ToxinPredictor: Computational models to predict the toxicity of molecules.

Predicting the toxicity of molecules is essential in fields like drug discovery, environmental protection, and industrial chemical management. While traditional experimental methods are time-consuming and costly, computational models offer an efficient alternative. In this study, we introduce ToxinPredictor, a machine learning-based model to predict the toxicity of small molecules using their structural properties.

FlavorDB2: An updated database of flavor molecules.

Flavor is expressed through the interaction of molecules via gustatory and olfactory mechanisms. Knowing the pivotal role of flavor molecules in food and fragrances, the existence of a comprehensive repository becomes valuable. Such a repository encompasses the information of flavor molecules characterizing their flavor profile, chemical properties, regulatory status, consumption statistics, taste/aroma threshold values, reported uses in food categories, and synthesis.

Computational gastronomy: capturing culinary creativity by making food computable.

Cooking, a quintessential creative pursuit, holds profound significance for individuals, communities, and civilizations. Food and cooking transcend mere sensory pleasure to influence nutrition and public health outcomes. Inextricably linked to culinary and cultural heritage, food systems play a pivotal role in sustainability and the survival of life on our planet.

Camouflage Detection and Its Association with Cognitive Style: A Functional Connectivity Study.

Individual differences exist in performance in tasks that require visual search, such as camouflage detection (CD). Field dependence/independence (FD/I), as assessed using the Group Embedded Figures Test (GEFT), is an extensively studied dimension of cognitive style that classifies participants based on their visual perceptual styles. In the present study, we utilized fMRI on 46 healthy participants to investigate the underlying neural mechanisms specific to the cognitive styles of FD/FI while performing a CD task using both activation magnitude and an exploratory functional connectivity (FC) analysis.

Investigating the link between miR-34a-5p and TLR6 signaling in sepsis-induced ARDS.

Unlabelled: Acute lung injury (ALI)/acute respiratory distress syndrome (ARDS) are lung complications diagnosed by impaired gaseous exchanges leading to mortality. From the diverse etiologies, sepsis is a prominent contributor to ALI/ARDS. In the present study, we retrieved sepsis-induced ARDS mRNA expression profile and identified 883 differentially expressed genes (DEGs).

RAV1 family members function as transcriptional regulators and play a positive role in plant disease resistance.

Phytopathogens pose a severe threat to agriculture and strengthening the plant defense response is an important strategy for disease control. Here, we report that AtRAV1, an AP2 and B3 domain-containing transcription factor, is required for basal plant defense in Arabidopsis thaliana. The atrav1 mutant lines demonstrate hyper-susceptibility against fungal pathogens (Rhizoctonia solani and Botrytis cinerea), whereas AtRAV1 overexpressing lines exhibit disease resistance against them.

Machine learning models to predict sweetness of molecules.

Sweetness is a vital taste to which humans are innately attracted. Given the increasing prevalence of type-2 diabetes, it is highly relevant to build computational models to predict the sweetness of small molecules. Such models are valuable for identifying sweeteners with low calorific value.

Computational gastronomy: A data science approach to food.

Cooking forms the core of our cultural identity other than being the basis of nutrition and health. The increasing availability of culinary data and the advent of computational methods for their scrutiny are dramatically changing the artistic outlook towards gastronomy. Starting with a seemingly simple question, 'Why do we eat what we eat?', data-driven research conducted in our lab has led to interesting explorations of traditional recipes, their flavor composition, and health associations.

RecipeDB: a resource for exploring recipes.

Cooking is the act of turning nature into the culture, which has enabled the advent of the omnivorous human diet. The cultural wisdom of processing raw ingredients into delicious dishes is embodied in their cuisines. Recipes thus are the cultural capsules that encode elaborate cooking protocols for evoking sensory satiation as well as providing nourishment.

BitterSweet: Building machine learning models for predicting the bitter and sweet taste of small molecules.

