Biology subjects

Sundar, D.

Publications and source records attributed to Sundar, D..

6 recordsLinked to original sources

bioRxiv2026

Atomistic Simulation of Blood Brain Barrier Permeability of Propolis Derived Natural Compounds

The ability of small molecules to cross the blood-brain barrier (BBB) remains a major bottleneck in neurotherapeutic development. While experimental assays and machine learning approaches provide approximate permeability estimates, they lack atomistic insight into the underlying transport mechanisms. Here, we employ all-atom molecular dynamics simulations of a compositionally realistic BBB lipid bilayer to characterize the passive permeation of two bioactive propolis-derived compounds, Caffeic Acid Phenethyl Ester (CAPE) and Artepillin-C (ARC). Using steered molecular dynamics and umbrella sampling, we computed free energy profiles, diffusion coefficients, and permeability metrics across the membrane. CAPE encounters a modest barrier at the lipid headgroup region but minimal resistance within the hydrophobic core, resulting in a low free energy barrier ([~]2-3 kcal/mol) and favorable permeability (logP_eff {approx} 0.28). In contrast, ARC exhibits a substantial energetic barrier within the membrane core, leading to high resistivity and strongly unfavorable permeability (logP_eff {approx} -10.91). The heterogeneous lipid model reproduces experimentally consistent membrane properties and reveals how lipid composition modulates transport energetics. These findings provide mechanistic insight into BBB permeability and demonstrate the utility of atomistic simulations for guiding the design of neuroactive therapeutics.

biophysics↗

bioRxiv2023

Estimating protein-ligand interactions with geometric deep learning and mixture density models

Understanding the interactions between a ligand and its molecular target is crucial in guiding the optimization of molecules for any in-silico drug-design workflow. Multiple experimental and computational methods have been developed to better understand these intermolecular interactions. With the availability of a large number of structural datasets, there is a need for developing statistical frameworks that improve upon existing physics-based solutions. Here, we report a method based on geometric deep learning that is capable of predicting the binding conformations of ligands to protein targets. A technique to generate graphical representations of protein was developed to exploit the topological and electrostatic properties of the binding region. The developed framework, based on graph neural networks, learns a statistical potential based on the distance likelihood, which is tailor-made for each ligand-target pair. This potential can be coupled with global optimization algorithms such as differential evolution to reproduce the experimental binding conformations of ligands. We show that the potential based on distance likelihood, described here, performs similarly or better than well-established scoring functions for docking and screening tasks. Overall, this method represents an example of how artificial intelligence can be used to improve structure-based drug design. Significance statementCurrent machine learning-based solutions to model protein-ligand interactions lack the level of interpretability that physics-based methods usually provide. Here, a workflow to embed protein binding surfaces as graphs was developed to serve as a viable data structure to be processed by geometric deep learning. The developed architecture based on mixture density models was employed to accurately estimate the position and conformation of the small molecule within the binding region. The likelihood-based scoring function was compared against existing physics-based alternatives, and significant performance improvements in terms of docking power, screening power and reverse screening power were observed. Taken together, the developed framework provides a platform for utilising geometric deep-learning models for interpretable prediction of protein-ligand interactions at a residue level.

bioinformatics↗

bioRxiv2023

M-Ionic: Prediction of metal ion binding sites from sequence using residue embeddings

MotivationUnderstanding metal-protein interaction can provide structural and functional insights into cellular processes. As the number of protein sequences increases, developing fast yet precise computational approaches to predict and annotate metal binding sites becomes imperative. Quick and resource-efficient pre-trained protein language model (PLM) embeddings have successfully predicted binding sites from protein sequences despite not using structural or evolutionary features (multiple sequence alignments). Using residue-level embeddings from the PLMs, we have developed a sequence-based method (M-Ionic) to identify metal-binding proteins and predict residues involved in metal-binding. ResultsOn independent validation of recent proteins, M-Ionic reports an area under the curve (AUROC) of 0.83 (recall=84.6%) in distinguishing metal-binding from non-binding proteins compared to AUROC of 0.74 (recall =61.8%) of the next best method. In addition to comparable performance to the state-of-the-art method for identifying metal-binding residues (Ca2+, Mg2+, Mn2+, Zn2+), M-Ionic provides binding probabilities for six additional ions (i.e., Cu2+, Po43-, So42-, Fe2+, Fe3+, Co2+). We show that the PLM embedding of a single residue contains sufficient information about its neighbours to predict its binding properties. Availability and ImplementationM-Ionic can be used on your protein of interest using a Google Colab Notebook (https://bit.ly/40FrRbK). GitHub repository (https://github.com/TeamSundar/m-ionic) contains all code and data. Contactarne@bioinfo.se Supplementary informationSupplementary data are available at Bioinformatics online.

