Identification of Protein Functional Regions

Nerattini, Francesca; Figliuzzi, Matteo; Cardelli, Chiara; Tubiana, Luca; Bianco, Valentino; Dellago, Christoph; Coluzza, Ivan

doi:10.1002/cphc.201900898

Cited by 2 publications

(2 citation statements)

References 97 publications

(197 reference statements)

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“…To this end, we considered three protein families, namely PDZ, FKBP, and Response regulator receiver domains (with Pfam codes PF00595, PF00254, PF00072 respectively). In a previous study ( 108 ), one representative member of each family was chosen as target structure from the Protein Data Bank (PDB) specifically 1WI2 (PDZ), 2PPN (FKBP), and 1NXW (Response regulator) and resulted in good designability for the caterpillar model.…”

Section: Bridging the Gap Between Natural And Artificial Sequencesmentioning

confidence: 99%

Machine learning in biological physics: From biomolecular prediction to design

Martin,

Lequerica Mateos,

Onuchic

et al. 2024

Proc. Natl. Acad. Sci. U.S.A.

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Machine learning has been proposed as an alternative to theoretical modeling when dealing with complex problems in biological physics. However, in this perspective, we argue that a more successful approach is a proper combination of these two methodologies. We discuss how ideas coming from physical modeling neuronal processing led to early formulations of computational neural networks, e.g., Hopfield networks. We then show how modern learning approaches like Potts models, Boltzmann machines, and the transformer architecture are related to each other, specifically, through a shared energy representation. We summarize recent efforts to establish these connections and provide examples on how each of these formulations integrating physical modeling and machine learning have been successful in tackling recent problems in biomolecular structure, dynamics, function, evolution, and design. Instances include protein structure prediction; improvement in computational complexity and accuracy of molecular dynamics simulations; better inference of the effects of mutations in proteins leading to improved evolutionary modeling and finally how machine learning is revolutionizing protein engineering and design. Going beyond naturally existing protein sequences, a connection to protein design is discussed where synthetic sequences are able to fold to naturally occurring motifs driven by a model rooted in physical principles. We show that this model is “learnable” and propose its future use in the generation of unique sequences that can fold into a target structure.

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Section: Bridging the Gap Between Natural And Artificial Sequencesmentioning

confidence: 99%

Machine learning in biological physics: From biomolecular prediction to design

Martin,

Lequerica Mateos,

Onuchic

et al. 2024

Proc. Natl. Acad. Sci. U.S.A.

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“…Nerattini et al [334] introduced a methodology to rank the residues according to their functional (F) or structural (S) nature within the ones that are involved in both events (OFSR, overlapping functional, structural residue [307]).…”

Section: Compare Artificial and Natural Sequencesmentioning

confidence: 99%

Key aspects of the past 30 years of protein design

Meconi¹,

Sasselli²,

Bianco³

et al. 2022

Rep. Prog. Phys.

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Proteins are the workhorse of life. They are the building infrastructure of living systems; they are the most efficient molecular machines known, and their enzymatic activity is still unmatched in versatility by any artificial system. Perhaps proteins’ most remarkable feature is their modularity. The large amount of information required to specify each protein’s function is analogically encoded with an alphabet of just ~20 letters. The protein folding problem is how to encode all such information in a sequence of 20 letters. In this review, we go through the last 30 years of research to summarize the state of the art and highlight some applications related to fundamental problems of protein evolution.

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Identification of Protein Functional Regions

Cited by 2 publications

References 97 publications

Machine learning in biological physics: From biomolecular prediction to design

Machine learning in biological physics: From biomolecular prediction to design

Key aspects of the past 30 years of protein design

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