scholarly journals Assessing Molecular Docking Tools to Guide Targeted Drug Discovery of CD38 Inhibitors

2020 ◽  
Vol 21 (15) ◽  
pp. 5183 ◽  
Author(s):  
Eric D. Boittier ◽  
Yat Yin Tang ◽  
McKenna E. Buckley ◽  
Zachariah P. Schuurs ◽  
Derek J. Richard ◽  
...  
Keyword(s):  
Scoring Function ◽  
Pose Prediction ◽  
Scoring Functions ◽  
Molecular Fingerprints ◽  
Biologically Relevant ◽  
Protein Ligand Interactions ◽  
Molecular Features ◽  
Ligand Interactions ◽  
Model Protein ◽  
Scoring Accuracy

A promising protein target for computational drug development, the human cluster of differentiation 38 (CD38), plays a crucial role in many physiological and pathological processes, primarily through the upstream regulation of factors that control cytoplasmic Ca2+ concentrations. Recently, a small-molecule inhibitor of CD38 was shown to slow down pathways relating to aging and DNA damage. We examined the performance of seven docking programs for their ability to model protein-ligand interactions with CD38. A test set of twelve CD38 crystal structures, containing crystallized biologically relevant substrates, were used to assess pose prediction. The rankings for each program based on the median RMSD between the native and predicted were Vina, AD4 > PLANTS, Gold, Glide, Molegro > rDock. Forty-two compounds with known affinities were docked to assess the accuracy of the programs at affinity/ranking predictions. The rankings based on scoring power were: Vina, PLANTS > Glide, Gold > Molegro >> AutoDock 4 >> rDock. Out of the top four performing programs, Glide had the only scoring function that did not appear to show bias towards overpredicting the affinity of the ligand-based on its size. Factors that affect the reliability of pose prediction and scoring are discussed. General limitations and known biases of scoring functions are examined, aided in part by using molecular fingerprints and Random Forest classifiers. This machine learning approach may be used to systematically diagnose molecular features that are correlated with poor scoring accuracy.

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.

Scoring Functions of Protein-Ligand Interactions

2016 ◽  
pp. 220-245 ◽  
Author(s):  
Zhiqiang Yan ◽  
Jin Wang
Keyword(s):  
Molecular Docking ◽  
Drug Discovery ◽  
Pharmaceutical Industry ◽  
Academic Research ◽  
Scoring Function ◽  
Scoring Functions ◽  
Virtual Database ◽  
Protein Ligand Interactions ◽  
Ligand Interactions ◽  
Database Screening

Scoring function of protein-ligand interactions is used to recognize the “native” binding pose of a ligand on the protein and to predict the binding affinity, so that the active small molecules can be discriminated from the non-active ones. Scoring function is widely used in computationally molecular docking and structure-based drug discovery. The development and improvement of scoring functions have broad implications in pharmaceutical industry and academic research. During the past three decades, much progress have been made in methodology and accuracy for scoring functions, and many successful cases have be witnessed in virtual database screening. In this chapter, the authors introduced the basic types of scoring functions and their derivations, the commonly-used evaluation methods and benchmarks, as well as the underlying challenges and current solutions. Finally, the authors discussed the promising directions to improve and develop scoring functions for future molecular docking-based drug discovery.

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.

Scoring Functions of Protein-Ligand Interactions

Oncology ◽  
2017 ◽  
pp. 915-940
Author(s):  
Zhiqiang Yan ◽  
Jin Wang
Keyword(s):  
Molecular Docking ◽  
Drug Discovery ◽  
Pharmaceutical Industry ◽  
Academic Research ◽  
Scoring Function ◽  
Scoring Functions ◽  
Virtual Database ◽  
Protein Ligand Interactions ◽  
Ligand Interactions ◽  
Database Screening

Scoring function of protein-ligand interactions is used to recognize the “native” binding pose of a ligand on the protein and to predict the binding affinity, so that the active small molecules can be discriminated from the non-active ones. Scoring function is widely used in computationally molecular docking and structure-based drug discovery. The development and improvement of scoring functions have broad implications in pharmaceutical industry and academic research. During the past three decades, much progress have been made in methodology and accuracy for scoring functions, and many successful cases have be witnessed in virtual database screening. In this chapter, the authors introduced the basic types of scoring functions and their derivations, the commonly-used evaluation methods and benchmarks, as well as the underlying challenges and current solutions. Finally, the authors discussed the promising directions to improve and develop scoring functions for future molecular docking-based drug discovery.

