Finding Patterns in Protein Sequences by Using a Hybrid Multiobjective Teaching Learning Based Optimization Algorithm.

Proteins are molecules that form the mass of living beings. These proteins exist in dissociated forms like amino-acids and carry out various biological functions, in fact, almost all body reactions occur with the participation of proteins. This is one of the reasons why the analysis of proteins has become a major issue in biology. In a more concrete way, the identification of conserved patterns in a set of related protein sequences can provide relevant biological information about these protein functions. In this paper, we present a novel algorithm based on teaching learning based optimization (TLBO) combined with a local search function specialized to predict common patterns in sets of protein sequences. This population-based evolutionary algorithm defines a group of individuals (solutions) that enhance their knowledge (quality) by means of different learning stages. Thus, if we correctly adapt it to the biological context of the mentioned problem, we can get an acceptable set of quality solutions. To evaluate the performance of the proposed technique, we have used six instances composed of different related protein sequences obtained from the PROSITE database. As we will see, the designed approach makes good predictions and improves the quality of the solutions found by other well-known biological tools.

Gene variants and haplotypes modifying transcription factor binding sites in the human cyclooxygenase 1 and 2 (PTGS1 and PTGS2) genes.

Cyclooxygenases (prostaglandin-endoperoxide synthases, (EC 1.14.99.1) 1 and 2 (COX-1 and COX-2)) are key enzymes with a highly functional and pharmacological relevance. Genetic variations in the corresponding genes PTGS1 and PTGS2 are related to diverse human disorders and adverse drug reactions. Although COX-2 is highly inducible, most genetic association studies have focused on coding region gene variants. The aim of this study is to analyze the genetic variants modifying transcription factor binding sites in human PTGS genes based on the combined use of bioinformatics with 1,000 genomes data and replication by next generation sequencing. Updated information on gene sequences and variants was obtained from the 1,000 genomes website and from a replication sequencing study. Of the 570 upstream PTGS1 gene variants, 43 altered binding sites, either by disrupting existing sequences or by creating new binding sites. The most relevant are the SNP rs72769722, which creates a new binding site for NFKB, and the SNPs rs73559017 and rs76403914, both disrupting binding sites for CDX1. Of the 682 upstream PTGS2 gene variants, 31 altered binding sites, the most relevant being rs689466 and rs20417, which disrupt binding sequences for MYB and E2F, respectively; rs689462 which creates a new binding site for POU3F2; and a haplotype combining the SNPs rs34984585+rs10911904, which creates a new binding site for SRY. This study provides a detailed catalog of variant and invariant transcription factor binding sites for PTGS genes and related haplotypes. This information can be useful to identify potential genetic targets for studies related to COX enzymes.