About

Aleksei Aksimentiev is an Associate Professor in the Department of Bioengineering at the University of Illinois Urbana-Champaign. He received his Ph.D. in chemistry cum laude from the Institute of Physical Chemistry, Warsaw, Poland, in 1999, and completed a master's degree in particle physics at Ivan Franko Lviv State University in Ukraine in 1996. His postdoctoral training was conducted at the Materials Science Laboratory R&D Center of Mitsui Chemicals in Tokyo, Japan, from 1999 to 2001, after which he joined the Theoretical and Computational Biophysics Group at the University of Illinois as a research associate. He became a faculty member in the Physics Department at Illinois in 2005. His primary research focuses on computational and systems biology, including biomolecular modeling, bionanotechnology, and nanosensors. His work involves understanding biological nanomachines, developing nanopore systems for single-molecule detection and sequencing, modeling DNA processing machinery, and exploring the physics of DNA assemblies and synthetic molecular systems. His research aims to elucidate the molecular mechanisms underlying biological processes and to design synthetic systems that surpass natural counterparts.

Research topics

• Computer Science
• Chemistry
• Biology
• Computational biology
• Physics
• Bioinformatics
• Operating system
• Parallel computing
• Genetics
• Biophysics
• Computational science
• Computational chemistry
• Nanotechnology
• Biochemistry
• Materials science

Selected publications

• Multiple rereads of single proteins at single–amino acid resolution using nanopores

  Science · 2021 · 429 citations

  • Computer Science
  • Chemistry
  • Computational biology

  in single amino acid variant identification. These proof-of-concept experiments constitute a promising basis for the development of a single-molecule protein fingerprinting and analysis technology.

  DOI

• The emerging landscape of single-molecule protein sequencing technologies

  Nature Methods · 2021 · 357 citations

  • Computer Science
  • Computational biology
  • Biology
  PublisherOA PDFDOI

• Scalable molecular dynamics on CPU and GPU architectures with NAMD

  The Journal of Chemical Physics · 2020 · 3184 citations

  • Computer Science
  • Computer Science
  • Parallel computing

  NAMDis a molecular dynamics program designed for high-performance simulations of very large biological objects on CPU- and GPU-based architectures. NAMD offers scalable performance on petascale parallel supercomputers consisting of hundreds of thousands of cores, as well as on inexpensive commodity clusters commonly found in academic environments. It is written in C++ and leans on Charm++ parallel objects for optimal performance on low-latency architectures. NAMD is a versatile, multipurpose code that gathers state-of-the-art algorithms to carry out simulations in apt thermodynamic ensembles, using the widely popular CHARMM, AMBER, OPLS, and GROMOS biomolecular force fields. Here, we review the main features of NAMD that allow both equilibrium and enhanced-sampling molecular dynamics simulations with numerical efficiency. We describe the underlying concepts utilized by NAMD and their implementation, most notably for handling long-range electrostatics; controlling the temperature, pressure, and pH; applying external potentials on tailored grids; leveraging massively parallel resources in multiple-copy simulations; and hybrid quantum-mechanical/molecular-mechanical descriptions. We detail the variety of options offered by NAMD for enhanced-sampling simulations aimed at determining free-energy differences of either alchemical or geometrical transformations and outline their applicability to specific problems. Last, we discuss the roadmap for the development of NAMD and our current efforts toward achieving optimal performance on GPU-based architectures, for pushing back the limitations that have prevented biologically realistic billion-atom objects to be fruitfully simulated, and for making large-scale simulations less expensive and easier to set up, run, and analyze. NAMD is distributed free of charge with its source code at www.ks.uiuc.edu.

  DOI

Recent grants

• Plasmonic nanopores for trapping, controlled motion and sequencing of DNA

  NIH · $2.4M · 2013–2018

• Elements: Enabling multi-resolution simulations at the interface of biology and nanotechnology with ARBD

  NSF · $597k · 2023–2026

• High Accuracy Nanopore Sequencing.

  NIH · $14.0M · 2009–2025

• Multi-resolution Approaches to Modeling the 3D Structure, Delivery, and Replication of Viral Genomes

  NIH · $1.2M · 2020–2025

• NIH Grant P41RR005969

  NIH · $6.7M · 2012

Frequent coauthors

• Christopher Maffeo

  University of Illinois Urbana-Champaign

  69 shared

• Jejoong Yoo

  Sungkyunkwan University

  45 shared

• Jeffrey Comer

  Kansas State University

  30 shared

• Himanshu Joshi

  30 shared

• Meni Wanunu

  Northeastern University

  25 shared

• Robert Hołyst

  Polish Academy of Sciences

  22 shared

• G. Timp

  University of Notre Dame

  20 shared

• Behzad Mehrafrooz

  University of Illinois Urbana-Champaign

  20 shared

Labs

• Everitt LabPI

Education

• Ph.D., Bioengineering

  University of Illinois Urbana-Champaign

  2005

• M.S., Bioengineering

  University of Illinois Urbana-Champaign

  2001

• B.S., Bioengineering

  University of Illinois Urbana-Champaign

  1999

Similar researchers at University of Illinois Urbana-Champaign

• Jiawei Hanh-146
• Maria T. Grosse Perdekamph-141
• Gan Zhangh-134
• Jeffrey S. Mooreh-130
• Shuming Nieh-128