Correlated Motions and Dynamics in Different Domains of Epidermal Growth Factor Receptor With L858R and T790M Mutations

Molecular Engineering L-Aspartate-Alpha-Decarboxylase to Enhance Catalytic Stability and Performance

L-aspartate-alpha-decarboxylase (ADC) catalyzes the decarboxylation of L-aspartate to produce β-alanine, which is the decisive step in the biosynthesis of β-alanine. However, the low catalytic stability and efficiency of ADC limit its industrial applications. In this study, a variant of ADC from Bacillus subtilis were used as a starting point for engineering. After constructing a random mutagenesis library by error-prone PCR, followed by high-throughput screening,four substitutions (S7 N, K63 N, A99T, and K113R) were identified. By screening saturation mutagenesis libraries on these positions and computational analysis, two recombined variants N3(S7 N/K63 N/I88 M/A99E/K113R/I126*) and Y1(S7Y/K63 N/I88 M/A99E/K113R/I126*) with improved performance were obtained. Compared to the wild type, the catalytic efficiency and catalytic stability of the best two variants were enhanced up to 95 %(variant N3) and up to 89 %(variant Y1), respectively. In addition, Y1 exhibited 3.37 times improved half-life and 2-fold improved total turnover number. Hydrophilicity analysis and molecular dynamics (MD) simulation revealed that the increased hydrophilicity and steric hindrance of key amino acid residues would affect the catalytic activity and stability. The improved catalytic performance of the variants could be attributed to their enhanced binding capacity to the substrate within the active pocket and the alleviation of mechanism-based inactivation.

Exploring the Therapeutic Potential of Petiveria alliacea L. Phytochemicals: A Computational Study on Inhibiting SARS-CoV-2's Main Protease (Mpro)

The outbreak of SARS-CoV-2, also known as the COVID-19 pandemic, is still a critical risk factor for both human life and the global economy. Although, several promising therapies have been introduced in the literature to inhibit SARS-CoV-2, most of them are synthetic drugs that may have some adverse effects on the human body. Therefore, the main objective of this study was to carry out an in-silico investigation into the medicinal properties of Petiveria alliacea L. (P. alliacea L.)-mediated phytocompounds for the treatment of SARS-CoV-2 infections since phytochemicals have fewer adverse effects compared to synthetic drugs. To explore potential phytocompounds from P. alliacea L. as candidate drug molecules, we selected the infection-causing main protease (Mpro) of SARS-CoV-2 as the receptor protein. The molecular docking analysis of these receptor proteins with the different phytocompounds of P. alliacea L. was performed using AutoDock Vina. Then, we selected the three top-ranked phytocompounds (myricitrin, engeletin, and astilbin) as the candidate drug molecules based on their highest binding affinity scores of -8.9, -8.7 and -8.3 (Kcal/mol), respectively. Then, a 100 ns molecular dynamics (MD) simulation study was performed for their complexes with Mpro using YASARA software, computed RMSD, RMSF, PCA, DCCM, MM/PBSA, and free energy landscape (FEL), and found their almost stable binding performance. In addition, biological activity, ADME/T, DFT, and drug-likeness analyses exhibited the suitable pharmacokinetics properties of the selected phytocompounds. Therefore, the results of this study might be a useful resource for formulating a safe treatment plan for SARS-CoV-2 infections after experimental validation in wet-lab and clinical trials.

Novel computational and drug design strategies for inhibition of human papillomavirus-associated cervical cancer and DNA polymerase theta receptor by Apigenin derivatives

