Biology subjects

Perez-Castillo, Y.

Publications and source records attributed to Perez-Castillo, Y..

6 recordsLinked to original sources

bioRxiv2020

SARS-CoV-2, an evolutionary perspective of interaction with human ACE2 reveals undiscovered amino acids necessary for complex stability

The emergence of SARS-CoV-2 has resulted in more than 200,000 infections and nearly 9,000 deaths globally so far. This novel virus is thought to have originated from an animal reservoir, and acquired the ability to infect human cells using the SARS-CoV cell receptor hACE2. In the wake of a global pandemic it is essential to improve our understanding of the evolutionary dynamics surrounding the origin and spread of a novel infectious disease. One way theory predicts selection pressures should shape viral evolution is to enhance binding with host cells. We first assessed evolutionary dynamics in select betacoronavirus spike protein genes to predict where these genomic regions are under directional or purifying selection between divergent viral lineages at various scales of relatedness. With this analysis, we determine a region inside the receptor-binding domain with putative sites under positive selection interspersed among highly conserved sites, which are implicated in structural stability of the viral spike protein and its union with human receptor hACE2. Next, to gain further insights into factors associated with coronaviruses recognition of the human host receptor, we performed modeling studies of five different coronaviruses and their potential binding to hACE2. Modeling results indicate that interfering with the salt bridges at hot spot 353 could be an effective strategy for inhibiting binding, and hence for the prevention of coronavirus infections. We also propose that a glycine residue at the receptor binding domain of the spike glycoprotein can have a critical role in permitting bat variants of the coronaviruses to infect human cells.

microbiology↗

bioRxiv2019

Prediction of breast cancer proteins using molecular descriptors and artificial neural networks: a focus on cancer immunotherapy proteins, metastasis driver proteins, and RNA-binding proteins

BackgroundBreast cancer (BC) is a heterogeneous disease characterized by an intricate interplay between different biological aspects such as ethnicity, genomic alterations, gene expression deregulation, hormone disruption, signaling pathway alterations and environmental determinants. Due to the complexity of BC, the prediction of proteins involved in this disease is a trending topic in drug design. MethodsThis work is proposing accurate prediction classifier for BC proteins using six sets of protein sequence descriptors and 13 machine learning methods. After using a univariate feature selection for the mix of five descriptor families, the best classifier was obtained using multilayer perceptron method (artificial neural network) and 300 features. ResultsThe performance of the model is demonstrated by the area under the receiver operating characteristics (AUROC) of 0.980 {+/-} 0.0037 and accuracy of 0.936 {+/-} 0.0056 (3-fold cross-validation). Regarding the prediction of 4504 cancer-associated proteins using this model, the best ranked cancer immunotherapy proteins related to BC were RPS27, SUPT4H1, CLPSL2, POLR2K, RPL38, AKT3, CDK3, RPS20, RASL11A and UBTD1; the best ranked metastasis driver proteins related to BC were S100A9, DDA1, TXN, PRNP, RPS27, S100A14, S100A7, MAPK1, AGR3 and NDUFA13; and the best ranked RNA-binding proteins related to BC were S100A9, TXN, RPS27L, RPS27, RPS27A, RPL38, MRPL54, PPAN, RPS20 and CSRP1. ConclusionsThis powerful model predicts several BC-related proteins which should be deeply studied to find new biomarkers and better therapeutic targets. The script and the results are available as a free repository at https://github.com/muntisa/neural-networks-for-breast-cancer-proteins.

bioinformatics↗

bioRxiv2019

Prediction of druggable proteins using machine learning and functional enrichment analysis: a focus on cancer-related proteins and RNA-binding proteins

BackgroundDruggable proteins are a trending topic in drug design. The druggable proteome can be defined as the percentage of proteins that have the capacity to bind an antibody or small molecule with adequate chemical properties and affinity. The screening and in silico modeling are critical activities for the reduction of experimental costs.\n\nMethodsThe current work proposes a unique prediction model for druggable proteins using amino acid composition descriptors of protein sequences and 13 machine learning linear and non-linear classifiers. After feature selection, the best classifier was obtained using the support vector machine method and 200 tri-amino acid composition descriptors.\n\nResultsThe high performance of the model is determined by an area under the receiver operating characteristics (AUROC) of 0.975 {+/-} 0.003 and accuracy of 0.929 {+/-} 0.006 (3-fold cross-validation). Regarding the prediction of cancer-associated proteins using this model, the best ranked druggable predicted proteins in the breast cancer protein set were CDK4, AP1S1, POLE, HMMR, RPL5, PALB2, TIMP1, RPL22, NFKB1 and TOP2A; in the cancer-driving protein set were TLL2, FAM47C, SAGE1, HTR1E, MACC1, ZFR2, VMA21, DUSP9, CTNNA3 and GABRG1; and in the RNA-binding protein set were PLA2G1B, CPEB2, NOL6, LRRC47, CTTN, CORO1A, SCAF11, KCTD12, DDX43 and TMPO.\n\nConclusionsThis powerful model predicts several druggable proteins which should be deeply studied to find better therapeutic targets and thus improve clinical trials. The scripts are freely available at https://github.com/muntisa/machine-learning-for-druggable-proteins.

