Publications

You can also find my articles on my Google Scholar profile.

The ManifoldEM method for cryo-EM: a step-by-step breakdown accompanied by a modern Python implementation

Published in Biological Crystallography, IUCr, 2025

Resolving continuous conformational heterogeneity in single-particle cryo-electron microscopy (cryo-EM) is a field in which new methods are now emerging regularly. Methods range from traditional statistical techniques to state-of-the-art neural network approaches. Such ongoing efforts continue to enhance the ability to explore and understand the continuous conformational variations in cryo-EM data. One of the first methods was the manifold embedding approach, or ManifoldEM. However, comparing it with more recent methods has been challenging due to software availability and usability issues. In this work, we introduce a modern Python implementation that is user-friendly, orders of magnitude faster than its previous versions, and designed with a developer-ready environment. This implementation allows a more thorough evaluation of the strengths and limitations of methods addressing continuous conformational heterogeneity in cryo-EM, paving the way for further community-driven improvements.

Recommended citation: Anupam Anand Ojha, Robert Blackwell, Eduardo R. Cruz-Chú, Raison Dsouza, Miro A. Astore, Peter Schwander, and Sonya M. Hanson. "The ManifoldEM method for cryo-EM: a step-by-step breakdown accompanied by a modern Python implementation." Biological Crystallography 81, no. 3 (2025). http://anandojha.github.io/files/paper16.pdf

Advances and Challenges in Milestoning Simulations for Drug–Target Kinetics

Published in Journal of Chemical Theory and Computation, ACS, 2024

Molecular dynamics simulations have become indispensable for exploring complex biological processes, yet their limitations in capturing rare events hinder our understanding of drug–target kinetics. In this Perspective, we investigate the domain of milestoning simulations to understand this challenge. The milestoning approach divides the phase space of the drug–target complex into discrete cells, offering extended time scale insights. This Perspective traces the history, applications, and future potential of milestoning simulations in the context of drug–target kinetics. It explores the fundamental principles of milestoning, highlighting the importance of probabilistic transitions and transition time independence. Markovian milestoning with Voronoi tessellations is revisited to address the traditional milestoning challenges. While observing the advancements in this field, this Perspective also addresses impending challenges in estimating drug–target unbinding rate constants through milestoning simulations, paving the way for more effective drug design strategies.

Recommended citation: Lane W. Votapka, Anupam Anand Ojha, Naoya Asada, and Rommie E. Amaro. "Prediction of Threonine-Tyrosine Kinase Receptor–Ligand Unbinding Kinetics with Multiscale Milestoning and Metadynamics." The Journal of Physical Chemistry Letters 15, no. 42 (2024): 10473-10478. http://anandojha.github.io/files/paper15.pdf

Prediction of Threonine-Tyrosine Kinase Receptor–Ligand Unbinding Kinetics with Multiscale Milestoning and Metadynamics

Published in Journal of Physical Chemistry Letters, ACS, 2024

Accurately describing protein–ligand binding and unbinding kinetics remains challenging. Computational calculations are difficult and costly, while experimental measurements often lack molecular detail and can be unobtainable. Here, we extend our multiscale milestoning method, Simulation-Enabled Estimation of Kinetics Rates (SEEKR), with metadynamics molecular dynamics simulations to yield accurate small molecule drug residence times. Using the pharmaceutically relevant threonine-tyrosine kinase (TTK) and eight long-residence-time (tens of seconds to hours) inhibitors, we demonstrate accurate prediction of absolute and rank-ordered ligand residence times and free energies of binding.

Simulation-Driven Design of Stabilized SARS-CoV-2 Spike S2 Immunogens

Published in Nature Communications, 2024

The full-length prefusion-stabilized SARS-CoV-2 spike (S) is the principal antigen of COVID-19 vaccines. Vaccine efficacy has been impacted by emerging variants of concern that accumulate most of the sequence modifications in the immunodominant S1 subunit. S2, in contrast, is the most evolutionarily conserved region of the spike and can elicit broadly neutralizing and protective antibodies. Yet, S2’s usage as an alternative vaccine strategy is hampered by its general instability. Here, we use a simulation-driven approach to design S2-only immunogens stabilized in a closed prefusion conformation. Molecular simulations provide a mechanistic characterization of the S2 trimer’s opening, informing the design of tryptophan substitutions that impart kinetic and thermodynamic stabilization. Structural characterization via cryo-EM shows the molecular basis of S2 stabilization in the closed prefusion conformation. Informed by molecular simulations and corroborated by experiments, we report an engineered S2 immunogen that exhibits increased protein expression, superior thermostability, and preserved immunogenicity against sarbecoviruses.

