Saikat Chakrabarti

Analysis and prediction of functionally important sites in proteins

The rapidly increasing volume of sequence and structure information available for proteins poses the daunting task of determining their functional importance. Computational methods can prove to be very useful in understanding and characterizing the biochemical and evolutionary information contained in this wealth of data, particularly at functionally important sites. Therefore, we perform a detailed survey of compositional and evolutionary constraints at the molecular and biological function level for a large set of known functionally important sites extracted from a wide range of protein families. We compare the degree of conservation across different functional categories and provide detailed statistical insight to decipher the varying evolutionary constraints at functionally important sites. The compositional and evolutionary information at functionally important sites has been compiled into a library of functional templates. We developed a module that predicts functionally important columns (FIC) of an alignment based on the detection of a significant “template match score” to a library template. Our template match score measures an alignment columns similarity to a library template and combines a term explicitly representing a columns residue composition with various evolutionary conservation scores (information content and position‐specific scoring matrix‐derived statistics). Our benchmarking studies show good sensitivity/specificity for the prediction of functional sites and high accuracy in attributing correct molecular function type to the predicted sites. This prediction method is based on information derived from homologous sequences and no structural information is required. Therefore, this method could be extremely useful for large‐scale functional annotation.

Coevolution in defining the functional specificity

Covariation between sites can arise due to a common evolutionary history. At the same time, structure and function of proteins play significant role in evolvability of different sites that are not directly connected with the common ancestry. The nature of forces which cause residues to coevolve is still not thoroughly understood, it is especially not clear how coevolutionary processes are related to functional diversification within protein families. We analyzed both functional and structural factors that might cause covariation of specificity determinants and showed that they more often participate in coevolutionary relationships with each other and other sites compared with functional sites and those sites that are not under strong functional constraints. We also found that protein sites with higher number of coevolutionary connections with other sites have a tendency to evolve slower. Our results indicate that in some cases coevolutionary connections exist between specificity sites that are located far away in space but are under similar functional constraints. Such correlated changes and compensations can be realized through the stepwise coevolutionary processes which in turn can shed light on the mechanisms of functional diversification. Proteins 2009. Published 2008 Wiley‐Liss, Inc.

Identification and molecular characterization of an Alba-family protein from human malaria parasite Plasmodium falciparum

We have investigated the DNA-binding nature as well as the function of a putative Alba (Acetylation lowers binding affinity) family protein (PfAlba3) from Plasmodium falciparum. PfAlba3 possesses DNA-binding property like Alba family proteins. PfAlba3 binds to DNA sequence non-specifically at the minor groove and acetylation lowers its DNA-binding affinity. The protein is ubiquitously expressed in all the erythrocytic stages of P. falciparum and it exists predominantly in the acetylated form. PfAlba3 inhibits transcription in vitro by binding to DNA. Plasmodium falciparum Sir2 (PfSir2A), a nuclear localized deacetylase interacts with PfAlba3 and deacetylates the lysine residue of N-terminal peptide of PfAlba3 specific for DNA binding. PfAlba3 is localized with PfSir2A in the periphery of the nucleus. Fluorescence in situ hybridization studies revealed the presence of PfAlba3 in the telomeric and subtelomeric regions. ChIP and ChIP ReChIP analyses further confirmed that PfAlba3 binds to the telomeric and subtelomeric regions as well as to var gene promoter.

Antiviral activity of baicalin against influenza virus H1N1-pdm09 is due to modulation of NS1-mediated cellular innate immune responses

