FLORA: a novel method to predict protein function from structure in diverse superfamilies

Predicting protein function from structure remains an active area of interest, particularly for the structural genomics initiatives where a substantial number of structures are initially solved with little or no functional characterisation. Although global structure comparison methods can be used to transfer functional annotations, the relationship between fold and function is complex, particularly in functionally diverse superfamilies that have evolved through different secondary structure embellishments to a common structural core. The majority of prediction algorithms employ local templates built on known or predicted functional residues. Here, we present a novel method (FLORA) that automatically generates structural motifs associated with different functional sub-families (FSGs) within functionally diverse domain superfamilies. Templates are created purely on the basis of their specificity for a given FSG, and the method makes no prior prediction of functional sites, nor assumes specific physico-chemical properties of residues. FLORA is able to accurately discriminate between homologous domains with different functions and substantially outperforms (a 2–3 fold increase in coverage at low error rates) popular structure comparison methods and a leading function prediction method. We benchmark FLORA on a large data set of enzyme superfamilies from all three major protein classes (α, β, αβ) and demonstrate the functional relevance of the motifs it identifies. We also provide novel predictions of enzymatic activity for a large number of structures solved by the Protein Structure Initiative. Overall, we show that FLORA is able to effectively detect functionally similar protein domain structures by purely using patterns of structural conservation of all residues

Protein structure prediction and structure-based protein function annotation

Nature tends to modify rather than invent function of protein molecules, and the log of the modifications is encrypted in the gene sequence. Analysis of these modification events in evolutionarily related genes is important for assigning function to hypothetical genes and their products surging in databases, and to improve our understanding of the bioverse. However, random mutations occurring during evolution chisel the sequence to an extent that both decrypting these codes and identifying evolutionary relatives from sequence alone becomes difficult. Thankfully, even after many changes at the sequence level, the protein three-dimensional structures are often conserved and hence protein structural similarity usually provide more clues on evolution of functionally related proteins. In this dissertation, I study the design of three bioinformatics modules that form a new hierarchical approach for structure prediction and function annotation of proteins based on sequence-to-structure-to-function paradigm. First, we design an online platform for structure prediction of protein molecules using multiple threading alignments and iterative structural assembly simulations (I-TASSER). I review the components of this module and have added features that provide function annotation to the protein sequences and help to combine experimental and biological data for improving the structure modeling accuracy. The online service of the system has been supporting more than 20,000 biologists from over 100 countries. Next, we design a new comparative approach (COFACTOR) to identify the location of ligand binding sites on these modeled protein structures and spot the functional residue constellations using an innovative global-to-local structural alignment procedure and functional sites in known protein structures. Based on both large-scale benchmarking and blind tests (CASP), the method demonstrates significant advantages over the state-of-the- art methods of the field in recognizing ligand-binding residues for both metal and non- metal ligands. The major advantage of the method is the optimal combination of the local and global protein structural alignments, which helps to recognize functionally conserved structural motifs among proteins that have taken different evolutionary paths. We further extend the COFACTOR global-to-local approach to annotate the gene- ontology and enzyme classifications of protein molecules. Here, we added two new components to COFACTOR. First, we developed a new global structural match algorithm that allows performing better structural search. Second, a sensitive technique was proposed for constructing local 3D-signature motifs of template proteins that lack known functional sites, which allows us to perform query-template local structural similarity comparisons with all template proteins. A scoring scheme that combines the confidence score of structure prediction with global-local similarity score is used for assigning a confidence score to each of the predicted function. Large scale benchmarking shows that the predicted functions have remarkably improved precision and recall rates and also higher prediction coverage than the state-of-art sequence based methods. To explore the applicability of the method for real-world cases, we applied the method to a subset of ORFs from Chlamydia trachomatis and the functional annotations provided new testable hypothesis for improving the understanding of this phylogenetically distinct bacterium

Accurate Protein Structure Annotation through Competitive Diffusion of Enzymatic Functions over a Network of Local Evolutionary Similarities

