Combined ligand and structure-based approaches on HIV-1 integrase strand transfer inhibitors

Reddy, Karnati Konda

doi:10.1016/j.cbi.2014.04.011

Cited by 28 publications

(6 citation statements)

References 40 publications

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“…The computational drug discovery process initiates from the drug target identification, target evaluation, and finding the suitable drug candidates [ 85 , 86 ]. Hence, target selection plays an imperial role in disease pathology, assessing the druggability of lead molecules and prioritizing candidate targets [ 87 , 88 ]. However, due to the complex nature of the human disease, the target selection process needs comprehensive methods that take part in the heterogeneous data and understand the molecular-level mechanism of disease phenotypes and also help to identify the patient-specific changes [ 89 ].…”

Section: Machine Learning Methodsmentioning

confidence: 99%

Artificial intelligence and machine learning approaches for drug design: challenges and opportunities for the pharmaceutical industries

Selvaraj

Chandra

2021

Mol Divers

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The global spread of COVID-19 has raised the importance of pharmaceutical drug development as intractable and hot research. Developing new drug molecules to overcome any disease is a costly and lengthy process, but the process continues uninterrupted. The critical point to consider the drug design is to use the available data resources and to find new and novel leads. Once the drug target is identified, several interdisciplinary areas work together with artificial intelligence (AI) and machine learning (ML) methods to get enriched drugs. These AI and ML methods are applied in every step of the computer-aided drug design, and integrating these AI and ML methods results in a high success rate of hit compounds. In addition, this AI and ML integration with high-dimension data and its powerful capacity have taken a step forward. Clinical trials output prediction through the AI/ML integrated models could further decrease the clinical trials cost by also improving the success rate. Through this review, we discuss the backend of AI and ML methods in supporting the computer-aided drug design, along with its challenge and opportunity for the pharmaceutical industry. Graphic abstract From the available information or data, the AI and ML based prediction for the high throughput virtual screening. After this integration of AI and ML, the success rate of hit identification has gained a momentum with huge success by providing novel drugs.

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Section: Machine Learning Methodsmentioning

confidence: 99%

Artificial intelligence and machine learning approaches for drug design: challenges and opportunities for the pharmaceutical industries

Selvaraj

Chandra

2021

Mol Divers

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“…The generated models were ranked according to discrete optimized potential energy (DOPE) and the lowest energy model was selected as the best model. Optimization followed by energy minimization of the best model was performed by Protein preparation wizard until the root mean square deviation (RMSD) of the non-hydrogen atoms touched 0.3Å by applying OPLS-AA force filed [ 34 , 35 ]. The minimized structure was evaluated using ProSA, and Saves server to check overall potential errors, dihedral angle distribution, and calculate the non-bonded interactions between the atoms respectively [ 36 , 37 ].…”

Section: Methodsmentioning

confidence: 99%

In silico identification of natural product inhibitors against Octamer-binding transcription factor 4 (Oct4) to impede the mechanism of glioma stem cells

