Structure-based Function Annotation Research Articles

Protein Depth Calculation and the Use for Improving Accuracy of Protein Fold Recognition

Protein structure and function are largely specified by the distribution of different atoms and residues relative to the core and surface of the molecule. Relative depths of atoms therefore are key attributions that have been widely used in protein structure modeling and function annotation. However, accurate calculation of depth is time consuming. Here, we developed an algorithm which uses Euclidean distance transform (EDT) to convert the target protein structure into a 3D gray-scale image, where depths of atoms in the protein can be conveniently and precisely derived from the minimum distance of the pixels to the surface of the protein. We tested the proposed EDT-based method on a set of 261 non-redundant protein structures, which shows that the method is 2.6 times faster than the widely used method proposed by Chakravarty and Varadarajan. Depth values by EDT method are highly accurate with a Pearson's correlation coefficient ≈1 compared to the calculations from exhaustive search. To explore the usefulness of the method in protein structure prediction, we add the calculated residue depth to the scoring function of the state of the art, profile-profile alignment based fold-recognition program, which shows an additional 3% improvement in the TM-score of the alignments. The data demonstrate that the EDT-based depth calculation program can be used as an efficient tool to assist protein structure analysis and structure-based function annotation.

Ab Initio structure prediction for Escherichia coli: towards genome-wide protein structure modeling and fold assignment

Genome-wide protein structure prediction and structure-based function annotation have been a long-term goal in molecular biology but not yet become possible due to difficulties in modeling distant-homology targets. We developed a hybrid pipeline combining ab initio folding and template-based modeling for genome-wide structure prediction applied to the Escherichia coli genome. The pipeline was tested on 43 known sequences, where QUARK-based ab initio folding simulation generated models with TM-score 17% higher than that by traditional comparative modeling methods. For 495 unknown hard sequences, 72 are predicted to have a correct fold (TM-score > 0.5) and 321 have a substantial portion of structure correctly modeled (TM-score > 0.35). 317 sequences can be reliably assigned to a SCOP fold family based on structural analogy to existing proteins in PDB. The presented results, as a case study of E. coli, represent promising progress towards genome-wide structure modeling and fold family assignment using state-of-the-art ab initio folding algorithms.

Fast and Accurate Calculation of Protein Depth by Euclidean Distance Transform.

The depth of each atom/residue in a protein structure is a key attribution that has been widely used in protein structure modeling and function annotation. However, the accurate calculation of depth is time consuming. Here, we propose to use the Euclidean distance transform (EDT) to calculate the depth, which conveniently converts the protein structure to a 3D gray-scale image with each pixel labeling the minimum distance of the pixel to the surface of the molecule (i.e. the depth). We tested the proposed EDT method on a set of 261 non-redundant protein structures. The data show that the EDT method is 2.6 times faster than the widely used method by Chakravarty and Varadarajan. The depth value by EDT method is also highly accurate, which is almost identical to the depth calculated by exhaustive search (Pearson's correlation coefficient≈1). We believe the EDT-based depth calculation program can be used as an efficient tool to assist the studies of protein fold recognition and structure-based function annotation.

Exploring the “dark matter” of a mammalian proteome by protein structure and function modeling

BackgroundA growing body of evidence shows that gene products encoded by short open reading frames play key roles in numerous cellular processes. Yet, they are generally overlooked in genome assembly, escaping annotation because small protein-coding genes are difficult to predict computationally. Consequently, there are still a considerable number of small proteins whose functions are yet to be characterized.ResultsTo address this issue, we apply a collection of structural bioinformatics algorithms to infer molecular function of putative small proteins from the mouse proteome. Specifically, we construct 1,743 confident structure models of small proteins, which reveal a significant structural diversity with a noticeably high helical content. A subsequent structure-based function annotation of small protein models exposes 178,745 putative protein-protein interactions with the remaining gene products in the mouse proteome, 1,100 potential binding sites for small organic molecules and 987 metal-binding signatures.ConclusionsThese results strongly indicate that many small proteins adopt three-dimensional structures and are fully functional, playing important roles in transcriptional regulation, cell signaling and metabolism. Data collected through this work is freely available to the academic community at http://www.brylinski.org/content/databases to support future studies oriented on elucidating the functions of hypothetical small proteins.

