
@article{tiwari_hbat_2007,
	title = {{HBAT}: {A} {Complete} {Package} for {Analysing} {Strong} and {Weak} {Hydrogen} {Bonds} in {Macromolecular} {Crystal} {Structures}},
	volume = {7},
	issn = {1386-6338},
	shorttitle = {{HBAT}},
	url = {https://doi.org/10.3233/ISI-2007-00337},
	doi = {10.3233/ISI-2007-00337},
	abstract = {The program HBAT is a tool to automate the analysis of potential hydrogen bonds and similar type of weak interactions like halogen bonds and non-canonical interactions in macromolecular structures, available in Brookhaven Protein Database (PDB) file format. HBAT is written using PERL and TK languages. The program generates an MSOFFICE Excel compatible output file for statistical analysis. HBAT identify potential interactions based on geometrical criteria. A series of analysis reports like frequency tables, geometry distribution tables, furcations list are generated. A user friendly GUI offers freedom to select several parameters and options. Graphviz based visualization of hydrogen bond networks in 2D helps to study the cooperativity and anticooperativity geometry in hydrogen bond. HBAT supports post docking interaction analysis between PDB files for any target/receptor (in PDB files) and docked ligands/poses (in SDF). This tool can be implemented in active site interaction analysis, structure based drug design and molecular dynamics simulations.},
	language = {EN},
	number = {6},
	urldate = {2025-08-08},
	journal = {In Silico Biology},
	author = {Tiwari, Abhishek and Panigrahi, Sunil K.},
	month = dec,
	year = {2007},
	note = {Publisher: SAGE Publications},
	pages = {651--661},
	file = {SAGE PDF Full Text:/Users/abhishektiwari/Zotero/storage/4UKLXUFK/Tiwari and Panigrahi - 2007 - HBAT A Complete Package for Analysing Strong and Weak Hydrogen Bonds in Macromolecular Crystal Stru.pdf:application/pdf},
}

@misc{noauthor_hbond_2020,
	title = {hbond - {AMBER}},
	url = {https://amberhub.chpc.utah.edu/hbond/},
	language = {en-US},
	urldate = {2025-08-08},
	month = may,
	year = {2020},
	file = {Snapshot:/Users/abhishektiwari/Zotero/storage/FNQZ3QZG/hbond.html:text/html},
}

@article{siemers_interactive_2021,
	title = {Interactive {Interface} for {Graph}-{Based} {Analyses} of {Dynamic} {H}-{Bond} {Networks}: {Application} to {Spike} {Protein} {S}},
	volume = {61},
	issn = {1549-9596},
	shorttitle = {Interactive {Interface} for {Graph}-{Based} {Analyses} of {Dynamic} {H}-{Bond} {Networks}},
	url = {https://doi.org/10.1021/acs.jcim.1c00306},
	doi = {10.1021/acs.jcim.1c00306},
	abstract = {Dynamic hydrogen-bond networks are key determinants of protein conformational dynamics. In the case of macromolecular protein complexes, which can have a large number of hydrogen bonds giving rise to extensive hydrogen-bond networks, efficient algorithms are required to analyze interactions that could be important for the dynamics and biological function of the complex. We present here a highly efficient, standalone interface designed for analyses of dynamical hydrogen-bond networks of biomolecules and macromolecular complexes. To facilitate a comprehensive description of protein dynamics, the interface includes analyses of hydrophobic interactions. We illustrate the usefulness and workflow of the interface by dissecting the dynamics of the ectodomain of SARS-CoV-2 protein S in its closed conformation. We find that protein S contains numerous local clusters of dynamic hydrogen bonds and identify hydrogen bonds that are sampled persistently. The receptor binding domain of the spike protein hosts only a handful of persistent hydrogen-bond clusters, suggesting structural plasticity. Our data analysis interface is released here for open use.},
	number = {6},
	urldate = {2025-08-08},
	journal = {Journal of Chemical Information and Modeling},
	author = {Siemers, Malte and Bondar, Ana-Nicoleta},
	month = jun,
	year = {2021},
	note = {Publisher: American Chemical Society},
	pages = {2998--3014},
}

@misc{siemers_maltesiebridge2_2024,
	title = {maltesie/bridge2},
	url = {https://github.com/maltesie/bridge2},
	abstract = {GUI for interactive graph-based analyses of hydrogen bond networks in crystal structures and MD trajectories.},
	urldate = {2025-08-08},
	author = {Siemers, Malte},
	month = nov,
	year = {2024},
	note = {original-date: 2020-12-10T23:43:14Z},
}

@misc{noauthor_durrantlabhbonanza_2025,
	title = {durrantlab/hbonanza},
	url = {https://github.com/durrantlab/hbonanza},
	urldate = {2025-08-08},
	publisher = {Durrant Lab},
	month = jun,
	year = {2025},
	note = {original-date: 2023-06-24T04:07:37Z},
}

@article{durrant_hbonanza_2011,
	title = {{HBonanza}: {A} {Computer} {Algorithm} for {Molecular}-{Dynamics}-{Trajectory} {Hydrogen}-{Bond} {Analysis}},
	volume = {31},
	issn = {1093-3263},
	shorttitle = {{HBonanza}},
	url = {https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3218086/},
	doi = {10.1016/j.jmgm.2011.07.008},
	abstract = {In the current work, we present a hydrogen-bond analysis of 2,673 ligand-receptor complexes that suggests the total number of hydrogen bonds formed between a ligand and its protein receptor is a poor predictor of ligand potency; furthermore, even that poor prediction does not suggest a statistically significant correlation between hydrogen-bond formation and potency. While we are not the first to suggest that hydrogen bonds on average do not generally contribute to ligand binding affinities, this additional evidence is nevertheless interesting. The primary role of hydrogen bonds may instead be to ensure specificity, to correctly position the ligand within the active site, and to hold the protein active site in a ligand-friendly conformation., We also present a new computer program called HBonanza (hydrogen-bond analyzer) that aids the analysis and visualization of hydrogen-bond networks. HBonanza, which can be used to analyze single structures or the many structures of a molecular dynamics trajectory, is open source and python implemented, making it easily editable, customizable, and platform independent. Unlike many other freely available hydrogen-bond analysis tools, HBonanza provides not only a text-based table describing the hydrogen-bond network, but also a Tcl script to facilitate visualization in VMD, a popular molecular visualization program. Visualization in other programs is also possible. A copy of HBonanza can be obtained free of charge from http://www.nbcr.net/hbonanza.},
	urldate = {2025-08-08},
	journal = {Journal of molecular graphics \& modelling},
	author = {Durrant, Jacob D. and McCammon, J. Andrew},
	month = nov,
	year = {2011},
	pmid = {21880522},
	pmcid = {PMC3218086},
	pages = {5--9},
	file = {Accepted Version:/Users/abhishektiwari/Zotero/storage/LLY9ZIBY/Durrant and McCammon - 2011 - HBonanza A Computer Algorithm for Molecular-Dynamics-Trajectory Hydrogen-Bond Analysis.pdf:application/pdf},
}

@misc{noauthor_vmd_nodate,
	title = {{VMD} {HBonds} {Plugin}, {Version} 1.2},
	url = {https://www.ks.uiuc.edu/Research/vmd/plugins/hbonds/},
	urldate = {2025-08-08},
	file = {HBonds Plugin, Version 1.2:/Users/abhishektiwari/Zotero/storage/QEPVD8QH/hbonds.html:text/html},
}

@article{wang_hbcalculator_2024,
	title = {{HBCalculator}: {A} {Tool} for {Hydrogen} {Bond} {Distribution} {Calculations} in {Molecular} {Dynamics} {Simulations}},
	volume = {64},
	issn = {1549-9596},
	shorttitle = {{HBCalculator}},
	url = {https://doi.org/10.1021/acs.jcim.4c00054},
	doi = {10.1021/acs.jcim.4c00054},
	abstract = {Hydrogen bonds, crucial noncovalent interactions in molecular systems, significantly impact biological, chemical, and energy-related processes; therefore, characterizing hydrogen bond information is of importance to fundamental studies. This work introduces the HBCalculator, a Tcl-based tool integrated with VMD for calculating 1D and 2D distributions of hydrogen bond density and strength. The tool facilitates spatial analysis, overcoming limitations in existing packages that lack direct spatial distribution output. By employing HBCalculator in MD simulations, three systems of cellulose/water and graphene/water interfaces, were tested to showcase its functionality. The 1D and 2D hydrogen bond distributions reveal insights into interfacial properties, reflecting the impact of material hydrophobicity. The simplicity of usage, along with its potential for diverse molecular systems, positions HBCalculator as a valuable tool for researchers exploring hydrogen bond networks.},
	number = {6},
	urldate = {2025-08-08},
	journal = {Journal of Chemical Information and Modeling},
	author = {Wang, Yulei and Wang, Yuxiang},
	month = mar,
	year = {2024},
	note = {Publisher: American Chemical Society},
	pages = {1772--1777},
}

@article{salentin_plip_2015,
	title = {{PLIP}: fully automated protein-ligand interaction profiler},
	volume = {43},
	issn = {0305-1048},
	shorttitle = {{PLIP}},
	url = {https://doi.org/10.1093/nar/gkv315},
	doi = {10.1093/nar/gkv315},
	abstract = {The characterization of interactions in protein-ligand complexes is essential for research in structural bioinformatics, drug discovery and biology. However, comprehensive tools are not freely available to the research community. Here, we present the protein-ligand interaction profiler (PLIP), a novel web service for fully automated detection and visualization of relevant non-covalent protein-ligand contacts in 3D structures, freely available at projects.biotec.tu-dresden.de/plip-web. The input is either a Protein Data Bank structure, a protein or ligand name, or a custom protein-ligand complex (e.g. from docking). In contrast to other tools, the rule-based PLIP algorithm does not require any structure preparation. It returns a list of detected interactions on single atom level, covering seven interaction types (hydrogen bonds, hydrophobic contacts, pi-stacking, pi-cation interactions, salt bridges, water bridges and halogen bonds). PLIP stands out by offering publication-ready images, PyMOL session files to generate custom images and parsable result files to facilitate successive data processing. The full python source code is available for download on the website. PLIP's command-line mode allows for high-throughput interaction profiling.},
	number = {W1},
	urldate = {2025-08-08},
	journal = {Nucleic Acids Research},
	author = {Salentin, Sebastian and Schreiber, Sven and Haupt, V. Joachim and Adasme, Melissa F. and Schroeder, Michael},
	month = jul,
	year = {2015},
	pages = {W443--W447},
	file = {Full Text PDF:/Users/abhishektiwari/Zotero/storage/JHNYT79R/Salentin et al. - 2015 - PLIP fully automated protein-ligand interaction profiler.pdf:application/pdf;Snapshot:/Users/abhishektiwari/Zotero/storage/M6CX3IWV/2467865.html:text/html},
}

