Filling-in void and sparse regions in protein sequence space by protein-like artificial sequences enables remarkable enhancement in remote homology detection capability

Protein functional annotation relies on the identification of accurate relationships, sequence divergence being a key factor. This is especially evident when distant protein relationships are demonstrated only with three-dimensional structures. To address this challenge, we describe a computational approach to purposefully bridge gaps between related protein families through directed design of protein-like "linker" sequences. For this, we represented SCOP domain families, integrated with sequence homologues, as multiple profiles and performed HMM-HMM alignments between related domain families. Where convincing alignments were achieved, we applied a roulette wheel-based method to design 3,611,010 protein-like sequences corresponding to 374 SCOP folds. To analyze their ability to link proteins in homology searches, we used 3024 queries to search two databases, one containing only natural sequences and another one additionally containing designed sequences. Our results showed that augmented database searches showed up to 30% improvement in fold coverage for over 74% of the folds, with 52 folds achieving all theoretically possible connections. Although sequences could not be designed between some families, the availability of designed sequences between other families within the fold established the sequence continuum to demonstrate 373 difficult relationships. Ultimately, as a practical and realistic extension, we demonstrate that such protein-like sequences can be "plugged-into" routine and generic sequence database searches to empower not only remote homology detection but also fold recognition. Our richly statistically supported findings show that complementary searches in both databases will increase the effectiveness of sequence-based searches in recognizing all homologues sharing a common fold.

Common recognition principles across diverse sequence and structural families of sialic acid binding proteins

Sialic acids form a large family of 9-carbon monosaccharides and are integral components of glycoconjugates. They are known to bind to a wide range of receptors belonging to diverse sequence families and fold classes and are key mediators in a plethora of cellular processes. Thus, it is of great interest to understand the features that give rise to such a recognition capability. Structural analyses using a non-redundant data set of known sialic acid binding proteins was carried out, which included exhaustive binding site comparisons and site alignments using in-house algorithms, followed by clustering and tree computation, which has led to derivation of sialic acid recognition principles. Although the proteins in the data set belong to several sequence and structure families, their binding sites could be grouped into only six types. Structural comparison of the binding sites indicates that all sites contain one or more different combinations of key structural features over a common scaffold. The six binding site types thus serve as structural motifs for recognizing sialic acid. Scanning the motifs against a non-redundant set of binding sites from PDB indicated the motifs to be specific for sialic acid recognition. Knowledge of determinants obtained from this study will be useful for detecting function in unknown proteins. As an example analysis, a genome-wide scan for the motifs in structures of Mycobacterium tuberculosis proteome identified 17 hits that contain combinations of the features, suggesting a possible function of sialic acid binding by these proteins.

Crowd sourcing a new paradigm for interactome driven drug target identification in Mycobacterium tuberculosis

A decade since the availability of Mycobacterium tuberculosis (Mtb) genome sequence, no promising drug has seen the light of the day. This not only indicates the challenges in discovering new drugs but also suggests a gap in our current understanding of Mtb biology. We attempt to bridge this gap by carrying out extensive re-annotation and constructing a systems level protein interaction map of Mtb with an objective of finding novel drug target candidates. Towards this, we synergized crowd sourcing and social networking methods through an initiative 'Connect to Decode' (C2D) to generate the first and largest manually curated interactome of Mtb termed 'interactome pathway' (IPW), encompassing a total of 1434 proteins connected through 2575 functional relationships. Interactions leading to gene regulation, signal transduction, metabolism, structural complex formation have been catalogued. In the process, we have functionally annotated 87% of the Mtb genome in context of gene products. We further combine IPW with STRING based network to report central proteins, which may be assessed as potential drug targets for development of drugs with least possible side effects. The fact that five of the 17 predicted drug targets are already experimentally validated either genetically or biochemically lends credence to our unique approach.

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/.

Mycobacterium tuberculosis and Clostridium difficille interactomes: demonstration of rapid development of computational system for bacterial interactome prediction

Background

Protein-protein interaction (PPI) networks (interactomes) of most organisms, except for some model organisms, are largely unknown. Experimental methods including high-throughput techniques are highly resource intensive. Therefore, computational discovery of PPIs can accelerate biological discovery by presenting "most-promising" pairs of proteins that are likely to interact. For many bacteria, genome sequence, and thereby genomic context of proteomes, is readily available; additionally, for some of these proteomes, localization and functional annotations are also available, but interactomes are not available. We present here a method for rapid development of computational system to predict interactome of bacterial proteomes. While other studies have presented methods to transfer interologs across species, here, we propose transfer of computational models to benefit from cross-species annotations, thereby predicting many more novel interactions even in the absence of interologs. Mycobacterium tuberculosis (Mtb) and Clostridium difficile (CD) have been used to demonstrate the work.

