ProtDec-LTR2.0: an improved method for protein remote homology detection by combining pseudo protein and supervised Learning to Rank

Summary As one of the most important tasks in protein sequence analysis, protein remote homology detection is critical for both basic research and practical applications. Here, we present an effective web server for protein remote homology detection called ProtDec-LTR2.0 by combining ProtDec-Learning to Rank (LTR) and pseudo protein representation. Experimental results showed that the detection performance is obviously improved. The web server provides a user-friendly interface to explore the sequence and structure information of candidate proteins and find their conserved domains by launching a multiple sequence alignment tool. Availability and implementation The web server is free and open to all users with no login requirement at http://bioinformatics.hitsz.edu.cn/ProtDec-LTR2.0/. Contact bliu@hit.edu.cn.

[1]  Xiaolong Wang,et al.  Combining evolutionary information extracted from frequency profiles with sequence-based kernels for protein remote homology detection , 2013, Bioinform..

[2]  Junjie Chen,et al.  Protein Remote Homology Detection Based on an Ensemble Learning Approach , 2016, BioMed research international.

[3]  Xiaolong Wang,et al.  A discriminative method for protein remote homology detection and fold recognition combining Top-n-grams and latent semantic analysis , 2008, BMC Bioinformatics.

[4]  Xing Gao,et al.  Enhanced Protein Fold Prediction Method Through a Novel Feature Extraction Technique , 2015, IEEE Transactions on NanoBioscience.

[5]  Jason Weston,et al.  Detecting Remote Evolutionary Relationships among Proteins by Large-Scale Semantic Embedding , 2011, PLoS Comput. Biol..

[6]  Michael Gribskov,et al.  Use of Receiver Operating Characteristic (ROC) Analysis to Evaluate Sequence Matching , 1996, Comput. Chem..

[7]  Q. Zou,et al.  Recent Progress in Machine Learning-Based Methods for Protein Fold Recognition , 2016, International journal of molecular sciences.

[8]  John D. Westbrook,et al.  The Structural Biology Knowledgebase: a portal to protein structures, sequences, functions, and methods , 2011, Journal of Structural and Functional Genomics.

[9]  Xiaolong Wang,et al.  Using distances between Top-n-gram and residue pairs for protein remote homology detection , 2014, BMC Bioinformatics.

[10]  Steven E Brenner,et al.  SCOPe: Manual Curation and Artifact Removal in the Structural Classification of Proteins - extended Database. , 2017, Journal of molecular biology.

[11]  Zhenhua Li,et al.  Use B-factor related features for accurate classification between protein binding interfaces and crystal packing contacts , 2014, BMC Bioinformatics.

[12]  B. Liu,et al.  Protein remote homology detection by combining Chou’s distance-pair pseudo amino acid composition and principal component analysis , 2015, Molecular Genetics and Genomics.

[13]  A G Murzin,et al.  SCOP: a structural classification of proteins database for the investigation of sequences and structures. , 1995, Journal of molecular biology.

[14]  Junjie Chen,et al.  Application of learning to rank to protein remote homology detection , 2015, Bioinform..

[15]  Ren Long,et al.  dRHP-PseRA: detecting remote homology proteins using profile-based pseudo protein sequence and rank aggregation , 2016, Scientific Reports.

[16]  Rodrigo Lopez,et al.  Clustal W and Clustal X version 2.0 , 2007, Bioinform..

[17]  David S. Goodsell,et al.  The RCSB protein data bank: integrative view of protein, gene and 3D structural information , 2016, Nucleic Acids Res..

[18]  Shanyi Wang,et al.  Protein Remote Homology Detection by Combining Pseudo Dimer Composition with an Ensemble Learning Method , 2016 .

[19]  K. Chou.  Some remarks on protein attribute prediction and pseudo amino acid composition , 2010, Journal of Theoretical Biology.

[20]  Steven E. Brenner,et al.  SCOPe: Structural Classification of Proteins—extended, integrating SCOP and ASTRAL data and classification of new structures , 2013, Nucleic Acids Res..

[21]  Thomas L. Madden,et al.  Gapped BLAST and PSI-BLAST: a new generation of protein database search programs. , 1997, Nucleic acids research.

[22]  Wei Chen,et al.  Detecting N6-methyladenosine sites from RNA transcriptomes using ensemble Support Vector Machines , 2017, Scientific Reports.

[23]  A. Biegert,et al.  HHblits: lightning-fast iterative protein sequence searching by HMM-HMM alignment , 2011, Nature Methods.

[24]  S. Eddy,et al.  Challenges in homology search: HMMER3 and convergent evolution of coiled-coil regions , 2013, Nucleic acids research.