Docking Methodologies and Recent Advances

Docking, a molecular modelling method, has wide applications in identification and optimization in modern drug discovery. This chapter addresses the recent advances in the docking methodologies like fragment docking, covalent docking, inverse docking, post processing, hybrid techniques, homology modeling etc. and its protocol like searching and scoring functions. Advances in scoring functions for e.g. consensus scoring, quantum mechanics methods, clustering and entropy based methods, fingerprinting, etc. are used to overcome the limitations of the commonly used force-field, empirical and knowledge based scoring functions. It will cover crucial necessities and different algorithms of docking and scoring. Further different aspects like protein flexibility, ligand sampling and flexibility, and the performance of scoring function will be discussed.

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Docking Methodologies and Recent Advances

Oncology ◽

10.4018/978-1-5225-0549-5.ch031 ◽

2017 ◽

pp. 804-828 ◽

Cited By ~ 1

Author(s):

Ashwani Kumar ◽

Ruchika Goyal ◽

Sandeep Jain

Keyword(s):

Quantum Mechanics ◽

Scoring Function ◽

Scoring Functions ◽

Post Processing ◽

Hybrid Techniques ◽

Knowledge Based ◽

Recent Advances ◽

Modern Drug ◽

Consensus Scoring ◽

Covalent Docking

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A knowledge–based scoring function to assess the stability of quaternary protein assemblies

10.1101/562520 ◽

2019 ◽

Cited By ~ 3

Author(s):

Abhilesh S. Dhawanjewar ◽

Ankit Roy ◽

M.S. Madhusudhan

Keyword(s):

Protein Interactions ◽

Binary Classification ◽

Scoring Function ◽

Protein Docking ◽

Scoring Functions ◽

Protein Protein Interactions ◽

Residue Contact ◽

Knowledge Based ◽

Cellular Biochemistry ◽

The Stability

AbstractMotivationElucidation of protein-protein interactions is a necessary step towards understanding the complete repertoire of cellular biochemistry. Given the enormity of the problem, the expenses and limitations of experimental methods, it is imperative that this problem is tackled computationally. In silico predictions of protein interactions entail sampling different conformations of the purported complex and then scoring these to assess for interaction viability. In this study we have devised a new scheme for scoring protein-protein interactions.ResultsOur method, PIZSA (Protein Interaction Z Score Assessment) is a binary classification scheme for identification of stable protein quaternary assemblies (binders/non-binders) based on statistical potentials. The scoring scheme incorporates residue-residue contact preference on the interface with per residue-pair atomic contributions and accounts for clashes. PIZSA can accurately discriminate between native and non-native structural conformations from protein docking experiments and outperform other recently published scoring functions, demonstrated through testing on a benchmark set and the CAPRI Score_set. Though not explicitly trained for this purpose, PIZSA potentials can identify spurious interactions that are artefacts of the crystallization process.AvailabilityPIZSA is implemented as awebserverat http://cospi.iiserpune.ac.in/pizsa/[email protected]

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AnnapuRNA: A scoring function for predicting RNA-small molecule binding poses

PLoS Computational Biology ◽

10.1371/journal.pcbi.1008309 ◽

2021 ◽

Vol 17 (2) ◽

pp. e1008309

Author(s):

Filip Stefaniak ◽

Janusz M. Bujnicki

Keyword(s):

Small Molecule ◽

Structure Prediction ◽

Scoring Function ◽

Scoring Functions ◽

Ligand Complex ◽

Computational Docking ◽

Knowledge Based ◽

Docking Method ◽

Docking Program ◽

Post Hoc

RNA is considered as an attractive target for new small molecule drugs. Designing active compounds can be facilitated by computational modeling. Most of the available tools developed for these prediction purposes, such as molecular docking or scoring functions, are parametrized for protein targets. The performance of these methods, when applied to RNA-ligand systems, is insufficient. To overcome these problems, we developed AnnapuRNA, a new knowledge-based scoring function designed to evaluate RNA-ligand complex structures, generated by any computational docking method. We also evaluated three main factors that may influence the structure prediction, i.e., the starting conformer of a ligand, the docking program, and the scoring function used. We applied the AnnapuRNA method for a post-hoc study of the recently published structures of the FMN riboswitch. Software is available at https://github.com/filipspl/AnnapuRNA.

