Computational Prediction of Potential Vaccine Candidates From tRNA Encoded peptides (tREP) Using a Bioinformatic Workflow and Molecular Dynamics Validations.

Transfer RNAs (tRNA) are non-coding RNAs. Encouraged by biological applications discovered for peptides derived from other non-coding genomic regions, we explore the possibility of deriving epitope-based vaccines from tRNA encoded peptides (tREP) in this study. Epitope-based vaccines have been identified as an effective strategy to mitigate safety and specificity concerns observed in vaccine development. In this study, we explore the potential of tREP as a source for epitope-based vaccines for virus pathogens. We present a computational workflow that uses verified data sources and community-validated predictive tools to produce a ranked list of plausible epitope-based vaccines starting from tRNA sequences. The top epitope, bound to the predicted HLA molecule, for the virus pathogen is computationally validated through 200 ns molecular dynamics (MD) simulations followed by binding free energy calculations. The simulation results indicate that two tRNA encoded epitope-based vaccines, RRHIDIVV and IMVRFSAE for Mamastrovirus 3 and Norovirus GII, respectively, are likely candidates. Peptides originating from tRNAs provide unexplored opportunities for vaccine design. Encouraged by our previous experimental study, which established the inhibitory properties of tREPs against infectious parasites, we have proposed a computationally validated set of peptides derived from tREPs as vaccines for viral pathogens.

Highly potent anti-malarial activity of benzopyrano(4,3-b)benzopyran derivatives and in silico interaction analysis with putative target Plasmodium falciparum lactate dehydrogenase.

Malaria infection caused by Plasmodium falciparum is majorly responsible for millions of deaths in humans every year. Moreover, a rapid increase in resistance to existing drugs has posed an urgent need for new anti-malarials. Herein, we report the highly potent anti-malarial activity of benzopyrano(4,3-b)benzopyran derivatives, inspired from naturally occurring dependensin against chloroquine (CQ) sensitive and resistant P. falciparum strains. Chemically synthesized, four dependensin analogs 85(A-D) exhibited growth inhibition at nanomolar concentrations ranging from 63.96 to 725.8 nM by blocking the parasite development at the ring and early trophozoite stages. The growth inhibitory activity of dependensin analogs was correlated with their anti-plasmodial lactate dehydrogenase activity by computational analysis. Molecular docking, 50 ns simulation and a 2D-Quantitative Structure-Activity Relationship (2D-QSAR) modelling revealed the interaction with their putative target P. falciparum lactate dehydrogenase (PfLDH). Here, developing the predictive 2D descriptors such as thermodynamic, spatial, electronic, and topological with multiple linear regression analysis (MLRA), the structural requirements for potent and selective PfLDH inhibitory activity has been identified. The strong binding of compound 85D to the catalytic Nicotinamide adenine dinucleotide (NADH) binding pocket of the PfLDH further supported the PfLDH targeting potential of dependensin analogs. Overall, this study revealed a highly potent anti-malarial activity of benzopyrano(4,3-b)benzopyran derivatives with their putative anti-PfLDH activity.Communicated by Ramaswamy H. Sarma.

Building Biofoundry India: challenges and path forward.

Biofoundry is a place where biomanufacturing meets automation. The highly modular structure of a biofoundry helps accelerate the design-build-test-learn workflow to deliver products fast and in a streamlined fashion. In this perspective, we describe our efforts to build Biofoundry India, where we see the facility add a substantial value in supporting research, innovation and entrepreneurship. We describe three key areas of our focus, harnessing the potential of non-expressing parts of the sequenced genomes, using deep learning in pathway reconstruction and synthesising enzymes and metabolites. Toward the end, we describe specific challenges in building such facility in India and the path to mitigate some of those working with the other biofoundries worldwide.

Association of miR-760 with cancer: An overview.

