scholarly journals SXGBsite: Prediction of Protein–Ligand Binding Sites Using Sequence Information and Extreme Gradient Boosting

Genes ◽  
2019 ◽  
Vol 10 (12) ◽  
pp. 965 ◽  
Author(s):  
Ziqi Zhao ◽  
Yonghong Xu ◽  
Yong Zhao
Keyword(s):  
Ligand Binding ◽  
Binding Site ◽  
Binding Sites ◽  
Gradient Boosting ◽  
Sequence Information ◽  
Ligand Binding Site ◽  
Extreme Gradient Boosting ◽  
Ligand Binding Sites ◽  
Independent Test ◽  
Test Sets

The prediction of protein–ligand binding sites is important in drug discovery and drug design. Protein–ligand binding site prediction computational methods are inexpensive and fast compared with experimental methods. This paper proposes a new computational method, SXGBsite, which includes the synthetic minority over-sampling technique (SMOTE) and the Extreme Gradient Boosting (XGBoost). SXGBsite uses the position-specific scoring matrix discrete cosine transform (PSSM-DCT) and predicted solvent accessibility (PSA) to extract features containing sequence information. A new balanced dataset was generated by SMOTE to improve classifier performance, and a prediction model was constructed using XGBoost. The parallel computing and regularization techniques enabled high-quality and fast predictions and mitigated overfitting caused by SMOTE. An evaluation using 12 different types of ligand binding site independent test sets showed that SXGBsite performs similarly to the existing methods on eight of the independent test sets with a faster computation time. SXGBsite may be applied as a complement to biological experiments.

Recognition of ion ligand binding sites based on amino acid features with the fusion of energy, physicochemical and structural features

2020 ◽  
Vol 26 ◽  
Author(s):  
Shan Wang ◽  
Xiuzhen Hu ◽  
Zhenxing Feng ◽  
Liu Liu ◽  
Kai Sun ◽  
...  
Keyword(s):  
Amino Acid ◽  
Ligand Binding ◽  
Binding Site ◽  
Binding Sites ◽  
Metal Ion ◽  
Structural Features ◽  
Support Vector ◽  
Sequence Information ◽  
Ligand Binding Site ◽  
Ligand Binding Sites

Background: Rational drug molecular design based on virtual screening requires the ligand binding site to be known. Recently, the recognition of ion ligand binding site has become an important research direction in pharmacology. Methods: In this work, we selected the binding residues of 4 acid radical ion ligands(NO2 - , CO3 2- , SO4 2- and PO4 3- ) and 10 metal ion ligands (Zn2+,Cu2+, Fe2+, Fe3+, Ca2+, Mg2+, Mn2+, Na+ , K+ and Co2+) as research objects. Based on the protein sequence information, we extracted amino acid features, energy, physicochemical and structure features. Then we incorporating the above features and input them into the MultilayerPerceptron (MLP) and support vector machine (SVM) algorithms. Results: In the independent test, the best accuracy was higher than 92.5%, which was better than the previous result on Conclusion: Finally, we set up a free web server for the prediction of protein-ion ligand binding sites (http://39.104.77.103:8081/lsb/HomePage/HomePage.html). This study is helpful for molecular drug design.

Protein-ligand binding site detection as an alternative route to molecular docking and drug repurposing

2018 ◽  
Vol 14 (2) ◽  
Author(s):  
Daniele Toti ◽  
Gabriele Macari ◽  
Fabio Polticelli
Keyword(s):  
Molecular Docking ◽  
Ligand Binding ◽  
Binding Site ◽  
Binding Sites ◽  
Web Application ◽  
Drug Repurposing ◽  
Full Potential ◽  
Ligand Binding Site ◽  
Docking Simulations ◽  
Ligand Binding Sites

