Simulating Protein-Ligand Binding with Neural Network Potentials

10.26434/chemrxiv.11051315 ◽

2019 ◽

Author(s):

Shae-Lynn Lahey ◽

Christopher Rowley

Keyword(s):

Neural Network ◽

Ligand Binding ◽

Computational Cost ◽

Bound State ◽

Drug Binding ◽

Cancer Drug ◽

Molecular Conformations ◽

Drug Molecules ◽

The Stability ◽

Dynamics Simulations

Drug molecules adopt a range of conformations both in solution and in their protein-bound state. The strain and reduced flexibility of bound drugs can partially counter the intermolecular interactions that drive protein–ligand binding. To make accurate computational predictions of drug binding affinities, computational chemists have attempted to develop efficient empirical models of these interactions, although these methods are not always reliable. Machine learning has allowed the development of highly-accurate neural-network potentials (NNPs), which are capable of predicting the stability of molecular conformations with accuracy comparable to state-of-the-art quantum chemical calculations but at a billionth of the computational cost. Here, we demonstrate that these methods can be used to represent the intramolecular forces of protein-bound drugs within molecular dynamics simulations. These simulations are shown to be capable of predicting the protein–ligand binding pose and conformational component of the absolute Gibbs energy of binding for a set of drug molecules. Notably, the conformational energy for anti-cancer drug erlotinib binding to its target was found to considerably overestimated by a molecular mechanical model, while the NNP predicts a more moderate value. Although the ANI-1ccX NNP was not trained to describe ionic molecules, reasonable binding poses are predicted for charged ligands, although this method is not suitable for modeling the ligands in solution.

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Multiscale simulations examining glycan shield effects on drug binding to influenza neuraminidase

10.1101/2020.08.12.248690 ◽

2020 ◽

Cited By ~ 1

Author(s):

Christian Seitz ◽

Lorenzo Casalino ◽

Robert Konecny ◽

Gary Huber ◽

Rommie E. Amaro ◽

...

Keyword(s):

Drug Development ◽

Ligand Binding ◽

Binding Site ◽

Brownian Dynamics ◽

Drug Binding ◽

Binding Mechanism ◽

Drug Effectiveness ◽

Future Drug ◽

Dynamics Simulations ◽

Inhibit Antibody

AbstractInfluenza neuraminidase is an important drug target. Glycans are present on neuraminidase, and are generally considered to inhibit antibody binding via their glycan shield. In this work we studied the effect of glycans on the binding kinetics of antiviral drugs to the influenza neuraminidase. We created all-atom in silico systems of influenza neuraminidase with experimentally-derived glycoprofiles consisting of four systems with different glycan conformations and one system without glycans. Using Brownian dynamics simulations, we observe a two- to eight-fold decrease in the rate of ligand binding to the primary binding site of neuraminidase due to the presence of glycans. These glycans are capable of covering much of the surface area of neuraminidase, and the ligand binding inhibition is derived from glycans sterically occluding the primary binding site on a neighboring monomer. Our work also indicates that drugs preferentially bind to the primary binding site (i.e. the active site) over the secondary binding site, and we propose a binding mechanism illustrating this. These results help illuminate the complex interplay between glycans and ligand binding on the influenza membrane protein neuraminidase.Statement of SignificanceThe influenza glycoprotein neuraminidase is the target for three FDA-approved influenza drugs in the US. However, drug resistance and low drug effectiveness merits further drug development towards neuraminidase, which is hindered by our limited understanding of glycan effects on ligand binding. Generally, drug developers do not include glycans in their development pipelines. Here, we show that even though glycans can reduce drug binding towards neuraminidase, we recommend future drug development work to focus on strong binders with a long lifetime. Furthermore, we examine the binding competition between the primary and secondary binding sites on neuraminidase, leading us to propose a new, to the best of our knowledge, multivalent binding mechanism.

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

Frontiers in Chemistry ◽

10.3389/fchem.2021.753002 ◽

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.

