Identification of potential HIV-1 integrase strand transfer inhibitors: in silico virtual screening and QM/MM docking studies

Potential inhibitors of VEGFR1, VEGFR2, and VEGFR3 developed through Deep Learning for the treatment of Cervical Cancer

Cervical cancer stands as a prevalent gynaecologic malignancy affecting women globally, often linked to persistent human papillomavirus infection. Biomarkers associated with cervical cancer, including VEGF-A, VEGF-B, VEGF-C, VEGF-D, and VEGF-E, show upregulation and are linked to angiogenesis and lymphangiogenesis. This research aims to employ in-silico methods to target tyrosine kinase receptor proteins-VEGFR-1, VEGFR-2, and VEGFR-3, and identify novel inhibitors for Vascular Endothelial Growth Factors receptors (VEGFRs). A comprehensive literary study was conducted which identified 26 established inhibitors for VEGFR-1, VEGFR-2, and VEGFR-3 receptor proteins. Compounds with high-affinity scores, including PubChem ID-25102847, 369976, and 208908 were chosen from pre-existing compounds for creating Deep Learning-based models. RD-Kit, a Deep learning algorithm, was used to generate 43 million compounds for VEGFR-1, VEGFR-2, and VEGFR-3 targets. Molecular docking studies were conducted on the top 10 molecules for each target to validate the receptor-ligand binding affinity. The results of Molecular Docking indicated that PubChem IDs-71465,645 and 11152946 exhibited strong affinity, designating them as the most efficient molecules. To further investigate their potential, a Molecular Dynamics Simulation was performed to assess conformational stability, and a pharmacophore analysis was also conducted for indoctrinating interactions.

Virtual Screening Process: A Guide in Modern Drug Designing

Due to its capacity to drastically cut the cost and time necessary for experimental screening of compounds, virtual screening (VS) has grown to be a crucial component of drug discovery and development. VS is a computational method used in drug design to identify potential drugs from enormous libraries of chemicals. This approach makes use of molecular modeling and docking simulations to assess the small molecule's ability to bind to the desired protein. Virtual screening has a bright future, as high computational power and modern techniques are likely to further enhance the accuracy and speed of the process.

Exploring the Role of Chemoinformatics in Accelerating Drug Discovery: A Computational Approach

Cheminformatics and its role in drug discovery is expected to be the privileged approach in handling large number of chemical datasets. This approach contributes toward the pharmaceutical development and assessment of chemical compounds at a faster rate efficiently. Additionally, as technological advancement impacts research, cheminformatics is being used more and more in the field of health science. This chapter describes the concepts of cheminformatics along with its involvement in drug discovery with a case study.

CLICK-chemoproteomics and molecular dynamics simulation reveals pregnenolone targets and their binding conformations in Th2 cells

Pregnenolone (P5) is synthesized as the first bioactive steroid in the mitochondria from cholesterol. Clusters of differentiation 4 (CD4+) and Clusters of differentiation 8 (CD8+) immune cells synthesize P5 de novo; P5, in turn, play important role in immune homeostasis and regulation. However, P5's biochemical mode of action in immune cells is still emerging. We envisage that revealing the complete spectrum of P5 target proteins in immune cells would have multifold applications, not only in basic understanding of steroids biochemistry in immune cells but also in developing new therapeutic applications. We employed a CLICK-enabled probe to capture P5-binding proteins in live T helper cell type 2 (Th2) cells. Subsequently, using high-throughput quantitative proteomics, we identified the P5 interactome in CD4+ Th2 cells. Our study revealed P5's mode of action in CD4+ immune cells. We identified novel proteins from mitochondrial and endoplasmic reticulum membranes to be the primary mediators of P5's biochemistry in CD4+ and to concur with our earlier finding in CD8+ immune cells. Applying advanced computational algorithms and molecular simulations, we were able to generate near-native maps of P5-protein key molecular interactions. We showed bonds and interactions between key amino acids and P5, which revealed the importance of ionic bond, hydrophobic interactions, and water channels. We point out that our results can lead to designing of novel molecular therapeutics strategies.

