A Computational Binding Affinity Estimation Protocol with Maximum Utilization of Experimental Data : A Case Study for Adenosine Receptor

2020 ◽  
Author(s):  
Il Kwon Cho ◽  
Sung Hyun Moon ◽  
Kwang-Hui Cho
Keyword(s):  
Experimental Data ◽  
Drug Discovery ◽  
Binding Affinity ◽  
Binding Free Energy ◽  
External Validation ◽  
Scoring Function ◽  
Linear Interaction ◽  
A2a Receptors ◽  
Validation Set

Estimating binding affinity between a target protein and the ligand is a crucial step in the drug discovery process. In computer aided drug design (CADD), the problem can be divided into two steps, finding the correct binding pose and estimating binding free energy. In this study, a new binding affinity estimation protocol, which uses molecular docking and binding affinity estimation with Molecular Dynamics (MD) simulation and maximizes the use of available experimental data, is suggested. Docking with a custom scoring function was used to find a better initial binding pose and Linear Interaction Energy (LIE) method with an optimized coefficient was used to estimate the binding affinity. The protocol has been validated with an external validation set and applied to five modafinil and its derivatives to set the order of binding affinity to Adenosine A2A receptors (ADORA2A, A2aR), which is a membrane protein, for a case study. This protocol could be time efficient and useful for computational drug discovery where limited experimental data is available.

scholarly journals A Computational Binding Affinity Estimation Protocol with Maximum Utilization of Experimental Data : A Case Study for Adenosine Receptor

2020 ◽  
Author(s):  
Il Kwon Cho ◽  
Sung Hyun Moon ◽  
Kwang-Hui Cho
Keyword(s):  
Experimental Data ◽  
Drug Discovery ◽  
Binding Affinity ◽  
Binding Free Energy ◽  
External Validation ◽  
Scoring Function ◽  
Linear Interaction ◽  
A2a Receptors ◽  
Validation Set

Estimating binding affinity between a target protein and the ligand is a crucial step in the drug discovery process. In computer aided drug design (CADD), the problem can be divided into two steps, finding the correct binding pose and estimating binding free energy. In this study, a new binding affinity estimation protocol, which uses molecular docking and binding affinity estimation with Molecular Dynamics (MD) simulation and maximizes the use of available experimental data, is suggested. Docking with a custom scoring function was used to find a better initial binding pose and Linear Interaction Energy (LIE) method with an optimized coefficient was used to estimate the binding affinity. The protocol has been validated with an external validation set and applied to five modafinil and its derivatives to set the order of binding affinity to Adenosine A2A receptors (ADORA2A, A2aR), which is a membrane protein, for a case study. This protocol could be time efficient and useful for computational drug discovery where limited experimental data is available.

scholarly journals A Computational Binding Affinity Estimation Protocol with Maximum Utilization of Experimental Data : A Case Study for Adenosine Receptor

2020 ◽  
Author(s):  
Il Kwon Cho ◽  
Sung Hyun Moon ◽  
Kwang-Hui Cho
Keyword(s):  
Experimental Data ◽  
Drug Discovery ◽  
Binding Affinity ◽  
Binding Free Energy ◽  
External Validation ◽  
Scoring Function ◽  
Linear Interaction ◽  
A2a Receptors ◽  
Validation Set

Estimating binding affinity between a target protein and the ligand is a crucial step in the drug discovery process. In computer aided drug design (CADD), the problem can be divided into two steps, finding the correct binding pose and estimating binding free energy. In this study, a new binding affinity estimation protocol, which uses molecular docking and binding affinity estimation with Molecular Dynamics (MD) simulation and maximizes the use of available experimental data, is suggested. Docking with a custom scoring function was used to find a better initial binding pose and Linear Interaction Energy (LIE) method with an optimized coefficient was used to estimate the binding affinity. The protocol has been validated with an external validation set and applied to five modafinil and its derivatives to set the order of binding affinity to Adenosine A2A receptors (ADORA2A, A2aR), which is a membrane protein, for a case study. This protocol could be time efficient and useful for computational drug discovery where limited experimental data is available.

scholarly journals OnionNet-2: A Convolutional Neural Network Model for Predicting Protein-Ligand Binding Affinity Based on Residue-Atom Contacting Shells