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CompScore: boosting structure-based virtual screening performance by incorporating docking scoring functions components into consensus scoring

10.1101/550590 ◽

2019 ◽

Author(s):

Yunierkis Perez-Castillo ◽

Stellamaris Sotomayor-Burneo ◽

Karina Jimenes-Vargas ◽

Mario Gonzalez-Rodriguez ◽

Maykel Cruz-Monteagudo ◽

...

Keyword(s):

Genetic Algorithms ◽

Virtual Screening ◽

High Performance ◽

Scoring Function ◽

Scoring Functions ◽

Traditional Use ◽

Screening Performance ◽

Consensus Scoring ◽

Improved Performance ◽

Virtual Screening Performance

AbstractConsensus scoring has become a commonly used strategy within structure-based virtual screening (VS) workflows with improved performance compared to those based in a single scoring function. However, no research has been devoted to analyze the worth of docking scoring functions components in consensus scoring. We implemented and tested a method that incorporates docking scoring functions components into the setting of high performance VS workflows. This method uses genetic algorithms for finding the combination of scoring components that maximizes the VS enrichment for any target. Our methodology was validated using a dataset that contains ligands and decoys for 102 targets that has been widely used in VS validation studies. Results show that our approach outperforms other methods for all targets. It also boosts the initial enrichment performance of the traditional use of whole scoring functions in consensus scoring by an average of 45%. CompScore is freely available at: http://bioquimio.udla.edu.ec/compscore/

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A knowledge-based scoring function to assess quaternary associations of proteins

Bioinformatics ◽

10.1093/bioinformatics/btaa207 ◽

2020 ◽

Vol 36 (12) ◽

pp. 3739-3748

Author(s):

Abhilesh S Dhawanjewar ◽

Ankit A Roy ◽

Mallur S Madhusudhan

Keyword(s):

Protein Interactions ◽

Statistical Physics ◽

Binary Classification ◽

Scoring Function ◽

Protein Docking ◽

Supplementary Information ◽

Scoring Functions ◽

Biological Interactions ◽

Protein Protein Interactions ◽

Knowledge Based

Abstract Motivation The elucidation of all inter-protein interactions would significantly enhance our knowledge of cellular processes at a molecular level. Given the enormity of the problem, the expenses and limitations of experimental methods, it is imperative that this problem is tackled computationally. In silico predictions of protein interactions entail sampling different conformations of the purported complex and then scoring these to assess for interaction viability. In this study, we have devised a new scheme for scoring protein–protein interactions. Results Our method, PIZSA (Protein Interaction Z-Score Assessment), is a binary classification scheme for identification of native protein quaternary assemblies (binders/nonbinders) based on statistical potentials. The scoring scheme incorporates residue–residue contact preference on the interface with per residue-pair atomic contributions and accounts for clashes. PIZSA can accurately discriminate between native and non-native structural conformations from protein docking experiments and outperform other contact-based potential scoring functions. The method has been extensively benchmarked and is among the top 6 methods, outperforming 31 other statistical, physics based and machine learning scoring schemes. The PIZSA potentials can also distinguish crystallization artifacts from biological interactions. Availability and implementation PIZSA is implemented as a web server at http://cospi.iiserpune.ac.in/pizsa and can be downloaded as a standalone package from http://cospi.iiserpune.ac.in/pizsa/Download/Download.html. Supplementary information Supplementary data are available at Bioinformatics online.

