Pep–Whisperer: Inhibitory peptide design

Abstract

Designing peptides for protein-protein interaction inhibition is of significant interest in computer-aided drug design. Such inhibitory peptides could mimic and compete with the binding of the partner protein to the inhibition target. Experimental peptide design is a laborious, time consuming, and expensive multi-step process. Therefore, in silico peptide design can be beneficial for achieving this task. We present a novel algorithm, Pep-Whisperer, which aims to design inhibitory peptides for proteinprotein interaction. The desirable peptides would have a relatively high predicted binding affinity to the target protein in a given protein-protein complex. The algorithm outputs linear peptides which are based on an initial template. The template could either be a peptide which is retrieved from the interaction site, or a patch of nonconsecutive amino acids from the protein-protein interface which is completed to a linear peptide by short polyalanine linkers. In addition, the algorithm takes into consideration the conservation of the amino acids in the ligand-protein binding site by using evolutionary information for choosing the preferred amino acids in each position of the designed peptide. Our algorithm was able to design peptides with high predicted binding affinity to the target protein. The method is fully automated and available as a web server at http://bioinfo3d.cs.tau.ac.il/PepWhisperer/.

SaLT&PepPr is an interface-predicting language model for designing peptide-guided protein degraders

“…1c ). We also trained and tested on the ScanNet PPBS dataset to compare our model to baseline and state-of-the-art models which require tertiary structure and/or multiple sequence alignments (MSAs) to identify protein interacting residues 12 , 14 . Despite not using structure as input, our model achieved competitive performance compared to structure-based benchmarks, and decreased performance compared to ScanNet (Fig.…”

Revolutionizing peptide‐based drug discovery: Advances in the post‐AlphaFold era

“…Alternatively, a known experimental lead is used as a foundational reference point for peptide design 49 . These methodologies either focus on direct optimization of structural attributes (e.g., peptide binding design) or combine Multiple Sequence Alignments (MSA) with design algorithms like PinaColada and pepCrawler to enhance binding affinity (e.g., PepWhisperer) 49–52 . Despite notable achievements, these methodologies are constrained to system‐specific investigations and present limited flexibility in terms of varying peptide length.…”

“…49 These methodologies either focus on direct optimization of structural attributes (e.g., peptide binding design) or combine Multiple Sequence Alignments (MSA) with design algorithms like PinaColada and pepCrawler to enhance binding affinity (e.g., PepWhisperer). [49][50][51][52] Despite notable achievements, these methodologies are constrained to system-specific investigations and present limited flexibility in terms of varying peptide length. Furthermore, even if a designed peptide outperforms the wild-type PPI interaction, that design could be far from the "best possible" design.…”

Design of Peptide-Guided Protein Degraders with Structure-Agnostic Language Models

“…We also trained and tested on the ScanNet PPBS dataset to compare our model to baseline and state-of-the-art models which use tertiary structure and/or multiple sequence alignments (MSAs) to identify protein interacting residues. 10,12 Despite not using structure as input, our model achieves competitive performance compared to structure-based benchmarks, with slightly decreased performance than ScanNet (Supplementary Figure 2A). Specifically, on the "Test none" split which reflects most distant proteins, SaLT&PepPr exhibited superior performance to baseline methods based on structural homology and handcrafted feature selection, suggesting strong generalization to non-homologous proteins from different families (Supplementary Figure 2A).…”