PremPLI: Predicting the Effects of Missense Mutations on Protein-Ligand Interactions

Abstract Protein-ligand interactions trigger a multitude of signal transduction processes and resistance to small-molecule drugs is the main cause of the failure of therapeutic drugs in clinical practice. Missense mutations altering the binding of ligands to proteins are one of the critical mechanisms that result in genetic disease and drug resistance. Computational methods have made a lot of progress for predicting binding affinity changes and identifying resistance mutations, but they are still not satisfied and need to be further improved in both accuracy and speed. To address these issues, we introduced PremPLI, a structure-based machine learning method for quantitatively estimating the effects of single mutations on ligand binding affinity changes. A comprehensive comparison of the predictive performance of PremPLI with other available methods on two benchmark datasets confirms that our approach performs robustly and presents similar or even higher predictive accuracy than the approaches relying on first-principle statistical mechanics and mixed physics- and knowledge-based potentials while requires much less computational resources. PremPLI can be used for guiding the design of ligand-binding proteins, identifying and understanding disease driver mutations, and finding potential resistance mutations for different drugs. PremPLI is freely available at https://lilab.jysw.suda.edu.cn/research/PremPLI/ and allows to do large-scale mutational scanning.

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PremPLI: a machine learning model for predicting the effects of missense mutations on protein-ligand interactions

Communications Biology ◽

10.1038/s42003-021-02826-3 ◽

2021 ◽

Vol 4 (1) ◽

Author(s):

Tingting Sun ◽

Yuting Chen ◽

Yuhao Wen ◽

Zefeng Zhu ◽

Minghui Li

Keyword(s):

Machine Learning ◽

Ligand Binding ◽

Binding Affinity ◽

Large Scale ◽

Predictive Accuracy ◽

Predictive Performance ◽

Driver Mutations ◽

Missense Mutations ◽

Resistance Mutations ◽

Benchmark Datasets

AbstractResistance to small-molecule drugs is the main cause of the failure of therapeutic drugs in clinical practice. Missense mutations altering the binding of ligands to proteins are one of the critical mechanisms that result in genetic disease and drug resistance. Computational methods have made a lot of progress for predicting binding affinity changes and identifying resistance mutations, but their prediction accuracy and speed are still not satisfied and need to be further improved. To address these issues, we introduce a structure-based machine learning method for quantitatively estimating the effects of single mutations on ligand binding affinity changes (named as PremPLI). A comprehensive comparison of the predictive performance of PremPLI with other available methods on two benchmark datasets confirms that our approach performs robustly and presents similar or even higher predictive accuracy than the approaches relying on first-principle statistical mechanics and mixed physics- and knowledge-based potentials while requires much less computational resources. PremPLI can be used for guiding the design of ligand-binding proteins, identifying and understanding disease driver mutations, and finding potential resistance mutations for different drugs. PremPLI is freely available at https://lilab.jysw.suda.edu.cn/research/PremPLI/ and allows to do large-scale mutational scanning.

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Defining a Global Map of Functional Group Based 3D Ligand-binding Motifs

10.1101/2020.09.27.315762 ◽

2020 ◽

Author(s):

Liu Yang ◽

Wei He ◽

Yuehui Yun ◽

Yongxiang Gao ◽

Zhongliang Zhu ◽

...

Keyword(s):

Ligand Binding ◽

Large Scale ◽

Wide Spectrum ◽

Systematic Analysis ◽

Structure Based Drug Design ◽

Binding Motifs ◽

Protein Ligand Interactions ◽

Target Drug ◽

Ligand Interactions ◽

Binding Pockets

AbstractUncovering conserved 3D protein-ligand binding patterns at the basis of functional groups (FGs) shared by a variety of small molecules can greatly expand our knowledge of protein-ligand interactions. Despite that conserved binding patterns for a few commonly used FGs have been reported in the literature, large-scale identification and evaluation of FG-based 3D binding motifs are still lacking. Here, we developed AFTME, an alignment-free method for automatic mapping of 3D motifs to different FGs of a specific ligand through two-dimensional clustering. Applying our method to 233 nature-existing ligands, we defined 481 FG-binding motifs that are highly conserved across different ligand-binding pockets. Systematic analysis further reveals four main classes of binding motifs corresponding to distinct sets of FGs. Combinations of FG-binding motifs facilitate proteins to bind a wide spectrum of ligands with various binding affinities. Finally, we showed that these general binding patterns are also applicable to target-drug interactions, providing new insights into structure-based drug design.