Abstract After the onset of the genomic era, the detection of ligand binding sites in proteins has emerged over the last few years as a powerful tool for protein function prediction. Several approaches, both sequence and structure based, have been developed, but the full potential of the corresponding tools has not been exploited yet. Here, we describe the development and classification of a large, almost exhaustive, collection of protein-ligand binding sites to be used, in conjunction with the Ligand Binding Site Recognition Application Web Application developed in our laboratory, as an alternative to virtual screening through molecular docking simulations to identify novel lead compounds for known targets. Ligand binding sites derived from the Protein Data Bank have been clustered according to ligand similarity, and given a known ligand, the binding mode of related ligands to the same target can be predicted. The collection of ligand binding sites contains more than 200,000 sites corresponding to more than 20,000 different ligands. Furthermore, the ligand binding sites of all Food and Drug Administration-approved drugs have been classified as well, allowing to investigate the possible binding of each of them (and related compounds) to a given target for drug repurposing and redesign initiatives. Sample usage cases are also described to demonstrate the effectiveness of this approach.

scholarly journals De novo protein fold families expand the designable ligand binding site space

2021 ◽  
Author(s):  
Xingjie Pan ◽  
Tanja Kortemme
Keyword(s):  
Ligand Binding ◽  
Binding Site ◽  
Binding Sites ◽  
Method Development ◽  
De Novo ◽  
Protein Structures ◽  
Protein Fold ◽  
Ligand Binding Site ◽  
Protein Families ◽  
Ligand Binding Sites

AbstractA major challenge in designing proteins de novo to bind user-defined ligands with high specificity and affinity is finding backbones structures that can accommodate a desired binding site geometry with high precision. Recent advances in methods to generate protein fold families de novo have expanded the space of accessible protein structures, but it is not clear to what extend de novo proteins with diverse geometries also expand the space of designable ligand binding functions. We constructed a library of 25,806 high-quality ligand binding sites and developed a fast protocol to place (“match”) these binding sites into both naturally occurring and de novo protein families with two fold topologies: Rossman and NTF2. 5,896 and 7,475 binding sites could be matched to the Rossmann and NTF2 fold families, respectively. De novo designed Rossman and NTF2 protein families can support 1,791 and 678 binding sites that cannot be matched to naturally existing structures with the same topologies, respectively. While the number of protein residues in ligand binding sites is the major determinant of matching success, ligand size and primary sequence separation of binding site residues also play important roles. The number of matched binding sites are power law functions of the number of members in a fold family. Our results suggest that de novo sampling of geometric variations on diverse fold topologies can significantly expand the space of designable ligand binding sites for a wealth of possible new protein functions.Author summaryDe novo design of proteins that can bind to novel and highly diverse user-defined small molecule ligands could have broad biomedical and synthetic biology applications. Because ligand binding site geometries need to be accommodated by protein backbone scaffolds at high accuracy, the diversity of scaffolds is a major limitation for designing new ligand binding functions. Advances in computational protein structure design methods have significantly increased the number of accessible stable scaffold structures. Understanding how many new ligand binding sites can be accommodated by the de novo scaffolds is important for designing novel ligand binding proteins. To answer this question, we constructed a large library of ligand binding sites from the Protein Data Bank (PDB). We tested the number of ligand binding sites that can be accommodated by de novo scaffolds and naturally existing scaffolds with same fold topologies. The results showed that de novo scaffolds significantly expanded the ligand binding space of their respective fold topologies. We also identified factors that affect difficulties of binding site accommodation, as well as the relationship between the number of scaffolds and the accessible ligand binding site space. We believe our findings will benefit future method development and applications of ligand binding protein design.

scholarly journals DeepPocket: Ligand Binding Site Detection and Segmentation using 3D Convolutional Neural Networks

2021 ◽  
Author(s):  
Rishal Aggarwal ◽  
Akash Gupta ◽  
Vineeth Chelur ◽  
C. V. Jawahar ◽  
U. Deva Priyakumar
Keyword(s):  
Neural Networks ◽  
Deep Learning ◽  
Ligand Binding ◽  
Binding Site ◽  
Convolutional Neural Networks ◽  
Binding Sites ◽  
Protein Structures ◽  
3D Structure ◽  
Ligand Binding Site ◽  
Structure Based Drug Design