bioinformatics↗

bioRxiv2021

Possible link between higher transmissibility of B.1.617 and B.1.1.7 variants of SARS-CoV-2 and increased structural stability of its spike protein and hACE2 affinity

The Severe Acute syndrome corona Virus 2 (SARS-CoV-2) outbreak in December 2019 has caused a global pandemic. The rapid mutation rate in the virus has caused alarming situations worldwide and is being attributed to the false negativity in RT-PCR tests, which also might lead to inefficacy of the available drugs. It has also increased the chances of reinfection and immune escape. We have performed Molecular Dynamic simulations of three different Spike-ACE2 complexes, namely Wildtype (WT), B.1.1.7 variant (N501Y Spike mutant) and B.1.617 variant (L452R, E484Q Spike mutant) and compared their dynamics, binding energy and molecular interactions. Our result shows that mutation has caused the increase in the binding energy between the Spike and hACE2. In the case of B.1.617 variant, the mutations at L452R and E484Q increased the stability and intra-chain interactions in the Spike protein, which may change the interaction ability of human antibodies to this Spike variant. Further, we found that the B.1.1.7 variant had increased hydrogen interaction with LYS353 of hACE2 and more binding affinity in comparison to WT. The current study provides the biophysical basis for understanding the molecular mechanism and rationale behind the increase in the transmissivity and infectivity of the mutants compared to wild-type SARS-CoV-2.

biophysics↗

bioRxiv2019

Computational and experimental evidence to the permeability of withanolides across the normal cell membrane

Poor bioavailability due to the inability to cross the cell membrane is one of the major reasons for the failure of a drug in the clinical trials. We have used molecular dynamics simulations to predict the membrane permeability of natural drugs - withanolides (withaferin-A and withanone) that have similar structures but remarkably differ in their cytotoxicity. We found that withaferin-A, but not withanone, could proficiently transverse through the model membrane. The free energy profiles obtained were in accordance with the physico-chemical properties of the investigated drug molecules. It was observed that the polar head group of the bilayer exhibits high resistance for the passage of withanone as compared to withaferin-A, while the interior of the membrane behaves similarly for both withanolides. The solvation analysis revealed that the high solvation of terminal O5 oxygen of withaferin-A was the major driving force. The impact of the favorable interaction of terminal oxygen (O5) of withaferin-A with the phosphate of the membrane led to its smooth passage across the bilayer. The computational predictions were validated by raising and recruiting unique antibodies that react to withaferin-A and withanone. Further, the time-lapsed analyses of control and treated human normal and cancer cells, demonstrated proficient permeation of withaferin-A, but not withanone, through normal cells. These data strongly validated our computational method for predicting permeability and hence bioavailability of candidate compounds in the drug development process.\n\nStatement of significanceWhat determines the bioavailability of a drug? Does the ability to cross cell membrane determine this? A combined simulation/experimental study of the permeability of two natural drugs - withanolides (Wi-A and Wi-N) across the cell membrane was conducted. In the computational portion of the study, steered MD simulations were performed to investigate the propensity of the two molecules to permeate across the cell. It is found that Wi-A proceeds relatively simply across the cell compared to Wi-N. This trend was found to be consistent with experiment. This work is an important step towards understanding the molecular basis of permeability of natural drug molecules.

biophysics↗

bioRxiv2018

The Complete Chloroplast Genome of Dendrobium nobile, an endangered medicinal orchid from Northeast India and its comparison with related chloroplast genomes of Dendrobium species.

The medicinal orchid genus Dendrobium belonging to the Orchidaceae family is the largest genus comprising about 800-1500 species. To better illustrate the species status in the genus Dendrobium, a comparative analysis of 33 newly sequenced chloroplast genomes retrieved from NCBI Refseq database was compared with that of the first complete chloroplast genome of D. nobile from north-east India based on next-generation sequencing methods (Illumina HiSeq 2500-PE150). Our results provide comparative chloroplast genomic information for taxonomical identification, alignment-free phylogenomic inference and other statistical features of Dendrobium plastomes, which can also provide valuable information on their mutational events and sequence divergence.

evolutionary biology↗