Progress in protein structural class prediction and its impact to bioinformatics and proteomics

Area of Science:

• Structural bioinformatics
• Computational biology
• Protein science

Background:

• The concept of protein structural class, crucial for characterizing protein folding, originated over 30 years ago from a small dataset.
• The rapid growth of protein structure databases (e.g., RCSB PDB) necessitates updated classification schemes.
• Numerous computational methods have been developed to predict protein structural classes.

Purpose of the Study:

• To review advancements in protein structural class prediction.
• To introduce updated protein structural classification schemes.
• To explore the interplay between structural class prediction and the identification of other protein attributes.

Main Methods:

• Review of existing literature on protein structural classification and prediction algorithms.
• Analysis of new classification schemes developed to accommodate the expanding protein data.
• Discussion of various computational approaches for predicting protein structural class.

Main Results:

• Significant progress has been made in protein structural class prediction methodologies.
• New classification schemes have been developed to better represent the diversity of known protein structures.
• The development of prediction tools has spurred advancements in identifying other protein attributes, creating a synergistic relationship.

Conclusions:

• Protein structural class prediction is a dynamic field benefiting from increased data and algorithmic development.
• The evolution of classification schemes and prediction methods enhances our understanding of protein structure-function relationships.
• Future research holds promise for further refining protein structural class prediction and its applications.