<div> A structure-based drug design pipeline involves the development of potential drug molecules or ligands that form stable complexes with a given receptor at its binding site. A prerequisite to this is finding druggable and functionally relevant binding sites on the 3D structure of the protein. Although several methods for detecting binding sites have been developed beforehand, a majority of them surprisingly fail in the identification and ranking of binding sites accurately. The rapid adoption and success of deep learning algorithms in various sections of structural biology beckons the usage of such algorithms for accurate binding site detection. As a combination of geometry based software and deep learning, we report a novel framework, DeepPocket that utilises 3D convolutional neural networks for the rescoring of pockets identified by Fpocket and further segments these identified cavities on the protein surface. Apart from this, we also propose another dataset SC6K containing protein structures submitted in the Protein Data Bank (PDB) from January 2018 till February 2020 for ligand binding site (LBS) detection. DeepPocket's results on various binding site datasets and SC6K highlights its better performance over current state-of-the-art methods and good generalization ability over novel structures. </div><div><br></div>

scholarly journals PrankWeb: a web server for ligand binding site prediction and visualization

2019 ◽  
Vol 47 (W1) ◽  
pp. W345-W349 ◽  
Author(s):  
Lukas Jendele ◽  
Radoslav Krivak ◽  
Petr Skoda ◽  
Marian Novotny ◽  
David Hoksza
Keyword(s):  
Ligand Binding ◽  
Binding Site ◽  
Binding Sites ◽  
Web Application ◽  
Ligand Binding Site ◽  
Site Prediction ◽  
Binding Site Prediction ◽  
Server Side ◽  
The Web ◽  
Ligand Binding Site Prediction

AbstractPrankWeb is an online resource providing an interface to P2Rank, a state-of-the-art method for ligand binding site prediction. P2Rank is a template-free machine learning method based on the prediction of local chemical neighborhood ligandability centered on points placed on a solvent-accessible protein surface. Points with a high ligandability score are then clustered to form the resulting ligand binding sites. In addition, PrankWeb provides a web interface enabling users to easily carry out the prediction and visually inspect the predicted binding sites via an integrated sequence-structure view. Moreover, PrankWeb can determine sequence conservation for the input molecule and use this in both the prediction and result visualization steps. Alongside its online visualization options, PrankWeb also offers the possibility of exporting the results as a PyMOL script for offline visualization. The web frontend communicates with the server side via a REST API. In high-throughput scenarios, therefore, users can utilize the server API directly, bypassing the need for a web-based frontend or installation of the P2Rank application. PrankWeb is available at http://prankweb.cz/, while the web application source code and the P2Rank method can be accessed at https://github.com/jendelel/PrankWebApp and https://github.com/rdk/p2rank, respectively.

scholarly journals Ligand-Binding-Site Structure Shapes Allosteric Signal Transduction and the Evolution of Allostery in Protein Complexes

2019 ◽  
Vol 36 (8) ◽  
pp. 1711-1727 ◽  
Author(s):  
György Abrusán ◽  
Joseph A Marsh
Keyword(s):  
Signal Transduction ◽  
Ligand Binding ◽  
Binding Site ◽  
Binding Sites ◽  
Quaternary Structure ◽  
Protein Complexes ◽  
Signal Transduction Pathways ◽  
Ligand Binding Site ◽  
Single Chain ◽  
Site Structure

Abstract The structure of ligand-binding sites has been shown to profoundly influence the evolution of function in homomeric protein complexes. Complexes with multichain binding sites (MBSs) have more conserved quaternary structure, more similar binding sites and ligands between homologs, and evolve new functions slower than homomers with single-chain binding sites (SBSs). Here, using in silico analyses of protein dynamics, we investigate whether ligand-binding-site structure shapes allosteric signal transduction pathways, and whether the structural similarity of binding sites influences the evolution of allostery. Our analyses show that: 1) allostery is more frequent among MBS complexes than in SBS complexes, particularly in homomers; 2) in MBS homomers, semirigid communities and critical residues frequently connect interfaces and thus they are characterized by signal transduction pathways that cross protein–protein interfaces, whereas SBS homomers usually not; 3) ligand binding alters community structure differently in MBS and SBS homomers; and 4) except MBS homomers, allosteric proteins are more likely to have homologs with similar binding site than nonallosteric proteins, suggesting that binding site similarity is an important factor driving the evolution of allostery.

