Adaptive landscape flattening in amino acid sequence space for the computational design of protein:peptide binding
| Autor: | Thomas Simonson, Francesco Villa, Nicolas Panel, Xingyu Chen |
|---|---|
| Přispěvatelé: | Laboratoire de Biochimie de l'Ecole polytechnique (BIOC), Centre National de la Recherche Scientifique (CNRS)-École polytechnique (X) |
| Rok vydání: | 2018 |
| Předmět: |
0301 basic medicine
Heuristic (computer science) Computer science Protein Conformation Monte Carlo method General Physics and Astronomy PDZ Domains Peptide Peptide binding [SDV.CAN]Life Sciences [q-bio]/Cancer MESH: Amino Acid Sequence [SDV.BC]Life Sciences [q-bio]/Cellular Biology MESH: Monte Carlo Method 01 natural sciences Sequence space 03 medical and health sciences MESH: Protein Conformation MESH: T-Lymphoma Invasion and Metastasis-inducing Protein 1/chemistry 0103 physical sciences MESH: PDZ Domains MESH: Protein Binding MESH: Syndecan-1/chemistry T-Lymphoma Invasion and Metastasis-inducing Protein 1 Amino Acid Sequence Physical and Theoretical Chemistry Peptide sequence chemistry.chemical_classification Sequence 010304 chemical physics Energy landscape MESH: Peptide Fragments/chemistry [SDV.BBM.BM]Life Sciences [q-bio]/Biochemistry Molecular Biology/Molecular biology [SDV.BIBS]Life Sciences [q-bio]/Quantitative Methods [q-bio.QM] Peptide Fragments 030104 developmental biology chemistry Thermodynamics Syndecan-1 MESH: Thermodynamics Algorithm Monte Carlo Method Protein Binding |
| Zdroj: | Journal of Chemical Physics Journal of Chemical Physics, American Institute of Physics, 2018, 149 (7), pp.072302. ⟨10.1063/1.5022249⟩ |
| ISSN: | 1089-7690 0021-9606 |
| Popis: | International audience; For the high throughput design of protein:peptide binding, one must explore a vast space of amino acid sequences in search of low binding free energies. This complex problem is usually addressed with either simple heuristic scoring or expensive sequence enumeration schemes. Far more efficient than enumeration is a recent Monte Carlo approach that adaptively flattens the energy landscape in sequence space of the unbound peptide and provides formally exact binding free energy differences. The method allows the binding free energy to be used directly as the design criterion. We propose several improvements that allow still more efficient sampling and can address larger design problems. They include the use of Replica Exchange Monte Carlo and landscape flattening for both the unbound and bound peptides. We used the method to design peptides that bind to the PDZ domain of the Tiam1 signaling protein and could serve as inhibitors of its activity. Four peptide positions were allowed to mutate freely. Almost 75 000 peptide variants were processed in two simulations of 109 steps each that used 1 CPU hour on a desktop machine. 96% of the theoretical sequence space was sampled. The relative binding free energies agreed qualitatively with values from experiment. The sampled sequences agreed qualitatively with an experimental library of Tiam1-binding peptides. The main assumption limiting accuracy is the fixed backbone approximation, which could be alleviated in future work by using increased computational resources and multi-backbone designs. |
| Databáze: | OpenAIRE |
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