About

Rommie E. Amaro holds the Distinguished Professorship in Theoretical and Computational Chemistry at the University of California, San Diego. She grew up on the south side of Chicago and received her B.S. in Chemical Engineering in 1999 and her Ph.D. in Chemistry in 2005 from the University of Illinois at Urbana-Champaign. Rommie was a NIH postdoctoral fellow with Prof. J. Andrew McCammon at UC San Diego from 2005-2009, and started her independent lab at the University of California, Irvine in 2009. In 2011 she moved to UC San Diego. Her scientific interests lie at the intersection of computer-aided drug discovery and biophysical simulation. Her scientific vision revolves around expanding the range and complexity of molecular constituents represented in atomic-level molecular dynamics simulations, the development of novel multiscale methods for elucidating their time-dependent dynamics, and the discovery of novel chemical matter controlling biological function.

Research topics

• Computer Science
• Virology
• Biology
• Medicine
• Physics
• Computational biology
• Genetics
• Computational science
• Biochemistry
• Computer graphics (images)
• Nanotechnology
• Chemistry
• Aerospace engineering
• Geometry
• Cell biology
• Meteorology
• Data science
• Mathematics
• Algorithm
• Materials science
• Bioinformatics
• Biophysics
• Engineering
• Pharmacology

Selected publications

• Detection of gas bubbles and local voids in molecular simulations using <i>burbuja</i>

  Protein Science · 2026-04-21

  articleOpen accessSenior authorCorresponding

  Abstract We present burbuja (Baring Unseen Regions of Bubbles Using Joint‐Density Analysis), an automated software tool for detecting and characterizing gas bubbles and other local voids in molecular structures and trajectories containing explicit aqueous solvent. We describe the burbuja algorithm and demonstrate its accuracy and utility across a range of example systems, including globular proteins, a membrane system, a coarse‐grained membrane trajectory with 25,000 frames, a large envelope capsid containing approximately 150 million atoms, and a very large respiratory aerosol system containing approximately 1 billion atoms. Burbuja supports optional GPU acceleration and can be run as a standalone command‐line utility or through a Python‐based API, facilitating integration with existing community tools for molecular system preparation and analysis. Burbuja is open‐source and freely available at https://github.com/Abrahammc90/Burbuja.git .

  PublisherDOI

• N-Glycans modulate tilting of HIV-1 envelope glycoprotein

  Nature Communications · 2026-04-15

  articleOpen accessSenior author

  Human immunodeficiency virus-1 (HIV-1) remains a global health crisis, with over 40 million people living with the virus and no effective vaccine available. Central to HIV infection and immune evasion is the envelope glycoprotein (Env), a heavily glycosylated class I fusion protein that mediates viral entry and is the sole immunogenic target. Despite the recent advancements provided by imaging techniques, the characterization of Env's structure and dynamics within its native membrane environment remains incomplete. Here, we present microsecond-long, all-atom molecular dynamics simulations of the full-length, glycosylated Env glycoprotein embedded in a biologically relevant lipid bilayer. Our simulations, corroborated by cryo-electron tomography, reveal a pronounced tilting motion of Env relative to the membrane. Importantly, we identify a critical role for N-linked glycans at N88 and N611 in modulating the transition to tilted conformations. Alongside illuminating the sites of vulnerability within the glycan shield, the results presented here underscore Env tilting dynamics as a feature that can be leveraged in immunogen design.

  PublisherDOI

• Revealing the Impact of pH on Lipase Structure and Surface Propensity at the Air–Water Interface and in Aqueous Aerosols

  The Journal of Physical Chemistry Letters · 2026-01-08 · 1 citations

  articleOpen accessCorresponding

  Sea spray aerosols (SSAs), generated through oceanic bubble bursting, are chemically complex particles that significantly influence climate processes and ecosystem health. These aerosols are enriched with biological macromolecules such as enzymes and proteins, whose structure and activity at the air–water interface remain poorly understood, particularly under the highly variable pH conditions of SSAs. In this study, we investigate the pH-dependent surface activity of Burkholderia cepacia lipase (BCL), a model extracellular enzyme commonly found in marine environments. Using surface tension and infrared reflection–absorption spectroscopy (IRRAS) measurements, we observe that BCL exhibits increased surface propensity at higher pH compared to acidic conditions. All-atom molecular dynamics simulations further reveal molecular-level insight into these observations, showing structural changes in BCL at the interface in acidic environments with new, highly atmosphere exposed conformations. Additionally, we explore the heterogeneous reactivity of BCL-containing aerosol particles with gaseous nitric acid to identify potential reactive sites relevant to interactions with atmospheric trace gases. Understanding these heterogeneous reaction pathways of biological macromolecules not only may be relevant for SSAs but also has broad implications for the atmospheric reactivity of bioaerosols.

