A new parameter-rich structure-aware mechanistic model for amino acid substitution during evolution.

Improvements in the description of amino acid substitution are required to develop better pseudo-energy-based protein structure-aware models for use in phylogenetic studies. These models are used to characterize the probabilities of amino acid substitution and enable better simulation of protein sequences over a phylogeny. A better characterization of amino acid substitution probabilities in turn enables numerous downstream applications, like detecting positive selection, ancestral sequence reconstruction, and evolutionarily-motivated protein engineering. Many existing Markov models for amino acid substitution in molecular evolution disregard molecular structure and describe the amino acid substitution process over longer evolutionary periods poorly. Here, we present a new model upgraded with a site-specific parameterization of pseudo-energy terms in a coarse-grained force field, which describes local heterogeneity in physical constraints on amino acid substitution better than a previous pseudo-energy-based model with minimum cost in runtime. The importance of each weight term parameterization in characterizing underlying features of the site, including contact number, solvent accessibility, and secondary structural elements was evaluated, returning both expected and biologically reasonable relationships between model parameters. This results in the acceptance of proposed amino acid substitutions that more closely resemble those observed site-specific frequencies in gene family alignments. The modular site-specific pseudo-energy function is made available for download through the following website: https://liberles.cst.temple.edu/Software/CASS/index.html.

Pet-Human Gut Microbiome Host Classifier Using Data from Different Studies.

(1) Background: microbiome host classification can be used to identify sources of contamination in environmental data. However, there is no ready-to-use host classifier. Here, we aimed to build a model that would be able to discriminate between pet and human microbiomes samples. The challenge of the study was to build a classifier using data solely from publicly available studies that normally contain sequencing data for only one type of host. (2) Results: we have developed a random forest model that distinguishes human microbiota from domestic pet microbiota (cats and dogs) with 97% accuracy. In order to prevent overfitting, samples from several (at least four) different projects were necessary. Feature importance analysis revealed that the model relied on several taxa known to be key components in domestic cat and dog microbiomes (such as Fusobacteriaceae and Peptostreptococcaeae), as well as on some taxa exclusively found in humans (as Akkermansiaceae). (3) Conclusion: we have shown that it is possible to make a reliable pet/human gut microbiome classifier on the basis of the data collected from different studies.

In Silico Analysis of the Minor Histocompatibility Antigen Landscape Based on the 1000 Genomes Project.

Allogeneic hematopoietic stem cell transplantation (allo-HSCT) is routinely used to treat hematopoietic malignancies. The eradication of residual tumor cells during engraftment is mediated by donor cytotoxic T lymphocytes reactive to alloantigens. In a HLA-matched transplantation context, alloantigens are encoded by various polymorphic genes situated outside the HLA locus, also called minor histocompatibility antigens (MiHAs). Recently, MiHAs have been recognized as promising targets for post-transplantation T-cell immunotherapy as they have several appealing advantages over tumor-associated antigens (TAAs) and neoantigens, i.e., they are more abundant than TAAs, which potentially facilitates multiple targeting; and unlike neoantigens, they are encoded by germline polymorphisms, some of which are common and thus, suitable for off-the-shelf therapy. The genetic sources of MiHAs are nonsynonymous polymorphisms that cause differences between the recipient and donor proteomes and subsequently, the immunopeptidomes. Systematic description of the alloantigen landscape in HLA-matched transplantation is still lacking as previous studies focused only on a few immunogenic and common MiHAs. Here, we perform a thorough in silico analysis of the public genomic data to classify genetic polymorphisms that lead to MiHA formation and estimate the number of potentially available MiHA mismatches. Our findings suggest that a donor/recipient pair is expected to have at least several dozen mismatched strong MHC-binding SNP-associated peptides per HLA allele (116 ± 26 and 65 ± 15 for non-related pairs and siblings respectively in European populations as predicted by two independent algorithms). Over 70% of them are encoded by relatively frequent polymorphisms (minor allele frequency > 0.1) and thus, may be targetable by off-the-shelf therapeutics. We showed that the most appealing targets (probability of mismatch over 20%) reside in the asymmetric allele frequency region, which spans from 0.15 to 0.47 and corresponds to an order of several hundred (213 ± 47) possible targets per HLA allele that can be considered for immunogenicity validation. Overall, these findings demonstrate the significant potential of MiHAs as targets for T-cell immunotherapy and emphasize the need for the systematic discovery of novel MiHAs.