Abdul Raafik Arattu Thodika

Computational Chemistry   |   Biophysics   |   Machine Learning Potentials

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As a graduate student pursuing a Ph.D. in the Department of Chemistry and Biochemistry at the University of Texas at Arlington, I am actively engaged in computational chemistry research under the mentorship of Dr. Kwangho Nam. My work focuses on developing and applying multiscale quantum mechanical/molecular mechanical (QM/MM) methodologies to investigate the thermodynamics and kinetics of chemical catalysis within enzyme environments. Alongside traditional sampling approaches, I’m exploring the integration of machine learning techniques to improve the efficiency of analyzing complex chemical systems.

Additionally, I am a trained electrochemist, particularly interested in the areas of fuel cells and rechargeable redox flow batteries. My current research in this direction involves studying the electrostatic effects of the electrical double layer (EDL) at electrode/electrolyte interfaces, contributing to advancements in energy storage and electrochemical processes.

Education

• Ph.D. in Physical Chemistry, University of Texas at Arlington, 2021 - Present, Advisor: Dr. Kwangho Nam
• BS & MS in Chemistry, Indian Institute of Science Education and Research(IISER Pune, India), 2015 - 2020
  • (Thesis: Outer Sphere Redox Chemistry for Rechargeable Flow Batteries, Advisor: Dr. Musthafa Ottakam Thotiyil)

Relevant Skills

• Programming Languages: Python, Bash, MATLAB
• Software: CHARMM, Amber, Gaussian, QChem, VMD, Schrödinger PyMOL, CHARMM GUI
• Machine Learning: PyTorch, Scikit-Learn, NumPy, Alphafold2

Career History

• Graduate Research Assistant, University of Texas at Arlington, 2021 - Present

• Undergraduate Research in Electrochemistry/Batteries, Indian Institute of Science Education and Research (Pune, India), 2017 - 2020

  • My research focussed on understanding thermodynamics & kinetics associated with electrochemical redox reactions and developing novel energy storage/conversion devices. The projects were supervised by Dr. Musthafa Ottakam Thotiyil.

Ongoing Projects

• Machine Learning Potentials (MLP) for Free Energy Simulations of Enzymatic Reactions
  • Collaboration: Dr. Xiaoliang Pan
  • Description: In the ongoing work, we undertake a comprehensive re-examination of the ML architecture introduced by Pan et al (for QM/MM free energy simulations) to rigorously evaluate the transferability of a pre-trained MLP and Δ-learning based MLP models.
  • Relevant Publications:
    • [Manuscript in preparation, 2024]
      
• Dynamic Perspectives on Catalytic Function in Adenylate Kinase
  • Collaboration: Dr. Magnus Wolf-Watz
  • Description: In our project, we aim to understand the catalytic mechanism of adenylate kinase using a combination of classical molecular dynamics and QM/MM simulations. Our particular interest lies in examining the role of protein dynamics in the enzyme’s catalytic function. Additionally, we are exploring the evolutionary trajectory of adenylate kinase’s thermoadaptation across different species with the aid of Alphafold2 predicted structures.
  • Relevant Publications:
    • Elucidating Dynamics of Adenylate Kinase from Enzyme Opening to Ligand Release. [J. Chem. Inf. Model., 2024]
    • Magnesium induced structural reorganization in the active site of adenylate kinase. [Sci. Adv., 2024]
      
• Electrostatic Effects of the Electrical Double Layer (EDL) at Electrode/Electrolyte Interfaces
  • Collaboration: Dr. Musthafa Ottakam Thotiyil
  • Description: In this project, we are exploring how modifications to the electrode surface might influence the electrostatic effects of the electrical double layer (EDL) at electrode/electrolyte interfaces. This investigation could impact the flux of ions, electrocatalytic activity, and the overall performance of electrochemical devices through the modulation of electrode surface potential.
  • Relevant Publications:
    • Directional Molecular Transport in Iron Redox Flow Batteries by Interfacial Electrostatic Forces. [J. Colloid Interface Sci., 2024]
      
• CHARMM-GUI QM/MM Interfacer Module
  • Collaboration:: Dr. Donghyuk Suh
  • Description: QM/MM Interfacer Module (part of CHARMM-GUI) provides a GUI-based platform to set up QM/MM simulations in CHARMM and Amber. In this project, I assist in debugging, generating template scripts, and testing the module for various QM/MM MD simulations applied to enzyme systems.
  • Relevant Publications:
    • CHARMM-GUI QM/MM Interfacer for a Quantum Mechanical and Molecular Mechanical (QM/MM) Simulation Setup: 1. Semiempirical Methods [J. Chem. Theory Comput., 2024]

Presentations

• Oral
  • 2024 ACS Meeting in Miniature (MiM) (UDallas, Texas, US): “Machine Learning Potential Models for Free Energy Simulations of Enzyme Reactions”
• Posters
  • 2024 Computational Chemistry Gordon Research Conference (GRC) (UMaine Portland, Maine, US): “Exploring Transferability of Machine Learning Potentials for Free Energy Simulations of Enzymatic Reaction: A Case Study on Dihydrofolate Reductase Catalyzed Reaction”
  • 2024 Chemistry at Harvard Molecular Mechanics (CHARMM) Developers' Meeting (UM Baltimore, Maryland, US): “Machine Learning Potential (MLP)-assisted QM/MM Molecular Dynamics Simulations in CHARMM Program”
  • 2023 Southwest Regional Meeting (SWRM) of The American Chemical Society (Oklahoma City, Oklahoma, US): “Dynamic Perspective on Catalytic Function in Adenylate Kinase: Decoding The Role of Magnesium Ion with Molecular Dynamics Simulations”
  • 2022 Discover Symposium (UT Arlington, Texas, US): “Insights on Evolutionary Thermoadaptation of Adenylate Kinase: Ancestral Protein Structure Prediction with Deep Learning”

Services

• Conference Host: 2019 Newton-Bhabha Fund Indo UK Researchers Link International Workshop

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Last updated: August 2024