Developing molecular docking methods to model protein interactions

2016 ◽  
Author(s):  
◽  
Chengfei Yan
Keyword(s):  
Molecular Docking ◽  
Protein Interactions ◽  
Peptide Binding ◽  
Scoring Functions ◽  
University Of Missouri ◽  
Protein Ligand Interactions ◽  
Mechanistic Investigation ◽  
Ligand Interactions ◽  
Therapeutic Development ◽  
Model Protein

[ACCESS RESTRICTED TO THE UNIVERSITY OF MISSOURI AT AUTHOR'S REQUEST.] In many cellular processes, proteins carry out their functions through interacting with other molecules, such as small compounds (i.e. ligands), peptides or other proteins. Computational modeling of these interactions with molecular docking has great applications in mechanistic investigation of protein binding and therapeutic development. This PhD dissertation presents my studies on developing molecular docking methods to model protein-ligand interactions and protein-peptide interactions. The studies include the improvement of statistical potential-based scoring functions for evaluating protein-ligand interactions, the development of a docking-based method for predicting peptide binding sites on protein surfaces, and the development of a fully blind docking method for predicting protein-peptide complex structures. The scoring functions are implemented in our docking software, and the peptide binding methods are implemented in web servers. Finally, for future work, the existing methods for modeling protein flexibility in molecular docking are reviewed in detail.

scholarly journals Evaluation of Scoring Function Performance on DNA-ligand Complexes

2019 ◽  
Vol 13 (1) ◽  
pp. 40-49 ◽  
Author(s):  
Pedro Fong ◽  
Hong-Kong Wong
Keyword(s):  
Drug Discovery ◽  
Scoring Function ◽  
Binding Mode ◽  
Scoring Functions ◽  
Discriminative Power ◽  
Ligand Complex ◽  
Protein Ligand Interactions ◽  
Minor Groove Binders ◽  
Ligand Interactions ◽  
Chemotherapy Agents

Background: DNA has been a pharmacological target for different types of treatment, such as antibiotics and chemotherapy agents, and is still a potential target in many drug discovery processes. However, most docking and scoring approaches were parameterised for protein-ligand interactions; their suitability for modelling DNA-ligand interactions is uncertain. Objective: This study investigated the performance of four scoring functions on DNA-ligand complexes. Material & Methods: Here, we explored the ability of four docking protocols and scoring functions to discriminate the native pose of 33 DNA-ligand complexes over a compiled set of 200 decoys for each DNA-ligand complexes. The four approaches were the AutoDock, ASP@GOLD, ChemScore@GOLD and GoldScore@GOLD. Results: Our results indicate that AutoDock performed the best when predicting binding mode and that ChemScore@GOLD achieved the best discriminative power. Rescoring of AutoDock-generated decoys with ChemScore@GOLD further enhanced their individual discriminative powers. All four approaches have no discriminative power in some DNA-ligand complexes, including both minor groove binders and intercalators. Conclusion: This study suggests that the evaluation for each DNA-ligand complex should be performed in order to obtain meaningful results for any drug discovery processes. Rescoring with different scoring functions can improve discriminative power.

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 MCCS, a novel characterization method for protein–ligand complex

2020 ◽  
Author(s):  
Maozi Chen ◽  
Zhiwei Feng ◽  
Siyi Wang ◽  
Weiwei Lin ◽  
Xiang-Qun Xie
Keyword(s):  
Rational Design ◽  
Scoring Function ◽  
Receptor Protein ◽  
Energy Contribution ◽  
Ligand Complex ◽  
Molecular Fingerprints ◽  
New Approach ◽  
Functional Regions ◽  
Biological Studies ◽  
Ligand Interactions

Abstract Delineating the fingerprint or feature vector of a receptor/protein will facilitate the structural and biological studies, as well as the rational design and development of drugs with high affinities and selectivity. However, protein is complicated by its different functional regions that can bind to some of its protein partner(s), substrate(s), orthosteric ligand(s) or allosteric modulator(s) where cogent methods like molecular fingerprints do not work well. We here elaborate a scoring-function-based computing protocol Molecular Complex Characterizing System to help characterize the binding feature of protein–ligand complexes. Based on the reported receptor-ligand interactions, we first quantitate the energy contribution of each individual residue which may be an alternative of MD-based energy decomposition. We then construct a vector for the energy contribution to represent the pattern of the ligand recognition at a receptor and qualitatively analyze the matching level with other receptors. Finally, the energy contribution vector is explored for extensive use in similarity and clustering. The present work provides a new approach to cluster proteins, a perspective counterpart for determining the protein characteristics in the binding, and an advanced screening technique where molecular docking is applicable.

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