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Using Short-Range Interactions and Simulated Genetic Strategy to Improve the Protein Contact Map Prediction [chapter]

Cosme E. Santiesteban Toca, Milton García-Borroto, Jesus S. Aguilar Ruiz
2012 Lecture Notes in Computer Science  
The proposed solution predicts protein contact maps by the combination of a forest of 400 decision trees with an input codification for short-range interactions and a genetic-based edition method.  ...  In this research we want to know how a decision tree predictor based on short-range interactions can learn the correlation among the covalent structures of a protein residues.  ...  and apported the financial support to the entire program and research visits.  ... 
doi:10.1007/978-3-642-31149-9_17 fatcat:bz54pyh675achg6elj7pwyti6a

Predicting protein residue-residue contacts using random forests and deep networks

Joseph Luttrell, Tong Liu, Chaoyang Zhang, Zheng Wang
2019 BMC Bioinformatics  
accuracy scores of 85.13% (short range), 74.49% (medium range), and 54.49% (long range).  ...  range), 60.26% (medium range), and 43.85% (long range) using the same evaluation.  ...  Availability of data and materials RFcon and our standalone DCA software package are freely available at http://dna.cs.miami.edu/RFcon/.  ... 
doi:10.1186/s12859-019-2627-6 fatcat:opq4m2wq2zaobkt4n2mlyvfpiu

Accurate prediction of helix interactions and residue contacts in membrane proteins

Peter Hönigschmid, Dmitrij Frishman
2016 Journal of Structural Biology  
16 Accurate prediction of intra-molecular interactions from amino acid sequence is an important pre-17 requisite for obtaining high-quality protein models.  ...  These models 71 attempt to infer causative correlations from the entire alignment and are thus able to distinguish 72 between direct structural contacts and transitive connections between residues.  ...  The output of the random forest is a value between 0 and 1, representing 209 the fraction of decision trees voting for the residue pair to be in contact.  ... 
doi:10.1016/j.jsb.2016.02.005 pmid:26851352 fatcat:f4aww3vbnrhwno2gihlj3wryqe

Multi-level machine learning prediction of protein–protein interactions inSaccharomyces cerevisiae

Julian Zubek, Marcin Tatjewski, Adam Boniecki, Maciej Mnich, Subhadip Basu, Dariusz Plewczynski
2015 PeerJ  
PIPE: a protein-protein interaction prediction engine based on the re-occurring short polypeptide sequences between known interacting protein pairs.  ...  First, we build linear descriptions of interacting and non-interacting sequence segment pairs based on their inter-residue distances.  ...  The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.  ... 
doi:10.7717/peerj.1041 pmid:26157620 pmcid:PMC4493684 fatcat:lknphk2k5vd2dhsuoccokdq4si

Tuning Intrinsic Disorder Predictors For Virus Proteins

Gal Almog, Abayomi S Olabode, Art Fy Poon
2021 Virus Evolution  
Lastly, we apply the random forest predictor to SARS-CoV-2 ORF6, an accessory gene that encodes a short (61 AA) and moderately disordered protein that inhibits the host innate immune response.  ...  In this study, we investigate whether some predictors outperform others in the context of virus proteins and compared our findings with data from non-viral proteins.  ...  This work was supported in part by grants from the Natural Sciences and Engineering Research Council of Canada (NSERC, RGPIN-2018-05516) and from the Canadian Institutes of Health Research (CIHR, PJT-155990  ... 
doi:10.1093/ve/veaa106 pmid:33614158 pmcid:PMC7882063 fatcat:j3z7duulnzdqfclhzuv64or2ti

An interpretable machine learning algorithm to predict disordered protein phase separation based on biophysical interactions [article]

Hao Cai, Robert M Vernon, Julie D Forman-Kay
2022 bioRxiv   pre-print
Here we describe LLPhyScore, a new predictor of IDR-driven phase separation, based on a broad set of physical interactions or features.  ...  LLPhyScore uses sequence-based statistics from the RCSB PDB database of folded structures for these interactions, and is trained on a manually curated set of phase separation driver proteins with different  ...  proteome and PDB sequences.  ... 
doi:10.1101/2022.07.06.499043 fatcat:nrx6dn35m5eg5c3ulqqbayzlai

