SAR-Guided Scoring Function and Mutational Validation Reveal the Binding Mode of CGS-8216 at the α1+/γ2– Benzodiazepine Site

Aim: This study aims to develop and establish a computational model that can identify potent molecules for p21-activating kinase 1 (PAK1). Background: PAK1 is a well-established drug target that has been explored for various therapeutic interventions. Control of this protein requires an indispensable inhibitor to curb the structural changes and subsequent activation of signalling effectors responsible for the progression of diseases, such as cancer, inflammatory, viral, and neurological disorders. Objective: To establish a computational model that could identify active molecules which will further provide a platform for developing potential PAK1 inhibitors. Method: A congeneric series of 27 compounds was considered for this study with Ki (nm) covering a minimum of 3 log range. The compounds were developed based on a previously reported Group-I PAK inhibitor, namely G-5555. The 27 compounds were subjected to the SP and XP mode of docking, to understand the binding mode, its conformation and interaction patterns. To understand the relevance of biological activity from computational approaches, the compounds were scored against generated water maps to obtain WM/MM ΔG binding energy. Moreover, molecular dynamics analysis was performed for the highly active compound, to understand the conformational variability and complex’s stability. We then evaluate the predictable binding pose obtained from the docking studies. Result: From the SP and XP modes of docking, the common interaction pattern with the amino acid residues Arg299 (cation-π), Glu345 (Aromatic hydrogen bond), hinge region Leu347, salt bridges Asp393 and Asp407 was observed, among the congeneric compounds. The interaction pattern was compared with the co-crystal inhibitor FRAX597 of the PAK1 crystal structure (PDB id: 4EQC). The correlation with different docking parameters in the SP and XP modes was insignificant and thereby revealed that the SP and XP’s scoring functions could not predict the active compounds. This was due to the limitations in the docking methodology that neglected the receptor flexibility and desolvation parameters. Hence, to recognise the desolvation and explicit solvent effects, as well as to study the Structure-Activity Relationships (SARs) extensively, WaterMap (WM) calculations were performed on the congeneric compounds. Based on displaceable unfavourable hydration sites (HS) and their associated thermodynamic properties, the WM calculations facilitated to understand the significance of correlation in the folds of activity of highly (19 and 17), moderate (16 and 21) and less active (26 and 25) compounds. Furthermore, the scoring function from WaterMap, namely WM/MM, led to a significant R2 value of 0.72, due to a coupled conjunction with MM treatment and displaced unfavourable waters at the binding site. To check the “optimal binding conformation”, molecular dynamics simulation was carried out with the highly active compound 19 to explain the binding mode, stability, interactions, solvent accessible area, etc., which could support the predicted conformation with bioactive conformation. Conclusion: This study determined the best scoring function, established SARs and predicted active molecules through a computational model. This will contribute towards development of the most potent PAK1 inhibitors.

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Development of a Fingerprint-Based Scoring Function for the Prediction of the Binding Mode of Carbonic Anhydrase II Inhibitors

International Journal of Molecular Sciences ◽

10.3390/ijms19071851 ◽

2018 ◽

Vol 19 (7) ◽

pp. 1851 ◽

Cited By ~ 8

Author(s):

Giulio Poli ◽

Vibhu Jha ◽

Adriano Martinelli ◽

Claudiu Supuran ◽

Tiziano Tuccinardi

Keyword(s):

Carbonic Anhydrase ◽

Scoring Function ◽

Binding Mode ◽

Carbonic Anhydrase Ii

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CSCORE: A SIMPLE YET EFFECTIVE SCORING FUNCTION FOR PROTEIN–LIGAND BINDING AFFINITY PREDICTION USING MODIFIED CMAC LEARNING ARCHITECTURE

Journal of Bioinformatics and Computational Biology ◽

10.1142/s021972001100577x ◽

2011 ◽

Vol 09 (supp01) ◽

pp. 1-14 ◽

Cited By ~ 20

Author(s):

XUCHANG OUYANG ◽

STEPHANUS DANIEL HANDOKO ◽

CHEE KEONG KWOH

Keyword(s):

