scholarly journals Four-body atomic potential for modeling protein-ligand binding affinity: application to enzyme-inhibitor binding energy prediction

2013 ◽  
Vol 13 (Suppl 1) ◽  
pp. S1 ◽  
Author(s):  
Majid Masso
Keyword(s):  
Binding Energy ◽  
Ligand Binding ◽  
Binding Affinity ◽  
Enzyme Inhibitor ◽  
Inhibitor Binding ◽  
Atomic Potential ◽  
Energy Prediction ◽  
Binding Energy Prediction

scholarly journals RASPD+: Fast Protein-Ligand Binding Free Energy Prediction Using Simplified Physicochemical Features

2020 ◽  
Author(s):  
Stefan Holderbach ◽  
Lukas Adam ◽  
Bhyravabhotla Jayaram ◽  
Rebecca Wade ◽  
Goutam Mukherjee
Keyword(s):  
Machine Learning ◽  
Ligand Binding ◽  
Protein Binding ◽  
Binding Affinity ◽  
Binding Free Energy ◽  
Rapid Screening ◽  
Scoring Functions ◽  
Energy Prediction ◽  
Molecular Features ◽  
Physicochemical Descriptors

The virtual screening of large numbers of compounds against target protein binding sites has become an integral component of drug discovery workflows. This screening is often done by computationally docking ligands into a protein binding site of interest, but this has the drawback that a large number of poses must be evaluated to obtain accurate estimates of protein-ligand binding affinity. We here introduce a fast prefiltering method for ligand prioritization that is based on a set of machine learning models and uses simple pose-invariant physicochemical descriptors of the ligands and the protein binding pocket. Our method, Rapid Screening with Physicochemical Descriptors + machine learning (RASPD+), is trained on PDBbind data and achieves a regression performance better than for the original RASPD method and comparable to traditional scoring functions on a range of different test sets without the need for generating ligand poses. Additionally, we use RASPD+ to identify molecular features important for binding affinity and assess the ability of RASPD+ to enrich active molecules from decoys.

Virtual Screening of the Indonesian Medicinal Plant and Zinc Databases for Potential Inhibitors of the RNA-Dependent RNA Polymerase (RdRp) of 2019 Novel Coronavirus

2020 ◽  
Vol 20 (6) ◽  
pp. 1430
Author(s):  
Muhammad Arba ◽  
Andry Nur-Hidayat ◽  
Ida Usman ◽  
Arry Yanuar ◽  
Setyanto Tri Wahyudi ◽  
...  
Keyword(s):  
Binding Energy ◽  
Rna Polymerase ◽  
Antiviral Drug ◽  
Dynamics Simulation ◽  
Human Beings ◽  
Rna Dependent Rna Polymerase ◽  
Energy Prediction ◽  
Binding Energy Prediction ◽  
Solvation Energies ◽  
Novel Coronavirus

The novel coronavirus disease 19 (Covid-19) which is caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has been a pandemic across the world, which necessitate the need for the antiviral drug discovery. One of the potential protein targets for coronavirus treatment is RNA-dependent RNA polymerase. It is the key enzyme in the viral replication machinery, and it does not exist in human beings, therefore its targeting has been considered as a strategic approach. Here we describe the identification of potential hits from Indonesian Herbal and ZINC databases. The pharmacophore modeling was employed followed by molecular docking and dynamics simulation for 40 ns. 151 and 14480 hit molecules were retrieved from Indonesian herbal and ZINC databases, respectively. Three hits that were selected based on the structural analysis were stable during 40 ns, while binding energy prediction further implied that ZINC1529045114, ZINC169730811, and 9-Ribosyl-trans-zeatin had tighter binding affinities compared to Remdesivir. The ZINC169730811 had the strongest affinity toward RdRp compared to the other two hits including Remdesivir and its binding was corroborated by electrostatic, van der Waals, and nonpolar contribution for solvation energies. The present study offers three hits showing tighter binding to RdRp based on MM-PBSA binding energy prediction for further experimental verification.

scholarly journals Noncontiguous regions in the extracellular domain of EGF receptor define ligand-binding specificity.