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Exploring the Stability of Ligand Binding Modes to Proteins by Molecular Dynamics Simulations: A Cross-docking Study

Journal of Chemical Information and Modeling ◽

10.1021/acs.jcim.7b00412 ◽

2017 ◽

Vol 57 (10) ◽

pp. 2514-2522 ◽

Cited By ~ 31

Author(s):

Kai Liu ◽

Hironori Kokubo

Keyword(s):

Molecular Dynamics ◽

Ligand Binding ◽

Molecular Dynamics Simulations ◽

Docking Study ◽

Binding Modes ◽

Cross Docking ◽

The Stability ◽

Dynamics Simulations

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Insights into the Binding and Covalent Inhibition Mechanism of PF-07321332 to SARS-CoV-2 Mpro

10.33774/chemrxiv-2021-fq3mg ◽

2021 ◽

Author(s):

Son Tung Ngo ◽

Trung Hai Nguyen ◽

Nguyen Thanh Tung ◽

Binh Khanh Mai

Keyword(s):

Ligand Binding ◽

Bound State ◽

Catalytic Triad ◽

Inhibition Mechanism ◽

Binding Process ◽

Covalent Inhibition ◽

Main Protease ◽

Dynamics Simulations ◽

Covalent Inhibitor

The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has been causing the COVID-19 pandemic resulting in several million death were reported. Numerous investigations have been carried out to discover a compound that can inhibit the biological activity of SARS-CoV-2 main protease, which is an enzyme related to the viral replication. Among these, PF-07321332 is currently under clinical trial for COVID-19 therapy. Therefore, in this work, atomistic and electronic simulations were performed to unravel the binding and covalent inhibition mechanism of the compound to Mpro. Initially, 5 µs of steered-molecular dynamics simulations were carried out to evaluate the ligand-binding process to SARS-CoV-2 Mpro. Successfully generated bound state between two molecules showed the important role of the PF-07321332 pyrrolidinyl group and the residues Glu166 and Gln189 in the ligand-binding process. Moreover, from the MD-refined structure, quantum mechanics/molecular mechanics (QM/MM) calculations were carried out to unravel the reaction mechanism for the formation of thioimidate product from SARS-CoV-2 Mpro and PF07321332 inhibitor. We found that the catalytic triad Cys145–His41–Asp187 of SARS-CoV-2 Mpro plays important role in the activation of PF-07321332 covalent inhibitor, which renders the deprotonation of Cys145 and, thus, facilitates further reaction. Our results are definitely beneficial for better understanding on the inhibition mechanism and designing new effective inhibitors for SARS-CoV-2 Mpro.

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Determinants of ligand selectivity in a cyclic nucleotide–regulated potassium channel

The Journal of General Physiology ◽

10.1085/jgp.201311145 ◽

2014 ◽

Vol 144 (1) ◽

pp. 41-54 ◽

Cited By ~ 5

Author(s):

João Pessoa ◽

Fátima Fonseca ◽

Simone Furini ◽

João H. Morais-Cabral

Keyword(s):

Ligand Binding ◽

Potassium Channel ◽

Cyclic Nucleotide ◽

Binding Pocket ◽

Bound State ◽

Triple Mutant ◽

Ligand Binding Pocket ◽

Ligand Selectivity ◽

Molecular Determinants ◽

Dynamics Simulations

Cyclic nucleotide–binding (CNB) domains regulate the activity of channels, kinases, exchange factors, and transcription factors. These proteins are highly variable in their ligand selectivity; some are highly selective for either cAMP or cGMP, whereas others are not. Several molecular determinants of ligand selectivity in CNB domains have been defined, but these do not provide a complete view of the selectivity mechanism. We performed a thorough analysis of the ligand-binding properties of mutants of the CNB domain from the MlotiK1 potassium channel. In particular, we defined which residues specifically favor cGMP or cAMP. Inversion of ligand selectivity, from favoring cAMP to favoring cGMP, was only achieved through a combination of three mutations in the ligand-binding pocket. We determined the x-ray structure of the triple mutant bound to cGMP and performed molecular dynamics simulations and a biochemical analysis of the effect of the mutations. We concluded that the increase in cGMP affinity and selectivity does not result simply from direct interactions between the nucleotide base and the amino acids introduced in the ligand-binding pocket residues. Rather, tighter cGMP binding over cAMP results from the polar chemical character of the mutations, from greater accessibility of water molecules to the ligand in the bound state, and from an increase in the structural flexibility of the mutated binding pocket.