Recommended citation: Xandra Nuqui, Lorenzo Casalino, Ling Zhou, Mohamed Shehata, Albert Wang, Alexandra L. Tse, Anupam Ojha, Fiona L. Kearns, Mia A. Rosenfeld, Emily Happy Miller, Cory M. Acreman, Surl-Hee Ahn, Kartik Chandran, Jason S. McLellan, Rommie E Amaro. "Simulation-Driven Design of Stabilized SARS-CoV-2 Spike S2 Immunogens." Nature Communications 15, no. 1 (2024): 7370 http://anandojha.github.io/files/paper13.pdf

Distinguishing Molecular Properties of OAT, OATP, and MRP Drug Substrates by Machine Learning

Published in Pharmaceutics, MDPI, 2024

The movement of organic anionic drugs across cell membranes is partly governed by interactions with SLC and ABC transporters in the intestine, liver, kidney, blood–brain barrier, placenta, breast, and other tissues. Major transporters involved include organic anion transporters (OATs, SLC22 family), organic anion transporting polypeptides (OATPs, SLCO family), and multidrug resistance proteins (MRPs, ABCC family). However, the sets of molecular properties of drugs that are necessary for interactions with OATs (OAT1, OAT3) vs. OATPs (OATP1B1, OATP1B3) vs. MRPs (MRP2, MRP4) are not well-understood. Defining these molecular properties is necessary for a better understanding of drug and metabolite handling across the gut–liver–kidney axis, gut–brain axis, and other multi-organ axes. It is also useful for tissue targeting of small molecule drugs and predicting drug–drug interactions and drug–metabolite interactions. Here, we curated a database of drugs shown to interact with these transporters in vitro and used chemoinformatic approaches to describe their molecular properties. We then sought to define sets of molecular properties that distinguish drugs interacting with OATs, OATPs, and MRPs in binary classifications using machine learning and artificial intelligence approaches. We identified sets of key molecular properties (e.g., rotatable bond count, lipophilicity, number of ringed structures) for classifying OATs vs. MRPs and OATs vs. OATPs. However, sets of molecular properties differentiating OATP vs. MRP substrates were less evident, as drugs interacting with MRP2 and MRP4 do not form a tight group owing to differing hydrophobicity and molecular complexity for interactions with the two transporters. If the results also hold for endogenous metabolites, they may deepen our knowledge of organ crosstalk, as described in the Remote Sensing and Signaling Theory. The results also provide a molecular basis for understanding how small organic molecules differentially interact with OATs, OATPs, and MRPs.

Recommended citation: Anisha K. Nigam, Jeremiah D. Momper, Anupam Anand Ojha, and Sanjay K. Nigam. "Distinguishing Molecular Properties of OAT, OATP, and MRP Drug Substrates by Machine Learning." Pharmaceutics 16, no. 5 (2024): 592. http://anandojha.github.io/files/paper12.pdf

An introductory tutorial to the SEEKR2 (Simulation enabled estimation of kinetic rates v. 2) multiscale milestoning software [Article v1. 0]

Published in Living Journal of Computational Molecular Science, 2024

SEEKR2 (Simulation enabled estimation of kinetic rates v. 2) is a powerful and versatile software tool designed to computationally estimate the kinetics and thermodynamics of complex molecular processes, particularly emphasizing the process of receptor-ligand binding and unbinding. We present a suite of tutorials for the SEEKR2 (Simulation enabled estimation of kinetic rates v. 2) multiscale milestoning software. This tutorial presents a comprehensive guide for users offering the best practices for preparing, executing, and analyzing molecular dynamics (MD) and Brownian dynamics (BD) simulations using SEEKR2. This tutorial highlights the advancements presented in SEEKR2 - the latest iteration within the SEEKR programs, including significant improvements in speed and capabilities compared to its earlier versions. SEEKR2 now supports both NAMD and OpenMM simulation engines, providing users with more flexibility in their simulation setups. Additionally, the BD component has been upgraded to the Browndye2 engine, enhancing the accuracy and efficiency of simulations. This tutorial aims to guide users to install SEEKR2, run MD and BD simulations within the framework of the SEEKR2 program, and analyze and interpret the kinetics and thermodynamics of binding and unbinding of model host-guest systems, thereby demonstrating its ease of usability and extensible features that allow for future expansions of the method. This tutorial equips users with the necessary knowledge to effectively prepare, execute, and analyze simulations using SEEKR2. By following the best practices outlined in the tutorial, users can leverage the power of the SEEKR2 program to gain insights into complex molecular processes and accelerate their understanding of key biomolecular interactions.