scholarly journals Recognizing ion ligand binding sites by SMO algorithm

2019 ◽  
Vol 20 (S3) ◽  
Author(s):  
Shan Wang ◽  
Xiuzhen Hu ◽  
Zhenxing Feng ◽  
Xiaojin Zhang ◽  
Liu Liu ◽  
...  
Keyword(s):  
Ligand Binding ◽  
Binding Sites ◽  
Protein Function ◽  
Metal Ion ◽  
Cross Validation ◽  
Sequence Information ◽  
Sequential Minimal Optimization ◽  
Ligand Binding Sites ◽  
Smo Algorithm ◽  
Fold Cross Validation

Abstract Background In many important life activities, the execution of protein function depends on the interaction between proteins and ligands. As an important protein binding ligand, the identification of the binding site of the ion ligands plays an important role in the study of the protein function. Results In this study, four acid radical ion ligands (NO2−,CO32−,SO42−,PO43−) and ten metal ion ligands (Zn2+,Cu2+,Fe2+,Fe3+,Ca2+,Mg2+,Mn2+,Na+,K+,Co2+) are selected as the research object, and the Sequential minimal optimization (SMO) algorithm based on sequence information was proposed, better prediction results were obtained by 5-fold cross validation. Conclusions An efficient method for predicting ion ligand binding sites was presented.

scholarly journals Transcriptional Repression of the VC2105 Protein by Vibrio FadR Suggests that It Is a New Auxiliary Member of thefadRegulon

2016 ◽  
Vol 82 (9) ◽  
pp. 2819-2832 ◽  
Author(s):  
Rongsui Gao ◽  
Jingxia Lin ◽  
Han Zhang ◽  
Youjun Feng
Keyword(s):  
Transcriptional Regulation ◽  
Fatty Acid ◽  
Ligand Binding ◽  
Binding Site ◽  
Binding Sites ◽  
Regulatory Proteins ◽  
Content Type ◽  
Ligand Binding Sites

ABSTRACTRecently, our group along with others reported that theVibrioFadR regulatory protein is unusual in that, unlike the prototypicalfadRproduct ofEscherichia coli, which has only one ligand-binding site,VibrioFadR has two ligand-binding sites and represents a new mechanism for fatty acid sensing. The promoter region of thevc2105gene, encoding a putative thioesterase, was mapped, and a putative FadR-binding site (AA CTG GTA AGA GCA CTT) was proposed. Different versions of the FadR regulatory proteins were prepared and purified to homogeneity. Both electrophoretic mobility shift assay (EMSA) and surface plasmon resonance (SPR) determined the direct interaction of thevc2105gene with FadR proteins of various origins. Further, EMSAs illustrated that the addition of long-chain acyl-coenzyme A (CoA) species efficiently dissociates thevc2105promoter from the FadR regulator. The expression level of theVibrio cholerae vc2105gene was elevated 2- to 3-fold in afadRnull mutant strain, validating that FadR is a repressor for thevc2105gene. The β-galactosidase activity of avc2105-lacZtranscriptional fusion was increased over 2-fold upon supplementation of growth medium with oleic acid. Unlike thefadDgene, a member of theVibrio fadregulon, the VC2105 protein played no role in bacterial growth and virulence-associated gene expression ofctxAB(cholera toxin A/B) andtcpA(toxin coregulated pilus A). Given that the transcriptional regulation ofvc2105fits the criteria for fatty acid degradation (fad) genes, we suggested that it is a new member of theVibrio fadregulon.IMPORTANCETheVibrioFadR regulator is unusual in that it has two ligand-binding sites. Different versions of the FadR regulatory proteins were prepared and characterizedin vitroandin vivo. An auxiliaryfadgene (vc2105) fromVibriowas proposed that encodes a putative thioesterase and has a predicted FadR-binding site (AAC TGG TA A GAG CAC TT). The function of this putative binding site was proved using both EMSA and SPR. Furtherin vitroandin vivoexperiments revealed that theVibrioFadR is a repressor for thevc2105gene. UnlikefadD, a member of theVibrio fadregulon, VC2105 played no role in bacterial growth and expression of the two virulence-associated genes (ctxABandtcpA). Therefore, since transcriptional regulation ofvc2105fits the criteria forfadgenes, it seems likely thatvc2105acts as a new auxiliary member of theVibrio fadregulon.