A computational overview of integrase strand transfer inhibitors (INSTIs) against emerging and evolving drug-resistant HIV-1 integrase mutants

AIDS (Acquired immunodeficiency syndrome) is one of the chronic and potentially life-threatening epidemics across the world. Hitherto, the non-existence of definitive drugs that could completely cure the Human immunodeficiency virus (HIV) implies an urgent necessity for the discovery of novel anti-HIV agents. Since integration is the most crucial stage in retroviral replication, hindering it can inhibit overall viral transmission. The 5 FDA-approved integrase inhibitors were computationally investigated, especially owing to the rising multiple mutations against their susceptibility. This comparative study will open new possibilities to guide the rational design of novel lead compounds for antiretroviral therapies (ARTs), more specifically the structure-based design of novel Integrase strand transfer inhibitors (INSTIs) that may possess a better resistance profile than present drugs. Further, we have discussed potent anti-HIV natural compounds and their interactions as an alternative approach, recommending the urgent need to tap into the rich vein of indigenous knowledge for reverse pharmacology. Moreover, herein, we discuss existing evidence that might change in the near future.

Machine learning-based predictive models for identifying high active compounds against HIV-1 integrase

HIV-integrase is an important drug target because it catalyzes chromosomal integration of proviral DNA towards establishing latent infection. Computer-aided drug design has immensely contributed to identifying and developing novel antiviral drugs. We have developed various machine learning-based predictive models for identifying high activity compounds against HIV-integrase. Multiclass models were built using support vector machine with reasonable accuracy on the test and evaluation sets. The developed models were evaluated by rigorous validation approaches and the best features were selected by Boruta method. As compared to the model developed from all descriptors set, a slight improvement was observed among the selected descriptors. Validated models were further used for virtual screening of potential compounds from ChemBridge library. Of the six high active compounds predicted from selected models, compounds 9103124, 6642917 and 9082952 showed the most reasonable binding-affinity and stable-interaction with HIV-integrase active-site residues Asp64, Glu152 and Asn155. This was in agreement with previous reports on the essentiality of these residues against a wide range of inhibitors. We therefore highlight the rigorosity of validated classification models for accurate prediction and ranking of high active lead drugs against HIV-integrase.

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

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.

High-Throughput Screening and Quantum Mechanics for Identifying Potent Inhibitors Against Mac1 Domain of SARS-CoV-2 Nsp3

SARS-CoV-2 encodes the Mac1 domain within the large nonstructural protein 3 (Nsp3), which has an ADP-ribosylhydrolase activity conserved in other coronaviruses. The enzymatic activity of Mac1 makes it an essential virulence factor for the pathogenicity of coronavirus (CoV). They have a regulatory role in counteracting host-mediated antiviral ADP-ribosylation, which is unique part of host response towards viral infections. Mac1 shows highly conserved residues in the binding pocket for the mono and poly ADP-ribose. Therefore, SARS-CoV-2 Mac1 enzyme is considered as an ideal drug target and inhibitors developed against them can possess a broad antiviral activity against CoV. ADP-ribose-1 phosphate bound closed form of Mac1 domain is considered for screening with large database of ZINC. XP docking and QPLD provides strong potential lead compounds, that perfectly fits inside the binding pocket. Quantum mechanical studies expose that, substrate and leads have similar electron donor ability in the head regions, that allocates tight binding inside the substrate-binding pocket. Molecular dynamics study confirms the substrate and new lead molecules presence of electron donor and acceptor makes the interactions tight inside the binding pocket. Overall binding phenomenon shows both substrate and lead molecules are well-adopt to bind with similar binding mode inside the closed form of Mac1.

