Page Not Found
Page not found. Your pixels are in another canvas.
Sitemap
A list of all the posts and pages found on the site. For you robots out there is an XML version available for digesting as well.
Pages
About me
About me
Page not in menu
This is a page not in th emain menu
Posts
Future Blog Post
Published:
This post will show up by default. To disable scheduling of future posts, edit config.yml and set future: false.
Blog Post number 4
Published:
This is a sample blog post. Lorem ipsum I can’t remember the rest of lorem ipsum and don’t have an internet connection right now. Testing testing testing this blog post. Blog posts are cool.
Blog Post number 3
Published:
This is a sample blog post. Lorem ipsum I can’t remember the rest of lorem ipsum and don’t have an internet connection right now. Testing testing testing this blog post. Blog posts are cool.
Blog Post number 2
Published:
This is a sample blog post. Lorem ipsum I can’t remember the rest of lorem ipsum and don’t have an internet connection right now. Testing testing testing this blog post. Blog posts are cool.
Blog Post number 1
Published:
This is a sample blog post. Lorem ipsum I can’t remember the rest of lorem ipsum and don’t have an internet connection right now. Testing testing testing this blog post. Blog posts are cool.
portfolio
Portfolio item number 1
Short description of portfolio item number 1
Portfolio item number 2
Short description of portfolio item number 2
publications
Adaptive Sampling Methods for Molecular Dynamics in the Era of Machine Learning
Published in The Journal of Physical Chemistry B, 2023
Abstract
Molecular dynamics (MD) simulations are fundamental computational tools for the study of proteins and their free energy landscapes. However, sampling protein conformational changes through MD simulations is challenging due to the relatively long time scales of these processes. Many enhanced sampling approaches have emerged to tackle this problem, including biased sampling and path-sampling methods. In this Perspective, we focus on adaptive sampling algorithms. These techniques differ from other approaches because the thermodynamic ensemble is preserved and the sampling is enhanced solely by restarting MD trajectories at particularly chosen seeds rather than introducing biasing forces. We begin our treatment with an overview of theoretically transparent methods, where we discuss principles and guidelines for adaptive sampling. Then, we present a brief summary of select methods that have been applied to realistic systems in the past. Finally, we discuss recent advances in adaptive sampling methodology powered by deep learning techniques, as well as their shortcomings.
Recommended citation: Diego E. Kleiman, Hassan Nadeem, and Diwakar Shukla The Journal of Physical Chemistry B 2023 127 (50), 10669-10681 DOI: 10.1021/acs.jpcb.3c04843 https://pubs.acs.org/doi/10.1021/acs.jpcb.3c04843
Ensemble Adaptive Sampling Scheme: Identifying an Optimal Sampling Strategy via Policy Ranking
Published in Journal of Chemical Theory and Computation, 2025
Abstract
Efficient sampling in biomolecular simulations is critical for accurately capturing the complex dynamic behaviors of biological systems. Adaptive sampling techniques aim to improve efficiency by focusing computational resources on the most relevant regions of the phase space. In this work, we present a framework for identifying the optimal sampling policy through metric-driven ranking. Our approach systematically evaluates the policy ensemble and ranks the policies based on their ability to explore the conformational space effectively. Through a series of biomolecular simulation case studies, we demonstrate that the choice of a different adaptive sampling policy at each round significantly outperforms single policy sampling, leading to faster convergence and improved sampling performance. This approach takes an ensemble of adaptive sampling policies and identifies the optimal policy for the next round based on current data. Beyond presenting this ensemble view of adaptive sampling, we also propose two sampling algorithms that approximate this ranking framework on the fly. The modularity of this framework allows incorporation of any adaptive sampling policy, making it versatile and suitable as a comprehensive adaptive sampling scheme.
Recommended citation: Hassan Nadeem, and Diwakar Shukla J. Chem. Theory Comput. 2025, 21, 9, 4626–4639 DOI: 10.1021/acs.jctc.4c01488 https://pubs.acs.org/doi/abs/10.1021/acs.jctc.4c01488
Structural Basis for Negative Regulation of ABA Signaling by ROP11 GTPase
Published in Journal of Chemical Information and Modeling, 2025
Abstract
Abscisic acid (ABA) is an essential plant hormone that is responsible for plant development and stress responses. Recent structural and biochemical studies have identified the key components involved in the ABA signaling cascade, including PYR/PYL/RCAR receptors, protein phosphatases PP2C, and protein kinases SnRK2. The plant-specific Rho-like (ROPs) small GTPases are negative regulators of ABA signal transduction by interacting with PP2C, which can shut off “leaky” ABA signal transduction caused by the constitutive activity of monomeric PYR/PYL/RCAR receptors. However, the structural basis for the negative regulation of ABA signaling by ROP GTPases remains elusive. In this study, we have utilized large-scale coarse-grained (10.05 ms) and all-atom molecular dynamics simulations and standard protein–protein binding free energy calculations to predict the complex structure of AtROP11 and phosphatase AtABI1. In addition, we have predicted the detailed complex association pathway and identified the critical residue pairs in AtROP11 and AtABI1 for complex stability. Overall, this study established a powerful framework for using large-scale molecular simulations to predict unknown protein complex structures and suggested the molecular mechanism of the negative regulation of ABA signal transduction by small GTPases..
Recommended citation: Chuankai Zhao, Hassan Nadeem, and Diwakar Shukla Journal of Chemical Information and Modeling 2025 DOI: 10.1021/acs.jcim.5c02002 https://pubs.acs.org/doi/abs/10.1021/acs.jcim.5c02002
teaching
BIOE 485: Computational Mathematics for Machine Learning and Imaging
Undergraduate/Graduate course, University of Illinois at Urbana-Champaign, Department of Bioengineering, 2024
Covers fundamental mathematical and computational methods needed to implement computational imaging and machine learning solutions. Topics : probability theory, matrix decompositions, vector calculus, stochastic sampling methods, numerical optimization-based formulations of inverse problems, first order deterministic and stochastic gradient-based methods, quasi-Newton and Hessian free methods
BIOE 210: Linear Algebra for Biomedical Data Science
Undergraduate/Graduate course, University of Illinois at Urbana-Champaign, Department of Bioengineering, 2025
BIOE 210 is a core course required for all bioengineering undergraduates. The goal is to introduce students to essential analytical and computational tools from linear algebra. In addition to describing vector and matrix arithmetic, students will solve systems of linear equations and explore linear regression. These methods can be applied to analyze large, multivariable datasets to quantify relationships between variables; decompose complex datasets into simpler representations; solve common problems in classification and image processing; and visualize high-dimensional data spaces. Course topics include definitions of vector spaces; solvability; rank; basis; linear transformation matrices; and vector & matrix decompositions (eigenanalysis, SVD, PCA). The course focuses on mathematical and computation aspects of problem solving, and consequently requires students to access Matlab