Prospective virtual screening combined with bio-molecular simulation enabled identification of new inhibitors for the KRAS drug target.

Lung cancer is a disease with a high mortality rate and it is the number one cause of cancer death globally. Approximately 12-14% of non-small cell lung cancers are caused by mutations in KRASG12C. The KRASG12C is one of the most prevalent mutants in lung cancer patients. KRAS was first considered undruggable. The sotorasib and adagrasib are the recently approved drugs that selectively target KRASG12C, and offer new treatment approaches to enhance patient outcomes however drug resistance frequently arises. Drug development is a challenging, expensive, and time-consuming process. Recently, machine-learning-based virtual screening are used for the development of new drugs. In this study, we performed machine-learning-based virtual screening followed by molecular docking, all atoms molecular dynamics simulation, and binding energy calculations for the identifications of new inhibitors against the KRASG12C mutant. In this study, four machine learning models including, random forest, k-nearest neighbors, Gaussian naïve Bayes, and support vector machine were used. By using an external dataset and 5-fold cross-validation, the developed models were validated. Among all the models the performance of the random forest (RF) model was best on the train/test dataset and external dataset. The random forest model was further used for the virtual screening of the ZINC15 database, in-house database, Pakistani phytochemicals, and South African Natural Products database. A total of 100 ns MD simulation was performed for the four best docking score complexes as well as the standard compound in complex with KRASG12C. Furthermore, the top four hits revealed greater stability and greater binding affinities for KRASG12C compared to the standard drug. These new hits have the potential to inhibit KRASG12C and may help to prevent KRAS-associated lung cancer. All the datasets used in this study can be freely available at ( https://github.com/Amar-Ajmal/Datasets-for-KRAS ).

In vivo analgesic, anti-inflammatory and molecular docking studies of S-naproxen derivatives.

In the current studies two naproxen derivatives (NPD) were evaluated for analgesic and anti-inflammatory properties. The acetic acid and hot plate animal models were used to screen the compounds for analgesic potential. While the anti-inflammatory potential was evaluated through animal paw edema, induced by several inflammatory mediators (carrageenan, bradykinin, and prostaglandin E2), the xylene-induced ear edema was also used as an inflammatory model. Both NPDs showed significant (p < 0.001) antinociceptive effects in the acetic acid-induced writhing paradigm. In the case of the hot plate, the NPD 1 at the tested dose of 5 mg/kg enhanced the latency time after 60 min of injection, which remained significant (p < 0.001) up to the end of the experiment duration. The maximum percent inhibition of NPD 1 was 87.53. The naloxone injection significantly lowered the latency time of NPD 1 as compared to NPD 2. Regarding the anti-inflammatory effect, both of the tested NPDs demonstrated a significant reduction in paw edema against various inflammatory mediators, as mentioned above; however, the anti-inflammatory effect of NPD 1 was better. The maximal percent inhibition by NPD 1 and 2 was 43.24 (after 60 min) and 45.93 (after 90 min). A considerable effect also resulted from xylene-induced ere edema. Further, a molecular docking study was carried out to investigate the binding modes of the NPD. The docking analysis revealed that the NPD significantly interacted with the COX2 enzyme. Furthermore, molecular dynamics simulation was carried out for the docked complexes. The MD simulation analysis revealed the high stability of the two naproxen derivatives.

Synthesis of new bis(dimethylamino)benzophenone hydrazone for diabetic management: In-vitro and in-silico approach.