@misc{noauthor_pharmaiplip_2025,
	title = {pharmai/plip},
	copyright = {GPL-2.0},
	url = {https://github.com/pharmai/plip},
	abstract = {Protein-Ligand Interaction Profiler - Analyze and visualize non-covalent protein-ligand interactions in PDB files according to 📝 Adasme et al. (2021), https://doi.org/10.1093/nar/gkab294},
	urldate = {2025-08-08},
	publisher = {PharmAI GmbH},
	month = aug,
	year = {2025},
	note = {original-date: 2014-12-16T09:06:43Z},
	keywords = {bioinformatics, docker, openbabel, pdb, plip, protein-structure, python-bindings, scientific-computing, singularity},
}

@article{jubb_arpeggio_2017,
	series = {Computation {Resources} for {Molecular} {Biology}},
	title = {Arpeggio: {A} {Web} {Server} for {Calculating} and {Visualising} {Interatomic} {Interactions} in {Protein} {Structures}},
	volume = {429},
	issn = {0022-2836},
	shorttitle = {Arpeggio},
	url = {https://www.sciencedirect.com/science/article/pii/S0022283616305332},
	doi = {10.1016/j.jmb.2016.12.004},
	abstract = {Interactions between proteins and their ligands, such as small molecules, other proteins, and DNA, depend on specific interatomic interactions that can be classified on the basis of atom type and distance and angle constraints. Visualisation of these interactions provides insights into the nature of molecular recognition events and has practical uses in guiding drug design and understanding the structural and functional impacts of mutations. We present Arpeggio, a web server for calculating interactions within and between proteins and protein, DNA, or small-molecule ligands, including van der Waals', ionic, carbonyl, metal, hydrophobic, and halogen bond contacts, and hydrogen bonds and specific atom-aromatic ring (cation-π, donor-π, halogen-π, and carbon-π) and aromatic ring-aromatic ring (π-π) interactions, within user-submitted macromolecule structures. PyMOL session files can be downloaded, allowing high-quality publication images of the interactions to be generated. Arpeggio is implemented in Python and available as a user-friendly web interface at http://structure.bioc.cam.ac.uk/arpeggio/ and as a downloadable package at https://bitbucket.org/harryjubb/arpeggio.},
	number = {3},
	urldate = {2025-08-08},
	journal = {Journal of Molecular Biology},
	author = {Jubb, Harry C and Higueruelo, Alicia P and Ochoa-Montaño, Bernardo and Pitt, Will R and Ascher, David B and Blundell, Tom L},
	month = feb,
	year = {2017},
	keywords = {drug design, molecular recognition, protein interactions, protein-ligand interactions, protein-protein interactions},
	pages = {365--371},
	file = {Full Text:/Users/abhishektiwari/Zotero/storage/ANJX9TA6/Jubb et al. - 2017 - Arpeggio A Web Server for Calculating and Visualising Interatomic Interactions in Protein Structure.pdf:application/pdf;ScienceDirect Snapshot:/Users/abhishektiwari/Zotero/storage/R99G4UJF/S0022283616305332.html:text/html},
}

@misc{jubb_harryjubbarpeggio_2025,
	title = {harryjubb/arpeggio},
	copyright = {GPL-3.0},
	url = {https://github.com/harryjubb/arpeggio},
	abstract = {Calculation of interatomic interactions in molecular structures},
	urldate = {2025-08-08},
	author = {Jubb, Harry},
	month = jul,
	year = {2025},
	note = {original-date: 2018-05-02T15:17:30Z},
	keywords = {bioinformatics, atomic-interactions, cheminformatics, structural-bioinformatics},
}

@article{schreyer_credo_2013,
	title = {{CREDO}: a structural interactomics database for drug discovery},
	volume = {2013},
	issn = {1758-0463},
	shorttitle = {{CREDO}},
	url = {https://doi.org/10.1093/database/bat049},
	doi = {10.1093/database/bat049},
	abstract = {CREDO is a unique relational database storing all pairwise atomic interactions of inter- as well as intra-molecular contacts between small molecules and macromolecules found in experimentally determined structures from the Protein Data Bank. These interactions are integrated with further chemical and biological data. The database implements useful data structures and algorithms such as cheminformatics routines to create a comprehensive analysis platform for drug discovery. The database can be accessed through a web-based interface, downloads of data sets and web services at http://www-cryst.bioc.cam.ac.uk/credo.Database URL:http://www-cryst.bioc.cam.ac.uk/credo},
	urldate = {2025-08-08},
	journal = {Database},
	author = {Schreyer, Adrian M. and Blundell, Tom L.},
	month = jan,
	year = {2013},
	pages = {bat049},
}

@book{desiraju_weak_2001,
	title = {The {Weak} {Hydrogen} {Bond}},
	isbn = {978-0-19-850970-7},
	url = {https://academic.oup.com/book/1817},
	urldate = {2025-08-08},
	publisher = {Oxford University Press},
	author = {Desiraju, Gautam and Steiner, Thomas},
	month = may,
	year = {2001},
	doi = {10.1093/acprof:oso/9780198509707.001.0001},
}

@article{sirimulla_halogen_2013,
	title = {Halogen {Interactions} in {Protein}-{Ligand} {Complexes}: {Implications} of {Halogen} {Bonding} for {Rational} {Drug} {Design}},
	volume = {53},
	issn = {1549-9596},
	shorttitle = {Halogen {Interactions} in {Protein}-{Ligand} {Complexes}},
	url = {https://doi.org/10.1021/ci400257k},
	doi = {10.1021/ci400257k},
	abstract = {Halogen bonding interactions between halogenated ligands and proteins were examined using the crystal structures deposited to date in the PDB. The data was analyzed as a function of halogen bonding to main chain Lewis bases, viz. oxygen of backbone carbonyl and backbone amide nitrogen. This analysis also examined halogen bonding to side-chain Lewis bases (O, N, and S) and to the electron-rich aromatic amino acids. All interactions were restricted to van der Waals radii with respective atoms. The data reveals that while fluorine and chlorine have strong tendencies favoring interactions with the backbone Lewis bases at glycine, the trend is not restricted to the achiral amino acid backbone for larger halogens. Halogen side-chain interactions are not restricted to amino acids containing O, N, and S as Lewis bases. Electron-rich aromatic amino acids host a high frequency of halogen bonds as does Leu. A closer examination of the latter hydrophobic side chain reveals that the “propensity of interactions” of halogen ligands at this oily residue is an outcome of strong classical halogen bonds with Lewis bases in the vicinity. Finally, an examination of Θ1 (C-X···O and C-X···N) and Θ2 (X···O-Z and X···N-Z) angles reveals that very few ligands adopt classical halogen bonding angles, suggesting that steric and other factors may influence these angles. The data is discussed in the context of ligand design for pharmaceutical applications.},
	number = {11},
	urldate = {2025-08-08},
	journal = {Journal of Chemical Information and Modeling},
	author = {Sirimulla, Suman and Bailey, Jake B. and Vegesna, Rahulsimham and Narayan, Mahesh},
	month = nov,
	year = {2013},
	note = {Publisher: American Chemical Society},
	pages = {2781--2791},
}

@article{cavallo_halogen_2016,
	title = {The {Halogen} {Bond}},
	volume = {116},
	issn = {0009-2665},
	url = {https://doi.org/10.1021/acs.chemrev.5b00484},
	doi = {10.1021/acs.chemrev.5b00484},
	abstract = {The halogen bond occurs when there is evidence of a net attractive interaction between an electrophilic region associated with a halogen atom in a molecular entity and a nucleophilic region in another, or the same, molecular entity. In this fairly extensive review, after a brief history of the interaction, we will provide the reader with a snapshot of where the research on the halogen bond is now, and, perhaps, where it is going. The specific advantages brought up by a design based on the use of the halogen bond will be demonstrated in quite different fields spanning from material sciences to biomolecular recognition and drug design.},
	number = {4},
	urldate = {2025-08-08},
	journal = {Chemical Reviews},
	author = {Cavallo, Gabriella and Metrangolo, Pierangelo and Milani, Roberto and Pilati, Tullio and Priimagi, Arri and Resnati, Giuseppe and Terraneo, Giancarlo},
	month = feb,
	year = {2016},
	note = {Publisher: American Chemical Society},
	pages = {2478--2601},
	file = {Full Text PDF:/Users/abhishektiwari/Zotero/storage/5WTW6CQ4/Cavallo et al. - 2016 - The Halogen Bond.pdf:application/pdf},
}

@misc{vries_halogen_2025,
	title = {Halogen bonds between ligands and proteins: can we use them in validation?},
	copyright = {© 2025, Posted by Cold Spring Harbor Laboratory. This pre-print is available under a Creative Commons License (Attribution-NonCommercial-NoDerivs 4.0 International), CC BY-NC-ND 4.0, as described at http://creativecommons.org/licenses/by-nc-nd/4.0/},
	shorttitle = {Halogen bonds between ligands and proteins},
	url = {https://www.biorxiv.org/content/10.1101/2025.06.24.661284v1},
	doi = {10.1101/2025.06.24.661284},
	abstract = {Halogen bonds are polar interactions between a halogen atom and its acceptor. They have characteristic geometry induced by the positively charged σ-hole on the halogen atom. Despite their importance in drug development, halogen bonds are often overlooked in the analysis and validation of ligand-protein complexes.
We analysed halogen bonds between ligands and proteins in structure models from the PDB-REDO databank and defined key geometric parameters. These are the donor-acceptor distance, and two bond angles (θ1 and θ2) for interatomic halogen bonds, or in the case of halogen-π interactions, the distance between the halogen and acceptor π-system and a bond angle (θ1).
Based on the distribution of these geometric parameters, we introduce a score, HalBS, that marks whether halogen bonds are adopting the preferred or allowed geometry or are an outlier and should be examined critically. A reference implementation of this score is now available in PDB-REDO. This is a first step towards improving halogen bond treatment in the analysis of macromolecular structure models.},
	language = {en},
	urldate = {2025-08-08},
	publisher = {bioRxiv},
	author = {Vries, Ida de and Tsiompanaki, Georgia and Perrakis, Anastassis and Joosten, Robbie P.},
	month = jun,
	year = {2025},
	note = {Pages: 2025.06.24.661284
Section: New Results},
	file = {Full Text PDF:/Users/abhishektiwari/Zotero/storage/KYX8F5C6/Vries et al. - 2025 - Halogen bonds between ligands and proteins can we use them in validation.pdf:application/pdf},
}

