QAUST: Protein Function Prediction Using Structure Similarity, Protein Interaction, and Functional Motifs

The number of available protein sequences in public databases is increasing exponentially. However, a significant percentage of these sequences lack functional annotation, which is essential for the understanding of how biological systems operate. Here, we propose a novel method, Quantitative Annotation of Unknown STructure (QAUST), to infer protein functions, specifically Gene Ontology (GO) terms and Enzyme Commission (EC) numbers. QAUST uses three sources of information: structure information encoded by global and local structure similarity search, biological network information inferred by protein–protein interaction data, and sequence information extracted from functionally discriminative sequence motifs. These three pieces of information are combined by consensus averaging to make the final prediction. Our approach has been tested on 500 protein targets from the Critical Assessment of Functional Annotation (CAFA) benchmark set. The results show that our method provides accurate functional annotation and outperforms other prediction methods based on sequence similarity search or threading. We further demonstrate that a previously unknown function of human tripartite motif-containing 22 (TRIM22) protein predicted by QAUST can be experimentally validated.

SOV_refine: A further refined definition of segment overlap score and its significance for protein structure similarity

Background

The segment overlap score (SOV) has been used to evaluate the predicted protein secondary structures, a sequence composed of helix (H), strand (E), and coil (C), by comparing it with the native or reference secondary structures, another sequence of H, E, and C. SOV’s advantage is that it can consider the size of continuous overlapping segments and assign extra allowance to longer continuous overlapping segments instead of only judging from the percentage of overlapping individual positions as Q3 score does. However, we have found a drawback from its previous definition, that is, it cannot ensure increasing allowance assignment when more residues in a segment are further predicted accurately.

Results

A new way of assigning allowance has been designed, which keeps all the advantages of the previous SOV score definitions and ensures that the amount of allowance assigned is incremental when more elements in a segment are predicted accurately. Furthermore, our improved SOV has achieved a higher correlation with the quality of protein models measured by GDT-TS score and TM-score, indicating its better abilities to evaluate tertiary structure quality at the secondary structure level. We analyzed the statistical significance of SOV scores and found the threshold values for distinguishing two protein structures (SOV_refine  > 0.19) and indicating whether two proteins are under the same CATH fold (SOV_refine > 0.94 and > 0.90 for three- and eight-state secondary structures respectively). We provided another two example applications, which are when used as a machine learning feature for protein model quality assessment and comparing different definitions of topologically associating domains. We proved that our newly defined SOV score resulted in better performance.

Conclusions

The SOV score can be widely used in bioinformatics research and other fields that need to compare two sequences of letters in which continuous segments have important meanings. We also generalized the previous SOV definitions so that it can work for sequences composed of more than three states (e.g., it can work for the eight-state definition of protein secondary structures). A standalone software package has been implemented in Perl with source code released. The software can be downloaded from http://dna.cs.miami.edu/SOV/.

Bioinformatics Based Approaches to Study Virus-Host Interactions During Chikungunya Virus Infection

The limitations of high-throughput genomic methods used for studying virus-host interactions make it difficult to directly obtain insights on virus pathogenesis. In this chapter, the central steps of a protein structure similarity based computational approach used to predict the host interactors of Chikungunya virus are explained by highlighting the important aspects that need to be considered. Identification of such conserved set of putative interactions that allow the virus to take control of the host has the potential to deepen our understanding of the virus-specific remodeling processes of the host cell and illuminate new arenas of disease intervention.

Cytochrome P450 BM3 of Bacillus megaterium - a possible endosulfan biotransforming gene

Computing chemistry was applied to understand biotransformation mechanism of an organochlorine pesticide, endosulfan. The stereo specific metabolic activity of human CYP-2B6 (cytochrome P450) on endosulfan has been well demonstrated. Sequence and structural similarity search revealed that the bacterium Bacillus megaterium encodes CYP-BM3, which is similar to CYP-2B6. The functional similarity was studied at organism level by batch-scale studies and it was proved that B. megaterium could metabolize endosulfan to endosulfan sulfate, as CYP-2B6 does in human system. The gene expression analyses also confirmed the possible role of CYP-BM3 in endosulfan metabolism. Thus, our results show that the protein structure based in-silico approach can help us to understand and identify microbes for remediation strategy development. To the best of our knowledge this is the first report which has extrapolated the bacterial gene for endosulfan biotransformation through in silico prediction approach for metabolic gene identification.