Paratome: an online tool for systematic identification of antigen-binding regions in antibodies based on sequence or structure

Kunik, Vered; Ashkenazi, Shaul; Ofran, Yishai

doi:10.1093/nar/gks480

Cited by 131 publications

(112 citation statements)

References 23 publications

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“…It also showed that the accuracy for the six CDR loops differ from each other, and that it is particularly low for L2 loops due to their low antigen-binding rates. Our discrimination accuracy is higher than that in a previous study with a similar rule-based method (Paratome, MCC 5 0.23 and F measure 5 0.48), 20,21 where the definition of antigen-binding residues is different from ours (at least one atom within 6 Å from any antigen atoms, compared to our 4Å distance threshold), while it is lower than the prediction accuracy of antigen-binding residues in full length antibodies by a random forest-based method (MCC 5 0.52). 22 These observations suggest that probable antigen-binding positions can be identified by using simple sequence and structural features.…”

Section: Characterization Of Antigen-binding Propensity Of Each Cdr Pcontrasting

confidence: 85%

The diversity of H3 loops determines the antigen‐binding tendencies of antibody CDR loops

Tsuchiya

Mizuguchi

2016

Protein Science

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Of the complementarity-determining regions (CDRs) of antibodies, H3 loops, with varying amino acid sequences and loop lengths, adopt particularly diverse loop conformations. The diversity of H3 conformations produces an array of antigen recognition patterns involving all the CDRs, in which the residue positions actually in contact with the antigen vary considerably. Therefore, for a deeper understanding of antigen recognition, it is necessary to relate the sequence and structural properties of each residue position in each CDR loop to its ability to bind antigens. In this study, we proposed a new method for characterizing the structural features of the CDR loops and obtained the antigen-binding ability of each residue position in each CDR loop. This analysis led to a simple set of rules for identifying probable antigen-binding residues. We also found that the diversity of H3 loop lengths and conformations affects the antigen-binding tendencies of all the CDR loops.

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Section: Characterization Of Antigen-binding Propensity Of Each Cdr Pcontrasting

confidence: 85%

The diversity of H3 loops determines the antigen‐binding tendencies of antibody CDR loops

Tsuchiya

Mizuguchi

2016

Protein Science

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“…SwarmDock [29,30], PyDock [31], and ZDOCK [32,33] are ab-initio methods, whereas HADDOCK uses bioinformatics predictions to drive the docking [34], in this particular case it uses CPORT to predict interface residues [35] and PARATOME [36] to identify CDR loops of antibodies (see Methods). Overall the success rates (at least one acceptable prediction for a benchmark case) ranged between 5–16% for the top prediction, 20–38% for the top 10 predictions, and 40–67% for the top 100 predictions, comparable to the success rates on version 4 of the docking benchmark using SwarmDock and ZDOCK [37,38].…”

Section: Resultsmentioning

confidence: 99%

Updates to the Integrated Protein–Protein Interaction Benchmarks: Docking Benchmark Version 5 and Affinity Benchmark Version 2

Vreven

Moal

Vangone

et al. 2015

Journal of Molecular Biology

403

538

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We present an updated and integrated version of our widely used protein-protein docking and binding affinity benchmarks. The benchmarks consist of non-redundant, high quality structures of protein-protein complexes along with the unbound structures of their components. Fifty-five new complexes were added to the docking benchmark, 35 of which have experimentally-measured binding affinities. These updated docking and affinity benchmarks now contain 230 and 179 entries, respectively. In particular, the number of antibody-antigen complexes has increased significantly, by 67% and 74% in the docking and affinity benchmarks, respectively. We tested previously developed docking and affinity prediction algorithms on the new cases. Considering only the top ten docking predictions per benchmark case, a prediction accuracy of 38% is achieved on all 55 cases, and up to 50% for the 32 rigid-body cases only. Predicted affinity scores are found to correlate with experimental binding energies up to r=0.52 overall, and r=0.72 for the rigid complexes.

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“…In a recent analysis (29) we utilized all available protein-Ab complexes in the PDB to identify the structural regions in which Ag binding actually occurs. This approach was implemented into a method dubbed Paratome (30, 74) that is based on a multiple structure alignment (MSTA) of all available Ab-Ag complexes in the PDB. The MSTA revealed regions of structural consensus where the pattern of structural positions that bind the Ag is highly similar among all Abs.…”

Section: The Role Of Cdrs and Their Definitionmentioning

confidence: 99%

The Structural Basis of Antibody-Antigen Recognition

2013

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The function of antibodies (Abs) involves specific binding to antigens (Ags) and activation of other components of the immune system to fight pathogens. The six hypervariable loops within the variable domains of Abs, commonly termed complementarity determining regions (CDRs), are widely assumed to be responsible for Ag recognition, while the constant domains are believed to mediate effector activation. Recent studies and analyses of the growing number of available Ab structures, indicate that this clear functional separation between the two regions may be an oversimplification. Some positions within the CDRs have been shown to never participate in Ag binding and some off-CDRs residues often contribute critically to the interaction with the Ag. Moreover, there is now growing evidence for non-local and even allosteric effects in Ab-Ag interaction in which Ag binding affects the constant region and vice versa. This review summarizes and discusses the structural basis of Ag recognition, elaborating on the contribution of different structural determinants of the Ab to Ag binding and recognition. We discuss the CDRs, the different approaches for their identification and their relationship to the Ag interface. We also review what is currently known about the contribution of non-CDRs regions to Ag recognition, namely the framework regions (FRs) and the constant domains. The suggested mechanisms by which these regions contribute to Ag binding are discussed. On the Ag side of the interaction, we discuss attempts to predict B-cell epitopes and the suggested idea to incorporate Ab information into B-cell epitope prediction schemes. Beyond improving the understanding of immunity, characterization of the functional role of different parts of the Ab molecule may help in Ab engineering, design of CDR-derived peptides, and epitope prediction.

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Paratome: an online tool for systematic identification of antigen-binding regions in antibodies based on sequence or structure

Cited by 131 publications

References 23 publications

The diversity of H3 loops determines the antigen‐binding tendencies of antibody CDR loops

The diversity of H3 loops determines the antigen‐binding tendencies of antibody CDR loops

Updates to the Integrated Protein–Protein Interaction Benchmarks: Docking Benchmark Version 5 and Affinity Benchmark Version 2

The Structural Basis of Antibody-Antigen Recognition

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