The dichotomy of sweet and bitter tastes is a salient evolutionary feature of human gustatory system with an innate attraction to sweet taste and aversion to bitterness. A better understanding of molecular correlates of bitter-sweet taste gradient is crucial for identification of natural as well as synthetic compounds of desirable taste on this axis. While previous studies have advanced our understanding of the molecular basis of bitter-sweet taste and contributed models for their identification, there is ample scope to enhance these models by meticulous compilation of bitter-sweet molecules and utilization of a wide spectrum of molecular descriptors.

Network controllability analysis of intracellular signalling reveals viruses are actively controlling molecular systems.

In recent years control theory has been applied to biological systems with the aim of identifying the minimum set of molecular interactions that can drive the network to a required state. However, in an intra-cellular network it is unclear how control can be achieved in practice. To address this limitation we use viral infection, specifically human immunodeficiency virus type 1 (HIV-1) and hepatitis C virus (HCV), as a paradigm to model control of an infected cell.

Data-driven analysis of biomedical literature suggests broad-spectrum benefits of culinary herbs and spices.

Spices and herbs are key dietary ingredients used across cultures worldwide. Beyond their use as flavoring and coloring agents, the popularity of these aromatic plant products in culinary preparations has been attributed to their antimicrobial properties. Last few decades have witnessed an exponential growth of biomedical literature investigating the impact of spices and herbs on health, presenting an opportunity to mine for patterns from empirical evidence.

A hierarchical anatomical classification schema for prediction of phenotypic side effects.

Prediction of adverse drug reactions is an important problem in drug discovery endeavors which can be addressed with data-driven strategies. SIDER is one of the most reliable and frequently used datasets for identification of key features as well as building machine learning models for side effects prediction. The inherently unbalanced nature of this data presents with a difficult multi-label multi-class problem towards prediction of drug side effects.

FlavorDB: a database of flavor molecules.

Flavor is an expression of olfactory and gustatory sensations experienced through a multitude of chemical processes triggered by molecules. Beyond their key role in defining taste and smell, flavor molecules also regulate metabolic processes with consequences to health. Such molecules present in natural sources have been an integral part of human history with limited success in attempts to create synthetic alternatives.

Differential Network Analysis Reveals Evolutionary Complexity in Secondary Metabolism of Rauvolfia serpentina over Catharanthus roseus.

Comparative co-expression analysis of multiple species using high-throughput data is an integrative approach to determine the uniformity as well as diversification in biological processes. Rauvolfia serpentina and Catharanthus roseus, both members of Apocyanacae family, are reported to have remedial properties against multiple diseases. Despite of sharing upstream of terpenoid indole alkaloid pathway, there is significant diversity in tissue-specific synthesis and accumulation of specialized metabolites in these plants.

Re-analysis of protein data reveals the germination pathway and up accumulation mechanism of cell wall hydrolases during the radicle protrusion step of seed germination in Podophyllum hexandrum- a high altitude plant.

Podophyllum hexandrum Royle is an important high-altitude plant of Himalayas with immense medicinal value. Earlier, it was reported that the cell wall hydrolases were up accumulated during radicle protrusion step of Podophyllum seed germination. In the present study, Podophyllum seed Germination protein interaction Network (PGN) was constructed by using the differentially accumulated protein (DAP) data set of Podophyllum during the radicle protrusion step of seed germination, with reference to Arabidopsis protein-protein interaction network (AtPIN).

Analysis of Food Pairing in Regional Cuisines of India.

Any national cuisine is a sum total of its variety of regional cuisines, which are the cultural and historical identifiers of their respective regions. India is home to a number of regional cuisines that showcase its culinary diversity. Here, we study recipes from eight different regional cuisines of India spanning various geographies and climates.

Control of Neuronal Network in Caenorhabditis elegans.