Recommended citation: Anupam Anand Ojha, Lane William Votapka, Gary Alexander Huber, Shang Gao, and Rommie Elizabeth Amaro. "An introductory tutorial to the SEEKR2 (Simulation enabled estimation of kinetic rates v. 2) multiscale milestoning software [Article v1. 0]." Living Journal of Computational Molecular Science 5, no. 1 (2023): 2359-2359. http://anandojha.github.io/files/paper11.pdf

QMrebind: incorporating quantum mechanical force field reparameterization at the ligand binding site for improved drug-target kinetics through milestoning simulations

Published in Chemical Science, RSC, 2023

Understanding the interaction of ligands with biomolecules is an integral component of drug discovery and development. Challenges for computing thermodynamic and kinetic quantities for pharmaceutically relevant receptor–ligand complexes include the size and flexibility of the ligands, large-scale conformational rearrangements of the receptor, accurate force field parameters, simulation efficiency, and sufficient sampling associated with rare events. Our recently developed multiscale milestoning simulation approach, SEEKR2 (Simulation Enabled Estimation of Kinetic Rates v.2), has demonstrated success in predicting unbinding (koff) kinetics by employing molecular dynamics (MD) simulations in regions closer to the binding site. The MD region is further subdivided into smaller Voronoi tessellations to improve the simulation efficiency and parallelization. To date, all MD simulations are run using general molecular mechanics (MM) force fields. The accuracy of calculations can be further improved by incorporating quantum mechanical (QM) methods into generating system-specific force fields through reparameterizing ligand partial charges in the bound state. The force field reparameterization process modifies the potential energy landscape of the bimolecular complex, enabling a more accurate representation of the intermolecular interactions and polarization effects at the bound state. We present QMrebind (Quantum Mechanical force field reparameterization at the receptor–ligand binding site), an ORCA-based software that facilitates reparameterizing the potential energy function within the phase space representing the bound state in a receptor–ligand complex. With SEEKR2 koff estimates and experimentally determined kinetic rates, we compare and interpret the receptor–ligand unbinding kinetics obtained using the newly reparameterized force fields for model host–guest systems and HSP90-inhibitor complexes. This method provides an opportunity to achieve higher accuracy in predicting receptor–ligand koff rate constants.

Recommended citation: Anupam Anand Ojha, Lane William Votapka, and Rommie Elizabeth Amaro. "QMrebind: incorporating quantum mechanical force field reparameterization at the ligand binding site for improved drug-target kinetics through milestoning simulations." Chemical Science 14, no. 45 (2023): 13159-13175. http://anandojha.github.io/files/paper10.pdf

Selectivity and Ranking of Tight-Binding JAK-STAT Inhibitors Using Markovian Milestoning with Voronoi Tessellations

Published in Journal of Chemical Information and Modeling, ACS, 2023

Janus kinases (JAK), a group of proteins in the nonreceptor tyrosine kinase (NRTKs) family, play a crucial role in growth, survival, and angiogenesis. They are activated by cytokines through the Janus kinase-signal transducer and activator of a transcription (JAK-STAT) signaling pathway. JAK-STAT signaling pathways have significant roles in the regulation of cell division, apoptosis, and immunity. Identification of the V617F mutation in the Janus homology 2 (JH2) domain of JAK2 leading to myeloproliferative disorders has stimulated great interest in the drug discovery community to develop JAK2-specific inhibitors. However, such inhibitors should be selective toward JAK2 over other JAKs and display an extended residence time. Recently, novel JAK2/STAT5 axis inhibitors (N-(1H-pyrazol-3-yl)pyrimidin-2-amino derivatives) have displayed extended residence times (hours or longer) on target and adequate selectivity excluding JAK3. To facilitate a deeper understanding of the kinase-inhibitor interactions and advance the development of such inhibitors, we utilize a multiscale Markovian milestoning with Voronoi tessellations (MMVT) approach within the Simulation-Enabled Estimation of Kinetic Rates v.2 (SEEKR2) program to rank order these inhibitors based on their kinetic properties and further explain the selectivity of JAK2 inhibitors over JAK3. Our approach investigates the kinetic and thermodynamic properties of JAK–inhibitor complexes in a user-friendly, fast, efficient, and accurate manner compared to other brute force and hybrid-enhanced sampling approaches.

