Swati Kaushik

Structural Analysis of Prolyl Oligopeptidases Using Molecular Docking and Dynamics: Insights into Conformational Changes and Ligand Binding

Prolyl oligopeptidase (POP) is considered as an important pharmaceutical target for the treatment of numerous diseases. Despite enormous studies on various aspects of POPs structure and function still some of the questions are intriguing like conformational dynamics of the protein and interplay between ligand entry/egress. Here, we have used molecular modeling and docking based approaches to unravel questions like differences in ligand binding affinities in three POP species (porcine, human and A. thaliana). Despite high sequence and structural similarity, they possess different affinities for the ligands. Interestingly, human POP was found to be more specific, selective and incapable of binding to a few planar ligands which showed extrapolation of porcine POP in human context is more complicated. Possible routes for substrate entry and product egress were also investigated by detailed analyses of molecular dynamics (MD) simulations for the three proteins. Trajectory analysis of bound and unbound forms of three species showed differences in conformational dynamics, especially variations in β-propeller pore size, which was found to be hidden by five lysine residues present on blades one and seven. During simulation, β-propeller pore size was increased by ∼2 Å in porcine ligand-bound form which might act as a passage for smaller product movement as free energy barrier was reduced, while there were no significant changes in human and A. thaliana POPs. We also suggest that these differences in pore size could lead to fundamental differences in mode of product egress among three species. This analysis also showed some functionally important residues which can be used further for in vitro mutagenesis and inhibitor design. This study can help us in better understanding of the etiology of POPs in several neurodegenerative diseases.

Decoding the structural events in substrate-gating mechanism of eukaryotic prolyl oligopeptidase using normal mode analysis and molecular dynamics simulations

Prolyl oligopeptidase (POP) is a serine protease, unique for its ability to cleave various small oligopeptides shorter than 30 amino acids. POP is an important drug target since it is implicated in various neurological disorders. Although there is structural evidence that bacterial POPs undergo huge interdomain movements and acquire an “open” state in the substrate‐unbound form, hitherto, no crystal structure is available in the substrate‐unbound domain‐open form of eukaryotic POPs. Indeed, there is no difference between the substrate‐unbound/bound states of eukaryotic POPs. This raises unanswered questions about whether difference in the substrate access pathway exists between bacterial and eukaryotic POPs. Here, we have used normal mode analysis and molecular dynamics to unravel the mechanism of substrate entry in mammalian POPs, which has been debated until now. Motions observed using normal modes of porcine and bacterial POPs were analyzed and compared, augmented by molecular dynamics of these proteins. Identical to bacterial POPs, interdomain opening was found to be the possible pathway for the substrate‐gating in mammals as well. On the basis of our analyses and evidences, a mechanistic model of substrate entry in POPs has been proposed. Up‐down movement of N‐terminal hydrolase domain resulted in twisting motion of two domains, followed by the conformational changes of interdomain loop regions, which facilitate interdomain opening. Similar to bacterial POPs, an open form of porcine POP is also proposed with domain‐closing motion. This work has direct implications for the development of novel inhibitors of mammalian POPs to understand the etiology of various neurological diseases. Proteins 2014; 82:1428–1443.

Improved Detection of Remote Homologues Using Cascade PSI-BLAST: Influence of Neighbouring Protein Families on Sequence Coverage

Background Development of sensitive sequence search procedures for the detection of distant relationships between proteins at superfamily/fold level is still a big challenge. The intermediate sequence search approach is the most frequently employed manner of identifying remote homologues effectively. In this study, examination of serine proteases of prolyl oligopeptidase, rhomboid and subtilisin protein families were carried out using plant serine proteases as queries from two genomes including A. thaliana and O. sativa and 13 other families of unrelated folds to identify the distant homologues which could not be obtained using PSI-BLAST. Methodology/Principal Findings We have proposed to start with multiple queries of classical serine protease members to identify remote homologues in families, using a rigorous approach like Cascade PSI-BLAST. We found that classical sequence based approaches, like PSI-BLAST, showed very low sequence coverage in identifying plant serine proteases. The algorithm was applied on enriched sequence database of homologous domains and we obtained overall average coverage of 88% at family, 77% at superfamily or fold level along with specificity of ∼100% and Mathew’s correlation coefficient of 0.91. Similar approach was also implemented on 13 other protein families representing every structural class in SCOP database. Further investigation with statistical tests, like jackknifing, helped us to better understand the influence of neighbouring protein families. Conclusions/Significance Our study suggests that employment of multiple queries of a family for the Cascade PSI-BLAST searches is useful for predicting distant relationships effectively even at superfamily level. We have proposed a generalized strategy to cover all the distant members of a particular family using multiple query sequences. Our findings reveal that prior selection of sequences as query and the presence of neighbouring families can be important for covering the search space effectively in minimal computational time. This study also provides an understanding of the ‘bridging’ role of related families.