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CSConv2d: A 2-D Structural Convolution Neural Network with a Channel and Spatial Attention Mechanism for Protein-Ligand Binding Affinity Prediction

Biomolecules ◽

10.3390/biom11050643 ◽

2021 ◽

Vol 11 (5) ◽

pp. 643

Author(s):

Xun Wang ◽

Dayan Liu ◽

Jinfu Zhu ◽

Alfonso Rodriguez-Paton ◽

Tao Song

Keyword(s):

Ligand Binding ◽

Binding Affinity ◽

Spatial Attention ◽

Kinase Inhibitors ◽

Drug Repositioning ◽

Attention Mechanism ◽

Receptor Proteins ◽

Protein Ligand Interactions ◽

Ligand Interactions ◽

Better Than

The binding affinity of small molecules to receptor proteins is essential to drug discovery and drug repositioning. Chemical methods are often time-consuming and costly, and models for calculating the binding affinity are imperative. In this study, we propose a novel deep learning method, namely CSConv2d, for protein-ligand interactions’ prediction. The proposed method is improved by a DEEPScreen model using 2-D structural representations of compounds as input. Furthermore, a channel and spatial attention mechanism (CS) is added in feature abstractions. Data experiments conducted on ChEMBLv23 datasets show that CSConv2d performs better than the original DEEPScreen model in predicting protein-ligand binding affinity, as well as some state-of-the-art DTIs (drug-target interactions) prediction methods including DeepConv-DTI, CPI-Prediction, CPI-Prediction+CS, DeepGS and DeepGS+CS. In practice, the docking results of protein (PDB ID: 5ceo) and ligand (Chemical ID: 50D) and a series of kinase inhibitors are operated to verify the robustness.

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Investigating structure function relationships in the NOTCH family through large-scale somatic DNA sequencing studies

10.1101/2020.03.31.018325 ◽

2020 ◽

Author(s):

Michael W J Hall ◽

David Shorthouse ◽

Philip H Jones ◽

Benjamin A Hall

Keyword(s):

Dna Sequencing ◽

Structure Function ◽

Calcium Binding ◽

Large Scale ◽

Driver Mutations ◽

Missense Mutations ◽

Mutant Selection ◽

Ligand Interaction ◽

Binding Interface

AbstractThe recent development of highly sensitive DNA sequencing techniques has detected large numbers of missense mutations of genes, including NOTCH1 and 2, in ageing normal tissues. Driver mutations persist and propagate in the tissue through a selective advantage over both wild-type cells and alternative mutations. This process of selection can be considered as a large scale, in vivo screen for mutations that increase clone fitness. It follows that the specific missense mutations that are observed in individual genes may offer us insights into the structure-function relationships. Here we show that the positively selected missense mutations in NOTCH1 and NOTCH2 in human oesophageal epithelium cause inactivation predominantly through protein misfolding. Once these mutations are excluded, we further find statistically significant evidence for selection at the ligand binding interface and calcium binding sites. In this, we observe stronger evidence of selection at the ligand interface on EGF12 over EGF11, suggesting that in this tissue EGF12 may play a more important role in ligand interaction. Finally, we show how a mutation hotspot in the NOTCH1 transmembrane helix arises through the intersection of both a high mutation rate and residue conservation. Together these insights offer a route to understanding the mechanism of protein function through in vivo mutant selection.

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nAPOLI: a graph-based strategy to detect and visualize conserved protein-ligand interactions in large-scale

IEEE/ACM Transactions on Computational Biology and Bioinformatics ◽

10.1109/tcbb.2019.2892099 ◽

2019 ◽

pp. 1-1 ◽

Cited By ~ 7

Author(s):

Alexandre Victor Fassio ◽

Lucianna H. Santos ◽

Sabrina A. Silveira ◽

Rafaela S. Ferreira ◽

Raquel Cardoso de Melo-Minardi

Keyword(s):

Large Scale ◽

Protein Ligand Interactions ◽

Ligand Interactions

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Improving protein–ligand binding prediction by considering the bridging water molecules in Autodock

Journal of Theoretical and Computational Chemistry ◽

10.1142/s0219633619500275 ◽

2019 ◽

Vol 18 (05) ◽

pp. 1950027 ◽

Cited By ~ 1

Author(s):

Qiangna Lu ◽

Lian-Wen Qi ◽

Jinfeng Liu

Keyword(s):

Ligand Binding ◽

Considerable Improvement ◽

Ligand Docking ◽

Water Molecules ◽

Binding Modes ◽

Binding Prediction ◽

Docking Simulations ◽

Protein Ligand Interactions ◽

Ligand Interactions ◽

Docking Program

Water plays a significant role in determining the protein–ligand binding modes, especially when water molecules are involved in mediating protein–ligand interactions, and these important water molecules are receiving more and more attention in recent years. Considering the effects of water molecules has gradually become a routine process for accurate description of the protein–ligand interactions. As a free docking program, Autodock has been most widely used in predicting the protein–ligand binding modes. However, whether the inclusion of water molecules in Autodock would improve its docking performance has not been systematically investigated. Here, we incorporate important bridging water molecules into Autodock program, and systematically investigate the effectiveness of these water molecules in protein–ligand docking. This approach was evaluated using 18 structurally diverse protein–ligand complexes, in which several water molecules bridge the protein–ligand interactions. Different treatment of water molecules were tested by using the fixed and rotatable water molecules, and a considerable improvement in successful docking simulations was found when including these water molecules. This study illustrates the necessity of inclusion of water molecules in Autodock docking, and emphasizes the importance of a proper treatment of water molecules in protein–ligand binding predictions.

