FLIGHTED: Inferring Fitness Landscapes from Noisy High-Throughput Experimental Data.

ABSTRACT: Machine learning (ML) for protein design requires large protein fitness datasets generated by high-throughput experiments for training, fine-tuning, and benchmarking models. However, most models do not account for experimental noise inherent in these datasets, harming model performance and changing model rankings in benchmarking studies. Here we develop FLIGHTED, a Bayesian method of accounting for uncertainty by generating probabilistic fitness landscapes from noisy high-throughput experiments. We demonstrate how FLIGHTED can improve model performance on two categories of experiments: single-step selection assays, such as phage display and SELEX, and a novel high-throughput assay called DHARMA that ties activity to base editing. We then compare the performance of standard machine-learning

SUBMITTER: Sundar V 

PROVIDER: S-EPMC10996587 | biostudies-literature | 2024 Sep

REPOSITORIES: biostudies-literature