Interrogation of Bacillus anthracis SrtA active site loop forming open/close lid conformations through extensive MD simulations for understanding binding selectivity of SrtA inhibitors

Bacillus anthracis is a gram positive, deadly spore forming bacteria causing anthrax and these bacteria having the complex mechanism in the cell wall envelope, which can adopt the changes in environmental conditions. In this, the membrane bound cell wall proteins are said to progressive drug target for the inhibition of Bacillus anthracis. Among the cell wall proteins, the SrtA is one of the important mechanistic protein, which mediate the ligation with LPXTG motif by forming the amide bonds. The SrtA plays the vital role in cell signalling, cell wall formation, and biofilm formations. Inhibition of SrtA leads to rupture of the cell wall and biofilm formation, and that leads to inhibition of Bacillus anthracis and thus, SrtA is core important enzyme to study the inhibition mechanism. In this study, we have examined 28 compounds, which have the inhibitory activity against the Bacillus anthracis SrtA for developing the 3D-QSAR and also, compounds binding selectivity with both open and closed SrtA conformations, obtained from 100 ns of MD simulations. The binding site loop deviate in forming the open and closed gate mechanism is investigated to understand the inhibitory profile of reported compounds, and results show the closed state active site conformations are required for ligand binding specificity. Overall, the present study may offer an opportunity for better understanding of the mechanism of action and can be aided to further designing of a novel and highly potent SrtA inhibitors.

In silico virtual screening of potent inhibitor to hamper the interaction between HIV-1 integrase and LEDGF/p75 interaction using E-pharmacophore modeling, molecular docking, and dynamics simulations

The rapid increase of HIV-1 infection throughout the globe has a high demand for a superior drug with lesser side effects. LEDGF/p75, the human Lens Epithelium-Derived Growth Factor is identified as a promising cellular cofactor with integrase in facilitating the viral replication in an early stage by acting as a tethering factor in the pre-integration to the chromatin. Therefore, the present study was designed to identify a potent inhibitor by applying an E-pharmacophore based virtual screening, molecular docking, and dynamics simulation approaches. Finally, ZINC22077550 and ZINC32124441 were best identified potent molecules with the efficient binding affinity, strong hydrogen bonding, and acceptable pharmacological properties to hamper the interaction between integrase and LEDGF/p75. Further, the DFT and MDS studies were also analyzed, and shown a favorable energetic state and dynamic stability then reference compound. In conclusion, we suggest that these findings could be novel therapeutics in the future and may increase the lifespan of individuals suffering from viral infection.

E-pharmacophore based screening to identify potential HIV-1 gp120 and CD4 interaction blockers for wild and mutant types

HIV-1 gp120 provides a multistage viral entry process through the conserved CD4 binding site. Hunting of potential blockers can diminish the interaction of gp120 with the CD4 host receptor leading to the suppression of HIV-1 infection. Structure-based pharmacophore virtual screening followed by binding free energy calculation, molecular dynamics (MD) simulation and density functional theory (DFT) calculation is applied to discriminate the potential blockers from six small molecule databases. Five compounds from six databases exhibited vital interactions with key residues ASP368, GLU370, ASN425, MET426, TRP427 and GLY473 of gp120, involved in the binding with CD4, host receptor. Most importantly, compound NCI-254200 displayed strong communication with key residues of wild type and drug resistance single mutant gp120 (M426L and W427V) even in the dynamic condition, evidenced from MD simulation. This investigation provided a potential compound NCI-254200 which may show inhibitory activity against HIV-1 gp120 variant interactions with CD4 host cell receptors.

Shape-based Machine Learning Models for the Potential Novel COVID-19 Protease Inhibitors Assisted by Molecular Dynamics Simulation

BACKGROUND

The vast geographical expansion of novel coronavirus and an increasing number of COVID-19 affected cases have overwhelmed health and public health services. Artificial Intelligence (AI) and Machine Learning (ML) algorithms have extended their major role in tracking disease patterns, and in identifying possible treatments.