OBJECTIVES Baicalin, a flavonoid, has been shown to have antiviral and anti-inflammatory activities, although the mechanism of action has been unknown. Therefore, attempts were made to analyse the mechanism behind the antiviral effects of baicalin using an influenza A virus (IAV) model in vitro and in vivo. METHODS Baicalins anti-influenza activity was elucidated (in vitro and in vivo) utilizing pandemic influenza strain A/H1N1/Eastern India/66/pdm09 (H1N1-pdm09). Anti-influenza activity was measured by plaque inhibition, fluorescent focus-forming units (ffu) and quantifying viral transcripts using quantitative real-time PCR following treatment with baicalin in a dose- and time-dependent manner. The role of the IAV non-structural protein 1 (NS1) gene in modulating host responses was measured by immunoblotting, co-immunoprecipitation and molecular docking. RESULTS Baicalin treatment following IAV infection revealed up-regulation of interferon (IFN)-induced antiviral signalling and decreased phosphoinositide 3-kinase/Akt (PI3K/Akt) activation compared with infected, untreated controls. Baicalin exerts its antiviral effects by modulating the function of the IAV-encoded NS1 protein. NS1 has been shown to counteract cellular antiviral responses by down-regulating IFN induction and up-regulating PI3K/Akt signalling. Baicalin disrupted NS1-p85β binding. Molecular docking predicted the binding site of baicalin in the RNA binding domain (RBD) of NS1. Site-directed mutagenesis within the RBD region of NS1 and the difference in the fluorescence quenching pattern of full-length NS1 and mutant NS1 proteins in the presence of baicalin confirmed the interaction of baicalin with the NS1 RBD. Amino acid residues 39-43 of the NS1 RBD were found to be crucial for the baicalin-NS1 interaction. CONCLUSIONS Overall, this study highlights that baicalin exerts its anti-influenza virus activity by modulating viral protein NS1, resulting in up-regulation of IFN-induced antiviral signalling and a decrease in PI3K/Akt signalling in cells.

A transient reversal of miRNA‐mediated repression controls macrophage activation

In mammalian macrophages, the expression of a number of cytokines is regulated by miRNAs. Upon macrophage activation, proinflammatory cytokine mRNAs are translated, although the expression of miRNAs targeting these mRNAs remains largely unaltered. We show that there is a transient reversal of miRNA‐mediated repression during the early phase of the inflammatory response in macrophages, which leads to the protection of cytokine mRNAs from miRNA‐mediated repression. This derepression occurs through Ago2 phosphorylation, which results in its impaired binding to miRNAs and to the corresponding target mRNAs. Macrophages expressing a mutant, non‐phosphorylatable AGO2—which remains bound to miRNAs during macrophage activation—have a weakened inflammatory response and fail to prevent parasite invasion. These findings highlight the relevance of the transient relief of miRNA repression for macrophage function.

DBETH: A Database of Bacterial Exotoxins for Human

Pathogenic bacteria produce protein toxins to survive in the hostile environments defined by the hosts defense systems and immune response. Recent progresses in high-throughput genome sequencing and structure determination techniques have contributed to a better understanding of mechanisms of action of the bacterial toxins at the cellular and molecular levels leading to pathogenicity. It is fair to assume that with time more and more unknown toxins will emerge not only by the discovery of newer species but also due to the genetic rearrangement of existing bacterial genomes. Hence, it is crucial to organize a systematic compilation and subsequent analyses of the inherent features of known bacterial toxins. We developed a Database for Bacterial ExoToxins (DBETH, http://www.hpppi.iicb.res.in/btox/), which contains sequence, structure, interaction network and analytical results for 229 toxins categorized within 24 mechanistic and activity types from 26 bacterial genuses. The main objective of this database is to provide a comprehensive knowledgebase for human pathogenic bacterial toxins where various important sequence, structure and physico-chemical property based analyses are provided. Further, we have developed a prediction server attached to this database which aims to identify bacterial toxin like sequences either by establishing homology with known toxin sequences/domains or by classifying bacterial toxin specific features using a support vector based machine learning techniques.

