Review on Computational Techniques to Identify Drug Targets from Whole Proteome of Fungi and Bacteria

Abstract Background Mosquito-borne diseases have a devastating impact on human civilization. A few species of Anopheles mosquitoes are responsible for malaria transmission, and while there has been a reduction in malaria-related deaths worldwide, growing insecticide resistance is a cause for concern. Aedes mosquitoes are known vectors of viral infections, including dengue, yellow fever, chikungunya, and Zika. Aminoacyl-tRNA synthetases (aaRSs) are key players in protein synthesis and are potent anti-infective drug targets. The structure–function activity relationship of aaRSs in mosquitoes (in particular, Anopheles and Aedes spp.) remains unexplored. Methods We employed computational techniques to identify aaRSs from five different mosquito species (Anopheles culicifacies, Anopheles stephensi, Anopheles gambiae, Anopheles minimus, and Aedes aegypti). The VectorBase database (https://vectorbase.org/vectorbase/app) and web-based tools were utilized to predict the subcellular localizations (TargetP-2.0, UniProt, DeepLoc-1.0), physicochemical characteristics (ProtParam), and domain arrangements (PfAM, InterPro) of the aaRSs. Structural models for prolyl (PRS)-, and phenylalanyl (FRS)-tRNA synthetases—were generated using the I-TASSER and Phyre protein modeling servers. Results Among the vector species, a total of 37 (An. gambiae), 37 (An. culicifacies), 37 (An. stephensi), 37 (An. minimus), and 35 (Ae. aegypti) different aaRSs were characterized within their respective mosquito genomes. Sequence identity amongst the aaRSs from the four Anopheles spp. was > 80% and in Ae. aegypti was > 50%. Conclusions Structural analysis of two important aminoacyl-tRNA synthetases [prolyl (PRS) and phenylanalyl (FRS)] of Anopheles spp. suggests structural and sequence similarity with potential antimalarial inhibitor [halofuginone (HF) and bicyclic azetidine (BRD1369)] binding sites. This suggests the potential for repurposing of these inhibitors against the studied Anopheles spp. and Ae. aegypti.

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Established and In-trial GPCR Families in Clinical Trials: A Review for Target Selection

Current Drug Targets ◽

10.2174/1389450120666181105152439 ◽

2019 ◽

Vol 20 (5) ◽

pp. 522-539 ◽

Cited By ~ 1

Author(s):

Surovi Saikia ◽

Manobjyoti Bordoloi ◽

Rajeev Sarmah

Keyword(s):

Clinical Trials ◽

Drug Discovery ◽

Phase I ◽

Human Genome ◽

Phase Ii ◽

Drug Targets ◽

Complex Diseases ◽

Phase Iii ◽

Computational Techniques ◽

Receptor Complexes

The largest family of drug targets in clinical trials constitute of GPCRs (G-protein coupled receptors) which accounts for about 34% of FDA (Food and Drug Administration) approved drugs acting on 108 unique GPCRs. Factors such as readily identifiable conserved motif in structures, 127 orphan GPCRs despite various de-orphaning techniques, directed functional antibodies for validation as drug targets, etc. has widened their therapeutic windows. The availability of 44 crystal structures of unique receptors, unexplored non-olfactory GPCRs (encoded by 50% of the human genome) and 205 ligand receptor complexes now present a strong foundation for structure-based drug discovery and design. The growing impact of polypharmacology for complex diseases like schizophrenia, cancer etc. warrants the need for novel targets and considering the undiscriminating and selectivity of GPCRs, they can fulfill this purpose. Again, natural genetic variations within the human genome sometimes delude the therapeutic expectations of some drugs, resulting in medication response differences and ADRs (adverse drug reactions). Around ~30 billion US dollars are dumped annually for poor accounting of ADRs in the US alone. To curb such undesirable reactions, the knowledge of established and currently in clinical trials GPCRs families can offer huge understanding towards the drug designing prospects including “off-target” effects reducing economical resource and time. The druggability of GPCR protein families and critical roles played by them in complex diseases are explained. Class A, class B1, class C and class F are generally established family and GPCRs in phase I (19%), phase II(29%), phase III(52%) studies are also reviewed. From the phase I studies, frizzled receptors accounted for the highest in trial targets, neuropeptides in phase II and melanocortin in phase III studies. Also, the bioapplications for nanoparticles along with future prospects for both nanomedicine and GPCR drug industry are discussed. Further, the use of computational techniques and methods employed for different target validations are also reviewed along with their future potential for the GPCR based drug discovery.

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TLR4-Targeting Therapeutics: Structural Basis and Computer-Aided Drug Discovery Approaches

Molecules ◽

10.3390/molecules25030627 ◽

2020 ◽

Vol 25 (3) ◽

pp. 627 ◽

Cited By ~ 9

Author(s):

Qurat ul Ain ◽

Maria Batool ◽

Sangdun Choi

Keyword(s):

Drug Discovery ◽

Lupus Erythematosus ◽

Drug Targets ◽

Pharmacophore Modeling ◽

Biological Data ◽

Structural Basis ◽

Computational Techniques ◽

Relationship Analysis ◽

Computer Aided ◽

Pharmacologically Active

The integration of computational techniques into drug development has led to a substantial increase in the knowledge of structural, chemical, and biological data. These techniques are useful for handling the big data generated by empirical and clinical studies. Over the last few years, computer-aided drug discovery methods such as virtual screening, pharmacophore modeling, quantitative structure-activity relationship analysis, and molecular docking have been employed by pharmaceutical companies and academic researchers for the development of pharmacologically active drugs. Toll-like receptors (TLRs) play a vital role in various inflammatory, autoimmune, and neurodegenerative disorders such as sepsis, rheumatoid arthritis, inflammatory bowel disease, Alzheimer’s disease, multiple sclerosis, cancer, and systemic lupus erythematosus. TLRs, particularly TLR4, have been identified as potential drug targets for the treatment of these diseases, and several relevant compounds are under preclinical and clinical evaluation. This review covers the reported computational studies and techniques that have provided insights into TLR4-targeting therapeutics. Furthermore, this article provides an overview of the computational methods that can benefit a broad audience in this field and help with the development of novel drugs for TLR-related disorders.