OBJECTIVE

This study aims to identify potential COVID-19 protease inhibitors through shape-based Machine Learning assisted by Molecular Docking and Molecular Dynamics simulations.

METHODS

31 Repurposed compounds have been selected targeting the main coronavirus protease (6LU7) and a machine learning approach was employed to generate shape-based molecules starting from the 3D shape to the pharmacophoric features of their seed compound. Ligand-Receptor Docking was performed with Optimized Potential for Liquid Simulations (OPLS) algorithms to identify highaffinity compounds from the list of selected candidates for 6LU7, which were subjected to Molecular Dynamic Simulations followed by ADMET studies and other analyses.

RESULTS

Shape-based Machine learning reported remdesivir, valrubicin, aprepitant, and fulvestrant as the best therapeutic agents with the highest affinity for the target protein. Among the best shape-based compounds, a novel compound identified was not indexed in any chemical databases (PubChem, Zinc, or ChEMBL). Hence, the novel compound was named 'nCorv-EMBS'. Further, toxicity analysis showed nCorv-EMBS to be suitable for further consideration as the main protease inhibitor in COVID-19.

CONCLUSION

Effective ACE-II, GAK, AAK1, and protease 3C blockers can serve as a novel therapeutic approach to block the binding and attachment of the main COVID-19 protease (PDB ID: 6LU7) to the host cell and thus inhibit the infection at AT2 receptors in the lung. The novel compound nCorv- EMBS herein proposed stands as a promising inhibitor to be evaluated further for COVID-19 treatment.

Current Computational Approaches for the Development of Anti-HIV Inhibitors: An Overview

BACKGROUND

Today, HIV-1 infection has become an extensive problem to public health and a greater challenge to all working researchers throughout the world. Since the beginning of HIV-1 virus, several antiviral therapeutic agents have been developed at various stages to combat HIV-1 infection. But, many of antiviral drugs are on the platform of drug resistance and toxicology issues, needs an urgent constructive investigation for the development of productive and protective therapeutics to make an improvement of individual life suffering with viral infection. As developing a novel agent is very costly, challenging and time taking route in the recent times.

METHODS

The review summarized about the modern approaches of computational aided drug discovery to developing a novel inhibitor within a short period of time and less cost.

RESULTS

The outcome suggests on the premise of reported information that the computational drug discovery is a powerful technology to design a defensive and fruitful therapeutic agents to combat HIV-1 infection and recover the lifespan of suffering one.

CONCLUSION

Based on survey of the reported information, we concluded that the current computational approaches is highly supportive in the progress of drug discovery and controlling the viral infection.

An Overview of Antiretroviral Agents for Treating HIV Infection in Paediatric Population

Paediatric Acquired ImmunoDeficiency Syndrome (AIDS) is a life-threatening and infectious disease in which the Human Immunodeficiency Virus (HIV) is mainly transmitted through Mother-To- Child Transmission (MTCT) during pregnancy, labour and delivery, or breastfeeding. This review provides an overview of the distinct therapeutic alternatives to abolish the systemic viral replication in paediatric HIV-1 infection. Numerous classes of antiretroviral agents have emerged as therapeutic tools for downregulation of different steps in the HIV replication process. These classes encompass Non- Nucleoside Analogue Reverse Transcriptase Inhibitors (NNRTIs), Nucleoside/Nucleotide Analogue Reverse Transcriptase Inhibitors (NRTIs/NtRTIs), INtegrase Inhibitors (INIs), Protease Inhibitors (PIs), and Entry Inhibitors (EIs). Co-administration of certain antiretroviral drugs with Pharmacokinetic Enhancers (PEs) may boost the effectiveness of the primary therapeutic agent. The combination of multiple antiretroviral drug regimens (Highly Active AntiRetroviral Therapy - HAART) is currently the standard therapeutic approach for HIV infection. So far, the use of HAART offers the best opportunity for prolonged and maximal viral suppression, and preservation of the immune system upon HIV infection. Still, the frequent administration of high doses of multiple drugs, their inefficient ability to reach the viral reservoirs in adequate doses, the development of drug resistance, and the lack of patient compliance compromise the complete HIV elimination. The development of nanotechnology-based drug delivery systems may enable targeted delivery of antiretroviral agents to inaccessible viral reservoir sites at therapeutic concentrations. In addition, the application of Computer-Aided Drug Design (CADD) approaches has provided valuable tools for the development of anti-HIV drug candidates with favourable pharmacodynamics and pharmacokinetic properties.

