De novo peptide structure prediction: an overview

Computational peptide engineering approach for selection of the new C05 antibody-driven peptide with potency to blocking influenza a virus attachment; from in silico to in vivo

Antiviral drugs are currently used to prevent or treat viral infections like influenza A Virus (IAV). Nonetheless, annual genetic mutations of influenza viruses make them resistant to efficient treatment by current medications. Antiviral peptides have recently attracted researchers' attention and can potentially supplant the current medications. This study aimed to design peptides against IAV propagation. For this purpose, P2 and P3 peptides were computationally designed based on the HCDR3 region of the C05 antibody (a monoclonal antibody that neutralizes influenza HA protein and inhibits the virus attachment). The synthesized peptides were tested against the influenza A virus (A/Puerto Rico/8/34 (H1N1)) in vitro, and the most efficient peptide was selected for in vivo experiments. It was shown that the designed peptide shows much more prophylactic and therapeutic effects against the virus. These findings demonstrated that the designed peptide can control the virus infection without any cytotoxicity effect. Antiviral peptide design is acknowledged as a critical tactic to manage viral infections by preventing viral binding to the host cells.Communicated by Ramaswamy H. Sarma.

Benchmarking AlphaFold2 on peptide structure prediction

Recent advancements in computational tools have allowed protein structure prediction with high accuracy. Computational prediction methods have been used for modeling many soluble and membrane proteins, but the performance of these methods in modeling peptide structures has not yet been systematically investigated. We benchmarked the accuracy of AlphaFold2 in predicting 588 peptide structures between 10 and 40 amino acids using experimentally determined NMR structures as reference. Our results showed AlphaFold2 predicts α-helical, β-hairpin, and disulfide-rich peptides with high accuracy. AlphaFold2 performed at least as well if not better than alternative methods developed specifically for peptide structure prediction. AlphaFold2 showed several shortcomings in predicting Φ/Ψ angles, disulfide bond patterns, and the lowest RMSD structures failed to correlate with lowest pLDDT ranked structures. In summary, computation can be a powerful tool to predict peptide structures, but additional steps may be necessary to analyze and validate the results.

Thermodynamics-Based Molecular Modeling of α-Helices in Membranes and Micelles

The Folding of Membrane-Associated Peptides (FMAP) method was developed for modeling α-helix formation by linear peptides in micelles and lipid bilayers. FMAP 2.0 identifies locations of α-helices in the amino acid sequence, generates their three-dimensional models in planar bilayers or spherical micelles, and estimates their thermodynamic stabilities and tilt angles, depending on temperature and pH. The method was tested for 723 peptides (926 data points) experimentally studied in different environments and for 170 single-pass transmembrane (TM) proteins with available crystal structures. FMAP 2.0 detected more than 95% of experimentally observed α-helices with an average error in helix end determination of around 2, 3, 4, and 5 residues per helix for peptides in water, micelles, bilayers, and TM proteins, respectively. Helical and nonhelical residue states were predicted with an accuracy from 0.86 to 0.96, and the Matthews correlation coefficient was from 0.64 to 0.88 depending on the environment. Experimental micelle- and membrane-binding energies and tilt angles of peptides were reproduced with a root-mean-square deviation of around 2 kcal/mol and 7°, respectively. The TM and non-TM states of hydrophobic and pH-triggered α-helical peptides in various lipid bilayers were reproduced in more than 95% of cases. The FMAP 2.0 web server (https://membranome.org/fmap) is publicly available to explore the structural polymorphism of antimicrobial, cell-penetrating, fusion, and other membrane-binding peptides, which is important for understanding the mechanisms of their biological activities.

Target-templated de novo design of macrocyclic d-/l-peptides: discovery of drug-like inhibitors of PD-1

Peptides are a rapidly growing class of therapeutics with various advantages over traditional small molecules, especially for targeting difficult protein-protein interactions. However, current structure-based methods are largely limited to natural peptides and are not suitable for designing bioactive cyclic topologies that go beyond natural l-amino acids. Here, we report a generalizable framework that exploits the computational power of Rosetta, in terms of large-scale backbone sampling, side-chain composition and energy scoring, to design heterochiral cyclic peptides that bind to a protein surface of interest. To showcase the applicability of our approach, we developed two new inhibitors (PD-i3 and PD-i6) of programmed cell death 1 (PD-1), a key immune checkpoint in oncology. A comprehensive biophysical evaluation was performed to assess their binding to PD-1 as well as their blocking effect on the endogenous PD-1/PD-L1 interaction. Finally, NMR elucidation of their in-solution structures confirmed our de novo design approach.

Using Local States To Drive the Sampling of Global Conformations in Proteins

Conformational changes associated with protein function often occur beyond the time scale currently accessible to unbiased molecular dynamics (MD) simulations, so that different approaches have been developed to accelerate their sampling. Here we investigate how the knowledge of backbone conformations preferentially adopted by protein fragments, as contained in precalculated libraries known as structural alphabets (SA), can be used to explore the landscape of protein conformations in MD simulations. We find that (a) enhancing the sampling of native local states in both metadynamics and steered MD simulations allows the recovery of global folded states in small proteins; (b) folded states can still be recovered when the amount of information on the native local states is reduced by using a low-resolution version of the SA, where states are clustered into macrostates; and (c) sequences of SA states derived from collections of structural motifs can be used to sample alternative conformations of preselected protein regions. The present findings have potential impact on several applications, ranging from protein model refinement to protein folding and design.

In silico design of low molecular weight protein-protein interaction inhibitors: Overall concept and recent advances

Protein-protein interactions (PPIs) are carrying out diverse functions in living systems and are playing a major role in the health and disease states. Low molecular weight (LMW) "drug-like" inhibitors of PPIs would be very valuable not only to enhance our understanding over physiological processes but also for drug discovery endeavors. However, PPIs were deemed intractable by LMW chemicals during many years. But today, with the new experimental and in silico technologies that have been developed, about 50 PPIs have already been inhibited by LMW molecules. Here, we first focus on general concepts about protein-protein interactions, present a consensual view about ligandable pockets at the protein interfaces and the possibilities of using fast and cost effective structure-based virtual screening methods to identify PPI hits. We then discuss the design of compound collections dedicated to PPIs. Recent financial analyses of the field suggest that LMW PPI modulators could be gaining momentum over biologics in the coming years supporting further research in this area.