PocketAnnotate: towards site-based function annotation

A computational pipeline PocketAnnotate for functional annotation of proteins at the level of binding sites has been proposed in this study. The pipeline integrates three in-house algorithms for site-based function annotation: PocketDepth, for prediction of binding sites in protein structures; PocketMatch, for rapid comparison of binding sites and PocketAlign, to obtain detailed alignment between pair of binding sites. A novel scheme has been developed to rapidly generate a database of non-redundant binding sites. For a given input protein structure, putative ligand-binding sites are identified, matched in real time against the database and the query substructure aligned with the promising hits, to obtain a set of possible ligands that the given protein could bind to. The input can be either whole protein structures or merely the substructures corresponding to possible binding sites. Structure-based function annotation at the level of binding sites thus achieved could prove very useful for cases where no obvious functional inference can be obtained based purely on sequence or fold-level analyses. An attempt has also been made to analyse proteins of no known function from Protein Data Bank. PocketAnnotate would be a valuable tool for the scientific community and contribute towards structure-based functional inference. The web server can be freely accessed at http://proline.biochem.iisc.ernet.in/pocketannotate/.

Assessment of structure-based functional annotation methods on protein 3D models

Motivations. The three-dimensional structure is more informative of the sole aminoacidic sequence to assign a molecular function to a new protein. For this reason many automated methods have been developed to infer the function of a protein structure using comparison approaches or analyzing its physicochemical characteristics. Unluckily while the genome sequencing projects of organisms have considerably increased the number of available protein sequences, protein structure determination with X-ray crystallography and NMR is still a complex and rather costly procedure. There are indeed more than 20 thousand entries in the database of protein sequences (UniProt) and only 79.600 entries in the database of protein structures (PDB). The big gap separating the number of known sequences from the number of solved structures is increasing every year and is strengthening the need for structure-based functional annotation methods capable to work on homology models instead of crystal structures. The applicability of the existing functional prediction methods to protein models has never been explored so far, even if most of the structural information now available is stored in 3D models. The aim of this work is to study the reliability of different structure-based functional annotation methods when used on protein models and to analyze how the prediction methods performance is correlated with the overall quality of the available homology model. Methods. We used an automated procedure to compare the performances of many structure-based functional prediction methods when they work on a set of homology models of different quality or on a crystallographic solved structure. Each different method is tested on the same dataset proposed by the authors in the original publication and on a set of homology models built for each structure in the dataset. All models were generated using MODELLER (v9.9) and evaluated using the GDT_TS score. To obtain models of different quality only templates are used having a sequence similarity with the solved structures under a set of fixed thresholds. Results. We have evaluated five methods: PDBinder, Concavity and Fpocket for the prediction of protein binding pockets and Pfinder and FINDSITE_metal for the prediction of phosphate and metals binding sites. The performances have been measured using the F-score or the MCC where applicable. Preliminary results show that when using models with a GDT higher than 99% on average the performances drop by about the 22%. When models quality decreases we have a significant decrease of prediction method performances up to 50% (with a GDT of 50%), with some methods that have shown a greater resilience to the decrease of the model quality. These are only to be considered as preliminary results since a number of other methods are being added to the analysis.

Sequence and Structure Signatures of Cancer Mutation Hotspots in Protein Kinases

Protein kinases are the most common protein domains implicated in cancer, where somatically acquired mutations are known to be functionally linked to a variety of cancers. Resequencing studies of protein kinase coding regions have emphasized the importance of sequence and structure determinants of cancer-causing kinase mutations in understanding of the mutation-dependent activation process. We have developed an integrated bioinformatics resource, which consolidated and mapped all currently available information on genetic modifications in protein kinase genes with sequence, structure and functional data. The integration of diverse data types provided a convenient framework for kinome-wide study of sequence-based and structure-based signatures of cancer mutations. The database-driven analysis has revealed a differential enrichment of SNPs categories in functional regions of the kinase domain, demonstrating that a significant number of cancer mutations could fall at structurally equivalent positions (mutational hotspots) within the catalytic core. We have also found that structurally conserved mutational hotspots can be shared by multiple kinase genes and are often enriched by cancer driver mutations with high oncogenic activity. Structural modeling and energetic analysis of the mutational hotspots have suggested a common molecular mechanism of kinase activation by cancer mutations, and have allowed to reconcile the experimental data. According to a proposed mechanism, structural effect of kinase mutations with a high oncogenic potential may manifest in a significant destabilization of the autoinhibited kinase form, which is likely to drive tumorigenesis at some level. Structure-based functional annotation and prediction of cancer mutation effects in protein kinases can facilitate an understanding of the mutation-dependent activation process and inform experimental studies exploring molecular pathology of tumorigenesis.

