Protein?protein interactions (PPIs) have many important cellular roles, including in transcription, protein degradation, protein translocation, signal transduction, and molecule and vesicular transport. The ability to selectively modulate PPIs thus provides a valuable means to control specific biological processes for therapeutic intervention. Unfortunately, owing to their flat and large interfaces, PPIs are challenging to target using traditional small molecule drugs. Cyclic peptides (CPs) represent a promising solution to target PPIs ? they can directly mimic the binding partners of the target protein interface and have enhanced biostability and bioavailability compared to their linear counterparts. Despite several examples of CPs successfully used as therapeutics, including as immune system suppressants, antibiotics, and antifungals, most of these examples are actually natural products or their derivatives, rather than the products of successful de novo CP development. One of the key reasons that novel, functional CPs are difficult to design is our current inability to efficiently and reliably predict CP three-dimensional structures. Our long-term objective is the rational design of functional CPs to target specific PPIs of interest. In this proposal, our specific aims are to (1) develop a computational method to address current challenges in CP structure prediction; (2) fill the substantial knowledge gap regarding CP sequence?structure relationships; and (3) validate a platform to rationally design CPs with desired structures. In our first aim, we will develop an enhanced sampling method for CP structure prediction by taking advantage of the constrained nature of CPs. We hypothesize that CPs have only a limited set of motions they can use to switch conformations and that these essential transitional motions of CPs can be leveraged to greatly accelerate conformational sampling, allowing efficient CP structure prediction in explicit solvent. In the second aim, we will systematically vary the sequences of CPs and apply our enhanced sampling methods to simulate their structures. From this study, we will extract general principles of how primary amino acid sequences affect CP structures. Moreover, we will integrate these principles into algorithms to predict CP structures and guide CP design. In our third aim, we will integrate our capability to simulate CP structures and our knowledge of CP sequence?structure relationships to design and experimentally validate CPs that mimic hot loops at PPIs. The proposed work will greatly enable the continued development of CPs as modulators of PPIs, advance our understanding of such important molecular interactions in normal and disease biology, and provide possible means to target specific PPIs for therapeutic interventions.
Cyclic peptides are an interesting class of drug molecules capable of engaging challenging biological targets and we have been using the cyclic peptides made by nature as immune system suppressants, antibiotics, and antifungals. However, it is extremely challenging for researchers to design new cyclic peptide drugs to treat specific diseases because it is currently very difficult to predict the three-dimensional structure any cyclic peptide will adopt. In this proposal, we will develop new computing algorithms that efficiently and accurately predict structures of cyclic peptides, thereby greatly enhancing their usefulness as biological perturbagens and potential therapeutic leads.
|Slough, Diana P; McHugh, Sean M; Cummings, Ashleigh E et al. (2018) Designing Well-Structured Cyclic Pentapeptides Based on Sequence-Structure Relationships. J Phys Chem B 122:3908-3919|
|Slough, Diana P; McHugh, Sean M; Lin, Yu-Shan (2018) Understanding and designing head-to-tail cyclic peptides. Biopolymers 109:e23113|