The present study deals with the advanced in-silico analyses of several Apigenin derivatives to explore human papillomavirus-associated cervical cancer and DNA polymerase theta inhibitor properties by molecular docking, molecular dynamics, QSAR, drug-likeness, PCA, a dynamic cross-correlation matrix and quantum calculation properties. The initial literature study revealed the potent antimicrobial and anticancer properties of Apigenin, prompting the selection of its potential derivatives to investigate their abilities as inhibitors of human papillomavirus-associated cervical cancer and DNA polymerase theta. In silico molecular docking was employed to streamline the findings, revealing promising energy-binding interactions between all Apigenin derivatives and the targeted proteins. Notably, Apigenin 4'-O-Rhamnoside and Apigenin-4'-Alpha-L-Rhamnoside demonstrated higher potency against the HPV45 oncoprotein E7 (PDB ID 2EWL), while Apigenin and Apigenin 5-O-Beta-D-Glucopyranoside exhibited significant binding energy against the L1 protein in humans. Similarly, a binding affinity range of - 7.5 kcal/mol to - 8.8 kcal/mol was achieved against DNA polymerase theta, indicating the potential of Apigenin derivatives to inhibit this enzyme (PDB ID 8E23). This finding was further validated through molecular dynamic simulation for 100 ns, analyzing parameters such as RMSD, RMSF, SASA, H-bond, and RoG profiles. The results demonstrated the stability of the selected compounds during the simulation. After passing the stability testing, the compounds underwent screening for ADMET, pharmacokinetics, and drug-likeness properties, fulfilling all the necessary criteria. QSAR, PCA, dynamic cross-correlation matrix, and quantum calculations were conducted, yielding satisfactory outcomes. Since this study utilized in silico computational approaches and obtained outstanding results, further validation is crucial. Therefore, additional wet-lab experiments should be conducted under in vivo and in vitro conditions to confirm the findings.

Elucidating the potential effects of point mutations on FGFR3 inhibitor resistance via combined molecular dynamics simulation and community network analysis

FGFR3 kinase mutations are associated with a variety of malignancies, but FGFR3 mutant inhibitors have rarely been studied. Furthermore, the mechanism of pan-FGFR inhibitors resistance caused by kinase domain mutations is still unclear. In this study, we try to explain the mechanism of drug resistance to FGFR3 mutation through global analysis and local analysis based on molecular dynamics simulation, binding free energy analysis, umbrella sampling and community network analysis. The results showed that FGFR3 mutations caused a decrease in the affinity between drugs and FGFR3 kinase, which was consistent with the reported experimental results. Possible mechanisms are that mutations affect drug-protein affinity by altering the environment of residues near the hinge region where the protein binds to the drug, or by affecting the A-loop and interfering with the allosteric communication networks. In conclusion, we systematically elucidated the underlying mechanism of pan-FGFR inhibitor resistance caused by FGFR3 mutation based on molecular dynamics simulation strategy, which provided theoretical guidance for the development of FGFR3 mutant kinase inhibitors.

Computational Methods for the Analysis and Prediction of EGFR-Mutated Lung Cancer Drug Resistance: Recent Advances in Drug Design, Challenges and Future Prospects

Lung cancer is a major cause of cancer deaths worldwide, and has a very low survival rate. Non-small cell lung cancer (NSCLC) is the largest subset of lung cancers, which accounts for about 85% of all cases. It has been well established that a mutation in the epidermal growth factor receptor (EGFR) can lead to lung cancer. EGFR Tyrosine Kinase Inhibitors (TKIs) are developed to target the kinase domain of EGFR. These TKIs produce promising results at the initial stage of therapy, but the efficacy becomes limited due to the development of drug resistance. In this paper, we provide a comprehensive overview of computational methods, for understanding drug resistance mechanisms. The important EGFR mutants and the different generations of EGFR-TKIs, with the survival and response rates are discussed. Next, we evaluate the role of important EGFR parameters in drug resistance mechanism, including structural dynamics, hydrogen bonds, stability, dimerization, binding free energies, and signaling pathways. Personalized drug resistance prediction models, drug response curve, drug synergy, and other data-driven methods are also discussed. Recent advancements in deep learning; such as AlphaFold2, deep generative models, big data analytics, and the applications of statistics and permutation are also highlighted. We explore limitations in the current methodologies, and discuss strategies to overcome them. We believe this review will serve as a reference for researchers; to apply computational techniques for precision medicine, analyzing structures of protein-drug complexes, drug discovery, and understanding the drug response and resistance mechanisms in lung cancer patients.

