Investigating the role of Sterol C24-Methyl transferase mutation on drug resistance in leishmaniasis and identifying potential inhibitors

Leishmaniasis is a fatal disease caused by the leishmania parasite. For the survival of the leishmania parasite, Sterol C24-Methyl Transferase (SMT) is essential which is an enzyme of the ergosterol pathway. SMT protein mutation is responsible for Amphotericin-B drug resistance in Leishmania, which is the main treatment for visceral leishmaniasis. Amphotericin-B resistance is caused by three mutated residues V131I, V321I and F72C. The underlying mechanisms and structural changes in SMT enzymes responsible for resistance due to mutation are still not well understood. In the current study, the potential mechanism of resistance due to these mutations and the structure variation of wild and mutant SMT proteins were investigated through molecular dynamics simulations and molecular docking analysis. The results showed that AmB established strong bonding interaction with wild SMT as compare to mutants SMT. The binding energy calculation showed that binding energy of AmB with mutants SMT increases as compare to the wild SMT. Further structural based virtual screening was carried out to design potential inhibitors for the mutant SMT. On the basis of structural-based virtual screening four inhibitors (SANC01057, SANC00882, SANC00414, SANC01047) were computationally identified as potential mutant SMT (F72C) inhibitors. This work provides valuable information for improved management of drug resistant Leishmaniasis.Communicated by Ramaswamy H. Sarma.

Identification of novel peptide inhibitors for oncogenic KRAS G12D as therapeutic options using mutagenesis-based remodeling and MD simulations

The Kirsten rat sarcoma 2 viral oncogene homolog (KRAS) serves as a molecular switch, cycling between guanosine triphosphate (GTP)-bound and inactive guanosine diphosphate (GDP)-bound states. KRAS modulates numerous signal transduction pathways including the conventional RAF-MEK-ERK pathway. Mutations in the RAS genes have been linked to the formation of malignant tumors. Human malignancies typically show mutations in the Ras gene including HRAS, KRAS, and NRAS. Among all the mutations in exon 12 and exon 13 of the KRAS gene, the G12D mutation is more prevalent in pancreatic and lung cancer and accounts for around 41% of all G12 mutations, making them potential anticancer therapeutic targets. The present study is aimed at repurposing the peptide inhibitor KD2 of the KRAS G12D mutant. We employed an in-silico mutagenesis approach to design novel peptide inhibitors from the experimentally reported peptide inhibitor, and it was found that substitutions (N8W, N8I, and N8Y) might enhance the peptide's binding affinity toward the KRAS. Molecular dynamics simulations and binding energy calculations confirmed that the newly designed peptide inhibitors are stable and that their binding affinities are stronger as compared to the wild-type peptide. The detailed analysis revealed that newly designed peptides have the potential to inhibit KRAS/Raf interaction and the oncogenic signal of the KRAS G12D mutant. Our findings strongly suggest that these peptides should be tested and clinically validated to combat the oncogenic activity of KRAS.Communicated by Ramaswamy H. Sarma.

Computational approaches for the design of modulators targeting protein-protein interactions

BACKGROUND

Protein-protein interactions (PPIs) are intriguing targets for designing novel small-molecule inhibitors. The role of PPIs in various infectious and neurodegenerative disorders makes them potential therapeutic targets . Despite being portrayed as undruggable targets, due to their flat surfaces, disorderedness, and lack of grooves. Recent progresses in computational biology have led researchers to reconsider PPIs in drug discovery.

AREAS COVERED

In this review, we introduce in-silico methods used to identify PPI interfaces and present an in-depth overview of various computational methodologies that are successfully applied to annotate the PPIs. We also discuss several successful case studies that use computational tools to understand PPIs modulation and their key roles in various physiological processes.

EXPERT OPINION

Computational methods face challenges due to the inherent flexibility of proteins, which makes them expensive, and result in the use of rigid models. This problem becomes more significant in PPIs due to their flexible and flat interfaces. Computational methods like molecular dynamics (MD) simulation and machine learning can integrate the chemical structure data into biochemical and can be used for target identification and modulation. These computational methodologies have been crucial in understanding the structure of PPIs, designing PPI modulators, discovering new drug targets, and predicting treatment outcomes.