Inhibiting α-glucosidase is a reliable method for reducing blood sugar levels in diabetic individuals. Bis(dimethylamino)benzophenone derivatives 1-27 were synthesized from bis(dimethylamino)benzophenone via two-step reaction. Different spectroscopic techniques, including EI-MS and 1H NMR, were employed to characterize all synthetic derivatives. The elemental composition of synthetic compounds was confirmed by elemental analysis and results were found in agreement with the calculated values. The synthetic compounds 1-27 were evaluated for α-glucosidase inhibitory activity, except five compounds all derivatives showed good to moderate inhibitory potential in the range of IC50 = 0.28 ± 2.65 - 0.94 ± 2.20 µM. Among them, the most active compounds were 5, 8, 9, and 12 with IC50 values of 0.29 ± 4.63, 0.29 ± 0.93, 0.28 ± 3.65, and 0.28 ± 2.65, respectively. Furthermore, all these compounds were found to be non-toxic on human fibroblast cell lines (BJ cell lines). Kinetics study of compounds 8 and 9 revealed competitive type of inhibition with Ki values 2.79 ± 0.011 and 3.64 ± 0.012 µM, respectively. The binding interactions of synthetic compounds were also confirmed through molecular docking studies that indicated that compounds fit well in the active site of enzyme. Furthermore, a total of 30ns MD simulation was carried out for the most potent complexes of the series. The molecular dynamics study revealed that compound-8 and compound-12 were stable during the MD simulation.

Identification of Drug Targets and Their Inhibitors in Yersinia pestis Strain 91001 through Subtractive Genomics, Machine Learning, and MD Simulation Approaches.

Yersinia pestis, the causative agent of plague, is a Gram-negative bacterium. If the plague is not properly treated it can cause rapid death of the host. Bubonic, pneumonic, and septicemic are the three types of plague described. Bubonic plague can progress to septicemic plague, if not diagnosed and treated on time. The mortality rate of pneumonic and septicemic plague is quite high. The symptom-defining disease is the bubo, which is a painful lymph node swelling. Almost 50% of bubonic plague leads to sepsis and death if not treated immediately with antibiotics. The host immune response is slow as compared to other bacterial infections. Clinical isolates of Yersinia pestis revealed resistance to many antibiotics such as tetracycline, spectinomycin, kanamycin, streptomycin, minocycline, chloramphenicol, and sulfonamides. Drug discovery is a time-consuming process. It always takes ten to fifteen years to bring a single drug to the market. In this regard, in silico subtractive proteomics is an accurate, rapid, and cost-effective approach for the discovery of drug targets. An ideal drug target must be essential to the pathogen's survival and must be absent in the host. Machine learning approaches are more accurate as compared to traditional virtual screening. In this study, k-nearest neighbor (kNN) and support vector machine (SVM) were used to predict the active hits against the beta-ketoacyl-ACP synthase III drug target predicted by the subtractive genomics approach. Among the 1012 compounds of the South African Natural Products database, 11 hits were predicted as active. Further, the active hits were docked against the active site of beta-ketoacyl-ACP synthase III. Out of the total 11 active hits, the 3 lowest docking score hits that showed strong interaction with the drug target were shortlisted along with the standard drug and were simulated for 100 ns. The MD simulation revealed that all the shortlisted compounds display stable behavior and the compounds formed stable complexes with the drug target. These compounds may have the potential to inhibit the beta-ketoacyl-ACP synthase III drug target and can help to combat Yersinia pestis-related infections. The dataset and the source codes are freely available on GitHub.

Novel flurbiprofen clubbed oxadiazole derivatives as potential urease inhibitors and their molecular docking study.

In this study, twenty eight novel oxadiazole derivatives (5-32) of the marketed available non-steroidal anti-inflammatory drug (NSAID), (S)-flurbiprofen (1), were synthesized via I2 mediated cyclo-addition reaction in better yields. The synthesized hydrazone-Schiff bases were cyclized with iodine by using potassium hydroxide as a base in DMSO solvent to obtain oxadiazole derivatives (5-32). Structures of the synthesized products were confirmed with HR-ESI-MS, 1H-NMR spectroscopy and CHN analysis. After structure confirmations all analogs were evaluated for urease (in vitro) inhibitory activity. Amongst the series, fourteen compounds 20, 26, 30, 24, 21, 16, 28, 31, 32, 7, 19, 13, 10, and 6 were found to be excellent inhibitors of urease enzyme, having IC50 values of 12 ± 0.9 to 20 ± 0.5 µM, better than the standard thiourea (IC50 = 22 ± 2.2 µM), whereas the remaining fourteen derivatives displayed good to moderate activity. The in silico study was executed to analyse the interaction between the active site of the enzyme (urease) and the produced compounds. The docking study revealed that compounds 20, 26, 30, 24, 21, 16, 28, 31, 32, 7, 19, 13, 10, and 6 had lower docking scores than the standard compound thiourea and revealed better interactions with the urease enzyme.