Improving accuracy and efficiency of blind protein‐ligand docking by focusing on predicted binding sites

The use of predicted binding sites (binding sites calculated from the protein structure alone) is evaluated here as a tool to focus the docking of small molecule ligands into protein structures, simulating cases where the real binding sites are unknown. The resulting approach consists of a few independent docking runs carried out on small boxes, centered on the predicted binding sites, as opposed to one larger blind docking run that covers the complete protein structure. The focused and blind approaches were compared using a set of 77 known protein-ligand complexes and 19 ligand-free structures. The focused approach is shown to: (1) identify the correct binding site more frequently than blind docking; (2) produce more accurate docking poses for the ligand; (3) require less computational time. Additionally, the results show that very few real binding sites are missed in spite of focusing on only three predicted binding sites per target protein. Overall the results indicate that, by improving the sampling in regions that are likely to correspond to binding sites, the focused docking approach increases accuracy and efficiency of protein ligand docking for those cases where the ligand-binding site is unknown. This is especially relevant in applications such as reverse virtual screening and structure-based functional annotation of proteins.

Piecing together the structure–function puzzle: Experiences in structure‐based functional annotation of hypothetical proteins

The combination of genomic sequencing with structural genomics has provided a wealth of new structures for previously uncharacterized ORFs, more commonly referred to as hypothetical proteins. This rapid growth has been the direct result of high-throughput, automated approaches in both the identification of new ORFs and the determination of high-resolution 3-D protein structures. A significant bottleneck is reached, however, at the stage of functional annotation in that the assignment of function is not readily automatable. It is often the case that the initial structural analysis at best indicates a functional family for a given hypothetical protein, but further identification of a relevant ligand or substrate is impeded by the diversity of function in a particular structural classification of proteins family, a highly selective and specific ligand-binding site, or the identification of a novel protein fold. Our approach to the functional annotation of hypothetical proteins relies on the combination of structural information with additional bioinformatics evidence garnered from operon prediction, loose functional information of additional operon members, conservation of catalytic residues, as well as cocrystallization trials and virtual ligand screening. The synthesis of all available information for each protein has permitted the functional annotation of several hypothetical proteins from Escherichia coli and each assignment has been confirmed through generally accepted biochemical methods.

Understanding the cell in terms of structure and function: insights from structural genomics

Structural genomics programs are only now moving into the large-scale production phase, yet have already produced around 2000 protein structures. Through a widespread if not exclusive emphasis on structural novelty, our knowledge of the protein fold universe is improving rapidly. With this information comes the challenge of structure-based function annotation for the many target proteins about which little or nothing is known. Recent years have therefore seen the emergence of impressively diverse bioinformatics approaches to predict the function of a protein structure. Attention is now turning to means of combining these predictions with information from various other sources.

Identification of an ITPase/XTPase in Escherichia coli by Structural and Biochemical Analysis

Inosine triphosphate (ITP) and xanthosine triphosphate (XTP) are formed upon deamination of ATP and GTP as a result of exposure to chemical mutagens and oxidative damage. Nucleic acid synthesis requires safeguard mechanisms to minimize undesired lethal incorporation of ITP and XTP. Here, we present the crystal structure of YjjX, a protein of hitherto unknown function. The three-dimensional fold of YjjX is similar to those of Mj0226 from Methanococcus janschii, which possesses nucleotidase activity, and of Maf from Bacillus subtilis, which can bind nucleotides. Biochemical analyses of YjjX revealed it to exhibit specific phosphatase activity for inosine and xanthosine triphosphates and have a possible interaction with elongation factor Tu. The enzymatic activity of YjjX as an inosine/xanthosine triphosphatase provides evidence for a plausible protection mechanism by clearing the noncanonical nucleotides from the cell during oxidative stress in E. coli.