AMaLa: Analysis of Directed Evolution Experiments via Annealed Mutational Approximated Landscape
| Autor: | Jorge Fernandez-de-Cossio-Diaz, Andrea Pagnani, Guido Uguzzoni, Luca Sesta |
|---|---|
| Přispěvatelé: | Department of Applied Science and Technology [Politecnico di Torino] (DISAT), Politecnico di Torino = Polytechnic of Turin (Polito), Laboratoire de physique de l'ENS - ENS Paris (LPENS), Centre National de la Recherche Scientifique (CNRS)-Université de Paris (UP)-Sorbonne Université (SU)-École normale supérieure - Paris (ENS Paris), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL), Physique Statistique et Inférence pour la Biologie, Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Centre National de la Recherche Scientifique (CNRS)-Université de Paris (UP)-Sorbonne Université (SU)-École normale supérieure - Paris (ENS Paris), Laboratoire de physique de l'ENS - ENS Paris (LPENS (UMR_8023)), École normale supérieure - Paris (ENS Paris), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Université de Paris (UP), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Université de Paris (UP)-École normale supérieure - Paris (ENS Paris) |
| Jazyk: | angličtina |
| Rok vydání: | 2021 |
| Předmět: |
Directed Evolution
fitness landscape Fitness landscape Computer science QH301-705.5 statistical modeling Inference Statistical weight Catalysis Article Inorganic Chemistry Evolution Molecular 03 medical and health sciences 0302 clinical medicine computational biology [SDV.BBM]Life Sciences [q-bio]/Biochemistry Molecular Biology Physical and Theoretical Chemistry Biology (General) Molecular Biology QD1-999 Spectroscopy Selection (genetic algorithm) 030304 developmental biology 0303 health sciences Models Genetic Organic Chemistry High-Throughput Nucleotide Sequencing Statistical model General Medicine Sequence Analysis DNA Directed evolution Deep Mutational Scanning Computer Science Applications Chemistry Mutation (genetic algorithm) Mutation Sequence space (evolution) Genetic Fitness Directed Molecular Evolution direct-coupling analysis Algorithm 030217 neurology & neurosurgery Algorithms |
| Zdroj: | International Journal of Molecular Sciences International Journal of Molecular Sciences, MDPI, 2021, 22 (20), pp.10908. ⟨10.3390/ijms222010908⟩ International Journal of Molecular Sciences, Vol 22, Iss 10908, p 10908 (2021) Volume 22 Issue 20 |
| ISSN: | 1661-6596 1422-0067 |
| DOI: | 10.3390/ijms222010908⟩ |
| Popis: | International audience; We present Annealed Mutational approximated Landscape (AMaLa), a new method to infer fitness landscapes from Directed Evolution experiments sequencing data. Such experiments typically start from a single wild-type sequence, which undergoes Darwinian in vitro evolution via multiple rounds of mutation and selection for a target phenotype. In the last years, Directed Evolution is emerging as a powerful instrument to probe fitness landscapes under controlled experimental conditions and as a relevant testing ground to develop accurate statistical models and inference algorithms (thanks to high-throughput screening and sequencing). Fitness landscape modeling either uses the enrichment of variants abundances as input, thus requiring the observation of the same variants at different rounds or assuming the last sequenced round as being sampled from an equilibrium distribution. AMaLa aims at effectively leveraging the information encoded in the whole time evolution. To do so, while assuming statistical sampling independence between sequenced rounds, the possible trajectories in sequence space are gauged with a time-dependent statistical weight consisting of two contributions: (i) an energy term accounting for the selection process and (ii) a generalized Jukes–Cantor model for the purely mutational step. This simple scheme enables accurately describing the Directed Evolution dynamics and inferring a fitness landscape that correctly reproduces the measures of the phenotype under selection (e.g., antibiotic drug resistance), notably outperforming widely used inference strategies. In addition, we assess the reliability of AMaLa by showing how the inferred statistical model could be used to predict relevant structural properties of the wild-type sequence. |
| Databáze: | OpenAIRE |
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