Structural and dynamics insights into the GBA variants associated with Parkinson's disease.

The GBA1 gene encodes for the lysosomal enzyme glucocerebrosidase (GCase), which maintains glycosphingolipid homeostasis and regulates the autophagy process. Genomic variants of GBA1 are associated with Goucher disease; however, several heterozygous variants of GBA (E326K, T369M, N370S, L444P) are frequent high-risk factors for Parkinson's disease (PD). The underlying mechanism of these variants has been revealed through functional and patient-centered research, but the structural and dynamical aspects of these variants have not yet been thoroughly investigated. In the current study, we used a thorough computational method to pinpoint the structural changes that GBA underwent because of genomic variants and drug binding mechanisms. According to our findings, PD-linked nsSNP variants of GBA showed structural variation and abnormal dynamics when compared to wild-typ. The docking analysis demonstrated that the mutants E326K, N370S, and L444P have higher binding affinities for Ambroxol. Root means square deviation (RMSD), Root mean square fluctuation analysis (RMSF), and MM-GBSA analysis confirmed that the Ambroxol are more stable in the binding site of N370S and L444P, and that their binding affinities are stronger as compared to the wild-type and T369M variants of GBA. The evaluation of hydrogen bonds and the calculation of the free binding energy provided additional evidence in favor of this conclusion. When docked with Ambroxol, GBA demonstrated an increase in binding affinity and catalytic activity. Understanding the therapeutic efficacy and potential against the aforementioned changes in the GBA will be beneficial in order to use more efficient methods for developing novel drugs.Communicated by Ramaswamy H. Sarma.

New drug target identification in Vibrio vulnificus by subtractive genome analysis and their inhibitors through molecular docking and molecular dynamics simulations.

Vibrio vulnificus is a rod shape, Gram-negative bacterium that causes sepsis (with a greater than 50% mortality rate), necrotizing fasciitis, gastroenteritis, skin, and soft tissue infection, wound infection, peritonitis, meningitis, pneumonia, keratitis, and arthritis. Based on pathogenicity V. vulnificus is categorized into three biotypes. Type 1 and type 3 cause diseases in humans while biotype 2 causes diseases in eel and fish. Due to indiscriminate use of antibiotics V. vulnificus has developed resistance to many antibiotics so curing is dramatically a challenge. V. vulnificus is resistant to cefazolin, streptomycin, tetracycline, aztreonam, tobramycin, cefepime, and gentamycin. Subtractive genome analysis is the most effective method for drug target identification. The method is based on the subtraction of homologous proteins from both pathogen and host. By this process set of proteins present only in the pathogen and perform essential functions in the pathogen can be identified. The entire proteome of Vibrio vulnificus strain ATCC 27562 was reduced step by step to a single protein predicted as the drug target. AlphaFold2 is one of the applications of deep learning algorithms in biomedicine and is correctly considered the game changer in the field of structural biology. Accuracy and speed are the major strength of AlphaFold2. In the PDB database, the crystal structure of the predicted drug target was not present, therefore the Colab notebook was used to predict the 3D structure by the AlphaFold2, and subsequently, the predicted model was validated. Potent inhibitors against the new target were predicted by virtual screening and molecular docking study. The most stable compound ZINC01318774 tightly attaches to the binding pocket of bisphosphoglycerate-independent phosphoglycerate mutase. The time-dependent molecular dynamics simulation revealed compound ZINC01318774 was superior as compared to the standard drug tetracycline in terms of stability. The availability of V. vulnificus strain ATCC 27562 has allowed in silico identification of drug target which will provide a base for the discovery of specific therapeutic targets against Vibrio vulnificus.

Computer-assisted drug repurposing for thymidylate kinase drug target in monkeypox virus.

