Improving Ligand-Ranking of AutoDock Vina by Changing the Empirical Parameters

2021 ◽  
Author(s):  
T. Ngoc Han Pham ◽  
Trung Hai Nguyen ◽  
Nguyen Minh Tam ◽  
Thien Y Vu ◽  
Nhat Truong Pham ◽  
...  
Keyword(s):  
Hydrogen Bond ◽  
Ligand Binding ◽  
Success Rate ◽  
Binding Affinity ◽  
Correlation Coefficient ◽  
Autodock Vina ◽  
Docking Simulations ◽  
Low Correlation ◽  
Very High ◽  
Ligand Ranking

AutoDock Vina (Vina) achieved a very high docking-success rate, p ̂, but give a rather low correlation coefficient, R, for binding affinity with respect to experiments. This low correlation can be an obstacle for ranking of ligand-binding affinity, which is the main objective of docking simulations. In this context, we evaluated the dependence of Vina R coefficient upon its empirical parameters. R is affected more by changing the gauss2 and rotation than other terms. The docking-success rate p ̂ is sensitive to the alterations of the gauss1, gauss2, repulsion, and hydrogen bond parameters. Based on our benchmarks, parameter set1 has been suggested to be the most optimal. The testing study over 800 complexes indicated that the modified Vina provided higher correlation with experiment R_set1=0.556±0.025 compared with R_Default=0.493±0.028 obtained by the original Vina and R_(Vina 1.2)=0.503±0.029 by Vina version 1.2. Besides, the modified Vina can be also applied more widely, giving R≥0.500 for 32/48 targets, compared with the default package, giving R≥0.500 for 31/48 targets. In addition, validation calculations for 1036 complexes obtained from version 2019 of PDBbind refined structures showed that the set1 of parameters gave higher correlation coefficient (R_set1=0.621±0.016) than the default package (R_Default=0.552±0.018) and Vina version 1.2 (R_(Vina 1.2)=0.549±0.017). The version of Vina with set1 of parameters can be downloaded at https://github.com/sontungngo/mvina. The outcomes would enhance the ranking of ligand-binding affinity using Autodock Vina.

scholarly journals Learning Protein-Ligand Binding Affinity with Atomic Environment Vectors

2020 ◽  
Author(s):  
Rocco Meli ◽  
Andrew Anighoro ◽  
Mike Bodkin ◽  
Garrett Morris ◽  
Philip Biggin
Keyword(s):  
Ligand Binding ◽  
Binding Affinity ◽  
Correlation Coefficient ◽  
Scoring Function ◽  
Scoring Functions ◽  
Autodock Vina ◽  
Pearson’S Correlation ◽  
Pearson’S Correlation Coefficient ◽  
Pearson's Correlation ◽  
Pearson's Correlation Coefficient

<div> <div> <div> <p>Scoring functions for the prediction of protein-ligand binding affinity have seen renewed interest in recent years when novel machine learning and deep learning methods started to consistently outperform classical scoring functions. Here we explore the use of atomic environment vectors (AEVs) and feed-forward neural networks, the building blocks of several neural network potentials, for the prediction of protein-ligand binding affinity. The AEV-based scoring function, which we term AEScore, is shown to perform as well or better than other state-of-the-art scoring functions on binding affinity prediction, with an RMSE of 1.22 pK units and a Pearson’s correlation coefficient of 0.83 for the CASF-2016 benchmark. However, AEScore does not perform as well in docking and virtual screening tasks. We therefore show that the model can be combined with the classical scoring function AutoDock Vina in the context of ∆-learning, where corrections to the AutoDock Vina scoring function are learned instead of the protein-ligand binding affinity itself. Combined with AutoDock Vina, ∆-AEScore has an RMSE of 1.32 pK units and a Pearson’s correlation coefficient of 0.80 on the CASF-2016 benchmark, while retaining the good docking and screening power of the underlying classical scoring function. </p> </div> </div> </div>

scholarly journals Learning Protein-Ligand Binding Affinity with Atomic Environment Vectors

2020 ◽  
Author(s):  
Rocco Meli ◽  
Andrew Anighoro ◽  
Mike Bodkin ◽  
Garrett Morris ◽  
Philip Biggin
Keyword(s):  
Ligand Binding ◽  
Binding Affinity ◽  
Correlation Coefficient ◽  
Scoring Function ◽  
Scoring Functions ◽  
Autodock Vina ◽  
Pearson’S Correlation ◽  
Pearson’S Correlation Coefficient ◽  
Pearson's Correlation ◽  
Pearson's Correlation Coefficient

