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Combining protein secondary structure prediction models with ensemble methods of optimal complexity

Yann Guermeur, Gianluca Pollastri, André Elisseeff, Dominique Zelus, Hélène Paugam-Moisy, Pierre Baldi
2004 Neurocomputing  
Two of these machines are used to combine some of the current best protein secondary structure prediction methods.  ...  Many sophisticated methods are currently available to perform protein secondary structure prediction.  ...  Two of these models have been used to combine protein secondary structure prediction methods.  ... 
doi:10.1016/j.neucom.2003.10.004 fatcat:4n3xl3kc3nepbfmnjovnnct62u

Computationally identifying hot spots in protein-DNA binding interfaces using an ensemble approach

Yuliang Pan, Shuigeng Zhou, Jihong Guan
2020 BMC Bioinformatics  
This method, called PreHots (the abbreviation of Predicting Hotspots), adopts an ensemble stacking classifier that integrates different machine learning classifiers to generate a robust model with 19 features  ...  89 protein-DNA complexes.  ...  The full contents of the supplement are available online at  ... 
doi:10.1186/s12859-020-03675-3 pmid:32938375 pmcid:PMC7495898 fatcat:6tqc5nfebzdyth3g3gsolhzgqi

Sequence Based Prediction of Antioxidant Proteins Using a Classifier Selection Strategy

Lina Zhang, Chengjin Zhang, Rui Gao, Runtao Yang, Qing Song, Gideon Schreiber
2016 PLoS ONE  
In this study, an ensemble method is presented to predict antioxidant proteins with hybrid features, incorporating SSI (Secondary Structure Information), PSSM (Position Specific Scoring Matrix), RSA (Relative  ...  A Relief combined with IFS (Incremental Feature Selection) method is adopted to obtain optimal features from hybrid features.  ...  of secondary structure element combinations from the secondary structure type of helix/strand/coil.  ... 
doi:10.1371/journal.pone.0163274 pmid:27662651 pmcid:PMC5035026 fatcat:dkw7gzw3ere4tkxcm6ka5hx5g4

Solving the Protein Secondary Structure Prediction Problem With the Hessian Free Optimization Algorithm

Konstantinos Charalampous, Michalis Agathocleous, Chris Christodoulou, Vasilis Promponas
2022 IEEE Access  
INDEX TERMS Hessian free optimization, neural networks, protein secondary structure prediction, second order learning algorithms.  ...  One typical problem which falls in this category is Protein Secondary Structure Prediction (PSSP). Recurrent Neural Networks (RNNs) have been successful in handling sequential data.  ...  The results from the ensembles of FFNNs are combined through majority voting ensemble methods which are then fed for filtering purposes to a SVM model for producing the final results.  ... 
doi:10.1109/access.2022.3156888 fatcat:ckswipdi65dxxafkxs3jkkjhxi

SESCA: Predicting the Circular Dichroism Spectra of Proteins from Molecular Structure [article]

Gabor Nagy, Soren V. Hoffmann, Nykola C. Jones, Helmut Grubmuller
2018 bioRxiv   pre-print
To this aim, we introduce a new computational method to calculate the electronic circular dichroism spectra of proteins from a three dimensional-model structure or structural ensemble.  ...  The method determines the CD spectrum from the average secondary structure composition of the protein using a pre-calculated set of basis spectra.  ...  Micsonai for prodiving CD spectra for the SP175 protein data set and the BestSel algorithm, to J. Hritz, S. Becker, and C. Griesinger for  ... 
doi:10.1101/279752 fatcat:udvybza6ufbuxemtx7vt5niyba

Challenges and Approaches to Predicting RNA with Multiple Functional Structures

Susan J Schroeder
2018 RNA: A publication of the RNA Society  
This review describes an efficient combinatorially complete method and three free energy minimization approaches to predicting RNA structures with more than one functional fold, as well as two methods  ...  for analysis of a thermodynamics-based Boltzmann ensemble of structures.  ...  This 120-nt sequence folds into a single secondary structure that is well predicted by A B C D FIGURE 1. Models of the RNA folding problem.  ... 
doi:10.1261/rna.067827.118 pmid:30143552 pmcid:PMC6239171 fatcat:xdelpczxufb7vksxa7vtierot4

Improved performance in protein secondary structure prediction by inhomogeneous score combination

Y. Guermeur, C. Geourjon, P. Gallinari, G. Deleage
1999 Bioinformatics  
Numerous systems have been developed for protein secondary structure prediction, based on different principles. Finding better ensemble methods for this task may thus become crucial.  ...  Experimental results establish that it can increase the recognition rate of protein secondary structure prediction methods that provide inhomogeneous scores, even though their individual prediction successes  ...  -M.Levin for the availability of the software for the GOR4 and SIMPA96 prediction methods.  ... 
doi:10.1093/bioinformatics/15.5.413 pmid:10366661 fatcat:hb4s5aiannauzon6gyyt7fuh6i

DNSS2: improved ab initio protein secondary structure prediction using advanced deep learning architectures [article]

Jie Hou, Zhiye Guo, Jianlin Cheng
2019 bioRxiv   pre-print
Most of the deep learning architectures are novel for protein secondary structure prediction.  ...  Motivation: Accurate prediction of protein secondary structure (alpha-helix, beta-strand and coil) is a crucial step for protein inter-residue contact prediction and ab initio tertiary structure prediction  ...  The ensemble of six networks from DNSS2 significantly improved the secondary structure prediction.  ... 
doi:10.1101/639021 fatcat:wbttv5kszrg2zniteeejyjzpke