  PublisherDOI

• Condensate-Like Organization in Respiratory Aerosols Modulates the Dynamics of an Airborne Virus

  bioRxiv (Cold Spring Harbor Laboratory) · 2026-05-04

  articleOpen accessSenior authorCorresponding

  Abstract The molecular behavior of viruses within respiratory aerosols plays a critical role in airborne disease transmission yet remains largely inaccessible to experimental characterization. Here, we use a billion-atom all-atom molecular dynamics simulation of a virus-laden respiratory aerosol to uncover how respiratory proteins, lipids, ions, and water collectively assemble around SARS-CoV-2, giving rise to structured microenvironments that influence viral stability and spike dynamics. We find that respiratory components rapidly evolve into heterogeneous networks characterized by protein-rich aggregates, patchy lipid assemblies, and spatially structured ion and water dynamics. These features create distinct microenvironments that constrain molecular transport and stabilize regions surrounding the virion. Within this crowded aerosol context, we observe sustained and selective interactions between aerosol components and the viral spike protein, including preferential recruitment of surfactant lipids and persistent coordination by divalent cations. These interactions modulate spike conformational dynamics, enhancing domain breathing motions and flexibility at key hinge regions while preserving a stable membrane anchor. Together, these observations reveal a condensate-like physical regime in which multivalent aerosol components coalesce into a soft, heterogeneous matrix that selectively modulates viral protein dynamics under extreme crowding. By framing virus-laden respiratory aerosols within this physical context, this work establishes an in situ molecular framework for understanding how aerosols influence viral persistence and offers a platform for exploring mechanisms relevant to airborne disease transmission and mitigation strategies. TOC Graphic Synopsis Respiratory aerosols exhibit condensate-like physical properties that govern the evolution of the particle and modulate the behavior of airborne SARS-CoV-2.

  PublisherDOI

• ReVesicle: Curation and Equilibration of Lipid Vesicles for Mesoscale Simulations

  Journal of Chemical Theory and Computation · 2026-03-19

  articleOpen accessSenior author

  , an iterative equilibration protocol designed to restore and stabilize quasi-spherical lipid vesicles with complex compositions and large dimensions. The proposed protocol combines selective identification and removal of infiltrated water molecules and flipped lipids with short nonequilibrium MD cycles and anisotropic pressure equilibration. These steps are organized into a modular, iterative sequence that progressively recovers bilayer continuity while preserving the vesicle geometry and enabling global density relaxation. Local vacuum-induced stress generated during nonequilibrium phases promotes lipid tail melting and hole curation, while anisotropic equilibration allows relaxation of box dimensions and system density. To demonstrate the robustness of ReVesicle, we applied the protocol to six biologically realistic vesicle systems: synaptic vesicles, plasma membranes, late endosomes, exosomes, mitochondria-derived vesicles, and the HIV-1 lipid envelope. These systems span diameters from 40 to 105 nm and reach total sizes of up to ∼150 million atoms with heterogeneous and asymmetric lipid compositions. Across all cases, ReVesicle consistently converges to continuous, tightly packed bilayers. Structural and biophysical analyses, including vesicle diameter, sphericity, area per lipid, and lipid acyl-chain order parameters, indicate preservation of quasi-spherical geometry and structural integrity. Overall, ReVesicle provides a reproducible framework for equilibrating large, heterogeneous lipid vesicles suitable for downstream all-atom simulations of complex biological environments.

  PublisherOA PDFDOI

• Toward In Situ Dynamics of DNA-Bound Full-Length p53 Tetramer

  Journal of Chemical Information and Modeling · 2026-01-06

  articleSenior authorCorresponding

  p53 is the most important tumor suppressor in humans as well as the most frequently mutated gene found in human cancers, with ∼50% of all human tumors bearing p53 missense mutations that leave p53 inactive. Restoring p53 activity has been shown to lead to tumor regression even in advanced tumors in mouse models and thus is among the most attractive potential strategies for novel cancer therapy. Full-length p53 (fl-p53) consists of 393 residues and multiple domains, some of which are folded while others are disordered. Using the crystal structures of folded domains and integrative molecular modeling techniques for disordered domains, we generated the first wild-type (WT) fl-p53 tetramer model bound to DNA. When solvated, the system size was ∼500 K atoms, challenging extensive sampling. Using the Anton2 supercomputer for microsecond timescale simulations in explicit solvent and the rigorous Markov state model (MSM) framework, we elucidated the conformational landscape of wild-type p53 as well as two of the p53 hotspot cancer mutants, Y220C and G245S, in a physiological DNA-bound, full-length tetramer context. In the simulated timescale, DNA-bound fl-p53 tetramer bent DNA and formed a compact complex with interactions between the N-terminal and DNA-binding domains (DBDs) and C-terminal domains (CTDs) with DNA. The WT fl-p53 tetramer also sampled a unique quaternary DBD organization that is not accessed by the cancer mutants. The free energy landscapes indicated differential dynamics for the inner and outer p53 DBDs due to the dimer-dimer interface. The dynamics of the druggable L1/S3 pocket was also closely monitored. Ultimately, MSMs identified an underexplored loop 6 (L6) cryptic pocket and captured the effect of p53 tetramerization and cancer mutations.

