Of the two problems, paratope prediction is much easier, as paratopes tend to correspond to CDR residues, while epitopes can be anywhere on an antigen

Of the two problems, paratope prediction is much easier, as paratopes tend to correspond to CDR residues, while epitopes can be anywhere on an antigen. made. 4.2. BCR clustering Structural studies of antibodies targeting antigens specific to HIV [67], influenza [68] and more recently SARS-CoV-2 [69] have demonstrated that antibodies produced in unrelated donors targeting common antigens and epitopes can share sequence and structural features. We note here that, since B cells can undergo affinity-driven maturation, such receptors need not derive from a similar common clone. Recently, the SAAB?+?tool was developed to characterize structural properties of CDRs from differentiated B cells [70]. It is likely that more tools trained to identify convergence of functionally related antibodies will appear in the future as more sequence data from donors with shared BCR epitopes become available. To this end, we recently developed InterClone, a method to cluster BCR sequences which are likely to share epitopes [71]. InterClone is based on a comparison of sequence and structural features of pairs of BCRs using a machine FRAP2 learning-based classifier that was trained on known antigen-BCR structures. Like TCRdist, InterClone assigns a universal similarity score to each BCR pair. Hierarchical clustering is then used to group sequences of high similarity. As such, InterClone can be used without requiring sequences to be enriched in a particular BCR motif. A sensitivity of 61.9% and specificity of 99.7% were obtained when InterClone was applied to an independent set of anti-HIV antibody sequences [71]. A more robust and computationally efficient version of InterClone that works for both BCRs and TCRs and can perform high-throughput analysis of up to 105 sequences is currently being developed. In addition to the above clustering methods, networks that describe antibody repertoire architecture can be used to compare repertoires. Miho and colleagues [72] developed a platform that builds similarity networks of hundreds of thousands of antibody sequences from both humans and mice. Using this approach, the authors detected global patterns in antibody repertoire architectures that were highly reproducible in different subjects, and tended HLCL-61 to converge despite independent VDJ recombination. Furthermore, these repertoire architectures were robust to clonal deletion of private clones. 5.?Epitope specificity 5.1. Predicting TCR epitopes TCRs recognize short peptides presented on class I or II MHC complexes. The ability to predict epitope(s) from TCR sequence and MHC allele would be highly valuable in elucidating disease etiology, monitoring the immune system, developing diagnostic assays and designing vaccines. Traditionally, identifying epitopes is carried out experimentally [73], and is both costly and time-consuming. There is necessarily great interest in methods that can accelerate HLCL-61 this process computationally. To this end, Fischer et al. [74] developed a deep learning approach on TCR CDR3 regions to predict the antigen-specificity of single T cells. Jokinen et al., [75] developed TCRGP to predict whether TCRs recognize certain epitopes using a novel Gaussian process HLCL-61 (GP). Their method uses CDR sequences from TCR alpha and beta and learns which CDR recognizes different epitopes. The tool was applied to HLCL-61 identify T cells specific to HBV. NetTCR by Jurtz VI et al. [43] utilized convolutional networks for sequence-based prediction of TCR-pMHC specificity. NetTCR uses the recent explosion of next-generation sequencing data to train a sequence based-predictor. Ogishi et al. [76] computationally defined immunogenicity scores through sequence-level simulation of interaction between pMHC complexes and public TCR repertoires. Though their focus is more on immunogenicity of peptides presented to MHC molecules, HLCL-61 they also observed correlation between individual TCR-pMHC affinities and the features important for immunogenicity score. Gielis et al. [77] applied random forest-based classifiers.