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Improving the prediction of protein binding sites by combining heterogeneous data and Voronoi diagrams
2011
BMC Bioinformatics
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The method makes use of broad set of heterogeneous data and defined of residue environment, by means of Voronoi Diagrams that are integrated by a two-steps Random Forest ensemble classifier. ...
Protein binding site prediction by computational means can yield valuable information that complements and guides experimental approaches to determine the structure of protein complexes. ...
This work was supported by the Research Councils United Kingdom (RCUK) Academic Fellow scheme (to NFF) and an internal scholarship awarded by the Leeds Institute of Molecular Medicine (to JM). ...
doi:10.1186/1471-2105-12-352
pmid:21861881
pmcid:PMC3171731
fatcat:lcrd3bztwjgyxgxbwvhwc5nyqy
PredHS: a web server for predicting protein–protein interaction hot spots by using structural neighborhood properties
2014
Nucleic Acids Research
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The PredHS prediction method integrates many novel structural and energetic features with two types of structural neighborhoods (Euclidian and Voronoi), and combines random forest and sequential backward ...
Identifying specific hot spot residues that contribute significantly to the affinity and specificity of protein interactions is a problem of the utmost importance. ...
PredHS predicts hot spots using an optimal feature set of 38 features, which are selected from the combination of 108 site features, 108 Euclidean features and 108 Voronoi features with the proposed two-step ...
doi:10.1093/nar/gku437
pmid:24852252
pmcid:PMC4086081
fatcat:2us6tip3gzejhnigcw4jrjdxp4
BIPSPI: a method for the prediction of partner-specific protein–protein interfaces
2018
Bioinformatics
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Contrary to most binding site prediction methods, the proposed approach takes into account a pair of interacting proteins rather than a single one in order to predict partnerspecific binding sites. ...
Results: We present BIPSPI, a new machine learning-based method for the prediction of partnerspecific PPI sites. ...
improvements in binding site prediction. ...
doi:10.1093/bioinformatics/bty647
pmid:30020406
fatcat:fmnxxuemvza37kmus4lm5gevsu
Computational Tools and Databases for the Study and Characterization of Protein Interactions
[chapter]
2012
Protein-Protein Interactions - Computational and Experimental Tools
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Other strategies combine structural data, docking and evolutionary conservation (Tuncbag et al. 2011
Prediction of protein binding sites As indicated by its name, binding site prediction methods seek ...
VORFFIP implements a novel definition of local environment by means of Voronoi Diagrams (see next and Fig. 2 ) that complements residue-based information improving the accuracy of predictions. ...
doi:10.5772/37268
fatcat:sd5xvcmfara7thw7goulplbwlq
Fpocket: An open source platform for ligand pocket detection
2009
BMC Bioinformatics
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Results: Fpocket is an open source pocket detection package based on Voronoi tessellation and alpha spheres built on top of the publicly available package Qhull. ...
For SBVS, the identification of candidate pockets in protein structures is a key feature, and the recent years have seen increasing interest in developing methods for pocket and cavity detection on protein ...
Acknowledgements The authors thank Xavier Barril Alonso for helpful discussions on PDB input and descriptor development for fpocket. ...
doi:10.1186/1471-2105-10-168
pmid:19486540
pmcid:PMC2700099
fatcat:u3npfu5shfgdxolb66hnc6wbsi
Predicting protein interface residues using easily accessible on-line resources
2015
Briefings in Bioinformatics
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Notwithstanding further improvements, easily accessible web servers already provide the scientific community with convenient resources for the identification of protein-protein interaction sites. ...
It has been more than a decade since the completion of the Human Genome Project that provided us with a complete list of human proteins. ...
Acknowledgments We thank Misagh Naderi who read the manuscript and provided critical comments. ...
doi:10.1093/bib/bbv009
pmid:25797794
fatcat:acovtz3stvehlolkhqpcpg55oe
A holistic in silico approach to predict functional sites in protein structures
2012
Computer applications in the biosciences : CABIOS
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Results: We have developed a novel computational method, Multi-VORFFIP (MV), a tool to predicts protein-, peptide-, DNA-and RNA-binding sites in proteins. ...
the use of the method and analysis of predictions to non-expert end-users. ...
Conflict of Interest: none declared. ...
doi:10.1093/bioinformatics/bts269
pmid:22563069
fatcat:rk6smikh2zbdvdcvdexswhttau
Algorithmic approaches to protein-protein interaction site prediction
2015
Algorithms for Molecular Biology
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Herein, we describe the intricacies of the biological theory, datasets, and features required for modern protein-protein interaction site (PPIS) prediction, and present an integrative analysis of the state-of-the-art ...