Caenorhabditis elegans, a soil dwelling nematode, is evolutionarily rudimentary and contains only ∼ 300 neurons which are connected to each other via chemical synapses and gap junctions. This structural connectivity can be perceived as nodes and edges of a graph. Controlling complex networked systems (such as nervous system) has been an area of excitement for mankind.

Phytochemica: a platform to explore phytochemicals of medicinal plants.

Unlabelled: Plant-derived molecules (PDMs) are known to be a rich source of diverse scaffolds that could serve as the basis for rational drug design. Structured compilation of phytochemicals from traditional medicinal plants can facilitate prospection for novel PDMs and their analogs as therapeutic agents. Atropa belladonna, Catharanthus roseus, Heliotropium indicum, Picrorhiza kurroa and Podophyllum hexandrum are important Himalayan medicinal plants, reported to have immense therapeutic properties against various diseases.

Phenotypic side effects prediction by optimizing correlation with chemical and target profiles of drugs.

Despite technological progresses and improved understanding of biological systems, discovery of novel drugs is an inefficient, arduous and expensive process. Research and development cost of drugs is unreasonably high, largely attributed to the high attrition rate of candidate drugs due to adverse drug reactions. Computational methods for accurate prediction of drug side effects, rooted in empirical data of drugs, have the potential to enhance the efficacy of the drug discovery process.

SerpentinaDB: a database of plant-derived molecules of Rauvolfia serpentina.

Background: Plant-derived molecules (PDMs) are known to be a rich source of diverse scaffolds that could serve as a basis for rational drug design. Structured compilation of phytochemicals from traditional medicinal plants can facilitate prospection for novel PDMs and their analogs as therapeutic agents. Rauvolfia serpentina is an important medicinal plant, endemic to Himalayan mountain ranges of Indian subcontinent, reported to be of immense therapeutic value against various diseases.

Tyrosine- and tryptophan-coated gold nanoparticles inhibit amyloid aggregation of insulin.

Here, we have strategically synthesized stable gold (AuNPs(Tyr), AuNPs(Trp)) and silver (AgNPs(Tyr)) nanoparticles which are surface functionalized with either tyrosine or tryptophan residues and have examined their potential to inhibit amyloid aggregation of insulin. Inhibition of both spontaneous and seed-induced aggregation of insulin was observed in the presence of AuNPs(Tyr), AgNPs(Tyr), and AuNPs(Trp) nanoparticles. These nanoparticles also triggered the disassembly of insulin amyloid fibrils.

Capsaicin inhibits collagen fibril formation and increases the stability of collagen fibers.

Capsaicin is a versatile plant product which has been ascribed several health benefits and anti-inflammatory and analgesic properties. We have investigated the effect of capsaicin on the molecular stability, self-assembly, and fibril stability of type-I collagen. It was found that capsaicin suppresses collagen fibril formation, increases the stability of collagen fibers in tendons, and has no effect on the molecular stability of collagen.

Identification of key nodes of type 2 diabetes mellitus protein interactome and study of their interactions with phloridzin.

Network biology-inspired approaches could be used effectively in probing regulatory processes by which small molecules intervene with disease mechanisms. The present study aims at identification of key targets of type 2 diabetes mellitus (T2DM) by network analysis of the underlying protein interactome, and probing for mechanisms by which phloridzin could be critical at altering the disease phenotype. Towards this goal, we constructed a protein-protein interaction network associated with T2DM, starting from candidate genes and systems-level interactions data available.

Prospecting for novel plant-derived molecules of Rauvolfia serpentina as inhibitors of Aldose Reductase, a potent drug target for diabetes and its complications.

Aldose Reductase (AR) is implicated in the development of secondary complications of diabetes, providing an interesting target for therapeutic intervention. Extracts of Rauvolfia serpentina, a medicinal plant endemic to the Himalayan mountain range, have been known to be effective in alleviating diabetes and its complications. In this study, we aim to prospect for novel plant-derived inhibitors from R.