Computational Design of Structured and Functional Peptide Macrocycles

A high-throughput molecular dynamics screening (HTMDS) approach to the design of novel cyclopeptide inhibitors of ATAD2B based on the non-canonical combinatorial library

Cyclic peptides (CPs) are a promising class of drugs because of their high biological activity and specificity. However, the design of CP remains challenging due to their conformational flexibility and difficulties in designing stable binding conformation. Herein, we present a high-throughput MD screening (HTMDS) process for the iterative design of stable CP binders with a combinatorial CP library composed of canonical and non-canonical amino acids. As a proof of concept, we apply our methods to design CP inhibitors for the bromodomain (BrD) of ATAD2B. 698,800 CP candidates with a total of 25,570 ns MD simulations were performed to study the protein-ligand binding interactions. The binding free energies (ΔGbind) estimated by MM/PBSA approach for eight lead CP designs were found to be low. CP-1st.43 was the best CP candidate with an estimated ΔGbind of -28.48 kcal/mol when compared to the standard inhibitor C-38 which has been experimentally validated and shown to exhibit ΔGbind of -17.11 kcal/mol. The major contribution of binding sites for BrD of ATAD2B involved the hydrogen-bonding anchor within the Aly-binding pocket, salt bridging, and hydrogen-bonding mediated stabilization of the ZA loop and BC loop, and the complementary Van der Waals attraction. Our methods demonstrate encouraging results by yielding conformationally stable and high-potential CP binders that should have potential applicability in future CP drug development.Communicated by Ramaswamy H. Sarma.

A simple method for developing lysine targeted covalent protein reagents

Peptide-based covalent probes can target shallow protein surfaces not typically addressable using small molecules, yet there is a need for versatile approaches to convert native peptide sequences into covalent binders that can target a broad range of residues. Here we report protein-based thio-methacrylate esters-electrophiles that can be installed easily on unprotected peptides and proteins via cysteine side chains, and react efficiently and selectively with cysteine and lysine side chains on the target. Methacrylate phosphopeptides derived from 14-3-3-binding proteins irreversibly label 14-3-3σ via either lysine or cysteine residues, depending on the position of the electrophile. Methacrylate peptides targeting a conserved lysine residue exhibit pan-isoform binding of 14-3-3 proteins both in lysates and in extracellular media. Finally, we apply this approach to develop protein-based covalent binders. A methacrylate-modified variant of the colicin E9 immunity protein irreversibly binds to the E9 DNAse, resulting in significantly higher thermal stability relative to the non-covalent complex. Our approach offers a simple and versatile route to convert peptides and proteins into potent covalent binders.

Design of Protein Segments and Peptides for Binding to Protein Targets

Recent years have witnessed a rise in methods for accurate prediction of structure and design of novel functional proteins. Design of functional protein fragments and peptides occupy a small, albeit unique, space within the general field of protein design. While the smaller size of these peptides allows for more exhaustive computational methods, flexibility in their structure and sparsity of data compared to proteins, as well as presence of noncanonical building blocks, add additional challenges to their design. This review summarizes the current advances in the design of protein fragments and peptides for binding to targets and discusses the challenges in the field, with an eye toward future directions.

Harnessing protein folding neural networks for peptide-protein docking

Highly accurate protein structure predictions by deep neural networks such as AlphaFold2 and RoseTTAFold have tremendous impact on structural biology and beyond. Here, we show that, although these deep learning approaches have originally been developed for the in silico folding of protein monomers, AlphaFold2 also enables quick and accurate modeling of peptide-protein interactions. Our simple implementation of AlphaFold2 generates peptide-protein complex models without requiring multiple sequence alignment information for the peptide partner, and can handle binding-induced conformational changes of the receptor. We explore what AlphaFold2 has memorized and learned, and describe specific examples that highlight differences compared to state-of-the-art peptide docking protocol PIPER-FlexPepDock. These results show that AlphaFold2 holds great promise for providing structural insight into a wide range of peptide-protein complexes, serving as a starting point for the detailed characterization and manipulation of these interactions.