Nayak

2021

PLoS ONE

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Octamer-binding transcription factor 4 (Oct4) is a core regulator in the retention of stemness, invasive, and self-renewal properties in glioma initiating cells (GSCs) and its overexpression inhibits the differentiation of glioma cells promoting tumor cell proliferation. The Pit-Oct-Unc (POU) domain comprising POU-specific domain (POUS) and POU-type homeodomain (POUHD) subdomains is the most critical part of the Oct4 for the generation of induced pluripotent stem cells from somatic cells that lead to tumor initiation, invasion, posttreatment relapse, and therapeutic resistance. Therefore, the present investigation hunts for natural product inhibitors (NPIs) against the POUHD domain of Oct4 by employing receptor-based virtual screening (RBVS) followed by binding free energy calculation and molecular dynamics simulation (MDS). RBVS provided 13 compounds with acceptable ranges of pharmacokinetic properties and good docking scores having key interactions with the POUHD domain. More Specifically, conformational and interaction stability analysis of 13 compounds through MDS unveiled two compounds ZINC02145000 and ZINC32124203 which stabilized the backbone of protein even in the presence of linker and POUS domain. Additionally, ZINC02145000 and ZINC32124203 exhibited stable and strong interactions with key residues W277, R242, and R234 of the POUHD domain even in dynamic conditions. Interestingly, ZINC02145000 and ZINC32124203 established communication not only with the POUHD domain but also with the POUS domain indicating their incredible potency toward thwarting the function of Oct4. ZINC02145000 and ZINC32124203 also reduced the flexibility and escalated the correlations between the amino acid residues of Oct4 evidenced by PCA and DCCM analysis. Finally, our examination proposed two NPIs that can impede the Oct4 function and may help to improve overall survival, diminish tumor relapse, and achieve a cure not only in deadly disease GBM but also in other cancers with minimal side effects.

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“…The prototype foamy virus (PFV) (PDB ID: 3OYA) is the surrogate model used in many studies to analyze the binding affinity of several strand transfer inhibitors including RAL [15- 17]. Though the PFV IN has structural and functional homology with HIV IN with virtually identical active sites but the overall HIV-1 IN specific sequence similarity is very limited [ 4 ]. Our aim of the study was to generate HIV-1 IN sequence specific clinical mutation models for this reason we had not used PFV (PDB ID: 3OYA).…”

Section: Methodsmentioning

confidence: 99%

“…The IN active site is located in the catalytic domain which is composed of 2 Asp residues and 1 Glu residue in the conserved DDE motif which are required for catalysis. The conserved DDE motif coordinates two divalent Mg2+ ions essential for the catalysis of integration [ 4 , 5 ]. The critical step during retroviral life cycle is the integration of the viral double stranded DNA into the host chromosome [ 6 ].…”

Section: Introductionmentioning

confidence: 99%

Effect of HIV-1 Subtype C integrase mutations implied using molecular modeling and docking data

et al. 2016

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The degree of sequence variation in HIV-1 integrase genes among infected patients and their impact on clinical response to Anti retroviral therapy (ART) is of interest. Therefore, we collected plasma samples from 161 HIV-1 infected individuals for subsequent integrase gene amplification (1087 bp). Thus, 102 complete integrase gene sequences identified as HIV-1 subtype-C was assembled. This sequence data was further used for sequence analysis and multiple sequence alignment (MSA) to assess position specific frequency of mutations within pol gene among infected individuals. We also used biophysical geometric optimization technique based molecular modeling and docking (Schrodinger suite) methods to infer differential function caused by position specific sequence mutations towards improved inhibitor selection. We thus identified accessory mutations (usually reduce susceptibility) leading to the resistance of some known integrase inhibitors in 14% of sequences in this data set. The Stanford HIV-1 drug resistance database provided complementary information on integrase resistance mutations to deduce molecular basis for such observation. Modeling and docking analysis show reduced binding by mutants for known compounds. The predicted binding values further reduced for models with combination of mutations among subtype C clinical strains. Thus, the molecular basis implied for the consequence of mutations in different variants of integrase genes of HIV-1 subtype C clinical strains from South India is reported. This data finds utility in the design, modification and development of a representative yet an improved inhibitor for HIV-1 integrase.

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Combined ligand and structure-based approaches on HIV-1 integrase strand transfer inhibitors

Cited by 28 publications

References 40 publications

Artificial intelligence and machine learning approaches for drug design: challenges and opportunities for the pharmaceutical industries

Artificial intelligence and machine learning approaches for drug design: challenges and opportunities for the pharmaceutical industries

In silico identification of natural product inhibitors against Octamer-binding transcription factor 4 (Oct4) to impede the mechanism of glioma stem cells

Effect of HIV-1 Subtype C integrase mutations implied using molecular modeling and docking data

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