@article{brandl_c-h-interactions_2001,
	title = {C-h⋯π-interactions in proteins},
	volume = {307},
	issn = {0022-2836},
	url = {https://www.sciencedirect.com/science/article/pii/S0022283600944735},
	doi = {10.1006/jmbi.2000.4473},
	abstract = {A non-redundant set of 1154 protein structures from the Protein Data Bank was examined with respect to close interactions between C-H-donor and π-acceptor groups. A total of 31,087 interactions were found to satisfy our selection criteria. Their geometric parameters suggest that these interactions can be classified as weak hydrogen bonds. A set of 12 interaction classes were defined based on the division of the donors into three groups and the acceptors into four groups. These classes were examined separately, and the respective interactions described in detail in each class. Most prominent were interactions between aliphatic C-H donors and aromatic π-acceptors and interactions between aromatic C-H donors and aromatic π-acceptors. About three-quarters of the Trp-rings, half of all Phe and Tyr-rings and a quarter of all His-rings were found to be involved as acceptors in C-H⋯π-interactions. On the donor side, a preference for aromatic C-H groups was observed, but also for the aliphatic side-chains of the long, extended amino acid residues Lys, Arg and Met, and the Pro ring. The average distance between the C-donor and the center-of-mass of the π-acceptor was observed to be significantly longer in the 174 protein structures determined at {\textgreater}2.5 Å resolution. Also, the distribution is significantly wider. This resolution dependance suggests that the force fields commonly used for the refinement of protein structures may not be adequate. C-H⋯π-interactions involving aromatic groups either as donor or as acceptor groups are found mostly in the interior of the protein. The more hydrophilic the participating groups are, the closer to the surface are the interactions located. About 40 \% of all C-H⋯π-interactions occur between amino acid residue side-chains that are separated by nine or less residues in sequence. Dependent on the interaction class, different preferences for secondary structure, residue type and side-chain conformation were observed. It is likely that the C-H⋯π-interactions contribute significantly to the overall stability of a protein.},
	number = {1},
	urldate = {2025-08-08},
	journal = {Journal of Molecular Biology},
	author = {Brandl, Maria and Weiss, Manfred S. and Jabs, Andreas and Sühnel, Jürgen and Hilgenfeld, Rolf},
	month = mar,
	year = {2001},
	keywords = {C-H⋯π-interaction, force fields, hydrogen bond, protein stability, protein structure},
	pages = {357--377},
	file = {ScienceDirect Snapshot:/Users/abhishektiwari/Zotero/storage/3GV4HHUN/S0022283600944735.html:text/html},
}

@article{xiao_proteinligand_2023,
	title = {Protein-{Ligand} {CH}-π {Interactions}: {Structural} {Informatics}, {Energy} {Function} {Development}, and {Docking} {Implementation}},
	volume = {19},
	issn = {1549-9618},
	shorttitle = {Protein-{Ligand} {CH}-π {Interactions}},
	url = {https://doi.org/10.1021/acs.jctc.3c00300},
	doi = {10.1021/acs.jctc.3c00300},
	abstract = {Here, we develop an empirical energy function based on quantum mechanical data for the interaction between methane and benzene that captures the contribution from CH-π interactions. Such interactions are frequently observed in protein-ligand crystal structures, particularly for carbohydrate ligands, but have been hard to quantify due to the absence of a model for CH-π interactions in typical molecular mechanical force fields or docking scoring functions. The CH-π term was added to the AutoDock Vina (AD VINA) scoring function enabling its performance to be evaluated against a cohort of more than 1600 occurrences in 496 experimental structures of protein-ligand complexes. By employing a conformational grid search algorithm, inclusion of the CH-π term was shown to improve the prediction of the preferred orientation of flexible ligands in protein-binding sites and to enhance the detection of carbohydrate-binding sites that display CH-π interactions. Last but not least, this term was also shown to improve docking performance for the CASF-2016 benchmark set and a carbohydrate set.},
	number = {16},
	urldate = {2025-08-08},
	journal = {Journal of Chemical Theory and Computation},
	author = {Xiao, Yao and Woods, Robert J.},
	month = aug,
	year = {2023},
	note = {Publisher: American Chemical Society},
	pages = {5503--5515},
	file = {Full Text PDF:/Users/abhishektiwari/Zotero/storage/IB4EE72N/Xiao and Woods - 2023 - Protein-Ligand CH-π Interactions Structural Informatics, Energy Function Development, and Docking I.pdf:application/pdf},
}

@article{wang_dissecting_2019,
	title = {Dissecting {C}-{H}∙∙∙π and {N}-{H}∙∙∙π {Interactions} in {Two} {Proteins} {Using} a {Combined} {Experimental} and {Computational} {Approach}},
	volume = {9},
	copyright = {2019 The Author(s)},
	issn = {2045-2322},
	url = {https://www.nature.com/articles/s41598-019-56607-4},
	doi = {10.1038/s41598-019-56607-4},
	abstract = {C-H∙∙∙π and N-H∙∙∙π interactions can have an important contribution for protein stability. However, direct measurements of these interactions in proteins are rarely reported. In this work, we combined the mutant cycle experiments and molecular dynamics (MD) simulations to characterize C-H∙∙∙π and N-H∙∙∙π interactions and their cooperativity in two model proteins. It is shown that the average C-H∙∙∙π interaction per residue pair is {\textasciitilde} -0.5kcal/mol while the N-H∙∙∙π interaction is slightly stronger. The triple mutant box measurement indicates that N-H∙∙∙π∙∙∙C-H∙∙∙π and C-H∙∙∙π∙∙∙C-H∙∙∙π can have a positive or negative cooperativity. MD simulations suggest that the cooperativity, depending on the local environment of the interactions, mainly arises from the geometric rearrangement when the nearby interaction is perturbed.},
	language = {en},
	number = {1},
	urldate = {2025-08-08},
	journal = {Scientific Reports},
	author = {Wang, Jia and Yao, Lishan},
	month = dec,
	year = {2019},
	note = {Publisher: Nature Publishing Group},
	keywords = {Biochemistry, Biophysics},
	pages = {20149},
	file = {Full Text PDF:/Users/abhishektiwari/Zotero/storage/EE6L9X4M/Wang and Yao - 2019 - Dissecting C-H∙∙∙π and N-H∙∙∙π Interactions in Two Proteins Using a Combined Experimental and Comput.pdf:application/pdf},
}

@article{rahim_reciprocal_2017,
	title = {Reciprocal carbonyl-carbonyl interactions in small molecules and proteins},
	volume = {8},
	copyright = {2017 The Author(s)},
	issn = {2041-1723},
	url = {https://www.nature.com/articles/s41467-017-00081-x},
	doi = {10.1038/s41467-017-00081-x},
	abstract = {Carbonyl-carbonyl n→π* interactions where a lone pair (n) of the oxygen atom of a carbonyl group is delocalized over the π* orbital of a nearby carbonyl group have attracted a lot of attention in recent years due to their ability to affect the 3D structure of small molecules, polyesters, peptides, and proteins. In this paper, we report the discovery of a “reciprocal” carbonyl-carbonyl interaction with substantial back and forth n→π* and π→π* electron delocalization between neighboring carbonyl groups. We have carried out experimental studies, analyses of crystallographic databases and theoretical calculations to show the presence of this interaction in both small molecules and proteins. In proteins, these interactions are primarily found in polyproline II (PPII) helices. As PPII are the most abundant secondary structures in unfolded proteins, we propose that these local interactions may have implications in protein folding.},
	language = {en},
	number = {1},
	urldate = {2025-08-08},
	journal = {Nature Communications},
	author = {Rahim, Abdur and Saha, Pinaki and Jha, Kunal Kumar and Sukumar, Nagamani and Sarma, Bani Kanta},
	month = jul,
	year = {2017},
	note = {Publisher: Nature Publishing Group},
	keywords = {Biophysical chemistry, Structural biology},
	pages = {78},
	file = {Full Text PDF:/Users/abhishektiwari/Zotero/storage/SQ84Q3IL/Rahim et al. - 2017 - Reciprocal carbonyl-carbonyl interactions in small molecules and proteins.pdf:application/pdf},
}


