Antibody modeling assessment

The Protein Data Bank

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Sequences of proteins of immunological interest

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Conformations of immunoglobulin hypervariable regions

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Development trends for human monoclonal antibody therapeutics

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Antibody Multispecificity Mediated by Conformational Diversity

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Standard conformations for the canonical structures of immunoglobulins 1 1Edited by I. A. Wilson

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Three-Dimensional Structure of Immunoglobulins

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A new clustering of antibody CDR loop conformations

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Expressed Murine and Human CDR-H3 Intervals of Equal Length Exhibit Distinct Repertoires that Differ in their Amino Acid Composition and Predicted Range of Structures

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The dynamo library for molecular simulations using hybrid quantum mechanical and molecular mechanical potentials

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Structural Families in Loops of Homologous Proteins: Automatic Classification, Modelling and Application to Antibodies

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Framework residue 71 is a major determinant of the position and conformation of the second hypervariable region in the VH domains of immunoglobulins

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Critical assessment of methods of protein structure prediction—Round VIII

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Toward high‐resolution homology modeling of antibody Fv regions and application to antibody–antigen docking

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Progress over the first decade of CASP experiments

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PIGS: automatic prediction of antibody structures

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H3‐rules: identification of CDR‐H3 structures in antibodies

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RosettaAntibody: antibody variable region homology modeling server

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WAM: an improved algorithm for modelling antibodies on the WEB

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LOOPER: a molecular mechanics-based algorithm for protein loop prediction

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Structural classification of CDR‐H3 revisited: A lesson in antibody modeling

Kuroda, D., Shirai, H., Kobori, M., & Nakamura, H. (2008) · Proteins Structure Function and Bioinformatics · doi:10.1002/prot.22087 · Cited by 127 · see citing works

Antibody Modeling: Implications for Engineering and Design

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Variable Region Sequences of Five Human Immunoglobulin Heavy Chains of the V H III Subgroup: Definitive Identification of Four Heavy Chain Hypervariable Regions

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Metrics for antibody therapeutics development

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The other 90% of the protein: Assessment beyond the Cαs for CASP8 template‐based and high‐accuracy models

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Systematic classification of CDR‐L3 in antibodies: Implications of the light chain subtypes and the VL–VH interface

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Construction of a Three-Dimensional Model of the Polypeptide Backbone of the Variable Region of Kappa Immunoglobulin Light Chains

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Antibody Variable Regions: Toward a Unified Modeling Method

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Antibody Structure and Function: The Basis for Engineering Therapeutics

Chiu, M. L., Goulet, D. R., Teplyakov, A., & Gilliland, G. L. (2019) · Antibodies · doi:10.3390/antib8040055 · Cited by 646 · see citing works

Serverification of Molecular Modeling Applications: The Rosetta Online Server That Includes Everyone (ROSIE)

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High-resolution <i>de novo</i> structure prediction from primary sequence

Wu, R., Ding, F., Wang, R., Shen, R., Zhang, X., Luo, S., Su, C., Wu, Z., Xie, Q., Berger, B., et al. (2022) · doi:10.1101/2022.07.21.500999 · Cited by 392 · see citing works

Homology modeling in drug discovery: Overview, current applications, and future perspectives

Muhammed, M. T., & Aki, E. (2018) · Chemical Biology & Drug Design · doi:10.1111/cbdd.13388 · Cited by 367 · see citing works

ABodyBuilder: automated antibody structure prediction with data-driven accuracy estimation

Leem, J., Dunbar, J., Georges, G., Shi, J., & Deane, C. M. (2016) · mAbs · doi:10.1080/19420862.2016.1205773 · Cited by 238 · see citing works

Antibody structure determination using a combination of homology modeling, energy-based refinement, and loop prediction

Zhu, K., Day, T., Warshaviak, D., Murrett, C. S., Friesner, R., & Pearlman, D. (2014) · Proteins: Structure, Function, and Bioinformatics · doi:10.1002/prot.24551 · Cited by 206 · see citing works

Computer-aided antibody design

Kuroda, D., Shirai, H., Jacobson, M. P., & Nakamura, H. (2012) · Protein Engineering Design and Selection · doi:10.1093/protein/gzs024 · Cited by 223 · see citing works