Recommended citation: Anupam Anand Ojha, Ambuj Srivastava, Lane William Votapka, and Rommie E. Amaro. "Selectivity and ranking of tight-binding JAK-STAT inhibitors using Markovian milestoning with Voronoi tessellations." Journal of Chemical Information and Modeling 63, no. 8 (2023): 2469-2482. http://anandojha.github.io/files/paper9.pdf

DeepWEST: Deep Learning of Kinetic Models with the Weighted Ensemble Simulation Toolkit for Enhanced Sampling

Published in Journal of Chemical Theory and Computation, ACS, 2023

Recent advances in computational power and algorithms have enabled molecular dynamics (MD) simulations to reach greater time scales. However, for observing conformational transitions associated with biomolecular processes, MD simulations still have limitations. Several enhanced sampling techniques seek to address this challenge, including the weighted ensemble (WE) method, which samples transitions between metastable states using many weighted trajectories to estimate kinetic rate constants. However, initial sampling of the potential energy surface has a significant impact on the performance of WE, i.e., convergence and efficiency. We therefore introduce deep-learned kinetic modeling approaches that extract statistically relevant information from short MD trajectories to provide a well-sampled initial state distribution for WE simulations. This hybrid approach overcomes any statistical bias to the system, as it runs short unbiased MD trajectories and identifies meaningful metastable states of the system. It is shown to provide a more refined free energy landscape closer to the steady state that could efficiently sample kinetic properties such as rate constants.

Recommended citation: Anupam Anand Ojha, Saumya Thakur, Surl-Hee Ahn, and Rommie E. Amaro. "DeepWEST: Deep learning of kinetic models with the Weighted Ensemble Simulation Toolkit for enhanced sampling." Journal of chemical theory and computation 19, no. 4 (2023): 1342-1359. http://anandojha.github.io/files/paper8.pdf

SEEKR2: Versatile Multiscale Milestoning Utilizing the OpenMM Molecular Dynamics Engine

Published in Journal of Chemical Theory and Computation, ACS, 2022

We present SEEKR2 (simulation-enabled estimation of kinetic rates version 2)─the latest iteration in the family of SEEKR programs for using multiscale simulation methods to computationally estimate the kinetics and thermodynamics of molecular processes, in particular, ligand-receptor binding. SEEKR2 generates equivalent, or improved, results compared to the earlier versions of SEEKR but with significant increases in speed and capabilities. SEEKR2 has also been built with greater ease of usability and with extensible features to enable future expansions of the method. Now, in addition to supporting simulations using NAMD, calculations may be run with the fast and extensible OpenMM simulation engine. The Brownian dynamics portion of the calculation has also been upgraded to Browndye 2. Furthermore, this version of SEEKR supports hydrogen mass repartitioning, which significantly reduces computational cost, while showing little, if any, loss of accuracy in the predicted kinetics.

Recommended citation: Lane W. Votapka, Andrew M. Stokely, Anupam A. Ojha, and Rommie E. Amaro. "SEEKR2: Versatile multiscale milestoning utilizing the OpenMM molecular dynamics engine." Journal of chemical information and modeling 62, no. 13 (2022): 3253-3262. http://anandojha.github.io/files/paper7.pdf

Gaussian-accelerated molecular dynamics with the weighted ensemble method: A hybrid method improves thermodynamic and kinetic sampling

