Hi and welcome to my website!
I am a postdoctoral scholar working with Prof. Andrej Sali at UCSF. I develop Bayesian inference based methods to improve integrative structure determination of protein complexes using the software framework IMP. Application areas constitute chromosomal maintenance proteins and DNA helicases.
I also work closely with the Pancreatic Beta Cell Consortium (PBCC), which is a large cross-functional team of experimentalists and modelers across the US west (UCSF, USC, UCLA, Scripps, Salk) and the east (Rutgers) coasts, Montreal (Universite de Montreal) and Sanghai (iHuman Institute at SanghaiTech), committed to studying beta cell biology. As part of the consortium, I use Bayesian networks to build integrative models of glucose induced insulin secretion which may contribute to better understanding the role of cellular enzymatic pathways and their impairments to type-II diabetes.
I completed my PhD in Chemical Engineering with a minor in Computational Science and Engineering from UCSB in 2018, supervised by Prof. M. Scott Shell. In my thesis, I developed novel coarse-grained models of liquid mixtures, polymers and protein folding for efficient (~30-100 times faster than detailed atomistic representation) molecular dynamics (MD) simulations using variational inference techniques.
Please feel free to reach out, if you have questions or comments about my research or want to collaborate.
Featured article + 2019 editor’s choice article in the Journal of Chemical Physics (Feb 2020): Coarse grained protein model paper first selected as a featured article and soon after as one of 88 “most innovative and influential articles” of 2019, by the Journal of Chemical Physics. The article is free to download through the end of 2020.
LAMMPS open-source contributions (Sep 2019): New manybody potential and post-processing tool for replica exchange MD simulations, now added to LAMMPS-Sep2019 stable release.
Joined a postdoc position in the Sali lab at UCSF: Integrative structural modeling of eukaryotic DNA replisomal machinery and whole cell modeling of pancreatic beta cells.Oh and also, awesome food trucks around the UCSF Mission-bay campus.
Ph(inally) D(one)! (Dec. 2018): Thesis defense done! Graduated from UCSB!! (thesis)
Integrative structure determination of chromosomal protein complexes from chemical crosslink data.
I develop Bayesian inference based methods to understand the effect of model representation choices on the accuracy and precision of protein structure determined from experimental data. In the context of protein structure modeling, representation can be defined as the collection of all decisions that are summarily taken before performing the computationally expensive process of conformational sampling (e.g. using molecular dynamics or monte-carlo methods), such as whether to use atomistic or coarse-grained resolution for the protein residues, what fragments of a protein complexe already have structural informational available and how to best use that information, how to account for multiple metastable states of a protein complex, and so on. I seek formal mathematical ways to pose these questions so that, they are then amenable to Bayesian statistical techniques.
Specificially, I investigate how to best perform rigid fragment decomposition of available structural information (such as from x-ray structures or homology models) such that the number of degrees of freedom is neither too restrictive nor too permissive during monte-carlo conformational sampling.
(2019 - ) Brian Chait, Rockefeller University, New York
(2020 - ) Xiaolan Zhao, Memorial Sloan Kettering Cancer Center
Meta-modeling of pancreatic beta cells
Bayesian meta-modeling is a tentative name for integrative modeling at cellular scales, where available data not only spans different scales, types and experimental protocols, but are also best explained by different submodels. To build the most complete, accurate and precise model for the entire pancreatic beta cell, we try to integrate different submodels as-is (together with data used to parameterize such models) using directed probabilistc graphical modeling. Current efforts are focused on combining massively simplified models of enzyme networks with a meso-scopic (i.e. partlcle based) model of secretory insulin granlues to build a metamodel of glucose-insulin dynamics.
Collaborators (2019 - ): The Pancreatic Beta Cell Consortium
(Short tutorial on whole cell modeling using toy sub-models)
Coarse-grained backbone forcefields for molecular dynamics simulations of protein folding
Developed simplistic coarse-grained models of hydrophilic and hydrophobic poly-amino acids which can be intelligently combined to produce remarkably accurate backbone models for folding short peptide fragments as well as globular protein domains. This work was a proof of principle for protein backbone models designed from polymers using variational inference methods, and using only native contact based sidechain interactions demonstrated the potential to successfully fold 200+ residue proteins. Possible future directions include combining reduced alphabet and full alphabet sidechain interactions with aforementioned backbone forcefields to produce sequence chemistry dependent protein models.
