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Motivation The scoring of antibody–antigen docked poses starting from unbound homology models has not been systematically optimized for a large and diverse set of input sequences. Results To address this need, we have developed AbAdapt, a webserver that accepts antibody and antigen sequences, models their 3D structures, predicts epitope and paratope, and then docks the modeled structures using two established docking engines (Piper and Hex). Each of the key steps has been optimized by developing and training new machine-learning models. The sequences from a diverse set of 622 antibody–antigen pairs with known structure were used as inputs for leave-one-out cross-validation. The final set of cluster representatives included at least one ‘Adequate’ pose for 550/622 (88.4%) of the queries. The median (interquartile range) ranks of these ‘Adequate’ poses were 22 (5–77). Similar results were obtained on a holdout set of 100 unrelated antibody–antigen pairs. When epitopes were repredicted using docking-derived features for specific antibodies, the median ROC AUC increased from 0.679 to 0.720 in cross-validation and from 0.694 to 0.730 in the holdout set. Availability and implementation AbAdapt and related data are available at https://sysimm.org/abadapt/. Supplementary information Supplementary data are available at Bioinformatics Advances online.

The repertoires of B cell receptors (BCRs), which can be captured by single cell-resolution sequencing technologies, contain a personal history of a donor's antigen exposure. One of the current challenges in analyzing such BCR sequence data is to assign sequences to groups with similar antigen and epitope binding specificity. This is a non-trivial task given the paucity of experimentally-determined antibody–antigen structures and the fact that different gene combinations in B cells can lead to receptors that target the same antigen and epitope. Here, we describe a method for clustering BCRs based on sequence and predicted structural features in order to predict groups with similar antigen and epitope binding specificity. We show that all known experimentally-determined structures of antibody–antigen complexes can be clustered accurately (AUC 0.981) and that use of predicted structural features improved the accuracy of the epitope classification. We next show that an independent and non-redundant set of 104 anti-HIV antibody sequences could be clustered corresponding to manually-assigned epitopes with a specificity of 99.7% and a sensitivity of 61.93%, with the imbalance in sensitivity due almost entirely to one group of antibodies—those that target the gp120 V3 loop, which do not form a single, well-defined cluster. We next examined a diverse set of anti-hemagglutinin BCR sequences from humans and mice. We observed clusters that included human or mouse sequences with anti-hemagglutinin antibodies of known structure. We also observed clusters that included both human and mouse sequences. Importantly, to the extent that the epitopes have been experimentally characterized, none of the observed clusters erroneously grouped different hemagglutinin binding regions. Taken together, these results demonstrate that the proposed clustering method provides high-throughput prediction of BCRs with common binding specificity across clonal lineages, donors and even species.

Repertoire Builder (https://sysimm.org/rep_builder/) is a method for generating atomic-resolution, three-dimensional models of B cell receptors (BCRs) or T cell receptors (TCRs) from their amino acid sequences. It is currently capable of handling batches of up to 104 sequences in approximately 30 minutes. This performance was achieved by applying a multiple sequence alignment extension technique originally developed for phylogenetic analysis to the template selection problem of complementarity determining region (CDR) loops. Under comparable conditions, average all-atom root-mean square deviations (RMSDs) from experimentally-determined structures of CDRH3 loops in BCRs were significantly lower than tested third-party high-throughput modeling methods, including ABodyBuilder, PigsPro, and LYRA. For TCRs, similar trends were observed when Repertoire Builder was compared with TCRmodel and LYRA. We also found that Repertoire Builder model errors were, in general, lower than those produced by our earlier Kotai Antibody Builder, even when CDRH3 loop refinement was used. However, in a subset of cases, which could be distinguished by poor Repertoire Builder scores, refinement by Kotai Antibody Builder or Rosetta Antibody, both of which utilize extensive structural sampling, improved the third heavy chain CDR (CDRH3) RMSD on average. Taken together, these results indicate that the MSA extension approach used by Repertoire Builder resulted in a favorable balance between speed and accuracy when compared to alternative methods. Furthermore, we conclude that more sensitive scoring, rather than extended structural sampling, is needed to further improve the accuracy of BCR and TCR modeling.

The Database of Aligned Structural Homologs (DASH) is a tool for efficiently navigating the Protein Data Bank (PDB) by means of pre-computed pairwise structural alignments. We recently showed that, by integrating DASH structural alignments with the multiple sequence alignment (MSA) software MAFFT, we were able to significantly improve MSA accuracy without dramatically increasing manual or computational complexity. In the latest DASH update, such queries are not limited to PDB entries but can also be launched from user-provided protein coordinates. Here, we describe a further extension of DASH that retrieves intermolecular interactions of all structurally similar domains in the PDB to a query domain of interest. We illustrate these new features using a model of the NYN domain of the ribonuclease N4BP1 as an example. We show that the protein-nucleotide interactions returned are distributed on the surface of the NYN domain in an asymmetric manner, roughly centered on the known nuclease active site.