In Silico Characterization of Withania coagulans Bioactive Compounds as Potential Inhibitors of Hydroxymethylglutaryl (HMG-CoA) Reductase of Mus musculus

Hypercholesterolemia is a mediator for the etiology of cardiovascular diseases, which are characterized as the global leading cause of mortality. We aimed to investigate the inhibitory activity of Withania coagulans compounds against 3-hydroxy-3-methylglutaryl-coenzyme A reductase (Hmgcr) of Mus musculus using an extensive in silico approach. The 3D structure of the Hmgcr protein is not yet known, so we performed the homology modeling using MODELLER and SWISS-MODEL tools, followed with structural validation and assessment. The PROCHECK web server showed that the top-ranked homology model from SWISS-MODEL has 93.4% of residues in the most-favorable region, the quality factor was 98%, and the Verify3D score was 91.43%, compared to the other generated models. The druggable protein-binding cavities in a 3D model of Hmgcr were investigated with the aid of commonly prescribed statin compounds using the CB-dock approach. We compiled a 3D compound library of W. coagulans, followed by drug-likeness evaluation, and found 20 eligible compounds. The pattern of consensus residues obtained from the CB-dock procedure was then used for grid-box docking of W. coagulans compounds and statin drugs using AutoDock 4.2, respectively. The results showed that withanolide R (-10.77 kcal/mol), withanolide Q (-10.56 kcal/mol), withanolide J (-10.52 kcal/mol), atorvastatin (-8.99 kcal/mol), simvastatin (-8.66 kcal/mol), and rosuvastatin (-8.58 kcal/mol) were promising candidates that bind Hmgcr protein. The key residues involved in protein-ligand (withanolide R) interactions were Y516, C526, V529, I530, M533, I535, and V537, and the formation of a H-bond was at C526, M533, and I535 residues. M533 was the consensus residue having a tendency to form a H-bond with withanolide Q, too. Molecular dynamics simulations were used to validate the top-ranked docked complexes for the stability of the modeled protein. We also predicted the pharmacokinetic properties of binding affinity-based top-ranked compounds and concluded that they could be used as potential inhibitors of Hmgcr. However, further in vitro and in vivo studies are essential to completing the drug development process.

Identification of novel inhibitors for SARS-CoV-2 as therapeutic options using machine learning-based virtual screening, molecular docking and MD simulation

The new coronavirus SARS-COV-2, which emerged in late 2019 from Wuhan city of China was regarded as causing agent of the COVID-19 pandemic. The primary protease which is also known by various synonymous i.e., main protease, 3-Chymotrypsin-like protease (3CL^PRO) has a vital role in the replication of the virus, which can be used as a potential drug target. The current study aimed to identify novel phytochemical therapeutics for 3CL^PRO by machine learning-based virtual screening. A total of 4,000 phytochemicals were collected from deep literature surveys and various other sources. The 2D structures of these phytochemicals were retrieved from the PubChem database, and with the use of a molecular operating environment, 2D descriptors were calculated. Machine learning-based virtual screening was performed to predict the active phytochemicals against the SARS-CoV-2 3CL^PRO. Random forest achieved 98% accuracy on the train and test set among the different machine learning algorithms. Random forest model was used to screen 4,000 phytochemicals which leads to the identification of 26 inhibitors against the 3CL^PRO. These hits were then docked into the active site of 3CL^PRO. Based on docking scores and protein-ligand interactions, MD simulations have been performed using 100 ns for the top 5 novel inhibitors, ivermectin, and the APO state of 3CL^PRO. The post-dynamic analysis i.e,. Root means square deviation (RMSD), Root mean square fluctuation analysis (RMSF), and MM-GBSA analysis reveal that our newly identified phytochemicals form significant interactions in the binding pocket of 3CL^PRO and form stable complexes, indicating that these phytochemicals could be used as potential antagonists for SARS-COV-2.

In Vitro Screening and MD Simulations of Thiourea Derivatives against SARS-CoV-2 in Association with Multidrug Resistance ABCB1 Transporter

Severe acute respiratory syndrome corona virus 2 (SARS-CoV-2) is considered a global public health concern since it causes high morbidity and mortality. Recently, it has been reported that repurposed anti-COVID-19 drugs might interact with multidrug resistance ABC transporter, particularly ABCB1. In the current study, a series of thiourea derivatives were screened as potential inhibitors against SARS-CoV-2 by targeting the attachment of receptor binding domain (RBD) of spike protein with ACE2 and their interaction with human ABCB1 has also been explored. The results indicated strong impairment of RBD-ACE2 attachment by BB IV-46 with a percentage inhibition of 95.73 ± 1.79% relative to the positive control, while BB V-19 was proven inactive with a percentage inhibition of 50.90 ± 0.84%. The same compound (BB IV-46) interacted with ABCB1 and potentially inhibited cell proliferation of P-gp overexpressing cell line with an IC₅₀ value of 4.651 ± 0.06 μM. BB V-19, which was inactive against SARS-CoV-2, was inactive against ABCB1 with a higher IC₅₀ value of 35.72 ± 0.09 μM. Furthermore, molecular dynamics simulations followed by binding free-energy analysis explored the binding interaction of BB IV-46 and BB V-19 to RBD region of spike protein of SARS-CoV-2. The results confirmed that compound BB IV-46 interacted strongly with RBD with a significant binding energy (-127.0 kJ/mol), while BB V-19 interacted weakly (-29.30 kJ/mol). The key interacting residues of the RBD involved in binding included Leu441, Lys444, and Tyr449. This study highlights the importance of BB IV-46 against SARS-CoV-2; however, further pharmacokinetic and pharmacodynamics studies are needed to be done.

