Openings Fall 2023!

August 1, 2023 - May 15, 2024

Graduate Student Openings, Fall 2023

There are multiple openings in the group for Fall 2023 in the different sub-areas within our group. Students could also choose the projects that fall at the interface between two research areas. Students will also have the freedom to shape their projects. Our current graduate students are from the departments of Chemical and Biomolecular Engineering, Chemistry, Bioengineering, Biophysics, Biochemistry and Information Sciences. In particular, we are planning to recruit students for the following projects.

Project 1. High-throughput characterization of membrane proteins using deep mutational scans (2 Experimental+Molecular Simulations Position). Deep mutagenesis, whereby tens of thousands of mutational effects are determined by combining in vitro selections of sequence variants with deep sequencing. The overall aim of the research is to use deep mutagenesis and molecular dynamics simulations synergistically to determine the mechanism of membrane proteins such as ion-coupled neurotransmitter transporters, Sugar transporters and G-protein coupled receptors (GPCRs). We plan to recruit two students working on experimental and computational aspects of this projects. This research project is supported by the National Institute of Health grant.


Project 2. Deep learning guided design of experiments for probing protein dynamics (1 Machine Learning+Experimental position). In this project, we plan to answer a broad question about design of experiments and validation of protein conformational ensembles, what is the minimum experimental information required for describing the conformational dynamics of a protein? We plan to combine ideas from machine learning and chemical physics – deep learning based hyperparameter selection via cross-validation and variational approaches for linear operator eigenproblems; to create a new method for predicting observables that optimally discriminate between alternative models for molecular kinetics constructed from MD simulations. These methods could also be used with a broad range of experimental techniques or a combination of experimental techniques. Students can work on both the computational aspects as well as conducting actual experiments to validate the machine learning based design.


Project 3. Interpretable machine learning of protein structure, function and dynamics (1 Machine Learning Position). Assessing the effect of mutations on protein function is a central question in protein engineering. The rise in popularity of machine learning approaches and its integration in the field of molecular biology gave rise to many advanced but uninterpretable models for assessing the effects of mutation. However, understanding these models can be pivotal for protein design by understanding the mechanism behind the biophysical effects of a given mutation. We plan to develop these interpretable methods using the data from high-throughput experiments and simulations conducted in our group. This project is supported by National Institutes of Health.


Project 4. Generative and large language models for ligand design (1 Molecular Simulations + Machine Learning positions). Large language models have revolutionized the development of new technologies in the fields of natural language processing. Deep generative models are powerful tools for navigating the chemical space and design of molecules with desired properties. Generative models, particularly pre-trained generative chemical language models could be employed to design novel ligands with enhanced binding affinity. These methods have been applied for drug design with the creation of bespoke focused virtual chemical libraries. Here, we propose to integrate the chemical language and deep generative models for design of focused virtual libraries for separation of REEs. The key advantage of our approach is that it leverages the unlabeled chemical datasets in chemical language models and labeled literature data on REE-ligand interactions for de novo generation of focused virtual chemical library for REE separation. This project is funded by NSF and is pursued in collaboration with Prof. Prashant Jain (Chemistry), Prof. Xiao Su (ChBE) and Prof. Alex Mironenko (ChBE).

Postdoctoral Fellow (Experimental), Fall 2023

We are seeking a talented Postdoctoral Associate to join our experimental team. The ideal candidate will possess excellent academic credentials in experimental molecular biology, as well as expertise or knowledge in chemical biology.

In this role, the successful applicant will be a key member within the laboratory and make significant contributions to the advancement of group’s protein engineering research with emphasis on engineering proteins in plants. Primarily, the candidate will be responsible for the conducting high-throughput experiments for either generating protein sequence-function data or high-throughput screening of ligands to enhance the potency and selectivity of potential drug candidates. Furthermore, the candidate will enjoy ample opportunities to learn and actively participate in multidisciplinary projects in our group, making a meaningful impact and engaging in independent ideation to drive the group’s research. We are also looking for candidates who are interested in learning the basics of AI and ML and apply these ideas in their project. There will be many opportunities for collaboration with other experimental groups as well.

The position presents an exciting and fulfilling prospect for career growth, as it involves pioneering work in the field of protein engineering and also provides ample opportunities for learning new skills.