Direct coupling analysis and the attention mechanism

Proteins are involved in nearly all cellular functions, encompassing roles in transport, signaling, enzymatic activity, and more. Their functionalities crucially depend on their complex three-dimensional arrangement. For this reason, being able to predict their structure from the amino acid sequence...

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Vydáno v:BMC bioinformatics Ročník 26; číslo 1; s. 41 - 21
Hlavní autoři: Caredda, Francesco, Pagnani, Andrea
Médium: Journal Article
Jazyk:angličtina
Vydáno: England BioMed Central Ltd 06.02.2025
Springer Nature B.V
BMC
Témata:
Accuracy
Algorithms
Amino acid sequence
Amino acids
Architecture
Attention mechanism
Cellular structure
Complexity
Computational Biology - methods
Computational linguistics
Coupling
Direct coupling analysis
Enzymatic activity
Enzymes
Evolution
Information processing
Language processing
Machine learning
Methods
Models, Molecular
Natural language interfaces
Protein Conformation
Protein families
Protein Folding
Protein structure
Protein structure prediction
Protein transport
Proteins
Proteins - chemistry
Structure-function relationships
Terminology
Transformer
Italy
Attention mechanism
Direct coupling analysis
Protein structure prediction
Transformer
ISSN:1471-2105, 1471-2105
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Shrnutí:Proteins are involved in nearly all cellular functions, encompassing roles in transport, signaling, enzymatic activity, and more. Their functionalities crucially depend on their complex three-dimensional arrangement. For this reason, being able to predict their structure from the amino acid sequence has been and still is a phenomenal computational challenge that the introduction of AlphaFold solved with unprecedented accuracy. However, the inherent complexity of AlphaFold's architectures makes it challenging to understand the rules that ultimately shape the protein's predicted structure. This study investigates a single-layer unsupervised model based on the attention mechanism. More precisely, we explore a Direct Coupling Analysis (DCA) method that mimics the attention mechanism of several popular Transformer architectures, such as AlphaFold itself. The model's parameters, notably fewer than those in standard DCA-based algorithms, can be directly used for extracting structural determinants such as the contact map of the protein family under study. Additionally, the functional form of the energy function of the model enables us to deploy a multi-family learning strategy, allowing us to effectively integrate information across multiple protein families, whereas standard DCA algorithms are typically limited to single protein families. Finally, we implemented a generative version of the model using an autoregressive architecture, capable of efficiently generating new proteins in silico.
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ISSN:1471-2105
1471-2105
DOI:10.1186/s12859-025-06062-y