Oberdorfer, G. | Search bioRxiv

bioRxiv2023

ESM-Scan - a tool to guide amino acid substitutions

Protein structure prediction and (re)design have gone through a revolution in the last three years. The tremendous progress in these fields has been almost exclusively driven by readily available machine-learning algorithms applied to protein folding and sequence design problems. Despite these advancements, predicting site-specific mutational effects on protein stability and function remains an unsolved problem. This is a persistent challenge mainly because the free energy of large systems is very difficult to compute with absolute accuracy and subtle changes to protein structures are also hard to capture with computational models. Here, we describe the implementation and use of ESM-Scan, which uses the ESM zero-shot predictor to scan entire protein sequences for preferential amino acid changes, thus enabling in-silico deep mutational scanning experiments. We benchmark ESM-Scan on its predictive capabilities for stability and functionality of sequence changes using three publicly available datasets and proceed by experimentally evaluating the tools performance on a challenging test case of a blue-light-activated diguanylate cyclase from Methylotenera species (MsLadC). We used ESM-Scan to predict conservative sequence changes in a highly conserved region of this enzyme responsible for allosteric product inhibition. Our experimental results show that the ESM-zero shot model emerges as a robust method for inferring the impact of amino acid substitutions, especially when evolutionary and functional insights are intertwined. ESM-Scan is publicly available at https://huggingface.co/spaces/thaidaev/zsp

biochemistry↗

bioRxiv2023

Flattening the curve - How to get better results with small deep-mutational-scanning datasets

Proteins are utilized in various biotechnological applications, often requiring the optimization of protein properties by introducing specific amino acid exchanges. Deep mutational scanning (DMS) is an effective high-throughput method for evaluating the effects of these exchanges on protein function. DMS data can then inform the training of a neural network to predict the impact of mutations. Most approaches employ some representation of the protein sequence for training and prediction. As proteins are characterized by complex structures and intricate residue interaction networks, directly providing structural information as input reduces the need to learn these features from the data. We introduce a method for encoding protein structures as stacked 2D contact maps, which capture residue interactions, their evolutionary conservation, and mutation-induced interaction changes. Furthermore, we explored techniques to augment neural network training performance on smaller DMS datasets. To validate our approach, we trained three neural network architectures originally used for image analysis on three DMS datasets, and we compared their performances with networks trained solely on protein sequences. The results confirm the effectiveness of the protein structure encoding in machine learning efforts on DMS data. Using structural representations as direct input to the networks, along with data augmentation and pre-training, significantly reduced demands on training data size and improved prediction performance, especially on smaller datasets, while performance on large datasets was on par with state-of-the-art sequence convolutional neural networks. The methods presented here have the potential to provide the same workflow as DMS without the experimental and financial burden of testing thousands of mutants. Additionally, we present an open-source, user-friendly software tool to make these data analysis techniques accessible, particularly to biotechnology and protein engineering researchers who wish to apply them to their mutagenesis data.

bioinformatics↗