Toward the Noninvasive Diagnosis of Alzheimer's Disease: Molecular Basis for the Specificity of Curcumin for Fibrillar Amyloid-β

Recent studies show that curcumin, a naturally fluorescent dye, can be used for the noninvasive optical imaging of retinal amyloid-β (Aβ) plaques. We investigated the molecular basis for curcumin's specificity for hierarchical Aβ structures using molecular dynamics simulations, with a focus on how curcumin is able to detect and discriminate different amyloid morphologies. Curcumin inhibits and breaks up β-sheet formation in Aβ monomers. With disordered Aβ structures, curcumin forms a coarse-grained composite structure. With an ordered fibril, curcumin's interaction is highly specific, and the curcumin molecules are deposited in the fibril groove. Curcumin tends to self-aggregate, which is finely balanced with its affinity for Aβ. This tendency concentrates curcumin molecules at Aβ deposition sites, potentially increasing the fluorescence signal. This is probably why curcumin is such an effective amyloid imaging agent.

Heparin-Assisted Amyloidogenesis Uncovered through Molecular Dynamics Simulations

Glycosaminoglycans (GAGs), in particular, heparan sulfate and heparin, are found colocalized with Aβ amyloid. They have been shown to enhance fibril formation, suggesting a possible pathological connection. We have investigated heparin's assembly of the KLVFFA peptide fragment using molecular dynamics simulation, to gain a molecular-level mechanistic understanding of how GAGs enhance fibril formation. The simulations reveal an exquisite process wherein heparin accelerates peptide assembly by first "gathering" the peptide molecules and then assembling them. Heparin does not act as a mere template but is tightly coupled to the peptides, yielding a composite protofilament structure. The strong intermolecular interactions suggest composite formation to be a general feature of heparin's interaction with peptides. Heparin's chain flexibility is found to be essential to its fibril promotion activity, and the need for optimal heparin chain length and concentration has been rationalized. These insights yield design rules (flexibility; chain-length) and protocol guidance (heparin:peptide molar ratio) for developing effective heparin mimetics and other functional GAGs.

In Silico Drug Designing for ala438 Deleted Ribosomal Protein S1 (RpsA) on the Basis of the Active Compound Zrl15

Pyrazinoic acid-resistant tuberculosis is a severe chronic disorder. First-line drugs specifically target the ribosomal protein subunit-1 (RpsA) and stop trans-translation in the wild-type bacterium, causing bacterial cell death. In mutant bacterial strain, the deletion of ala438 does not let the pyrazinoic acid to bind to the active site of RpsA and ensures that the bacterium survives. Hence, such tuberculosis cases require an immediate and successful regime. The current study was designed to identify inhibitors that could bind to the mutant state of the RpsA protein. Initially, a pharmacophore model was generated based on the recently published most potent inhibitor for the mutant state of RpsA, i.e., zrl15. The validated pharmacophore model was further used for virtual screening of two chemical libraries, i.e., ZINC and ChemBridge. After applying the Lipinski rule of five (Ro5), a total of 260 and 749 hits from the ChemBridge and ZINC libraries, respectively, were identified using pharmacophore mapping. These hits were then docked into the active site of the mutant state of the RpsA protein, and later, the top 150 compounds from each library were chosen based on the docking score. A total of 21 compounds were shortlisted from each library based on the best protein-ligand interactions. Finally, a total of 05 compounds were subjected to molecular dynamics study to examine the dynamic behavior of each compound in the active site of the mutant state of the RpsA protein. The results revealed that all compounds had good chemical properties such as absorption, distribution, metabolism, excretion, and toxicity (ADMET), and there was no Pan Assay Interference (PAINS) or deviation from Ro5, indicating that these compounds could be useful antagonists for the mutant state of the RpsA protein.

Hidden allosteric sites and De-Novo drug design

INTRODUCTION

Hidden allosteric sites are not visible in apo-crystal structures, but they may be visible in holo-structures when a certain ligand binds and maintains the ligand intended conformation. Several computational and experimental techniques have been used to investigate these hidden sites but identifying them remains a challenge.

AREAS COVERED

This review provides a summary of the many theoretical approaches for predicting hidden allosteric sites in disease-related proteins. Furthermore, promising cases have been thoroughly examined to reveal the hidden allosteric site and its modulator.

EXPERT OPINION

In the recent past, with the development in scientific techniques and bioinformatics tools, the number of drug targets for complex human diseases has significantly increased but unfortunately most of these targets are undruggable due to several reasons. Alternative strategies such as finding cryptic (hidden) allosteric sites are an attractive approach for exploitation of the discovery of new targets. These hidden sites are difficult to recognize compared to allosteric sites, mainly due to a lack of visibility in the crystal structure. In our opinion, after many years of development, MD simulations are finally becoming successful for obtaining a detailed molecular description of drug-target interaction.