Computer simulation of molecular recognition in biomolecular system: from in silico screening to generalized ensembles

Abstract

Prediction of ligand-receptor complex structure is important in both the basic science and the industry such as drug discovery. We report various computation molecular docking methods: fundamental in silico (virtual) screening, ensemble docking, enhanced sampling (generalized ensemble) methods, and other methods to improve the accuracy of the complex structure. We explain not only the merits of these methods but also their limits of application and discuss some interaction terms which are not considered in the in silico methods. In silico screening and ensemble docking are useful when one focuses on obtaining the native complex structure (the most thermodynamically stable complex). Generalized ensemble method provides a free-energy landscape, which shows the distribution of the most stable complex structure and semi-stable ones in a conformational space. Also, barriers separating those stable structures are identified. A researcher should select one of the methods according to the research aim and depending on complexity of the molecular system to be studied.

Interaction of Camptothecin Anticancer Drugs with Ribosomal Proteins L15 and L11: A Molecular Docking Study

“…This in silico investigation was performed using validated methods and based on past experience and expertise learned through multiple rigorous studies with other drug–target systems [ 58 , 59 , 60 ]. We are well informed of the merits of the method, but also the limits of application [ 61 , 62 ]. Computer-aided drug discovery (CADD) is a useful approach, but experimental validation (wet-lab experiments) of the in silico data will be essential [ 63 ].…”

Binding Mechanism of Riboswitch to Natural Ligand Elucidated by McMD-Based Dynamic Docking Simulations

“…In this aspect, molecular dynamics (MD) simulation studies have contributed to capture the structural and thermodynamic properties of RNAs. 20,21 Docking using MD simulations, 22 called dynamic docking, which is one the most powerful computational methods to analyze molecular recognition processes, 23 can be used to explore binding configurations between receptor proteins and their ligands. We have developed 24,25 a dynamic docking implementation 26,27 based on multicanonical molecular dynamics (McMD, see Section S1 for an explanation of the McMD theory), 28 which we have applied to a number of cases from small-molecule ligands 29,24,25,30 to medium-sized ligands 31,32 and peptides.…”

“…Docking using MD simulations, called dynamic docking, which is one the most powerful computational methods to analyze molecular recognition processes, can be used to explore binding configurations between receptor proteins and their ligands. We have developed , a dynamic docking implementation , based on multicanonical molecular dynamics (McMD, see Section S1 for an explanation of the McMD theory), which we have applied to a number of cases from small-molecule ligands ,,, to medium-sized ligands , and peptides. − We have also applied McMD simulations to the conformational sampling of proteins and peptides , and the loop structure prediction of an antibody, With McMD, the bias is correlated with the temperature, enabling McMD simulations to adaptively modulate the bias given the density of states.…”

Cytotoxicity Profile Prediction of Different Flavonoids in NCI-H187, LS174T, and MCF7 Cell Lines Using In Silico Simulations

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To Editor, Computer simulation of the effects of a compound, based on its chemical and spatial structure, on biological systems (in silico or virtual investigation) is one of the most important and first steps in designing and evaluating the effects of chemical agents. 1 Cell-line cytotoxicity profile prediction (CLC-Pred) based on chemical structural formula is an online predictive tool for investigating the probability of cytotoxicity of various chemical compounds in healthy and cancer cells based on the structure, which allows this possibility in the form of experimental screening. 2,3 Considering extensive in vitro studies evaluating the effects of flavonoids on different tumor cell lines and the lack of comparison between hundreds of different compounds from this family, this study was conducted to comparatively evaluate the effects of flavonoids of different categories on three simulated cell lines in an in silico model.In this study, the CLC-Pred based on the specific spatial chemical structure of more than 2000 flavonoids (families of anthocyanins, benzoflavones, bioflavonoids (flavonoid dimers), catechins, chalcones, flavanones, flavones, flavonolignans, flavonols, isoflavones, and proanthocyanidins) which have been recorded in the PubChem database (https://pubchem.ncbi.nlm.nih.gov/, until November 10, 2022) was performed on three human cancer cell lines of lung (NCI-H187), colon (LS174T), and breast (MCF7), separately.According to the recommended manner in similar studies, after receiving the molecular structure of each compound in SDF format from the PubChem database, the corresponding file was submitted to the web service for the CLC-Pred (http://way2drug.com/Cell-line/), and the probability of activity (Pa) was estimated (between 0-1).Higher Pa values indicate a great probability of having anti-tumor characteristics in the evaluated cell line.

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“…To Editor, Computer simulation of the effects of a compound, based on its chemical and spatial structure, on biological systems (in silico or virtual investigation) is one of the most important and first steps in designing and evaluating the effects of chemical agents. 1 Cell-line cytotoxicity profile prediction (CLC-Pred) based on chemical structural formula is an online predictive tool for investigating the probability of cytotoxicity of various chemical compounds in healthy and cancer cells based on the structure, which allows this possibility in the form of experimental screening. 2,3 Considering extensive in vitro studies evaluating the effects of flavonoids on different tumor cell lines and the lack of comparison between hundreds of different compounds from this family, this study was conducted to comparatively evaluate the effects of flavonoids of different categories on three simulated cell lines in an in silico model.…”