Ashwini Patil

Disordered domains and high surface charge confer hubs with the ability to interact with multiple proteins in interaction networks

We investigate the structural properties of hubs that enable them to interact with several partners in protein–protein interaction networks. We find that hubs have more observed and predicted disordered residues with fewer loops/coils, and more charged residues on the surface as compared to non‐hubs. Smaller hubs have fewer disordered residues and more charged residues on the surface than larger hubs. We conclude that the global flexibility provided by disordered domains, and high surface charge are complementary factors that play a significant role in the binding ability of hubs.

Filtering high-throughput protein-protein interaction data using a combination of genomic features.

BackgroundProtein-protein interaction data used in the creation or prediction of molecular networks is usually obtained from large scale or high-throughput experiments. This experimental data is liable to contain a large number of spurious interactions. Hence, there is a need to validate the interactions and filter out the incorrect data before using them in prediction studies.ResultsIn this study, we use a combination of 3 genomic features – structurally known interacting Pfam domains, Gene Ontology annotations and sequence homology – as a means to assign reliability to the protein-protein interactions in Saccharomyces cerevisiae determined by high-throughput experiments. Using Bayesian network approaches, we show that protein-protein interactions from high-throughput data supported by one or more genomic features have a higher likelihood ratio and hence are more likely to be real interactions. Our method has a high sensitivity (90%) and good specificity (63%). We show that 56% of the interactions from high-throughput experiments in Saccharomyces cerevisiae have high reliability. We use the method to estimate the number of true interactions in the high-throughput protein-protein interaction data sets in Caenorhabditis elegans, Drosophila melanogaster and Homo sapiens to be 27%, 18% and 68% respectively. Our results are available for searching and downloading at http://helix.protein.osaka-u.ac.jp/htp/.ConclusionA combination of genomic features that include sequence, structure and annotation information is a good predictor of true interactions in large and noisy high-throughput data sets. The method has a very high sensitivity and good specificity and can be used to assign a likelihood ratio, corresponding to the reliability, to each interaction.

Hub Promiscuity in Protein-Protein Interaction Networks

Hubs are proteins with a large number of interactions in a protein-protein interaction network. They are the principal agents in the interaction network and affect its function and stability. Their specific recognition of many different protein partners is of great interest from the structural viewpoint. Over the last few years, the structural properties of hubs have been extensively studied. We review the currently known features that are particular to hubs, possibly affecting their binding ability. Specifically, we look at the levels of intrinsic disorder, surface charge and domain distribution in hubs, as compared to non-hubs, along with differences in their functional domains.

HitPredict: a database of quality assessed protein–protein interactions in nine species

Despite the availability of a large number of protein–protein interactions (PPIs) in several species, researchers are often limited to using very small subsets in a few organisms due to the high prevalence of spurious interactions. In spite of the importance of quality assessment of experimentally determined PPIs, a surprisingly small number of databases provide interactions with scores and confidence levels. We introduce HitPredict (http://hintdb.hgc.jp/htp/), a database with quality assessed PPIs in nine species. HitPredict assigns a confidence level to interactions based on a reliability score that is computed using evidence from sequence, structure and functional annotations of the interacting proteins. HitPredict was first released in 2005 and is updated annually. The current release contains 36 930 proteins with 176 983 non-redundant, physical interactions, of which 116 198 (66%) are predicted to be of high confidence.

Domain distribution and intrinsic disorder in hubs in the human protein―protein interaction network

Intrinsic disorder and distributed surface charge have been previously identified as some of the characteristics that differentiate hubs (proteins with a large number of interactions) from non‐hubs in protein–protein interaction networks. In this study, we investigated the differences in the quantity, diversity, and functional nature of Pfam domains, and their relationship with intrinsic disorder, in hubs and non‐hubs. We found that proteins with a more diverse domain composition were over‐represented in hubs when compared with non‐hubs, with the number of interactions in hubs increasing with domain diversity. Conversely, the fraction of intrinsic disorder in hubs decreased with increasing number of ordered domains. The difference in the levels of disorder was more prominent in hubs and non‐hubs with fewer domains. Functional analysis showed that hubs were enriched in kinase and adaptor domains acting primarily in signal transduction and transcription regulation, whereas non‐hubs had more DNA‐binding domains and were involved in catalytic activity. Consistent with the differences in the functional nature of their domains, hubs with two or more domains were more likely to connect distinct functional modules in the interaction network when compared with single domain hubs. We conclude that the availability of greater number and diversity of ordered domains, in addition to the tendency to have promiscuous domains, differentiates hubs from non‐hubs and provides an additional means of achieving interaction promiscuity. Further, hubs with fewer domains use greater levels of intrinsic disorder to facilitate interaction promiscuity with the prevalence of disorder decreasing with increasing number of ordered domains.

Methods for protein complex prediction and their contributions towards understanding the organisation, function and dynamics of complexes

Complexes of physically interacting proteins constitute fundamental functional units responsible for driving biological processes within cells. A faithful reconstruction of the entire set of complexes is therefore essential to understand the functional organisation of cells. In this review, we discuss the key contributions of computational methods developed till date (approximately between 2003 and 2015) for identifying complexes from the network of interacting proteins (PPI network). We evaluate in depth the performance of these methods on PPI datasets from yeast, and highlight their limitations and challenges, in particular at detecting sparse and small or sub‐complexes and discerning overlapping complexes. We describe methods for integrating diverse information including expression profiles and 3D structures of proteins with PPI networks to understand the dynamics of complex formation, for instance, of time‐based assembly of complex subunits and formation of fuzzy complexes from intrinsically disordered proteins. Finally, we discuss methods for identifying dysfunctional complexes in human diseases, an application that is proving invaluable to understand disease mechanisms and to discover novel therapeutic targets. We hope this review aptly commemorates a decade of research on computational prediction of complexes and constitutes a valuable reference for further advancements in this exciting area.

