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.

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 Prediction of Protein–Ligand Interaction Based on the Positional Similarity Scores Derived from Amino Acid Sequences

2019 ◽  
Vol 21 (1) ◽  
pp. 24 ◽  
Author(s):  
Dmitry Karasev ◽  
Boris Sobolev ◽  
Alexey Lagunin ◽  
Dmitry Filimonov ◽  
Vladimir Poroikov
Keyword(s):  
Prediction Accuracy ◽  
Sequence Similarity ◽  
Amino Acid Sequences ◽  
Protein Families ◽  
Pairwise Sequence Alignment ◽  
Ligand Interaction ◽  
Protein Ligand Interactions ◽  
General Chemical ◽  
Local Sequence ◽  
Ligand Interactions

The affinity of different drug-like ligands to multiple protein targets reflects general chemical–biological interactions. Computational methods estimating such interactions analyze the available information about the structure of the targets, ligands, or both. Prediction of protein–ligand interactions based on pairwise sequence alignment provides reasonable accuracy if the ligands’ specificity well coincides with the phylogenic taxonomy of the proteins. Methods using multiple alignment require an accurate match of functionally significant residues. Such conditions may not be met in the case of diverged protein families. To overcome these limitations, we propose an approach based on the analysis of local sequence similarity within the set of analyzed proteins. The positional scores, calculated by sequence fragment comparisons, are used as input data for the Bayesian classifier. Our approach provides a prediction accuracy comparable or exceeding those of other methods. It was demonstrated on the popular Gold Standard test sets, presenting different sequence heterogeneity and varying from the group, including different protein families to the more specific groups. A reasonable prediction accuracy was also found for protein kinases, displaying weak relationships between sequence phylogeny and inhibitor specificity. Thus, our method can be applied to the broad area of protein–ligand interactions.

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 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 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 Evolution of (p)ppGpp-HPRT regulation through diversification of an allosteric oligomeric interaction

2019 ◽  
Author(s):  
Brent W. Anderson ◽  
Kuanqing Liu ◽  
Christine Wolak ◽  
Katarzyna Dubiel ◽  
Kenneth A. Satyshur ◽  
...  
Keyword(s):  
Ligand Binding ◽  
Binding Site ◽  
Protein Interactions ◽  
Binding Motif ◽  
Protein Ligand Interactions ◽  
Protein Interfaces ◽  
Ligand Interactions ◽  
Binding Residues ◽  
The Common ◽  
Ligand Regulation

ABSTRACTThe signaling ligand (p)ppGpp binds diverse targets across bacteria, yet the mechanistic and evolutionary basis underlying these ligand-protein interactions remains poorly understood. Here we identify a novel (p)ppGpp binding motif in the enzyme HPRT, where (p)ppGpp shares identical binding residues for PRPP and nucleobase substrates to regulate purine homeostasis. Intriguingly, HPRTs across species share the conserved binding site yet strongly differ in ligand binding, from strong inhibition by basal (p)ppGpp levels to weak regulation at induced concentrations. Surprisingly, strong ligand binding requires an HPRT dimer-dimer interaction that allosterically opens the (p)ppGpp pocket. This dimer-dimer interaction is absent in the common ancestor but evolved to favor (p)ppGpp binding in the vast majority of bacteria. We propose that the evolutionary plasticity of oligomeric interfaces enables allosteric adjustment of ligand regulation, bypassing constraints of the ligand binding site. Since most ligands bind near protein-protein interfaces, this principle likely extends to other protein-ligand interactions.

scholarly journals ABS–Scan: In silico alanine scanning mutagenesis for binding site residues in protein–ligand complex

F1000Research ◽  
2014 ◽  
Vol 3 ◽  
pp. 214 ◽  
Author(s):  
Praveen Anand ◽  
Deepesh Nagarajan ◽  
Sumanta Mukherjee ◽  
Nagasuma Chandra
Keyword(s):  
Binding Site ◽  
In Silico ◽  
Ligand Recognition ◽  
Loss Of Function ◽  
Ligand Complex ◽  
Alanine Scanning Mutagenesis ◽  
Ligand Interaction ◽  
Alanine Scanning ◽  
Protein Ligand Interactions ◽  
Ligand Interactions

Most physiological processes in living systems are fundamentally regulated by protein–ligand interactions. Understanding the process of ligand recognition by proteins is a vital activity in molecular biology and biochemistry. It is well known that the residues present at the binding site of the protein form pockets that provide a conducive environment for recognition of specific ligands. In many cases, the boundaries of these sites are not well defined. Here, we provide a web-server to systematically evaluate important residues in the binding site of the protein that contribute towards the ligand recognition through in silico alanine-scanning mutagenesis experiments. Each of the residues present at the binding site is computationally mutated to alanine. The ligand interaction energy is computed for each mutant and the corresponding ΔΔG values are computed by comparing it to the wild type protein, thus evaluating individual residue contributions towards ligand interaction. The server will thus provide clues to researchers about residues to obtain loss-of-function mutations and to understand drug resistant mutations. This web-tool can be freely accessed through the following address: http://proline.biochem.iisc.ernet.in/abscan/.

scholarly journals Prediction of Dissociation Constant in Protein-Ligand Interaction

2021 ◽  
Author(s):  
Masatoshi Kawashima
Keyword(s):  
Dissociation Constant ◽  
Dissociation Constants ◽  
Coefficient Of Determination ◽  
Ligand Interaction ◽  
Protein Ligand Interactions ◽  
Ligand Interactions ◽  
Predicted Values ◽  
Equilibrium Dissociation ◽  
Antigen Antibody ◽  
Antibody Interactions

In protein-ligand interactions, such as antigen-antibody interactions and hormone-receptor interactions, a correlation between the equilibrium dissociation constant <i>K</i><sub>D</sub> and the reduced mass of the protein and ligand was found. The correlation of dissociation constants as p<i>K</i><sub>D</sub> (-log<i>K</i><sub>D</sub>) between literature values and predicted values was confirmed in high coefficient of determination R<sup>2</sup> over 0.98.

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