Published in Journal of Chemical Theory and Computation, ACS, 2021

Gaussian-accelerated molecular dynamics (GaMD) is a well-established enhanced sampling method for molecular dynamics simulations that effectively samples the potential energy landscape of the system by adding a boost potential, which smoothens the surface and lowers the energy barriers between states. GaMD is unable to give time-dependent properties such as kinetics directly. On the other hand, the weighted ensemble (WE) method can efficiently sample transitions between states with its many weighted trajectories, which directly yield rates and pathways. However, convergence to equilibrium conditions remains a challenge for the WE method. Hence, we have developed a hybrid method that combines the two methods, wherein GaMD is first used to sample the potential energy landscape of the system and WE is subsequently used to further sample the potential energy landscape and kinetic properties of interest. We show that the hybrid method can sample both thermodynamic and kinetic properties more accurately and quickly compared to using either method alone.

Recommended citation: Surl-Hee Ahn, Anupam A. Ojha, Rommie E. Amaro, and J. Andrew McCammon. "Gaussian-accelerated molecular dynamics with the weighted ensemble method: A hybrid method improves thermodynamic and kinetic sampling." Journal of chemical theory and computation 17, no. 12 (2021): 7938-7951. http://anandojha.github.io/files/paper6.pdf

Molecular Properties of Drugs Handled by Kidney OATs and Liver OATPs Revealed by Chemoinformatics and Machine Learning: Implications for Kidney and Liver Disease

Published in Pharmaceutics, MDPI, 2021

In patients with liver or kidney disease, it is especially important to consider the routes of metabolism and elimination of small-molecule pharmaceuticals. Once in the blood, numerous drugs are taken up by the liver for metabolism and/or biliary elimination, or by the kidney for renal elimination. Many common drugs are organic anions. The major liver uptake transporters for organic anion drugs are organic anion transporter polypeptides (OATP1B1 or SLCO1B1; OATP1B3 or SLCO1B3), whereas in the kidney they are organic anion transporters (OAT1 or SLC22A6; OAT3 or SLC22A8). Since these particular OATPs are overwhelmingly found in the liver but not the kidney, and these OATs are overwhelmingly found in the kidney but not liver, it is possible to use chemoinformatics, machine learning (ML) and deep learning to analyze liver OATP-transported drugs versus kidney OAT-transported drugs. Our analysis of >30 quantitative physicochemical properties of OATP- and OAT-interacting drugs revealed eight properties that in combination, indicate a high propensity for interaction with “liver” transporters versus “kidney” ones based on machine learning (e.g., random forest, k-nearest neighbors) and deep-learning classification algorithms. Liver OATPs preferred drugs with greater hydrophobicity, higher complexity, and more ringed structures whereas kidney OATs preferred more polar drugs with more carboxyl groups. The results provide a strong molecular basis for tissue-specific targeting strategies, understanding drug–drug interactions as well as drug–metabolite interactions, and suggest a strategy for how drugs with comparable efficacy might be chosen in chronic liver or kidney disease (CKD) to minimize toxicity.

Recommended citation: Anisha K. Nigam, Anupam A. Ojha, Julia G. Li, Da Shi, Vibha Bhatnagar, Kabir B. Nigam, Ruben Abagyan, and Sanjay K. Nigam. "Molecular properties of drugs handled by kidney OATs and liver OATPs revealed by chemoinformatics and machine learning: implications for kidney and liver disease." Pharmaceutics 13, no. 10 (2021): 1720. http://anandojha.github.io/files/paper5.pdf

Predicting Ligand Binding Kinetics Using a Markovian Milestoning with Voronoi Tessellations Multiscale Approach

Published in Journal of Chemical Theory and Computation, ACS, 2020

Accurate and efficient computational predictions of ligand binding kinetics can be useful to inform drug discovery campaigns, particularly in the screening and lead optimization phases. Simulation enabled estimation of kinetic rates (SEEKR) is a multiscale molecular dynamics, Brownian dynamics, and milestoning simulation approach for calculating receptor–ligand association and dissociation rates. Here, we present the implementation of a Markovian milestoning with Voronoi tessellations approach that significantly reduces the simulation cost of calculations as well as further improving their parallelizability. The new approach is applied to a host–guest system to assess its effectiveness for rank-ordering compounds by kinetic rates and to the model protein system, trypsin, with the noncovalent inhibitor benzamidine. For both applications, we demonstrate that the new approach requires up to a factor of 10 less simulation time to achieve results with comparable or increased accuracy.

