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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...

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...

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,...

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

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Conserved Binding Sites

Conserved Binding Sites

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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Optimization of Synthetic Proteins: Identification of Interpositional Dependencies Indicating Structurally and/or Functionally Linked Residues

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Published on: July 14, 2015

Knot or not? Identifying unknotted proteins in knotted families with sequence-based Machine Learning model.

Maciej Sikora^1,2, Eva Klimentova^3,4, Dawid Uchal^1,5

¹Centre of New Technologies, University of Warsaw, Warsaw, Poland.

Protein Science : a Publication of the Protein Society

|June 18, 2024

Summary

This study uses AlphaFold predictions to identify knotted proteins in the UniProt database. A machine learning model predicts protein knots from sequences, revealing conserved knot structures in most families.

Keywords:

AlphaFold SPOUT family proteins deep learning knotted proteins protein topology

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Area of Science:

Structural biology
Computational biology
Machine learning

Background:

Knotted proteins are rare but structurally vital, with ongoing research into their functions.
AlphaFold provides extensive protein structure predictions, enabling large-scale computational studies.

Purpose of the Study:

To computationally identify and analyze knotted and unknotted proteins within the UniProt database using AlphaFold predictions.
To develop a machine learning model for predicting protein knot presence from amino acid sequences.
To investigate the structural and evolutionary conservation of knots across protein families.

Main Methods:

Utilized AlphaFold predictions to build a comprehensive dataset of knotted and unknotted proteins from UniProt.
Developed and validated a machine learning model for sequence-based knot prediction.
Classified knotted proteins into structural families and analyzed sequence conservation.

Main Results:

Successfully created a robust dataset of knotted and unknotted proteins.
Achieved 92% agreement on a test set of 100 proteins for sequence-based knot prediction.
Identified that all predicted knotted proteins belong to only 17 families.
Discovered three new families (UCH, DUF4253, DUF2254) with both knotted and unknotted members, potentially due to deletions.
Found knotted topology is conserved in 11 out of 15 families, even with low sequence similarity.
Determined that predicted unknotted proteins are structurally accurate but may be nonfunctional fragments.

Conclusions:

Machine learning models can accurately predict protein knotting from amino acid sequences.
Knotted protein structures are highly conserved within specific protein families.
Unknotted proteins predicted by AlphaFold may represent truncated or non-functional variants.