MicroRNAs (miRNAs) are small non-coding RNA molecules of around 22 nucleotides in length. They are crucially involved in the post transcriptional regulation and thus play a significant role in the modulation of different diseases. Several studies have suggested that miRNA expression is dysregulated in various cancers through different mechanisms and the dysregulated miRNA in return affects different cancer hallmarks including cell proliferation, cell death suppression, metastasis and angiogenesis. Compilation of the available miRNA data can be a stimulator for proper understanding of the correlation between the miRNA expression and cancer progression. In this review, we have focussed on the role of miR-760 in the progression of different cancer. MicroRNA-760 (miR-760) has been found to be down regulated in various cancers, thus it can be utilized as a possible prognostic marker for cancer detection. Here, we have tried to fill a gap regarding the role of miR-760 in relation to cervical cancer also. Moreover, unravelling the role of miR-760 in different cancers will enlighten the researchers with proper understanding of biology of miR-760 in regulation of different cancers.

HYPO: A Database of Human Hypothetical Proteins.

BACKGROUND: There are genes whose function remains obscure as they may not have similarities to known regions in the genome. Such known 'unknown' genes constituting the Open Reading Frames (ORF) that remain in the epigenome are termed as orphan genes and the proteins encoded by them but having no experimental evidence of translation are termed as 'Hypothetical Proteins' (HPs). OBJECTIVES: We have enhanced our former database of Hypothetical Proteins (HP) in human (HypoDB) with added annotation, application programming interfaces and descriptive features. The database hosts 1000+ manually curated records of the known 'unknown' regions in the human genome. The new updated version of HypoDB with functionalities (Blast, Match) is freely accessible at http://www.bioclues.org/hypo2. METHODS: The total collection of HPs were checked using experimentally validated sets (from Swiss-Prot) or non-experimentally validated set (TrEMBL) or the complete set (UniProtKB). The database was designed with java at the core backend, integrated with databases, viz. EMBL, PIR, HPRD and those including descriptors for structural databases, interaction and association databases. RESULTS: The HypoDB constituted Application Programming Interfaces (API) for implicitly searching resources linking them to other databases like NCBI Link-out in addition to multiple search capabilities along with advanced searches using integrated bio-tools, viz. Match and BLAST were incorporated. CONCLUSION: The HypoDB is perhaps the only open-source HP database with a range of tools for common bioinformatics retrievals and serves as a standby reference to researchers who are interested in finding candidate sequences for their potential experimental work.

Deciphering the role of microRNA - A step by step guide.

MicroRNAs (miRNAs), are small non-coding RNAs of approximately 22 nucleotides in length, playing an important role in regulating gene expression post-transcriptionally. Understanding the effect of miRNA regulation in a pathway-specific manner unravels the approaches adopted to apprehend biological mechanisms, the information, which is scanty for researchers, not primed already for miR related research. Here, we describe a quick perspective in 5 steps with probable approaches and assays at every level to unravel the specific role of a microRNA, miR-145a-5p, as an example. This perspective as a guide would help in identifying novel targets for a microRNA, as shown for miR-145a-5p, which down-regulated the mRNA expression of ADD3 and BRCA2, using bioinformatic tools and experimental assays.

Computational study of 'HUB' microRNA in human cardiac diseases.

MicroRNAs (miRNAs) are small non-coding RNAs ~22 nucleotides long that do not encode for proteins but have been reported to influence gene expression in normal and abnormal health conditions. Though a large body of scientific literature on miRNAs exists, their network level profile linking molecules with their corresponding phenotypes, is less explored. Here, we studied a network of 191 human miRNAs reported to play a role in 30 human cardiac diseases. Our aim was to study miRNA network properties like hubness and preferred associations, using data mining, network graph theory and statistical analysis. A total of 16 miRNAs were found to have a disease node connectivity of >5 edges (i.e., they were linked to more than 5 diseases) and were considered hubs in the miRNAcardiac disease network. Alternatively, when diseases were considered as hubs, >10 of miRNAs showed up on each 'disease hub node'. Of all the miRNAs associated with diseases, 19 miRNAs (19/24= 79.1% of upregulated events) were found to be upregulated in atherosclerosis. The data suggest micro RNAs as early stage biological markers in cardiac conditions with potential towards microRNA based therapeutics.

