Two sequence- and two structure-based ML models have learned different aspects of protein biochemistry.

ABSTRACT: Deep learning models are seeing increased use as methods to predict mutational effects or allowed mutations in proteins. The models commonly used for these purposes include large language models (LLMs) and 3D Convolutional Neural Networks (CNNs). These two model types have very different architectures and are commonly trained on different representations of proteins. LLMs make use of the transformer architecture and are trained purely on protein sequences whereas 3D CNNs are trained on voxelized representations of local protein structure. While comparable overall prediction accuracies have been reported for both types of models, it is not known to what extent these models make comparable specific predictions and/or generalize protein biochemistry in similar ways. Here, we perform a systema

SUBMITTER: Kulikova AV 

PROVIDER: S-EPMC10055221 | biostudies-literature | 2023 Jul

REPOSITORIES: biostudies-literature