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

Biomolecules ◽  
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.

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

2021 ◽  
Author(s):  
Tingting Sun ◽  
Yuting Chen ◽  
Yuhao Wen ◽  
Zefeng Zhu ◽  
Minghui Li
Keyword(s):  
Ligand Binding ◽  
Binding Affinity ◽  
Large Scale ◽  
Predictive Accuracy ◽  
Predictive Performance ◽  
Driver Mutations ◽  
Missense Mutations ◽  
Resistance Mutations ◽  
Protein Ligand Interactions ◽  
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.

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

2019 ◽  
Vol 18 (05) ◽  
pp. 1950027 ◽  
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.

scholarly journals An Investigation of Small GTPases in relation to Liver Tumorigenesis Using Traditional Chinese Medicine

2014 ◽  
Vol 2014 ◽  
pp. 1-11 ◽  
Author(s):  
Tzu-Chieh Hung ◽  
Wen-Yuan Lee ◽  
Kuen-Bao Chen ◽  
Yueh-Chiu Chan ◽  
Calvin Yu-Chian Chen
Keyword(s):  
Molecular Dynamics ◽  
Chinese Medicine ◽  
Traditional Chinese Medicine ◽  
Hydrophobic Interactions ◽  
Small Gtpases ◽  
Ligand Interaction ◽  
Protein Ligand Interactions ◽  
Ligand Interactions ◽  
Protein Ligand Interaction ◽  
Better Than

Recently, an important topic of liver tumorigenesis had been published in 2013. In this report, Ras and Rho had defined the relation of liver tumorigenesis. The traditional Chinese medicine (TCM) database has been screened for molecular compounds by simulating molecular docking and molecular dynamics to regulate Ras and liver tumorigenesis. Saussureamine C, S-allylmercaptocysteine, and Tryptophan are selected based on the highest docking score than other TCM compounds. The molecular dynamics are helpful in the analysis and detection of protein-ligand interactions. Based on the docking poses, hydrophobic interactions, and hydrogen bond variations, this research surmises are the main regions of important amino acids in Ras. In addition to the detection of TCM compound efficacy, we suggest Saussureamine C is better than the others for protein-ligand interaction.

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

2007 ◽  
Vol 79 (2) ◽  
pp. 193-200 ◽  
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.

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

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.

scholarly journals 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

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>

scholarly journals 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

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>

scholarly journals Systematic domain-based aggregation of protein structures highlights DNA-, RNA- and other ligand-binding positions

2018 ◽  
Vol 47 (2) ◽  
pp. 582-593 ◽  
Author(s):  
Shilpa Nadimpalli Kobren ◽  
Mona Singh
Keyword(s):  
Ligand Binding ◽  
Small Molecules ◽  
Binding Sites ◽  
Protein Structures ◽  
Complex Structures ◽  
Disease Etiology ◽  
Systematic Assessment ◽  
Protein Ligand Interactions ◽  
Ligand Binding Sites ◽  
Ligand Interactions

Abstract Domains are fundamental subunits of proteins, and while they play major roles in facilitating protein–DNA, protein–RNA and other protein–ligand interactions, a systematic assessment of their various interaction modes is still lacking. A comprehensive resource identifying positions within domains that tend to interact with nucleic acids, small molecules and other ligands would expand our knowledge of domain functionality as well as aid in detecting ligand-binding sites within structurally uncharacterized proteins. Here, we introduce an approach to identify per-domain-position interaction ‘frequencies’ by aggregating protein co-complex structures by domain and ascertaining how often residues mapping to each domain position interact with ligands. We perform this domain-based analysis on ∼91000 co-complex structures, and infer positions involved in binding DNA, RNA, peptides, ions or small molecules across 4128 domains, which we refer to collectively as the InteracDome. Cross-validation testing reveals that ligand-binding positions for 2152 domains are highly consistent and can be used to identify residues facilitating interactions in ∼63–69% of human genes. Our resource of domain-inferred ligand-binding sites should be a great aid in understanding disease etiology: whereas these sites are enriched in Mendelian-associated and cancer somatic mutations, they are depleted in polymorphisms observed across healthy populations. The InteracDome is available at http://interacdome.princeton.edu.

Effects of ligand binding upon measurement of Gc by rocket immunoelectrophoresis: Implications for protein determination and for studies of protein/ligand interactions

1985 ◽  
Vol 6 (4) ◽  
pp. 155-161 ◽  
Author(s):  
Pascal J. Goldschmidt-Clermont ◽  
Robert M. Galbraith ◽  
David L. Emerson ◽  
Andre E. Nel ◽  
Philip A. M. Werner ◽  
...  
Keyword(s):  
Ligand Binding ◽  
Protein Determination ◽  
Protein Ligand Interactions ◽  
Ligand Interactions

scholarly journals Graphlet signature-based scoring method to estimate protein–ligand binding affinity

2014 ◽  
Vol 1 (4) ◽  
pp. 140306 ◽  
Author(s):  
Omkar Singh ◽  
Kunal Sawariya ◽  
Polamarasetty Aparoy
Keyword(s):  
Hydrogen Bond ◽  
Ligand Binding ◽  
Interaction Networks ◽  
Scoring Method ◽  
Ligand Interaction ◽  
Ligand Affinity ◽  
Protein Ligand Interactions ◽  
Residue Interaction ◽  
Ligand Interactions ◽  
Cox 2

Over the years, various computational methodologies have been developed to understand and quantify receptor–ligand interactions. Protein–ligand interactions can also be explained in the form of a network and its properties. The ligand binding at the protein-active site is stabilized by formation of new interactions like hydrogen bond, hydrophobic and ionic. These non-covalent interactions when considered as links cause non-isomorphic sub-graphs in the residue interaction network. This study aims to investigate the relationship between these induced sub-graphs and ligand activity. Graphlet signature-based analysis of networks has been applied in various biological problems; the focus of this work is to analyse protein–ligand interactions in terms of neighbourhood connectivity and to develop a method in which the information from residue interaction networks, i.e. graphlet signatures, can be applied to quantify ligand affinity. A scoring method was developed, which depicts the variability in signatures adopted by different amino acids during inhibitor binding, and was termed as GSUS (graphlet signature uniqueness score). The score is specific for every individual inhibitor. Two well-known drug targets, COX-2 and CA-II and their inhibitors, were considered to assess the method. Residue interaction networks of COX-2 and CA-II with their respective inhibitors were used. Only hydrogen bond network was considered to calculate GSUS and quantify protein–ligand interaction in terms of graphlet signatures. The correlation of the GSUS with pIC 50 was consistent in both proteins and better in comparison to the Autodock results. The GSUS scoring method was better in activity prediction of molecules with similar structure and diverse activity and vice versa. This study can be a major platform in developing approaches that can be used alone or together with existing methods to predict ligand affinity from protein–ligand complexes.

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