bioinformatics↗

bioRxiv2019

ShadowCaster: compositional methods under the shadow of phylogenetic models for the detection of horizontal gene transfer events in prokaryotes.

Horizontal gene transfer (HGT) plays an important role in the evolution of many organisms, especially in prokaryotes where commonly occurs. Microbial communities can improve survival due to the evolutionary innovations induced by HGT events. Thus, several computational approaches have arisen to identify such events in recipient genomes. However, this has been proven to be a complex task due to the generation of a great number of false positives and the prediction disagreement among the existing methods. Phylogenetic reconstruction methods turned out to be the most reliable but they are not extensible to all genes/species and are computationally demanding when dealing with large datasets. On the other hand, the so-called surrogate methods that use heuristic solutions either based on nucleotide composition patterns or phyletic distribution of BLAST hits can be applied easily to genomic scale, however, they fail in identifying common HGT events. Here, we present ShadowCaster, a hybrid approach that sequentially combines compositional features under the shadow of phylogenetic models independent of tree reconstruction to improve the detection of HTG events in prokaryotes. ShadowCaster predicted successfully close and distant HTG events in both artificial and bacterial genomes. It detected HGT related to heavy metal resistance in the genome of Rhodanobacter denitrificans with higher accuracy than the most popular state-of-the-art computational approaches. ShadowCasters predictions showed the highest agreement among those obtained with other assayed methods. ShadowCaster is released as an open-source software under the GPLv3 license. Source code is hosted at https://github.com/dani2s/ShadowCaster and documentation at https://shadowcaster.readthedocs.io/en/latest/.

bioinformatics↗

bioRxiv2019

OncoOmics approaches to reveal essential genes in breast cancer: a panoramic view from pathogenesis to precision medicine

Breast cancer (BC) is a heterogeneous disease where each OncoOmics approach needs to be fully understood as a part of a complex network. Therefore, the main objective of this study was to analyze genetic alterations, signaling pathways, protein-protein interaction networks, protein expression, dependency maps and enrichment maps in 230 previously prioritized genes by the Consensus Strategy, the Pan-Cancer Atlas, the Pharmacogenomics Knowledgebase and the Cancer Genome Interpreter, in order to reveal essential genes to accelerate the development of precision medicine in BC. The OncoOmics essential genes were rationally filtered to 144, 48 (33%) of which were hallmarks of cancer and 20 (14%) were significant in at least three OncoOmics approaches: RAC1, AKT1 CCND1, PIK3CA, ERBB2, CDH1, MAPK14, TP53, MAPK1, SRC, RAC3, PLCG1, GRB2, MED1, TOP2A, GATA3, BCL2, CTNNB1, EGFR and CDK2. According to the Open Targets Platform, there are 111 drugs that are currently being analyzed in 3151 clinical trials in 39 genes. Lastly, there are more than 800 clinical annotations associated with 94 genes in BC pharmacogenomics.

genomics↗

bioRxiv2019

CompScore: boosting structure-based virtual screening performance by incorporating docking scoring functions components into consensus scoring

Consensus scoring has become a commonly used strategy within structure-based virtual screening (VS) workflows with improved performance compared to those based in a single scoring function. However, no research has been devoted to analyze the worth of docking scoring functions components in consensus scoring. We implemented and tested a method that incorporates docking scoring functions components into the setting of high performance VS workflows. This method uses genetic algorithms for finding the combination of scoring components that maximizes the VS enrichment for any target. Our methodology was validated using a dataset that contains ligands and decoys for 102 targets that has been widely used in VS validation studies. Results show that our approach outperforms other methods for all targets. It also boosts the initial enrichment performance of the traditional use of whole scoring functions in consensus scoring by an average of 45%. CompScore is freely available at: http://bioquimio.udla.edu.ec/compscore/

bioinformatics↗