Enzymes play a crucial role in sustainable industrial applications, with their optimization posing a formidable challenge due to the intricate interplay among residues. Computational methodologies predominantly rely on evolutionary insights of homologous sequences. However, deciphering the evolutionary variability and complex dependencies among residues presents substantial hurdles. Here, we present a new machine-learning method based on variational autoencoders and evolutionary sampling strategy to address those limitations. We customized our method to generate novel sequences of model enzymes, haloalkane dehalogenases. Three design-build-test cycles improved the solubility of variants from 11% to 75%. Thorough experimental validation including the microfluidic device MicroPEX resulted in 20 multiple-point variants. Nine of them, sharing as little as 67% sequence similarity with the template, showed a melting temperature increase of up to 9 °C and an average improvement of 3 °C. The most stable variant demonstrated a 3.5-fold increase in activity compared to the template. High-quality experimental data collected with 20 variants represent a valuable data set for the critical validation of novel protein design approaches. Python scripts, jupyter notebooks, and data sets are available on GitHub (https://github.com/loschmidt/vae-dehalogenases), and interactive calculations will be possible via https://loschmidt.chemi.muni.cz/fireprotasr/.

• Publikační typ
• časopisecké články MeSH

Recent progress in engineering highly promising biocatalysts has increasingly involved machine learning methods. These methods leverage existing experimental and simulation data to aid in the discovery and annotation of promising enzymes, as well as in suggesting beneficial mutations for improving known targets. The field of machine learning for protein engineering is gathering steam, driven by recent success stories and notable progress in other areas. It already encompasses ambitious tasks such as understanding and predicting protein structure and function, catalytic efficiency, enantioselectivity, protein dynamics, stability, solubility, aggregation, and more. Nonetheless, the field is still evolving, with many challenges to overcome and questions to address. In this Perspective, we provide an overview of ongoing trends in this domain, highlight recent case studies, and examine the current limitations of machine learning-based methods. We emphasize the crucial importance of thorough experimental validation of emerging models before their use for rational protein design. We present our opinions on the fundamental problems and outline the potential directions for future research.

• Publikační typ
• časopisecké články MeSH
• přehledy MeSH

The fibroblast growth factors (FGF) family holds significant potential for addressing chronic diseases. Specifically, recombinant FGF18 shows promise in treating osteoarthritis by stimulating cartilage formation. However, recent phase 2 clinical trial results of sprifermin (recombinant FGF18) indicate insufficient efficacy. Leveraging our expertise in rational protein engineering, we conducted a study to enhance the stability of FGF18. As a result, we obtained a stabilized variant called FGF18-E4, which exhibited improved stability with 16 °C higher melting temperature, resistance to trypsin and a 2.5-fold increase in production yields. Moreover, the FGF18-E4 maintained mitogenic activity after 1-week incubation at 37 °C and 1-day at 50 °C. Additionally, the inserted mutations did not affect its binding to the fibroblast growth factor receptors, making FGF18-E4 a promising candidate for advancing FGF-based osteoarthritis treatment.

• Klíčová slova
• Computer-assisted stabilization, FGF-18, Fibroblast growth factor, Improved yield, Protease, Resistance to, Thermostability,
• Publikační typ
• časopisecké články MeSH