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Discovering loop conformational flexibility in T4 lysozyme mutants through artificial intelligence aided molecular dynamics

10.1101/2020.04.08.032748 ◽

2020 ◽

Cited By ~ 1

Author(s):

Zachary Smith ◽

Pavan Ravindra ◽

Yihang Wang ◽

Rory Cooley ◽

Pratyush Tiwary

Keyword(s):

Artificial Intelligence ◽

Molecular Dynamics ◽

Protein Function ◽

Computational Cost ◽

Conformational Flexibility ◽

T4 Lysozyme ◽

Reaction Coordinates ◽

Accurate Comparison ◽

The Stability ◽

Dynamics Simulations

Proteins sample a variety of conformations distinct from their crystal structure. These structures, their propensities, and pathways for moving between them contain enormous information about protein function that is hidden from a purely structural perspective. Molecular dynamics simulations can uncover these higher energy states but often at a prohibitively high computational cost. Here we apply our recent statistical mechanics and artificial intelligence based molecular dynamics framework for enhanced sampling of protein loops in three mutants of the protein T4 lysozyme. We are able to correctly rank these according to the stability of their excited state. By analyzing reaction coordinates, we also obtain crucial insight into why these specific perturbations in sequence space lead to tremendous variations in conformational flexibility. Our framework thus allows accurate comparison of loop conformation populations with minimal prior human bias, and should be directly applicable to a range of macromolecules in biology, chemistry and beyond.

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Virtual Screening Studies for Discovery of Novel Inhibitors of Inflammatory Process Targets

Current Pharmaceutical Design ◽

10.2174/1381612824666180403122410 ◽

2018 ◽

Vol 24 (14) ◽

pp. 1617-1638 ◽

Cited By ~ 2

Author(s):

Marcus Tullius Scotti ◽

Mateus Feitosa Alves ◽

Chonny Alexander Herrera-Acevedo ◽

Luciana Scotti

Keyword(s):

Virtual Screening ◽

Infectious Diseases ◽

Inflammatory Diseases ◽

Computational Cost ◽

Inflammatory Conditions ◽

Inflammatory Drugs ◽

The Stability ◽

Dynamics Simulations ◽

Potential Inhibitors ◽

Pharmacophore Models

Inflammation has been very evident in infectious diseases, but in recent times research has increasingly shown that a range of non-infectious diseases may present with inflammatory conditions. This fact becomes important as new anti-inflammatory drugs emerge with different targets for treatment of diseases. Virtual screening (VS) involves applying computational methods to discover new ligands for biological structures from the formation of large libraries composed of a large number of compounds. This review aims to report several studies employing a variety of VS: ligand-based and structure-based VS are being used more frequently in combination to decrease the probability of choosing false positive candidates. There are also studies that use only one approach. Docking is widely employed as structure-based VS methodology, however pharmacophore models based on the structure are becoming more prevalent. Molecular dynamics simulations, despite their computational cost, are still utilized to validate docking scores and analyze the stability of the complex ligand-structure. It is important to note that several studies employed several drug-like rules to screen structures, as well, decoys and PAINS to validate the models. Natural product databases, despite the lower number of the compounds compared to other databases that are available, are commonly referred to as a source of drug-like molecules. There is a literal explosion of software being released for a variety of purposes and several of them are free tools and/or web tools. Overall, VS studies are nowadays a normal part of medicinal chemistry to determine novel potential inhibitors for targets of inflammatory diseases.

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