Predicting Residue-Residue Contacts and Helix-Helix Interactions in Transmembrane Proteins Using an Integrative Feature-Based Random Forest Approach

Xiao-Feng Wang, Zhen Chen, Chuan Wang, Ren-Xiang Yan, Ziding Zhang, Jiangning Song, Ruben Claudio Aguilar
2011 PLoS ONE  
The predicted residue contacts were further employed to predict interacting helical pairs and achieved the Matthew's correlation coefficients of 0.430 and 0.424, according to two different residue contact  ...  Using a strict leave-one-protein-out jackknifing procedure, they were capable of reaching the top L/5 prediction accuracies of 49.5% and 48.8% for two different residue contact definitions, respectively  ...  Author Contributions Predicting Residue Contacts and Helix Interactions PLoS ONE | www.plosone.org  ... 
doi:10.1371/journal.pone.0026767 pmid:22046350 pmcid:PMC3203928 fatcat:fedc3huxkrhwleo5o5bzeswjra

ADH-PPI: An Attention based Deep Hybrid Model for Protein Protein Interaction Prediction

Muhammad Nabeel Asim, Muhammad Ali Ibrahim, Muhammad Imran Malik, Andreas Dengel, Sheraz Ahmed
2022 iScience  
This is a PDF file of an article that has undergone enhancements after acceptance, such as the addition of a cover page and metadata, and formatting for readability, but it is not yet the definitive version  ...  Please note that, during the production process, errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.  ...  Using LSTM with 120 hidden units, ADH-PPI model extracts the short and long range dependencies of features which are important to distinguish interactive protein sequence pairs from non-interactive protein  ... 
doi:10.1016/j.isci.2022.105169 fatcat:d2yc7hfu6net5oqdsfqga7mf6y

Predicting intrinsic disorder in proteins: an overview

Bo He, Kejun Wang, Yunlong Liu, Bin Xue, Vladimir N Uversky, A Keith Dunker
2009 Cell Research  
and is a key for the elaboration of a new structural and functional hierarchy of proteins.  ...  The discovery of intrinsically disordered proteins (IDP) (i.e., biologically active proteins that do not possess stable secondary and/or tertiary structures) came as an unexpected surprise, as the existence  ...  Acknowledgments This work was supported in part by the grants R01 LM007688-01A1 (to AKD and VNU) and GM071714-01A2 (to AKD and VNU) from the National Institutes of Health and the Program of the Russian  ... 
doi:10.1038/cr.2009.87 pmid:19597536 fatcat:3meuomgupjegbop5gqkdzqacwq

Recent Applications of Deep Learning Methods on Evolution- and Contact-Based Protein Structure Prediction

Donghyuk Suh, Jai Woo Lee, Sun Choi, Yoonji Lee
2021 International Journal of Molecular Sciences  
The prediction of proteins' 3D structural components is now heavily dependent on machine learning techniques that interpret how protein sequences and their homology govern the inter-residue contacts and  ...  We also look at the potential opportunities for deep learning methods to identify unknown protein structures and functions to be discovered and help guide drug–target interactions.  ...  The authors used four FFNN-based models to distinguish four classes of contact ranges: 0-8, 8-13, 13-18, and 18-23 Å.  ... 
doi:10.3390/ijms22116032 pmid:34199677 pmcid:PMC8199773 fatcat:7znk2khhhfgj7err5roqnygz6i

Identification of residue pairing in interacting β-strands from a predicted residue contact map

Wenzhi Mao, Tong Wang, Wenxuan Zhang, Haipeng Gong
2018 BMC Bioinformatics  
In this work, we propose a novel ridge-detectionbased β-β contact predictor to identify residue pairing in β strands from any predicted residue contact map.  ...  However, information of residue pairing in β strands could be extracted from a noisy contact map, due to the presence of characteristic contact patterns in β-β interactions.  ...  Jinbo Xu for his help in the job submission using the RaptorX-Contact server.  ... 
doi:10.1186/s12859-018-2150-1 pmid:29673311 pmcid:PMC5907701 fatcat:4qmj4nzlizfojltwn7i2g7fal4