Recommended citation: Benjamin R. Jagger, Anupam A. Ojha, and Rommie E. Amaro. "Predicting ligand binding kinetics using a Markovian milestoning with Voronoi tessellations multiscale approach." Journal of Chemical Theory and Computation 16, no. 8 (2020): 5348-5357. http://anandojha.github.io/files/paper4.pdf

Paper based field deployable sensor for naked eye monitoring of copper (II) ions; elucidation of binding mechanism by DFT studies

Published in Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy, ScienceDirect, 2019

The study demonstrates the fabrication of test strips made from newly synthesized ortho-Vanillin based colorimetric chemosensor (probe P) that could be employed as field deployable tool for rapid and naked-eye detection of copper ions. Upon addition of copper ions to the chemosensor, it exhibits a rapid pink color from colorless and can be easily seen through the naked eye. This probe exhibits a remarkable colorimetric ON response, and the absorbance intensity of the probe is enhanced significantly in the presence of copper ions. The sensing mechanism has been deduced using FTIR, XPS, LCMS, and DFT studies. The binding mechanism of the probe to copper ions was substantiated by DFT studies. HOMO of the probe suggests that a high electronic density resides on oxygen and nitrogen atoms, and thus, these are the favorable binding sites for the metal ions.

Recommended citation: Halali V. Vishaka, Manav Saxena, H. R. Chandan, Anupam Anand Ojha, and R. Geetha Balakrishna. "Paper based field deployable sensor for naked eye monitoring of copper (II) ions; elucidation of binding mechanism by DFT studies." Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy 223 (2019): 117291. http://anandojha.github.io/files/paper3.pdf

Water-stable nanoscale zirconium-based metal-organic frameworks for the effective removal of glyphosate from aqueous media

Published in ACS Omega, 2018

Two water-stable zirconium-based metal-organic frameworks (MOFs) (NU-1000 and UiO-67) have been synthesized in various size scales (100–2000 nm) for the adsorptive removal of glyphosate from the aqueous media. Both NU-1000 and UiO-67 possess a three-dimensional structure; NU-1000 consists of triangular micropores and wide mesoporous channels (31 Å), whereas UiO-67 has cage-like pores [octahedral (16 Å) and tetrahedral (14 Å) cages]. NU-1000 and UiO-67 contain different secondary building units. These units act as Lewis acid nodes and can interact with the Lewis base phosphate group of the glyphosate. The time taken to reach equilibrium is found to be reduced considerably as the size of the MOF decreases. The smaller the particle size, the lesser the diffusion barrier for the analyte, which enhances the interaction between Lewis acidic metal nodes and the Lewis basic center of the glyphosate molecule. NU-1000 was found to be better compared to UiO-67, both in terms of efficiency and reusability. This might be due to the larger pore diameters of the NU-1000. Theoretical calculations revealed that the interaction energy of glyphosate with the nodes of NU-1000 is higher compared to UiO-67, which might be the possible reason for the higher efficiency of NU-1000.

Recommended citation: Asha Pankajakshan, Mekhola Sinha, Anupam Anand Ojha, and Sukhendu Mandal. "Water-stable nanoscale zirconium-based metal–organic frameworks for the effective removal of glyphosate from aqueous media." ACS omega 3, no. 7 (2018): 7832-7839. http://anandojha.github.io/files/paper2.pdf

“Turn-on” fluorescence sensing and discriminative detection of aliphatic amines using a 5-fold-interpenetrated coordination polymer

Published in Inorganic Chemistry, ACS, 2017

A 5-fold-interpenetrated zinc-based coordination polymer can discriminately detect aliphatic amines through a fluorescence “turn-on” method. This compound can sense aliphatic amines in the solid state, solution state, and vapor phase. Theoretical calculations revealed that the ground-state dipole moment of the corresponding amines guides the order of enhancement.

Recommended citation: Prabu Mani, Anupam Anand Ojha, Vennapusa Sivaranjana Reddy, and Sukhendu Mandal. "“Turn-on” fluorescence sensing and discriminative detection of aliphatic amines using a 5-fold-interpenetrated coordination polymer." Inorganic Chemistry 56, no. 12 (2017): 6772-6775. http://anandojha.github.io/files/paper1.pdf

Anupam Anand Ojha, Ph.D.

Publications