Binding Affinity ◽

Scoring Function ◽

Binding Mode ◽

Computational Method ◽

Data Driven ◽

Machine Learning Techniques ◽

Ligand Docking ◽

Scoring Functions ◽

Binding Affinity Prediction ◽

Affinity Prediction

Protein–ligand docking is a computational method to identify the binding mode of a ligand and a target protein, and predict the corresponding binding affinity using a scoring function. This method has great value in drug design. After decades of development, scoring functions nowadays typically can identify the true binding mode, but the prediction of binding affinity still remains a major problem. Here we present CScore, a data-driven scoring function using a modified Cerebellar Model Articulation Controller (CMAC) learning architecture, for accurate binding affinity prediction. The performance of CScore in terms of correlation between predicted and experimental binding affinities is benchmarked under different validation approaches. CScore achieves a prediction with R = 0.7668 and RMSE = 1.4540 when tested on an independent dataset. To the best of our knowledge, this result outperforms other scoring functions tested on the same dataset. The performance of CScore varies on different clusters under the leave-cluster-out validation approach, but still achieves competitive result. Lastly, the target-specified CScore achieves an even better result with R = 0.8237 and RMSE = 1.0872, trained on a much smaller but more relevant dataset for each target. The large dataset of protein–ligand complexes structural information and advances of machine learning techniques enable the data-driven approach in binding affinity prediction. CScore is capable of accurate binding affinity prediction. It is also shown that CScore will perform better if sufficient and relevant data is presented. As there is growth of publicly available structural data, further improvement of this scoring scheme can be expected.

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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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MOLECULAR DOCKING: AN EXPLANATORY APPROACH IN STRUCTURE-BASED DRUG DESIGNING AND DISCOVERY

International Journal of Pharmacy and Pharmaceutical Sciences ◽

10.22159/ijpps.2021v13i6.40830 ◽

2021 ◽

pp. 6-12

Author(s):

ADITI SHARMA ◽

SALONI KUNWAR ◽

VAISHALI ◽

VAISHALI AGARWAL ◽

CHHAYA SINGH ◽

...

Keyword(s):

Molecular Docking ◽

Drug Design ◽

De Novo ◽

Scoring Function ◽

Binding Mode ◽

De Novo Drug Design ◽

Structure Based Drug Design ◽

Binding Characteristics ◽

Stable Product ◽

Novel Concept

Molecular docking is a modeling tool of Bioinformatics which includes two or more molecules which interact to provide a stable product in the form of a complex. Molecular docking is helpful in predicting the 3-d structure of a complex which depends on the binding characteristics of Ligand and target. Also, it is a structure-based virtual screening (SBVS) utilized to keep the 3-d structures of small molecule which are generated by computers into a target structure in various types of conformations, positions and orientations. This molecular docking has come out to be a novel concept with various types of advantages. It behaves as a highly exploring domain due to its significant structure-based drug design (SBDD), Assessment of Biochemical pathways, Lead Optimization and in De Novo drug design. In spite of all potential approaches, there are certain challenges which are-scoring function (differentiate the true binding mode), ligand chemistry (tautomerism and ionization) and receptor flexibility (single conformation of rigid receptor). The area of computer-aided drug design and discovery (CADDD) has achieved large favorable outcomes in the past few years. CADD has been adopted by various big pharmaceutical companies for leading discoveries of drugs. Many researchers have worked in order to examine different docking algorithms and to predict molecules' active site. Hence, this Review article depicts the whole sole of Molecular Docking.

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PatchSearch: a web server for off-target protein identification

Nucleic Acids Research ◽

10.1093/nar/gkz478 ◽

2019 ◽

Vol 47 (W1) ◽

pp. W365-W372 ◽

Cited By ~ 1

Author(s):

Julien Rey ◽

Inès Rasolohery ◽

Pierre Tufféry ◽

Frédéric Guyon ◽

Gautier Moroy

Keyword(s):