MOBI: a web server to define and visualize structural mobility in NMR protein ensembles

A. J. M. Martin, I. Walsh, S. C. E. Tosatto
2010 Bioinformatics  
At least three different use cases can be envisaged: (i) visualization of NMR mobility for structural analysis; (ii) definition of regions for reliable comparative modelling in protein structure prediction  ...  Motivation: MOBI is a web server for the identification of structurally mobile regions in NMR protein ensembles.  ...  ACKNOWLEDGEMENTS The authors are grateful to Joel Sussman and Orly Noivirt-Brik for providing the CASP8 disorder definitions and to members of the BioComputing UP lab for insightful discussions.  ... 
doi:10.1093/bioinformatics/btq537 pmid:20861031 fatcat:vwmwdzjkizahdod5eg4xk2bawe

Computational Design of a DNA- and Fc-Binding Fusion Protein

Jonas Winkler, Giuliano Armano, J. Nikolaj Dybowski, Oliver Kuhn, Filippo Ledda, Dominik Heider
2011 Advances in Bioinformatics  
The optimization was guided by directed evolution based on hydrophobicity scores, molecular weight, and secondary structure predictions.  ...  Computational design of novel proteins with well-defined functions is an ongoing topic in computational biology.  ...  Acknowledgment This work has been supported by a Young Investigator Grand of the University of Duisburg-Essen and the Italian Ministry of Education-Investment funds for basic research, under the project  ... 
doi:10.1155/2011/457578 pmid:21941539 pmcid:PMC3173724 fatcat:re3ytwhgnrfndlioohi3causvq

Understanding the challenges of protein flexibility in drug design

Dinler A Antunes, Didier Devaurs, Lydia E Kavraki
2015 Expert Opinion on Drug Discovery  
More generally, the challenge of correctly predicting the structure of a protein-peptide complex is becoming more and more related to ab initio protein modeling and proteinprotein docking [9] .  ...  Sampling consists of exploring (some of) the structural degrees of freedom (DoFs) of a protein-ligand complex and predicting its binding mode [5] .  ... 
doi:10.1517/17460441.2015.1094458 pmid:26414598 fatcat:3cdp5prnkrdddd6sh5rita7yja

Building Ensemble Classifier Based on Complex Network for Predicting Protein Structural Class

Peng Wu, Tao Xu, Li Kai Dong, Zhen Liu, Yue Hui Chen
2012 Advanced Engineering Forum  
In this paper, ensemble classifier based on complex network (mainly scale-free network) is firstly used to predict protein structural class.  ...  In recent years, complex network models were developed to solve classification and time series prediction problems.  ...  Acknowledgments The authors acknowledge the financial support from the National Natural Science Foundation of University of Jinan (Nos.  ... 
doi:10.4028/ fatcat:fyn4hco66fdorhy3cig2m2jlui

Accurate Prediction of Immunogenic T-Cell Epitopes from Epitope Sequences Using the Genetic Algorithm-Based Ensemble Learning

Wen Zhang, Yanqing Niu, Hua Zou, Longqiang Luo, Qianchao Liu, Weijian Wu, Francesco Pappalardo
2015 PLoS ONE  
The accurate prediction of immunogenic T-cell epitopes is significant for designing useful vaccines and understanding the immune system.  ...  In this work, we extract 18 protein sequence-derived features that are commonly used to predict protein functions, with the aim of obtaining diversity.  ...  A lot of studies [22] [23] [24] have been focused on crystal structures of the MHC-peptide complexes, but few useful conclusions were drawn because of the limited number of complex structures.  ... 
doi:10.1371/journal.pone.0128194 pmid:26020952 pmcid:PMC4447411 fatcat:fwwnvxmeojaqpep45udgya5hs4

Protein and RNA Structure Prediction by Integration of Co-Evolutionary Information into Molecular Simulation

Eleonora De Leonardis, Benjamin Lutz, Simona Cocco, Remi Monasson, Hendrik Szurmant, Martin Weigt, Alexander Schug
2015 Biophysical Journal  
By combining sequence optimization of existing repeats and de novo design of capping structures, we designed leucine-rich repeat (LRR) proteins where the building blocks assemble with a novel geometry.  ...  Prediction of G-Quadruplex Formation Guanine-rich regions of genomic DNA can spontaneously fold into secondary structures called G-quadruplexes (GQs).  ...  By combining sequence optimization of existing repeats and de novo design of capping structures, we designed leucine-rich repeat (LRR) proteins where the building blocks assemble with a novel geometry.  ... 
doi:10.1016/j.bpj.2014.11.099 fatcat:a7mq7lfypbayhnsg5bzyhm4rxi

Computational approaches for inferring the functions of intrinsically disordered proteins

Mihaly Varadi, Wim Vranken, Mainak Guharoy, Peter Tompa
2015 Frontiers in Molecular Biosciences  
The critical biological roles of these proteins, despite not adopting a well-defined fold, encouraged structural biologists to revisit their views on the protein structure-function paradigm.  ...  prediction algorithms viable and reliable tools for large scale analyses, while on the structure level the in silico integration of fundamentally different experimental data types is essential to describe  ...  Toward the Functional Interpretation of IDP Ensembles While ensemble models do not yet possess a predictive power comparable to that of the structure of folded proteins/domains, these models can already  ... 
doi:10.3389/fmolb.2015.00045 pmid:26301226 pmcid:PMC4525029 fatcat:k7elzzyfybfozks475jpkvytwe
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