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.

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.

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.

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.

Using molecular docking and molecular dynamics to investigate protein-ligand interactions

2021 ◽  
Vol 35 (08) ◽  
pp. 2130002
Author(s):  
Connor J. Morris ◽  
Dennis Della Corte
Keyword(s):  
Molecular Dynamics ◽  
Molecular Docking ◽  
Drug Discovery ◽  
State Of The Art ◽  
Ligand Complex ◽  
Protein Ligand Interactions ◽  
Art Methods ◽  
Ligand Interactions ◽  
Docking Calculations ◽  
Ligand Bond

Molecular docking and molecular dynamics (MD) are powerful tools used to investigate protein-ligand interactions. Molecular docking programs predict the binding pose and affinity of a protein-ligand complex, while MD can be used to incorporate flexibility into docking calculations and gain further information on the kinetics and stability of the protein-ligand bond. This review covers state-of-the-art methods of using molecular docking and MD to explore protein-ligand interactions, with emphasis on application to drug discovery. We also call for further research on combining common molecular docking and MD methods.

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.

A Frame Work for Learning Drug Designing through Molecular Modelling Software Techniques and Biological Databases for Protein-Ligand Interactions

2016 ◽  
Vol 27 ◽  
pp. 111-118 ◽  
Author(s):  
R. Kannadasan ◽  
M.S. Saleembasha ◽  
I. Arnold Emerson
Keyword(s):  
Molecular Docking ◽  
Drug Discovery ◽  
Ligand Docking ◽  
Biological Databases ◽  
Discovery Process ◽  
Drug Discovery Process ◽  
Protein Ligand Interactions ◽  
Protein Receptors ◽  
Ligand Interactions ◽  
Strong Candidate

Applications of computer and information technology are indispensable in various fields especially in the field of biology. The use of computer aided tools plays a key role in solving biological problems. The spontaneous process of molecular docking is important for finding potentially strong candidate of drug for various viruses. The binding of protein receptors with ligand molecules is essential in drug discovery process. The aim of molecular docking tools is to predict the interaction between protein and ligand. This review outlines the major tools for protein - ligand docking which in turn emphasize the importance of molecular docking in modern drug discovery process.

scholarly journals Recent Advances in Molecular Docking for the Research and Discovery of Potential Marine Drugs

Marine Drugs ◽  
2020 ◽  
Vol 18 (11) ◽  
pp. 545
Author(s):  
Guilin Chen ◽  
Armel Jackson Seukep ◽  
Mingquan Guo
Keyword(s):  
Molecular Docking ◽  
Drug Discovery ◽  
Binding Mode ◽  
Mechanisms Of Action ◽  
Clinical Practices ◽  
Drug Target Discovery ◽  
Protein Ligand Interactions ◽  
Active Chemical ◽  
Ligand Interactions ◽  
Discovery Potential

Marine drugs have long been used and exhibit unique advantages in clinical practices. Among the marine drugs that have been approved by the Food and Drug Administration (FDA), the protein–ligand interactions, such as cytarabine–DNA polymerase, vidarabine–adenylyl cyclase, and eribulin–tubulin complexes, are the important mechanisms of action for their efficacy. However, the complex and multi-targeted components in marine medicinal resources, their bio-active chemical basis, and mechanisms of action have posed huge challenges in the discovery and development of marine drugs so far, which need to be systematically investigated in-depth. Molecular docking could effectively predict the binding mode and binding energy of the protein–ligand complexes and has become a major method of computer-aided drug design (CADD), hence this powerful tool has been widely used in many aspects of the research on marine drugs. This review introduces the basic principles and software of the molecular docking and further summarizes the applications of this method in marine drug discovery and design, including the early virtual screening in the drug discovery stage, drug target discovery, potential mechanisms of action, and the prediction of drug metabolism. In addition, this review would also discuss and prospect the problems of molecular docking, in order to provide more theoretical basis for clinical practices and new marine drug research and development.

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.

19F-NMR in Target-based Drug Discovery

2019 ◽  
Vol 26 (26) ◽  
pp. 4964-4983 ◽  
Author(s):  
CongBao Kang
Keyword(s):  
Drug Discovery ◽  
Conformational Changes ◽  
Protein Structures ◽  
19F Nmr ◽  
Binding Affinities ◽  
Protein Ligand Interactions ◽  
Compound Libraries ◽  
Ligand Interactions ◽  
Reference Ligand ◽  
Hit Identification

Solution NMR spectroscopy plays important roles in understanding protein structures, dynamics and protein-protein/ligand interactions. In a target-based drug discovery project, NMR can serve an important function in hit identification and lead optimization. Fluorine is a valuable probe for evaluating protein conformational changes and protein-ligand interactions. Accumulated studies demonstrate that 19F-NMR can play important roles in fragment- based drug discovery (FBDD) and probing protein-ligand interactions. This review summarizes the application of 19F-NMR in understanding protein-ligand interactions and drug discovery. Several examples are included to show the roles of 19F-NMR in confirming identified hits/leads in the drug discovery process. In addition to identifying hits from fluorinecontaining compound libraries, 19F-NMR will play an important role in drug discovery by providing a fast and robust way in novel hit identification. This technique can be used for ranking compounds with different binding affinities and is particularly useful for screening competitive compounds when a reference ligand is available.

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