The Database of Aligned Structural Homologs (DASH) is a tool for efficiently navigating the Protein Data Bank (PDB) by means of pre-computed pairwise structural alignments. We recently showed that, by integrating DASH structural alignments with the multiple sequence alignment (MSA) software MAFFT, we were able to significantly improve MSA accuracy without dramatically increasing manual or computational complexity. In the latest DASH update, such queries are not limited to PDB entries but can also be launched from user-provided protein coordinates. Here, we describe a further extension of DASH that retrieves intermolecular interactions of all structurally similar domains in the PDB to a query domain of interest. We illustrate these new features using a model of the NYN domain of the ribonuclease N4BP1 as an example. We show that the protein-nucleotide interactions returned are distributed on the surface of the NYN domain in an asymmetric manner, roughly centered on the known nuclease active site.

Graphical abstract, B cell receptors (BCRs) and T cell receptors (TCRs) make up an essential network of defense molecules that, collectively, can distinguish self from non-self and facilitate destruction of antigen-bearing cells such as pathogens or tumors. The analysis of BCR and TCR repertoires plays an important role in both basic immunology as well as in biotechnology. Because the repertoires are highly diverse, specialized software methods are needed to extract meaningful information from BCR and TCR sequence data. Here, we review recent developments in bioinformatics tools for analysis of BCR and TCR repertoires, with an emphasis on those that incorporate structural features. After describing the recent sequencing technologies for immune receptor repertoires, we survey structural modeling methods for BCR and TCRs, along with methods for clustering such models. We review downstream analyses, including BCR and TCR epitope prediction, antibody-antigen docking and TCR-peptide-MHC Modeling. We also briefly discuss molecular dynamics in this context.

This article describes several features in the MAFFT online service for multiple sequence alignment (MSA). As a result of recent advances in sequencing technologies, huge numbers of biological sequences are available and the need for MSAs with large numbers of sequences is increasing. To extract biologically relevant information from such data, sophistication of algorithms is necessary but not sufficient. Intuitive and interactive tools for experimental biologists to semiautomatically handle large data are becoming important. We are working on development of MAFFT toward these two directions. Here, we explain (i) the Web interface for recently developed options for large data and (ii) interactive usage to refine sequence data sets and MSAs.

Structural modeling plays a key role in protein function prediction on a genome-wide scale. For B and T lymphocyte receptors, the critical functional question is: which antigens and epitopes are targeted? With emerging B cell receptor (BCR) and T cell receptor (TCR) sequencing methods improving in both breadth and depth, there is a growing need for methods that can help answer this question. Since lymphocyte-antigen recognition depends on complementarity, structural modeling is likely to play an important role in understanding antigen specificity and affinity. In the case of BCRs, such modeling methods have a long history in the study and design of antibodies. However, for TCRs there are relatively few publicly available modeling tools, and, to our knowledge, none that incorporate interaction between TCRs and peptide-MHC (pMHC) complexes. Here, we provide a web-based tool, ImmuneScape ( https://sysimm.org/immune-scape/ ), to carry out TCR-pMHC modeling as a first step toward structure-based function prediction.

Here, we describe a web server that integrates structural alignments with the MAFFT multiple sequence alignment (MSA) tool. For this purpose, we have prepared a web-based Database of Aligned Structural Homologs (DASH), which provides structural alignments at the domain and chain levels for all proteins in the Protein Data Bank (PDB), and can be queried interactively or by a simple REST-like API. MAFFT-DASH integration can be invoked with a single flag on either the web (https://mafft.cbrc.jp/alignment/server/) or command-line versions of MAFFT. In our benchmarks using 878 cases from the BAliBase, HomFam, OXFam, Mattbench and SISYPHUS datasets, MAFFT-DASH showed 10–20% improvement over standard MAFFT for MSA problems with weak similarity, in terms of Sum-of-Pairs (SP), a measure of how well a program succeeds at aligning input sequences in comparison to a reference alignment. When MAFFT alignments were supplemented with homologous sequences, further improvement was observed. Potential applications of DASH beyond MSA enrichment include functional annotation through detection of remote homology and assembly of template libraries for homology modeling.

Structural modeling plays a key role in protein function prediction on a genome-wide scale. For B and T lymphocyte receptors, the critical functional question is: which antigens and epitopes are targeted? With emerging B cell receptor (BCR) and T cell receptor (TCR) sequencing methods improving in both breadth and depth, there is a growing need for methods that can help answer this question. Since lymphocyte-antigen recognition depends on complementarity, structural modeling is likely to play an important role in understanding antigen specificity and affinity. In the case of BCRs, such modeling methods have a long history in the study and design of antibodies. However, for TCRs there are relatively few publicly available modeling tools, and, to our knowledge, none that incorporate interaction between TCRs and peptide-MHC (pMHC) complexes. Here, we provide a web-based tool, ImmuneScape ( https://sysimm.org/immune-scape/ ), to carry out TCR-pMHC modeling as a first step toward structure-based function prediction.