High-throughput Structural Genomics yields many new protein structures without known molecular function. This study aims to uncover these missing annotations by globally comparing select functional residues across the structural proteome. First, Evolutionary Trace Annotation, or ETA, identifies which proteins have local evolutionary and structural features in common; next, these proteins are linked together into a proteomic network of ETA similarities; then, starting from proteins with known functions, competing functional labels diffuse link-by-link over the entire network. Every node is thus assigned a likelihood z-score for every function, and the most significant one at each node wins and defines its annotation. In high-throughput controls, this competitive diffusion process recovered enzyme activity annotations with 99% and 97% accuracy at half-coverage for the third and fourth Enzyme Commission (EC) levels, respectively. This corresponds to false positive rates 4-fold lower than nearest-neighbor and 5-fold lower than sequence-based annotations. In practice, experimental validation of the predicted carboxylesterase activity in a protein from Staphylococcus aureus illustrated the effectiveness of this approach in the context of an increasingly drug-resistant microbe. This study further links molecular function to a small number of evolutionarily important residues recognizable by Evolutionary Tracing and it points to the specificity and sensitivity of functional annotation by competitive global network diffusion. A web server is at http://mammoth.bcm.tmc.edu/networks

Evolutionary Trace Annotation Server: automated enzyme function prediction in protein structures using 3D templates

Summary:The Evolutionary Trace Annotation (ETA) Server predicts enzymatic activity. ETA starts with a structure of unknown function, such as those from structural genomics, and with no prior knowledge of its mechanism uses the phylogenetic Evolutionary Trace (ET) method to extract key functional residues and propose a function-associated 3D motif, called a 3D template. ETA then searches previously annotated structures for geometric template matches that suggest molecular and thus functional mimicry. In order to maximize the predictive value of these matches, ETA next applies distinctive specificity filters—evolutionary similarity, function plurality and match reciprocity. In large scale controls on enzymes, prediction coverage is 43% but the positive predictive value rises to 92%, thus minimizing false annotations. Users may modify any search parameter, including the template. ETA thus expands the ET suite for protein structure annotation, and can contribute to the annotation efforts of metaservers

In Silico Site-Directed Mutagenesis of Ser11 and Lys107 on the Predicted 3D Structure of glutathione s-transferase from Acidovoras sp. KKS102

Bacterial glutathione s-transferases (GSTs) are known to have variety of functions in detoxification processes. It is familiar that this detoxification ability is achieved through the attack of the thiolate form of glutathione on the electrophilic centres of toxic compounds. Indeed, cytosolic glutathione s-tranferase from Acidovorax sp. KKS102 is now known to have a dehalogenation function. However, little is known about the specific aminoacids involved in this catalytic process. In this study, we investigated the effect of in silico site-directed mutagenesis of the evolutionarily conserved amino acids, Ser11 and Lys107, on the theoretical 3D structure of GST from Acidovorax sp. KKS102 (GST-KKS102) using Deep View/Swiss-Pdb Viewer. The substitution of Ser11, with aromatic amino acids, Tyr, Phe and Trp and positively charged amino acids, Arg, His and lys produced the greatest effect on the stability of the 3D structure of GST-KKS102. Indeed, at Lys107 position, substitution with nonpolar amino acids, Pro and Gly produced the highest structural stability effect on the theoretical 3D structure of the GST-KKS102. This in silico analysis suggests that Deep View rotamer scores could aid in planning in vitro site-directed mutagenesis studies in protein engineering.Keywords: Glutathione s-transferase, Acidovoras sp. KKS102, Site-directed mutagenesis, 3D structure, Ser11, Lys10

In Silico Site-Directed Mutagenesis of some Amino Acids in the C-Terminal Domain of Glutathione s-transferase from Acidovoras sp. KKS102

A cytosolic glutathione s-tranferase from Acidovorax sp. KKS102, a biphenyl/polychlorobiphenyl degrading organism is recently known to have a dehalogenation function on various organochlorine substrates. However, little is known about the specific amino acids involved in its structural stability and catalytic process. The in silico site-directed mutagenesis of a highly conserved region, Ala154, Asp155 and Tyr157 in the C-terminal domain of the cytosolic glutathione s-transferase from Acidovorax sp. was carried out using Deep View/Swiss-Pdb Viewer molecular graphics program for all the proteinogenic amino acids. The amino acid substitutions in this region directly affected the theoretical 3D model of the transferase protein entity through alteration of the predicted stabilization forces which may in turn affect the structural stability and perhaps the activity of the enzyme