About

Ioan Andricioaei is a faculty member at the University of California, Irvine, within the Department of Chemistry. His research interests encompass Theoretical and Computational Physical Chemistry and Chemical Physics, as well as Chemical Biology. He is involved in advancing understanding in these fields through his academic and research activities at UC Irvine, contributing to the department's scientific community and educational mission.

Research topics

• Computer Science
• Chemistry
• Biophysics
• Physics
• Medicine
• Statistical physics
• Internal medicine
• Computational chemistry
• Thermodynamics
• Biology

Selected publications

• The scientific legacy of Martin Karplus from the perspective of his collaborators

  Biophysical Journal · 2026-04-01

  article1st authorCorresponding
  PublisherDOI

• Author Correction: Slowing down single-molecule trafficking through a protein nanopore reveals intermediates for peptide translocation

  Scientific Reports · 2025-02-28

  erratumOpen access
  PublisherOA PDFDOI

• Discriminant Analysis Optimizes Progress Coordinate in Weighted Ensemble Simulations of Rare Event Kinetics

  ChemRxiv · 2025-04-09

  preprintOpen accessSenior author

  Calculating the kinetics of rare-but-important conformational transitions in complex biomolecules is a significant challenge in computational biophysics. Because of the long timescales needed to observe such processes, regular molecular dynamics simulations are too slow to sample these events by direct integration of the equations of motion. Recently, the weighted ensemble method has gained significant popularity for its ability to compute the rates of conformational transitions in biomolecular systems using unbiased simulations. However, the progress coordinate(s) of the weighted ensemble simulation should be carefully designed to capture the slow degrees of freedom of the system. Here, we demonstrate the application of a machine learning approach, harmonic linear discriminant analysis, which builds a predictive model for class membership, to design progress coordinates for weighted ensemble simulations. We test the accuracy and efficiency of this technique for computing the kinetics of the conformational transition of alanine dipeptide and the unfolding of a small protein. The key advantage of our data-driven approach is its minimal system knowledge requirement, which potentially extends its applicability to more complex and physiologically relevant systems.

  PublisherOA PDFDOI

• Discriminant analysis optimizes progress coordinate in weighted ensemble simulations of rare event kinetics

  The Journal of Chemical Physics · 2025-08-19 · 2 citations

  articleSenior author

  Calculating the kinetics of rare-but-important conformational transitions in complex biomolecules is a significant challenge in computational biophysics. Because of the long timescales needed to observe such processes, regular molecular dynamics simulations are too slow to sample these events by direct integration of the equations of motion. Recently, the weighted ensemble method has gained significant popularity for its ability to compute the rates of conformational transitions in biomolecular systems using unbiased simulations. However, the progress coordinate(s) of the weighted ensemble simulation should be carefully designed to capture the slow degrees of freedom of the system. Here, we demonstrate the application of a machine learning approach, harmonic linear discriminant analysis, which builds a predictive model for class membership, to design progress coordinates for weighted ensemble simulations. We test the accuracy and efficiency of this technique for computing the kinetics of the conformational transition of alanine dipeptide and the unfolding of a small protein. The key advantage of our data-driven approach is its minimal system knowledge requirement, which potentially extends its applicability to more complex and physiologically relevant systems.

  PublisherDOI

• Estimation of free energies of FKBP-12-ligand systems using enhanced sampling methods

  Biophysical Journal · 2024-02-01

  articleSenior author
  PublisherDOI

• Discriminant Analysis Optimizes Progress Coordinate in Weighted Ensemble Simulations of Rare Event Kinetics

  ChemRxiv · 2024-11-25

  preprintOpen accessSenior author

  Calculating the kinetics of rare-but-important conformational transitions in complex biomolecules is a significant challenge in computational biophysics. Because of the long timescales needed to observe such processes, regular molecular dynamics simulations are too slow to sample these events by direct integration of the equations of motion. Recently, the weighted ensemble method has gained significant popularity for its ability to compute the rates of conformational transitions in biomolecular systems using unbiased simulations. However, the progress coordinate(s) of the weighted ensemble simulation should be carefully designed to capture the slow degrees of freedom of the system. Here, we demonstrate the application of a machine learning approach, harmonic linear discriminant analysis, which builds a predictive model for class membership, to design progress coordinates for weighted ensemble simulations. We test the accuracy and efficiency of this technique for computing the kinetics of the conformational transition of alanine dipeptide and the unfolding of a small protein. The key advantage of our data-driven approach is its minimal system knowledge requirement, which potentially extends its applicability to more complex and physiologically relevant systems.

  PublisherOA PDFDOI

• Machine learning guided weighted ensemble for rare event simulation in biophysics

  Biophysical Journal · 2023-02-01

  articleOpen accessSenior author
  PublisherOA PDFDOI

• Predicting residue cooperativity during protein folding: A combined, molecular dynamics and unsupervised learning approach

