Classification and identification of proteins by means of common and specific amino acid n-tuples in unaligned sequences

In Silico and In Vitro Evaluation of the Molecular Mimicry of the SARS-CoV-2 Spike Protein by Common Short Constituent Sequences (cSCSs) in the Human Proteome: Toward Safer Epitope Design for Vaccine Development

Spike protein sequences in SARS-CoV-2 have been employed for vaccine epitopes, but many short constituent sequences (SCSs) in the spike protein are present in the human proteome, suggesting that some anti-spike antibodies induced by infection or vaccination may be autoantibodies against human proteins. To evaluate this possibility of "molecular mimicry" in silico and in vitro, we exhaustively identified common SCSs (cSCSs) found both in spike and human proteins bioinformatically. The commonality of SCSs between the two systems seemed to be coincidental, and only some cSCSs were likely to be relevant to potential self-epitopes based on three-dimensional information. Among three antibodies raised against cSCS-containing spike peptides, only the antibody against EPLDVL showed high affinity for the spike protein and reacted with an EPLDVL-containing peptide from the human unc-80 homolog protein. Western blot analysis revealed that this antibody also reacted with several human proteins expressed mainly in the small intestine, ovary, and stomach. Taken together, these results showed that most cSCSs are likely incapable of inducing autoantibodies but that at least EPLDVL functions as a self-epitope, suggesting a serious possibility of infection-induced or vaccine-induced autoantibodies in humans. High-risk cSCSs, including EPLDVL, should be excluded from vaccine epitopes to prevent potential autoimmune disorders.

Otaki J. Search for Human-Specific Proteins Based on Availability Scores of Short Constituent Sequences: Identification of a WRWSH Protein in Human Testis

Little is known about protein sequences unique in humans. Here, we performed alignment-free sequence comparisons based on the availability (frequency bias) of short constituent amino acid (aa) sequences (SCSs) in proteins to search for human-specific proteins. Focusing on 5-aa SCSs (pentats), exhaustive comparisons of availability scores among the human proteome and other nine mammalian proteomes in the nonredundant (nr) database identified a candidate protein containing WRWSH, here called FAM75, as human-specific. Examination of various human genome sequences revealed that FAM75 had genomic DNA sequences for either WRWSH or WRWSR due to a single nucleotide polymorphism (SNP). FAM75 and its related protein FAM205A were found to be produced through alternative splicing. The FAM75 transcript was found only in humans, but the FAM205A transcript was also present in other mammals. In humans, both FAM75 and FAM205A were expressed specifically in testis at the mRNA level, and they were immunohistochemically located in cells in seminiferous ducts and in acrosomes in spermatids at the protein level, suggesting their possible function in sperm development and fertilization. This study highlights a practical application of SCS-based methods for protein searches and suggests possible contributions of SNP variants and alternative splicing of FAM75 to human evolution.

N-gram analysis of 970 microbial organisms reveals presence of biological language models

Background

It has been suggested previously that genome and proteome sequences show characteristics typical of natural-language texts such as "signature-style" word usage indicative of authors or topics, and that the algorithms originally developed for natural language processing may therefore be applied to genome sequences to draw biologically relevant conclusions. Following this approach of 'biological language modeling', statistical n-gram analysis has been applied for comparative analysis of whole proteome sequences of 44 organisms. It has been shown that a few particular amino acid n-grams are found in abundance in one organism but occurring very rarely in other organisms, thereby serving as genome signatures. At that time proteomes of only 44 organisms were available, thereby limiting the generalization of this hypothesis. Today nearly 1,000 genome sequences and corresponding translated sequences are available, making it feasible to test the existence of biological language models over the evolutionary tree.

Results

We studied whole proteome sequences of 970 microbial organisms using n-gram frequencies and cross-perplexity employing the Biological Language Modeling Toolkit and Patternix Revelio toolkit. Genus-specific signatures were observed even in a simple unigram distribution. By taking statistical n-gram model of one organism as reference and computing cross-perplexity of all other microbial proteomes with it, cross-perplexity was found to be predictive of branch distance of the phylogenetic tree. For example, a 4-gram model from proteome of Shigellae flexneri 2a, which belongs to the Gammaproteobacteria class showed a self-perplexity of 15.34 while the cross-perplexity of other organisms was in the range of 15.59 to 29.5 and was proportional to their branching distance in the evolutionary tree from S. flexneri. The organisms of this genus, which happen to be pathotypes of E.coli, also have the closest perplexity values with E. coli.

