F. Anjum, Ali Hazazi, F. Alsaeedi, Maha Bakhuraysah, Alaa Shafie, Norah Ali Alshehri, Nahed Hawsawi, A. Ashour, H. Banjer, Afaf Alharthi, Maryam Ishrat Niaz
{"title":"Identification and Dynamics Understanding of Novel Inhibitors of Peptidase Domain of Collagenase G from Clostridium histolyticum","authors":"F. Anjum, Ali Hazazi, F. Alsaeedi, Maha Bakhuraysah, Alaa Shafie, Norah Ali Alshehri, Nahed Hawsawi, A. Ashour, H. Banjer, Afaf Alharthi, Maryam Ishrat Niaz","doi":"10.3390/computation12080153","DOIUrl":null,"url":null,"abstract":"Clostridium histolyticum is a Gram-positive anaerobic bacterium belonging to the Clostridium genus. It produces collagenase, an enzyme involved in breaking down collagen which is a key component of connective tissues. However, antimicrobial resistance (AMR) poses a great challenge in combating infections caused by this bacteria. The lengthy nature of traditional drug development techniques has resulted in a shift to computer-aided drug design and other modern drug discovery approaches. The above method offers a cost-effective means for gathering comprehensive information about how ligands interact with their target proteins. The objective of this study is to create novel, explicit drugs that specifically inhibit the C. histolyticum collagenase enzyme. Through structure-based virtual screening, a library containing 1830 compounds was screened to identify potential drug candidates against collagenase enzymes. Following that, molecular dynamic (MD) simulation was performed in an aqueous solution to evaluate the behavior of protein and ligand in a dynamic environment while density functional theory (DFT) analysis was executed to predict the molecular properties and structure of lead compounds, and the WaterSwap technique was utilized to obtain insights into the drug–protein interaction with water molecules. Furthermore, principal component analysis (PCA) was performed to reveal conformational changes, salt bridges to express electrostatic interaction and protein stability, and absorption, distribution, metabolism, excretion, and toxicity (ADMET) to assess the pharmacokinetics profile of top compounds and control molecules. Three potent drug candidates were identified MSID000001, MSID000002, MSID000003, and the control with a binding score of −10.7 kcal/mol, −9.8 kcal/mol, −9.5 kcal/mol, and −8 kcal/mol, respectively. Furthermore, Molecular Mechanics Poisson–Boltzmann Surface Area (MMPBSA) analysis of the simulation trajectories revealed energy scores of −79.54 kcal/mol, −73.99 kcal/mol, −62.26 kcal/mol, and −70.66 kcal/mol, correspondingly. The pharmacokinetics properties exhibited were under the acceptable range. The compounds hold the potential to be novel drugs; therefore, further investigation needs to be conducted to find out their anti-collagenase action against C. histolyticum infections and antibiotic resistance.","PeriodicalId":52148,"journal":{"name":"Computation","volume":null,"pages":null},"PeriodicalIF":1.9000,"publicationDate":"2024-07-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Computation","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.3390/computation12080153","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"MATHEMATICS, INTERDISCIPLINARY APPLICATIONS","Score":null,"Total":0}
引用次数: 0
Abstract
Clostridium histolyticum is a Gram-positive anaerobic bacterium belonging to the Clostridium genus. It produces collagenase, an enzyme involved in breaking down collagen which is a key component of connective tissues. However, antimicrobial resistance (AMR) poses a great challenge in combating infections caused by this bacteria. The lengthy nature of traditional drug development techniques has resulted in a shift to computer-aided drug design and other modern drug discovery approaches. The above method offers a cost-effective means for gathering comprehensive information about how ligands interact with their target proteins. The objective of this study is to create novel, explicit drugs that specifically inhibit the C. histolyticum collagenase enzyme. Through structure-based virtual screening, a library containing 1830 compounds was screened to identify potential drug candidates against collagenase enzymes. Following that, molecular dynamic (MD) simulation was performed in an aqueous solution to evaluate the behavior of protein and ligand in a dynamic environment while density functional theory (DFT) analysis was executed to predict the molecular properties and structure of lead compounds, and the WaterSwap technique was utilized to obtain insights into the drug–protein interaction with water molecules. Furthermore, principal component analysis (PCA) was performed to reveal conformational changes, salt bridges to express electrostatic interaction and protein stability, and absorption, distribution, metabolism, excretion, and toxicity (ADMET) to assess the pharmacokinetics profile of top compounds and control molecules. Three potent drug candidates were identified MSID000001, MSID000002, MSID000003, and the control with a binding score of −10.7 kcal/mol, −9.8 kcal/mol, −9.5 kcal/mol, and −8 kcal/mol, respectively. Furthermore, Molecular Mechanics Poisson–Boltzmann Surface Area (MMPBSA) analysis of the simulation trajectories revealed energy scores of −79.54 kcal/mol, −73.99 kcal/mol, −62.26 kcal/mol, and −70.66 kcal/mol, correspondingly. The pharmacokinetics properties exhibited were under the acceptable range. The compounds hold the potential to be novel drugs; therefore, further investigation needs to be conducted to find out their anti-collagenase action against C. histolyticum infections and antibiotic resistance.
期刊介绍:
Computation a journal of computational science and engineering. Topics: computational biology, including, but not limited to: bioinformatics mathematical modeling, simulation and prediction of nucleic acid (DNA/RNA) and protein sequences, structure and functions mathematical modeling of pathways and genetic interactions neuroscience computation including neural modeling, brain theory and neural networks computational chemistry, including, but not limited to: new theories and methodology including their applications in molecular dynamics computation of electronic structure density functional theory designing and characterization of materials with computation method computation in engineering, including, but not limited to: new theories, methodology and the application of computational fluid dynamics (CFD) optimisation techniques and/or application of optimisation to multidisciplinary systems system identification and reduced order modelling of engineering systems parallel algorithms and high performance computing in engineering.