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Related Experiment Video

Updated: Dec 31, 2025

Author Spotlight: Advancements in DNA Nanosensors – Addressing Sensitivity and Selectivity Challenges in Molecular Detection

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Published on: February 9, 2024

Fold recognition methods.

Adam Godzik¹

¹Program in Bioinformatics and Biological Diversity, Burnham Institute, La Jolla, CA, USA.

Methods of Biochemical Analysis

|March 22, 2003

Summary

This summary is machine-generated.

Protein fold recognition accurately predicts distant protein relationships and structures. However, predicting function remains challenging, requiring new tools to bridge the gap between structure and function analysis.

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Electronic Tongue Generating Continuous Recognition Patterns for Protein Analysis

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Area of Science:

* Structural bioinformatics and computational biology.

Background:

* Fundamental understanding of protein sequence/structure/function relationships remains incomplete.
* Analogy-based prediction algorithms are currently the most reliable tools for protein fold prediction.

Purpose of the Study:

* To explore the capabilities and limitations of advanced protein fold recognition methods.
* To address the challenge of predicting protein function from predicted structures, especially for distantly related proteins.

Main Methods:

* Utilized analogy-based prediction algorithms, including threading and hybrid threading/sequence fold recognition.
* Applied these methods to analyze newly sequenced genomes and identify protein superfamilies.

Main Results:

* Advanced methods can recognize distant homologues and even unrelated proteins with similar structures.
* A majority of newly sequenced genomes can be tentatively assigned to known protein superfamilies.
* Fold prediction is insufficient for accurate function prediction at distant evolutionary scales.

Conclusions:

* While fold recognition has advanced significantly, predicting function remains a key challenge.
* Development of novel tools is crucial to integrate structure prediction with function prediction and analysis.