First, the major sources of data used by predictors are reviewed, including training sets, evaluation sets, and methods for their procurement. ...
knowledge of an interacting partner, to predict the binding site of that protein at the amino acid scale. ...
doi:10.1186/s13015-015-0033-9
pmid:25713596
pmcid:PMC4338852
fatcat:3ayzig6w4rbtnk2efe7573qsgq
Protein sequence-to-structure learning: Is this the end(-to-end revolution)?
[article]
2021
arXiv
pre-print
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The potential of deep learning has been recognized in the protein structure prediction community for some time, and became indisputable after CASP13. ...
attention; (iii) equivariant architectures preserving the symmetry of 3D space; (iv) use of large meta-genome databases; (v) combinations of protein representations; (vi) and finally truly end-to-end ...
The founder had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. ...
arXiv:2105.07407v2
fatcat:6szubg7q2rajlj3l4vyzqri3nm
GraphDTI: A robust deep learning predictor of drug-target interactions from multiple heterogeneous data
2021
Journal of Cheminformatics
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AbstractTraditional techniqueset identification, we developed GraphDTI, a robust machine learning framework integrating the molecular-level information on drugs, proteins, and binding sites with the system-level ...
To improve the state-of-the-art in drug targrotocol. ...
Acknowledgements Portions of this research were conducted with high-performance computational resources provided by Louisiana State University. ...
doi:10.1186/s13321-021-00540-0
fatcat:bogrqny6pjd5lhdowfxziflvv4
The Role of the Antigen GAD 65 in Diabetes Mellitus Type 1: A Molecular Analysis
[chapter]
2012
Autoimmune Diseases - Contributing Factors, Specific Cases of Autoimmune Diseases, and Stem Cell and Other Therapies
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The entry point to the structural protein data is the PDB web site: http://www.rcsb.org/pdb. ...
The first type of surface binds many different proteins, whereas the second type binds only specific molecules, such as: antibodies, factors, DNA binding proteins and substrates. ...
H (r ) H (r )dV The energy of the molecular system, in the independent-particle model, is then given by: The first expression describes the mean kinetic energy of the electrons and the potential energy ...
doi:10.5772/48329
fatcat:xmbw67cymzbsdkyyzwt2u3maze
Emerging Computational Methods for the Rational Discovery of Allosteric Drugs
2016
Chemical Reviews
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Here, we review algorithms that predict allosteric sites based on sequence data and molecular dynamics simulations, describe tools that assess the druggability of these pockets, and discuss how Markov ...
To date, the discovery of allosteric binding sites and lead compounds has been mostly serendipitous, achieved through high-throughput screening. ...
In contrast, CAST generates a Voronoi diagram based on protein-atom locations, removes any Voronoi edges and vertices that fall completely outside the receptor, and identifies pockets as collections of ...
doi:10.1021/acs.chemrev.5b00631
pmid:27074285
pmcid:PMC4901368
fatcat:x72bgr3if5acdlyz2rznhwqtge
Nuclear morphometry, nucleomics and prostate cancer progression
2012
Asian Journal of Andrology
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DNA double-strand breaks), followed by a multistep process of progression. ...
' site where PCa proliferation and growth may occur over time. ...
Hence, this model confirmed that when QNG is combined with the Gleason score and PSA, an improved prediction of pathological stage is possible. ...
doi:10.1038/aja.2011.148
pmid:22504875
pmcid:PMC3720156
fatcat:etprwmygkjezlo7e63ymxgssmy
Towards Structured Prediction in Bioinformatics with Deep Learning
[article]
2020
arXiv
pre-print
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The structured outputs cover 1D signals, 2D images, 3D structures, hierarchical labeling, and heterogeneous networks. ...
Due to the properties of those structured prediction problems, such as having problem-specific constraints and dependency within the labeling space, the straightforward application of existing deep learning ...
Class P, R and X, corresponding to Phosphate, Ribose and Non-RNA Binding Sites, are omitted from the logo diagram. ...
arXiv:2008.11546v1
fatcat:5in2a642b5cj3lweuynl7sniaa
Minireview: Multiomic candidate biomarkers for clinical manifestations of sickle cell severity: Early steps to precision medicine
2016
Experimental biology and medicine
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It will also require the analysis of big data sets. ...
Once validated, the hope is that informative biomarkers will be used for the identification of individuals most likely to experience severe complications, and thereby be applied for the design of patientspecific ...
The work on metabolomic profiling in SCD is funded by NIH grants HL119549 (to YX) and P01HL114457 (Project 3 to YX). ...
doi:10.1177/1535370216640150
pmid:27022133
pmcid:PMC4950385
fatcat:zqu4hvjgtzgkjorkuseblpbrmq
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