Collaborators (2017 - 2018): Jeetain Mittal, Lehigh University
A hybrid, bottom-up, structurally accurate, Go-like coarse-grained protein model, Tanmoy Sanyal, Jeetain Mittal and M. Scott Shell, Journal of Chemical Physics, 2019, 151, 044111
This repository is a little sketchy; documentation will be updated soon. Also, a substantial part of this code uses the package
sim written in Python-2.7. A copy can be obtained through personal request to M. Scott Shell, UCSB.
Local density potentials for coarse grained molecular dynamics simulations of liquid mixtures
Developed computationally efficient manybody potentials for improving solvent models in implicit solvent simulations of polymer collapse and liquid-liquid phase separation in coarse-grained binary solutions of small hydrophobes (such as benzene or methanol in water). Depending only on the mean-field local density around solute particles, such potentials signficantly improved predictions of pair structure and clustering behavior of either component across widely varying mixture compositions. This work constitutes some of the very few structurally accurate molecular models of liquid-liquid phase separation in the chemical engineering literature.
Collaborators (2016-2019): Nico van der Vegt, TU Darmstadt
Coarse-grained models using local-density potentials optimized with the relative entropy: Application to implicit solvation, Tanmoy Sanyal and M. Scott Shell, Journal of Chemical Physics, 2016, 145, 034109
Transferable coarse-grained models of liquid-liquid equilibrium using local density potentials optimized with the relative entropy, Tanmoy Sanyal and M. Scott Shell, Journal of Physical Chemistry B, 2018, 122 (21), 5678-5693
Transferability of local density-assisted implicit solvation models for homogeneous fluid mixtures, David Rosenberger, Tanmoy Sanyal, M. Scott Shell and Nico F.A. van der Vegt, Journal of Chemical Theory and Computation, 2019, 15 (5), 2881-2895
Link to my Google Scholar profile.
A hybrid, bottom-up, structurally accurate, Go-like coarse-grained protein model , SI
Tanmoy Sanyal, Jeetain Mittal and M. Scott Shell
Journal of Chemical Physics, 2019, 151, 044111
(2019 editor’s pick article, July 2019 featured article)
Transferability of local density-assisted implicit solvation models for homogeneous fluid mixtures
David Rosenberger, Tanmoy Sanyal, M. Scott Shell and Nico F.A. van der Vegt
Journal of Chemical Theory and Computation, 2019, 15 (5), 2881-2895
Evaporation-induced assembly of colloidal crystals , SI
Michael P. Howard, Wesley F. Reinhart, Tanmoy Sanyal, M. Scott Shell
Arash Nikoubashman and Athanassios Z. Panagiotopoulos, Journal of Chemical Physics, 2018, 149, 209902
(2018 editor’s pick article)
Transferable coarse-grained models of liquid-liquid equilibrium using local density potentials optimized with the relative entropy , SI
Tanmoy Sanyal and M. Scott Shell
Journal of Physical Chemistry B, 2018, 122 (21), 5678-5693
Coarse-grained models using local-density potentials optimized with the relative entropy: Application to implicit solvation , SI
Tanmoy Sanyal and M. Scott Shell
Journal of Chemical Physics, 2016, 145, 034109
Multiscale analysis of simultaneous uptake of two reactive gases in the human lungs and its application to methemoglobin anemia
Tanmoy Sanyal and Saikat Chakraborty
Computers & Chemical Engineering, 2013, 59 (5), 226-242
Jan. 2019-present: Postdoctoral scholar, Sali lab, University of California San Francisco (UCSF), San Francisco, CA
2013-2018: PhD, Chemical Engineering, University of California Santa Barbara (UCSB), Santa Barbara, CA
2008-2013: B.Tech (Hons.) + M.Tech integrated dual degree, Chemical Engineering, Indian Institute of Technology Kharagpur (IIT-KGP), Kharagpur, WB, India