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abhik-compbio/README.md

Hi ๐Ÿ‘‹, I'm Abhik

Postdoctoral Research Associate Department of Chemistry, College of Staten Island

City University of New York, USA ๐Ÿ‡ฎ๐Ÿ‡ณ

abhikgmoulick

GIF

Research interests:

  • Structural & dynamical properties of bio molecules like protein.
  • Conformational thermodynamics & enhanced sampling.
  • Dynamics of hydration water.
  • Application of machine learning in biomolecular simulations.
  • Allostery.
  • Protein-ligand interactions.
  • Coarse grained modelling of protein

Manuscript accepted:

  1. Sequence Dependence in Nucleosome Dynamics, Prabir Khatua, Phu K Tang, A.G. Moulick, Rutika Patel, Anjela Manandhar, Sharon Loverde, J. Phys. Chem. B 2024, 128, 13, 3090โ€“3101
  2. Fluctuation dominated ligand binding in molten globule protein, Abhik Ghosh Moulick & J. Chakrabarti, Journal of Chemical Information and Modeling, 2023, 63, 17, 5583โ€“5591.
  3. Conformational fluctuations in molten globule state of ฮฑ-lactalbumin, Abhik Ghosh Moulick & J. Chakrabarti, Physical Chemistry Chemical Physics, 2022, 24, 21348.
  4. Correlated dipolar and dihedral fluctuations in a protein, Abhik Ghosh Moulick & J. Chakrabarti, Chemical Physics Letters 797 (2022) 139574.
  5. A.G.Moulick & J. Chakrabarti, Correlation between protein bond vector and dihedral fluctuations, AIP Conference Proceedings 2265, 030036 (2020).

Manuscript under preparation:

  1. Coarse-grained model of protein with structural information, Abhik Ghosh Moulick & J. Chakrabarti.

Computational Skill & Softwares:

  • Operating system: MacOS, Linux.
  • Programming Language: Fortran, Python, R, Bash Script.
  • Software: Gromacs, NAMD, Amber.
  • Visualisation & Analysis: VMD, Pymol.
  • HPC Experience: CRAY (SNBNCBS).
  • Others: GNUPlot, XMGrace, Latex, Lyx.

Skills & Techniques:

  • Classical all atom Molecular Dynamics.
  • Analysis of time dependent cross correlation functions.
  • Numerical analysis e.g. Gaussian quadra- ture e.t.c.
  • Constant pH molecular dynamics in implicit & explicit solvent.
  • Clustering (Geometrical clustering, Density based cluystering).
  • Machine learning model (XGBoost).
  • Protein-ligand docking.
  • Enhanced sampling.
  • Self-Van Hove function, Mean Squared Dis- placement, Survival probability.
  • Monte carlo simulation.
  • Bead-spring polymer model.

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