ArticleMolecular biology and evolution2021
Unsupervised Inference of Protein Fitness Landscape from Deep Mutational Scan.
Article in Molecular biology and evolution, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers.
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20 citing papers in PubMed, 34 citations in OpenAlex.
- How far can you go? Extrapolating values of catalytic activity from known protein landscapes in natural and directed evolution.Chemical Society reviews · 2026Review
- Designing molecular RNA switches with Restricted Boltzmann machines.Nature communications · 2025Article
- Influence of contact map topology on RNA structure prediction.Nucleic acids research · 2025Article
- Investigating the determinants of performance in machine learning for protein fitness prediction.Protein science : a publication of the Protein Society · 2025Article
- Direct coupling analysis and the attention mechanism.BMC bioinformatics · 2025Article
- EvoAI enables extreme compression and reconstruction of the protein sequence space.Nature methods · 2025Article
- Bioengineering hybrid artificial life.Frontiers in bioinformatics · 2025Review
- Inference and design of antibody specificity: From experiments to models and back.PLoS computational biology · 2024Article
- Unsupervised modeling of mutational landscapes of adeno-associated viruses viability.BMC bioinformatics · 2024Article
- Genotype sampling for deep-learning assisted experimental mapping of a combinatorially complete fitness landscape.Bioinformatics (Oxford, England) · 2024Article
- EvoAI enables extreme compression and reconstruction of the protein sequence space.Research square · 2024Article
- Inference of annealed protein fitness landscapes with AnnealDCA.PLoS computational biology · 2024Article
- ACIDES: on-line monitoring of forward genetic screens for protein engineering.Nature communications · 2023Article
- Inferring protein fitness landscapes from laboratory evolution experiments.PLoS computational biology · 2023Article
- Intragenic compensation through the lens of deep mutational scanning.Biophysical reviews · 2022Review
- From systems to structure - using genetic data to model protein structures.Nature reviews. Genetics · 2022Review
- Facilitating Machine Learning-Guided Protein Engineering with Smart Library Design and Massively Parallel Assays.Advanced genetics (Hoboken, N.J.) · 2021Article
- AMaLa: Analysis of Directed Evolution Experiments via Annealed Mutational Approximated Landscape.International journal of molecular sciences · 2021Article
- Deep mutational scanning of the plasminogen activator inhibitor-1 functional landscape.Scientific reports · 2021Article
- Understanding the Origins of Loss of Protein Function by Analyzing the Effects of Thousands of Variants on Activity and Abundance.Molecular biology and evolution · 2021Article
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Authors and funding
3 authors at 2 institutions in 3 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The recent technological advances underlying the screening of large combinatorial libraries in high-throughput mutational scans deepen our understanding of adaptive protein evolution and boost its applications in protein design. Nevertheless, the large number of possible genotypes requires suitable computational methods for data analysis, the prediction of mutational effects, and the generation of optimized sequences. We describe a computational method that, trained on sequencing samples from multiple rounds of a screening experiment, provides a model of the genotype-fitness relationship. We tested the method on five large-scale mutational scans, yielding accurate predictions of the mutational effects on fitness. The inferred fitness landscape is robust to experimental and sampling noise and exhibits high generalization power in terms of broader sequence space exploration and higher fitness variant predictions. We investigate the role of epistasis and show that the inferred model provides structural information about the 3D contacts in the molecular fold.
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