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Computational Biology and Bioinformatics in Anti-SARS-CoV-2 Drug Development
Published in Debmalya Barh, Kenneth Lundstrom, COVID-19, 2022
Selection of host proteins as drug targets and/or the discovery of drug candidates depends on the knowledge of the SARS-CoV-2/human interactome [101–103]. MS-assisted proteomics represents an important means for a better understanding of the roles of viral and host proteins during SARS-CoV-2 infection, their protein–protein interactions, and post-translational modifications. Bittremieux et al. describe freely available data and computational resources that can be used to facilitate mass spectrometry-based analysis of SARS-CoV-2 [104]. Important information on the potentially druggable host proteins and the molecular mechanisms at play during infection can also be retrieved from the comparisons of SARS-CoV-2 with other viruses [105].
Computational approaches for the design of modulators targeting protein-protein interactions
Published in Expert Opinion on Drug Discovery, 2023
Ashfaq Ur Rehman, Beenish Khurshid, Yasir Ali, Salman Rasheed, Abdul Wadood, Ho-Leung Ng, Hai-Feng Chen, Zhiqiang Wei, Ray Luo, Jian Zhang
Due to their significance in cell signaling and regulation, PPIs are considered as potential therapeutic targets. But there are still many issues to explore about their interactions and modulation to fully define these massive networks and address PPI-based drug discovery challenges. No doubt, modern experimental techniques have expanded our knowledge of PPIs, but unfortunately, the size of the human interactome makes experimental methods insufficient, demanding more robust and efficient computational methods. Computational methods facilitate the characterization of PPIs by identifying their chemical structures, which in turn expedites and improves the design of PPI modulators. Advancement in computational resources and algorithms, coupled with a molecular-level understanding of proteins’ dynamics, has made in silico approaches successful in PPI drug discovery.
Knowledge graphs and their applications in drug discovery
Published in Expert Opinion on Drug Discovery, 2021
Marshall Nirenberg famously stated that science progresses best using simple assays to rapidly generate large data sets [19]. Whilst large-scale and genome-wide screens are certainly the gold standard of systematic drug discovery, their high costs often prohibit their use only for all but the most common (and thus profitable) of diseases. Machine learning has demonstrated its potential as a complementary approach: rapidly and inexpensively generating data in unexplored areas of the biological and chemical space. In particular, graph-based machine learning (GML; also referred to as geometric machine learning) methods have shown promise in this field. By representing biological systems as KGs, it has allowed for the exploitation of graph theory and powerful network science algorithms; drawing new insights into this otherwise silo-ed data. GML has been applied to systemically screen compounds for new interactions, and shed light upon areas unknown of the human interactome.
Polygenic and Network-based studies in risk identification and demystification of cancer
Published in Expert Review of Molecular Diagnostics, 2022
Christopher El Hadi, Georges Ayoub, Yara Bachir, Michèle Haykal, Nadine Jalkh, Hampig Raphael Kourie
Li et al. introduced a new statistical method by analyzing data from five different human interactome databases (Lit-BM, PrePPI, HI-II-14, ci-Frac, and AP-MS) to identify cancer-related genes [56]. After constructing their network, they identified sets of genes that formed dense areas of interaction. They then proved by functional analysis that their ‘top-ranked genes,’ having superior topological characteristics, coincided with commonly known cancer genes. Clustering methods other than that of Li et al. (e.g. DAPPLE, Metaranker, PRINCE, and other bi-ranking methods) are also exploited to predict disease-related genes.