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Browsing by Author "Joshi, Soumil Y."

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    Coarse-grained molecular dynamics integrated with convolutional neural network for comparing shapes of temperature sensitive bottlebrushes
    Joshi, Soumil Y.; Singh, Samrendra; Deshmukh, Sanket A. (Nature Portfolio, 2022-03-18)
    Quantification of shape changes in nature-inspired soft material architectures of stimuli-sensitive polymers is critical for controlling their properties but is challenging due to their softness and flexibility. Here, we have computationally designed uniquely shaped bottlebrushes of a thermosensitive polymer, poly(N-isopropylacrylamide) (PNIPAM), by controlling the length of side chains along the backbone. Coarse-grained molecular dynamics simulations of solvated bottlebrushes were performed below and above the lower critical solution temperature of PNIPAM. Conventional analyses (free volume, asphericity, etc.) show that lengths of side chains and their immediate environments dictate the compactness and bending in these architectures. We further developed 100 unique convolutional neural network models that captured molecular-level features and generated a statistically significant quantification of the similarity between different shapes. Thus, our study provides insights into the shapes of complex architectures as well as a general method to analyze them. The shapes presented here may inspire the synthesis of new bottlebrushes.
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    Supramolecular Peptide Nanostructures Regulate Catalytic Efficiency and Selectivity
    Li, Zhao; Joshi, Soumil Y.; Wang, Yin; Deshmukh, Sanket A.; Matson, John B. (Wiley-V C H, 2023-05)
    We report three constitutionally isomeric tetrapeptides, each comprising one glutamic acid (E) residue, one histidine (H) residue, and two lysine (K-S) residues functionalized with side-chain hydrophobic S-aroylthiooxime (SATO) groups. Depending on the order of amino acids, these amphiphilic peptides self-assembled in aqueous solution into different nanostructures:nanoribbons, a mixture of nanotoroids and nanoribbons, or nanocoils. Each nanostructure catalyzed hydrolysis of a model substrate, with the nanocoils exhibiting the greatest rate enhancement and the highest enzymatic efficiency. Coarse-grained molecular dynamics simulations, analyzed with unsupervised machine learning, revealed clusters of H residues in hydrophobic pockets along the outer edge of the nanocoils, providing insight for the observed catalytic rate enhancement. Finally, all three supramolecular nanostructures catalyzed hydrolysis of the l-substrate only when a pair of enantiomeric Boc-l/d-Phe-ONp substrates were tested. This study highlights how subtle molecular-level changes can influence supramolecular nanostructures, and ultimately affect catalytic efficiency.