Solvated Interaction Energy (SIE) for Scoring Protein−Ligand Binding Affinities. 1. Exploring the Parameter Space

2007 ◽  
Vol 47 (1) ◽  
pp. 122-133 ◽  
Author(s):  
Marwen Naïm ◽  
Sathesh Bhat ◽  
Kathryn N. Rankin ◽  
Sheldon Dennis ◽  
Shafinaz F. Chowdhury ◽  
...  
Keyword(s):  
Ligand Binding ◽  
Interaction Energy ◽  
Parameter Space ◽  
Binding Affinities ◽  
Solvated Interaction Energy

scholarly journals Assessment of Solvated Interaction Energy Function for Ranking Antibody–Antigen Binding Affinities

2016 ◽  
Vol 56 (7) ◽  
pp. 1292-1303 ◽  
Author(s):  
Traian Sulea ◽  
Victor Vivcharuk ◽  
Christopher R. Corbeil ◽  
Christophe Deprez ◽  
Enrico O. Purisima
Keyword(s):  
Interaction Energy ◽  
Energy Function ◽  
Antigen Binding ◽  
Binding Affinities ◽  
Solvated Interaction Energy

Computational methods for calculation of protein-ligand binding affinities in structure-based drug design

2020 ◽  
Vol 0 (0) ◽  
Author(s):  
Zbigniew Dutkiewicz
Keyword(s):  
Drug Design ◽  
Ligand Binding ◽  
Binding Free Energy ◽  
Development Project ◽  
Quantum Mechanical ◽  
Prediction Methods ◽  
Binding Affinities ◽  
Structure Based Drug Design ◽  
Quantum Mechanical Description ◽  
Optimization Stage

AbstractDrug design is an expensive and time-consuming process. Any method that allows reducing the time the costs of the drug development project can have great practical value for the pharmaceutical industry. In structure-based drug design, affinity prediction methods are of great importance. The majority of methods used to predict binding free energy in protein-ligand complexes use molecular mechanics methods. However, many limitations of these methods in describing interactions exist. An attempt to go beyond these limits is the application of quantum-mechanical description for all or only part of the analyzed system. However, the extensive use of quantum mechanical (QM) approaches in drug discovery is still a demanding challenge. This chapter briefly reviews selected methods used to calculate protein-ligand binding affinity applied in virtual screening (VS), rescoring of docked poses, and lead optimization stage, including QM methods based on molecular simulations.

scholarly journals SMPLIP-Score: predicting ligand binding affinity from simple and interpretable on-the-fly interaction fingerprint pattern descriptors

2021 ◽  
Vol 13 (1) ◽  
Author(s):  
Surendra Kumar ◽  
Mi-hyun Kim
Keyword(s):  
Ligand Binding ◽  
Binding Affinity ◽  
Scoring Functions ◽  
Binding Affinities ◽  
Ligand Interaction ◽  
Fingerprint Pattern ◽  
Comparable Performance ◽  
Direct Interpretation ◽  
Benchmark Datasets ◽  
Complex Features

AbstractIn drug discovery, rapid and accurate prediction of protein–ligand binding affinities is a pivotal task for lead optimization with acceptable on-target potency as well as pharmacological efficacy. Furthermore, researchers hope for a high correlation between docking score and pose with key interactive residues, although scoring functions as free energy surrogates of protein–ligand complexes have failed to provide collinearity. Recently, various machine learning or deep learning methods have been proposed to overcome the drawbacks of scoring functions. Despite being highly accurate, their featurization process is complex and the meaning of the embedded features cannot directly be interpreted by human recognition without an additional feature analysis. Here, we propose SMPLIP-Score (Substructural Molecular and Protein–Ligand Interaction Pattern Score), a direct interpretable predictor of absolute binding affinity. Our simple featurization embeds the interaction fingerprint pattern on the ligand-binding site environment and molecular fragments of ligands into an input vectorized matrix for learning layers (random forest or deep neural network). Despite their less complex features than other state-of-the-art models, SMPLIP-Score achieved comparable performance, a Pearson’s correlation coefficient up to 0.80, and a root mean square error up to 1.18 in pK units with several benchmark datasets (PDBbind v.2015, Astex Diverse Set, CSAR NRC HiQ, FEP, PDBbind NMR, and CASF-2016). For this model, generality, predictive power, ranking power, and robustness were examined using direct interpretation of feature matrices for specific targets.

