Protein-peptide interactions are prevalent in many cellular processes, such as signal transduction, transcription
regulation, and immune response. Peptide-based therapeutics have attracted much attention in recent years,
and a significantly growing number of peptide-based medicines have been designed and approved for a variety
of diseases. Therefore, studying protein-peptide interactions is of great significance for mechanistic investigation
of many biological processes and for peptide therapeutic development. However, because of the difficulties and
cost for determining such structures by X-ray crystallography and NMR spectroscopy, currently there are only a
limited number of protein-peptide complex structures in the Protein Data Bank. Thus, the ability to predict protein-
peptide complex structures will have a far-reaching impact on understanding important biological processes and
on designing therapeutic interventions. However, structure prediction for protein-peptide complexes is
challenging, particularly due to peptide flexibility. In this project, we will address this challenging issue by
innovative integration of bioinformatics and physical modeling approaches. Specifically, we propose to achieve
Goal #1: We will develop novel deep-learning models for protein-peptide structure prediction. Despite successful
application of deep learning to protein structure prediction and protein-ligand interaction, deep learning has not
been applied to protein-peptide structure prediction yet, due to the flexibility and the resulting large degrees of
freedom in peptides.
Goal #2: We will develop the first in silico screening method for the search of peptide-based inhibitors, and will
construct novel peptide libraries for screening. Our in silico method will be an attractive complement to valuable
experimental technologies such as phage display and yeast two-hybrid system for rapid peptide screening at
much lower cost.
Goal #3: We will convert our computational algorithms into a modular, extensible, open-source software
package that can be disseminated to the computational modeling community at no cost.
Goal #4. As a proof-of-concept application of our in silico screening method, we will screen for novel peptide
leads by targeting ß-lactamase to combat antibiotic resistance, in collaboration with my experimental collaborator
whose expertise is in molecular biology, biochemistry and microbiology.