<div> <div> <div> <p>Scoring functions for the prediction of protein-ligand binding affinity have seen renewed interest in recent years when novel machine learning and deep learning methods started to consistently outperform classical scoring functions. Here we explore the use of atomic environment vectors (AEVs) and feed-forward neural networks, the building blocks of several neural network potentials, for the prediction of protein-ligand binding affinity. The AEV-based scoring function, which we term AEScore, is shown to perform as well or better than other state-of-the-art scoring functions on binding affinity prediction, with an RMSE of 1.22 pK units and a Pearson’s correlation coefficient of 0.83 for the CASF-2016 benchmark. However, AEScore does not perform as well in docking and virtual screening tasks. We therefore show that the model can be combined with the classical scoring function AutoDock Vina in the context of ∆-learning, where corrections to the AutoDock Vina scoring function are learned instead of the protein-ligand binding affinity itself. Combined with AutoDock Vina, ∆-AEScore has an RMSE of 1.32 pK units and a Pearson’s correlation coefficient of 0.80 on the CASF-2016 benchmark, while retaining the good docking and screening power of the underlying classical scoring function. </p> </div> </div> </div>

scholarly journals Molecular Design of Anticancer Drug from Marine Fungi Derivatives

2021 ◽  
Author(s):  
Duc Tuan Cao ◽  
Thi Mai Huong DOAN ◽  
Van Cuong PHAM ◽  
Thi Hong Lien HOANG ◽  
Jung-Woo Chae ◽  
...  
Keyword(s):  
Cancer Therapy ◽  
Binding Affinity ◽  
Marine Fungi ◽  
Molecular Design ◽  
Pearson Correlation ◽  
Scoring Function ◽  
Heat Shock Protein 90 ◽  
Autodock Vina ◽  
Docking Simulations ◽  
Potential Inhibitors

Heat shock protein 90 (HSP90) is known as one of the most potential target in cancer therapy. In this context, we have demonstrated that marine fungi derivatives can play as possible inhibitors for preventing the biological activity of HSP90 using a combination of molecular docking and fast pulling of ligand (FPL) simulations. In particular, the computational approaches were validated since compared with the respective experiments. Based on a benchmark on available inhibitors of HsP90, GOLD docking package using ChemPLP scoring function was found to be dominated over both Autodock Vina and Autodock4 in preliminary estimation the ligand binding affinity and binding pose with the Pearson correlation, R=-0.62. Moreover, FPL calculations were also indicated to be a suitable approach to refine docking simulations with a correlation coefficient with the respective experimental data of R=-0.81. Therefore, the binding affinity of marine fungi derivatives to Hsp90 was evaluated. Docking and FPL calculations suggested that five compounds including 23, 40, 46, 48, and 52 are as highly potential inhibitors for HSP90. The obtained results probably enhance the cancer therapy. <br>

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

PeerJ ◽  
2019 ◽  
Vol 7 ◽  
pp. e7362 ◽  
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.

scholarly journals Molecular Design of Anticancer Drug from Marine Fungi Derivatives

2021 ◽  
Author(s):  
Duc Tuan Cao ◽  
Thi Mai Huong DOAN ◽  
Van Cuong PHAM ◽  
Thi Hong Lien HOANG ◽  
Jung-Woo Chae ◽  
...  
Keyword(s):  
Cancer Therapy ◽  
Binding Affinity ◽  
Marine Fungi ◽  
Molecular Design ◽  
Pearson Correlation ◽  
Scoring Function ◽  
Heat Shock Protein 90 ◽  
Autodock Vina ◽  
Docking Simulations ◽  
Potential Inhibitors

Heat shock protein 90 (HSP90) is known as one of the most potential target in cancer therapy. In this context, we have demonstrated that marine fungi derivatives can play as possible inhibitors for preventing the biological activity of HSP90 using a combination of molecular docking and fast pulling of ligand (FPL) simulations. In particular, the computational approaches were validated since compared with the respective experiments. Based on a benchmark on available inhibitors of HsP90, GOLD docking package using ChemPLP scoring function was found to be dominated over both Autodock Vina and Autodock4 in preliminary estimation the ligand binding affinity and binding pose with the Pearson correlation, R=-0.62. Moreover, FPL calculations were also indicated to be a suitable approach to refine docking simulations with a correlation coefficient with the respective experimental data of R=-0.81. Therefore, the binding affinity of marine fungi derivatives to Hsp90 was evaluated. Docking and FPL calculations suggested that five compounds including 23, 40, 46, 48, and 52 are as highly potential inhibitors for HSP90. The obtained results probably enhance the cancer therapy. <br>

scholarly journals Simulating Protein Structure to Support Clinical Decisions: Predicting the Ligand Specific Impact of Genomic Variation on Opioid Binding