  PublisherDOI

• Advancing Reproducibility and Open Data in Theoretical and Computational Chemistry

  Journal of Chemical Theory and Computation · 2026-04-23

  article1st author
  PublisherDOI

• BPS2026 – More than a shield: Glycans modulate tilting dynamics of the HIV-1 Env glycoprotein

  Biophysical Journal · 2026-02-01

  articleSenior author
  PublisherDOI

• Towards In Situ Dynamics of DNA-bound Full-Length p53 Tetramer

  bioRxiv (Cold Spring Harbor Laboratory) · 2025-10-15

  preprintOpen accessSenior authorCorresponding

  p53 is the most important tumor suppressor in humans as well as the most frequently mutated gene found in human cancers with ~50% of all human tumors bearing p53 missense mutations that leave p53 inactive. Restoring the p53 activity proved to lead to tumor regression even in advanced tumors in mouse models- and thus, is among the most attractive potential strategies for novel cancer therapy. Full-length p53 (fl-p53) consists of 393 residues and multiple domains; some folded and some disordered. Using crystal structures of folded domains and integrative molecular modelling techniques for disordered domains, we generated the first wild-type fl-p53 tetramer model bound to DNA. When solvated, the system size nears 500K atoms challenging extensive sampling. Using Anton2 supercomputer for microsecond-timescale simulations in explicit solvent and the rigorous Markov state model (MSM) framework, we elucidated the conformational landscape of wild-type p53 as well as two of the p53 hot-spot cancer mutants, Y220C and G245S, in a physiological DNA-bound, full-length tetramer context. In the simulated timescale, DNA-bound fl-p53 tetramer bent DNA and formed a compact complex with interactions between the N-terminal and DNA-binding domains (DBDs), and the C-terminal domains (CTDs) with DNA. WT fl-p53 tetramer also sampled a unique quaternary DBD organization not accessed by the cancer mutants. Free energy landscapes indicated differential dynamics for inner and outer p53 DBDs due to the dimer-dimer interface. The dynamics of the druggable L1/S3 pocket is also closely monitored. Ultimately the MSMs identified an underexplored loop 6 (L6) cryptic pocket and captured the effect of p53 tetramerization and cancer mutations.

  PublisherOA PDFDOI

• Optimal message passing for molecular prediction is simple, attentive and spatial

  arXiv (Cornell University) · 2025-09-13

  preprintOpen accessSenior author

  Strategies to improve the predicting performance of Message-Passing Neural-Networks for molecular property predictions can be achieved by simplifying how the message is passed and by using descriptors that capture multiple aspects of molecular graphs. In this work, we designed model architectures that achieved state-of-the-art performance, surpassing more complex models such as those pre-trained on external databases. We assessed dataset diversity to complement our performance results, finding that structural diversity influences the need for additional components in our MPNNs and feature sets. In most datasets, our best architecture employs bidirectional message-passing with an attention mechanism, applied to a minimalist message formulation that excludes self-perception, highlighting that relatively simpler models, compared to classical MPNNs, yield higher class separability. In contrast, we found that convolution normalization factors do not benefit the predictive power in all the datasets tested. This was corroborated in both global and node-level outputs. Additionally, we analyzed the influence of both adding spatial features and working with 3D graphs, finding that 2D molecular graphs are sufficient when complemented with appropriately chosen 3D descriptors. This approach not only preserves predictive performance but also reduces computational cost by over 50%, making it particularly advantageous for high-throughput screening campaigns.

  PublisherOA PDFDOI

Recent grants

• NATIONAL BIOMEDICAL COMPUTATION RESOURCE

  NIH · $12.1M · 1997–2020

• Structural and Dynamical Determinants of Influenza Transmissibility

  NSF · $11k · 2018–2019

• AN OPEN RESOURCE FOR COLLABORATIVE BIOMEDICAL BIG DATA TRAINING

  NIH · $646k · 2014–2017

• PROJECT 3 – BIOLOGY OF DNA DEAMINASES IN CANCER

  NIH · $21.4M · 2019–2030

• AN OPEN RESOURCE TO ADVANCE COMPUTER-AIDED DRUG DESIGN

  NIH · $3.6M · 2014–2020

Frequent coauthors

• J. Andrew McCammon

  University of California, San Diego

  173 shared

• Wilfred W. Li

  University of California, San Diego

  77 shared

• Lorenzo Casalino

  University of California, San Diego

  62 shared

• Özlem Demir

  Erzincan University

  59 shared

• Fiona L. Kearns

  University of South Florida

  52 shared

• Reuben S. Harris

  50 shared

• Mia A. Rosenfeld

  43 shared

• Dong Xu

  Huazhong University of Science and Technology

  42 shared

Awards & honors

• NIH New Innovator Award
• Presidential Early Career Award for Scientists and Engineers
• ACS COMP OpenEye Outstanding Junior Faculty Award
• ACS Kavli Foundation Emerging Leader in Chemistry
• Corwin Hansch Award