Predicting gene ontology functions from protein's regional surface structures

Abstract Background Annotation of protein functions is an important task in the post-genomic era. Most early approaches for this task exploit only the sequence or global structure information. However, protein surfaces are believed to be crucial to protein functions because they are the main interfaces to facilitate biological interactions. Recently, several databases related to structural surfaces, such as pockets and cavities, have been constructed with a comprehensive library of identified surface structures. For example, CASTp provides identification and measurements of surface accessible pockets as well as interior inaccessible cavities. Results A novel method was proposed to predict the Gene Ontology (GO) functions of proteins from the pocket similarity network, which is constructed according to the structure similarities of pockets. The statistics of the networks were presented to explore the relationship between the similar pockets and GO functions of proteins. Cross-validation experiments were conducted to evaluate the performance of the proposed method. Results and codes are available at: http://zhangroup.aporc.org/bioinfo/PSN/. Conclusion The computational results demonstrate that the proposed method based on the pocket similarity network is effective and efficient for predicting GO functions of proteins in terms of both computational complexity and prediction accuracy. The proposed method revealed strong relationship between small surface patterns (or pockets) and GO functions, which can be further used to identify active sites or functional motifs. The high quality performance of the prediction method together with the statistics also indicates that pockets play essential roles in biological interactions or the GO functions. Moreover, in addition to pockets, the proposed network framework can also be used for adopting other protein spatial surface patterns to predict the protein functions.</p

Automated methods for the determination of homologous relationships and functional similarities between protein domains.

CATH is a protein database of structural domains which are assigned to superfamilies through evidence of a common evolutionary ancestor. These superfamilies are further grouped by overall structural similarity into folds. This thesis explores several automated methods for recognising homologous relationships between these domains using the structural data from the Protein Data Bank (PDB). The aim of this work was to aid the manual classification of domains into the database and provide putative functional assignments to structures solved by the structural genomics initiatives. A fast and novel algorithm, CATHEDRAL, was developed to make fold assignments to regions of polypeptide chains. By combining a fast secondary-structure method (GRATH) and a slower residue-based method (SSAP), the algorithm was able to accurately assign boundaries for distant relatives, undetectable by sequence methods. Sequence and structural conservation patterns were combined in a novel algorithm, FLORA, to develop structural templates specific to catalytic function. FLORA was able to predict the correct functional site in 80% of cases and combined with global structure comparison, it was able to assign domains to enzyme families within diverse superfamilies. Techniques in structure comparison were also applied to ab initio models of protein domains, in order to assign them to fold groups within the CATH database. A novel scoring method was developed to pre-select models that were more likely to have adopted the correct fold. A selected sample of models for each target structure was then compared against representatives from the CATH database using the MAMMOTH and SSAP algorithms. Data from these alignments were combined using a Support Vector Machine to assign the target to a fold group within CATH. This work was generously supported by the Engineering and Physical Sciences Research Council