@article{newberry_n_2017,
	title = {The n→π* {Interaction}},
	volume = {50},
	issn = {0001-4842},
	url = {https://doi.org/10.1021/acs.accounts.7b00121},
	doi = {10.1021/acs.accounts.7b00121},
	abstract = {ConspectusThe carbonyl group holds a prominent position in chemistry and biology not only because it allows diverse transformations but also because it supports key intermolecular interactions, including hydrogen bonding. More recently, carbonyl groups have been found to interact with a variety of nucleophiles, including other carbonyl groups, in what we have termed an n→π* interaction. In an n→π* interaction, a nucleophile donates lone-pair (n) electron density into the empty π* orbital of a nearby carbonyl group. Mixing of these orbitals releases energy, resulting in an attractive interaction. Hints of such interactions were evident in small-molecule crystal structures as early as the 1970s, but not until 2001 was the role of such interactions articulated clearly.These non-covalent interactions were first discovered during investigations into the thermostability of the proline-rich protein collagen, which achieves a robust structure despite a relatively low potential for hydrogen bonding. It was found that by modulating the distance between two carbonyl groups in the peptide backbone, one could alter the conformational preferences of a peptide bond to proline. Specifically, only the trans conformation of a peptide bond to proline allows for an attractive interaction with an adjacent carbonyl group, so when one increases the proximity of the two carbonyl groups, one enhances their interaction and promotes the trans conformation of the peptide bond, which increases the thermostability of collagen.More recently, attention has been paid to the nature of these interactions. Some have argued that rather than resulting from electron donation, carbonyl interactions are a particular example of dipolar interactions that are well-approximated by classical mechanics. However, experimental evidence has demonstrated otherwise. Numerous examples now exist where an increase in the dipole moment of a carbonyl group decreases the strength of its interactions with other carbonyl groups, demonstrating unequivocally that a dipolar mechanism is insufficient to describe these interactions. Rather, these interactions have important quantum-mechanical character that can be evaluated through careful experimental analysis and judicious use of computation.Although individual n→π* interactions are relatively weak (∼0.3-0.7 kcal/mol), the ubiquity of carbonyl groups across chemistry and biology gives the n→π* interaction broad impact. In particular, the n→π* interaction is likely to play an important role in dictating protein structure. Indeed, bioinformatics analysis suggests that approximately one-third of residues in folded proteins satisfy the geometric requirements to engage in an n→π* interaction, which is likely to be of particular importance for the α-helix. Other carbonyl-dense polymeric materials like polyesters and peptoids are also influenced by n→π* interactions, as are a variety of small molecules, some with particular medicinal importance. Research will continue to identify molecules whose conformation and activity are affected by the n→π* interaction and will clarify their specific contributions to the structures of biomacromolecules.},
	number = {8},
	urldate = {2025-08-08},
	journal = {Accounts of Chemical Research},
	author = {Newberry, Robert W. and Raines, Ronald T.},
	month = aug,
	year = {2017},
	note = {Publisher: American Chemical Society},
	pages = {1838--1846},
	file = {Full Text PDF:/Users/abhishektiwari/Zotero/storage/897EPGFD/Newberry and Raines - 2017 - The n→π Interaction.pdf:application/pdf},
}

@article{choudhary_nature_2009,
	title = {Nature of {Amide} {Carbonyl}-{Carbonyl} {Interactions} in {Proteins}},
	volume = {131},
	copyright = {http://pubs.acs.org/page/policy/authorchoice\_termsofuse.html},
	issn = {0002-7863, 1520-5126},
	url = {https://pubs.acs.org/doi/10.1021/ja901188y},
	doi = {10.1021/ja901188y},
	language = {en},
	number = {21},
	urldate = {2025-08-08},
	journal = {Journal of the American Chemical Society},
	author = {Choudhary, Amit and Gandla, Deepa and Krow, Grant R. and Raines, Ronald T.},
	month = jun,
	year = {2009},
	pages = {7244--7246},
	file = {PDF:/Users/abhishektiwari/Zotero/storage/MFINE7G6/Choudhary et al. - 2009 - Nature of Amide Carbonyl-Carbonyl Interactions in Proteins.pdf:application/pdf},
}

@article{mcgaughey_-stacking_1998,
	title = {π-{Stacking} {Interactions}: {ALIVE} {AND} {WELL} {IN} {PROTEINS}*},
	volume = {273},
	issn = {0021-9258},
	shorttitle = {π-{Stacking} {Interactions}},
	url = {https://www.sciencedirect.com/science/article/pii/S0021925818808158},
	doi = {10.1074/jbc.273.25.15458},
	abstract = {A representative set of high resolution x-ray crystal structures of nonhomologous proteins have been examined to determine the preferred positions and orientations of noncovalent interactions between the aromatic side chains of the amino acids phenylalanine, tyrosine, histidine, and tryptophan. To study the primary interactions between aromatic amino acids, care has been taken to examine only isolated pairs (dimers) of amino acids because trimers and higher order clusters of aromatic amino acids behave differently than their dimer counterparts. We find that pairs (dimers) of aromatic side chain amino acids preferentially align their respective aromatic rings in an off-centered parallel orientation. Further, we find that this parallel-displaced structure is 0.5-0.75 kcal/mol more stable than a T-shaped structure for phenylalanine interactions and 1 kcal/mol more stable than a T-shaped structure for the full set of aromatic side chain amino acids. This experimentally determined structure and energy difference is consistent with ab initio and molecular mechanics calculations of benzene dimer, however, the results are not in agreement with previously published analyses of aromatic amino acids in proteins. The preferred orientation is referred to as parallel displaced π-stacking.},
	number = {25},
	urldate = {2025-08-08},
	journal = {Journal of Biological Chemistry},
	author = {McGaughey, Georgia B. and Gagné, Marc and Rappé, Anthony K.},
	month = jun,
	year = {1998},
	pages = {15458--15463},
	file = {Full Text:/Users/abhishektiwari/Zotero/storage/ZI5KPGPD/McGaughey et al. - 1998 - π-Stacking Interactions ALIVE AND WELL IN PROTEINS.pdf:application/pdf;ScienceDirect Snapshot:/Users/abhishektiwari/Zotero/storage/LVF3KBB9/S0021925818808158.html:text/html},
}

@article{vernon_pi-pi_nodate,
	title = {Pi-{Pi} contacts are an overlooked protein feature relevant to phase separation},
	volume = {7},
	issn = {2050-084X},
	url = {https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5847340/},
	doi = {10.7554/eLife.31486},
	abstract = {Protein phase separation is implicated in formation of membraneless organelles, signaling puncta and the nuclear pore. Multivalent interactions of modular binding domains and their target motifs can drive phase separation. However, forces promoting the more common phase separation of intrinsically disordered regions are less understood, with suggested roles for multivalent cation-pi, pi-pi, and charge interactions and the hydrophobic effect. Known phase-separating proteins are enriched in pi-orbital containing residues and thus we analyzed pi-interactions in folded proteins. We found that pi-pi interactions involving non-aromatic groups are widespread, underestimated by force-fields used in structure calculations and correlated with solvation and lack of regular secondary structure, properties associated with disordered regions. We present a phase separation predictive algorithm based on pi interaction frequency, highlighting proteins involved in biomaterials and RNA processing.},
	urldate = {2025-08-08},
	journal = {eLife},
	author = {Vernon, Robert McCoy and Chong, Paul Andrew and Tsang, Brian and Kim, Tae Hun and Bah, Alaji and Farber, Patrick and Lin, Hong and Forman-Kay, Julie Deborah},
	pmid = {29424691},
	pmcid = {PMC5847340},
	pages = {e31486},
	year = {2018},
	file = {Full Text PDF:/Users/abhishektiwari/Zotero/storage/L3WDHA5L/Vernon et al. - Pi-Pi contacts are an overlooked protein feature relevant to phase separation.pdf:application/pdf},
}

@misc{noauthor_harryjubb_nodate,
	title = {harryjubb / arpeggio - {Bitbucket}},
	url = {https://bitbucket.org/harryjubb/arpeggio/src/master/},
	urldate = {2025-08-08},
	file = {harryjubb / arpeggio - Bitbucket:/Users/abhishektiwari/Zotero/storage/W7WEXI5S/master.html:text/html},
}

@article{krieger_watfinder_2024,
	title = {{WatFinder}: a {ProDy} tool for protein-water interactions},
	volume = {40},
	issn = {1367-4803},
	shorttitle = {{WatFinder}},
	url = {https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11349191/},
	doi = {10.1093/bioinformatics/btae516},
	abstract = {Summary
We introduce WatFinder, a tool designed to identify and visualize protein-water interactions (water bridges, water-mediated associations, or water channels, fluxes, and clusters) relevant to protein stability, dynamics, and function. WatFinder is integrated into ProDy, a Python API broadly used for structure-based prediction of protein dynamics. WatFinder provides a suite of functions for generating raw data as well as outputs from statistical analyses. The ProDy framework facilitates comprehensive automation and efficient analysis of the ensembles of structures resolved for a given protein or the time-evolved conformations from simulations in explicit water, as illustrated in five case studies presented in the .

Availability and implementation

ProDy is open-source and freely available under MIT License from https://github.com/ProDy/ProDy.},
	number = {8},
	urldate = {2025-08-08},
	journal = {Bioinformatics},
	author = {Krieger, James M and Doljanin, Frane and Bogetti, Anthony T and Zhang, Feng and Manivarma, Thiliban and Bahar, Ivet and Mikulska-Ruminska, Karolina},
	month = aug,
	year = {2024},
	pmid = {39152994},
	pmcid = {PMC11349191},
	pages = {btae516},
}

@misc{noauthor_431_nodate,
	title = {4.3.1. {Hydrogen} {Bond} {Analysis} - {MDAnalysis}.analysis.hydrogenbonds.hbond\_analysis - {MDAnalysis} 1.1.0 documentation},
	url = {https://docs.mdanalysis.org/1.1.0/documentation_pages/analysis/hydrogenbonds.html},
	urldate = {2025-08-08},
	file = {4.3.1. Hydrogen Bond Analysis - MDAnalysis.analysis.hydrogenbonds.hbond_analysis - MDAnalysis 1.1.0 documentation:/Users/abhishektiwari/Zotero/storage/3VJG877H/hydrogenbonds.html:text/html},
}

@misc{noauthor_hbnet_nodate,
	title = {{HBNet}},
	url = {https://docs.rosettacommons.org/docs/latest/scripting_documentation/RosettaScripts/Movers/movers_pages/HBNetMover},
	urldate = {2025-08-08},
	file = {HBNet:/Users/abhishektiwari/Zotero/storage/WC6ET3TA/HBNetMover.html:text/html},
}