SAbPred: a structure-based antibody prediction server

Dunbar, J., Krawczyk, K., Leem, J., Marks, C., Nowak, J., Regep, C., Georges, G. J., Kelm, S., Popovic, B., & Deane, C. M. (2016) · Nucleic Acids Research · doi:10.1093/nar/gkw361 · Cited by 202 · see citing works

Phage Display Libraries for Antibody Therapeutic Discovery and Development

Almagro, J. C., Pedraza‐Escalona, M., Arrieta-Oliva, H. I., & Pérez‐Tapia, S. M. (2019) · Antibodies · doi:10.3390/antib8030044 · Cited by 163 · see citing works

Antibody recognition of carbohydrate epitopes

Haji‐Ghassemi, O., Blackler, R. J., Young, N. M., & Evans, S. V. (2015) · Glycobiology · doi:10.1093/glycob/cwv037 · Cited by 145 · see citing works

Second antibody modeling assessment (AMA-II)

Almagro, J. C., Teplyakov, A., Luo, J., Sweet, R. W., Kodangattil, S., Hernandez‐Guzman, F., & Gilliland, G. L. (2014) · Proteins Structure Function and Bioinformatics · doi:10.1002/prot.24567 · Cited by 144 · see citing works

Fully automated antibody structure prediction using BIOVIA tools: Validation study

Kemmish, H., Fasnacht, M., & Yan, L. (2017) · PLoS ONE · doi:10.1371/journal.pone.0177923 · Cited by 141 · see citing works

Molecular Signatures of Hemagglutinin Stem-Directed Heterosubtypic Human Neutralizing Antibodies against Influenza A Viruses

Avnir, Y., Tallarico, A. S. C., Zhu, Q. K., Bennett, A. S., Connelly, G., Sheehan, J., Sui, J., Fahmy, A., Huang, C., Cadwell, G., et al. (2014) · PLoS Pathogens · doi:10.1371/journal.ppat.1004103 · Cited by 127 · see citing works

ABangle: characterising the VH-VL orientation in antibodies

Dunbar, J., Fuchs, A., Shi, J., & Deane, C. M. (2013) · Protein Engineering Design and Selection · doi:10.1093/protein/gzt020 · Cited by 125 · see citing works

A Systematic Comparison of Free and Bound Antibodies Reveals Binding-Related Conformational Changes

Sela‐Culang, I., Alon, S., & Ofran, Y. (2012) · The Journal of Immunology · doi:10.4049/jimmunol.1201493 · Cited by 115 · see citing works

Aggregation risk prediction for antibodies and its application to biotherapeutic development

Obrezanova, O., Arnell, A., de la Cuesta, R. G., Berthelot, M. E., Gallagher, T. R., Zurdo, J., & Stallwood, Y. (2015) · mAbs · doi:10.1080/19420862.2015.1007828 · Cited by 107 · see citing works

The Origin of CDR H3 Structural Diversity

Weitzner, B. D., Dunbrack, R. L., & Gray, J. J. (2015) · Structure · doi:10.1016/j.str.2014.11.010 · Cited by 101 · see citing works

Assessing developability early in the discovery process for novel biologics

Fernández‐Quintero, M. L., Ljungars, A., Waibl, F., Greiff, V., Andersen, J. T., Gjølberg, T. T., Jenkins, T. P., Voldborg, B. G. R., Grav, L. M., Kumar, S., et al. (2023) · mAbs · doi:10.1080/19420862.2023.2171248 · Cited by 99 · see citing works

Understanding the Conformational Impact of Chemical Modifications on Monoclonal Antibodies with Diverse Sequence Variation Using Hydrogen/Deuterium Exchange Mass Spectrometry and Structural Modeling

Zhang, A., Hu, P., MacGregor, P. A., Xue, Y., Fan, H., Suchecki, P., Olszewski, L., & Liu, A. (2014) · Analytical Chemistry · doi:10.1021/ac404130a · Cited by 98 · see citing works

Blind prediction performance of RosettaAntibody 3.0: Grafting, relaxation, kinematic loop modeling, and full CDR optimization

Weitzner, B. D., Kuroda, D., Marze, N. A., Xu, J., & Gray, J. J. (2014) · Proteins Structure Function and Bioinformatics · doi:10.1002/prot.24534 · Cited by 97 · see citing works

