Communications in information and systems

Molecular docking stands as a pivotal element in the realm of computer-aided drug design (CADD), consistently contributing to advancements in pharmaceutical research. In essence, it employs computer algorithms to identify the "best" match between two molecules, akin to solving intricate three-dimensional jigsaw puzzles. At a more stringent level, the molecular docking challenge entails predicting the accurate bound association state based on the atomic coordinates of two molecules. This process assumes particular significance in unraveling the mechanistic intricacies of physicochemical interactions at the atomic scale. Notably, the application of docking, especially in the context of protein-small molecule interactions, holds wide-ranging implications for structure-based drug design, given the prevalent use of small compounds as drug candidates. This study provides an overview of docking methodologies, delves into recent key developments, elucidates the physicochemical underpinnings of molecular recognition in protein-ligand interactions, and concludes by addressing the applications of docking in virtual screening, alongside current challenges within existing docking methods.

Zhiwei Ma

Abeeb Ajibade

Xiaoqin Zou

Communications in Information and Systems

Docking strategies for predicting protein-ligand interactions and their application to structure-based drug design

预测蛋白质-配体相互作用的对接策略及其在基于结构的药物设计中的应用

Single-molecule Förster Resonance Energy Transfer (smFRET) is a powerful biophysical technique that utilizes the distance-dependent energy transfer between donor and acceptor dyes linked to individual molecules, providing insights into molecular conformational changes and interactions at the single-molecule level. Prior investigations leveraged smFRET to study the conformational dynamics of single truncated Ubc4 pre-mRNA molecules during splicing, yet these efforts did not prioritize structural modeling. In this study, we develop an smFRET-assisted RNA prediction method to predict the 2D and 3D structures of this pre-mRNA. To achieve this, we initiate the process by generating RNA structural ensembles through coarse-grained molecular dynamics (MD) simulations. Subsequently, inter-dye distances are calculated for these RNA structural ensembles by performing all-atom MD simulations of the dye groups. The ultimate determination of the 2D and 3D structures for the pre-mRNA is achieved by comparing the calculated inter-dye distances with experimental counterparts. Notably, our computational results demonstrate a significant alignment with experimental findings, which involve a conformational change at the 2D level.

Jun Li

Nils G Walter

Shi-Jie Chen

smFRET-assisted RNA structure prediction

单分子荧光共振能量转移辅助的RNA结构预测

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) caused by coronavirus disease 2019 (COVID-19) has led to a tremendous human fatality and economic loss. SARS-CoV-2 infectivity is a key reason for the widespread viral transmission, but its rigorous experimental measurement is essentially impossible due to the ongoing genome evolution around the world. We show that artificial intelligence (AI) and algebraic topology (AT) offer an accurate and efficient alternative to the experimental determination of viral infectivity. AI and AT analysis indicates that the on-going mutations make SARS-CoV-2 more infectious.

Jiahui Chen

Rui Wang

Guo-Wei Wei

SARS-CoV-2 becoming more infectious as revealed by algebraic topology and deep learning

代数拓扑与深度学习揭示SARS-CoV-2传染性增强

Elizabeth Hawkins

Yuanzhen Shao

Zhan Chen

New variational analysis on the sharp interface of multiscale implicit solvation: general expressions and applications

多尺度隐式溶剂化锐利界面新变分分析：一般表达式及应用

Because of their potential utility in predicting conformational changes and assessing folding dynamics, coarse-grained (CG) RNA folding models are appealing for rapid characterization of RNA molecules. Previously, we reported the iterative simulated RNA reference state (IsRNA) method for parameterizing a CG force field for RNA folding, which consecutively updates the simulation force field to reflect marginal distributions of folding coordinates in the structure database and extract various energy terms. While the IsRNA model was validated by showing close agreement between the IsRNA-simulated and experimentally observed distributions, here, we expand our theoretical understanding of the model and, in doing so, improve the parameterization process to optimize the subset of included folding coordinates, which leads to accelerated simulations. Using statistical mechanical theory, we analyze the underlying, Bayesian concept that drives parameterization of the energy function, providing a general method for developing predictive, knowledge-based, polymer force fields on the basis of limited data. Furthermore, we propose an optimal parameterization procedure, based on the principal of maximum entropy.

Travis Hurst

Dong Zhang

Yuanzhe Zhou

A Bayes-inspired theory for optimally building an efficient coarse-grained folding force field

Abbreviation	Commun. Inf. Syst.
Journal Impact	0.74
Quartiles(Global)	COMPUTER SCIENCE, INFORMATION SYSTEMS(Q4)

Publication Information	Publisher: International Press of Boston，Publishing cycle: ，Journal Type: journal，Open Access Journals: No
Basic data	Year of publication: ，Proportion of original research papers: 100.00%，Self Citation Rate:0.00%， Gold OA Rate: 0.00%

Communications in information and systems

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Docking strategies for predicting protein-ligand interactions and their application to structure-based drug design

smFRET-assisted RNA structure prediction

SARS-CoV-2 becoming more infectious as revealed by algebraic topology and deep learning

New variational analysis on the sharp interface of multiscale implicit solvation: general expressions and applications

A Bayes-inspired theory for optimally building an efficient coarse-grained folding force field

Docking strategies for predicting protein-ligand interactions and their application to structure-based drug design

smFRET-assisted RNA structure prediction

Machine learning predicting COVID-19 in Algeria

Automatic insight extraction method from ECG scatterplot

Robust porous sheet structure generation for 3D printing

Communications in information and systems

Journal Citation Format

A journal article with 1 author

A journal article with 2 authors

A journal article with 3 authors

A journal article with 5 or more authors

Books Citation Format

Thesis Citation Format

Web sites Citation Format

Patent Citation Format

Staying up late manually editing references? ivySCI automatically matches journals and helps you generate references with a single click.

Share Submission Experience

Docking strategies for predicting protein-ligand interactions and their application to structure-based drug design

smFRET-assisted RNA structure prediction

SARS-CoV-2 becoming more infectious as revealed by algebraic topology and deep learning

New variational analysis on the sharp interface of multiscale implicit solvation: general expressions and applications

A Bayes-inspired theory for optimally building an efficient coarse-grained folding force field

Docking strategies for predicting protein-ligand interactions and their application to structure-based drug design

smFRET-assisted RNA structure prediction

Machine learning predicting COVID-19 in Algeria

Automatic insight extraction method from ECG scatterplot

Robust porous sheet structure generation for 3D&nbsp;printing

Robust porous sheet structure generation for 3D printing