3/13/2025 Maddie Stover
ProfessorAleksei Aksimentiev is advancing the field of single-molecule protein sequencing through a new award from the DOE’s INCITE program. His team is developing a computational model to map nanopore current signals to amino acid sequences, overcoming the challenge of distinguishing the 20 amino acids that make up proteins. This breakthrough could revolutionize personalized medicine, disease diagnostics, and biosecurity by providing unprecedented insight into individual proteins. Leveraging cutting-edge molecular dynamics simulations and machine learning, Aksimentiev’s work positions Illinois at the forefront of next-generation protein sequencing technology.
Written by Maddie Stover
Protein sequencing allows scientists to determine the string of amino acids that make up a protein. Currently, proteins can only be sequenced by averaging several copies in a process that loses critical information about unique attributes of individual proteins. Developing capability for single-molecule protein sequencing would allow scientists to read the entire amino-acid sequence of each protein. This level of detailed insight into individual proteins would represent a giant step forward for the field, analogous to the breakthrough in genetic understanding after DNA was first sequenced. It would have broad implications for personalized medicine and disease diagnostics and treatment, because disease is typically caused by a malignant protein mutation. Beyond healthcare, protein identification can benefit biodiversity studies, archaeology, crop sciences, and biosecurity.
Illinois physics and bioengineering professor Aleksei Aksimentiev has received an award to develop a computational model for single-molecule nanopore protein sequencing at Oak Ridge National Laboratory through the DOE’sInnovative and Novel Computational Impact on Theory and Experiment (INCITE) program.
There is a huge diversity of proteins in the human body—up to one million different variations. This variety arises from modifications to proteins after they are built from the instructions in the messenger molecule ribonucleic acid (RNA).
Aksimentiev explains, “To make a protein, you start with a DNA blueprint. RNA reads the instructions in DNA and becomes a template for protein assembly. However, once the proteins are assembled, they can be modified in multiple ways. That’s how we end up with one million different protein types even though we have only 20,000 genes—protein coding segments of DNA.”
Nanopore technology represents one promising avenue to sequence individual proteins. In this technique, a protein is threaded through the nanopore, a small protein structure that transports molecules through the cellular membrane. In this method, as a protein moves through the nanopore, it blocks the electric current that is also flowing through the nanopore. The change in current is then interpreted as an amino acid sequence. Aksimentiev’s research group has built a computational model to demonstrate that nanopores can be used to sequence nucleic acids such as DNA and RNA (this work can be found in theBiophysical Journal). However, sequencing proteins through nanopores poses a much greater challenge because amino acids are more difficult to distinguish from each other than the nucleic bases that make up nucleic acids. Illinois Physics postdoctoral researcher Monika Kumari, who is collaborating with Aksimentiev on this study, elaborates, “In the case of proteins, we have more heterogeneity. Instead of four nucleic bases, we have 20 amino acids.”
A major challenge in the development of single-molecule protein sequencing will be developing the capacity to translate the blocked electric current as an amino-acid sequence. This is the goal of Aksimentiev’s and Kumari’s work: to create a computational model that directly maps nanopore current signals to the amino-acid sequences.
Aksimentiev and Kumari plan to accomplish this goal by creating an all-atom simulation of the dynamics of a protein passing through a nanopore. This level of detail is necessary because the differences between amino acids are only a few atoms. They will take advantage of in-house technologyNanoscale Molecular Dynamics (NAMD)in developing their simulation of over 20,000 atoms. “We are really fortunate to be part of theBeckman Institute, which develops molecular dynamics code NAMD. We wouldn’t be able to do this study without NAMD3, which was just completed in the last year,” says Aksimentiev. Once Aksimentiev and Kumari have detailed data relating the blocked current to a known amino acid sequence, they will develop a machine-learning algorithm to create the map from electric current to protein identity.
Aksimentiev is optimistic that this study will contribute to development of groundbreaking new technology. “In this massive study, we hope to provide some clarity into what actually happens in the experimental measurement and also provide a blueprint for interpreting current signatures in terms of sequence.”
This work is supported by the Innovative and Novel Computational Impact on Theory and Experiment Award under grant No. BIP246 and the National Human Genome Research Institute under grant No. R01-HG012553. The findings presented are those of the researchers and not necessarily those of the funding agencies.
This artical originally appeared on The Grainger College of Engineering Physics website here.