2021 ◽  
Vol 9 ◽  
Author(s):  
Zechen Wang ◽  
Liangzhen Zheng ◽  
Yang Liu ◽  
Yuanyuan Qu ◽  
Yong-Qiang Li ◽  
...  
Keyword(s):  
Neural Network ◽  
Ligand Binding ◽  
Convolutional Neural Network ◽  
Binding Affinity ◽  
Binding Free Energy ◽  
Computational Cost ◽  
Scoring Function ◽  
Quality Data ◽  
Great Success ◽  
Data Set

One key task in virtual screening is to accurately predict the binding affinity (△G) of protein-ligand complexes. Recently, deep learning (DL) has significantly increased the predicting accuracy of scoring functions due to the extraordinary ability of DL to extract useful features from raw data. Nevertheless, more efforts still need to be paid in many aspects, for the aim of increasing prediction accuracy and decreasing computational cost. In this study, we proposed a simple scoring function (called OnionNet-2) based on convolutional neural network to predict △G. The protein-ligand interactions are characterized by the number of contacts between protein residues and ligand atoms in multiple distance shells. Compared to published models, the efficacy of OnionNet-2 is demonstrated to be the best for two widely used datasets CASF-2016 and CASF-2013 benchmarks. The OnionNet-2 model was further verified by non-experimental decoy structures from docking program and the CSAR NRC-HiQ data set (a high-quality data set provided by CSAR), which showed great success. Thus, our study provides a simple but efficient scoring function for predicting protein-ligand binding free energy.

scholarly journals A Benchmark of Popular Free Energy Approaches Revealing the Inhibitors Binding to SARS-CoV2 Mpro

2020 ◽  
Author(s):  
Son Tung Ngo ◽  
Nguyen Minh Tam ◽  
Pham Minh Quan ◽  
Trung Hai Nguyen
Keyword(s):  
Free Energy ◽  
Ligand Binding ◽  
Binding Affinity ◽  
Drug Target ◽  
Binding Free Energy ◽  
Essential Elements ◽  
Dominant Factor ◽  
Linear Interaction ◽  
Energy Perturbation ◽  
Binding Cleft

COVID-19 pandemic has killed millions of people worldwide since its outbreak in Dec 2019. The pandemic is caused by the SARS-CoV-2 virus whose main protease (Mpro) is a promising drug target since it plays a key role in viral proliferation and replication. Currently, designing an effective therapy is an urgent task, which requires accurately estimating ligand-binding free energy to the SARS-CoV-2 Mpro. However, it should be noted that the accuracy of a free energy method probably depends on the protein target. A highly accurate approach for some targets may fail to produce a reasonable correlation with experiment when a novel enzyme is considered as a drug target. Therefore, in this context, the ligand-binding affinity to SARS-CoV-2 Mpro was calculated via various approaches. The Autodock Vina (Vina) and Autodock4 (AD4) packages were manipulated to preliminary investigate the ligand-binding affinity and pose to the SARS-CoV-2 Mpro. The binding free energy was then refined using the fast pulling of ligand (FPL), linear interaction energy (LIE), molecular mechanics-Poission Boltzmann surface area (MM-PBSA), and free energy perturbation (FEP) methods. The benchmark results indicated that for docking calculations, Vina is more accurate than AD4 and for free energy methods, FEP is the most accurate followed by LIE, FPL and MM-PBSA (FEP > LIE > FPL > MM-PBSA). Moreover, the binding mechanism was also revealed by atomistic simulations. The vdW interaction is the dominant factor. The residues <i>Thr25</i>, <i>Thr26</i>, <i>His41</i>, <i>Ser46</i>, <i>Asn142</i>, <i>Gly143</i>, <i>Cys145</i>, <i>Glu166</i>, and <i>Gln189</i> are essential elements affecting on the binding process. Furthermore, the <i>Ser46</i> and related residues probably are important elements affecting the enlarge/dwindle of the SARS-CoV-2 Mpro binding cleft. The benchmark probably guide for further investigations using computational approaches.

scholarly journals Aquatic Toxicity Integrated Testing and Assessment Strategies (ITS) for Difficult Substances: Case Study With Thiochemicals

