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Computational Biology - Europe PMC

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Last Updated: 19 September 2022

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A comprehensive review on genomics, systems biology and structural biology approaches for combating antimicrobial resistance in ESKAPE pathogens: computational tools and recent advancements

Antimicrobial resistance has been heightened in recent decades as a global threat to public health due to the global dissemination of multidrug-resistant strains from various ESKAPE pathogens. A slew of computational approaches involving genomics, systems biology, and structural biology have all emerged among molecular biologists, with some of them increasing in popularity and providing valuable insight into AMR research in ESKAPE pathogens. These computational techniques can be helpful in elucidating the AMR pathways, determining key hub genes/proteins, and their promising targets together with their interactions with key drug targets, which is a critical step in drug discovery. To solve the current issue of AMR in ESKAPE pathogens, the present study seeks to provide comprehensive information on currently employed bioinformatic techniques and their use in the discovery of multifunctional novel therapeutic drugs.

Source link: https://europepmc.org/article/MED/IND607819732


A survey on protein-DNA-binding sites in computational biology.

Transcription factors are important cellular components of gene expression regulation's process. Transcription factors are key cellular components of gene expression control. To fine-tune spatiotemporal gene regulation, transcription factor binding sites are locations where transcription factors specifically recognize DNA sequences, targeting gene-specific regions and recruiting transcription factors or chromatin regulators. It is critical to understand the protein's physical appearance and function in the face of the increase in the protein sequence. Protein-DNA-binding site prediction methods are based on traditional machine learning algorithms and deep learning algorithms at the time. Based on CNN-RNN, existing deep learning techniques for predicting protein-DNA-binding sites can be roughly divided into three categories: convolutional neural network, recursive neural network, and hybrid neural network. This paper discusses the methods of traditional machine learning and deep learning in protein-DNA-binding site estimation, as well as differences between basic learning model frameworks.

Source link: https://europepmc.org/article/MED/35652477


Computational biology insights into genotype-clinical phenotype-protein phenotype relationships between novel SLC26A2 variants identified in inherited skeletal dysplasias.

In a total of four foetuses, we found five unusual autosomal recessive SLC26A2 [NM_000112. 4] variants, including three homozygous c. 796dupA, c. 1724delA, and c. 1377dup, two heterozygous variants, as well as two heterozygous variants and in compound heterozygous form. The c. 1382C> T variant of the 14. 036C > T variant is predicted to distort alpha helix conformation and alter the bonding properties and free energy dynamics of transmembrane domains, as well as the loss of both main transmembrane and STAS domains of the SLC26A2 protein in clinically moderate atelosteogenesis type 2 phenotype. The Qualitative Model Energy Analysis, which affects key geometrical aspects of the SLC26A2 protein structure, is expected to decrease the Qualitative Model Energy Analysis, which is expected to decrease the c. 1375-1377dup variant that was clinically milder atelosteogenesis type II-diastrophic dysplasia spectrum lethal phenotype's lethal phenotype's lethal phenotype's lethal phenometry de phenometria de kinetic model Energy Analysis phenometry of the SLC26A2 protein kinetic and phenometric phenometry of the phenogenesis type II-dia spectrum lethal phenogenesis type II-dia spectrum lethal phenomyr pheno phenoteologeoogenesis type II-deologue duol phenologue duosoma spectrum lethal phenotypic phenomysteotype II-deodeogenesis type II-dotype'steotype II Conclusions We continue to develop the spectrum of SLC26A2-related lethal dysplasia, as well as three novel variants relating to medical severity and protein phenotype within the lethal range of this uncommon dysplasia.

Source link: https://europepmc.org/article/MED/36007841


ISCB RSG-Spain and highlights from the VIII Spanish Student Symposium in Bioinformatics and Computational Biology in 2021

The Student Council and the International Society for Computational Biology have both lauded Spain's Regional Student Group. The Regional Student Group of the Spanish Student Group is recognized by the Student Council and the International Society for Computational Biology. We want to present and promote RSG-Spain, the Spanish ISCB regional student body, by focusing on the growing community of students and young professionals as well as how it aids in their growth in the field.

Source link: https://europepmc.org/article/PPR/PPR534961

* Please keep in mind that all text is summarized by machine, we do not bear any responsibility, and you should always check original source before taking any actions

* Please keep in mind that all text is summarized by machine, we do not bear any responsibility, and you should always check original source before taking any actions