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Related Concept Videos

Proteomics01:33

Proteomics

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A proteome is the entire set of proteins that a cell type produces. We can study proteomes using the knowledge of genomes because genes code for mRNAs, and the mRNAs encode proteins. Although mRNA analysis is a step in the right direction, not all mRNAs are translated into proteins.
Proteomics is the study of proteomes' function. It involves the large-scale systematic study of the proteome to denote the protein complement expressed by a genome. Scientist Mark Wilkins coined the term...
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Conservation of Protein Domains Over Different Proteins02:26

Conservation of Protein Domains Over Different Proteins

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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...
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Protein-protein Interfaces02:04

Protein-protein Interfaces

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Many proteins form complexes to carry out their functions, making protein-protein interactions (PPIs) essential for an organism's survival. Most PPIs are stabilized by numerous weak noncovalent chemical forces. The physical shape of the interfaces determines the way two proteins interact. Many globular proteins have closely-matching shapes on their surfaces, which form a large number of weak bonds. Additionally, many PPIs occur between two helices or between a surface cleft and a...
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Conserved Binding Sites01:49

Conserved Binding Sites

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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...
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Protein Families02:47

Protein Families

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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...
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Protein Networks02:26

Protein Networks

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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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Updated: May 30, 2025

Author Spotlight: A Computational Approach to Decipher Amino Acid Preferences in Multispecific Protein-Protein Interactions
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Modern machine learning methods for protein property prediction.

Arjun Dosajh1, Prakul Agrawal1, Prathit Chatterjee1

  • 1Center for Computational Natural Sciences and Bioinformatics, International Institute of Information Technology, Hyderabad, 500032, Telangana, India.

Current Opinion in Structural Biology
|January 30, 2025
PubMed
Summary
This summary is machine-generated.

Artificial intelligence and machine learning (AI/ML) advance protein science by predicting functional properties and enabling reverse-engineering of protein sequence and structure from known characteristics.

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Area of Science:

  • Biomolecular science
  • Computational biology
  • Artificial intelligence

Background:

  • Artificial intelligence and machine learning (AI/ML) are increasingly powerful tools for complex biomolecular problems.
  • AI/ML models excel at identifying patterns in data to make predictions on new inputs.
  • Generative AI (GenAI) offers capabilities for generating protein structures or sequences based on desired properties.

Purpose of the Study:

  • To review the applications of AI/ML in predicting crucial protein functional properties.
  • To explore the potential of reverse-engineering protein sequence and structure from property data.

Main Methods:

  • Review of current AI/ML techniques applied to protein property prediction.
  • Analysis of generative AI capabilities for protein sequence and structure generation.
  • Examination of reverse-engineering approaches using protein-property relationships.

Main Results:

  • AI/ML models demonstrate significant success in predicting diverse protein functional properties.
  • GenAI shows promise in designing novel protein sequences and structures with targeted characteristics.
  • Reverse-engineering approaches are emerging for inferring protein details from functional data.

Conclusions:

  • AI/ML is revolutionizing protein science, enabling accurate property prediction and de novo design.
  • The integration of AI/ML facilitates a deeper understanding and manipulation of protein sequence-structure-function relationships.
  • Future prospects include enhanced protein engineering and discovery through advanced AI/ML applications.