@article{ferruz_proteintools_2021,
	title = {{ProteinTools}: a toolkit to analyze protein structures},
	volume = {49},
	copyright = {http://creativecommons.org/licenses/by/4.0/},
	issn = {0305-1048, 1362-4962},
	shorttitle = {{ProteinTools}},
	url = {https://academic.oup.com/nar/article/49/W1/W559/6279848},
	doi = {10.1093/nar/gkab375},
	abstract = {Abstract
            The experimental characterization and computational prediction of protein structures has become increasingly rapid and precise. However, the analysis of protein structures often requires researchers to use several software packages or web servers, which complicates matters. To provide long-established structural analyses in a modern, easy-to-use interface, we implemented ProteinTools, a web server toolkit for protein structure analysis. ProteinTools gathers four applications so far, namely the identification of hydrophobic clusters, hydrogen bond networks, salt bridges, and contact maps. In all cases, the input data is a PDB identifier or an uploaded structure, whereas the output is an interactive dynamic web interface. Thanks to the modular nature of ProteinTools, the addition of new applications will become an easy task. Given the current need to have these tools in a single, fast, and interpretable interface, we believe that ProteinTools will become an essential toolkit for the wider protein research community. The web server is available at https://proteintools.uni-bayreuth.de.},
	language = {en},
	number = {W1},
	urldate = {2025-08-08},
	journal = {Nucleic Acids Research},
	author = {Ferruz, Noelia and Schmidt, Steffen and Höcker, Birte},
	month = jul,
	year = {2021},
	pages = {W559--W566},
	file = {Full Text:/Users/abhishektiwari/Zotero/storage/Y42M2FPE/Ferruz et al. - 2021 - ProteinTools a toolkit to analyze protein structures.pdf:application/pdf},
}

@misc{noauthor_hbplus_nodate,
	title = {{HBPLUS} home page},
	url = {https://www.ebi.ac.uk/thornton-srv/software/HBPLUS/},
	urldate = {2025-08-08},
	file = {HBPLUS home page:/Users/abhishektiwari/Zotero/storage/7BJIMNS4/HBPLUS.html:text/html},
}

@misc{noauthor_tool_nodate,
	title = {Tool: {H}-{Bonds}},
	url = {https://www.cgl.ucsf.edu/chimerax/docs/user/tools/hbonds.html},
	urldate = {2025-08-08},
	file = {Tool\: H-Bonds:/Users/abhishektiwari/Zotero/storage/Q9XJARRZ/hbonds.html:text/html},
}

@article{kabsch_dictionary_1983,
	title = {Dictionary of protein secondary structure: {Pattern} recognition of hydrogen‐bonded and geometrical features},
	volume = {22},
	copyright = {http://onlinelibrary.wiley.com/termsAndConditions\#vor},
	issn = {0006-3525, 1097-0282},
	shorttitle = {Dictionary of protein secondary structure},
	url = {https://onlinelibrary.wiley.com/doi/10.1002/bip.360221211},
	doi = {10.1002/bip.360221211},
	abstract = {Abstract
            
              For a successful analysis of the relation between amino acid sequence and protein structure, an unambiguous and physically meaningful definition of secondary structure is essential. We have developed a set of simple and physically motivated criteria for secondary structure, programmed as a pattern‐recognition process of hydrogen‐bonded and geometrical features extracted from x‐ray coordinates. Cooperative secondary structure is recognized as repeats of the elementary hydrogen‐bonding patterns “turn” and “bridge.” Repeating turns are “helices,” repeating bridges are “ladders,” connected ladders are “sheets.” Geometric structure is defined in terms of the concepts torsion and curvature of differential geometry. Local chain “chirality” is the torsional handedness of four consecutive C
              α
              positions and is positive for right‐handed helices and negative for ideal twisted β‐sheets. Curved pieces are defined as “bends.” Solvent “exposure” is given as the number of water molecules in possible contact with a residue. The end result is a compilation of the primary structure, including SS bonds, secondary structure, and solvent exposure of 62 different globular proteins. The presentation is in linear form: strip graphs for an overall view and strip tables for the details of each of 10.925 residues. The dictionary is also available in computer‐readable form for protein structure prediction work.},
	language = {en},
	number = {12},
	urldate = {2025-08-08},
	journal = {Biopolymers},
	author = {Kabsch, Wolfgang and Sander, Christian},
	month = dec,
	year = {1983},
	pages = {2577--2637},
}

@misc{noauthor_protoss_nodate,
	title = {Protoss},
	url = {https://www.zbh.uni-hamburg.de/forschung/amd/software/protoss.html},
	language = {de},
	urldate = {2025-08-08},
	file = {Snapshot:/Users/abhishektiwari/Zotero/storage/64PVZ6AS/protoss.html:text/html},
}

@misc{noauthor_gmx_nodate,
	title = {gmx hbond - {GROMACS} 2025.2 documentation},
	url = {https://manual.gromacs.org/current/onlinehelp/gmx-hbond.html},
	urldate = {2025-08-08},
	file = {gmx hbond - GROMACS 2025.2 documentation:/Users/abhishektiwari/Zotero/storage/DUN6E5U7/gmx-hbond.html:text/html},
}

@article{mcdonald_satisfying_1994,
	title = {Satisfying hydrogen bonding potential in proteins},
	volume = {238},
	issn = {0022-2836},
	doi = {10.1006/jmbi.1994.1334},
	abstract = {We have analysed the frequency with which potential hydrogen bond donors and acceptors are satisfied in protein molecules. There are a small percentage of nitrogen or oxygen atoms that do not form hydrogen bonds with either solvent or protein atoms, when standard criteria are used. For high resolution structures 9.5\% and 5.1\% of buried main-chain nitrogen and oxygen atoms, respectively, fail to hydrogen bond under our standard criteria, representing 5.8\% and 2.1\% of all main-chain nitrogen and oxygen atoms. We find that as the resolution of the data improves, the percentages fall. If the hydrogen bond criteria are relaxed many of these unsatisfied atoms form weak hydrogen bonds. However, there remain some buried atoms (1.3\% NH and 1.8\% CO) that fail to hydrogen bond without any immediately obvious compensating interactions.},
	language = {eng},
	number = {5},
	journal = {Journal of Molecular Biology},
	author = {McDonald, I. K. and Thornton, J. M.},
	month = may,
	year = {1994},
	pmid = {8182748},
	keywords = {Algorithms, Amino Acids, Hydrogen, Hydrogen Bonding, Nitrogen, Oxygen, Protein Structure, Secondary, Proteins},
	pages = {777--793},
}

@article{berman_protein_2000,
	title = {The {Protein} {Data} {Bank}},
	volume = {28},
	issn = {0305-1048},
	url = {https://doi.org/10.1093/nar/28.1.235},
	doi = {10.1093/nar/28.1.235},
	abstract = {The Protein Data Bank (PDB; http://www.rcsb.org/pdb/ ) is the single worldwide archive of structural data of biological macromolecules. This paper describes the goals of the PDB, the systems in place for data deposition and access, how to obtain further information, and near-term plans for the future development of the resource.},
	number = {1},
	urldate = {2025-08-08},
	journal = {Nucleic Acids Research},
	author = {Berman, Helen M. and Westbrook, John and Feng, Zukang and Gilliland, Gary and Bhat, T. N. and Weissig, Helge and Shindyalov, Ilya N. and Bourne, Philip E.},
	month = jan,
	year = {2000},
	pages = {235--242},
	file = {Full Text PDF:/Users/abhishektiwari/Zotero/storage/WI5376UV/Berman et al. - 2000 - The Protein Data Bank.pdf:application/pdf;Snapshot:/Users/abhishektiwari/Zotero/storage/9GF6A36X/28.1.html:text/html},
}

@misc{noauthor_openmmpdbfixer_2025,
	title = {openmm/pdbfixer},
	url = {https://github.com/openmm/pdbfixer},
	abstract = {PDBFixer fixes problems in PDB files},
	urldate = {2025-08-08},
	publisher = {OpenMM},
	month = aug,
	year = {2025},
	note = {original-date: 2013-08-29T22:29:24Z},
}

@article{eastman_openmm_2013,
	title = {{OpenMM} 4: {A} {Reusable}, {Extensible}, {Hardware} {Independent} {Library} for {High} {Performance} {Molecular} {Simulation}},
	volume = {9},
	issn = {1549-9618},
	shorttitle = {{OpenMM} 4},
	url = {https://doi.org/10.1021/ct300857j},
	doi = {10.1021/ct300857j},
	abstract = {OpenMM is a software toolkit for performing molecular simulations on a range of high performance computing architectures. It is based on a layered architecture: the lower layers function as a reusable library that can be invoked by any application, while the upper layers form a complete environment for running molecular simulations. The library API hides all hardware-specific dependencies and optimizations from the users and developers of simulation programs: they can be run without modification on any hardware on which the API has been implemented. The current implementations of OpenMM include support for graphics processing units using the OpenCL and CUDA frameworks. In addition, OpenMM was designed to be extensible, so new hardware architectures can be accommodated and new functionality (e.g., energy terms and integrators) can be easily added.},
	number = {1},
	urldate = {2025-08-08},
	journal = {Journal of Chemical Theory and Computation},
	author = {Eastman, Peter and Friedrichs, Mark S. and Chodera, John D. and Radmer, Randall J. and Bruns, Christopher M. and Ku, Joy P. and Beauchamp, Kyle A. and Lane, Thomas J. and Wang, Lee-Ping and Shukla, Diwakar and Tye, Tony and Houston, Mike and Stich, Timo and Klein, Christoph and Shirts, Michael R. and Pande, Vijay S.},
	month = jan,
	year = {2013},
	note = {Publisher: American Chemical Society},
	pages = {461--469},
	file = {Accepted Version:/Users/abhishektiwari/Zotero/storage/PMN8YGXJ/Eastman et al. - 2013 - OpenMM 4 A Reusable, Extensible, Hardware Independent Library for High Performance Molecular Simula.pdf:application/pdf},
}

@article{oboyle_pybel_2008,
	title = {Pybel: a {Python} wrapper for the {OpenBabel} cheminformatics toolkit},
	volume = {2},
	issn = {1752-153X},
	shorttitle = {Pybel},
	url = {https://bmcchem.biomedcentral.com/articles/10.1186/1752-153X-2-5},
	doi = {10.1186/1752-153X-2-5},
	language = {en},
	number = {1},
	urldate = {2025-08-08},
	journal = {Chemistry Central Journal},
	author = {O'Boyle, Noel M and Morley, Chris and Hutchison, Geoffrey R},
	month = dec,
	year = {2008},
	pages = {5},
	file = {Full Text:/Users/abhishektiwari/Zotero/storage/D3M9CYPD/O'Boyle et al. - 2008 - Pybel a Python wrapper for the OpenBabel cheminformatics toolkit.pdf:application/pdf},
}