Results

We developed a random forest classifier over features derived from Gene Ontology annotations and genetic context scores provided by STRING database for predicting Mtb and CD interactions independently. The Mtb classifier gave a precision of 94% and a recall of 23% on a held out test set. The Mtb model was then run on all the 8 million protein pairs of the Mtb proteome, resulting in 708 new interactions (at 94% expected precision) or 1,595 new interactions at 80% expected precision. The CD classifier gave a precision of 90% and a recall of 16% on a held out test set. The CD model was run on all the 8 million protein pairs of the CD proteome, resulting in 143 new interactions (at 90% expected precision) or 580 new interactions (at 80% expected precision). We also compared the overlap of predictions of our method with STRING database interactions for CD and Mtb and also with interactions identified recently by a bacterial 2-hybrid system for Mtb. To demonstrate the utility of transfer of computational models, we made use of the developed Mtb model and used it to predict CD protein-pairs. The cross species model thus developed yielded a precision of 88% at a recall of 8%. To demonstrate transfer of features from other organisms in the absence of feature-based and interaction-based information, we transferred missing feature values from Mtb orthologs into the CD data. In transferring this data from orthologs (not interologs), we showed that a large number of interactions can be predicted.

Conclusions

Rapid discovery of (partial) bacterial interactome can be made by using existing set of GO and STRING features associated with the organisms. We can make use of cross-species interactome development, when there are not even sufficient known interactions to develop a computational prediction system. Computational model of well-studied organism(s) can be employed to make the initial interactome prediction for the target organism. We have also demonstrated successfully, that annotations can be transferred from orthologs in well-studied organisms enabling accurate predictions for organisms with no annotations. These approaches can serve as building blocks to address the challenges associated with feature coverage, missing interactions towards rapid interactome discovery for bacterial organisms.

Availability

The predictions for all Mtb and CD proteins are made available at: http://severus.dbmi.pitt.edu/TB and http://severus.dbmi.pitt.edu/CD respectively for browsing as well as for download.

Structural annotation of Mycobacterium tuberculosis proteome

Of the ∼4000 ORFs identified through the genome sequence of Mycobacterium tuberculosis (TB) H37Rv, experimentally determined structures are available for 312. Since knowledge of protein structures is essential to obtain a high-resolution understanding of the underlying biology, we seek to obtain a structural annotation for the genome, using computational methods. Structural models were obtained and validated for ∼2877 ORFs, covering ∼70% of the genome. Functional annotation of each protein was based on fold-based functional assignments and a novel binding site based ligand association. New algorithms for binding site detection and genome scale binding site comparison at the structural level, recently reported from the laboratory, were utilized. Besides these, the annotation covers detection of various sequence and sub-structural motifs and quaternary structure predictions based on the corresponding templates. The study provides an opportunity to obtain a global perspective of the fold distribution in the genome. The annotation indicates that cellular metabolism can be achieved with only 219 folds. New insights about the folds that predominate in the genome, as well as the fold-combinations that make up multi-domain proteins are also obtained. 1728 binding pockets have been associated with ligands through binding site identification and sub-structure similarity analyses. The resource (http://proline.physics.iisc.ernet.in/Tbstructuralannotation), being one of the first to be based on structure-derived functional annotations at a genome scale, is expected to be useful for better understanding of TB and for application in drug discovery. The reported annotation pipeline is fairly generic and can be applied to other genomes as well.

Stress and phase purity analyses of diamond films deposited through laser-assisted combustion synthesis

Diamond films were deposited on silicon and tungsten carbide substrates in open air through laser-assisted combustion synthesis. Laser-induced resonant excitation of ethylene molecules was achieved in the combustion process to promote diamond growth rate. In addition to microstructure study by scanning electron microscopy, Raman spectroscopy was used to analyze the phase purity and residual stress of the diamond films. High-purity diamond films were obtained through laser-assisted combustion synthesis. The levels of residual stress were in agreement with corresponding thermal expansion coefficients of diamond, silicon, and tungsten carbide. Diamond-film purity increases while residual stress decreases with an increasing film thickness. Diamond films deposited on silicon substrates exhibit higher purity and lower residual stress than those deposited on tungsten carbide substrates.

