TheorChromo

TheorChromo predicts sequence-dependent peptide and protein retention times in linear gradient liquid chromatography using the BioLCCC model for C18 reversed-phase HPLC.

Key Features:

• Sequence-dependent retention time prediction: Predicts retention times from full amino acid sequences rather than additive retention coefficients.
• BioLCCC model foundation: Implements the BioLCCC concept incorporating a random walk model for macromolecule chains, entropy and energy compensation within adsorbent pores, and phenomenological parameters that define effective interaction energies between amino acid residues and the adsorbent surface.
• Parameterization for C18 reversed-phase HPLC: Model parameters are specifically fitted for C18 reversed-phase high-performance liquid chromatography.
• Empirical validation with LC-MS data: Validated against LTQ FT LC-MS and LC-MS/MS data from Escherichia coli and peptide standards showing R² = 0.97 for peptide standards and R² = 0.90 for E. coli datasets with standard error below one minute.
• Addresses limitations of additive models: Capable of predicting retention times for any amino acid sequence in specific HPLC experiments, overcoming chain-length and composition limits of additive models.

Scientific Applications:

• Proteomics: Supports peptide and protein identification by providing sequence-dependent retention time predictions for LC-MS/MS experiments.
• Mass spectrometry integration: Enhances interpretation of LTQ FT LC-MS and LC-MS/MS datasets by supplying expected retention times for peptide standards and complex samples.
• High-throughput peptide characterization: Enables large-scale peptide retention time prediction to aid high-throughput proteomic workflows.
• Biological studies and disease research: Facilitates characterization of complex biological systems and disease-related proteome changes through improved retention time prediction.

Methodology:

Calibrating the HPLC system using peptide retention standards, applying the BioLCCC model to predict retention times based on sequence-specific interactions, and validating predictions against experimental LTQ FT LC-MS and LC-MS/MS data.