IntFOLD: an integrated server for modelling protein structures and functions from amino acid sequences

IntFOLD is an independent web server that integrates our leading methods for structure and function prediction. The server provides a simple unified interface that aims to make complex protein modelling data more accessible to life scientists. The server web interface is designed to be intuitive and integrates a complex set of quantitative data, so that 3D modelling results can be viewed on a single page and interpreted by non-expert modellers at a glance. The only required input to the server is an amino acid sequence for the target protein. Here we describe major performance and user interface updates to the server, which comprises an integrated pipeline of methods for: tertiary structure prediction, global and local 3D model quality assessment, disorder prediction, structural domain prediction, function prediction and modelling of protein-ligand interactions. The server has been independently validated during numerous CASP (Critical Assessment of Techniques for Protein Structure Prediction) experiments, as well as being continuously evaluated by the CAMEO (Continuous Automated Model Evaluation) project. The IntFOLD server is available at: http://www.reading.ac.uk/bioinf/IntFOLD/

[1]  Liam J. McGuffin,et al.  Rapid model quality assessment for protein structure predictions using the comparison of multiple models without structural alignments , 2010, Bioinform..

[2]  Liam J. McGuffin,et al.  The ModFOLD4 server for the quality assessment of 3D protein models , 2013, Nucleic Acids Res..

[3]  Anna Tramontano,et al.  Assessment of the assessment: Evaluation of the model quality estimates in CASP10 , 2014, Proteins.

[4]  Yaoqi Zhou,et al.  Improving protein fold recognition and template-based modeling by employing probabilistic-based matching between predicted one-dimensional structural properties of query and corresponding native properties of templates , 2011, Bioinform..

[5]  Daniel B. Roche,et al.  Automated tertiary structure prediction with accurate local model quality assessment using the intfold‐ts method , 2011, Proteins.

[6]  Liam J. McGuffin,et al.  The IntFOLD server: an integrated web resource for protein fold recognition, 3D model quality assessment, intrinsic disorder prediction, domain prediction and ligand binding site prediction , 2011, Nucleic Acids Res..

[7]  Liam J. McGuffin,et al.  FunFOLDQA: A Quality Assessment Tool for Protein-Ligand Binding Site Residue Predictions , 2012, PloS one.

[8]  David A. Lee,et al.  New functional families (FunFams) in CATH to improve the mapping of conserved functional sites to 3D structures , 2012, Nucleic Acids Res..

[9]  Torsten Schwede,et al.  Assessment of template based protein structure predictions in CASP9 , 2011, Proteins.

[10]  Anna Tramontano,et al.  Assessment of protein disorder region predictions in CASP10 , 2014, Proteins.

[11]  Liam J. McGuffin,et al.  The binding site distance test score: a robust method for the assessment of predicted protein binding sites , 2010, Bioinform..

[12]  Osvaldo Graña,et al.  Assessment of domain boundary predictions and the prediction of intramolecular contacts in CASP8 , 2009, Proteins.

[13]  Torsten Schwede,et al.  Assessment of ligand‐binding residue predictions in CASP9 , 2011, Proteins.

[14]  David T. Jones,et al.  DISOPRED3: precise disordered region predictions with annotated protein-binding activity , 2014, Bioinform..

[15]  Torsten Schwede,et al.  Assessment of ligand binding site predictions in CASP10 , 2014, Proteins.

[16]  Liam J. McGuffin,et al.  FunFOLD: an improved automated method for the prediction of ligand binding residues using 3D models of proteins , 2011, BMC Bioinformatics.

[17]  David Kim,et al.  Assessment of predictions submitted for the CASP7 domain prediction category , 2007, Proteins.

[18]  Juergen Haas,et al.  The Protein Model Portal—a comprehensive resource for protein structure and model information , 2013, Database J. Biol. Databases Curation.

[19]  John D. Westbrook,et al.  The Protein Model Portal , 2008, Journal of Structural and Functional Genomics.

[20]  Yang Zhang,et al.  How significant is a protein structure similarity with TM-score = 0.5? , 2010, Bioinform..

[21]  Liam J. McGuffin,et al.  The FunFOLD2 server for the prediction of protein–ligand interactions , 2013, Nucleic Acids Res..

[22]  Liam J. McGuffin,et al.  Improvement of 3D protein models using multiple templates guided by single-template model quality assessment , 2012, Bioinform..

[23]  Anna Tramontano,et al.  Evaluation of disorder predictions in CASP9 , 2011, Proteins.

[24]  Yang Zhang,et al.  BioLiP: a semi-manually curated database for biologically relevant ligand–protein interactions , 2012, Nucleic Acids Res..

[25]  Liam J. McGuffin,et al.  Intrinsic disorder prediction from the analysis of multiple protein fold recognition models , 2008, Bioinform..

[26]  James M Aramini,et al.  Assessment of template‐based protein structure predictions in CASP10 , 2014, Proteins.

[27]  Ilya N. Shindyalov,et al.  PDP: protein domain parser , 2003, Bioinform..

[28]  Anna Tramontano,et al.  Evaluation of model quality predictions in CASP9 , 2011, Proteins.

[29]  Sitao Wu,et al.  LOMETS: A local meta-threading-server for protein structure prediction , 2007, Nucleic acids research.

[30]  Jaime Prilusky,et al.  Assessment of disorder predictions in CASP8 , 2009, Proteins.