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Updated: Feb 16, 2026

A Protocol for Computer-Based Protein Structure and Function Prediction

A Protocol for Computer-Based Protein Structure and Function Prediction

Published on: November 3, 2011

3DCONS-DB: A Database of Position-Specific Scoring Matrices in Protein Structures.

Ruben Sanchez-Garcia¹, Carlos Oscar Sanchez Sorzano², Jose Maria Carazo³

¹GN7 of the Spanish National Institute for Bioinformatics (INB) and Biocomputing Unit, National Center of Biotechnology (CSIC)/Instruct Image Processing Center, 28049 Madrid, Spain. rsanchez@cnb.csic.es.

Molecules (Basel, Switzerland)

|December 16, 2017

Summary

This study introduces a new database of Position-Specific Scoring Matrix (PSSM) profiles for Protein Data Bank (PDB) proteins. This resource aims to reduce computational costs for researchers studying protein structures and features.

Keywords:

machine learning position-specific scoring matrices protein databases protein structure

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Area of Science:

Biochemistry
Structural Biology
Bioinformatics

Background:

Position-Specific Scoring Matrix (PSSM) profiles are crucial for characterizing protein residues and predicting protein features.
Recalculating PSSM profiles for Protein Data Bank (PDB) entries is common but computationally intensive, especially for large-scale studies.
Existing methods face substantial computational costs when utilizing thousands of PSSM profiles for statistical or machine learning analyses.

Purpose of the Study:

To develop and present a centralized database of pre-compiled PSSM profiles for proteins within the PDB.
To alleviate the significant computational burden associated with generating PSSM profiles for large datasets.
To provide a valuable resource for researchers in structural biology and bioinformatics.

Main Methods:

Compilation of PSSM profiles from a comprehensive set of Protein Data Bank (PDB) entries.
Development of a database infrastructure to store and manage a large collection of protein chain profiles.
Ensuring the accessibility and usability of the compiled PSSM profiles for downstream applications.

Main Results:

The database currently houses 333,532 protein chain profiles.
These profiles represent 123,135 distinct Protein Data Bank (PDB) entries.
The database offers a readily available resource, saving researchers considerable computational time and resources.

Conclusions:

The newly compiled PSSM profile database significantly reduces the computational overhead for protein structure and feature analysis.
This resource facilitates broader application of PSSM-based methods in statistical and machine learning studies.
The database serves as a key asset for advancing research in protein science and bioinformatics.