Computational Structural Biology for Drug Discovery

Ilatovskiy, Andrey V.; Abagyan, Ruben

doi:10.1002/9781118681121.ch15

Cited by 3 publications

(2 citation statements)

References 95 publications

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“…One of the most important aspects of computational structure prediction is the accurate evaluation of the 3D model [ 27 ]. As a result of novel sequencing methods that have arisen in recent years, scientists have made ground-breaking discoveries in computational structural biology [ 28 ]. The introduction of widely accepted and extremely efficient techniques for structure evaluation has opened up new avenues for qualitatively estimating protein structures.…”

Section: Methodsmentioning

confidence: 99%

Potential Therapeutic Candidates against Chlamydia pneumonia Discovered and Developed In Silico Using Core Proteomics and Molecular Docking and Simulation-Based Approaches

Kadi

Altammar

Hassan

et al. 2022

IJERPH

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Chlamydia pneumonia, a species of the family Chlamydiacea, is a leading cause of pneumonia. Failure to eradicate C. pneumoniae can lead to chronic infection, which is why it is also considered responsible for chronic inflammatory disorders such as asthma, arthritis, etc. There is an urgent need to tackle the major concerns arising due to persistent infections caused by C. pneumoniae as no FDA-approved drug is available against this chronic infection. In the present study, an approach named subtractive proteomics was employed to the core proteomes of five strains of C. pneumonia using various bioinformatic tools, servers, and software. However, 958 non-redundant proteins were predicted from the 4754 core proteins of the core proteome. BLASTp was used to analyze the non-redundant genes against the proteome of humans, and the number of potential genes was reduced to 681. Furthermore, based on subcellular localization prediction, 313 proteins with cytoplasmic localization were selected for metabolic pathway analysis. Upon subsequent analysis, only three cytoplasmic proteins, namely 30S ribosomal protein S4, 4-hydroxybenzoate decarboxylase subunit C, and oligopeptide binding protein, were identified, which have the potential to be novel drug target candidates. The Swiss Model server was used to predict the target proteins’ three-dimensional (3D) structure. The molecular docking technique was employed using MOE software for the virtual screening of a library of 15,000 phytochemicals against the interacting residues of the target proteins. Molecular docking experiments were also evaluated using molecular dynamics simulations and the widely used MM-GBSA and MM-PBSA binding free energy techniques. The findings revealed a promising candidate as a novel target against C. pneumonia infections.

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Section: Methodsmentioning

confidence: 99%

Potential Therapeutic Candidates against Chlamydia pneumonia Discovered and Developed In Silico Using Core Proteomics and Molecular Docking and Simulation-Based Approaches

Kadi

Altammar

Hassan

et al. 2022

IJERPH

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“…In some extreme low-resolution cases, it may be impossible to determine whether the electron density is attributable to the binding of a compound or noise [10]. For this reason, it is believed that a density map with a resolution of 2.0-2.5 Å or higher is necessary for drug discovery or simulation studies [11]. Unfortunately, these criteria are not always met, and only 40% of the registered entities in the Protein Data Bank (PDB) have a resolution higher than 2 Å (Fig.…”

Section: Introductionmentioning

confidence: 99%

QAEmap: A Novel Local Quality Assessment Method for Protein Crystal Structures Using Machine Learning

Miyaguchi¹,

Sato²,

Kashima³

et al. 2021

Preprint

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Low-resolution electron density maps can pose a major obstacle in the determination and use of protein structures. Herein, we describe a novel method, quality assessment based on an electron density map (QAEmap), that evaluates local protein structures determined by X-ray crystallography and corrects structural errors using low-resolution maps. QAEmap uses a three-dimensional deep convolutional neural network with electron density maps and their corresponding coordinates as input and predicts the correlation between the local structure and the putative high-resolution experimental electron density map. This estimates how well the structure fits the high-resolution map. Further, we propose that this method may be applied to evaluate ligand binding, which can be difficult to determine at low resolution.

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Machine learning to estimate the local quality of protein crystal structures

Miyaguchi

Sato

Kashima

et al. 2021

Sci Rep

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Low-resolution electron density maps can pose a major obstacle in the determination and use of protein structures. Herein, we describe a novel method, called quality assessment based on an electron density map (QAEmap), which evaluates local protein structures determined by X-ray crystallography and could be applied to correct structural errors using low-resolution maps. QAEmap uses a three-dimensional deep convolutional neural network with electron density maps and their corresponding coordinates as input and predicts the correlation between the local structure and putative high-resolution experimental electron density map. This correlation could be used as a metric to modify the structure. Further, we propose that this method may be applied to evaluate ligand binding, which can be difficult to determine at low resolution.

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Computational Structural Biology for Drug Discovery

Cited by 3 publications

References 95 publications

Potential Therapeutic Candidates against Chlamydia pneumonia Discovered and Developed In Silico Using Core Proteomics and Molecular Docking and Simulation-Based Approaches

Potential Therapeutic Candidates against Chlamydia pneumonia Discovered and Developed In Silico Using Core Proteomics and Molecular Docking and Simulation-Based Approaches

QAEmap: A Novel Local Quality Assessment Method for Protein Crystal Structures Using Machine Learning

Machine learning to estimate the local quality of protein crystal structures

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