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Protein Families

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...

Protein Families

Protein Families

Conservation of Protein Domains Over Different Proteins

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...

Evolutionary Relationships through Genome Comparisons

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...

Conservation of Protein Domains

Conservation of Protein Domains

Protein Networks

Protein Networks

An organism can have thousands of different proteins, and these proteins must cooperate to ensure the health of an organism. Proteins bind to other proteins and form complexes to carry out their functions. Many proteins interact with multiple other proteins creating a complex network of protein interactions.
These interactions can be represented through maps depicting protein-protein interaction networks, represented as nodes and edges. Nodes are circles that are representative of a protein,...

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Related Experiment Video

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Creating and Applying a Reference to Facilitate the Discussion and Classification of Proteins in a Diverse Group

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Published on: August 16, 2017

Critiquing Protein Family Classification Models Using Sufficient Input Subsets.

Brandon Carter^1,2, Maxwell Bileschi², Jamie Smith²

¹MIT Computer Science and Artificial Intelligence Laboratory, Cambridge, Massachusetts, USA.

Journal of Computational Biology : a Journal of Computational Molecular Cell Biology

|December 25, 2019

Summary

This summary is machine-generated.

Researchers developed new methods to understand deep learning models in protein classification. These tools reveal how models make predictions, showing their biological relevance can vary with model design.

Keywords:

interpretability machine learning model selection neural networks protein classification protein domain

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Area of Science:

Computational Biology
Bioinformatics
Machine Learning

Background:

Deep learning models achieve high accuracy in many domains but lack interpretability.
Lack of transparency hinders adoption in life and medical sciences, where biological basis for predictions is crucial.
Understanding model decision-making is essential for reliable application in scientific research.

Purpose of the Study:

To develop and apply methods for critiquing deep learning models for protein family classification.
To investigate the decision-making criteria used by deep learning models in this task.
To assess the biological relevance of model predictions and their variability.

Main Methods:

Extended the Sufficient Input Subsets (SIS) technique to identify feature subsets crucial for classification.
Developed a suite of tools to analyze these subsets and interpret model behavior.
Applied methods to protein sequence classification tasks using deep learning models.

Main Results:

Identified subsets of protein sequence features sufficient for classification by deep learning models.
Demonstrated that while models can classify based on biologically relevant features, their behavior is sensitive to architecture and initialization.
Highlighted variability in model decision-making criteria across different configurations.

Conclusions:

The developed methods provide insights into the decision-making processes of deep learning models in scientific applications.
Model interpretability is crucial for validating predictions in fields like life and medical sciences.
The approach is generalizable to various scientific contexts requiring model transparency.