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Drug resistance to anticancer drugs is a serious complication in patients with cancer. Typically, drug resistance occurs due to amino acid substitutions (AAS) in drug target proteins. The study aimed at developing and validating a new approach to the creation of structure-property relationships (SPR) classification models to predict AASs leading to drug resistance to inhibitors of tyrosine-protein kinase ABL1. The approach was based on the representation of AASs as peptides described in terms of structural formulas. The data on drug-resistant and non-resistant variants of AAS for two isoforms of ABL1 were extracted from the COSMIC database. The given training sets (approximately 700 missense variants) were used for the creation of SPR models in MultiPASS software based on substructural atom-centric multiple neighborhoods of atom (MNA) descriptors for the description of the structural formula of protein fragments and a Bayesian-like algorithm for revealing structure-property relationships. It was found that MNA descriptors of the 6th level and peptides from 11 amino acid residues were the best combination for ABL1 isoform 1 with the prediction accuracy (AUC) of resistance to imatinib (0.897) and dasatinib (0.996). For ABL1 isoform 2 (resistance to imatinib), the best combination was MNA descriptors of the 6th level, peptides form 15 amino acids (AUC value was 0.909). The prediction of possible drug-resistant AASs was made for dbSNP and gnomAD data. The six selected most probable imatinib-resistant AASs were additionally validated by molecular modeling and docking, which confirmed the possibility of resistance for the E334V and T392I variants.

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Long non-coding RNAs (lncRNAs) are of fundamental biological importance; however, their functional role is often unclear or loosely defined as experimental characterization is challenging and bioinformatic methods are limited. We developed a novel integrated method protocol for the annotation and detailed functional characterization of lncRNAs within the genome. It combines annotation, normalization and gene expression with sequence-structure conservation, functional interactome and promoter analysis. Our protocol allows an analysis based on the tissue and biological context, and is powerful in functional characterization of experimental and clinical RNA-Seq datasets including existing lncRNAs. This is demonstrated on the uncharacterized lncRNA GATA6-AS1 in dilated cardiomyopathy.

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Doramapimod (BIRB-796) is widely recognized as one of the most potent and selective type II inhibitors of human p38α mitogen-activated protein kinase (MAPK); however, the understanding of its binding mechanism remains incomplete. Previous studies indicated high affinity of the ligand to a so-called allosteric pocket revealed only in the 'out' state of the DFG motif (i.e. Asp168-Phe169-Gly170) when Phe169 becomes fully exposed to the solvent. The possibility of alternative binding in the DFG-in state was hypothesized, but the molecular mechanism was not known. Methods of bioinformatics, docking and long-time scale classical and accelerated molecular dynamics have been applied to study the interaction of Doramapimod with the human p38α MAPK. It was shown that Doramapimod can bind to the protein even when the Phe169 is fully buried inside the allosteric pocket and the kinase activation loop is in the DFG-in state. Orientation of the inhibitor in such a complex is significantly different from that in the known crystallographic complex formed by the kinase in the DFG-out state; however, the Doramapimod's binding is followed by the ligand-induced conformational changes, which finally improve accommodation of the inhibitor. Molecular modelling has confirmed that Doramapimod combines the features of type I and II inhibitors of p38α MAPK, i.e. can directly and indirectly compete with the ATP binding. It can be concluded that optimization of the initial binding in the DFG-in state and the final accommodation in the DFG-out state should be both considered at designing novel efficient type II inhibitors of MAPK and homologous proteins. Communicated by Ramaswamy H. Sarma.

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Aided by a host of bioinformatics tools, primary and secondary structural analyses of the internal transcribed spacer 2 (ITS2) from the eukaryotic ribosomal RNA repeat have a long and enviable record of service to diversity studies of fungi, plants and protists. Automation of annotation, secondary structure estimation and sequence alignment have become routine for the vast majority of ITS2 sequences. Challenges to the bioinformatics pipeline for ITS2 analysis generally arise in cases where the sequence length lies well outside the norm. These sequences generally defy protocols for annotation and secondary structure prediction. The long ITS2 sequences (ca. 600 nucleotides) from the green alga, Jenufa, offered an opportunity to explore this problem. Custom BLAST parameters revealed the presence of 4-helix structures (200-250 nucleotides) embedded in the 5' portion of several long ITS2 sequences of Jenufa. Of special note is the ITS2 sequence of J. lobulosa where a 4-helix structure was obtained for both the embedded ITS2 and for the complete sequence. Phylogenetic analysis of these typically-sized sequences resolved Golenkinia longispicula as the sister to Jenufa. Our observations indicate that other long ITS2 sequences should be examined for evidence of expansion or duplication. In addition, if the embedded ITS2 sequences are functional, then ribogenesis is almost certainly more diverse than is already apparent from studies of humans and yeast.

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Normal blood glucose level depends on the availability of insulin and its ability to bind insulin receptor (IR) that regulates the downstream signaling pathway. Insulin sequence and blood glucose level usually vary among animals due to species specificity. The study of genetic variation of insulin, blood glucose level and diabetics symptoms development in Aves is interesting because of its optimal high blood glucose level than mammals. Therefore, it is of interest to study its evolutionary relationship with other mammals using sequence data. Hence, we compiled 32 Aves insulin from GenBank to compare its sequence-structure features with phylogeny for evolutionary inference. The analysis shows long conserved motifs (about 14 residues) for functional inference. These sequences show high leucine content (20%) with high instability index (>40). Amino acid position 11, 14, 16 and 20 are variable that may have contribution to binding to IR. We identified functionally critical variable residues in the dataset for possible genetic implication. Structural models of these sequences were developed for surface analysis towards functional representation. These data find application in the understanding of insulin function across species.