Challenges in the computational design of proteins

Suárez, María; Jaramillo, Alfonso

doi:10.1098/rsif.2008.0508.focus

Cited by 59Protein Structure and DynamicsRNA and protein synthesis mechanismsChemical Synthesis and Analysis

Challenges in the computational design of proteins

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Suárez, María, Jaramillo, Alfonso·2009·Journal of The Royal Society Interface

DOI 10.1098/rsif.2008.0508.focusPMC PMC2843960

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Abstract

Protein design has many applications not only in biotechnology but also in basic science. It uses our current knowledge in structural biology to predict, by computer simulations, an amino acid sequence that would produce a protein with targeted properties. As in other examples of synthetic biology, this approach allows the testing of many hypotheses in biology. The recent development of automated computational methods to design proteins has enabled proteins to be designed that are very different from any known ones. Moreover, some of those methods mostly rely on a physical description of atomic interactions, which allows the designed sequences not to be biased towards known proteins. In this paper, we will describe the use of energy functions in computational protein design, the use of atomic models to evaluate the free energy in the unfolded and folded states, the exploration and optimization of amino acid sequences, the problem of negative design and the design of biomolecular function. We will also consider its use together with the experimental techniques such as directed evolution. We will end by discussing the challenges ahead in computational protein design and some of their future applications.

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Most existing machine-learning methods use physical quantities as input features, requiring considerable computational time.
“Most of the existing machine-learning methods use physical quantities as input features, which require considerable computational time to solve [17, 18].”
From Deep geometric representations for modeling effects of mutations on protein-protein binding affinity (2021) · cites this paper as [17]

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Authors: Suárez, M., & Jaramillo, A.
Affiliations: Laboratoire de Biochimie, Ecole Polytechnique, CNRS, 91128 Palaiseau Cedex, France
Centre National de la Recherche Scientifique (FR)
Corresponding author: Jaramillo, Alfonso
ORCID: Jaramillo, Alfonso
Venue: Journal of The Royal Society Interface
Publisher: The Royal Society
Published: 2009-03-11
Volume: 6
Issue: suppl_4
Pages: S477-91
ISSN: 1742-5662 (ISSN-L)
1742-5689
DOI: 10.1098/rsif.2008.0508.focus
PMC: PMC2843960
OpenAlex: W2132851824
Cited by: 59
References: 130
Citations by year: 2026: 2 · 2025: 3 · 2023: 2 · 2022: 1 · 2021: 1 · 2020: 1 · 2018: 1 · 2017: 2 · 2016: 5 · 2015: 2 · 2014: 4 · 2013: 3 · 2012: 10
FWCI: 0.82
Type: journal article
Language: en
Open access: green
www.ncbi.nlm.nih.gov
Licence: royalsociety.org/journals/ethics-policies/data-sharing-mining/ (tdm)
Locations: Journal of The Royal Society Interface (publishedVersion)
PubMed Central (publishedVersion)
PubMed (publishedVersion)
HAL (submittedVersion)
Topics: Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Protein Structure and Dynamics
Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › RNA and protein synthesis mechanisms
Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Chemical Synthesis and Analysis
Keywords: computational protein design, energy function, free energy calculation, atomic models, sequence optimization, negative design, directed evolution, synthetic biology, protein folding, amino acid sequence prediction
MeSH terms: Amino Acid Sequence, Amino Acids, Automation, Computer Simulation, Molecular Conformation, Molecular Sequence Data, Peptides, Protein Denaturation, Proteins, Software, Thermodynamics, Protein Engineering, Protein Folding, Computational Biology, Static Electricity

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