1991 ◽  
Vol 2 (5) ◽  
pp. 337-345 ◽  
Author(s):  
I Lax ◽  
R Fischer ◽  
C Ng ◽  
J Segre ◽  
A Ullrich ◽  
...  
Keyword(s):  
Binding Energy ◽  
Ligand Binding ◽  
Binding Site ◽  
Binding Affinity ◽  
Egf Receptor ◽  
Binding Properties ◽  
Amino Terminal ◽  
High Affinity ◽  
Domain Iii ◽  
Domain I

Murine epidermal growth factor (EGF) binds with approximately 250-fold higher binding affinity to the human EGF receptor (EGFR) than to the chicken EGFR. This difference in binding affinity enabled the identification of a major ligand-binding domain for EGF by studying the binding properties of various chicken/human EGFR chimera expressed in transfected cells lacking endogenous EGFR. It was shown that domain III of EGFR is a major ligand-binding region. Here, we analyze the binding properties of novel chicken/human chimera to further delineate the contact sequences in domain III and to assess the role of other regions of EGFR for their contribution to the display of high-affinity EGF binding. The chimeric receptors include chicken EGFR containing domain I of the human EGFR, chicken receptor containing domain I and III of the human EGFR, and two chimeric chicken EGFR containing either the amino terminal or the carboxy terminal halves of domain III of human EGFR, respectively. In addition, the binding of various human-specific anti-EGFR monoclonal antibodies that interfere with EGF binding is also compared. It is concluded that noncontiguous regions of the EGFR contribute additively to the binding of EGF. Each of the two halves of domain III has a similar contribution to the binding energy, and the sum of both is close to that of the entire domain III. This suggests that the folding of domain III juxtaposes sequences that together constitute the ligand-binding site. Domain I also provides a contribution to the binding energy, and the added contributions of both domain I and III to the binding energy generate the high-affinity binding site typical of human EGFR.

scholarly journals RASPD+: Fast Protein-Ligand Binding Free Energy Prediction Using Simplified Physicochemical Features

2020 ◽  
Author(s):  
Stefan Holderbach ◽  
Lukas Adam ◽  
Bhyravabhotla Jayaram ◽  
Rebecca Wade ◽  
Goutam Mukherjee
Keyword(s):  
Machine Learning ◽  
Ligand Binding ◽  
Protein Binding ◽  
Binding Affinity ◽  
Binding Free Energy ◽  
Rapid Screening ◽  
Scoring Functions ◽  
Energy Prediction ◽  
Molecular Features ◽  
Physicochemical Descriptors

The virtual screening of large numbers of compounds against target protein binding sites has become an integral component of drug discovery workflows. This screening is often done by computationally docking ligands into a protein binding site of interest, but this has the drawback that a large number of poses must be evaluated to obtain accurate estimates of protein-ligand binding affinity. We here introduce a fast prefiltering method for ligand prioritization that is based on a set of machine learning models and uses simple pose-invariant physicochemical descriptors of the ligands and the protein binding pocket. Our method, Rapid Screening with Physicochemical Descriptors + machine learning (RASPD+), is trained on PDBbind data and achieves a regression performance better than for the original RASPD method and comparable to traditional scoring functions on a range of different test sets without the need for generating ligand poses. Additionally, we use RASPD+ to identify molecular features important for binding affinity and assess the ability of RASPD+ to enrich active molecules from decoys.

scholarly journals RASPD+: Fast Protein-Ligand Binding Free Energy Prediction Using Simplified Physicochemical Features

2020 ◽  
Vol 7 ◽  
Author(s):  
Stefan Holderbach ◽  
Lukas Adam ◽  
B. Jayaram ◽  
Rebecca C. Wade ◽  
Goutam Mukherjee
Keyword(s):  
Machine Learning ◽  
Ligand Binding ◽  
Protein Binding ◽  
Binding Affinity ◽  
Binding Free Energy ◽  
Rapid Screening ◽  
Scoring Functions ◽  
Energy Prediction ◽  
Molecular Features ◽  
Physicochemical Descriptors

The virtual screening of large numbers of compounds against target protein binding sites has become an integral component of drug discovery workflows. This screening is often done by computationally docking ligands into a protein binding site of interest, but this has the drawback of a large number of poses that must be evaluated to obtain accurate estimates of protein-ligand binding affinity. We here introduce a fast pre-filtering method for ligand prioritization that is based on a set of machine learning models and uses simple pose-invariant physicochemical descriptors of the ligands and the protein binding pocket. Our method, Rapid Screening with Physicochemical Descriptors + machine learning (RASPD+), is trained on PDBbind data and achieves a regression performance that is better than that of the original RASPD method and traditional scoring functions on a range of different test sets without the need for generating ligand poses. Additionally, we use RASPD+ to identify molecular features important for binding affinity and assess the ability of RASPD+ to enrich active molecules from decoys.

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.

Sign in / Sign up

Export Citation Format

Share Document