Advantages of Structure-Based Drug Design Approaches in Neurological Disorders

OBJECTIVE

The purpose of the review is to portray the theoretical concept on neurological disorders from research data.

BACKGROUND

The freak changes in chemical response of nerve impulse causes neurological disorders. The research evidence of the effort done in the older history suggests that the biological drug targets and their effective feature with responsive drugs could be valuable in promoting the future development of health statistics structure for improved treatment for curing the nervous disorders.

METHODS

In this review, we summarized the most iterative theoretical concept of structure based drug design approaches in various neurological disorders to unfathomable understanding of reported information for future drug design and development.

RESULTS

On the premise of reported information we analyzed the model of theoretical drug designing process for understanding the mechanism and pathology of the neurological diseases which covers the development of potentially effective inhibitors against the biological drug targets. Finally, it also suggests the management and implementation of the current treatment in improving the human health system behaviors.

CONCLUSION

With the survey of reported information we concluded the development strategies of diagnosis and treatment against neurological diseases which leads to supportive progress in the drug discovery.

Quantum mechanics implementation in drug-design workflows: does it really help?

The pharmaceutical industry is progressively operating in an era where development costs are constantly under pressure, higher percentages of drugs are demanded, and the drug-discovery process is a trial-and-error run. The profit that flows in with the discovery of new drugs has always been the motivation for the industry to keep up the pace and keep abreast with the endless demand for medicines. The process of finding a molecule that binds to the target protein using in silico tools has made computational chemistry a valuable tool in drug discovery in both academic research and pharmaceutical industry. However, the complexity of many protein-ligand interactions challenges the accuracy and efficiency of the commonly used empirical methods. The usefulness of quantum mechanics (QM) in drug-protein interaction cannot be overemphasized; however, this approach has little significance in some empirical methods. In this review, we discuss recent developments in, and application of, QM to medically relevant biomolecules. We critically discuss the different types of QM-based methods and their proposed application to incorporating them into drug-design and -discovery workflows while trying to answer a critical question: are QM-based methods of real help in drug-design and -discovery research and industry?

Development of HIV-1 integrase inhibitors: recent molecular modeling perspectives

Of the three viral enzymes essential to HIV replication, HIV-1 integrase (IN) is gaining popularity as a target for the antiviral therapy of AIDS. Substantial work focusing on IN has been done over the past three decades, which has facilitated and led to the approval of three drugs. Here, we discuss in detail the development of IN inhibitors between January 2012 and May 2014, with a particular focus on molecular simulation. We highlight controversial aspects of computational drug design and refer to alternative practices where appropriate. The analysis of these computational approaches provides some useful clues to the possible future discovery of novel IN inhibitors.