Introduction: Monkeypox is a zoonotic disease caused by brick-shaped enveloped monkeypox (Mpox) virus that belongs to the family of ancient viruses known as Poxviridae. Subsequently, the viruses have been reported in various countries. The virus is transmitted by respiratory droplets, skin lesions, and infected body fluids. The infected patients experience fluid-filled blisters, maculopapular rash, myalgia, and fever. Due to the lack of effective drugs or vaccines, there is a need to identify the most potent and effective drugs to reduce the spread of monkeypox. The current study aimed to use computational methods to quickly identify potentially effective drugs against the Mpox virus. Methods: In our study, the Mpox protein thymidylate kinase (A48R) was targeted because it is a unique drug target. We screened a library of 9000 FDA-approved compounds of the DrugBank database by using various in silico approaches, such as molecular docking and molecular dynamic (MD) simulation. Results: Based on docking score and interaction analysis, compounds DB12380, DB13276, DB13276, DB11740, DB14675, DB11978, DB08526, DB06573, DB15796, DB08223, DB11736, DB16250, and DB16335 were predicted as the most potent. To examine the dynamic behavior and stability of the docked complexes, three compounds-DB16335, DB15796, and DB16250 -along with the Apo state were simulated for 300ns. The results revealed that compound DB16335 revealed the best docking score (-9.57 kcal/mol) against the Mpox protein thymidylate kinase. Discussion: Additionally, during the 300 ns MD simulation period, thymidylate kinase DB16335 showed great stability. Further, in vitro and in vivo study is recommended for the final predicted compounds.

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 (3CLPRO) 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 3CLPRO 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 3CLPRO. 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 3CLPRO. These hits were then docked into the active site of 3CLPRO. 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 3CLPRO. 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 3CLPRO and form stable complexes, indicating that these phytochemicals could be used as potential antagonists for SARS-COV-2.

Identification of novel peptide inhibitors for the KRas-G12C variant to prevent oncogenic signaling.

Kirsten rat sarcoma viral oncogene homolog (KRas) activating mutations are common in solid tumors, accounting for 90%, 45%, and 35% of pancreatic, colorectal, and lung cancers (LC), respectively. Each year, nearly 150k new cases (both men and women) of KRas-mutated malignancies are reported in the United States. NSCLC (non-small cell lung cancer) accounts for 80% of all LC cases. KRas mutations are found in 15% to 25% of NSCLC patients. The main cause of NSCLC is the KRas-G12C mutation. The drugs Sotorasib and Adagrasib were recently developed to treat advanced NSCLC caused by the KRas-G12C mutation. Most patients do not respond to KRas-G12C inhibitors due to cellular, molecular, and genetic resistance. Because of their safety, efficacy, and selectivity, peptide inhibitors have the potential to treat newly developing KRas mutations. Based on the KRas mutations, peptide inhibitors that are highly selective and specific to individual lung cancers can be rationally designed. The current study uses an alanine and residue scanning approach to design peptide inhibitors for KRas-G12C based on the known peptide. Our findings show that substitution of F3K, G11T, L8C, T14C, K13D, G11S, and G11P considerably enhances the binding affinity of the novel peptides, whereas F3K, G11T, L8C, and T14C peptides have higher stability and favorable binding to the altered peptides. Overall, our study paves the road for the development of potential therapeutic peptidomimetics that target the KRas-G12C complex and may inhibit the KRas and SOS complex from interacting.Communicated by Ramaswamy H. Sarma.

Machine Learning-based Virtual Screening for STAT3 Anticancer Drug Target.