2020 ◽  
Author(s):  
Vikram Shivakumar ◽  
Whitney Reid ◽  
Subha Madhavan ◽  
Matthew D. McCoy
Keyword(s):  
Genetic Variation ◽  
Binding Energy ◽  
Ligand Binding ◽  
Binding Affinity ◽  
Opioid Receptor ◽  
Drug Dosage ◽  
Drug Binding ◽  
Docking Simulations ◽  
Simulation Results ◽  
The Impact

Abstract Background: Predicting the impact of missense protein variants on drug binding would have a widespread implications on the practice of genomic medicine, including matching a molecular therapy and dosage to an individual’s genome sequence. Genetic variation is widespread within G-protein-coupled receptors, which can affect overall structure and conformation of the receptors. These structural changes in turn impact ligand binding interactions, which may change the overall dosage requirements for target drugs. In this study, we used molecular docking simulations to explore the effect of missense variants on opioid drug binding affinity to the opioid receptor mu 1 (OPMR1). Methods: Using high-throughput, in silico docking simulations, the binding interactions of 27 opioid drugs to naturally occurring variants in opioid receptor mu 1 (OPRM1) were used to predict changes to ligand binding affinity. The binding energy of the small molecules to the wild-type receptor was compared to an experimentally derived inhibitory constant (Ki) for validation, and the variant-induced disruptions in variant:drug interactions used to predict the impact on the effective drug dosage. Results: The binding energies for each drug-variant receptor pair relative to the wildtype receptor and drug showed trends across drugs, with some variants showing enhancing (238I, 302I) or diminishing (235M, 235N) effects on binding affinity. The 153V variant showed increased binding affinity for certain drugs, and decreased affinity for others. The simulation results correlated well with experimentally derived inhibitory constants (R2 = 0.69), and an exponential regression model revealed how changes in relative binding energy between wildtype and variant structures predict changes to Ki.Conclusions: The simulation results illustrate the potential for integrating genetic variation into the process of development of small molecule therapies to support genomic-driven medicine. Depending on the drug and location, amino acid variation can either increase or decrease the strength of the molecular interactions and should be considered when determining drug dosage. The scale of variation and the cost of experimental characterization underscores the potential for accurate simulation based methods to inform clinical decisions.

Molecular Docking Studies of Indolyl-Benzodioxyl-Chalcone, Pyrrolyl-Benzodioxyl- Chalcone, and Thiophenyl-Benzodioxyl-Chalcone as an Antimalarial Agent

2021 ◽  
Vol 874 ◽  
pp. 136-142
Author(s):  
Kamilia Mustikasari ◽  
Joshua Eka Harap ◽  
Tanto Budi Susilo ◽  
Noer Komari
Keyword(s):  
Hydrogen Bond ◽  
Molecular Docking ◽  
Binding Affinity ◽  
Docking Studies ◽  
Kcal Mole ◽  
Docking Simulations ◽  
Drug Candidates ◽  
Hydrogen Bond Interactions ◽  
New Compounds ◽  
Native Ligand

The drug resistance condition of P. falciparum pose a major challenge in the fight against malaria. This prompts a comprehensive research in an effort to discover new drug candidates. Therefore, chalcone was modified into 24 new compounds, including indolyl-benzodioxyl-chalcone, pyrrolyl-benzodioxyl-chalcone, and thiophenyl-benzodioxyl-chalcone in the course of this study. Moreover, these compounds are commercial malaria mediciations screened for their inhibitory activity using molecular docking simulations. Subsequent results of combined indolyl-benzodioxyl-chalcone and PfDHFR-TS showed the intrinsic indolyl components produced stronger interactions referenced to pyrrolyl-benzodioxyl-chalcone, thiophenyl-benzodioxyl-chalcone, and chloroguanide. Under these circumstances, intense PfDHFR-TS-indolyl-benzodioxyl-chalcone complex was produced with lower binding affinity values (-7.32 to -8.43 kcal/mole) referenced to PfDHFR-TS-pyrrolyl-benzodioxyl-chalcone (-6.38 to -6.68 kcal/mole), PfDHFR-TS-Thiophenyl-benzodioxyl-chalcone (-6.47 to -6.52 kcal/mole), and PfDHFR-TS-chloroguanide (-6.75 kcal/mole). Furthermore, the hydrogen bond interactions developed by indolyl-benzodioxyl-chalcone (7-10) are observably similar to standard chloroguanide compounds and WR99210. These compounds also possess a binding affinity similar to WR99210 (native ligand) and are expected to be potentially anti-malarial candidates.