Molecular insights on analogs of HIV PR inhibitors toward HTLV-1 PR through QM/MM interactions and molecular dynamics studies: comparative structure analysis of wild and mutant HTLV-1 PR

Retroviruses HTLV-1 and HIV-1 are the primary causative agents of fatal adult T-cell leukemia and acquired immune deficiency syndrome (AIDS) disease. Both retroviruses are similar in characteristics mechanism, and it encodes for protease that mainly involved in the viral replication process. On the basis of the therapeutic success of HIV-1 PR inhibitors, the protease of HTLV-1 is mainly considered as a potential target for chemotherapy. At the same time, structural similarities in both enzymes that originate HIV PR inhibitors can also be an HTLV-1 PR inhibitor. But the expectations failed because of rejection of HIV PR inhibitors from the HTLV-1 PR binding pocket. In this present study, the reason for the HIV PR inhibitor rejection from the HTLV-1 binding site was identified through sequence analysis and molecular dynamics simulation method. Functional analysis of M37A mutation in HTLV PR clearly shows that the MET37 specificity and screening of potential inhibitors targeting MET37 is performed by using approved 90% similar HIV PR inhibitor compounds. From this approach, we report few compounds with a tendency to accept/donate electron specifically to an important site residue MET37 in HTLV-1 PR binding pocket.

Anti-HIV drug development through computational methods

Although highly active antiretroviral therapy (HAART) is effective in controlling the progression of AIDS, the emergence of drug-resistant strains increases the difficulty of successful treatment of patients with HIV infection. Increasing numbers of patients are facing the dilemma that comes with the running out of drug combinations for HAART. Computational methods play a key role in anti-HIV drug development. A substantial number of studies have been performed in anti-HIV drug development using various computational methods, such as virtual screening, QSAR, molecular docking, and homology modeling, etc. In this review, we summarize recent advances in the application of computational methods to anti-HIV drug development for five key targets as follows: reverse transcriptase, protease, integrase, CCR5, and CXCR4. We hope that this review will stimulate researchers from multiple disciplines to consider computational methods in the anti-HIV drug development process.

Investigations on the interactions of λphage-derived peptides against the SrtA mechanism in Bacillus anthracis

Bacillus anthracis is a well-known bioweapon pathogen, which coordinates the expression of its virulence factors in response to a specific environmental signal by its protein architecture. Absences of sortase signal functioning may fail to assemble the surface linked proteins and so B. anthracis cannot sustain an infection with host cells. Targeting the signaling mechanism of B. anthracis can be achieved by inhibition of SrtA enzyme through λphage-derived plyG. The lysin enzyme plyG is experimentally proven as bacteriolytic agent, specifically kill's B. anthracis by inhibiting the SrtA. Here, we have screened the peptides from λphage lysin, and these peptides are having the ability as LPXTG competitive inhibitors. In comparison to the activator peptide LPXTG binding motif, λphage lysin based inhibitor peptides are having much supremacy towards binding of SrtA. Finally, peptide structures extracted from PlyG are free from toxic, allergic abilities and also have the ability to terminate the signal transduction mechanism in B. anthracis.

Validation of potential inhibitors for SrtA against Bacillus anthracis by combined approach of ligand-based and molecular dynamics simulation

The development of SrtA inhibitors targeting the biothreat organism namely Bacillus anthracis was achieved by the combined approach of pharmacophore modeling, binding interactions, electron transferring capacity, ADME, and Molecular dynamics studies. In this study, experimentally reported Ba-SrtA inhibitors (pyridazinone and pyrazolethione derivatives) were considered for the development of enhanced pharmacophoric model. The obtained AAAHR hypothesis was a pure theoretical concept that accounts for common molecular interaction network present in experimentally active pyridazinone and pyrazolethione derivatives. Pharmacophore-based screening of AAAHR hypothesis provides several new compounds, and those compounds were treated with four phases of docking protocols with combined Glide-QPLD docking approach. In this approach, scoring and charge accuracy variations were seen to be dominated by QM/MM approach through the allocation of partial charges. Finally, we reported the best compounds from binding db, Chembridge db, and Toslab based on scoring values, energy parameters, electron transfer reaction, ADME, and cell adhesion inhibition activity. The dynamic state of interaction and binding energy assess that new compounds are more active inside the binding pocket and these compounds on experimental validations will survive as better inhibitors for targeting the cell adhesion mechanism of Ba-SrtA.