Highly accurate classification and discovery of microbial protein-coding gene functions using FunGeneTyper: an extensible deep learning framework.

High-throughput DNA sequencing technologies decode tremendous amounts of microbial protein-coding gene sequences. However, accurately assigning protein functions to novel gene sequences remain a challenge. To this end, we developed FunGeneTyper, an extensible framework with two new deep learning models (i.

Protocol to use protein language models predicting and following experimental validation of function-enhancing variants of thymine-N-glycosylase.

Protein language models (PLMs) are machine learning tools trained to predict masked amino acids within protein sequences, offering opportunities to enhance protein function without prior knowledge of their specific roles. Here, we present a protocol for optimizing thymine-DNA-glycosylase (TDG) using PLMs. We describe steps for "zero-shot" enzyme optimization, construction of plasmids, double plasmid transfection, and high-throughput sequencing and data analysis.

Protein language models-assisted optimization of a uracil-N-glycosylase variant enables programmable T-to-G and T-to-C base editing.

Current base editors (BEs) use DNA deaminases, including cytidine deaminase in cytidine BE (CBE) or adenine deaminase in adenine BE (ABE), to facilitate transition nucleotide substitutions. Combining CBE or ABE with glycosylase enzymes can induce limited transversion mutations. Nonetheless, a critical demand remains for BEs capable of generating alternative mutation types, such as T>G corrections.