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Related Experiment Video

Updated: Jan 3, 2026

Application of I TASSER, trRosetta, UCSF Chimera, HADDOCK server, and HEX loria for De Novo and In Silico Design of Proteins

Application of I TASSER, trRosetta, UCSF Chimera, HADDOCK server, and HEX loria for De Novo and In Silico Design of Proteins

Published on: July 8, 2025

Assessing Protein Sequence Database Suitability Using De Novo Sequencing.

Richard S Johnson¹, Brian C Searle², Brook L Nunn¹

¹Department of Genome Sciences, University of Washington, Seattle, Washington.

Molecular & Cellular Proteomics : MCP

|November 17, 2019

Summary

This summary is machine-generated.

Automated de novo sequencing assesses proteomic data quality and database suitability for unsequenced species and complex samples. This method aids in analyzing diverse proteomes, including extinct organisms and metaproteomics.

Keywords:

Algorithms Caenorhabditis elegans data evaluation de novo sequencing mass spectrometry metaproteomics peptides*protein identification quality control and metrics sequencing ms tandem mass spectrometry

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Novel Sequence Discovery by Subtractive Genomics

Novel Sequence Discovery by Subtractive Genomics

Published on: January 25, 2019

Area of Science:

Proteomics
Bioinformatics
Mass Spectrometry

Background:

Analyzing proteomes from unsequenced or understudied species presents challenges.
Determining the presence of peptide tandem mass spectra and the availability of suitable protein sequence databases are key hurdles.

Purpose of the Study:

To introduce automated de novo sequencing as a tool for evaluating proteomic data quality.
To assess the suitability of protein sequence databases for proteomic searches with challenging samples.

Main Methods:

Utilizing automated de novo sequencing to analyze peptide tandem mass spectra.
Evaluating the quality of spectral data from unusual sample types.
Assessing the appropriateness of protein sequence databases for proteomic searches.

Main Results:

Automated de novo sequencing effectively evaluates the quality of tandem mass spectra.
The method determines the suitability of protein sequence databases for proteomic data analysis.
Demonstrated applicability in diverse proteomic scenarios.

Conclusions:

Automated de novo sequencing is a valuable method for quality control in proteomics.
This technique addresses challenges in analyzing proteomes from unsequenced species and complex biological systems.
Facilitates proteomic analysis in fields like metaproteomics and paleoproteomics.