EPSILON-CP: using deep learning to combine information from multiple sources for protein contact prediction

Kolja Stahl, Michael Schneider, Oliver Brock
2017 BMC Bioinformatics  
We develop a new contact predictor (EPSILON-CP) that goes beyond current methods by combining evolutionary, physicochemical, and sequence-based information.  ...  Accurately predicted contacts allow to compute the 3D structure of a protein.  ...  Funding This work was supported by the Alexander-von-Humboldt Foundation through an Alexander-von-Humboldt professorship sponsored by the German Federal Ministry for Education and Research (BMBF).  ... 
doi:10.1186/s12859-017-1713-x pmid:28623886 pmcid:PMC5474060 fatcat:2hjnwqcjlzccfi4e7hf3scgfnm

DISCRIMINATION OF NATIVE FOLDS USING NETWORK PROPERTIES OF PROTEIN STRUCTURES

ALPER KÜÇÜKURAL, O. UĞUR SEZERMAN, AYTÜL ERÇİL
2007 Proceedings of the 6th Asia-Pacific Bioinformatics Conference  
We used two different graph representations: Delaunay tessellations of proteins and contact map graphs.  ...  Graph theoretic properties of proteins can be used to perceive the differences between correctly folded proteins and well designed decoy sets. 3D protein structures of proteins are represented with graphs  ...  93.41%± 0.94 Norm. dens. based quad. 98.87%± 0.49 98.08%± 1.32 94.81%± 1.20 92.91%± 0.52 Binary decision tree 95.61%± 1.97 94.04%± 1.88 85.77%± 2.01 82.23%± 4.17 Quadratic classifier 98.54%  ... 
doi:10.1142/9781848161092_0009 fatcat:63zwy5zrmfdkhgezv5b2ythzzy

High-accuracy prediction of transmembrane inter-helix contacts and application to GPCR 3D structure modeling

Jing Yang, Richard Jang, Yang Zhang, Hong-Bin Shen
2013 Computer applications in the biosciences : CABIOS  
These results demonstrate significant progress in contact prediction and a potential for contact-driven structure modeling of transmembrane proteins.  ...  However, contact determination through experiments is difficult because most transmembrane proteins are hard to crystallize.  ...  ., 2011) , which used 100 decision trees, all the existing predictors were trained with a single model using a 1:1 or 1:4 ratio of contact and non-contact samples.  ... 
doi:10.1093/bioinformatics/btt440 pmid:23946502 pmcid:PMC3789543 fatcat:5nvcrritjffwjfhgvwnorbahui

Protein Science Meets Artificial Intelligence: A Systematic Review and a Biochemical Meta-Analysis of an Inter-Field

Jalil Villalobos-Alva, Luis Ochoa-Toledo, Mario Javier Villalobos-Alva, Atocha Aliseda, Fernando Pérez-Escamirosa, Nelly F. Altamirano-Bustamante, Francine Ochoa-Fernández, Ricardo Zamora-Solís, Sebastián Villalobos-Alva, Cristina Revilla-Monsalve, Nicolás Kemper-Valverde, Myriam M. Altamirano-Bustamante
2022 Frontiers in Bioengineering and Biotechnology  
There are many tasks such as novel protein design, protein folding pathways, and synthetic metabolic routes, as well as protein-aggregation mechanisms, pathogenesis of protein misfolding and disease, and  ...  Proteins are some of the most fascinating and challenging molecules in the universe, and they pose a big challenge for artificial intelligence.  ...  We are also thankful for the contributions of Perla Sueiras, Daniela Monroy, Maria Fernanda Frlas, Pablo Cardenas and Mattea Cussel for translation and proofread the manuscript, and Rogelio Ezequiel and  ... 
doi:10.3389/fbioe.2022.788300 pmid:35875501 pmcid:PMC9301016 doaj:8e8e60e57969460994a513e490c6b1cd fatcat:3blbtec65ra2hia2oigbku3b24
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