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Related Concept Videos

Evolutionary Relationships through Genome Comparisons02:54

Evolutionary Relationships through Genome Comparisons

Genome comparison is one of the excellent ways to interpret the evolutionary relationships between organisms. The basic principle of genome comparison is that if two species share a common feature, it is likely encoded by the DNA sequence conserved between both species. The advent of genome sequencing technologies in the late 20th century enabled scientists to understand the concept of conservation of domains between species and helped them to deduce evolutionary relationships across diverse...
Protein Families02:47

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...
Conservation of Protein Domains Over Different Proteins02:26

Conservation of Protein Domains Over Different Proteins

Protein domains are small structurally independent units that are part of a single amino acid chain.  Although these domains are often structurally independent, they may rely on synergistic effects to perform their functions as part of a larger protein. Protein domains may be conserved within the same organism, as well as across different organisms.
A limited set of protein domains often duplicate and recombine during evolution. These domains can be organized in different combinations to form...

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Related Experiment Video

Updated: May 24, 2026

Identification of Plant Ice-binding Proteins Through Assessment of Ice-recrystallization Inhibition and Isolation Using Ice-affinity Purification
09:43

Identification of Plant Ice-binding Proteins Through Assessment of Ice-recrystallization Inhibition and Isolation Using Ice-affinity Purification

Published on: May 5, 2017

Using support vector machine and evolutionary profiles to predict antifreeze protein sequences.

Xiaowei Zhao1,2,3, Zhiqiang Ma1,2,3, Minghao Yin1,2

  • 1College of Computer Science and Information Technology, Northeast Normal University, 2555 Jingyue Street, Changchun 130117, China.

International Journal of Molecular Sciences
|March 13, 2012
PubMed
Summary
This summary is machine-generated.

Researchers developed AFP_PSSM, a novel predictor for identifying antifreeze proteins (AFPs). This method accurately identifies AFPs, aiding in understanding ice-protein interactions and designing new ice-binding proteins.

Keywords:
antifreeze proteinsevolutionary informationposition specific scoring matrixsupport vector machineweb sever

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Determining the Ice-binding Planes of Antifreeze Proteins by Fluorescence-based Ice Plane Affinity
08:46

Determining the Ice-binding Planes of Antifreeze Proteins by Fluorescence-based Ice Plane Affinity

Published on: January 15, 2014

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Last Updated: May 24, 2026

Identification of Plant Ice-binding Proteins Through Assessment of Ice-recrystallization Inhibition and Isolation Using Ice-affinity Purification
09:43

Identification of Plant Ice-binding Proteins Through Assessment of Ice-recrystallization Inhibition and Isolation Using Ice-affinity Purification

Published on: May 5, 2017

Determining the Ice-binding Planes of Antifreeze Proteins by Fluorescence-based Ice Plane Affinity
08:46

Determining the Ice-binding Planes of Antifreeze Proteins by Fluorescence-based Ice Plane Affinity

Published on: January 15, 2014

Area of Science:

  • Biochemistry
  • Structural Biology
  • Bioinformatics

Background:

  • Antifreeze proteins (AFPs) are crucial for organisms surviving in sub-zero environments.
  • Understanding AFP structure and function is key to advancing cryobiology and biomaterials.
  • Accurate identification of novel AFPs is essential for exploring ice-protein interactions.

Purpose of the Study:

  • To develop an accurate computational method for predicting antifreeze proteins.
  • To leverage evolutionary information for enhanced AFP prediction.
  • To provide a freely accessible web server for AFP prediction.

Main Methods:

  • Utilized support vector machine (SVM) algorithm.
  • Incorporated position-specific scoring matrix (PSSM) profiles for evolutionary information.
  • Employed 10-fold cross-validation and independent testing for performance evaluation.

Main Results:

  • The AFP_PSSM method achieved 82.67% accuracy on the training dataset.
  • The predictor demonstrated high performance with 93.01% accuracy on the independent testing dataset.
  • Evolutionary information via PSSM profiles proved effective for AFP prediction.

Conclusions:

  • The developed AFP_PSSM predictor is a reliable tool for identifying antifreeze proteins.
  • The study highlights the utility of PSSM profiles in predicting protein function.
  • A web server is available, facilitating further research in antifreeze proteins and ice-binding domains.