SearcharxivSearch

arXiv · 2108.09747

Neuromodulators in food ingredients: insights from network pharmacological evaluation of Ayurvedic herbs

Abstract

The global burden of neurological diseases, the second leading cause of death after heart dis-eases constitutes one of the major challenges of modern medicine. Ayurveda, the traditional Indian medicinal systemenrooted in the Vedic literature and considered as a schema for the holistic management of health, characterizes various neurological diseases disorders (NDDs) and prescribes several herbs, formulations, and bio-cleansing regimes for their care and cure. In this work, we examined neuro-phytoregulatory potential of 34,472 phytochemicals among 3,038 herbs (including their varieties) mentioned in Ayurveda using network pharmacology approach and found that 45% of these Ayurvedic phytochemicals (APCs) have regulatory associations with 1,643 approved protein targets. Metabolite interconversion enzymes and protein modifying enzymes were found to be the major target classes of APCs against NDDs. The study further suggests that the actions of Ayurvedic herbs in managing NDDs were majorly via regulating signalling processes, like, G-protein signaling, acetylcholine signaling, chemokine signaling pathway and GnRH signaling. A high confidence network specific to 219 pharmaceutically relevant neuro-phytoregulators (NPRs) from 1,197 Ayurvedic herbs against 102 approved protein-targets involved in NDDs was developed and analyzed for gaining mechanistic insights. The key protein targets of NPRs to elicit their neuro-regulatory effect were highlighted as CYP and TRPA, while estradiol and melatonin were identified as the NPRs with high multi-targeting ability. 32 herbs enriched in NPRs were identified that include some of the well-known Ayurvedic neurological recommendations, like, Papaver somniferum, Glycyrrhiza glabra, Citrus aurantium, Cannabis sativa etc. Herbs enriched in NPRs may be used as a chemical source library for drug-discovery against NDDs from systems medicine perspectives.

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Neha Choudhary, Vikram Singh. 2021-08-22. Neuromodulators in food ingredients: insights from network pharmacological evaluation of Ayurvedic herbs. https://arxiv.org/abs/2108.09747

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related papers

Sequence-Informed Geometric Evaluation of RNA 3D Structures

Computational RNA structure pipelines generate many candidate conformations for the same sequence. Reliable evaluation therefore requires more than recognising plausible geometry, it requires determining whether that geometry is compatible with the sequence. We introduce SIRGE, a sequence-informed geometric evaluator that conditions structural representations on nucleotide embeddings from a pretrained RNA language model. Early results show that SIRGE outperforms established evaluators in Kendall--$\tau$ alignment, Top-1 selection, and Top-3 ranking. Controlled comparisons further show that sequence conditioning corrects errors made by an otherwise matched geometric model and improves target-level rank structure. These findings provide initial evidence that pretrained sequence representations supply ranking information that complements geometric reasoning.

q-bio.BM

PocketVE: Stable and Property-Guided Structure-Based Drug Design with Variance-Exploding Diffusion

Protein-conditioned 3D molecule generation is a central challenge in structure-based drug design, requiring a balance between pocket compatibility, molecular properties, and physical geometry. We propose \textbf{PocketVE}, a protein-pocket-conditioned variance-exploding (VE) diffusion framework that couples stable coordinate denoising with inference-time property guidance. Specifically, PocketVE combines an EDM-style training and sampling setup for 3D denoising, classifier-free guidance for multi-property steering without external property classifiers, and adaptive protein perturbation as a training-time pocket regularizer. Evaluated on CrossDocked2020 under the GenBench3D protocol, PocketVE improves Valid$_{3\text{D}}$ from 58.6 to 80.6 and reduces strain energy from 457.4 to 127.9 relative to its TAGMol architectural baseline, while retaining competitive docking and molecular-property scores under moderate guidance. A guidance-scale study shows that moderate guidance gives a favorable balance between target-related objectives and geometric quality, whereas stronger guidance can degrade geometry and distributional fidelity. Pocket-permutation and PoseCheck diagnostics further support pocket-specific spatial compatibility with reduced steric conflicts. Overall, the results suggest that geometric stability and inference-time property guidance should be considered as coupled design objectives.

q-bio.BM

Predicting directional flexibility in proteins

Predicting protein dynamics is a long-standing problem in computational structural biology. Often, protein function critically depends on local directed motions, such as hinge movements, catalytic loop rearrangements and domain reorientations, which can be characterized by directional flexibility and correlated structural motions of the protein backbone. While Molecular Dynamics (MD) simulations provide an established but often prohibitively expensive approach, recent deep generative models aim to reduce this cost by directly predicting conformational ensembles, emulating MD. However, due to their large size and the need to generate several states until the derived dynamical properties converge, these models remain expensive. In this work, we propose BackFlip-2: a fast SE(3)-equivariant graph neural network trained to directly predict dynamical descriptors, such as directional backbone flexibility and pairwise dynamic correlations, from an equilibrium structure. In a series of experiments, we show that our model matches the accuracy of substantially larger ensemble generation models while being orders of magnitude faster, and demonstrate that the proposed equivariant architecture is especially well-suited for capturing anisotropic motions in proteins. BackFlip-2 model weights, training and inference code are available at https://github.com/graeter-group/backflip.

q-bio.BM