Characterizing the Diversity of the CDR-H3 Loop Conformational Ensembles in Relationship to Antibody Binding Properties

Fernández‐Quintero, M. L., Loeffler, J. R., Kraml, J., Kahler, U., Kamenik, A. S., & Liedl, K. R. (2019) · Frontiers in Immunology · doi:10.3389/fimmu.2018.03065 · Cited by 96 · see citing works

Assessment of fully automated antibody homology modeling protocols in molecular operating environment

Maier, J. K. X., & Labute, P. (2014) · Proteins Structure Function and Bioinformatics · doi:10.1002/prot.24576 · Cited by 91 · see citing works

LYRA, a webserver for lymphocyte receptor structural modeling

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Metal-ion-chelating phenylalanine nanostructures reverse immune dysfunction and sensitize breast tumour to immune checkpoint blockade

Tan, M., Cao, G., Wang, R., Cheng, L., Huang, W., Yin, Y., Ma, H., Ho, S., Wang, Z., Zhu, M., et al. (2024) · Nature Nanotechnology · doi:10.1038/s41565-024-01758-3 · Cited by 83 · see citing works

Shape complementarity and hydrogen bond preferences in protein–protein interfaces: implications for antibody modeling and protein–protein docking

Kuroda, D., & Gray, J. J. (2016) · Bioinformatics · doi:10.1093/bioinformatics/btw197 · Cited by 81 · see citing works

Natural and man-made V-gene repertoires for antibody discovery

Finlay, W. J. J., & Almagro, J. C. (2012) · Frontiers in Immunology · doi:10.3389/fimmu.2012.00342 · Cited by 76 · see citing works

Antibody modeling assessment II. Structures and models

Teplyakov, A., Luo, J., Obmolova, G., Malia, T. J., Sweet, R., Stanfield, R. L., Kodangattil, S., Almagro, J. C., & Gilliland, G. L. (2014) · Proteins: Structure, Function, and Bioinformatics · doi:10.1002/prot.24554 · Cited by 72 · see citing works

A Review of Deep Learning Methods for Antibodies

Graves, J., Byerly, J., Priego, E., Makkapati, N., Parish, S., Medellin, B. P., & Berrondo, M. (2020) · Antibodies · doi:10.3390/antib9020012 · Cited by 73 · see citing works

Structural diversity in a human antibody germline library

Teplyakov, A., Obmolova, G., Malia, T., Luo, J., Muzammil, S., Sweet, R. W., Almagro, J. C., & Gilliland, G. L. (2016) · mAbs · doi:10.1080/19420862.2016.1190060 · Cited by 62 · see citing works

PIGSPro: prediction of immunoGlobulin structures v2

Lepore, R., Olimpieri, P. P., Messih, M. A., & Tramontano, A. L. (2017) · Nucleic Acids Research · doi:10.1093/nar/gkx334 · Cited by 61 · see citing works

Modeling Immunity with Rosetta: Methods for Antibody and Antigen Design

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Antibody modeling using the Prediction of ImmunoGlobulin Structure (PIGS) web server

Marcatili, P., Olimpieri, P. P., Chailyan, A., & Tramontano, A. L. (2014) · Nature Protocols · doi:10.1038/nprot.2014.189 · Cited by 58 · see citing works

Antibody H3 structure prediction

Marks, C., & Deane, C. M. (2017) · Computational and Structural Biotechnology Journal · doi:10.1016/j.csbj.2017.01.010 · Cited by 56 · see citing works

Prediction of VH-VL domain orientation for antibody variable domain modeling

Bujotzek, A., Dunbar, J., Lipsmeier, F., Schäfer, W., Antes, I., Deane, C. M., & Georges, G. (2015) · Proteins Structure Function and Bioinformatics · doi:10.1002/prot.24756 · Cited by 56 · see citing works

Challenges in antibody structure prediction

Fernández-Quintero, M. L., Kokot, J., Waibl, F., Fischer, A.-L. M., Quoika, P. K., Deane, C. M., & Liedl, K. R. (2023) · mAbs · doi:10.1080/19420862.2023.2175319 · Cited by 55 · see citing works

