Niide, T. | Search bioRxiv

bioRxiv2025

A Method for Predicting Enzyme Substrate Specificity Residues Using Homologous Sequence Information

Identifying amino acid residues that are critical for the catalytic function of enzymes is essential for elucidating reaction mechanisms, facilitating drug discovery, and advancing protein engineering. However, experimentally and computationally distinguishing residues that maintain structural integrity from those directly involved in enzymatic function remains a major challenge. In this study, we developed a methodology to identify amino acid residues that influence substrate specificity in enzymes with homologous structures. We framed the sequence comparison as a classification problem, treating each residue as a feature, thereby enabling the rapid and objective identification of key residues responsible for functional differences. To validate the proposed method, we applied it to three enzyme pairs-- trypsin/chymotrypsin, adenylyl cyclase/guanylyl cyclase, and lactate dehydrogenase (LDH)/malate dehydrogenase (MDH). The results confirmed the accurate prediction of previously identified specificity-determining residues. Furthermore, we conducted experiments on the LDH/MDH pair and successfully introduced mutations into key residues to alter substrate specificity, enabling LDH to utilize oxaloacetate while maintaining its expression levels. These findings demonstrate the potential of this method for efficiently identifying residues that govern substrate specificity. We have further developed this approach into a practical tool, the EZSCAN: Enzyme Substrate-specificity and Conservation Analysis Navigator (https://ezscan.pe-tools.com/), which enables rapid identification of amino acid residues critical for enzyme function. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=75 SRC="FIGDIR/small/656053v1_ufig1.gif" ALT="Figure 1"> View larger version (29K): org.highwire.dtl.DTLVardef@d97b76org.highwire.dtl.DTLVardef@38bbe8org.highwire.dtl.DTLVardef@b8b6edorg.highwire.dtl.DTLVardef@f1b858_HPS_FORMAT_FIGEXP M_FIG C_FIG

bioengineering↗

bioRxiv2018

A machine-learning-guided mutagenesis platform for accelerated discovery of novel functional proteins

Molecular evolution based on mutagenesis is widely used in protein engineering. However, optimal proteins are often difficult to obtain due to a large sequence space that requires high costs for screening experiments. Here, we propose a novel approach that combines molecular evolution with machine learning. In this approach, we conduct two rounds of mutagenesis where an initial library of protein variants is used to train a machine-learning model to guide mutagenesis for the second-round library. This enables to prepare a small library suited for screening experiments with high enrichment of functional proteins. We demonstrated a proof-of-concept of our approach by altering the reference green fluorescent protein (GFP) so that its fluorescence is changed to yellow while improving its fluorescence intensity. Using 155 and 78 variants for the initial and the second-round libraries, respectively, we successfully obtained a number of proteins showing yellow fluorescence, 12 of which had better fluorescence performance than the reference yellow fluorescent protein (YFP). These results show the potential of our approach as a powerful platform for accelerated discovery of functional proteins.

bioengineering↗