@article{meyer_interactions_2003,
	title = {Interactions with {Aromatic} {Rings} in {Chemical} and {Biological} {Recognition}},
	volume = {42},
	copyright = {© 2002 WILEY-VCH Verlag GmbH \& Co. KGaA, Weinheim},
	issn = {1521-3773},
	url = {https://onlinelibrary.wiley.com/doi/abs/10.1002/anie.200390319},
	doi = {10.1002/anie.200390319},
	abstract = {Intermolecular interactions involving aromatic rings are key processes in both chemical and biological recognition. Their understanding is essential for rational drug design and lead optimization in medicinal chemistry. Different approaches-biological studies, molecular recognition studies with artificial receptors, crystallographic database mining, gas-phase studies, and theoretical calculations-are pursued to generate a profound understanding of the structural and energetic parameters of individual recognition modes involving aromatic rings. This review attempts to combine and summarize the knowledge gained from these investigations. The review focuses mainly on examples with biological relevance since one of its aims it to enhance the knowledge of molecular recognition forces that is essential for drug development.},
	language = {en},
	number = {11},
	urldate = {2025-08-08},
	journal = {Angewandte Chemie International Edition},
	author = {Meyer, Emmanuel A. and Castellano, Ronald K. and Diederich, François},
	year = {2003},
	note = {\_eprint: https://onlinelibrary.wiley.com/doi/pdf/10.1002/anie.200390319},
	keywords = {molecular recognition, arenes, hydrogen bonds, noncovalent interactions, receptors},
	pages = {1210--1250},
	file = {Snapshot:/Users/abhishektiwari/Zotero/storage/V8GLGWS7/anie.html:text/html},
}

@incollection{hubbard_hydrogen_2010,
	title = {Hydrogen {Bonds} in {Proteins}: {Role} and {Strength}},
	copyright = {Copyright © 2010 John Wiley \& Sons, Ltd. All rights reserved.},
	isbn = {978-0-470-01590-2},
	shorttitle = {Hydrogen {Bonds} in {Proteins}},
	url = {https://onlinelibrary.wiley.com/doi/abs/10.1002/9780470015902.a0003011.pub2},
	abstract = {Hydrogen bonds provide most of the directional interactions that underpin protein folding, protein structure and molecular recognition. The core of most protein structures is composed of secondary structures such as α helix and β sheet. This satisfies the hydrogen-bonding potential between main chain carbonyl oxygen and amide nitrogen buried in the hydrophobic core of the protein. Hydrogen bonding between a protein and its ligands (protein, nucleic acid, substrate, effector or inhibitor) provides a directionality and specificity of interaction that is a fundamental aspect of molecular recognition. The energetics and kinetics of hydrogen bonding therefore need to be optimal to allow the rapid sampling and kinetics of folding, conferring stability to the protein structure and providing the specificity required for selective macromolecular interactions. Key concepts: A hydrogen bond is formed by the interaction of a hydrogen atom that is covalently bonded to an electronegative atom (donor) with another electronegative atom (acceptor). Hydrogen bonding confers rigidity to the protein structure and specificity to intermolecular interactions. The accepted (and most frequently observed) geometry for a hydrogen bond is a distance of less than 2.5 Å (1.9 Å) between hydrogen and the acceptor and a donor-hydrogen-acceptor angle of between 90° and 180° (160°). During protein folding, the burial of hydrophobic side-chains requires intramolecular hydrogen bonds to be formed between the main chain polar groups. The most stable conformations of polypeptide chains that maximize intrachain hydrogen-bonding potential are α helices and β sheets. Specificity in molecular recognition is driven by the interaction of complementary hydrogen-bonding groups on interacting surfaces.},
	language = {en},
	urldate = {2025-08-08},
	booktitle = {{eLS}},
	publisher = {John Wiley \& Sons, Ltd},
	author = {Hubbard, Roderick E and Kamran Haider, Muhammad},
	year = {2010},
	doi = {10.1002/9780470015902.a0003011.pub2},
	note = {\_eprint: https://onlinelibrary.wiley.com/doi/pdf/10.1002/9780470015902.a0003011.pub2},
	keywords = {molecular recognition, hydrogen bond, protein stability, protein structure, secondary structure},
	file = {Snapshot:/Users/abhishektiwari/Zotero/storage/JEKZHSZV/9780470015902.a0003011.html:text/html},
}

@article{salonen_aromatic_2011,
	title = {Aromatic {Rings} in {Chemical} and {Biological} {Recognition}: {Energetics} and {Structures}},
	volume = {50},
	copyright = {Copyright © 2011 WILEY-VCH Verlag GmbH \& Co. KGaA, Weinheim},
	issn = {1521-3773},
	shorttitle = {Aromatic {Rings} in {Chemical} and {Biological} {Recognition}},
	url = {https://onlinelibrary.wiley.com/doi/abs/10.1002/anie.201007560},
	doi = {10.1002/anie.201007560},
	abstract = {This review describes a multidimensional treatment of molecular recognition phenomena involving aromatic rings in chemical and biological systems. It summarizes new results reported since the appearance of an earlier review in 2003 in host-guest chemistry, biological affinity assays and biostructural analysis, data base mining in the Cambridge Structural Database (CSD) and the Protein Data Bank (PDB), and advanced computational studies. Topics addressed are arene-arene, perfluoroarene-arene, S⋅⋅⋅aromatic, cation-π, and anion-π interactions, as well as hydrogen bonding to π systems. The generated knowledge benefits, in particular, structure-based hit-to-lead development and lead optimization both in the pharmaceutical and in the crop protection industry. It equally facilitates the development of new advanced materials and supramolecular systems, and should inspire further utilization of interactions with aromatic rings to control the stereochemical outcome of synthetic transformations.},
	language = {en},
	number = {21},
	urldate = {2025-08-08},
	journal = {Angewandte Chemie International Edition},
	author = {Salonen, Laura M. and Ellermann, Manuel and Diederich, François},
	year = {2011},
	note = {\_eprint: https://onlinelibrary.wiley.com/doi/pdf/10.1002/anie.201007560},
	keywords = {molecular recognition, arenes, noncovalent interactions, receptors, biological complexation, host-guest systems},
	pages = {4808--4842},
	file = {Snapshot:/Users/abhishektiwari/Zotero/storage/TBIKEFT5/anie.html:text/html},
}

@book{metrangolo_halogen_2008,
	address = {Berlin, Heidelberg},
	series = {Structure and {Bonding}},
	title = {Halogen {Bonding}},
	volume = {126},
	copyright = {http://www.springer.com/tdm},
	isbn = {978-3-540-74329-3 978-3-540-74330-9},
	url = {http://link.springer.com/10.1007/978-3-540-74330-9},
	language = {en},
	urldate = {2025-08-08},
	publisher = {Springer},
	editor = {Metrangolo, P. and Resnati, G.},
	year = {2008},
	doi = {10.1007/978-3-540-74330-9},
	keywords = {analytical chemistry, bonding, chemical structure, inorganic chemistry, physical chemistry, structure},
}

@article{dannenberg_introduction_1998,
	title = {An {Introduction} to {Hydrogen} {Bonding} {By} {George} {A}. {Jeffrey} ({University} of {Pittsburgh}). {Oxford} {University} {Press}: {New} {York} and {Oxford}. 1997. ix + 303 pp. \$60.00. {ISBN} 0-19-509549-9.},
	volume = {120},
	issn = {0002-7863},
	shorttitle = {An {Introduction} to {Hydrogen} {Bonding} {By} {George} {A}. {Jeffrey} ({University} of {Pittsburgh}). {Oxford} {University} {Press}},
	url = {https://doi.org/10.1021/ja9756331},
	doi = {10.1021/ja9756331},
	number = {22},
	urldate = {2025-08-08},
	journal = {Journal of the American Chemical Society},
	author = {Dannenberg, J. J.},
	month = jun,
	year = {1998},
	note = {Publisher: American Chemical Society},
	pages = {5604--5604},
}

@article{tiwari_hbng_2007,
	title = {{HBNG}: {Graph} theory based visualization of hydrogen bond networks in protein structures},
	volume = {2},
	issn = {0973-2063},
	shorttitle = {{HBNG}},
	url = {https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2139992/},
	doi = {10.6026/97320630002028},
	abstract = {HBNG is a graph theory based tool for visualization of hydrogen bond network in 2D. Digraphs generated by HBNG facilitate visualization of cooperativity 
	    and anticooperativity chains and rings in protein structures. HBNG takes hydrogen bonds list files (output from HBAT, HBEXPLORE, HBPLUS and STRIDE) as input 
	    and generates a DOT language script and constructs digraphs using freeware AT and T Graphviz tool. HBNG is useful in the enumeration of favorable topologies 
	    of hydrogen bond networks in protein structures and determining the effect of cooperativity and anticooperativity on protein stability and folding. HBNG can 
	    be applied to protein structure comparison and in the identification of secondary structural regions in protein structures.},
	number = {1},
	urldate = {2025-08-08},
	journal = {Bioinformation},
	author = {Tiwari, Abhishek and Tiwari, Vivek},
	month = jul,
	year = {2007},
	pmid = {18084648},
	pmcid = {PMC2139992},
	pages = {28--30},
	file = {PubMed Central Full Text PDF:/Users/abhishektiwari/Zotero/storage/YIU5YD2A/Tiwari and Tiwari - 2007 - HBNG Graph theory based visualization of hydrogen bond networks in protein structures.pdf:application/pdf},
}

@misc{laboratory_rlabdukereduce_2025,
	title = {rlabduke/reduce},
	url = {https://github.com/rlabduke/reduce},
	abstract = {Reduce - tool for adding and correcting hydrogens in PDB files},
	urldate = {2025-08-08},
	author = {Laboratory, Richardson},
	month = aug,
	year = {2025},
	note = {original-date: 2015-01-21T18:26:47Z},
}