scholarly journals Novel Method for Quantifying AhR-Ligand Binding Affinities Using Microscale Thermophoresis

Biosensors ◽  
2021 ◽  
Vol 11 (3) ◽  
pp. 60
Author(s):  
Anne Stinn ◽  
Jens Furkert ◽  
Stefan H. E. Kaufmann ◽  
Pedro Moura-Alves ◽  
Michael Kolbe
Keyword(s):  
Ligand Binding ◽  
Environmental Pollutants ◽  
Screening Strategy ◽  
Pharmacological Intervention ◽  
Binding Affinities ◽  
Free System ◽  
Microscale Thermophoresis ◽  
Aryl Hydrocarbon ◽  
Promising Target ◽  
A Cell

The aryl hydrocarbon receptor (AhR) is a highly conserved cellular sensor of a variety of environmental pollutants and dietary-, cell- and microbiota-derived metabolites with important roles in fundamental biological processes. Deregulation of the AhR pathway is implicated in several diseases, including autoimmune diseases and cancer, rendering AhR a promising target for drug development and host-directed therapy. The pharmacological intervention of AhR processes requires detailed information about the ligand binding properties to allow specific targeting of a particular signaling process without affecting the remaining. Here, we present a novel microscale thermophoresis-based approach to monitoring the binding of purified recombinant human AhR to its natural ligands in a cell-free system. This approach facilitates a precise identification and characterization of unknown AhR ligands and represents a screening strategy for the discovery of potential selective AhR modulators.

scholarly journals Robust, Efficient and Automated Methods for Accurate Prediction of Protein-Ligand Binding Affinities in AMBER Drug Discovery Boost

2021 ◽  
Author(s):  
Tai-Sung Lee ◽  
Hsu-Chun Tsai ◽  
Abir Ganguly ◽  
Timothy J Giese ◽  
Darrin M. York
Keyword(s):  
Drug Discovery ◽  
Ligand Binding ◽  
Binding Affinity ◽  
Single Step ◽  
Simulation Software ◽  
Rational Drug Design ◽  
Computer Hardware ◽  
Binding Affinities ◽  
Methodology Development ◽  
Industry Applications

Recent concurrent advances in methodology development, computer hardware and simulation software has transformed our ability to make practical, quantitative predictions of relative ligand binding affinities to guide rational drug design. In the past, these calculations have been hampered by the lack of affordable software with highly efficient implementations of state-of-the-art methods on specialized hardware such as graphical processing units, combined with the paucity of available workflows to streamline throughput for real-world industry applications. Herein we discuss recent methodology development, GPU-accelerated implementation, and workflow creation for alchemical free energy simulation methods in the AMBER Drug Discovery Boost (AMBER-DD Boost) package available as a patch to AMBER20. Among the methodological advances are 1) new methods for the treatment of softcore potentials that overcome long standing end-point catastrophe and softcore imbalance problems and enable single-step alchemical transformations between ligands, and 2) new adaptive enhanced sampling methods in the "alchemical" (or "λ") dimension to accelerate convergence and obtain high precision ligand binding affinity predictions, 3) robust network-wide analysis methods that include cycle closure and reference constraints and restraints, and 4) practical workflows that enable streamlined calculations on large datasets to be performed. Benchmark calculations on various systems demonstrate that these tools deliver an outstanding combination of accuracy and performance, resulting in reliable high-throughput binding affinity predictions at affordable cost.<br>

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