scholarly journals GRaSP: a graph-based residue neighborhood strategy to predict binding sites

2020 ◽  
Vol 36 (Supplement_2) ◽  
pp. i726-i734
Author(s):  
Charles A Santana ◽  
Sabrina de A Silveira ◽  
João P A Moraes ◽  
Sandro C Izidoro ◽  
Raquel C de Melo-Minardi ◽  
...  
Keyword(s):  
Ligand Binding ◽  
Binding Site ◽  
Binding Sites ◽  
Protein Function ◽  
Learning Strategy ◽  
State Of The Art ◽  
Supplementary Information ◽  
Major Step ◽  
Ligand Binding Sites ◽  
Residue Neighborhood

Abstract Motivation The discovery of protein–ligand-binding sites is a major step for elucidating protein function and for investigating new functional roles. Detecting protein–ligand-binding sites experimentally is time-consuming and expensive. Thus, a variety of in silico methods to detect and predict binding sites was proposed as they can be scalable, fast and present low cost. Results We proposed Graph-based Residue neighborhood Strategy to Predict binding sites (GRaSP), a novel residue centric and scalable method to predict ligand-binding site residues. It is based on a supervised learning strategy that models the residue environment as a graph at the atomic level. Results show that GRaSP made compatible or superior predictions when compared with methods described in the literature. GRaSP outperformed six other residue-centric methods, including the one considered as state-of-the-art. Also, our method achieved better results than the method from CAMEO independent assessment. GRaSP ranked second when compared with five state-of-the-art pocket-centric methods, which we consider a significant result, as it was not devised to predict pockets. Finally, our method proved scalable as it took 10–20 s on average to predict the binding site for a protein complex whereas the state-of-the-art residue-centric method takes 2–5 h on average. Availability and implementation The source code and datasets are available at https://github.com/charles-abreu/GRaSP. Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals PrankWeb: web server for ligand binding-site prediction and visualization

2019 ◽  
Author(s):  
Lukas Jendele ◽  
Radoslav Krivak ◽  
Petr Skoda ◽  
Marian Novotny ◽  
David Hoksza
Keyword(s):  
Ligand Binding ◽  
Binding Site ◽  
Binding Sites ◽  
Web Application ◽  
Ligand Binding Site ◽  
Site Prediction ◽  
Binding Site Prediction ◽  
Link Type ◽  
The Web ◽  
Ligand Binding Site Prediction

ABSTRACTPrankWeb is an online resource providing an interface to P2Rank, a state-of-the-art ligand binding site prediction method. P2Rank is a template-free machine learning method which is based on the prediction of ligandability of local chemical neighborhoods centered on points placed on a solvent accessible surface of a protein. Points with high ligandability score are then clustered to form the resulting ligand binding sites. On top of that, PrankWeb then provides a web interface enabling users to easily carry out the prediction and visually inspect the predicted binding sites via an integrated sequence-structure view. Moreover, PrankWeb can determine sequence conservation for the input molecule and use it in both the prediction and results visualization steps. Alongside its online visualization options, PrankWeb also offers the possibility to export the results as a PyMOL script for offline visualization. The web frontend communicates with the serer side via a REST API. Therefore, in high-throughput scenarios users can utilize the server API directly, bypassing the need for a webbased front end or installation of the P2Rank application. PrankWeb is available at http://prankweb.cz/. The source code of the web application and the P2Rank method can be accessed at https://github.com/jendelel/PrankWebApp and https://github.com/rdk/p2rank, respectively.

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