CycloPs

CycloPs generates and evaluates virtual libraries of constrained peptides composed of natural and non‑natural commercially available amino acids for peptide discovery and design.


Key Features:

  • Virtual Library Generation: Generates large, structurally diverse libraries of constrained peptides and exports structures in one-dimensional SMILES and three-dimensional SDF formats.
  • Empirical Filtering Capabilities: Applies empirical-property filters to evaluate synthesizability (including assessment for standard solid-phase peptide synthesis), stability, and drug-like properties.
  • Structural Diversity and Synthesizability: Rapidly enumerates structurally diverse peptides while emphasizing synthesizability for experimental follow-up.
  • Peptide Topologies Supported: Handles both cyclised and linear peptides.
  • Physicochemical Property Assessment: Computes drug-like attributes including the Octanol-water partition coefficient.

Scientific Applications:

  • Drug Discovery and Design: Supports virtual screening and design of constrained peptides as potential therapeutic agents with enhanced stability and specificity.
  • Candidate Prioritization for Pharmacokinetics: Enables prioritization of peptide candidates based on synthesizability, stability, and drug-like properties to inform pharmacokinetic assessment.

Methodology:

Leverages computational algorithms to simulate peptide synthesis and structural characteristics, generates SMILES and SDF representations, and assesses the Octanol-water partition coefficient and other drug-like attributes for both cyclised and linear peptides.

Topics

Details

Tool Type:
command-line tool
Operating Systems:
Linux, Windows, Mac
Programming Languages:
Python
Added:
12/18/2017
Last Updated:
11/25/2024

Operations

Publications

Duffy FJ, Verniere M, Devocelle M, Bernard E, Shields DC, Chubb AJ. CycloPs: Generating Virtual Libraries of Cyclized and Constrained Peptides Including Nonnatural Amino Acids. Journal of Chemical Information and Modeling. 2011;51(4):829-836. doi:10.1021/ci100431r. PMID:21434641.

Documentation

Links