Computational Biophysical Chemistry · SNBNCBS · Est. 2023
Abdul Aziz Mandal — Research Scholar
Working at the intersection of computational chemistry, biophysics, and machine learning to unravel molecular aggregation pathways and interactions of complex biological systems at S.N. Bose National Centre for Basic Sciences, Kolkata.
About Me
I am a Research Scholar in Computational Chemistry (MD Simulation) at the S. N. Bose National Centre for Basic Sciences (SNBNCBS), Kolkata. My work sits at the intersection of physical chemistry, biophysics, and computational science.
My research aims to unravel the molecular aggregation pathways and molecular interactions of small molecules in complex biological systems — particularly disease-linked peptide and protein aggregation processes and their modulation by small organic molecules and metal ions.
I am currently developing expertise in machine learning potentials and enhanced-sampling techniques to push the boundaries of what classical MD simulations can reveal about biological systems.
Research Interests
Education
Achievements & Awards
Research Themes
Exploring the aggregation pathways of disease-linked proteins and peptides (Alzheimer's, Parkinson's) and mechanistic inhibition by small molecules and metal ions using atomistic MD simulations.
Computational BiologyDeveloping expertise in ML-based force fields and enhanced sampling methods — metadynamics, replica exchange, OPES — to access timescales beyond classical MD.
ML / AIInvestigating the role of water structure and solvation dynamics in modulating protein stability, aggregation, and small-molecule binding thermodynamics.
Physical ChemistryStudying binding affinities and interaction mechanisms of small organic molecules with aggregation-prone proteins using docking and free-energy perturbation methods.
MD SimulationsCharacterising the conformational ensemble and aggregation propensity of intrinsically disordered regions using all-atom simulations.
BiophysicsLearning and applying umbrella sampling, metadynamics, thermodynamic integration, and MBAR to compute binding free energies and conformational free energy landscapes.
Statistical MechanicsComputational Tools & Software
Selected Work
People
Resources
Step-by-step molecular dynamics tutorials for beginners and advanced users.
Open ResourceAmber is a suite of biomolecular simulation programs.
Open ResourceOfficial tutorials, examples, and user guides for molecular dynamics simulations using LAMMPS.
Open ResourceStep-by-step molecular dynamics tutorials and documentation for beginners and advanced OpenMM users.
Open ResourceLearn molecular visualization, trajectory analysis, and simulation setup.
Open TutorialPython module for post-processing, analyzing, and visualizing molecular dynamics trajectories and simulation data.
Open ResourceGenerate topology and parameter files for small molecules using AMBER, OPLS-AA, CHARMM, and GROMOS force fields.
ACPYPE (AMBER) AmberTools LigParGen (OPLS-AA) CHARMM CHARMM-GUI Ligand Reader CGenFF (CHARMM) ATB (GROMOS)Generate initial configurations for molecular dynamics simulations by packing molecules into simulation boxes using geometric optimization.
Open Resourcegmx_MMPBSA is a new tool based on AMBER's MMPBSA.py aiming to perform end-state free energy calculations with GROMACS files.
Open ResourceGenerate hybrid structures and topologies for amino acid mutations and perform alchemical free energy calculations using PMX.
Open ResourceTutorials and examples for MARTINI coarse-grained simulations.
Open TutorialSingle worldwide archive of structural data of biological macromolecules.
Open TutorialWeb-based platform for preparing simulation-ready biomolecular systems.
Open WebsitePredicted protein structures generated using AlphaFold.
Browse DatabaseReference for LaTeX symbols and special character formatting.
View GuideCreate custom DNA and RNA PDB structures from user-defined sequences.
NAFlexPredict liquid–liquid phase separation propensity of proteins.
Run PredictionOfficial CHARMM force-field distributions compatible with GROMACS.
Download FFComprehensive documentation for collective variables and enhanced sampling methods.
Read DocsNetwork
Find Me
I am open to research collaborations, seminar invitations, and discussions on computational biophysical chemistry. Graduate students and postdocs interested in joining are also welcome to write.
Please include a brief description of your background and what you'd like to work on. I typically respond within 3–5 working days.
Send an Email