Project Info

Molecular Simulations of SARS-CoV-2 Peptide Therapeutics

Alex Pak

Project Goals and Description:

This research project aims to use molecular dynamics (MD) simulations to study the assembly and binding of small peptides that selectively target viral structural proteins, thereby acting as therapeutic inhibitors. In particular, the focus of this study is on lung receptor-mimetic peptides that target SARS-CoV-2 spike proteins. Spike proteins are the key agents that mediate SARS-CoV-2 virion binding and entry into new cells. Therefore, inhibition of spike activity would reduce viral infectivity. By combining targeting peptides with self-assembling peptides, a multivalent therapeutic can be designed with enhanced binding and efficacy. Through computational mutagenesis and free energy calculations, the goal of this study is to understand how self-assembling scaffolds and sequence motifs mediate spike protein binding and inhibition.

More Information:

Grand Challenge: Engineer better medicines.
  1. Chan et al., “Engineering human ACE2 to optimize binding to the spike protein of SARS coronavirus 2,” Science, 2020, 369, 1261-5.
  2. Larue et al., “Rationally designed ACE2-derived peptides inhibit SARS-CoV-2,” Bioconjugate Chem., 2021, 32, 215-23.

Primary Contacts:

Alex Pak,  

Student Preparation


The student should have prior knowledge on thermodynamics and molecular biology from formal coursework or self-study. The student should be highly motivated to learn and to develop new skills. Prior experience with programming (Python or C++) or Linux is beneficial but not required.




  • Molecular dynamics simulations
  • Enhanced sampling techniques
  • High-performance computing
  • Dimensional reduction
  • Workflow management (Git, Lab notebooks, etc.)
  • Coding: Python and Bash
  • Literature review
  • Oral/written communication (e.g. presentations and manuscripts)


The student will give brief updates over Slack once a week and have an extended meeting (joint with other group members in the same research area) with the PI every two weeks. The student will also participate in regular biweekly group meetings. The group members and PI will be accessible both in-person and via Slack.


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