@article{lindauer_hbexplorenew_1996,
	title = {{HBexplore}-a new tool for identifying and analysing hydrogen bonding patterns in biological macromolecules},
	volume = {12},
	issn = {1367-4803},
	url = {https://doi.org/10.1093/bioinformatics/12.4.281},
	doi = {10.1093/bioinformatics/12.4.281},
	abstract = {The program HBexplore is a new tool for identifying and analysing H-bonding patterns in biological macromolecules. It selects all potential H-bonds according to geometrical criteria. The H-bond table can then be subjected to further automatic or interactive analysis tools. These tools include the calculation of mean values and distributions of geometrical H-bond parameters for parts of a single structure, for complete single structures and for structure sets, the classification of each H-bond according to the participation of backbone, side chain or base, ligand and water parts of nucleic acids or proteins, identification of Watson-Crick nucleotide pairs and of H-bonded pairs of equal nucleotides, the calculation of the mean number of H-bonds per residue, and of the fraction of potential donor and acceptor atoms involved in H-bonds. HBexplore further generates automatically a H-bond residue interaction table. This table lists for all residues of the structure the other residues, ligands or water molecules directly connected via a H-bond. By means of a binary tree search algorithm, this table is then converted into a H-bond cluster table. Clusters are understood here as an uninterrupted network of H-bonded residues. For nucleic acids, secondary structures and tertiary interactions are automatically derived from the H-bonding pattern. HBexplore is applied to two example RNA structures: a pseudoknot and a hairpin. It provides a comprehensive listing of individual H-bonds and statistical information for larger structure sets. In addition, it can identify interesting new H-bond motjfs. One example is a pentanucleotide base-base H-bond interaction mot in the RNA pseudoknot. HBexplore is intended to contribute both to the elucidation of general principles of the architecture of biological macromolecules, and to the prediction and refinement of single structures.},
	number = {4},
	urldate = {2025-08-08},
	journal = {Bioinformatics},
	author = {Lindauer, Klaus and Bendic, Cezar and Sühnel, Jürgen},
	month = aug,
	year = {1996},
	pages = {281--289},
	file = {Full Text PDF:/Users/abhishektiwari/Zotero/storage/SS52FS98/Lindauer et al. - 1996 - HBexplore-a new tool for identifying and analysing hydrogen bonding patterns in biological macromole.pdf:application/pdf;Snapshot:/Users/abhishektiwari/Zotero/storage/WWB9H9IR/12.4.html:text/html},
}

@article{word_asparagine_1999,
	title = {Asparagine and glutamine: using hydrogen atom contacts in the choice of side-chain amide orientation1},
	volume = {285},
	issn = {0022-2836},
	shorttitle = {Asparagine and glutamine},
	url = {https://www.sciencedirect.com/science/article/pii/S0022283698924019},
	doi = {10.1006/jmbi.1998.2401},
	abstract = {Small-probe contact dot surface analysis, with all explicit hydrogen atoms added and their van der Waals contacts included, was used to choose between the two possible orientations for each of 1554 asparagine (Asn) and glutamine (Gln) side-chain amide groups in a dataset of 100 unrelated, high-quality protein crystal structures at 0.9 to 1.7 Å resolution. For the movable-H groups, each connected, closed set of local H-bonds was optimized for both H-bonds and van der Waals overlaps. In addition to the Asn/Gln “flips”, this process included rotation of OH, SH, NH3+, and methionine methyl H atoms, flip and protonation state of histidine rings, interaction with bound ligands, and a simple model of water interactions. However, except for switching N and O identity for amide flips (or N and C identity for His flips), no non-H atoms were shifted. Even in these very high-quality structures, about 20\% of the Asn/Gln side-chains required a 180° flip to optimize H-bonding and/or to avoid NH2clashes with neighboring atoms (incorporating a conservative score penalty which, for marginal cases, favors the assignment in the original coordinate file). The programs Reduce, Probe, and Mage provide not only a suggested amide orientation, but also a numerical score comparison, a categorization of the marginal cases, and a direct visualization of all relevant interactions in both orientations. Visual examination allowed confirmation of the raw score assignment for about 40\% of those Asn/Gln flips placed within the “marginal” penalty range by the automated algorithm, while uncovering only a small number of cases whose automated assignment was incorrect because of special circumstances not yet handled by the algorithm. It seems that the H-bond and the atomic-clash criteria independently look at the same structural realities: when both criteria gave a clear answer they agreed every time. But consideration of van der Waals clashes settled many additional cases for which H-bonding was either absent or approximately equivalent for the two main alternatives. With this extra information, 86\% of all side-chain amide groups could be oriented quite unambiguously. In the absence of further experimental data, it would probably be inappropriate to assign many more than this. Some of the remaining 14\% are ambiguous because of coordinate error or inadequacy of the theoretical model, but the great majority of ambiguous cases probably occur as a dynamic mix of both flip states in the actual protein molecule. The software and the 100 coordinate files with all H atoms added and optimized and with amide flips corrected are publicly available.},
	number = {4},
	urldate = {2025-08-08},
	journal = {Journal of Molecular Biology},
	author = {Word, J. Michael and Lovell, Simon C. and Richardson, Jane S. and Richardson, David C.},
	month = jan,
	year = {1999},
	keywords = {Asn/Gln flips, hydrogen atom placement, hydrogen bond network, side-chain amide orientation, small-probe contact dots},
	pages = {1735--1747},
	file = {ScienceDirect Snapshot:/Users/abhishektiwari/Zotero/storage/PDCWUNX6/S0022283698924019.html:text/html},
}

@misc{noauthor_jensengrouppropka_2025,
	title = {jensengroup/propka},
	copyright = {LGPL-2.1},
	url = {https://github.com/jensengroup/propka},
	abstract = {PROPKA predicts the pKa values of ionizable groups in proteins and protein-ligand complexes based in the 3D structure.},
	urldate = {2025-08-08},
	publisher = {Jensen Group},
	month = jul,
	year = {2025},
	note = {original-date: 2012-10-31T15:16:12Z},
}

@article{afshinpour_aqcalc_2023,
	title = {{AQcalc}: {A} web server that identifies weak molecular interactions in protein structures},
	volume = {32},
	copyright = {© 2023 The Protein Society.},
	issn = {1469-896X},
	shorttitle = {{AQcalc}},
	url = {https://onlinelibrary.wiley.com/doi/abs/10.1002/pro.4762},
	doi = {10.1002/pro.4762},
	abstract = {Weak molecular interactions play an important role in protein structure and function. Computational tools that identify weak molecular interactions are, therefore, valuable for the study of proteins. Here, we present AQcalc, a web server (https://aqcalcbiocomputing.com/) that can be used to identify anion-quadrupole (AQ) interactions, which are weak interactions involving aromatic residue (Trp, Tyr, and Phe) ring edges and anions (Asp, Glu, and phosphate ion) both within proteins and at their interfaces (protein-protein, protein-nucleic acids, and protein-lipid bilayer). AQcalc identifies AQ interactions as well as clusters involving AQ, cation-π, and salt bridges, among others. Utilizing AQcalc we analyzed weak interactions in protein models, even in the absence of experimental structures, to understand the contributions of weak interactions to deleterious structural changes, including those associated with oncogenic and germline disease variants. We identified several deleterious variants with disrupted AQ interactions (comparable in frequency to cation-π disruptions). Amyloid fibrils utilize AQ to bury anions at frequencies that far exceed those observed for globular proteins. AQ interactions were detected three and five times more frequently than the hydrogen-bonded AQ (HBAQ) in fibril structures and protein-lipid bilayer interfaces, respectively. By contrast, AQ and HBAQ interactions were detected with similar frequencies in globular proteins. Collectively, these findings suggest AQcalc will be effective in facilitating fine structural analysis. As other web utilities designed to identify protein residue interaction networks do not report AQ interactions, wide use of AQcalc will enrich our understanding of residue interaction networks and facilitate hypothesis testing by identifying and experimentally characterizing these comparably weak but important interactions.},
	language = {en},
	number = {10},
	urldate = {2025-08-23},
	journal = {Protein Science},
	author = {Afshinpour, Maral and Smith, Logan A. and Chakravarty, Suvobrata},
	year = {2023},
	note = {\_eprint: https://onlinelibrary.wiley.com/doi/pdf/10.1002/pro.4762},
	keywords = {AlphaFold2, anion-quadrupole, cation-π, disordered regions, interfaces, MemProtMD, post-translational modification, variants, weak molecular interactions},
	pages = {e4762},
	file = {Full Text PDF:/Users/abhishektiwari/Zotero/storage/RAMLLT64/Afshinpour et al. - 2023 - AQcalc A web server that identifies weak molecular interactions in protein structures.pdf:application/pdf;Snapshot:/Users/abhishektiwari/Zotero/storage/WYQ2L7RQ/pro.html:text/html},
}

@article{roe_ptraj_2013,
	title = {{PTRAJ} and {CPPTRAJ}: {Software} for {Processing} and {Analysis} of {Molecular} {Dynamics} {Trajectory} {Data}},
	volume = {9},
	issn = {1549-9618, 1549-9626},
	shorttitle = {{PTRAJ} and {CPPTRAJ}},
	url = {https://pubs.acs.org/doi/10.1021/ct400341p},
	doi = {10.1021/ct400341p},
	language = {en},
	number = {7},
	urldate = {2025-08-23},
	journal = {Journal of Chemical Theory and Computation},
	author = {Roe, Daniel R. and Cheatham, Thomas E.},
	month = jul,
	year = {2013},
	pages = {3084--3095},
}

@article{siemers_interactive_2021-1,
	title = {Interactive {Interface} for {Graph}-{Based} {Analyses} of {Dynamic} {H}-{Bond} {Networks}: {Application} to {Spike} {Protein} {S}},
	volume = {61},
	issn = {1549-9596},
	shorttitle = {Interactive {Interface} for {Graph}-{Based} {Analyses} of {Dynamic} {H}-{Bond} {Networks}},
	url = {https://doi.org/10.1021/acs.jcim.1c00306},
	doi = {10.1021/acs.jcim.1c00306},
	abstract = {Dynamic hydrogen-bond networks are key determinants of protein conformational dynamics. In the case of macromolecular protein complexes, which can have a large number of hydrogen bonds giving rise to extensive hydrogen-bond networks, efficient algorithms are required to analyze interactions that could be important for the dynamics and biological function of the complex. We present here a highly efficient, standalone interface designed for analyses of dynamical hydrogen-bond networks of biomolecules and macromolecular complexes. To facilitate a comprehensive description of protein dynamics, the interface includes analyses of hydrophobic interactions. We illustrate the usefulness and workflow of the interface by dissecting the dynamics of the ectodomain of SARS-CoV-2 protein S in its closed conformation. We find that protein S contains numerous local clusters of dynamic hydrogen bonds and identify hydrogen bonds that are sampled persistently. The receptor binding domain of the spike protein hosts only a handful of persistent hydrogen-bond clusters, suggesting structural plasticity. Our data analysis interface is released here for open use.},
	number = {6},
	urldate = {2025-08-23},
	journal = {Journal of Chemical Information and Modeling},
	author = {Siemers, Malte and Bondar, Ana-Nicoleta},
	month = jun,
	year = {2021},
	note = {Publisher: American Chemical Society},
	pages = {2998--3014},
}