Computational identification of novel microRNAs and their targets in the malarial vector, Anopheles stephensi.

MicroRNAs are a ~22 nucleotide small non-coding RNAs found in animals, plants and viruses. They regulate key cellular processes by enhancing, degrading or silencing protein coding targets. Currently most of the data on miRNA is available from Drosophila . Given their important post-transcriptional role in several organisms, there is a need to understand the miRNA mediated processes in normal and abnormal conditions. Here we report four novel microRNAs ast - mir - 2502, ast - mir - 2559, ast - mir - 3868 and ast - mir - 9891 in Anopheles stephensi identified from a set of 3,052 transcriptome sequences, showing average minimum free energy of -31.8 kcal/mol of duplex formation with mRNA indicating their functional relevance. Phylogenetic study shows conservation of sequence signatures within the Class Insecta. Furthermore, 26 potential targets of these four miRNAs have been predicted that play an important role in the mosquito life-cycle. This work leads to novel leads and experimental possibilities for improved understanding of gene regulatory processes in mosquito.

In silico study of peptide inhibitors against BACE 1.

Peptides are increasingly used as inhibitors of various disease specific targets. Several naturally occurring and synthetically developed peptides are undergoing clinical trials. Our work explores the possibility of reusing the non-expressing DNA sequences to predict potential drug-target specific peptides. Recently, we experimentally demonstrated the artificial synthesis of novel proteins from non-coding regions of Escherichia coli genome. In this study, a library of synthetic peptides (Synpeps) was constructed from 2500 intergenic E. coli sequences and screened against Beta-secretase 1 protein, a known drug target for Alzheimer's disease (AD). Secondary and tertiary protein structure predictions followed by protein-protein docking studies were performed to identify the most promising enzyme inhibitors. Interacting residues and favorable binding poses of lead peptide inhibitors were studied. Though initial results are encouraging, experimental validation is required in future to develop efficient target specific inhibitors against AD.

Predicting stable functional peptides from the intergenic space of E. coli.

Expression of synthetic proteins from intergenic regions of E. coli and their functional association was recently demonstrated (Dhar et al. in J Biol Eng 3:2, 2009. doi:10.1186/1754-1611-3-2). This gave birth to the question: if one can make 'user-defined' genes from non-coding genome-how big is the artificially translatable genome? (Dinger et al. in PLoS Comput Biol 4, 2008; Frith et al. in RNA Biol 3(1):40-48, 2006a; Frith et al. in PLoS Genet 2(4):e52, 2006b). To answer this question, we performed a bioinformatics study of all reported E. coli intergenic sequences, in search of novel peptides and proteins, unexpressed by nature. Overall, 2500 E. coli intergenic sequences were computationally translated into 'protein sequence equivalents' and matched against all known proteins. Sequences that did not show any resemblance were used for building a comprehensive profile in terms of their structure, function, localization, interactions, stability so on. A total of 362 protein sequences showed evidence of stable tertiary conformations encoded by the intergenic sequences of E. coli genome. Experimental studies are underway to confirm some of the key predictions. This study points to a vast untapped repository of functional molecules lying undiscovered in the non-expressed genome of various organisms.

Making novel proteins from pseudogenes.

MOTIVATION: Recently, we made synthetic proteins from non-coding DNA of Escherichia coli. Encouraged by this, we asked: can we artificially express pseudogenes into novel and functional proteins? What kind of structures would be generated? Would these proteins be stable? How would the organism respond to the artificial reactivation of pseudogenes? RESULTS: To answer these questions, we studied 16 full-length protein equivalents of pseudogenes. The sequence-based predictions indicated interesting molecular and cellular functional roles for pseudogene-derived proteins. Most of the proteins were predicted to be involved in the amino acid biosynthesis, energy metabolism, purines and pyrimidine biosynthesis, central intermediary metabolism, transport and binding. Interestingly, many of the pseudogene-derived proteins were predicted to be enzymes. Furthermore, proteins showed strong evidence of stable tertiary structures. The prediction scores for structure, function and stability were found to be favorable in most of the cases. IMPACT: To our best knowledge, this is the first such report that predicts the possibility of making functional and stable proteins from pseudogenes. In future, it would be interesting to experimentally synthesize and validate these predictions.

