Knowledge-Based Scoring Functions in Drug Design. 1. Developing a Target-Specific Method for Kinase−Ligand Interactions

2010 ◽  
Vol 50 (8) ◽  
pp. 1378-1386 ◽  
Author(s):  
Mengzhu Xue ◽  
Mingyue Zheng ◽  
Bing Xiong ◽  
Yanlian Li ◽  
Hualiang Jiang ◽  
...  
Keyword(s):  
Drug Design ◽  
Specific Method ◽  
Scoring Functions ◽  
Knowledge Based ◽  
Ligand Interactions

Knowledge-Based Scoring Functions in Drug Design: 2. Can the Knowledge Base Be Enriched?

2010 ◽  
Vol 51 (2) ◽  
pp. 386-397 ◽  
Author(s):  
Qiancheng Shen ◽  
Bing Xiong ◽  
Mingyue Zheng ◽  
Xiaomin Luo ◽  
Cheng Luo ◽  
...  
Keyword(s):  
Drug Design ◽  
Knowledge Base ◽  
Scoring Functions ◽  
Knowledge Based

scholarly journals KORP-PL: a coarse-grained knowledge-based scoring function for protein–ligand interactions

2020 ◽  
Author(s):  
Maria Kadukova ◽  
Karina dos Santos Machado ◽  
Pablo Chacón ◽  
Sergei Grudinin
Keyword(s):  
Joint Probability ◽  
Coarse Grained ◽  
Supplementary Information ◽  
Joint Probability Distribution ◽  
Pose Prediction ◽  
Scoring Functions ◽  
Widespread Application ◽  
Knowledge Based ◽  
Protein Ligand Interactions ◽  
Ligand Interactions

Abstract Motivation Despite the progress made in studying protein–ligand interactions and the widespread application of docking and affinity prediction tools, improving their precision and efficiency still remains a challenge. Computational approaches based on the scoring of docking conformations with statistical potentials constitute a popular alternative to more accurate but costly physics-based thermodynamic sampling methods. In this context, a minimalist and fast sidechain-free knowledge-based potential with a high docking and screening power can be very useful when screening a big number of putative docking conformations. Results Here, we present a novel coarse-grained potential defined by a 3D joint probability distribution function that only depends on the pairwise orientation and position between protein backbone and ligand atoms. Despite its extreme simplicity, our approach yields very competitive results with the state-of-the-art scoring functions, especially in docking and screening tasks. For example, we observed a twofold improvement in the median 5% enrichment factor on the DUD-E benchmark compared to Autodock Vina results. Moreover, our results prove that a coarse sidechain-free potential is sufficient for a very successful docking pose prediction. Availabilityand implementation The standalone version of KORP-PL with the corresponding tests and benchmarks are available at https://team.inria.fr/nano-d/korp-pl/ and https://chaconlab.org/modeling/korp-pl. Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals ASFP (Artificial Intelligence based Scoring Function Platform): a web server for the development of customized scoring functions

2021 ◽  
Vol 13 (1) ◽  
Author(s):  
Xujun Zhang ◽  
Chao Shen ◽  
Xueying Guo ◽  
Zhe Wang ◽  
Gaoqi Weng ◽  
...  
Keyword(s):  
High Efficiency ◽  
Low Cost ◽  
Pearson Correlation ◽  
Scoring Function ◽  
Web Server ◽  
Scoring Functions ◽  
Protein Ligand Interactions ◽  
Prediction Module ◽  
Ligand Interactions ◽  
Benchmark Datasets

AbstractVirtual screening (VS) based on molecular docking has emerged as one of the mainstream technologies of drug discovery due to its low cost and high efficiency. However, the scoring functions (SFs) implemented in most docking programs are not always accurate enough and how to improve their prediction accuracy is still a big challenge. Here, we propose an integrated platform called ASFP, a web server for the development of customized SFs for structure-based VS. There are three main modules in ASFP: (1) the descriptor generation module that can generate up to 3437 descriptors for the modelling of protein–ligand interactions; (2) the AI-based SF construction module that can establish target-specific SFs based on the pre-generated descriptors through three machine learning (ML) techniques; (3) the online prediction module that provides some well-constructed target-specific SFs for VS and an additional generic SF for binding affinity prediction. Our methodology has been validated on several benchmark datasets. The target-specific SFs can achieve an average ROC AUC of 0.973 towards 32 targets and the generic SF can achieve the Pearson correlation coefficient of 0.81 on the PDBbind version 2016 core set. To sum up, the ASFP server is a powerful tool for structure-based VS.

scholarly journals Structure- and ligand-based drug design approaches for neglected tropical diseases

2012 ◽  
Vol 84 (9) ◽  
pp. 1857-1866 ◽  
Author(s):  
Rafael V. C. Guido ◽  
Glaucius Oliva ◽  
Adriano D. Andricopulo
Keyword(s):  
Drug Design ◽  
Neglected Tropical Diseases ◽  
Poor People ◽  
Tropical Diseases ◽  
Protein Ligand Interactions ◽  
New Chemical Entities ◽  
Ligand Interactions ◽  
Complementary Nature ◽  
Further Development ◽  
D Investment