BACKGROUND: Signal transducers and activators of the transcription (STAT) family is composed of seven structurally similar and highly conserved members, including STAT1, STAT2, STAT3, STAT4, STAT5a, STAT5b, and STAT6. The STAT3 signaling cascade is activated by upstream kinase signals and undergoes phosphorylation, homo-dimerization, nuclear translocation, and DNA binding, resulting in the expression of target genes involved in tumor cell proliferation, metastasis, angiogenesis, and immune editing. STAT3 hyperactivation has been documented in a number of tumors, including head and neck, breast, lung, liver, kidney, prostate, pancreas cancer, multiple myeloma, and acute myeloid leukemia. Drug discovery is a timeconsuming and costly process; it may take ten to fifteen years to bring a single drug to the market. Machine learning algorithms are very fast and effective and commonly used in the field, such as drug discovery. These algorithms are ideal for the virtual screening of large compound libraries to classify molecules as active or inactive. OBJECTIVE: The present work aims to perform machine learning-based virtual screening for the STAT3 drug target. METHODS: Machine learning models, such as k-nearest neighbor, support vector machine, Gaussian naïve Bayes, and random forest for classifying the active and inactive inhibitors against a STAT3 drug target, were developed. Ten-fold cross-validation was used for model validation. Then the test dataset prepared from the zinc database was screened using the random forest model. A total of 20 compounds with 88% accuracy was predicted as active against STAT3. Furthermore, these twenty compounds were docked into the active site of STAT3. The two complexes with good docking scores as well as the reference compound were subjected to MD simulation. A total of 100ns MD simulation was performed. RESULTS: Compared to all other models, the random forest model revealed better results. Compared to the standard reference compound, the top two hits revealed greater stability and compactness. CONCLUSION: In conclusion, our predicted hits have the ability to inhibit STAT3 overexpression to combat STAT3-associated diseases.

Strategies for Targeting KRAS: A Challenging Drug Target.

In the developed world, cancer is the most common cause of death. Among the 36 human genes of the RAS family, KRAS, NRAS, and HRAS play a prominent role in human cancer. KRAS belongs to the Ras superfamily of proteins and is a small GTPase signal transduction protein. Among the RAS isoform, KRAS is the dominant mutant that induces approximately 86% of the RAS mutations. The most frequently mutated KRAS isoform is KRAS4B. About 90% of pancreatic cancer, 30-40% of colon cancer, and 15 to 20% of lung cancers are caused by mutations KRAS4B isoform. Liver cancer, bladder cancer, breast cancer, and myeloid leukaemia are also caused by mutations in KRAS but are rare. The FDA has recently approved sotorasib for the treatement of KRASG12C-mutated advanced non-small cell lung cancer (NSCLC) patients. However, no FDAapproved drugs are available for other KRAS-driven cancer. As the KRAS proteins lack a druggable pocket accessible to the chemical inhibitors, the cancer-causing mutant proteins are almost identical to their essential wild-type counterparts. Therefore, they are considered undruggable. The new insights into the structure and function of RAS have changed this understanding and encouraged the development of many drug candidates. This review provides information about the different strategies for targeting KRAS, a challenging drug target that might be valuable for the scientific community.

Core proteome mediated therapeutic target mining and multi-epitope vaccine design for Helicobacter pylori.

Helicobacter pylori is a Gram-negative spiral-shaped bacterium that infects half of the human population worldwide and causes chronic inflammation. In the present study, we used the art of computational biology for therapeutic drug targets identification and a multi-epitope vaccine against multi-strains of H. pylori. For drug target identification, we used different tools and softwares to identify human non-homologous but pathogen essential proteins, with virulent properties and involved in unique metabolic pathways of H. pylori. For this purpose, the core proteome of 84 strains of H. pylori was retrieved from EDGAR 2.3 database. There were 59,808 proteins sequences in these strains. Duplicates and paralogous protein sequence removal was followed by human non-homologous protein miningPathogen essential and virulent proteins were subjected to pathway analysis Subcellular localization of the virulent proteins was predicted and druggability was also checked, leading to 30 druggable targets based on their similarity with the approved drug targets in Drugbank. For immunoinformatics analysis, we selected two outer membrane proteins (HPAKL86_RS06305 and HPSNT_RS00950) and subjected to determined immunogenic B and T-Cell epitopes. The B and T-Cell overlapped epitopes were selected to design 9 different vaccine constructs by using linkers and adjuvants. Least allergenic and most antigenic construct (C-8) was selected as a promiscuous vaccine to elicit host immune response. Cloning and in silico expression of the constructed vaccine (C-8) was done to produce a clone having the desired (gene) vaccine construct. In conclusion, the prioritized therapeutic targets for 84 strains of H.pylori will be useful for future therapy design. Vaccine design may also prove useful in the quest for targeting multi-strains of H. pylori in patients.