Decahydroquinolines from the venom of a formicinae ant, Oecophylla smaragdina.

Ecologically significant species in controlling pests, Oecophylla smaragdina uses its venom to paralyze their prey and to communicate with their colony mates. But no significant analysis of the ant's venom gland secretions has been carried out hitherto. This study describes the identification of venom constituents of Oecophylla smaragdina using coupled gas chromatography and mass spectroscopy (GC-MS) analysis. The results indicate the anticipated presence of a neurotoxin i.e., 2, 5 dipropyl decahydroquinoline and phenol, 2, 4-bis (1, 1 dimethylethyl). This is the first report on presence of decahydroquinolines in the venom of formicinae ant species of genera Oecophylla.

Looking for a sequence based allostery definition: a statistical journey at different resolution scales.

The aim of this work was to detect allosteric hotspots signatures characterizing protein regions acting as the 'key drivers' of global allosteric conformational change. We computationally estimated the relative strength of intra-molecular interaction in allosteric proteins between two putative allostery-susceptible sites using a co-evolution model based upon the optimization of the cross-correlation in terms of free-energy-transfer hydrophobicity scale (Tanford scale) distribution along the chain. Cross-Recurrence Quantification Analysis (Cross-RQA) applied on the sequences of allostery susceptible sites showed evidence of strong interaction amongst allosteric susceptible sites. This could be due to transient weak molecular bonds between allostery susceptible patches enabling regions far-apart to come together. Further, using a large protein dataset, by comparing allosteric protein set with a randomly generated sequence population as well as a generic protein set, we reconfirmed our earlier findings that hydrophobicity patterning (as formalized by Recurrence Quantification Analysis (RQA) descriptors) may serve as determinant of allostery and its relevance in the transmission of allosteric conformational change. We applied RQA to free-energy-transfer hydrophobicity-transformed amino acid sequences of the allostery dataset to extract allostery specific global sequence features. These free-energy-transfer hydrophobicity-based RQA markers proved to be representative of allosteric signatures and not related to the differences between randomly generated and real proteins. These free-energy-transfer hydrophobicity-based RQA markers when evaluated by pattern recognition tools could distinguish allosteric proteins with 92% accuracy.

Laws of biology: why so few?

Finding fundamental organizing principles is the current intellectual front end of systems biology. From a hydrogen atom to the whole cell level, organisms manage massively parallel and massively interactive processes over several orders of magnitude of size. To manage this scale of informational complexity it is natural to expect organizing principles that determine higher order behavior. Currently, there are only hints of such organizing principles but no absolute evidences. Here, we present an approach as old as Mendel that could help uncover fundamental organizing principles in biology. Our approach essentially consists of identifying constants at various levels and weaving them into a hierarchical chassis. As we identify and organize constants, from pair-wise interactions to networks, our understanding of the fundamental principles in biology will improve, leading to a theory in biology.

Building momentum for systems and synthetic biology in India.

Biological systems are inherently noisy. Predicting the outcome of a perturbation is extremely challenging. Traditional reductionist approach of describing properties of parts, vis-a-vis higher level behaviour has led to enormous understanding of fundamental molecular level biology. This approach typically consists of converting genes into junk (knock-down) and garbage (knock-out) and observe how a system responds. To enable broader understanding of biological dynamics, an integrated computational and experimental strategy was formally proposed in mid 1990s leading to the re-emergence of Systems Biology. However, soon it became clear that natural systems were far more complex than expected. A new strategy to address biological complexity was proposed at MIT (Massachusetts Institute of Technology) in June 2004, when the first meeting of synthetic biology was held. Though the term 'synthetic biology' was proposed during 1970s (Szybalski in Control of gene expression, Plenum Press, New York, 1974), the usage of the original concept found an experimental proof in 2000 with the demonstration of a three-gene circuit called repressilator (Elowitz and Leibler in Nature, 403:335-338, 2000). This encouraged people to think of forward engineering biology from a set of well described parts.