@misc{fang_learning_2025,
	title = {Learning {Universal} {Representations} of {Intermolecular} {Interactions} with {ATOMICA}},
	copyright = {© 2025, Posted by Cold Spring Harbor Laboratory. This pre-print is available under a Creative Commons License (Attribution 4.0 International), CC BY 4.0, as described at http://creativecommons.org/licenses/by/4.0/},
	url = {https://www.biorxiv.org/content/10.1101/2025.04.02.646906v2},
	doi = {10.1101/2025.04.02.646906},
	abstract = {Molecular interactions underlie nearly all biological processes, but most machine learning models treat molecules in isolation or specialize in a single type of interaction, such as protein-ligand or protein-protein binding. Here, we introduce ATOMICA, a geometric deep learning model that learns atomic-scale representations of intermolecular interfaces across five modalities, including proteins, small molecules, metal ions, lipids, and nucleic acids. ATOMICA is trained on 2,037,972 interaction complexes using self-supervised denoising and masking to generate embeddings of interaction interfaces at the levels of atoms, chemical blocks, and molecular interfaces. ATOMICA’s latent space is compositional and captures physicochemical features shared across molecular classes, enabling representations of new molecular interactions to be generated by algebraically combining embeddings of interaction interfaces. The representation quality of this space improves with increased data volume and modality diversity. As in pre-trained natural language models, this scaling law implies predictable gains in performance as structural datasets expand. We construct modality-specific interfaceome networks, termed ATOMICANETs, which connect proteins based on interaction similarity with ions, small molecules, nucleic acids, lipids, and proteins. By overlaying disease-associated proteins of 27 diseases onto ATOMICANETs, we find strong associations for asthma in lipid networks and myeloid leukemia in ion networks. We use ATOMICA to annotate the dark proteome-proteins lacking known function-by predicting 2,646 uncharacterized ligand-binding sites, including putative zinc finger motifs and transmembrane cytochrome subunits. We experimentally confirm heme binding for five ATOMICA predictions in the dark proteome. By modeling molecular interactions, ATOMICA opens new avenues for understanding and annotating molecular function at scale.},
	language = {en},
	urldate = {2025-09-01},
	publisher = {bioRxiv},
	author = {Fang, Ada and Desgagné, Michael and Zhang, Zaixi and Zhou, Andrew and Loscalzo, Joseph and Pentelute, Bradley L. and Zitnik, Marinka},
	month = jul,
	year = {2025},
	note = {ISSN: 2692-8205
Pages: 2025.04.02.646906
Section: New Results},
	file = {Full Text PDF:/Users/abhishektiwari/Zotero/storage/H67Z6AJP/Fang et al. - 2025 - Learning Universal Representations of Intermolecular Interactions with ATOMICA.pdf:application/pdf},
}

@article{procacci_pdbrestore_2025,
	title = {{PDBrestore}: {A} {Free} {Web} {Interface} for {Processing} and {Fixing} {Protein} {Chains} {From} {Raw} {PDB} {Files}},
	volume = {46},
	issn = {0192-8651},
	shorttitle = {{PDBrestore}},
	url = {https://pmc.ncbi.nlm.nih.gov/articles/PMC12076534/},
	doi = {10.1002/jcc.70124},
	abstract = {We present PDBrestore, a free web interface for repairing protein PDB chains extracted from either a local PDB file or a PDB file downloaded from the Protein Data Bank. PDBrestore performs several key tasks: It adds hydrogen atoms, completes missing atoms in side chains, fills gaps in the sequence, derives the itp parameter file for a ligand according to the GAFF2 force field for GROMACS applications, and generates a reasonably pre‐equilibrated solvated simulation box. The interface is designed to streamline the cumbersome preparatory work required to set up an initial protein‐ligand coordinates PDB file for use in drug design projects, such as free energy perturbation or thermodynamic integration calculations of ligand binding affinities. Additionally, PDBrestore is available as a command‐line application within the open‐source ORAC distribution, which can be freely downloaded from the website: www1.chim.unifi.it/orac., PDBrestore is a freely accessible web interface to repair raw PDB files fetched from the Protein Data Bank for molecular dynamics applications. The interface, with minimal end‐user intervention, repairs common PDB defects such as missing atoms, incomplete side chains, missing residues, non‐standard residue names or non‐standard atom labels, manages the presence of cofactors, ligands, or post‐translational modifications such as disulfide bridges, and provides a pre‐equilibrated simulation box including explicit solvent.},
	number = {13},
	urldate = {2025-10-14},
	journal = {Journal of Computational Chemistry},
	author = {Procacci, Piero},
	month = may,
	year = {2025},
	pmid = {40365838},
	pmcid = {PMC12076534},
	pages = {e70124},
	file = {Full Text PDF:/Users/abhishektiwari/Zotero/storage/I6ZZECMC/Procacci - 2025 - PDBrestore A Free Web Interface for Processing and Fixing Protein Chains From Raw PDB Files.pdf:application/pdf},
}

@article{jimenez-garcia_pdb-tools_2021,
	title = {{PDB}-tools web: {A} user-friendly interface for the manipulation of {PDB} files},
	volume = {89},
	issn = {1097-0134},
	shorttitle = {{PDB}-tools web},
	doi = {10.1002/prot.26018},
	abstract = {The Protein Data Bank (PDB) file format remains a popular format used and supported by many software to represent coordinates of macromolecular structures. It however suffers from drawbacks such as error-prone manual editing. Because of that, various software toolkits have been developed to facilitate its editing and manipulation, but, to date, there is no online tool available for this purpose. Here we present PDB-Tools Web, a flexible online service for manipulating PDB files. It offers a rich and user-friendly graphical user interface that allows users to mix-and-match more than 40 individual tools from the pdb-tools suite. Those can be combined in a few clicks to perform complex pipelines, which can be saved and uploaded. The resulting processed PDB files can be visualized online and downloaded. The web server is freely available at https://wenmr.science.uu.nl/pdbtools.},
	language = {eng},
	number = {3},
	journal = {Proteins},
	author = {Jiménez-García, Brian and Teixeira, João M. C. and Trellet, Mikael and Rodrigues, João P. G. L. M. and Bonvin, Alexandre M. J. J.},
	month = mar,
	year = {2021},
	pmid = {33111403},
	pmcid = {PMC7855443},
	keywords = {bioinformatics, Proteins, Database Management Systems, Databases, Protein, Internet, Models, Molecular, PDB, Protein Conformation, structural biology, User-Computer Interface, web server},
	pages = {330--335},
	file = {Full Text:/Users/abhishektiwari/Zotero/storage/LHZJ29QG/Jiménez-García et al. - 2021 - PDB-tools web A user-friendly interface for the manipulation of PDB files.pdf:application/pdf},
}

@article{nnyigide_protein_2022,
	title = {Protein {Repair} and {Analysis} {Server}: {A} {Web} {Server} to {Repair} {PDB} {Structures}, {Add} {Missing} {Heavy} {Atoms} and {Hydrogen} {Atoms}, and {Assign} {Secondary} {Structures} by {Amide} {Interactions}},
	volume = {62},
	issn = {1549-960X},
	shorttitle = {Protein {Repair} and {Analysis} {Server}},
	doi = {10.1021/acs.jcim.2c00571},
	abstract = {The Protein Data Bank (PDB) file format developed at Brookhaven National Laboratory is the most popular file format to store biological data and is widely supported by many software programs for the editing and visualization of macromolecular structures. Unfortunately, many of these structures have variety of issues ranging from missing side chains and/or atoms to alternate locations (rotamers). To fix these flaws, one has to learn a new program, compile modules, and install libraries. To overcome these challenges, we present Protein Repair and Analysis Server (PRAS), an easy-to-use web server to repair protein structures and add missing atoms. The PRAS program flow has two main sections. In the first section, several subroutines are called to deal with sequence microheterogeneity, rotamers, missing side chains, heavy atoms, or hydrogen atoms. A log file is generated detailing irregularities and their fixes as carried out by the program. In the later section, the program uses the error-corrected protein structure to assign secondary structure elements based on amide-amide interactions of the backbone. Plots of four Ramachandran types (general, glycine, proline, and pre-proline) and percentage of secondary structure elements; a log file and the error-corrected PDB file can be downloaded by clicking on the download link generated by the server. The server is freely available and is accessed through its web address at www.protein-science.com. Alternatively, users can download the source code from the server or install the program on their local machines following the instructions at https://pypi.org/project/Pras-Server/ or https://github.com/osita-sunday-nnyigide/Pras\_Server. While the present work focuses on the repair of structural data in the PDB format, the server is capable of fixing similar errors in the extensible and newly adopted mmCIF format.},
	language = {eng},
	number = {17},
	journal = {Journal of Chemical Information and Modeling},
	author = {Nnyigide, Osita Sunday and Nnyigide, Tochukwu Olunna and Lee, Sun-Gu and Hyun, Kyu},
	month = sep,
	year = {2022},
	pmid = {36000562},
	keywords = {Hydrogen, Protein Structure, Secondary, Proteins, Databases, Protein, Internet, Protein Conformation, Amides, Proline, Software},
	pages = {4232--4246},
}

@misc{osita-sunday-nnyigide_osita-sunday-nnyigidepras_server_2025,
	title = {osita-sunday-nnyigide/{Pras}\_Server},
	url = {https://github.com/osita-sunday-nnyigide/Pras_Server},
	abstract = {Pras Server is a library to repair PDB or mmCIF structures, add missing heavy atoms and hydrogen atoms and assign secondary structure by amide-amide interactions of the backbone},
	urldate = {2025-10-14},
	author = {osita-sunday-nnyigide},
	month = sep,
	year = {2025},
	note = {original-date: 2022-07-25T19:35:08Z},
	keywords = {bioinformatics, biophysics},
}
