Impairment Screening Utilizing Biophysical Measurements and Machine Learning Algorithms

Roshan, S.; Park, Edward J.

doi:10.1109/embc46164.2021.9630022

Cited by 2 publications

(1 citation statement)

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“…These personally adapted protocols are hard to compare and transfer to other software set-ups, resulting in user frustration and lack of reproducibility. After embedding the evolutionary scale modeling (ESM) protein language model (PLM) family 14,22,[24][25][26] in Rosetta 27,28 , we realized the benefits of a shared interface and testing environment using the C++ Tensorflow 29 and LibTorch 30 libraries, and therefore set out to streamline and expand this interface to other models.…”

Section: Introductionmentioning

confidence: 99%

Self-supervised machine learning methods for protein design improve sampling, but not the identification of high-fitness variants

Ertelt,

Moretti,

Meiler

et al. 2024

Preprint

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Machine learning (ML) is changing the world of computational protein design, with data- driven methods surpassing biophysical-based methods in experimental success rates. However, they are most often reported as case studies, lack integration and standardization across platforms, and are therefore hard to objectively compare. In this study, we established a streamlined and diverse toolbox for methods that predict amino acid probabilities inside the Rosetta software framework that allows for the side-by-side comparison of these models. Subsequently, existing protein fitness landscapes were used to benchmark novel self- supervised machine learning methods in realistic protein design settings. We focused on the traditional problems of protein sequence design: sampling and scoring. A major finding of our study is that novel ML approaches are better at purging the sampling space from deleterious mutations. Nevertheless, scoring resulting mutations without model fine-tuning showed no clear improvement over scoring with Rosetta. This study fills an important gap in the field and allows for the first time a comprehensive head-to-head comparison of different ML and biophysical methods. We conclude that ML currently acts as a complement to, rather than a replacement for, biophysical methods in protein design.

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