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

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PISCES: a protein sequence culling server.

Guoli Wang¹, Roland L Dunbrack

¹Institute for Cancer Research, Fox Chase Cancer Center, 7701 Burholme Avenue, Philadelphia, PA 19111, USA.

Bioinformatics (Oxford, England)

|August 13, 2003

Summary

This summary is machine-generated.

PISCES is a public server that refines protein sequence datasets from the Protein Data Bank (PDB) using sequence identity and structural quality. It offers improved accuracy over other methods for identifying protein relationships.

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Area of Science:

Bioinformatics
Structural Biology
Computational Biology

Background:

High-throughput structural biology generates vast amounts of protein data.
Redundant or low-quality data can hinder downstream analyses.
Accurate sequence identity assessment is crucial for understanding protein relationships.

Purpose of the Study:

To introduce PISCES, a public server for culling protein sequence datasets.
To provide a tool for selecting high-quality, non-redundant protein sequences.
To improve upon existing methods for sequence identity calculation.

Main Methods:

Utilizes Position-Specific Iterated BLAST (PSI-BLAST) for sequence alignment.
Employs position-specific substitution matrices derived from non-redundant databases.
Applies user-defined sequence identity and structural quality thresholds.
Supports culling from the entire Protein Data Bank (PDB) or user-provided lists.

Main Results:

PISCES generates more accurate sequence identity lists compared to standard BLAST.
It effectively identifies relationships below 40% sequence identity.
The server provides weekly updated PDB sequence lists.
PISCES can process non-PDB sequences from various input formats.

Conclusions:

PISCES offers a robust and accurate method for curating protein sequence datasets.
The server enhances the quality of data for structural biology and bioinformatics research.
It provides a valuable resource for researchers working with large protein sequence collections.