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

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.

2014 ◽  
Vol 2014 ◽  
pp. 1-11 ◽  
Author(s):  
Tzu-Chieh Hung ◽  
Wen-Yuan Lee ◽  
Kuen-Bao Chen ◽  
Yueh-Chiu Chan ◽  
Calvin Yu-Chian Chen

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.


Author(s):  
D. Filimonov ◽  
B. Sobolev ◽  
A. Lagunin

The method for computer prediction of protein-ligand interactions was developed. The amino acid sequences of target proteins and structural descriptions of small molecule ligands are used as the input data. The method was tested on protein families representing perspective drug targets. The developed approach allows one to predict ligand-protein interactions with high efficiency.


2014 ◽  
Vol 1 (4) ◽  
pp. 140306 ◽  
Author(s):  
Omkar Singh ◽  
Kunal Sawariya ◽  
Polamarasetty Aparoy

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.


F1000Research ◽  
2014 ◽  
Vol 3 ◽  
pp. 214 ◽  
Author(s):  
Praveen Anand ◽  
Deepesh Nagarajan ◽  
Sumanta Mukherjee ◽  
Nagasuma Chandra

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/.


2021 ◽  
Author(s):  
Masatoshi Kawashima

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.


2020 ◽  
Vol 44 (42) ◽  
pp. 18250-18255
Author(s):  
Lunxi Duan ◽  
Hongliang Yao ◽  
Yong Xie ◽  
Ke Pan

Label-free fluorescence monitoring protein–ligand interaction based on binding induced enzymatic cleavage protection.


2017 ◽  
Vol 73 (3) ◽  
pp. 195-202 ◽  
Author(s):  
Daria A. Beshnova ◽  
Joana Pereira ◽  
Victor S. Lamzin

Macromolecular X-ray crystallography is one of the main experimental techniques to visualize protein–ligand interactions. The high complexity of the ligand universe, however, has delayed the development of efficient methods for the automated identification, fitting and validation of ligands in their electron-density clusters. The identification and fitting are primarily based on the density itself and do not take into account the protein environment, which is a step that is only taken during the validation of the proposed binding mode. Here, a new approach, based on the estimation of the major energetic terms of protein–ligand interaction, is introduced for the automated identification of crystallographic ligands in the indicated binding site withARP/wARP. The applicability of the method to the validation of protein–ligand models from the Protein Data Bank is demonstrated by the detection of models that are `questionable' and the pinpointing of unfavourable interatomic contacts.


Molecules ◽  
2021 ◽  
Vol 26 (15) ◽  
pp. 4625
Author(s):  
Bing Bai ◽  
Rongfeng Zou ◽  
H. C. Stephen Chan ◽  
Hongchun Li ◽  
Shuguang Yuan

Protein–ligand interaction analysis is important for drug discovery and rational protein design. The existing online tools adopt only a single conformation of the complex structure for calculating and displaying the interactions, whereas both protein residues and ligand molecules are flexible to some extent. The interactions evolved with time in the trajectories are of greater interest. MolADI is a user-friendly online tool which analyzes the protein–ligand interactions in detail for either a single structure or a trajectory. Interactions can be viewed easily with both 2D graphs and 3D representations. MolADI is available as a web application.


2012 ◽  
Vol 27 ◽  
pp. 373-379 ◽  
Author(s):  
Olga V. Stepanenko ◽  
Olesya V. Stepanenko ◽  
Alexander V. Fonin ◽  
Vladislav V. Verkhusha ◽  
Irina M. Kuznetsova ◽  
...  

In this paper we have studied peculiarities of protein-ligand interaction under different conditions. We have shown that guanidine hydrochloride (GdnHCI) unfolding-refolding of GGBP in the presence of glucose (Glc) is reversible, but the equilibrium curves of complex refolding-unfolding have been attained only after 10-day incubation of GGBP/Glc in the presence of GdnHCl. This effect has not been revealed at heat-induced GGBP/Glc denaturation. Slow equilibration between the native protein in GGBP/Glc complex and the unfolded state of protein in the GdnHCl presence is connected with increased viscosity of solution at moderate and high GdnHCl concentrations which interferes with diffusion of glucose molecules. Thus, the limiting step of the unfolding-refolding process of the complex GGBP/Glc is the disruption/tuning of the configuration fit between the protein in the native state and the ligand.


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