Exploring binding stability of hydroxy-3-(4-hydroxyphenyl)-5-(4-nitrophenyl)-5,5a,7,8,9,9a-hexahydrothiazolo[2,3-b] quinazolin-6-one with T790M/L858R EGFR-TKD

Cancer causes innumerable deaths every year globally. Breast cancer and non-small cell lung carcinoma are the most prevalent worldwide. EGFR-TKD is a neoplastic survival therapeutic target in a wide array of carcinoma cells. Various non-specific tyrosine kinase inhibitors lead to hyperphosphorylation and overexpression of EGFR-TKD and further mutations recognise deletion of exon 19. In this work, we study the binding affinity, binding stability, and strength of hydroxy-3-(4-hydroxyphenyl)-5-(4-nitrophenyl)-5,5a,7,8,9,9a-hexahydrothiazolo[2,3-b] quinazolin-6-one with TMLR mutated EGFR-TKD (T790M/L858R). The collective motions, residual mobility, and flexibility of TMLR mutated EGFR-TKD bound with reference and title molecule were calculated by principal component analysis. The meta-state conformations of both the simulated complexes were determined by Gibb's energy landscape analysis. The binding affinity exhibited by thiazolo-[2,3-b] quinazolinone and the reference molecule was found to be -7.95 ± 0.088 Kcal/mol and -9.13 ± 0.018 kcal/mol with TMLR mutated EGFR-TKD. The alignment of both the docked complexes was done by blosum40 matrix. Similar spatial orientations were exhibited by the synthesised ligand in the binding pocket of TMLR mutated EGFR-TKD, corresponding to the reference ligand. The ligand stability was computed for 100 ns. In addition, the radius of gyration, solvent accessible surface area, hydrogen bonds formed was calculated. The average ΔGbind of thiazolo-[2,3-b] quinazolinone was -41.212 ± 0.834 kJ/mol and for reference ligand -71.938 ± 0.367 kJ/mol, calculated by MM-PBSA. ADMET analysis concludes thiazolo-[2,3-b] quinazolinone derivative is safe. Further research work is encouraged to determine the efficacy of thiazolo-[2,3-b] quinazolinone against in vivo models.Communicated by Ramaswamy H. Sarma.

In Silico Drug Repurposing Approach: Investigation of Mycobacterium tuberculosis FadD32 Targeted by FDA-Approved Drugs

Background: Despite the enormous efforts made towards combating tuberculosis (TB), the disease remains a major global threat. Hence, new drugs with novel mechanisms against TB are urgently needed. Fatty acid degradation protein D32 (FadD32) has been identified as a promising drug target against TB, the protein is required for the biosynthesis of mycolic acids, hence, essential for the growth and multiplication of the mycobacterium. However, the FadD32 mechanism upon the binding of FDA-approved drugs is not well established. Herein, we applied virtual screening (VS), molecular docking, and molecular dynamic (MD) simulation to identify potential FDA-approved drugs against FadD32. Methodology/Results: VS technique was found promising to identify four FDA-approved drugs (accolate, sorafenib, mefloquine, and loperamide) with higher molecular docking scores, ranging from -8.0 to -10.0 kcal/mol. Post-MD analysis showed that the accolate hit displayed the highest total binding energy of -45.13 kcal/mol. Results also showed that the accolate hit formed more interactions with FadD32 active site residues and all active site residues displayed an increase in total binding contribution. RMSD, RMSF, Rg, and DCCM analysis further supported that the presence of accolate exhibited more structural stability, lower bimolecular flexibility, and more compactness into the FadD32 protein. Conclusions: Our study revealed accolate as the best potential drug against FadD32, hence a prospective anti-TB drug in TB therapy. In addition, we believe that the approach presented in the current study will serve as a cornerstone to identifying new potential inhibitors against a wide range of biological targets.

Visualization of Protein-Drug Interactions for the Analysis of Drug Resistance in Lung Cancer

Non-small cell lung cancer (NSCLC) caused by mutation of the epidermal growth factor receptor (EGFR) is a major cause of death worldwide. Tyrosine kinase inhibitors (TKIs) of EGFR have been developed and show promising results at the initial stage of therapy. However, in most cases, their efficacy becomes limited due to the emergence of secondary mutations causing drug resistance after about a year. In this work, we investigated the mechanism of drug resistance due to these mutations. We performed molecular dynamics (MD) simulations of EGFR-drug interactions to obtain Euclidean distance and binding free energy values to analyse drug resistance and visualize drug-protein interactions. A PCA-based method is proposed to find normal, rigid, flexible, and critical residues. We have established a systematic method for the visualization of protein-drug interactions, which provides an effective framework for the analysis of drug resistance in lung cancer at the atomic level.