A new approach to assess and predict the functional roles of proteins across all known structures

Data to knowledge: how to get meaning from your result

Structural and functional studies require the development of sophisticated 'Big Data' technologies and software to increase the knowledge derived and ensure reproducibility of the data. This paper presents summaries of the Structural Biology Knowledge Base, the VIPERdb Virus Structure Database, evaluation of homology modeling by the Protein Model Portal, the ProSMART tool for conformation-independent structure comparison, the LabDB 'super' laboratory information management system and the Cambridge Structural Database. These techniques and technologies represent important tools for the transformation of crystallographic data into knowledge and information, in an effort to address the problem of non-reproducibility of experimental results.

In silico prediction of structure and functions for some proteins of male-specific region of the human Y chromosome

Male-specific region of the human Y chromosome (MSY) comprises 95% of its length that is functionally active. This portion inherits in block from father to male offspring. Most of the genes in the MSY region are involved in male-specific function, such as sex determination and spermatogenesis; also contains genes probably involved in other cellular functions. However, a detailed characterization of numerous MSY-encoded proteins still remains to be done. In this study, 12 uncharacterized proteins of MSY were analyzed through bioinformatics tools for structural and functional characterization. Within these 12 proteins, a total of 55 domains were found, with DnaJ domain signature corresponding to be the highest (11%) followed by both FAD-dependent pyridine nucleotide reductase signature and fumarate lyase superfamily signature (9%). The 3D structures of our selected proteins were built up using homology modeling and the protein threading approaches. These predicted structures confirmed in detail the stereochemistry; indicating reasonably good quality model. Furthermore the predicted functions and the proteins with whom they interact established their biological role and their mechanism of action at molecular level. The results of these structure-functional annotations provide a comprehensive view of the proteins encoded by MSY, which sheds light on their biological functions and molecular mechanisms. The data presented in this study may assist in future prognosis of several human diseases such as Turner syndrome, gonadal sex reversal, spermatogenic failure, and gonadoblastoma.

KB-Rank: efficient protein structure and functional annotation identification via text query

The KB-Rank tool was developed to help determine the functions of proteins. A user provides text query and protein structures are retrieved together with their functional annotation categories. Structures and annotation categories are ranked according to their estimated relevance to the queried text. The algorithm for ranking first retrieves matches between the query text and the text fields associated with the structures. The structures are next ordered by their relative content of annotations that are found to be prevalent across all the structures retrieved. An interactive web interface was implemented to navigate and interpret the relevance of the structures and annotation categories retrieved by a given search. The aim of the KB-Rank tool is to provide a means to quickly identify protein structures of interest and the annotations most relevant to the queries posed by a user. Informational and navigational searches regarding disease topics are described to illustrate the tool's utilities. The tool is available at the URL http://protein.tcmedc.org/KB-Rank.

Quality of computationally inferred gene ontology annotations

Gene Ontology (GO) has established itself as the undisputed standard for protein function annotation. Most annotations are inferred electronically, i.e. without individual curator supervision, but they are widely considered unreliable. At the same time, we crucially depend on those automated annotations, as most newly sequenced genomes are non-model organisms. Here, we introduce a methodology to systematically and quantitatively evaluate electronic annotations. By exploiting changes in successive releases of the UniProt Gene Ontology Annotation database, we assessed the quality of electronic annotations in terms of specificity, reliability, and coverage. Overall, we not only found that electronic annotations have significantly improved in recent years, but also that their reliability now rivals that of annotations inferred by curators when they use evidence other than experiments from primary literature. This work provides the means to identify the subset of electronic annotations that can be relied upon—an important outcome given that >98% of all annotations are inferred without direct curation.

In the UniProt Gene Ontology Annotation database, the largest repository of functional annotations, over 98% of all function annotations are inferred in silico, without curator oversight. Yet these “electronic GO annotations” are generally perceived as unreliable; they are disregarded in many studies. In this article, we introduce novel methodology to systematically evaluate the quality of electronic annotations. We then provide the first comprehensive assessment of the reliability of electronic GO annotations. Overall, we found that electronic annotations are more reliable than generally believed, to an extent that they are competitive with annotations inferred by curators when they use evidence other than experiments from primary literature. But we also report significant variations among inference methods, types of annotations, and organisms. This work provides guidance for Gene Ontology users and lays the foundations for improving computational approaches to GO function inference.