Sequence- and Species-Dependence of Proteasomal Processivity

The proteasome is the degradation machine at the center of the ubiquitin-proteasome system and controls the concentrations of many proteins in eukaryotes. It is highly processive so that substrates are degraded completely into small peptides, avoiding the formation of potentially toxic fragments. Nonetheless, some proteins are incompletely degraded, indicating the existence of factors that influence proteasomal processivity. We have quantified proteasomal processivity and determined the underlying rates of substrate degradation and release. We find that processivity increases with species complexity over a 5-fold range between yeast and mammalian proteasome, and the effect is due to slower but more persistent degradation by proteasomes from more complex organisms. A sequence stretch that has been implicated in causing incomplete degradation, the glycine-rich region of the NFκB subunit p105, reduces the proteasomes ability to unfold its substrate, and polyglutamine repeats such as found in Huntingtons disease reduce the processivity of the proteasome in a length-dependent manner.

Phytochemical Analysis and Free Radical Scavenging Activity of Medicinal Plants Gnidia glauca and Dioscorea bulbifera

Gnidia glauca and Dioscorea bulbifera are traditional medicinal plants that can be considered as sources of natural antioxidants. Herein we report the phytochemical analysis and free radical scavenging activity of their sequential extracts. Phenolic and flavonoid content were determined. Scavenging activity was checked against pulse radiolysis generated ABTS•+ and OH radical, in addition to DPPH, superoxide and hydroxyl radicals by biochemical methods followed by principal component analysis. G. glauca leaf extracts were rich in phenolic and flavonoid content. Ethyl acetate extract of D. bulbifera bulbs and methanol extract of G. glauca stem exhibited excellent scavenging of pulse radiolysis generated ABTS•+ radical with a second order rate constant of 2.33×106 and 1.72×106, respectively. Similarly, methanol extract of G. glauca flower and ethyl acetate extract of D. bulbifera bulb with second order rate constants of 4.48×106 and 4.46×106 were found to be potent scavengers of pulse radiolysis generated OH radical. G. glauca leaf and stem showed excellent reducing activity and free radical scavenging activity. HPTLC fingerprinting, carried out in mobile phase, chloroform: toluene: ethanol (4: 4: 1, v/v) showed presence of florescent compound at 366 nm as well as UV active compound at 254 nm. GC-TOF-MS analysis revealed the predominance of diphenyl sulfone as major compound in G. glauca. Significant levels of n-hexadecanoic acid and octadecanoic acid were also present. Diosgenin (C27H42O3) and diosgenin (3á,25R) acetate were present as major phytoconstituents in the extracts of D. bulbifera. G. glauca and D. bulbifera contain significant amounts of phytochemicals with antioxidative properties that can be exploited as a potential source for herbal remedy for oxidative stress induced diseases. These results rationalize further investigation in the potential discovery of new natural bioactive principles from these two important medicinal plants.

Linking Transcriptional Changes over Time in Stimulated Dendritic Cells to Identify Gene Networks Activated during the Innate Immune Response

The innate immune response is primarily mediated by the Toll-like receptors functioning through the MyD88-dependent and TRIF-dependent pathways. Despite being widely studied, it is not yet completely understood and systems-level analyses have been lacking. In this study, we identified a high-probability network of genes activated during the innate immune response using a novel approach to analyze time-course gene expression profiles of activated immune cells in combination with a large gene regulatory and protein-protein interaction network. We classified the immune response into three consecutive time-dependent stages and identified the most probable paths between genes showing a significant change in expression at each stage. The resultant network contained several novel and known regulators of the innate immune response, many of which did not show any observable change in expression at the sampled time points. The response network shows the dominance of genes from specific functional classes during different stages of the immune response. It also suggests a role for the protein phosphatase 2a catalytic subunit α in the regulation of the immunoproteasome during the late phase of the response. In order to clarify the differences between the MyD88-dependent and TRIF-dependent pathways in the innate immune response, time-course gene expression profiles from MyD88-knockout and TRIF-knockout dendritic cells were analyzed. Their response networks suggest the dominance of the MyD88-dependent pathway in the innate immune response, and an association of the circadian regulators and immunoproteasomal degradation with the TRIF-dependent pathway. The response network presented here provides the most probable associations between genes expressed in the early and the late phases of the innate immune response, while taking into account the intermediate regulators. We propose that the method described here can also be used in the identification of time-dependent gene sub-networks in other biological systems.

Evaluation of sequence features from intrinsically disordered regions for the estimation of protein function.

With the exponential increase in the number of sequenced organisms, automated annotation of proteins is becoming increasingly important. Intrinsically disordered regions are known to play a significant role in protein function. Despite their abundance, especially in eukaryotes, they are rarely used to inform function prediction systems. In this study, we extracted seven sequence features in intrinsically disordered regions and developed a scheme to use them to predict Gene Ontology Slim terms associated with proteins. We evaluated the function prediction performance of each feature. Our results indicate that the residue composition based features have the highest precision while bigram probabilities, based on sequence profiles of intrinsically disordered regions obtained from PSIBlast, have the highest recall. Amino acid bigrams and features based on secondary structure show an intermediate level of precision and recall. Almost all features showed a high prediction performance for GO Slim terms related to extracellular matrix, nucleus, RNA and DNA binding. However, feature performance varied significantly for different GO Slim terms emphasizing the need for a unique classifier optimized for the prediction of each functional term. These findings provide a first comprehensive and quantitative evaluation of sequence features in intrinsically disordered regions and will help in the development of a more informative protein function predictor.