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Correlation between Virtual Screening Performance and Binding Site Descriptors of Protein Targets

International Journal of Medicinal Chemistry ◽

10.1155/2018/3829307 ◽

2018 ◽

Vol 2018 ◽

pp. 1-10 ◽

Cited By ~ 3

Author(s):

Jamal Shamsara

Keyword(s):

Virtual Screening ◽

Binding Site ◽

Center Of Mass ◽

Scoring Function ◽

Scoring Functions ◽

Autodock Vina ◽

Protein Targets ◽

Shape Complementarity ◽

Screening Performance ◽

Consensus Scoring

Rescoring is a simple approach that theoretically could improve the original docking results. In this study AutoDock Vina was used as a docked engine and three other scoring functions besides the original scoring function, Vina, as well as their combinations as consensus scoring functions were employed to explore the effect of rescoring on virtual screenings that had been done on diverse targets. Rescoring by DrugScore produces the most number of cases with significant changes in screening power. Thus, the DrugScore results were used to build a simple model based on two binding site descriptors that could predict possible improvement by DrugScore rescoring. Furthermore, generally the screening power of all rescoring approach as well as original AutoDock Vina docking results correlated with the Maximum Theoretical Shape Complementarity (MTSC) and Maximum Distance from Center of Mass and all Alpha spheres (MDCMA). Therefore, it was suggested that, with a more complete set of binding site descriptors, it could be possible to find robust relationship between binding site descriptors and response to certain molecular docking programs and scoring functions. The results could be helpful for future researches aiming to do a virtual screening using AutoDock Vina and/or rescoring using DrugScore.

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Random Forest Refinement of Pairwise Potentials for Protein-ligand Decoy Detection

10.26434/chemrxiv.8047820.v1 ◽

2019 ◽

Cited By ~ 1

Author(s):

Jun Pei ◽

Zheng Zheng ◽

Hyunji Kim ◽

Lin Song ◽

Sarah Walworth ◽

...

Keyword(s):

Machine Learning ◽

Random Forest ◽

Probability Function ◽

Pair Potential ◽

Scoring Function ◽

Stable Structure ◽

Scoring Functions ◽

Atom Pair ◽

Data Set ◽

Atom Pairs

An accurate scoring function is expected to correctly select the most stable structure from a set of pose candidates. One can hypothesize that a scoring function’s ability to identify the most stable structure might be improved by emphasizing the most relevant atom pairwise interactions. However, it is hard to evaluate the relevant importance for each atom pair using traditional means. With the introduction of machine learning methods, it has become possible to determine the relative importance for each atom pair present in a scoring function. In this work, we use the Random Forest (RF) method to refine a pair potential developed by our laboratory (GARF6) by identifying relevant atom pairs that optimize the performance of the potential on our given task. Our goal is to construct a machine learning (ML) model that can accurately differentiate the native ligand binding pose from candidate poses using a potential refined by RF optimization. We successfully constructed RF models on an unbalanced data set with the ‘comparison’ concept and, the resultant RF models were tested on CASF-2013.5 In a comparison of the performance of our RF models against 29 scoring functions, we found our models outperformed the other scoring functions in predicting the native pose. In addition, we used two artificial designed potential models to address the importance of the GARF potential in the RF models: (1) a scrambled probability function set, which was obtained by mixing up atom pairs and probability functions in GARF, and (2) a uniform probability function set, which share the same peak positions with GARF but have fixed peak heights. The results of accuracy comparison from RF models based on the scrambled, uniform, and original GARF potential clearly showed that the peak positions in the GARF potential are important while the well depths are not. <br>

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Faculty Opinions recommendation of Large-scale validation of a quantum mechanics based scoring function: predicting the binding affinity and the binding mode of a diverse set of protein-ligand complexes.

Faculty Opinions – Post-Publication Peer Review of the Biomedical Literature ◽

10.3410/f.1005266.329471 ◽

2005 ◽

Author(s):

Themis Lazaridis

Keyword(s):

Quantum Mechanics ◽

Binding Affinity ◽

Large Scale ◽

Scale Validation ◽

Scoring Function ◽

Binding Mode

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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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