Structural and Functional Roles of Coevolved Sites in Proteins

Background Understanding the residue covariations between multiple positions in protein families is very crucial and can be helpful for designing protein engineering experiments. These simultaneous changes or residue coevolution allow protein to maintain its overall structural-functional integrity while enabling it to acquire specific functional modifications. Despite the significant efforts in the field there is still controversy in terms of the preferable locations of coevolved residues on different regions of protein molecules, the strength of coevolutionary signal and role of coevolution in functional diversification. Methodology In this paper we study the scale and nature of residue coevolution in maintaining the overall functionality and structural integrity of proteins. We employed a large scale study to investigate the structural and functional aspects of coevolved residues. We found that the networks representing the coevolutionary residue connections within our dataset are in general of ‘small-world’ type as they have clustering coefficient values higher than random networks and also show smaller mean shortest path lengths similar and/or lower than random and regular networks. We also found that altogether 11% of functionally important sites are coevolved with any other sites. Active sites are found more frequently to coevolve with any other sites (15%) compared to protein (11%) and ligand (9%) binding sites. Metal binding and active sites are also found to be more frequently coevolved with other metal binding and active sites, respectively. Analysis of the coupling between coevolutionary processes and the spatial distribution of coevolved sites reveals that a high fraction of coevolved sites are located close to each other. Moreover, ∼80% of charge compensatory substitutions within coevolved sites are found at very close spatial proximity (< = 5Å), pointing to the possible preservation of salt bridges in evolution. Conclusion Our findings show that a noticeable fraction of functionally important sites undergo coevolution and also point towards compensatory substitutions as a probable coevolutionary mechanism within spatially proximal coevolved functional sites.

Ensemble approach to predict specificity determinants: benchmarking and validation

BackgroundIt is extremely important and challenging to identify the sites that are responsible for functional specification or diversification in protein families. In this study, a rigorous comparative benchmarking protocol was employed to provide a reliable evaluation of methods which predict the specificity determining sites. Subsequently, three best performing methods were applied to identify new potential specificity determining sites through ensemble approach and common agreement of their prediction results.ResultsIt was shown that the analysis of structural characteristics of predicted specificity determining sites might provide the means to validate their prediction accuracy. For example, we found that for smaller distances it holds true that the more reliable the prediction method is, the closer predicted specificity determining sites are to each other and to the ligand.ConclusionWe observed certain similarities of structural features between predicted and actual subsites which might point to their functional relevance. We speculate that majority of the identified potential specificity determining sites might be indirectly involved in specific interactions and could be ideal target for mutagenesis experiments.

A survey on prediction of specificity-determining sites in proteins

Specificity-determining sites (SDS) are the key positions of a protein family that show a specific conservation of amino acids, related to the subfamily members of that family. SDS play crucial role in developing functional variation within the protein family during the course of evolution. Thus, it is important to identify SDS to understand the evolutionary process of diversification of biological functions within a protein family. A wide range of computational tools have been designed to detect such SDS. In this review, we intend to examine the concept of SDS in more details along with the advancements and drawbacks of different computational approaches designed towards successful prediction of SDS. Further, we discussed the algorithms behind the computational approaches developed till date and provide an exhaustive comparison of performance of each method. We also introduce a new ensemble approach, SubSite as another tool to predict SDS through a user-friendly webserver available at www.hpppi.iicb.res.in/subsite.

MegaMotifBase: a database of structural motifs in protein families and superfamilies

Structural motifs are important for the integrity of a protein fold and can be employed to design and rationalize protein engineering and folding experiments. Such conserved segments represent the conserved core of a family or superfamily and can be crucial for the recognition of potential new members in sequence and structure databases. We present a database, MegaMotifBase, that compiles a set of important structural segments or motifs for protein structures. Motifs are recognized on the basis of both sequence conservation and preservation of important structural features such as amino acid preference, solvent accessibility, secondary structural content, hydrogen-bonding pattern and residue packing. This database provides 3D orientation patterns of the identified motifs in terms of inter-motif distances and torsion angles. Important applications of structural motifs are also provided in several crucial areas such as similar sequence and structure search, multiple sequence alignment and homology modeling. MegaMotifBase can be a useful resource to gain knowledge about structure and functional relationship of proteins. The database can be accessed from the URL http://caps.ncbs.res.in/MegaMotifbase/index.html