Rule-based modelling of iron homeostasis in tuberculosis

To establish itself within the host system, Mycobacterium tuberculosis (Mtb) has formulated various means of attacking the host system. One such crucial strategy is the exploitation of the iron resources of the host system. Obtaining and maintaining the required concentration of iron becomes a matter of contest between the host and the pathogen, both trying to achieve this through complex molecular networks. The extent of complexity makes it important to obtain a systems perspective of the interplay between the host and the pathogen with respect to iron homeostasis. We have reconstructed a systems model comprising 92 components and 85 protein-protein or protein-metabolite interactions, which have been captured as a set of 194 rules. Apart from the interactions, these rules also account for protein synthesis and decay, RBC circulation and bacterial production and death rates. We have used a rule-based modelling approach, Kappa, to simulate the system separately under infection and non-infection conditions. Various perturbations including knock-outs and dual perturbation were also carried out to monitor the behavioral change of important proteins and metabolites. From this, key components as well as the required controlling factors in the model that are critical for maintaining iron homeostasis were identified. The model is able to re-establish the importance of iron-dependent regulator (ideR) in Mtb and transferrin (Tf) in the host. Perturbations, where iron storage is increased, appear to enhance nutritional immunity and the analysis indicates how they can be harmful for the host. Instead, decreasing the rate of iron uptake by Tf may prove to be helpful. Simulation and perturbation studies help in identifying Tf as a possible drug target. Regulating the mycobactin (myB) concentration was also identified as a possible strategy to control bacterial growth. The simulations thus provide significant insight into iron homeostasis and also for identifying possible drug targets for tuberculosis.

MTHFR Gene variants C677T, A1298C and association with Down syndrome: A Case-control study from South India

BACKGROUND

The 5,10-methylenetetrahydrofolate reductase (MTHFR) polymorphisms and low folate levels are associated with inhibition of DNA methyltransferase and consequently DNA hypomethylation. The expanding spectrum of common conditions linked with MTHFR polymorphisms includes certain adverse birth outcome, pregnancy complications, cancers, adult cardiovascular diseases and psychiatric disorders, with several of these associations remaining still controversial. Trisomy 21 or Down syndrome (DS) is the most common genetic cause of mental retardation. It stems predominantly from the failure of chromosome 21 to segregate normally during meiosis. Despite substantial research, the molecular mechanisms underlying non-disjunction leading to trisomy 21 are poorly understood.

MATERIALS AND METHODS

Two common variants C677T and A1298C of the MTHFR gene were screened in 36 parents with DS children and 60 healthy couples from Tamil Nadu and Karnataka. The MTHFR genotypes were studied by RFLP analysis of PCR-amplified products and confirmed by sequencing.

RESULTS

The CT genotype was seen in three each (8.3%) of case mothers and fathers. One case father showed TT genotype. All the control individuals exhibited the wild type CC genotype. A similar frequency for the uncommon allele C of the second polymorphism was recorded in case mothers (0.35) and fathers (0.37) in comparison with the control mothers (0.39) and fathers (0.37).

CONCLUSION

This first report on MTHFR C677T and A1298C polymorphisms in trisomy 21 parents from south Indian population revealed that MTHFR 677CT polymorphism was associated with a risk for Down syndrome.

PocketAlign a novel algorithm for aligning binding sites in protein structures

A fundamental task in bioinformatics involves a transfer of knowledge from one protein molecule onto another by way of recognizing similarities. Such similarities are obtained at different levels, that of sequence, whole fold, or important substructures. Comparison of binding sites is important to understand functional similarities among the proteins and also to understand drug cross-reactivities. Current methods in literature have their own merits and demerits, warranting exploration of newer concepts and algorithms, especially for large-scale comparisons and for obtaining accurate residue-wise mappings. Here, we report the development of a new algorithm, PocketAlign, for obtaining structural superpositions of binding sites. The software is available as a web-service at http://proline.physics.iisc.ernet.in/pocketalign/. The algorithm encodes shape descriptors in the form of geometric perspectives, supplemented by chemical group classification. The shape descriptor considers several perspectives with each residue as the focus and captures relative distribution of residues around it in a given site. Residue-wise pairings are computed by comparing the set of perspectives of the first site with that of the second, followed by a greedy approach that incrementally combines residue pairings into a mapping. The mappings in different frames are then evaluated by different metrics encoding the extent of alignment of individual geometric perspectives. Different initial seed alignments are computed, each subsequently extended by detecting consequential atomic alignments in a three-dimensional grid, and the best 500 stored in a database. Alignments are then ranked, and the top scoring alignments reported, which are then streamed into Pymol for visualization and analyses. The method is validated for accuracy and sensitivity and benchmarked against existing methods. An advantage of PocketAlign, as compared to some of the existing tools available for binding site comparison in literature, is that it explores different schemes for identifying an alignment thus has a better potential to capture similarities in ligand recognition abilities. PocketAlign, by finding a detailed alignment of a pair of sites, provides insights as to why two sites are similar and which set of residues and atoms contribute to the similarity.