2021 ◽  
Author(s):  
Monika Nendza ◽  
Jan Ahlers
Keyword(s):  
Experimental Data ◽  
Water Solubility ◽  
Aquatic Toxicity ◽  
External Validation ◽  
Weighted Average ◽  
Test Results ◽  
Worst Case ◽  
Chemical Structures ◽  
Integrated Testing

Abstract Background An Integrated Testing and Assessment Strategy (ITS) for aquatic toxicity of 16 thiochemicals to be registered under REACH revealed 12 data gaps, which had to be filled by experimental data. These test results are now available and offer the unique opportunity to subject previous estimates obtained by read-across (analogue and category approaches) to an external validation. The case study thiochemicals are so-called difficult substances due to instability and poor water solubility, challenging established ITS. Results The new experimental data confirm the previous predictions of acute aquatic toxicity with the new test results indicating a 2-5 times lower toxicity than previously predicted. The previous predictions thus are conservative and closer to the experimental results than expected. The good agreement can be attributed to the fact that we had limited the extrapolations to narrow chemical groups with similar SH-group reactivities. The new experimental data further strengthen and externally validate the existing trends based on similarity in chemical structures, mode of action (MoA), water solubility and stability of source and target compounds in aquatic media. Based on the new experimental data, reliable revised PNECs could be derived and the REACH requirements for these thiochemicals are largely fulfilled. Appropriately adapted ITS are therefore able to reduce in vivo tests with fish even for difficult substances and replace them with alternative information. Conclusions Both experimental and alternative information for difficult substances such as thiochemicals that are rapidly transformed in water are subject to considerable uncertainty. For example, the use of nominal, initial or time-weighted average concentrations, contribute variability in the determination of aquatic toxicity. The use of nominal concentrations is likely to be the most appropriate choice as it reflects realistic worst-case environmental conditions in these cases. In general, uncertainties in (historical) test results and alternative information (read-across) must be considered in terms of how much uncertainty is acceptable for environmental protection on the one hand and how much certainty is technically feasible on the other.

scholarly journals A Benchmark of Popular Free Energy Approaches Revealing the Inhibitors Binding to SARS-CoV2 Mpro

2020 ◽  
Author(s):  
Son Tung Ngo ◽  
Nguyen Minh Tam ◽  
Pham Minh Quan ◽  
Trung Hai Nguyen
Keyword(s):  
Free Energy ◽  
Ligand Binding ◽  
Binding Affinity ◽  
Drug Target ◽  
Binding Free Energy ◽  
Essential Elements ◽  
Dominant Factor ◽  
Linear Interaction ◽  
Urgent Task ◽  
Energy Perturbation

COVID-19 pandemic has killed millions of people worldwide since its outbreak in Dec 2019. The pandemic is caused by the SARS-CoV-2 virus whose main protease (Mpro) is a promising drug target since it plays a key role in viral proliferation and replication. Currently, designing an effective therapy is an urgent task, which requires accurately estimating ligand-binding free energy to the SARS-CoV-2 Mpro. However, it should be noted that the accuracy of a free energy method probably depends on the protein target. A highly accurate approach for some targets may fail to produce a reasonable correlation with experiment when a novel enzyme is considered as a drug target. Therefore, in this context, the ligand-binding affinity to SARS-CoV-2 Mpro was calculated via various approaches. The Autodock Vina (Vina) and Autodock4 (AD4) packages were manipulated to preliminary investigate the ligand-binding affinity and pose to the SARS-CoV-2 Mpro. The binding free energy was then refined using the fast pulling of ligand (FPL), linear interaction energy (LIE), molecular mechanics-Poission Boltzmann surface area (MM-PBSA), and free energy perturbation (FEP) methods. The benchmark results indicated that for docking calculations, Vina is more accurate than AD4 and for free energy methods, FEP is the most accurate followed by LIE, FPL and MM-PBSA (FEP > LIE ≈ FPL > MM-PBSA). Moreover, the binding mechanism was also revealed by atomistic simulations. The vdW interaction is the dominant factor. The residues <i>Thr26</i>, <i>His41</i>, <i>Ser46</i>, <i>Asn142</i>, <i>Gly143</i>, <i>Cys145</i>, <i>His164</i>, <i>Glu166</i>, and <i>Gln189</i> are essential elements affecting on the binding process. The benchmark probably guide for further investigations using computational approaches.

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