Automated, Accurate, and Scalable Relative Protein-Ligand Binding Free Energy Calculations using Lambda Dynamics

2020 ◽  
Author(s):  
E. Prabhu Raman ◽  
Thomas J. Paul ◽  
Ryan L. Hayes ◽  
Charles L. Brooks III
Keyword(s):  
Free Energy ◽  
Ligand Binding ◽  
Binding Affinity ◽  
Binding Free Energy ◽  
Computational Cost ◽  
Combinatorial Libraries ◽  
Free Energy Calculations ◽  
Lead Optimization ◽  
Efficient Estimation ◽  
Lead Compound

<p>Accurate predictions of changes to protein-ligand binding affinity in response to chemical modifications are of utility in small molecule lead optimization. Relative free energy perturbation (FEP) approaches are one of the most widely utilized for this goal, but involve significant computational cost, thus limiting their application to small sets of compounds. Lambda dynamics, also rigorously based on the principles of statistical mechanics, provides a more efficient alternative. In this paper, we describe the development of a workflow to setup, execute, and analyze Multi-Site Lambda Dynamics (MSLD) calculations run on GPUs with CHARMm implemented in BIOVIA Discovery Studio and Pipeline Pilot. The workflow establishes a framework for setting up simulation systems for exploratory screening of modifications to a lead compound, enabling the calculation of relative binding affinities of combinatorial libraries. To validate the workflow, a diverse dataset of congeneric ligands for seven proteins with experimental binding affinity data is examined. A protocol to automatically tailor fit biasing potentials iteratively to flatten the free energy landscape of any MSLD system is developed that enhances sampling and allows for efficient estimation of free energy differences. The protocol is first validated on a large number of ligand subsets that model diverse substituents, which shows accurate and reliable performance. The scalability of the workflow is also tested to screen more than a hundred ligands modeled in a single system, which also resulted in accurate predictions. With a cumulative sampling time of 150ns or less, the method results in average unsigned errors of under 1 kcal/mol in most cases for both small and large combinatorial libraries. For the multi-site systems examined, the method is estimated to be more than an order of magnitude more efficient than contemporary FEP applications. The results thus demonstrate the utility of the presented MSLD workflow to efficiently screen combinatorial libraries and explore chemical space around a lead compound, and thus are of utility in lead optimization.</p>

Distinct Modulation of Wild-Type and Selective Gene Mutated Vitamin D Receptor by Essential Poly Unsaturated Fatty Acids

2021 ◽  
Vol 21 ◽  
Author(s):  
Hari Balaji ◽  
Selvaraj Ayyamperuma ◽  
Niladri Saha ◽  
Shyam Sundar Pottabathula ◽  
Jubie Selvaraj ◽  
...  
Keyword(s):  
Fatty Acids ◽  
Vitamin D ◽  
Ligand Binding ◽  
Vitamin D Deficiency ◽  
Binding Affinity ◽  
Unsaturated Fatty Acids ◽  
Binding Domain ◽  
Ligand Binding Domain ◽  
Binding Energies ◽  
Wild Type

: Vitamin-D deficiency is a global concern. Gene mutations in the vitamin D receptor’s (VDR) ligand binding domain (LBD) variously alter the ligand binding affinity, heterodimerization with retinoid X receptor (RXR) and inhibit coactivator interactions. These LBD mutations may result in partial or total hormone unresponsiveness. A plethora of evidence report that selective long chain polyunsaturated fatty acids (PUFAs) including eicosapentaenoic acid (EPA), docosahexaenoic acid (DHA) and arachidonic acid (AA) bind to the ligand-binding domain of VDR and lead to transcriptional activation. We therefore hypothesize that selective PUFAs would modulate the dynamics and kinetics of VDRs, irrespective bioactive of vitamin-D binding. The spatial arrangements of the selected PUFAs in VDR active site were examined by in-silico docking studies. The docking results revealed that PUFAs have fatty acid structure-specific binding affinity towards VDR. The calculated EPA, DHA & AA binding energies (Cdocker energy) were lesser compared to vitamin-D in wild type of VDR (PDB id: 2ZLC). Of note, the DHA has higher binding interactions to the mutated VDR (PDB id: 3VT7) when compared to the standard Vitamin-D. Molecular dynamic simulation was utilized to confirm the stability of potential compound binding of DHA with mutated VDR complex. These findings suggest the unique roles of PUFAs in VDR activation and may offer alternate strategy to circumvent vitamin-D deficiency.

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