A Quantitative Chemotherapy Genetic Interaction Map Reveals Factors Associated with PARP Inhibitor Resistance

SUMMARY Chemotherapy is used to treat most cancer patients, yet our understanding of factors that dictate response and resistance to such drugs remains limited. We report the generation of a quantitative chemical-genetic interaction map in human mammary epithelial cells charting the impact of the knockdown of 625 genes related to cancer and DNA repair on sensitivity to 29 drugs, covering all classes of chemotherapy. This quantitative map is predictive of interactions maintained in other cell lines, identifies DNA-repair factors, predicts cancer cell line responses to therapy, and prioritizes synergistic drug combinations. We identify that ARID1A loss confers resistance to PARP inhibitors in cells and ovarian cancer patients and that loss of GPBP1 causes resistance to cisplatin and PARP inhibitors through the regulation of genes involved in homologous recombination. This map helps navigate patient genomic data and optimize chemotherapeutic regimens by delineating factors involved in the response to specific types of DNA damage.

Rapid and enhanced remote homology detection by cascading hidden Markov model searches in sequence space

MOTIVATION In the post-genomic era, automatic annotation of protein sequences using computational homology-based methods is highly desirable. However, often protein sequences diverge to an extent where detection of homology and automatic annotation transfer is not straightforward. Sophisticated approaches to detect such distant relationships are needed. We propose a new approach to identify deep evolutionary relationships of proteins to overcome shortcomings of the available methods. RESULTS We have developed a method to identify remote homologues more effectively from any protein sequence database by using several cascading events with Hidden Markov Models (C-HMM). We have implemented clustering of hits and profile generation of hit clusters to effectively reduce the computational timings of the cascaded sequence searches. Our C-HMM approach could cover 94, 83 and 40% coverage at family, superfamily and fold levels, respectively, when applied on diverse protein folds. We have compared C-HMM with various remote homology detection methods and discuss the trade-offs between coverage and false positives. AVAILABILITY AND IMPLEMENTATION A standalone package implemented in Java along with a detailed documentation can be downloaded from https://github.com/RSLabNCBS/C-HMM SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online. CONTACT mini@ncbs.res.in.

Distribution, classification, domain architectures and evolution of prolyl oligopeptidases in prokaryotic lineages

BackgroundProlyl oligopeptidases (POPs) are proteolytic enzymes, widely distributed in all the kingdoms of life. Bacterial POPs are pharmaceutically important enzymes, yet their functional and evolutionary details are not fully explored. Therefore, current analysis is aimed at understanding the distribution, domain architecture, probable biological functions and gene family expansion of POPs in bacterial and archaeal lineages.ResultsExhaustive sequence analysis of 1,202 bacterial and 91 archaeal genomes revealed ~3,000 POP homologs, with only 638 annotated POPs. We observed wide distribution of POPs in all the analysed bacterial lineages. Phylogenetic analysis and co-clustering of POPs of different phyla suggested their common functions in all the prokaryotic species. Further, on the basis of unique sequence motifs we could classify bacterial POPs into eight subtypes. Analysis of coexisting domains in POPs highlighted their involvement in protein-protein interactions and cellular signaling. We proposed significant extension of this gene family by characterizing 39 new POPs and 158 new α/β hydrolase members.ConclusionsOur study reflects diversity and functional importance of POPs in bacterial species. Many genomes with multiple POPs were identified with high sequence variations and different cellular localizations. Such anomalous distribution of POP genes in different bacterial genomes shows differential expansion of POP gene family primarily by multiple horizontal gene transfer events.

Connecting tumor genomics with therapeutics through multi-dimensional network modules

Recent efforts have catalogued genomic, transcriptomic, epigenetic and proteomic changes in tumors, but connecting these data with effective therapeutics remains a challenge. In contrast, cancer cell lines can model therapeutic responses but only partially reflect tumor biology. Bridging this gap requires new methods of data integration to identify a common set of pathways and molecular events. Using MAGNETIC, a new method to integrate molecular profiling data using functional networks, we identify 219 gene modules in TCGA breast cancers that capture recurrent alterations, reveal new roles for H3K27 tri-methylation and accurately quantitate various cell types within the tumor microenvironment. We show that a significant portion of gene expression and methylation in tumors is poorly reproduced in cell lines due to differences in biology and microenvironment and MAGNETIC identifies therapeutic biomarkers that are robust to these differences. This work addresses a fundamental challenge in pharmacogenomics that can only be overcome by the joint analysis of patient and cell line data.