Protein threading using context-specific alignment potential

By Jianzhu Ma, Sheng Wang, Feng Zhao, Jinbo Xu. 2013

143 citations recorded by the source corpus.

Motivation: Template-based modeling, including homology modeling and protein threading, is the most reliable method for protein 3D structure prediction. However, alignment errors and template selection are still the main bottleneck for current template-base modeling methods, especially when proteins under consideration are distantly related. Results: We present a novel context-specific alignment potential for protein threading, including alignment and template selection. Our alignment potential measures the log-odds ratio of one alignment being generated from two related proteins to being generated from two unrelated proteins, by integrating both local and global context-specific information. The local alignment potential quantifies how well one sequence residue can be aligned to one template residue based on context-specific information of the residues. The global alignment potential quantifies how well two sequence residues can be placed into two template positions at a given distance, again based on context-specific information. By accounting for correlation among a variety of protein features and making use of context-specific information, our alignment potential is much more sensitive than the widely used context-independent or profile-based scoring function. Experimental results confirm that our method generates significantly better alignments and threading results than the best profile-based methods on several large benchmarks. Our method works particularly well for distantly related proteins or proteins with sparse sequence profiles because of the effective integration of context-specific, structure and global information. Availability: http://raptorx.uchicago.edu/download/. Contact: jinboxu@gmail.com

Subfields: Materials Chemistry, Molecular Biology

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Citations in the corpus

IntFOLD: an integrated web resource for high performance protein structure and function prediction (2019, methodology): “The IntFOLD5-TS method was newly developed for CASP13 and worked via iterative multi-template based modelling (19) using the target-template alignments from 14 alternative methods (SP3 (31), SPARKS2 (31), HHsearch (32), COMA (33), SPARKSX (34), CNFsearch (35) and the eight alternative threading meth”
MRFalign: Protein Homology Detection through Alignment of Markov Random Fields (2014, methodology): “In particular, we use the following three datasets to test the alignment accuracy, which are subsets of the test data used in [4] to benchmark protein modeling methods.”
DeepCNF-D: Predicting Protein Order/Disorder Regions by Weighted Deep Convolutional Neural Fields (2015, background): “The order/disorder state of a residue has a strong relationship with the residue’s substitution and evolution [46–48].”
Protein Homology Detection Through Alignment of Markov Random Fields (2015, methodology): “We use a Conditional Neural Fields (CNF) [12] method to develop such a node alignment potential, using a procedure very similar to what is described in the protein threading paper [13, 14].”
CoinFold: a web server for protein contact prediction and contact-assisted protein folding (2016, methodology): “In addition to co-evolution information, CoinFold uses the following features for supervised contact prediction: sequence profile (16), contact or distance potential (17), and some non-evolutionary information (18) (see our method paper (12) for their description).”