NGP

NGP simulates next-generation proteomics experiments to evaluate single-molecule peptide and protein sequencing performance, amino-acid readout, and protein identification accuracy.

Key Features:

• Single-Molecule Sequencing: Emphasizes sequencing individual peptide or protein molecules rather than bulk or mass spectrometry-based methods.
• Amino Acid Labeling: Simulates technologies that distinguish specific subsets of proteinogenic amino acids via labeling or other differentiation techniques.
• Theoretical Simulations: Conducts simulations under both ideal and non-ideal conditions to predict performance and limitations of sequencing strategies.
• Partial Peptide Sequencing: Models single-molecule partial peptide sequencing scenarios to assess identifiable sequence information.
• Benchmarking Capabilities: Establishes benchmarks for achievable outcomes in amino acid reading and discrimination power through simulation experiments.
• Agnostic Platform Design: Produces results intended to be applicable across various potential future proteomics platforms.

Scientific Applications:

• Protein Identification: Assesses the ability to uniquely identify human proteins based on the number and type of readable amino acids.
• Enzymatic Efficiency Analysis: Evaluates how enzymatic processes impact sequencing efficiency to inform optimization of reactions.
• Read Length Optimization: Determines optimal read lengths and their implications for protein identification accuracy.
• Discrimination Power Analysis: Analyzes the discrimination performance of reading N amino acids compared with reading N+1 amino acids on average.
• Frequency-Based Scaling: Demonstrates that amino acid discrimination power correlates with their frequency in the proteome.

Methodology:

Uses theoretical models to simulate single-molecule partial peptide sequencing under ideal and non-ideal conditions and performs simulation-based benchmarking of amino acid readout and discrimination power with results reported agnostically across potential platforms.