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Related Concept Videos

Conserved Binding Sites01:49

Conserved Binding Sites

Many proteins’ biological role depends on their interactions with their ligands, small molecules that bind to specific locations on the protein known as ligand-binding sites. Ligand-binding sites are often conserved among homologous proteins as these sites are critical for protein function.
Binding sites are often located in large pockets, and if their location on a protein’s surface is unknown, it can be predicted using various approaches. The energetic method computationally analyses the...
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Protein Families

Protein families are groups of homologous proteins; that is, they have similarities in amino acid sequences and three-dimensional structures. Protein families usually occur because of gene duplication, where an additional copy of a gene is inserted into the genome of an organism.   Mutations that change the amino acids but still allow the protein to be properly synthesized, will lead to new protein family members.   If these new proteins contain similar amino acids in key locations, protein...
Genome Annotation and Assembly03:36

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The genome refers to all of the genetic material in an organism. It can range from a few million base pairs in microbial cells to several billion base pairs in many eukaryotic organisms. Genome assembly refers to the process of taking the DNA sequencing data and putting it all back together in a correct order to create a close representation of the original genome. This is followed by the identification of functional elements on the newly assembled genome, a process called genome annotation.

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Updated: Jun 27, 2026

A Protocol for Computer-Based Protein Structure and Function Prediction
16:41

A Protocol for Computer-Based Protein Structure and Function Prediction

Published on: November 3, 2011

Structural descriptor database: a new tool for sequence-based functional site prediction.

Juliana S Bernardes1, Jorge H Fernandez, Ana Tereza R Vasconcelos

  • 1Laboratório Nacional de Computação Científica LNCC/MTC, Quitandinha, Petrópolis, RJ, Brazil. julibinho@gmail.com

BMC Bioinformatics
|November 27, 2008
PubMed
Summary
This summary is machine-generated.

The Structural Descriptor Database (SDDB) predicts protein function and active sites using Hidden Markov Models (HMM) and structural data. SDDB shows improved performance, especially with reliable training data, achieving over 70% precision in predictions.

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Last Updated: Jun 27, 2026

A Protocol for Computer-Based Protein Structure and Function Prediction
16:41

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Published on: November 3, 2011

An Integrated Approach for Microprotein Identification and Sequence Analysis
09:37

An Integrated Approach for Microprotein Identification and Sequence Analysis

Published on: July 12, 2022

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Area of Science:

  • Bioinformatics
  • Computational Biology
  • Structural Biology

Background:

  • The Structural Descriptor Database (SDDB) is a web tool for predicting protein function and functional site positions.
  • It utilizes structural properties of protein families and Hidden Markov Models (HMM) descriptors built from known protein sets.
  • SDDB integrates data from PDB, PDBSUM, CSA, and SCOP, accepting FASTA format queries.

Purpose of the Study:

  • To assess the performance of the Structural Descriptor Database (SDDB) in predicting protein functional sites.
  • To compare SDDB's prediction accuracy with existing methods, particularly for active and binding sites.
  • To evaluate the impact of training data quality on prediction outcomes.

Main Methods:

  • Development and application of HMM descriptors based on structural alignments and functional residues.
  • Utilizing curated datasets like Trypsin-like Serine protease and SCOP families for performance evaluation.
  • Comparing SDDB's ATP-binding site prediction against current state-of-the-art methods.

Main Results:

  • SDDB demonstrated significant improvements across all evaluated datasets.
  • Performance was notably better when using curated and reliable training data.
  • The database achieved prediction precision exceeding 70% for functional sites.

Conclusions:

  • SDDB's predictive accuracy is enhanced by the availability of trustworthy training data.
  • Active site prediction accuracy is superior to binding site prediction due to higher conservation of active sites.
  • The developed method offers a valuable tool for predicting protein function and functional residues.