Length-independent structural similarities enrich the antibody CDR canonical class model

Nowak, J., BAKER, T. S., Georges, G. J., Kelm, S., Klostermann, S., Shi, J., Sridharan, S., & Deane, C. M. (2016) · mAbs · doi:10.1080/19420862.2016.1158370 · Cited by 54 · see citing works

Prediction of methionine oxidation risk in monoclonal antibodies using a machine learning method

Sankar, K., Hoi, K. H., Yin, Y., Ramachandran, P., Andersen, N., Hilderbrand, A. E., McDonald, P., Spiess, C., & Zhang, Q. (2018) · mAbs · doi:10.1080/19420862.2018.1518887 · Cited by 53 · see citing works

High-resolution modeling of antibody structures by a combination of bioinformatics, expert knowledge, and molecular simulations

Shirai, H., Ikeda, K., Yamashita, K., Tsuchiya, Y., Sarmiento, J., Liang, S., Morokata, T., Mizuguchi, K., Higo, J., Standley, D. M., et al. (2014) · Proteins Structure Function and Bioinformatics · doi:10.1002/prot.24591 · Cited by 51 · see citing works

Lateral Clustering of TLR3:dsRNA Signaling Units Revealed by TLR3ecd:3Fabs Quaternary Structure

Luo, J., Obmolova, G., Malia, T., Wu, S., Duffy, K. E., Marion, J. D., Bell, J. K., Ge, P., Zhou, Z., Teplyakov, A., et al. (2012) · Journal of Molecular Biology · doi:10.1016/j.jmb.2012.05.006 · Cited by 51 · see citing works

Physics‐based enzyme design: Predicting binding affinity and catalytic activity

Sirin, S., Pearlman, D. A., & Sherman, W. (2014) · Proteins Structure Function and Bioinformatics · doi:10.1002/prot.24694 · Cited by 49 · see citing works

Kotai Antibody Builder: automated high-resolution structural modeling of antibodies

Yamashita, K., Ikeda, K., Amada, K. M., Liang, S., Tsuchiya, Y., Nakamura, H., Shirai, H., & Standley, D. M. (2014) · Bioinformatics · doi:10.1093/bioinformatics/btu510 · Cited by 48 · see citing works

How B-Cell Receptor Repertoire Sequencing Can Be Enriched with Structural Antibody Data

Kovaltsuk, A., Krawczyk, K., Galson, J. D., Kelly, D. F., Deane, C. M., & Trück, J. (2017) · Frontiers in Immunology · doi:10.3389/fimmu.2017.01753 · Cited by 48 · see citing works

In silico analysis of plant phytochemicals against secreted aspartic proteinase enzyme of Candida albicans

Meenambiga, S. S., Venkataraghavan, R., & Biswal, R. A. (2018) · Journal of Applied Pharmaceutical Science · doi:10.7324/japs.2018.81120 · Cited by 45 · see citing works

Leveraging SBDD in Protein Therapeutic Development: Antibody Engineering

Gilliland, G. L., Luo, J., Vafa, O., & Almagro, J. C. (2011) · Methods in molecular biology · doi:10.1007/978-1-61779-520-6_14 · Cited by 42 · see citing works

Acknowledging Errors: Advanced Molecular Replacement with Phaser

McCoy, A. J. (2017) · Methods in molecular biology · doi:10.1007/978-1-4939-7000-1_18 · Cited by 42 · see citing works

Differential performance of RoseTTAFold in antibody modeling

Liang, T., Jiang, C., Yuan, J., Othman, Y., Xie, X., & Feng, Z. (2022) · Briefings in Bioinformatics · doi:10.1093/bib/bbac152 · Cited by 41 · see citing works

Molecular docking of various bioactive compounds from essential oil of Trachyaspermum ammi against the fungal enzyme Candidapepsin-1

Biswal, R. A., Venkataraghavan, R., Pazhamalai, V., & Ivo Romauld, S. (2019) · Journal of Applied Pharmaceutical Science · doi:10.7324/japs.2019.90503 · Cited by 41 · see citing works

Revisiting antibody modeling assessment for CDR-H3 loop

Nishigami, H., Kamiya, N., & Nakamura, H. (2016) · Protein Engineering Design and Selection · doi:10.1093/protein/gzw028 · Cited by 39 · see citing works