Conclusion

Whole proteome sequences of microbial organisms have been shown to contain particular n-gram sequences in abundance in one organism but occurring very rarely in other organisms, thereby serving as proteome signatures. Further it has also been shown that perplexity, a statistical measure of similarity of n-gram composition, can be used to predict evolutionary distance within a genus in the phylogenetic tree.

n-Gram characterization of genomic islands in bacterial genomes

The paper presents a novel, n-gram-based method for analysis of bacterial genome segments known as genomic islands (GIs). Identification of GIs in bacterial genomes is an important task since many of them represent inserts that may contribute to bacterial evolution and pathogenesis. In order to characterize and distinguish GIs from rest of the genome, binary classification of islands based on n-gram frequency distribution have been performed. It consists of testing the agreement of islands n-gram frequency distributions with the complete genome and backbone sequence. In addition, a statistic based on the maximal order Markov model is used to identify significantly overrepresented and underrepresented n-grams in islands. The results may be used as a basis for Zipf-like analysis suggesting that some of the n-grams are overrepresented in a subset of islands and underrepresented in the backbone, or vice versa, thus complementing the binary classification. The method is applied to strain-specific regions in the Escherichia coli O157:H7 EDL933 genome (O-islands), resulting in two groups of O-islands with different n-gram characteristics. It refines a characterization based on other compositional features such as G+C content and codon usage, and may help in identification of GIs, and also in research and development of adequate drugs targeting virulence genes in them.

Potential implications of availability of short amino acid sequences in proteins: an old and new approach to protein decoding and design

Three-dimensional structure of a protein molecule is primarily determined by its amino acid sequence, and thus the elucidation of general rules embedded in amino acid sequences is of great importance in protein science and engineering. To extract valuable information from sequences, we propose an analytical method in which a protein sequence is considered to be constructed by serial superimpositions of short amino acid sequences of n amino acid sets, especially triplets (3-aa sets). Using the comprehensive nonredundant protein database, we first examined "availability" of all possible combinatorial sets of 8,000 triplet species. Availability score was mathematically defined as an indicator for the relative "preference" or "avoidance" for a given short constituent sequence to be used in protein chain. Availability scores of real proteins were clearly biased against those of randomly generated proteins. We found many triplet species that occurred in the database more than expected or less than expected. Such bias was extended to longer sets, and we found that some species of pentats (5-aa sets) that occurred reasonably frequently in the randomly generated protein population did not occur at all in any real proteins known today. Availability score was dependent on species, potentially serving as a phylogenetic indicator. Furthermore, we suggest possibilities of various biotechnological applications of characteristic short sequences such as human-specific and pathogen-specific short sequences obtained from availability analysis. Availability score was also dependent on secondary structures, potentially serving as a structural indicator. Availability analysis on triplets may be combined with a comprehensive data collection on the varphi and psi peptide-bond angles of the amino acid at the center of each triplet, i.e., a collection of Ramachandran plots for each triplet. These triplet characters, together with other physicochemical data, will provide us with basic information between protein sequence and structure, by which structure prediction and engineering may be greatly facilitated. Availability analysis may also be useful in identifying word processing units in amino acid sequences based on an analogy to natural languages. Together with other approaches, availability analysis will elucidate general rules hidden in the primary sequences and eventually contributes to rebuilding the paradigm of protein science.

Primary sequences of proteins from complete genomes display a singular periodicity: Alignment-free N-gram analysis

A method is proposed to represent and to analyze complete genome sequences (52 species from procaryotes and eukaryotes), based upon n-gram sequence's frequencies of amino acid pairs (bigrams), separated by a given number of other residues. For each of the species analyzed, it allows us to construct over-abundant and over-deficient occurrence profiles, summarizing amino acid bigram frequencies over the entire genome. The method deals efficiently with a sparseness of statistical representations of individual sequences, and describes every gene sequence in the same way, independently of its length and of the genome sizes. The frequency of over-abundant and over-deficient occurrences of bigrams presents a singular periodicity around 3.5 peptide bonds, suggesting a relation with the alpha helical secondary structure.

Depicting a protein's two faces: GPCR classification by phylogenetic tree-based HMMs

Related proteins with similar biological functions generally share common features, allowing us to extract the common sequence features. These common features enable us to build statistical models that can be used to classify proteins, to predict new members, and to study the sequence-function relationship of this protein function group. Although evolution underlies the basis of multiple sequence analysis methods, most methods ignore phylogenetic relationships and the evolutionary process in building these statistical models. Previously we have shown that a phylogenetic tree-based profile hidden Markov model (T-HMM) is superior in generating a profile for a group of similar proteins. In this study we used the method to generate common features of G protein-coupled receptors (GPCRs). The profile generated by T-HMM gives high accuracy in GPCR function classification, both by ligand and by coupled G protein.