Accurate computational evolution of proteins and its dependence on deep learning and machine learning strategies

A protein fitness predictive framework based on feature combination and intelligent searching

Machine learning (ML) constructs predictive models by understanding the relationship between protein sequences and their functions, enabling efficient identification of protein sequences with high fitness values without falling into local optima, like directional evolution. However, how to extract the most pertinent functional feature information from a limited number of protein sequences is vital for optimizing the performance of ML models. Here, we propose scut_ProFP (Protein Fitness Predictor), a predictive framework that integrates feature combination and feature selection techniques. Feature combination offers comprehensive sequence information, while feature selection searches for the most beneficial features to enhance model performance, enabling accurate sequence-to-function mapping. Compared to similar frameworks, scut_ProFP demonstrates superior performance and is also competitive with more complex deep learning models-ECNet, EVmutation, and UniRep. In addition, scut_ProFP enables generalization from low-order mutants to high-order mutants. Finally, we utilized scut_ProFP to simulate the engineering of the fluorescent protein CreiLOV and highly enriched mutants with high fluorescence based on only a small number of low-fluorescence mutants. Essentially, the developed method is advantageous for ML in protein engineering, providing an effective approach to data-driven protein engineering. The code and datasets for scut_ProFP are available at https://github.com/Zhang66-star/scut_ProFP.

Accessing Active Fragments for Drug Discovery Utilising Nitroreductase Biocatalysis

Biocatalysis has played a limited role in the early stages of drug discovery. This is often attributed to the limited substrate scope of enzymes not affording access to vast areas of novel chemical space. Here, we have shown a promiscuous nitroreductase enzyme (NR-55) can be used to produce a panel of functionalised anilines from a diverse panel of aryl nitro starting materials. After screening on analytical scale, we show that sixteen substrates could be scaled to 1 mmol scale, with several poly-functional anilines afforded with ease under the standard conditions. The aniline products were also screened for activity against several cell lines of interest, with modest activity observed for one compound. This study demonstrates the potential for nitroreductase biocatalysis to provide access to functional fragments under benign conditions.

Biocatalysis for the asymmetric synthesis of Active Pharmaceutical Ingredients (APIs): this time is for real

INTRODUCTION

Biocatalysis has emerged as a powerful and useful strategy for the synthesis of active pharmaceutical ingredients (APIs). The outstanding developments in molecular biology techniques allow nowadays the screening, large-scale production, and designing of biocatalysts, adapting them to desired reactions. Many enzymes can perform reactions both in aqueous and non-aqueous media, broadening even further the opportunities to integrate them in complex pharmaceutical multi-step syntheses.

AREAS COVERED

This paper showcases several examples of biocatalysis in the pharmaceutical industry, covering examples of different enzymes, such as lipases, oxidoreductases, and transaminases, to deliver active drugs through complex synthetic routes. Examples are critically discussed in terms of reaction conditions, motivation for using an enzyme, and how biocatalysts can be integrated in multi-step syntheses. When possible, biocatalytic routes are benchmarked with chemical reactions.

EXPERT OPINION

The reported enzymatic examples are performed with high substrate loadings (>100 g L^-1) and with excellent selectivity, making them inspiring strategies for present and future industrial applications. The combination of powerful molecular biology techniques with the needs of the pharmaceutical industry can be aligned, creating promising platforms for synthesis under more sustainable conditions.