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An Overview of Bioinformatics Methods for Analyzing Autism Spectrum Disorders

Current Pharmaceutical Design ◽

10.2174/1381612825666191111154837 ◽

2020 ◽

Vol 25 (43) ◽

pp. 4552-4559 ◽

Cited By ~ 3

Author(s):

Shogo Nakashima ◽

Jose C. Nacher ◽

Jiangning Song ◽

Tatsuya Akutsu

Keyword(s):

Autism Spectrum Disorders ◽

Biological Networks ◽

Drug Targets ◽

Autism Spectrum ◽

Computational Techniques ◽

Protein Protein Interaction ◽

High Prevalence ◽

Computational Aspects ◽

Spectrum Disorders ◽

Novel Drug

Autism Spectrum Disorders (ASD) are a group of neurodevelopmental disorders and are well recognized to be biologically heterogeneous in which various factors are associated, including genetic, metabolic, and environmental ones. Despite its high prevalence, only a few drugs have been approved for the treatment of ASD. Therefore, extensive studies have been conducted to identify ASD risk genes and novel drug targets. Since many genes and many other factors are associated with ASD, various bioinformatics methods have also been developed for the analysis of ASD. In this paper, we review bioinformatics methods for analyzing ASD data with the focus on computational aspects. We classify existing methods into two categories: (i) methods based on genomic variants and gene expression data, and (ii) methods using biological networks, which include gene co-expression networks and protein-protein interaction networks. Next, for each method, we provide an overall flow and elaborate on the computational techniques used. We also briefly review other approaches and discuss possible future directions and strategies for developing bioinformatics approaches to analyze ASD.

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An Updated Review of Computer-Aided Drug Design and Its Application to COVID-19

BioMed Research International ◽

10.1155/2021/8853056 ◽

2021 ◽

Vol 2021 ◽

pp. 1-18

Author(s):

Arun Bahadur Gurung ◽

Mohammad Ajmal Ali ◽

Joongku Lee ◽

Mohammad Abul Farah ◽

Khalid Mashay Al-Anazi

Keyword(s):

Drug Discovery ◽

Drug Design ◽

Drug Targets ◽

Pharmacophore Modeling ◽

Review Article ◽

Drug Candidate ◽

Computational Techniques ◽

Computer Aided Drug Design ◽

Structure Based Drug Design ◽

Computer Aided

The recent outbreak of the deadly coronavirus disease 19 (COVID-19) pandemic poses serious health concerns around the world. The lack of approved drugs or vaccines continues to be a challenge and further necessitates the discovery of new therapeutic molecules. Computer-aided drug design has helped to expedite the drug discovery and development process by minimizing the cost and time. In this review article, we highlight two important categories of computer-aided drug design (CADD), viz., the ligand-based as well as structured-based drug discovery. Various molecular modeling techniques involved in structure-based drug design are molecular docking and molecular dynamic simulation, whereas ligand-based drug design includes pharmacophore modeling, quantitative structure-activity relationship (QSARs), and artificial intelligence (AI). We have briefly discussed the significance of computer-aided drug design in the context of COVID-19 and how the researchers continue to rely on these computational techniques in the rapid identification of promising drug candidate molecules against various drug targets implicated in the pathogenesis of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). The structural elucidation of pharmacological drug targets and the discovery of preclinical drug candidate molecules have accelerated both structure-based as well as ligand-based drug design. This review article will help the clinicians and researchers to exploit the immense potential of computer-aided drug design in designing and identification of drug molecules and thereby helping in the management of fatal disease.

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Application of Computational Methods in Understanding Mutations in Mycobacterium tuberculosis Drug Resistance

Frontiers in Molecular Biosciences ◽

10.3389/fmolb.2021.643849 ◽

2021 ◽

Vol 8 ◽

Author(s):

Grace Mugumbate ◽

Brilliant Nyathi ◽

Albert Zindoga ◽

Gadzikano Munyuki

Keyword(s):

Drug Resistance ◽

Mycobacterium Tuberculosis ◽

Computational Methods ◽

Drug Targets ◽

Point Mutations ◽

Machine Learning Techniques ◽

World Health ◽

Computational Techniques ◽

Health Organization ◽

Drug Resistant Strains

The emergence of drug-resistant strains of Mycobacterium tuberculosis (Mtb) impedes the End TB Strategy by the World Health Organization aiming for zero deaths, disease, and suffering at the hands of tuberculosis (TB). Mutations within anti-TB drug targets play a major role in conferring drug resistance within Mtb; hence, computational methods and tools are being used to understand the mechanisms by which they facilitate drug resistance. In this article, computational techniques such as molecular docking and molecular dynamics are applied to explore point mutations and their roles in affecting binding affinities for anti-TB drugs, often times lowering the protein’s affinity for the drug. Advances and adoption of computational techniques, chemoinformatics, and bioinformatics in molecular biosciences and resources supporting machine learning techniques are in abundance, and this has seen a spike in its use to predict mutations in Mtb. This article highlights the importance of molecular modeling in deducing how point mutations in proteins confer resistance through destabilizing binding sites of drugs and effectively inhibiting the drug action.

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