scholarly journals Improving correlated mutation analysis

2015 ◽  
Vol 12 (11) ◽  
pp. 1009-1009
Keyword(s):  
Mutation Analysis ◽  
Correlated Mutation

Optimal data collection for correlated mutation analysis

2009 ◽  
Vol 74 (3) ◽  
pp. 545-555 ◽  
Author(s):  
Haim Ashkenazy ◽  
Ron Unger ◽  
Yossef Kliger
Keyword(s):  
Data Collection ◽  
Mutation Analysis ◽  
Correlated Mutation

scholarly journals Reducing phylogenetic bias in correlated mutation analysis

2010 ◽  
Vol 23 (5) ◽  
pp. 321-326 ◽  
Author(s):  
Haim Ashkenazy ◽  
Yossef Kliger
Keyword(s):  
Mutation Analysis ◽  
Correlated Mutation

scholarly journals Detection and reduction of evolutionary noise in correlated mutation analysis

2005 ◽  
Vol 18 (5) ◽  
pp. 247-253 ◽  
Author(s):  
Orly Noivirt ◽  
Miriam Eisenstein ◽  
Amnon Horovitz
Keyword(s):  
Mutation Analysis ◽  
Correlated Mutation

scholarly journals Correlated Mutation Analysis on the Catalytic Domains of Serine/Threonine Protein Kinases

PLoS ONE ◽  
2009 ◽  
Vol 4 (6) ◽  
pp. e5913 ◽  
Author(s):  
Feng Xu ◽  
Pan Du ◽  
Hongbo Shen ◽  
Hairong Hu ◽  
Qi Wu ◽  
...  
Keyword(s):  
Protein Kinases ◽  
Mutation Analysis ◽  
Catalytic Domains ◽  
Correlated Mutation

Prediction of inter domain interactions in modular polyketide synthases by docking and correlated mutation analysis

2013 ◽  
Vol 31 (1) ◽  
pp. 17-29 ◽  
Author(s):  
Gitanjali Yadav ◽  
Swadha Anand ◽  
Debasisa Mohanty
Keyword(s):  
Mutation Analysis ◽  
Polyketide Synthases ◽  
Domain Interactions ◽  
Correlated Mutation

scholarly journals PREDICTION OF CONTACT MAPS USING SUPPORT VECTOR MACHINES

2005 ◽  
Vol 14 (05) ◽  
pp. 849-865 ◽  
Author(s):  
YING ZHAO ◽  
GEORGE KARYPIS
Keyword(s):  
Support Vector Machines ◽  
Secondary Structure ◽  
Mutation Analysis ◽  
3D Structure ◽  
Prediction Algorithm ◽  
Support Vector ◽  
Contact Map ◽  
Vector Machines ◽  
Sequence Profiles ◽  
Correlated Mutation

Contact map prediction is of great interest for its application in fold recognition and protein 3D structure determination. In this paper we present a contact-map prediction algorithm that employs Support Vector Machines as the machine learning tool and incorporates various features such as sequence profiles and their conservations, correlated mutation analysis based on various amino acid physicochemical properties, and secondary structure. In addition, we evaluated the effectiveness of the different features on contact map prediction for different fold classes. On average, our predictor achieved a prediction accuracy of 0.224 with an improvement over a random predictor of a factor 11.7, which is better than reported studies. Our study showed that predicted secondary structure features play an important roles for the proteins containing beta-structures. Models based on secondary structure features and correlated mutation analysis features produce different sets of predictions. Our study also suggests that models learned separately for different protein fold families may achieve better performance than a unified model.

scholarly journals Correction: Correlated Mutation Analysis on the Catalytic Domains of Serine/Threonine Protein Kinases

PLoS ONE ◽  
2009 ◽  
Vol 4 (6) ◽  
Author(s):  
Feng Xu ◽  
Pan Du ◽  
Hongbo Shen ◽  
Hairong Hu ◽  
Qi Wu ◽  
...  
Keyword(s):  
Protein Kinases ◽  
Mutation Analysis ◽  
Catalytic Domains ◽  
Correlated Mutation

scholarly journals Improving intermolecular contact prediction through protein-protein interaction prediction using coevolutionary analysis with expectation-maximization

2018 ◽  
Author(s):  
Miguel Correa Marrero ◽  
Richard G.H. Immink ◽  
Dick de Ridder ◽  
Aalt D.J van Dijk
Keyword(s):  
Mutation Analysis ◽  
Protein Interaction ◽  
Sequence Data ◽  
Correct Identification ◽  
Interacting Proteins ◽  
Intermolecular Contact ◽  
Sequence Alignments ◽  
Contact Prediction ◽  
Correlated Mutations ◽  
Correlated Mutation

Predicting residue-residue contacts between interacting proteins is an important problem in bioinformatics. The growing wealth of sequence data can be used to infer these contacts through correlated mutation analysis on multiple sequence alignments of interacting homologs of the proteins of interest. This requires correct identification of pairs of interacting proteins for many species, in order to avoid introducing noise (i.e. non-interacting sequences) in the analysis that will decrease predictive performance. We have designed Ouroboros, a novel algorithm to reduce such noise in intermolecular contact prediction. Our method iterates between weighting proteins according to how likely they are to interact based on the correlated mutations signal, and predicting correlated mutations based on the weighted sequence alignment. We show that this approach accurately discriminates between protein interaction versus noninteraction and simultaneously improves the prediction of intermolecular contact residues compared to a naive application of correlated mutation analysis. Furthermore, the method relaxes the assumption of one-to-one interaction of previous approaches, allowing for the study of many-to-many interactions. Source code and test data are available at www.bif.wur.nl/

Sign in / Sign up

Export Citation Format

Share Document