Homology modeling and structure-based design improve hydrophobic interaction chromatography behavior of integrin binding antibodies

Jetha, A., Thorsteinson, N., Jmeian, Y., Jeganathan, A., Giblin, P. A., & Fransson, J. (2018) · mAbs · doi:10.1080/19420862.2018.1475871 · Cited by 36 · see citing works

Computationally Guided Design of Single-Chain Variable Fragment Improves Specificity of Chimeric Antigen Receptors

Krokhotin, A., Du, H., Hirabayashi, K., Popov, K. I., Kurokawa, T., Wan, X., Ferrone, S., Dotti, G., & Dokholyan, N. V. (2019) · Molecular Therapy — Oncolytics · doi:10.1016/j.omto.2019.08.008 · Cited by 36 · see citing works

In Silico Methods in Antibody Design

Zhao, J., Nussinov, R., Wu, W., & Ma, B. (2018) · Antibodies · doi:10.3390/antib7030022 · Cited by 36 · see citing works

Automated antibody structure prediction using Accelrys tools: Results and best practices

Fasnacht, M., Butenhof, K. J., Lamy, A., Hernandez-Guzman, F. G., Huang, H., & Yan, L. (2014) · Proteins Structure Function and Bioinformatics · doi:10.1002/prot.24604 · Cited by 35 · see citing works

Humanization and Simultaneous Optimization of Monoclonal Antibody

Kuramochi, T., Igawa, T., Tsunoda, H., & Hattori, K. (2013) · Methods in molecular biology · doi:10.1007/978-1-62703-586-6_7 · Cited by 35 · see citing works

Ab initio structure prediction of the antibody hypervariable H3 loop

Zhu, K., & Day, T. (2013) · Proteins Structure Function and Bioinformatics · doi:10.1002/prot.24240 · Cited by 34 · see citing works

Quantifying the Risks of Asparagine Deamidation and Aspartate Isomerization in Biopharmaceuticals by Computing Reaction Free-Energy Surfaces

Plotnikov, N. V., Singh, S. K., Rouse, J. C., & Kumar, S. (2017) · The Journal of Physical Chemistry B · doi:10.1021/acs.jpcb.6b11614 · Cited by 31 · see citing works

Mechanism of Insoluble Aggregate Formation in a Reconstituted Solution of Spray-Dried Protein Powder

Tao, Y., Chen, Y., Howard, W. W., Ibrahim, M., Patel, S. M., McMahon, W. P., Kim, Y. J., Delmar, J. A., & Davis, D. (2023) · Pharmaceutical Research · doi:10.1007/s11095-023-03524-x · Cited by 29 · see citing works

Examining Variable Domain Orientations in Antigen Receptors Gives Insight into TCR-Like Antibody Design

Dunbar, J., Knapp, B., Fuchs, A., Shi, J., & Deane, C. M. (2014) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1003852 · Cited by 29 · see citing works

Improving the accuracy of the structure prediction of the third hypervariable loop of the heavy chains of antibodies

Messih, M. A., Lepore, R., Marcatili, P., & Tramontano, A. L. (2014) · Bioinformatics · doi:10.1093/bioinformatics/btu194 · Cited by 28 · see citing works

Improving Loop Modeling of the Antibody Complementarity-Determining Region 3 Using Knowledge-Based Restraints

Finn, J. A., Leman, J. K., Willis, J. R., Cisneros, A., Crowe, J. E., & Meiler, J. (2016) · PLoS ONE · doi:10.1371/journal.pone.0154811 · Cited by 25 · see citing works

Promiscuous antibodies characterised by their physico-chemical properties: From sequence to structure and back

Laffy, J., Dodev, T. S., Macpherson, J. A., Townsend, C. L., Lu, H. C., Dunn‐Walters, D. K., & Fraternali, F. (2016) · Progress in Biophysics and Molecular Biology · doi:10.1016/j.pbiomolbio.2016.09.002 · Cited by 25 · see citing works

Surprisingly Fast Interface and Elbow Angle Dynamics of Antigen-Binding Fragments

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Structural modeling of antibody variable regions using deep learning—progress and perspectives on drug discovery

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