  The Journal of Chemical Physics · 2023-03-15 · 6 citations

  articleSenior author

  Allostery in proteins involves, broadly speaking, ligand-induced conformational transitions that modulate function at active sites distal to where the ligand binds. In contrast, the concept of cooperativity (in the sense used in phase transition theory) is often invoked to understand protein folding and, therefore, function. The modern view on allostery is one based on dynamics and hinges on the time-dependent interactions between key residues in a complex network, interactions that determine the free-energy profile for the reaction at the distal site. Here, we merge allostery and cooperativity, and we discuss a joint model with features of both. In our model, the active-site reaction is replaced by the reaction pathway that leads to protein folding, and the presence or absence of the effector is replaced by mutant-vs-wild type changes in key residues. To this end, we employ our recently introduced time-lagged independent component analysis (tICA) correlation approach [Ray et al. Proc. Natl. Acad. Sci. 118(43) (2021), e2100943118] to identify the allosteric role of distant residues in the folded-state dynamics of a large protein. In this work, we apply the technique to identify key residues that have a significant role in the folding of a small, fast folding-protein, chignolin. Using extensive enhanced sampling simulations, we critically evaluate the accuracy of the predictions by mutating each residue one at a time and studying how the mutations change the underlying free energy landscape of the folding process. We observe that mutations in those residues whose associated backbone torsion angles have a high correlation score can indeed lead to loss of stability of the folded configuration. We also provide a rationale based on interaction energies between individual residues with the rest of the protein to explain this effect. From these observations, we conclude that the tICA correlation score metric is a useful tool for predicting the role of individual residues in the correlated dynamics of proteins and can find application to the problem of identifying regions of protein that are either most vulnerable to mutations or-mutatis mutandis-to binding events that affect their functionality.

  PublisherDOI

• A Suite of Tutorials for the WESTPA 2.0 Rare-Events Sampling Software [Article v2.0]

  Living Journal of Computational Molecular Science · 2023-01-18 · 15 citations

  articleOpen access

  The weighted ensemble (WE) strategy has been demonstrated to be highly efficient in generating pathways and rate constants for rare events such as protein folding and protein binding using atomistic molecular dynamics simulations. Here we present two sets of tutorials instructing users in the best practices for preparing, carrying out, and analyzing WE simulations for various applications using the WESTPA software. The first set of more basic tutorials describes a range of simulation types, from a molecular association process in explicit solvent to more complex processes such as host-guest association, peptide conformational sampling, and protein folding. The second set ecompasses six advanced tutorials instructing users in the best practices of using key new features and plugins/extensions of the WESTPA 2.0 software package, which consists of major upgrades for larger systems and/or slower processes. The advanced tutorials demonstrate the use of the following key features: (i) a generalized resampler module for the creation of "binless" schemes, (ii) a minimal adaptive binning scheme for more efficient surmounting of free energy barriers, (iii) streamlined handling of large simulation datasets using an HDF5 framework, (iv) two different schemes for more efficient rate-constant estimation, (v) a Python API for simplified analysis of WE simulations, and (vi) plugins/extensions for Markovian Weighted Ensemble Milestoning and WE rule-based modeling for systems biology models. Applications of the advanced tutorials include atomistic and non-spatial models, and consist of complex processes such as protein folding and the membrane permeability of a drug-like molecule. Users are expected to already have significant experience with running conventional molecular dynamics or systems biology simulations.

  PublisherDOI

• Considerable slowdown of short DNA fragment translocation across a protein nanopore using pH-induced generation of enthalpic traps inside the permeation pathway

  Nanoscale · 2023-01-01 · 11 citations

  article

  A pressing challenge in the realm of nanopore-based sensing technologies for nucleic acid characterization has been the cheap and efficient control of analyte translocation. To address this, a plethora of methods were tested, including mutagenesis, molecular motors, enzymes, or the optimization of experimental conditions. Herein, we present a paradigm exploiting the manipulation of electrostatic interactions between 22-mer single-stranded DNAs (22_ssDNA) and low pH-induced charges in the alpha-hemolysin (α-HL) nanopore, to efficiently control the passage of captured molecules. We discovered that in electrolytes buffered at pH = 5 and pH = 4.5 where the nanopore's vestibule and lumen become oppositely charged as compared to that at neutral pH, the electrostatic anchoring at these regions of a 22_ssDNA fragment leads to a dramatic increase of the translocation time, orders of magnitude larger compared to that at neutral pH. This pH-dependent tethering effect is reversible, side invariant, and sensitive to the ionic strength and ssDNA contour length. In the long run, our discovery has the potential to provide a simple read-out of the sequence of bases pertaining to short nucleotide sequences, thus extending the efficacy of current nanopore-based sequencers.

  PublisherDOI

Recent grants

• CAREER: Methods for Enhanced Kinetics: Application to Long-Time Biomolecular Relaxation, Conformational Transitions and Single-Molecular Manipulations

  NSF · $560k · 2006–2009

• CDI-Type 1 Collaborative Research: Multi-scale Modeling of Protein-Modulated DNA Large-Scale Dynamics by Free Energy Surface Matching

  NSF · $319k · 2009–2012

• RAPID: Computational studies of the structural dynamics, function and inhibition of the SARS-CoV-2 coronavirus spike protein

  NSF · $200k · 2020–2022

• CAREER: Methods for Enhanced Kinetics: Application to Long-Time Biomolecular Relaxation, Conformational Transitions and Single-Molecular Manipulations

  NSF · $422k · 2008–2011

• CDS&E/Collaborative Research: Exposing the Injection Machinery Dynamics of Bacteriophage T4 through Multi-Scale Modeling

  NSF · $273k · 2014–2018

Frequent coauthors

• Dhiman Ray

  Italian Institute of Technology

  28 shared

• Hashim M. Al‐Hashimi

  Columbia University

  20 shared

• Noel C. Perkins

  University of Michigan–Ann Arbor

  19 shared

• John E. Straub

  Boston University

  16 shared

• Mark M. Banaszak Holl

  University of Alabama at Birmingham

  12 shared

• Maryna Taranova

  Stanford University

  11 shared

• Martin Karplus

  Harvard University

  11 shared

• Aaron T. Frank

  Arrakis Therapeutics (United States)

  10 shared