Sequence signatures of allosteric proteins towards rational design.

Allostery is the phenomenon of changes in the structure and activity of proteins that appear as a consequence of ligand binding at sites other than the active site. Studying mechanistic basis of allostery leading to protein design with predetermined functional endpoints is an important unmet need of synthetic biology. Here, we screened the amino acid sequence landscape in search of sequence-signatures of allostery using Recurrence Quantitative Analysis (RQA) method. A characteristic vector, comprised of 10 features extracted from RQA was defined for amino acid sequences. Using Principal Component Analysis, four factors were found to be important determinants of allosteric behavior. Our sequence-based predictor method shows 82.6% accuracy, 85.7% sensitivity and 77.9% specificity with the current dataset. Further, we show that Laminarity-Mean-hydrophobicity representing repeated hydrophobic patches is the most crucial indicator of allostery. To our best knowledge this is the first report that describes sequence determinants of allostery based on hydrophobicity. As an outcome of these findings, we plan to explore possibility of inducing allostery in proteins.

Synthesizing non-natural parts from natural genomic template.

BACKGROUND: The current knowledge of genes and proteins comes from 'naturally designed' coding and non-coding regions. It would be interesting to move beyond natural boundaries and make user-defined parts. To explore this possibility we made six non-natural proteins in E. coli. We also studied their potential tertiary structure and phenotypic outcomes. RESULTS: The chosen intergenic sequences were amplified and expressed using pBAD 202/D-TOPO vector. All six proteins showed significantly low similarity to the known proteins in the NCBI protein database. The protein expression was confirmed through Western blot. The endogenous expression of one of the proteins resulted in the cell growth inhibition. The growth inhibition was completely rescued by culturing cells in the inducer-free medium. Computational structure prediction suggests globular tertiary structure for two of the six non-natural proteins synthesized. CONCLUSION: To our best knowledge, this is the first study that demonstrates artificial synthesis of non-natural proteins from existing genomic template, their potential tertiary structure and phenotypic outcome. The work presented in this paper opens up a new avenue of investigating fundamental biology. Our approach can also be used to synthesize large numbers of non-natural RNA and protein parts for useful applications.

Amino-acid residue association models for large scale protein-protein interaction prediction.

UNLABELLED: The computational prediction of protein-protein interactions (PPI) is an essential complement to direct experimental evidence. Traditional approaches rely on less available or computationally predicted surface properties, show database-specific performances and are computationally expensive for large-scale datasets. Several sensitivity and specificity issues remain. Here, we report a novel method based on 'Amino-acid Residue Associations' (ARA) among interacting proteins which utilizes the accurate and easily available primary sequence. Large scale PPI datasets for six model species (from E. coli to human) were studied. The ARA method shows up to 73%sensitivity and 78% specificity. Furthermore, the method performs remarkably well in terms of stability and generalizability. The performance of ARA method benchmarked against existing prediction techniques shows performance improvement upto 25%. Ability of ARA method to predict PPI across species and across databases is also demonstrated. Overall, the ARA method provides a significant improvement over existing ones in correctly identifying large scale protein-protein interactions,irrespective of the data resource, network size or organism. AVAILABILITY: The MATLAB code for ARA approach will be made available upon request.

Rich can get poor: conversion of hub to non-hub proteins.

Hubs are ubiquitous network elements with high connectivity. One of the common observations about hub proteins is their preferential attachment leading to scale-free network topology. Here we examine the question: does rich protein always get richer, or can it get poor too? To answer this question, we compared similar and well-annotated hub proteins in six organisms, from prokaryotes to eukaryotes. Our findings indicate that hub proteins retain, gain or lose connectivity based on the context. Furthermore, the loss or gain of connectivity appears to correlate with the functional role of the protein in a given system.