A new class of models for computing receptor-ligand binding affinities

AbstractFormation of protein–ligand complexes causes various changes in both the receptor and the ligand. This review focuses on changes in pK and protonation states of ionizable groups that accompany protein–ligand binding. Physical origins of these effects are outlined, followed by a brief overview of the computational methods to predict them and the associated corrections to receptor–ligand binding affinities. Statistical prevalence, magnitude and spatial distribution of the pK and protonation state changes in protein–ligand binding are discussed in detail, based on both experimental and theoretical studies. While there is no doubt that these changes occur, they do not occur all the time; the estimated prevalence varies, both between individual complexes and by method. The changes occur not only in the immediate vicinity of the interface but also sometimes far away. When receptor–ligand binding is associated with protonation state change at particular pH, the binding becomes pH dependent: we review the interplay between sub-cellular characteristic pH and optimum pH of receptor–ligand binding. It is pointed out that there is a tendency for protonation state changes upon binding to be minimal at physiologically relevant pH for each complex (no net proton uptake/release), suggesting that native receptor–ligand interactions have evolved to reduce the energy cost associated with ionization changes. As a result, previously reported statistical prevalence of these changes – typically computed at the same pH for all complexes – may be higher than what may be expected at optimum pH specific to each complex. We also discuss whether proper account of protonation state changes appears to improve practical docking and scoring outcomes relevant to structure-based drug design. An overview of some of the existing challenges in the field is provided in conclusion.

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Three-dimensional structure of homodimeric androgen receptor ligand-binding domain

Endocrine Abstracts ◽

10.1530/endoabs.42.il3 ◽

2016 ◽

Author(s):

Eva Estebanez Perpina

Keyword(s):

Androgen Receptor ◽

Ligand Binding ◽

Three Dimensional ◽

Binding Domain ◽

Ligand Binding Domain ◽

Dimensional Structure ◽

Receptor Ligand ◽

Three Dimensional Structure

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

Physical Sciences Reviews ◽

10.1515/psr-2020-0034 ◽

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.

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

Journal of Cheminformatics ◽

10.1186/s13321-021-00507-1 ◽

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.

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

Biosensors ◽

10.3390/bios11030060 ◽

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.

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