Evaluation of Scoring Function Performance on DNA-ligand Complexes

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.

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Scoring Functions of Protein-Ligand Interactions

Methods and Algorithms for Molecular Docking-Based Drug Design and Discovery - Advances in Medical Technologies and Clinical Practice ◽

10.4018/978-1-5225-0115-2.ch009 ◽

2016 ◽

pp. 220-245 ◽

Cited By ~ 4

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.

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Scoring Functions of Protein-Ligand Interactions

Oncology ◽

10.4018/978-1-5225-0549-5.ch036 ◽

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

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

Journal of Cheminformatics ◽

10.1186/s13321-021-00486-3 ◽

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.

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

Modern Physics Letters B ◽

10.1142/s0217984921300027 ◽

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.

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

International Journal of Molecular Sciences ◽

10.3390/ijms21155183 ◽

2020 ◽

Vol 21 (15) ◽

pp. 5183 ◽

Cited By ~ 1

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.

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DeepBindRG: a deep learning based method for estimating effective protein–ligand affinity

PeerJ ◽

10.7717/peerj.7362 ◽

2019 ◽

Vol 7 ◽

pp. e7362 ◽

Cited By ~ 13

Author(s):

Haiping Zhang ◽

Linbu Liao ◽

Konda Mani Saravanan ◽

Peng Yin ◽

Yanjie Wei

Keyword(s):

Deep Learning ◽

Ligand Binding ◽

Binding Affinity ◽

Mean Squared Error ◽

Scoring Function ◽

Binding Mode ◽

Scoring Functions ◽

Autodock Vina ◽

Cellular Functions ◽

Ligand Complex

Proteins interact with small molecules to modulate several important cellular functions. Many acute diseases were cured by small molecule binding in the active site of protein either by inhibition or activation. Currently, there are several docking programs to estimate the binding position and the binding orientation of protein–ligand complex. Many scoring functions were developed to estimate the binding strength and predict the effective protein–ligand binding. While the accuracy of current scoring function is limited by several aspects, the solvent effect, entropy effect, and multibody effect are largely ignored in traditional machine learning methods. In this paper, we proposed a new deep neural network-based model named DeepBindRG to predict the binding affinity of protein–ligand complex, which learns all the effects, binding mode, and specificity implicitly by learning protein–ligand interface contact information from a large protein–ligand dataset. During the initial data processing step, the critical interface information was preserved to make sure the input is suitable for the proposed deep learning model. While validating our model on three independent datasets, DeepBindRG achieves root mean squared error (RMSE) value of pKa (−logKd or −logKi) about 1.6–1.8 and R value around 0.5–0.6, which is better than the autodock vina whose RMSE value is about 2.2–2.4 and R value is 0.42–0.57. We also explored the detailed reasons for the performance of DeepBindRG, especially for several failed cases by vina. Furthermore, DeepBindRG performed better for four challenging datasets from DUD.E database with no experimental protein–ligand complexes. The better performance of DeepBindRG than autodock vina in predicting protein–ligand binding affinity indicates that deep learning approach can greatly help with the drug discovery process. We also compare the performance of DeepBindRG with a 4D based deep learning method “pafnucy”, the advantage and limitation of both methods have provided clues for improving the deep learning based protein–ligand prediction model in the future.

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

Marine Drugs ◽

10.3390/md18110545 ◽

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.

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Combining Molecular Dynamic Information and an Aspherical-Atom Data Bank in the Evaluation of the Electrostatic Interaction Energy in Multimeric Protein-Ligand Complex: A Case Study for HIV-1 Protease

Molecules ◽

10.3390/molecules26133872 ◽

2021 ◽

Vol 26 (13) ◽

pp. 3872

Author(s):

Prashant Kumar ◽

Paulina Maria Dominiak

Keyword(s):

Interaction Energy ◽

Molecular Dynamic ◽

Binding Affinity ◽

Electrostatic Interaction ◽

Electrostatic Interactions ◽

Scoring Functions ◽

Ligand Complex ◽

Protein Ligand Interactions ◽

Ligand Interactions ◽

Hiv 1

Computational analysis of protein–ligand interactions is of crucial importance for drug discovery. Assessment of ligand binding energy allows us to have a glimpse of the potential of a small organic molecule to be a ligand to the binding site of a protein target. Available scoring functions, such as in docking programs, all rely on equations that sum each type of protein–ligand interactions in order to predict the binding affinity. Most of the scoring functions consider electrostatic interactions involving the protein and the ligand. Electrostatic interactions constitute one of the most important part of total interactions between macromolecules. Unlike dispersion forces, they are highly directional and therefore dominate the nature of molecular packing in crystals and in biological complexes and contribute significantly to differences in inhibition strength among related enzyme inhibitors. In this study, complexes of HIV-1 protease with inhibitor molecules (JE-2147 and darunavir) were analyzed by using charge densities from the transferable aspherical-atom University at Buffalo Databank (UBDB). Moreover, we analyzed the electrostatic interaction energy for an ensemble of structures, using molecular dynamic simulations to highlight the main features of electrostatic interactions important for binding affinity.

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19F-NMR in Target-based Drug Discovery

Current Medicinal Chemistry ◽

10.2174/0929867326666190610160534 ◽

2019 ◽

Vol 26 (26) ◽

pp. 4964-4983 ◽

Cited By ~ 7

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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Combiphore (Structure and Ligand Based Pharmacophore) - Approach for the Design of GPR40 Modulators in the Management of Diabetes

Current Drug Discovery Technologies ◽

10.2174/1570163815666181008165822 ◽

2020 ◽

Vol 17 (2) ◽

pp. 233-247

Author(s):

Krishna A. Gajjar ◽

Anuradha K. Gajjar

Keyword(s):

Molecular Docking ◽

Structural Information ◽

Docking Studies ◽

Structural Motif ◽

Roc Curve Analysis ◽

Ligand Complex ◽

Discovery Studio ◽

Protein Ligand Interactions ◽

Ligand Interactions ◽

Pharmacophore Models

Background: Pharmacophore mapping and molecular docking can be synergistically integrated to improve the drug design and discovery process. A rational strategy, combiphore approach, derived from the combined study of Structure and Ligand based pharmacophore has been described to identify novel GPR40 modulators. Methods: DISCOtech module from Discovery studio was used for the generation of the Structure and Ligand based pharmacophore models which gave hydrophobic aromatic, ring aromatic and negative ionizable as essential pharmacophoric features. The generated models were validated by screening active and inactive datasets, GH scoring and ROC curve analysis. The best model was exposed as a 3D query to screen the hits from databases like GLASS (GPCR-Ligand Association), GPCR SARfari and Mini-Maybridge. Various filters were applied to retrieve the hit molecules having good drug-like properties. A known protein structure of hGPR40 (pdb: 4PHU) having TAK-875 as ligand complex was used to perform the molecular docking studies; using SYBYL-X 1.2 software. Results and Conclusion: Clustering both the models gave RMSD of 0.89. Therefore, the present approach explored the maximum features by combining both ligand and structure based pharmacophore models. A common structural motif as identified in combiphore for GPR40 modulation consists of the para-substituted phenyl propionic acid scaffold. Therefore, the combiphore approach, whereby maximum structural information (from both ligand and biological protein) is explored, gives maximum insights into the plausible protein-ligand interactions and provides potential lead candidates as exemplified in this study.

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