Predicting Ligand Binding Sites on Protein Surfaces by 3-Dimensional Probability Density Distributions of Interacting Atoms

Jian, Jhih-Wei; Pavadai, Elumalai; Pitti, Thejkiran; Wu, Cheng-Shong; Tsai, Keng‐Chang; Chang, Jeng-Yih; Peng, Hung-Pin; Yang, An‐Suei

doi:10.1371/journal.pone.0160315

Cited by 19 publications

(11 citation statements)

References 52 publications

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“…A simple solution to the Jmol issue is to use JSmol (10), a JavaScript replacement for Jmol. This is the avenue taken by 3DLigandSite (11), COFACTOR (12,13), COACH (14) ISMBLAB-LIG (15) and LIBRA (16). Though JSmol supports complex visualization options, it suffers from performance issues due to inefficiencies introduced when migrating Jmol code from Java to JavaScript.…”

Section: Introductionmentioning

confidence: 99%

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

Jendele

Krivák

Škoda

et al. 2019

Nucleic Acids Research

286

177

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PrankWeb 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.

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Section: Introductionmentioning

confidence: 99%

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

Jendele

Krivák

Škoda

et al. 2019

Nucleic Acids Research

286

177

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“…The general principles and technical details of the ISMBLab-PPI/PEP predictors have been published previously. 18,[24][25][26][27][28][29] Details of the computational methods are described in Supplementary Methods.…”

Section: Ismblab-ppi and Ismblab-pep Predictorsmentioning

confidence: 99%

“…The synthetic antibody scFv libraries were constructed with oligonucleotide-directed mutagenesis; [19][20][21][22][23][24] the enriched antigen-recognition determinants on the scFv variants were calculated with the ISMBLab package, which is a collection of machine learning predictors for quantitative antigen-recognition propensities on the antigen binding sites of the antibodies. 18,[24][25][26][27][28][29] The antigen-recognition propensity computation indicated that the scFv variants of the designed synthetic antibody libraries were encoded with multiple folds of CDR hot spot residues with high protein antigen recognition propensities compared with those of the human antibody germline sequences. Selected anti-protein antibodies from the synthetic antibody libraries were highly specific against the corresponding protein antigens with sub-nanomolar affinity without in vivo affinity maturation, indicating that the antibodies encoded with enhanced population of CDR hot spot residues could bind to protein antigens with high specificity and affinity, bypassing the in vivo affinity maturation processes involving somatic hyper mutations and clonal selections.…”

Section: Introductionmentioning

confidence: 99%

Effective binding to protein antigens by antibodies from antibody libraries designed with enhanced protein recognition propensities

Jian

Chen

Chiu

et al. 2019

mAbs

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Antibodies provide immune protection by recognizing antigens of diverse chemical properties, but elucidating the amino acid sequence-function relationships underlying the specificity and affinity of antibody-antigen interactions remains challenging. We designed and constructed phage-displayed synthetic antibody libraries with enriched protein antigen-recognition propensities calculated with machine learning predictors, which indicated that the designed single-chain variable fragment variants were encoded with enhanced distributions of complementarity-determining region (CDR) hot spot residues with high protein antigen recognition propensities in comparison with those in the human antibody germline sequences. Antibodies derived directly from the synthetic antibody libraries, without affinity maturation cycles comparable to those in in vivo immune systems, bound to the corresponding protein antigen through diverse conformational or linear epitopes with specificity and affinity comparable to those of the affinity-matured antibodies from in vivo immune systems. The results indicated that more densely populated CDR hot spot residues were sustainable by the antibody structural frameworks and could be accompanied by enhanced functionalities in recognizing protein antigens. Our study results suggest that synthetic antibody libraries, which are not limited by the sequences found in antibodies in nature, could be designed with the guidance of the computational machine learning algorithms that are programmed to predict interaction propensities to molecules of diverse chemical properties, leading to antibodies with optimal characteristics pertinent to their medical applications.

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“…Machine learning has been also employed to solve partial tasks in complex eFindSite and COACH methods. Tools based primarily on machine learning have been introduced only very recently [ 32 , 33 ] (with notable earlier exception [ 34 ]). The latest one of them is DeepSite, a method based on multi-layer (for different atom types) voxelized representation of 3D space and deep convolutional neural networks.…”

Section: Introductionmentioning

confidence: 99%

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

2018

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BackgroundLigand binding site prediction from protein structure has many applications related to elucidation of protein function and structure based drug discovery. It often represents only one step of many in complex computational drug design efforts. Although many methods have been published to date, only few of them are suitable for use in automated pipelines or for processing large datasets. These use cases require stability and speed, which disqualifies many of the recently introduced tools that are either template based or available only as web servers.ResultsWe present P2Rank, a stand-alone template-free tool for prediction of ligand binding sites based on machine learning. It is based on prediction of ligandability of local chemical neighbourhoods that are centered on points placed on the solvent accessible surface of a protein. We show that P2Rank outperforms several existing tools, which include two widely used stand-alone tools (Fpocket, SiteHound), a comprehensive consensus based tool (MetaPocket 2.0), and a recent deep learning based method (DeepSite). P2Rank belongs to the fastest available tools (requires under 1 s for prediction on one protein), with additional advantage of multi-threaded implementation.ConclusionsP2Rank is a new open source software package for ligand binding site prediction from protein structure. It is available as a user-friendly stand-alone command line program and a Java library. P2Rank has a lightweight installation and does not depend on other bioinformatics tools or large structural or sequence databases. Thanks to its speed and ability to make fully automated predictions, it is particularly well suited for processing large datasets or as a component of scalable structural bioinformatics pipelines.Electronic supplementary materialThe online version of this article (10.1186/s13321-018-0285-8) contains supplementary material, which is available to authorized users.

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Predicting Ligand Binding Sites on Protein Surfaces by 3-Dimensional Probability Density Distributions of Interacting Atoms

Cited by 19 publications

References 52 publications

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

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

Effective binding to protein antigens by antibodies from antibody libraries designed with enhanced protein recognition propensities

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

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