P2Rank: machine learning based tool for rapid and accurate prediction of ligand binding sites from protein structure

Aim and Objective: The accurate identification of protein-ligand binding sites helps elucidate protein function and facilitate the design of new drugs. Machine-learning-based methods have been widely used for the prediction of protein-ligand binding sites. Nevertheless, the severe class imbalance phenomenon, where the number of nonbinding (majority) residues is far greater than that of binding (minority) residues, has a negative impact on the performance of such machine-learning-based predictors. Materials and Methods: In this study, we aim to relieve the negative impact of class imbalance by Boosting Multiple Granular Support Vector Machines (BGSVM). In BGSVM, each base SVM is trained on a granular training subset consisting of all minority samples and some reasonably selected majority samples. The efficacy of BGSVM for dealing with class imbalance was validated by benchmarking it with several typical imbalance learning algorithms. We further implemented a protein-nucleotide binding site predictor, called BGSVM-NUC, with the BGSVM algorithm. Results: Rigorous cross-validation and independent validation tests for five types of proteinnucleotide interactions demonstrated that the proposed BGSVM-NUC achieves promising prediction performance and outperforms several popular sequence-based protein-nucleotide binding site predictors. The BGSVM-NUC web server is freely available at http://csbio.njust.edu.cn/bioinf/BGSVM-NUC/ for academic use.

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Bioinformatics Analysis Unveils Certain Mutations Implicated in Spike Structure Damage and Ligand-Binding Site of Severe Acute Respiratory Syndrome Coronavirus 2

Bioinformatics and Biology Insights ◽

10.1177/11779322211018200 ◽

2021 ◽

Vol 15 ◽

pp. 117793222110182

Author(s):

Emre Aktas

Keyword(s):

Protein Structure ◽

North America ◽

Severe Acute Respiratory Syndrome ◽

South Korea ◽

Ligand Binding ◽

Binding Sites ◽

Spike Protein ◽

Structure Damage ◽

Ligand Binding Sites ◽

New Mutations

There are certain mutations related to severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). In addition to these known mutations, other new mutations have been found across regions in this study. Based on the results, in which 4,326 SARS-CoV-2 whole sequences were used, some mutations are found to be peculiar with certain regions, while some other mutations are found in all regions. In Asia, mutations (3 different mutations in QLA46612 isolated from South Korea) were found in the same sequence. Although huge number of mutations are detected (more than 70 in Asia) by regions, according to bioinformatics tools, some of them which are G75V (isolated from North America), T95I (isolated from South Korea), G143V (isolated from North America), M177I (isolated from Asia), L293M (isolated from Asia), P295H (isolated from Asia), T393P (isolated from Europe), P507S (isolated from Asia), and D614G (isolated from all regions) (These color used only make correct) predicted a damage to spike’ protein structure. Furthermore, this study also aimed to reveal how binding sites of ligands change if the spike protein structure is damaged, and whether more than one mutation affects ligand binding. Mutations that were predicted to damage the structure did not affect the ligand-binding sites, whereas ligands’ binding sites were affected in those with multiple mutations. It is thought that this study will give a different perspective to both the vaccine SARS-CoV studies and the change in the structure of the spike protein belonging to this virus against mutations.

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The utility of geometrical and chemical restraint information extracted from predicted ligand-binding sites in protein structure refinement

Journal of Structural Biology ◽

10.1016/j.jsb.2010.09.009 ◽

2011 ◽

Vol 173 (3) ◽

pp. 558-569 ◽

Cited By ~ 4

Author(s):

Michal Brylinski ◽

Seung Yup Lee ◽

Hongyi Zhou ◽

Jeffrey Skolnick

Keyword(s):

Protein Structure ◽

Ligand Binding ◽

Binding Sites ◽

Structure Refinement ◽

Protein Structure Refinement ◽

Chemical Restraint ◽

Ligand Binding Sites

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Predicting lipid and ligand binding sites in TRPV1 channel by molecular dynamics simulation and machine learning

Proteins Structure Function and Bioinformatics ◽

10.1002/prot.26075 ◽

2021 ◽

Author(s):

Wenjun Zheng ◽

Han Wen

Keyword(s):

Machine Learning ◽

Molecular Dynamics ◽

Molecular Dynamics Simulation ◽

Ligand Binding ◽

Binding Sites ◽

Dynamics Simulation ◽

Trpv1 Channel ◽

Ligand Binding Sites

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PUResNet: prediction of protein-ligand binding sites using deep residual neural network

Journal of Cheminformatics ◽

10.1186/s13321-021-00547-7 ◽

2021 ◽

Vol 13 (1) ◽

Author(s):

Jeevan Kandel ◽

Hilal Tayara ◽

Kil To Chong

Keyword(s):

Protein Structure ◽

Ligand Binding ◽

Binding Sites ◽

Protein Structures ◽

Structural Similarity ◽

Vital Role ◽

Biological Functions ◽

True Nature ◽

Ligand Binding Sites ◽

Deep Learning Model

Abstract Background Predicting protein-ligand binding sites is a fundamental step in understanding the functional characteristics of proteins, which plays a vital role in elucidating different biological functions and is a crucial step in drug discovery. A protein exhibits its true nature after binding to its interacting molecule known as a ligand that binds only in the favorable binding site of the protein structure. Different computational methods exploiting the features of proteins have been developed to identify the binding sites in the protein structure, but none seems to provide promising results, and therefore, further investigation is required. Results In this study, we present a deep learning model PUResNet and a novel data cleaning process based on structural similarity for predicting protein-ligand binding sites. From the whole scPDB (an annotated database of druggable binding sites extracted from the Protein DataBank) database, 5020 protein structures were selected to address this problem, which were used to train PUResNet. With this, we achieved better and justifiable performance than the existing methods while evaluating two independent sets using distance, volume and proportion metrics.

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Bioinformatic Analysis Reveals That Some Mutations May Affect On Both Spike Structure Damage and Ligand Binding Site

10.1101/2020.08.10.244632 ◽

2020 ◽

Author(s):

Emre Aktas

Keyword(s):

Protein Structure ◽

North America ◽

South Korea ◽

Ligand Binding ◽

Binding Sites ◽

Bioinformatic Analysis ◽

Spike Protein ◽

Huge Number ◽

Structure Damage ◽

Ligand Binding Sites

AbstractThere are some mutations are known related to SARS-CoV-2. Together with these mutations known, I tried to show other newly mutations regionally. According to my results which 4326 whole sequences are used, I found that some mutations occur only in a certain region, while some other mutations are found in each regions. Especially in Asia, more than one mutation(three different mutations are found in QLA46612 isolated from South Korea) was seen in the same sequence. Although I detected a huge number of mutations (more than seventy in Asia) by regions, some of them were predicted that damage spike’s protein structure by using bioinformatic tools.The predicted results are G75V(isolated from North America), T95I(isolated from South Korea), G143V(isolated from North America), M177I(isolated Asia), L293M(isolated from Asia), P295H(isolated from Asia), T393P(isolated from Europe), P507S(isolated from Asia), D614G(isolated from all regions) respectively. Also, in this study, I tried to show how possible binding sites of ligands change if the spike protein structure is damaged and whether more than one mutation affects ligand binding was estimated using bioinformatics tools. Interestingly, mutations that predicted to damage the structure do not affect ligand binding sites, whereas ligands’ binding sites were affected in those with multiple mutations.Focusing on mutations may opens up the window to exploit new future therapeutic targets.

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