Drug discovery has moved toward more rational strategies based on our increasing understanding of the fundamental principles of protein–ligand interactions. Structure- (SBDD) and ligand-based drug design (LBDD) approaches bring together the most powerful concepts in modern chemistry and biology, linking medicinal chemistry with structural biology. The definition and assessment of both chemical and biological space have revitalized the importance of exploring the intrinsic complementary nature of experimental and computational methods in drug design. Major challenges in this field include the identification of promising hits and the development of high-quality leads for further development into clinical candidates. It becomes particularly important in the case of neglected tropical diseases (NTDs) that affect disproportionately poor people living in rural and remote regions worldwide, and for which there is an insufficient number of new chemical entities being evaluated owing to the lack of innovation and R&D investment by the pharmaceutical industry. This perspective paper outlines the utility and applications of SBDD and LBDD approaches for the identification and design of new small-molecule agents for NTDs.

A knowledge-based halogen bonding scoring function for predicting protein-ligand interactions

2013 ◽  
Vol 19 (11) ◽  
pp. 5015-5030 ◽  
Author(s):  
Yingtao Liu ◽  
Zhijian Xu ◽  
Zhuo Yang ◽  
Kaixian Chen ◽  
Weiliang Zhu
Keyword(s):  
Scoring Function ◽  
Halogen Bonding ◽  
Knowledge Based ◽  
Protein Ligand Interactions ◽  
Ligand Interactions

scholarly journals An efficient use of X-ray information, homology modeling, molecular dynamics and knowledge-based docking techniques to predict protein–monosaccharide complexes

Glycobiology ◽  
2018 ◽  
Vol 29 (2) ◽  
pp. 124-136 ◽  
Author(s):  
Juan I Blanco Capurro ◽  
Matias Di Paola ◽  
Marcelo Daniel Gamarra ◽  
Marcelo A Martí ◽  
Carlos P Modenutti
Keyword(s):  
Molecular Dynamics ◽  
Model Building ◽  
Essential Element ◽  
Homology Model ◽  
Conformational Ensemble ◽  
Scoring Functions ◽  
Knowledge Based ◽  
Hydrophilic Nature ◽  
Conformational Diversity ◽  
Biological Recognition

Abstract Unraveling the structure of lectin–carbohydrate complexes is vital for understanding key biological recognition processes and development of glycomimetic drugs. Molecular Docking application to predict them is challenging due to their low affinity, hydrophilic nature and ligand conformational diversity. In the last decade several strategies, such as the inclusion of glycan conformation specific scoring functions or our developed solvent-site biased method, have improved carbohydrate docking performance but significant challenges remain, in particular, those related to receptor conformational diversity. In the present work we have analyzed conventional and solvent-site biased autodock4 performance concerning receptor conformational diversity as derived from different crystal structures (apo and holo), Molecular Dynamics snapshots and Homology-based models, for 14 different lectin–monosaccharide complexes. Our results show that both conventional and biased docking yield accurate lectin–monosaccharide complexes, starting from either apo or homology-based structures, even when only moderate (45%) sequence identity templates are available. An essential element for success is a proper combination of a middle-sized (10–100 structures) conformational ensemble, derived either from Molecular dynamics or multiple homology model building. Consistent with our previous works, results show that solvent-site biased methods improve overall performance, but that results are still highly system dependent. Finally, our results also show that docking can select the correct receptor structure within the ensemble, underscoring the relevance of joint evaluation of both ligand pose and receptor conformation.

Physics-based predictions of RNA loop stability and structures

2012 ◽  
Author(s):  
◽  
Liang Liu
Keyword(s):  
Free Energy ◽  
Building Blocks ◽  
Conformational Ensemble ◽  
Scoring Functions ◽  
Rna Structures ◽  
Cellular Functions ◽  
Entropy Model ◽  
University Of Missouri ◽  
Knowledge Based ◽  
Three Body

[ACCESS RESTRICTED TO THE UNIVERSITY OF MISSOURI AT AUTHOR'S REQUEST.] RNA (ribonucleic acid) molecules play a variety of crucial roles in cellular functions at the level of transcription, translation and gene regulation. RNA functions are tied to structures. We aim to develop a novel free energy-based model for RNA structures, especially for RNA loops and junctions. In the first project, we develop a new conformational entropy model for RNA structures consisting of multiple helices connected by cross-linked loops. The basic strategy of our approach is to decompose the whole structure into a number of three-body building blocks, where each building block consists of a loop and two helices that are directly connected to the two ends of the loop. Assembly of the building blocks gives the entropy of the whole structure. The method provide a solid first step toward a systematic development of an entropy and free energy model for complex tertiary folds for RNA and other biopolymer. In the second project, based on the survey of all the known RNA structures, we derive a set of virtual bond-based scoring functions for the different types of dinucleotides. To circumvent the problem of reference state selection, we apply an iterative method to extract the effective potential, based on the complete conformational ensemble. With such a set of knowledge-based energy parameters, for a given sequence, we can successfully identify the native structure (the best-scored structure) from a set of structural decoys.

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