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Preorganization in biological systems: Are conformational constraints worth the energy?

Pure and Applied Chemistry ◽

10.1351/pac200779020193 ◽

2007 ◽

Vol 79 (2) ◽

pp. 193-200 ◽

Cited By ~ 27

Author(s):

Stephen F. Martin

Keyword(s):

Experimental Evidence ◽

Binding Affinity ◽

Three Dimensional ◽

Common Belief ◽

Flexible Control ◽

Protein Ligand Interactions ◽

Conformational Constraints ◽

Ligand Interactions ◽

Dimensional Shape ◽

Three Dimensional Shape

It is generally assumed that preorganizing a flexible ligand in the three-dimensional shape it adopts when bound to a macromolecular receptor will provide a derivative having an increased binding affinity, primarily because the rigidified molecule is expected to benefit from a lesser entropic penalty during complexation. We now provide the first experimental evidence that demonstrates this common belief is not universally true. Indeed, we find that ligand preorganization may be accompanied by an unfavorable entropy of binding, even when the constrained ligand exhibits a higher binding affinity than its flexible control. Thus, the effects that ligand preorganization have upon energetics and structure in protein-ligand interactions must be reevaluated.

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Estimation of Binding Rates and Affinities from Multiensemble Markov Models and Ligand Decoupling

10.26434/chemrxiv.14728206.v1 ◽

2021 ◽

Author(s):

Yunhui Ge ◽

Vincent Voelz

Keyword(s):

Ligand Binding ◽

Markov Models ◽

Time Correlation ◽

Test System ◽

Binding Affinities ◽

Limited Data ◽

Markov State Model ◽

Protein Ligand Interactions ◽

Ligand Interactions ◽

Kinetics Of

Accurate and efficient simulation of the thermodynamics and kinetics of protein-ligand interactions is crucial for computational drug discovery. Multiensemble Markov Model (MEMM) estimators can provide estimates of both binding rates and affinities from collections of short trajectories, but have not been systematically explored for situations when a ligand is decoupled through scaling of non-bonded interactions. In this work, we compare the performance of two MEMM approaches for estimating ligand binding affinities and rates: (1) the transition-based reweighting analysis method (TRAM) and (2) a Maximum Caliber (MaxCal) based method. As a test system, we construct a small host-guest system where the ligand is a single uncharged Lennard-Jones (LJ) particle, and the receptor is an 11-particle icosahedral pocket made from the same atom type. To realistically mimic a protein-ligand binding system, the LJ ε parameter was tuned, and the system placed in a periodic box with 860 TIP3P water molecules. A benchmark was performed using over 80 μs of unbiased simulation, and an 18-state Markov state model used to estimate reference binding affinities and rates. We then tested the performance of TRAM and MaxCal when challenged with limited data. Both TRAM and MaxCal approaches perform better than conventional MSMs, with TRAM showing better convergence and accuracy. We find that subsampling of trajectories to remove time correlation improves the accuracy of both TRAM and MaxCal, and that in most cases only a single biased ensemble to enhance sampled transitions is required to make accurate estimates.

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Combining Molecular Dynamic Information and an Aspherical-Atom Data Bank in the Evaluation of the Electrostatic Interaction Energy in Multimeric Protein-Ligand Complex: A Case Study for HIV-1 Protease

10.26434/chemrxiv.13513161 ◽

2021 ◽

Author(s):

Prashant Kumar ◽

Paulina Dominiak

Keyword(s):

Interaction Energy ◽

Molecular Dynamic ◽

Binding Affinity ◽

Electrostatic Interaction ◽

Electrostatic Interactions ◽

Data Bank ◽

Scoring Functions ◽

Protein Ligand Interactions ◽

Ligand Interactions ◽

Hiv 1

<div> <div> <div> <p>Computational analysis of protein-ligand interactions is of crucial importance for drug discovery. Assessment of ligand binding energy allows us to have a glimpse on the potential of a small organic molecule to be a ligand to the binding site of a protein target. Available scoring functions such as in docking programs, we could say that they all rely on equations that sum each type of protein-ligand interactions to model the binding affinity. Most of the scoring functions consider electrostatic interactions involving the protein and the ligand. Electrostatic interactions contribute one of the most important part of total interaction energies between macromolecules, unlike dispersion forces they are highly directional and therefore dominate the nature of molecular packing in crystals and in biological complexes and contribute significantly to differences in inhibition strength among related enzyme inhibitors. In this paper, complexes of HIV-1 protease with inhibitor molecules (JE-2147 and Darunavir) have been analysed using charge densities from a transferable aspherical-atom data bank. Moreover, we analyse the electrostatic interaction energy for an ensemble of structures using molecular dynamic simulation to highlight the main features related to the importance of this interaction for binding affinity. </p> </div> </div> </div>

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