Protein Identification ◽

Protein Structures ◽

Scoring Function ◽

Web Server ◽

Binding Mode ◽

Target Protein ◽

Local Alignment ◽

Protein Surfaces ◽

Target Proteins ◽

Potential Interactions

Abstract The large number of proteins found in the human body implies that a drug may interact with many proteins, called off-target proteins, besides its intended target. The PatchSearch web server provides an automated workflow that allows users to identify structurally conserved binding sites at the protein surfaces in a set of user-supplied protein structures. Thus, this web server may help to detect potential off-target protein. It takes as input a protein complexed with a ligand and identifies within user-defined or predefined collections of protein structures, those having a binding site compatible with this ligand in terms of geometry and physicochemical properties. It is based on a non-sequential local alignment of the patch over the entire protein surface. Then the PatchSearch web server proposes a ligand binding mode for the potential off-target, as well as an estimated affinity calculated by the Vinardo scoring function. This novel tool is able to efficiently detects potential interactions of ligands with distant off-target proteins. Furthermore, by facilitating the discovery of unexpected off-targets, PatchSearch could contribute to the repurposing of existing drugs. The server is freely available at http://bioserv.rpbs.univ-paris-diderot.fr/services/PatchSearch.

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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

Journal of Medicinal Chemistry ◽

10.1021/jm048973n ◽

2005 ◽

Vol 48 (14) ◽

pp. 4558-4575 ◽

Cited By ~ 143

Author(s):

Kaushik Raha ◽

Kenneth M. Merz

Keyword(s):

Quantum Mechanics ◽

Binding Affinity ◽

Large Scale ◽

Scale Validation ◽

Scoring Function ◽

Binding Mode

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Evaluation of Scoring Function Performance on DNA-ligand Complexes

The Open Medicinal Chemistry Journal ◽

10.2174/1874104501913010040 ◽

2019 ◽

Vol 13 (1) ◽

pp. 40-49 ◽

Cited By ~ 2

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.

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Hotspot coevolution at protein-protein interfaces is a key identifier of native protein complexes

10.1101/698233 ◽

2019 ◽

Cited By ~ 1

Author(s):

Sambit K. Mishra ◽

Sarah J. Cooper ◽

Jerry M. Parks ◽

Julie C. Mitchell

Keyword(s):

Protein Interactions ◽

Protein Complexes ◽

Fundamental Problem ◽

Scoring Function ◽

Binding Mode ◽

Protein Docking ◽

Evolutionary Information ◽

Protein Protein Interactions ◽

Novel Approach ◽

Protein Interfaces

AbstractProtein-protein interactions play a key role in mediating numerous biological functions, with more than half the proteins in living organisms existing as either homo- or hetero-oligomeric assemblies. Protein subunits that form oligomers minimize the free energy of the complex, but exhaustive computational search-based docking methods have not comprehensively addressed the protein docking challenge of distinguishing a natively bound complex from non-native forms. In this study, we propose a scoring function, KFC-E, that accounts for both conservation and coevolution of putative binding hotspot residues at protein-protein interfaces. For a benchmark set of 53 bound complexes, KFC-E identifies a near-native binding mode as the top-scoring pose in 38% and in the top 5 in 55% of the complexes. For a set of 17 unbound complexes, KFC-E identifies a near-native pose in the top 10 ranked poses in more than 50% of the cases. By contrast, a scoring function that incorporates information on coevolution at predicted non-hotspots performs poorly by comparison. Our study highlights the importance of coevolution at hotspot residues in forming natively bound complexes and suggests a novel approach for coevolutionary scoring in protein docking.Author SummaryA fundamental problem in biology is to distinguish between the native and non-native bound forms of protein-protein complexes. Experimental methods are often used to detect the native bound forms of proteins but, are demanding in terms of time and resources. Computational approaches have proven to be a useful alternative; they sample the different binding configurations for a pair of interacting proteins and then use an heuristic or physical model to score them. In this study we propose a new scoring approach, KFC-E, which focuses on the evolutionary contributions from a subset of key interface residues (hotspots) to identify native bound complexes. KFC-E capitalizes on the wealth of information in protein sequence databases by incorporating residue-level conservation and coevolution of putative binding hotspots. As hotspot residues mediate the binding energetics of protein-protein interactions, we hypothesize that the knowledge of putative hotspots coupled with their evolutionary information should be helpful in the identification of native bound protein-protein complexes.

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