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

Haloalkane dehalogenase (HLD) enzymes employ an SN 2 nucleophilic substitution mechanism to erase halogen substituents in diverse organohalogen compounds. Subfamily I and II HLDs are well-characterized enzymes, but the mode and purpose of multimerization of subfamily III HLDs are unknown. Here we probe the structural organization of DhmeA, a subfamily III HLD-like enzyme from the archaeon Haloferax mediterranei, by combining cryo-electron microscopy (cryo-EM) and x-ray crystallography. We show that full-length wild-type DhmeA forms diverse quaternary structures, ranging from small oligomers to large supramolecular ring-like assemblies of various sizes and symmetries. We optimized sample preparation steps, enabling three-dimensional reconstructions of an oligomeric species by single-particle cryo-EM. Moreover, we engineered a crystallizable mutant (DhmeAΔGG ) that provided diffraction-quality crystals. The 3.3 Å crystal structure reveals that DhmeAΔGG forms a ring-like 20-mer structure with outer and inner diameter of ~200 and ~80 Å, respectively. An enzyme homodimer represents a basic repeating building unit of the crystallographic ring. Three assembly interfaces (dimerization, tetramerization, and multimerization) were identified to form the supramolecular ring that displays a negatively charged exterior, while its interior part harboring catalytic sites is positively charged. Localization and exposure of catalytic machineries suggest a possible processing of large negatively charged macromolecular substrates.

• Klíčová slova
• DhmeA, Haloferax mediterranei, catalysis, cryo-EM, haloalkane dehalogenase, multimerization, x-ray crystallography,
• MeSH
• elektronová kryomikroskopie metody   MeSH
• hydrolasy * chemie   MeSH
• krystalografie rentgenová MeSH
• substrátová specifita MeSH
• Publikační typ
• časopisecké články MeSH
• práce podpořená grantem MeSH
• Názvy látek
• haloalkane dehalogenase MeSH Prohlížeč
• hydrolasy * MeSH

Haloalkane dehalogenases (EC 3.8.1.5) play an important role in hydrolytic degradation of halogenated compounds, resulting in a halide ion, a proton, and an alcohol. They are used in biocatalysis, bioremediation, and biosensing of environmental pollutants and also for molecular tagging in cell biology. The method of ancestral sequence reconstruction leads to prediction of sequences of ancestral enzymes allowing their experimental characterization. Based on the sequences of modern haloalkane dehalogenases from the subfamily II, the most common ancestor of thoroughly characterized enzymes LinB from Sphingobium japonicum UT26 and DmbA from Mycobacterium bovis 5033/66 was in silico predicted, recombinantly produced and structurally characterized. The ancestral enzyme AncLinB-DmbA was crystallized using the sitting-drop vapor-diffusion method, yielding rod-like crystals that diffracted X-rays to 1.5 Å resolution. Structural comparison of AncLinB-DmbA with their closely related descendants LinB and DmbA revealed some differences in overall structure and tunnel architecture. Newly prepared AncLinB-DmbA has the highest active site cavity volume and the biggest entrance radius on the main tunnel in comparison to descendant enzymes. Ancestral sequence reconstruction is a powerful technique to study molecular evolution and design robust proteins for enzyme technologies.

• Klíčová slova
• ancestral sequence reconstruction, haloalkane dehalogenase, halogenated pollutants, structural analysis,
• MeSH
• hydrolasy chemie   metabolismus   MeSH
• hydrolýza MeSH
• katalytická doména MeSH
• krystalografie rentgenová metody   MeSH
• molekulární evoluce MeSH
• molekulární modely MeSH
• Mycobacterium bovis enzymologie   MeSH
• proteinové inženýrství metody   MeSH
• sekvenční analýza proteinů metody   MeSH
• Sphingomonadaceae enzymologie   MeSH
• vazebná místa MeSH
• Publikační typ
• časopisecké články MeSH
• Názvy látek
• haloalkane dehalogenase MeSH Prohlížeč
• hydrolasy MeSH

Ancestral sequence reconstruction is a powerful method for inferring ancestors of modern enzymes and for studying structure-function relationships of enzymes. We have previously applied this approach to haloalkane dehalogenases (HLDs) from the subfamily HLD-II and obtained thermodynamically highly stabilized enzymes (ΔT m up to 24 °C), showing improved catalytic properties. Here we combined crystallographic structural analysis and computational molecular dynamics simulations to gain insight into the mechanisms by which ancestral HLDs became more robust enzymes with novel catalytic properties. Reconstructed ancestors exhibited similar structure topology as their descendants with the exception of a few loop deviations. Strikingly, molecular dynamics simulations revealed restricted conformational dynamics of ancestral enzymes, which prefer a single state, in contrast to modern enzymes adopting two different conformational states. The restricted dynamics can potentially be linked to their exceptional stabilization. The study provides molecular insights into protein stabilization due to ancestral sequence reconstruction, which is becoming a widely used approach for obtaining robust protein catalysts.

• Klíčová slova
• Ancestral sequence reconstruction, Conformational flexibility, Enzyme, Haloalkane dehalogenase, Protein design, Protein simulations, Thermostability, X-ray crystallography,
• Publikační typ
• časopisecké články MeSH