SMISS: a protein function prediction server by integrating multiple sources

SMISS is a novel web server for protein function prediction. Three different predictors can be selected for different usage. It integrates different sources to improve the protein function prediction accuracy, including the query protein sequence, protein-protein interaction network, gene-gene interaction network, and the rules mined from protein function associations. SMISS automatically switch to ab initio protein function prediction based on the query sequence when there is no homologs in the database. It takes fasta format sequences as input, and several sequences can submit together without influencing the computation speed too much. PHP and Perl are two primary programming language used in the server. The CodeIgniter MVC PHP web framework and Bootstrap front-end framework are used for building the server. It can be used in different platforms in standard web browser, such as Windows, Mac OS X, Linux, and iOS. No plugins are needed for our website. Availability: this http URL.

[1]  Renzhi Cao,et al.  Three-Level Prediction of Protein Function by Combining Profile-Sequence Search, Profile-Profile Search, and Domain Co-Occurrence Networks , 2013, BMC Bioinformatics.

[2]  Karolis Uziela,et al.  ProQ2: estimation of model accuracy implemented in Rosetta , 2016, Bioinform..

[3]  Gary Stacey,et al.  A Protein Domain Co-Occurrence Network Approach for Predicting Protein Function and Inferring Species Phylogeny , 2011, PloS one.

[4]  Jilong Li,et al.  A large-scale conformation sampling and evaluation server for protein tertiary structure prediction and its assessment in CASP11 , 2015, BMC Bioinformatics.

[5]  Jianpeng Ma,et al.  OPUS‐Ca: A knowledge‐based potential function requiring only Cα positions , 2007, Protein science : a publication of the Protein Society.

[6]  J. Skolnick,et al.  GOAP: a generalized orientation-dependent, all-atom statistical potential for protein structure prediction. , 2011, Biophysical journal.

[7]  Vincent B. Chen,et al.  Correspondence e-mail: , 2000 .

[8]  Jilong Li,et al.  Massive integration of diverse protein quality assessment methods to improve template based modeling in CASP11 , 2016, Proteins.

[9]  Anthony Widjaja,et al.  Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond , 2003, IEEE Transactions on Neural Networks.

[10]  Jianlin Cheng,et al.  CONFOLD: Residue‐residue contact‐guided ab initio protein folding , 2015, Proteins.

[11]  Yang Zhang,et al.  The I-TASSER Suite: protein structure and function prediction , 2014, Nature Methods.

[12]  Taeho Jo,et al.  Improving Protein Fold Recognition by Deep Learning Networks , 2015, Scientific Reports.

[13]  Yaoqi Zhou,et al.  Ab initio folding of terminal segments with secondary structures reveals the fine difference between two closely related all‐atom statistical energy functions , 2008, Protein science : a publication of the Protein Society.

[14]  Zheng Wang,et al.  Designing and evaluating the MULTICOM protein local and global model quality prediction methods in the CASP10 experiment , 2014, BMC Structural Biology.

[15]  Anna Tramontano,et al.  Methods of model accuracy estimation can help selecting the best models from decoy sets: Assessment of model accuracy estimations in CASP11 , 2016, Proteins.

[16]  D. Kihara,et al.  PFP: Automated prediction of gene ontology functional annotations with confidence scores using protein sequence data , 2009, Proteins.

[17]  Jianlin Cheng,et al.  Predicting protein residue-residue contacts using deep networks and boosting , 2012, Bioinform..

[18]  Yang Zhang,et al.  A Novel Side-Chain Orientation Dependent Potential Derived from Random-Walk Reference State for Protein Fold Selection and Structure Prediction , 2010, PloS one.

[19]  Renzhi Cao,et al.  Deciphering the association between gene function and spatial gene-gene interactions in 3D human genome conformation , 2015, BMC Genomics.

[20]  Jilong Li,et al.  The MULTICOM protein tertiary structure prediction system. , 2014, Methods in molecular biology.

[21]  Yang Zhang,et al.  3DRobot: automated generation of diverse and well-packed protein structure decoys , 2016, Bioinform..

[22]  B. Rost.  Protein Structure Prediction in 1D, 2D, and 3D , 2002 .

[23]  Jilong Li,et al.  Large-scale model quality assessment for improving protein tertiary structure prediction , 2015, Bioinform..

[24]  Renzhi Cao,et al.  SMOQ: a tool for predicting the absolute residue-specific quality of a single protein model with support vector machines , 2013, BMC Bioinformatics.

[25]  Renzhi Cao,et al.  Integrated protein function prediction by mining function associations, sequences, and protein-protein and gene-gene interaction networks. , 2016, Methods.

[26]  Taeho Jo,et al.  Improving protein fold recognition by random forest , 2014, BMC Bioinformatics.

[27]  Daniel W. A. Buchan,et al.  A large-scale evaluation of computational protein function prediction , 2013, Nature Methods.

[28]  Yaoqi Zhou,et al.  Specific interactions for ab initio folding of protein terminal regions with secondary structures , 2008, Proteins.