Prediction of site-specific interactions in antibody-antigen complexes: the proABC method and server

Olimpieri, Pier Paolo; Chailyan, Anna; Tramontano, Anna; Marcatili, Paolo

doi:10.1093/bioinformatics/btt369

Cited by 87 publications

(69 citation statements)

References 37 publications

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“…All these shared predictions are aromatic residues and 13 of the shared predictions are true positives. These two sets of results are the most accurate predictions from the known algorithms available in the public domain (30,31,33,34).…”

Section: Validation Of Computationally Predicted Hot Spot Residues Inmentioning

confidence: 92%

“…One set of predictions was carried out with our previously published computational method [In-silico Molecular Biology Labprotein-protein interaction (ISMBLab-PPI)], where the proteinprotein interaction confidence level (PPI_CL) for protein surface atoms to participate in protein-protein interaction is strongly correlated (r 2 = 0.99) with the averaged burial level of the atoms in the PPI interfaces (30). Another set of predictions was carried out with a recently published random forest algorithm, prediction of antibody contacts (proABC) (31), which was trained specifically with antibody-antigen complex structures in PDB with additional information from antibody germ-line family sequences, CDR residue positions, multiple antibody sequence alignments, CDR lengths and canonical structures, and antigen volume. Both sets of predicted functional paratope-epitope interfaces consistently led to the conclusion that antibodies, with relatively limited sequence and structural diversities in the antigen binding sites, can recognize unlimited protein antigens through recognizing the common and ubiquitous physicochemical features on all protein surfaces.…”

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confidence: 99%

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Origins of specificity and affinity in antibody–protein interactions

Peng

Lee

Jian

et al. 2014

Proc. Natl. Acad. Sci. U.S.A.

178

283

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Natural antibodies are frequently elicited to recognize diverse protein surfaces, where the sequence features of the epitopes are frequently indistinguishable from those of nonepitope protein surfaces. It is not clearly understood how the paratopes are able to recognize sequence-wise featureless epitopes and how a natural antibody repertoire with limited variants can recognize seemingly unlimited protein antigens foreign to the host immune system. In this work, computational methods were used to predict the functional paratopes with the 3D antibody variable domain structure as input. The predicted functional paratopes were reasonably validated by the hot spot residues known from experimental alanine scanning measurements. The functional paratope (hot spot) predictions on a set of 111 antibody-antigen complex structures indicate that aromatic, mostly tyrosyl, side chains constitute the major part of the predicted functional paratopes, with short-chain hydrophilic residues forming the minor portion of the predicted functional paratopes. These aromatic side chains interact mostly with the epitope main chain atoms and side-chain carbons. The functional paratopes are surrounded by favorable polar atomistic contacts in the structural paratope-epitope interfaces; more that 80% these polar contacts are electrostatically favorable and about 40% of these polar contacts form direct hydrogen bonds across the interfaces. These results indicate that a limited repertoire of antibodies bearing paratopes with diverse structural contours enriched with aromatic side chains among short-chain hydrophilic residues can recognize all sorts of protein surfaces, because the determinants for antibody recognition are common physicochemical features ubiquitously distributed over all protein surfaces.protein antigenic site | interface hot spot | paratope prediction | epitope prediction | functional epitope I t is incompletely understood as to how functional antibodies can almost always be elicited against unlimited possibilities of protein antigens from a limited repertoire of antibodies. Antibodies provide protection against foreign protein antigens by recognizing the antigen proteins with exquisite specificity and remarkable affinity, but the principles underlying the antibody affinity and specificity remain elusive. Consequently, current antibody discoveries are by and large limited by the uncontrollable animal immune systems (1) or by the recombinant antibody libraries with relatively infinitesimal coverage of the vast combinatorial sequence space in antibody-antigen interaction interfaces (2). In developing the efficacy of a therapeutic antibody, optimizing the affinity and specificity of the antibody-antigen interaction mostly relies on selecting and screening from a large pool of random candidates. As antibodies are becoming the most prominent class of protein therapeutics (3), a better understanding of the principles governing antibody affinity and specificity will facilitate in understanding humoral immunity and in developing novel ...

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Section: Validation Of Computationally Predicted Hot Spot Residues Inmentioning

confidence: 92%

mentioning

confidence: 99%

Origins of specificity and affinity in antibody–protein interactions

Peng

Lee

Jian

et al. 2014

Proc. Natl. Acad. Sci. U.S.A.

178

283

View full text Add to dashboard Cite

show abstract

“…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 Pmentioning

confidence: 94%

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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“…While the lack of a crystal structure for a target may have limited the potential utility of structure-based methods, in recent years, modeling techniques (and the structural databases upon which they rely, according to their characteristic fold and overall high sequence identity) have improved sufficiently such that antibody structures can routinely be reliably modeled, [28][29][30][31] including the hypervariable CDRs. [32][33][34] Furthermore, structure-based computational protein design has already been widely and successfully used in other protein engineering contexts, [35][36][37] and rich computational tools specialized for antibodies [38][39][40] have also enabled antibody engineering and humanization to be more accessible. Olimpieri et al 41 recently released a comprehensive webserver that provides helpful tools for antibody humanization.…”

Section: Introductionmentioning

confidence: 99%

Antibody humanization by structure-based computational protein design

Choi

Hua

Sentman

et al. 2015

mAbs

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(2015) Antibody humanization by structure-based computational protein design, mAbs, 7:6, 1045-1057, DOI: 10.1080DOI: 10. /19420862.2015 To link to this article: https://doi.org/10. 1080/19420862.2015 Keywords: antibody, computational protein design, human string content, humanization, pareto optimization, protein structure analysisAbbreviations: MHC, major histocompatibility complex; HSC, Human String Content; RMSD, root mean square deviation; BLI, bio-layer interferometry; IDT, Integrated DNA Technologies; PBS, phosphate-buffered saline; PBS-T, PBS C 0.1% Tween-20; PBS-F, PBS C 0.1% Bovine serum albuminAntibodies derived from non-human sources must be modified for therapeutic use so as to mitigate undesirable immune responses. While complementarity-determining region (CDR) grafting-based humanization techniques have been successfully applied in many cases, it remains challenging to maintain the desired stability and antigen binding affinity upon grafting. We developed an alternative humanization approach called CoDAH ("Computationally-Driven Antibody Humanization") in which computational protein design methods directly select sets of amino acids to incorporate from human germline sequences to increase humanness while maintaining structural stability. Retrospective studies show that CoDAH is able to identify variants deemed beneficial according to both humanness and structural stability criteria, even for targets lacking crystal structures. Prospective application to TZ47, a murine antihuman B7H6 antibody, demonstrates the approach. Four diverse humanized variants were designed, and all possible unique VH/VL combinations were produced as full-length IgG1 antibodies. Soluble and cell surface expressed antigen binding assays showed that 75% (6 of 8) of the computationally designed VH/VL variants were successfully expressed and competed with the murine TZ47 for binding to B7H6 antigen. Furthermore, 4 of the 6 bound with an estimated K D within an order of magnitude of the original TZ47 antibody. In contrast, a traditional CDR-grafted variant could not be expressed. These results suggest that the computational protein design approach described here can be used to efficiently generate functional humanized antibodies and provide humanized templates for further affinity maturation.

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Prediction of site-specific interactions in antibody-antigen complexes: the proABC method and server

Cited by 87 publications

References 37 publications

Origins of specificity and affinity in antibody–protein interactions

Origins of specificity and affinity in antibody–protein interactions

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

Antibody humanization by structure-based computational protein design

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