Most cited article - PubMed ID 28419658
Ancestral Haloalkane Dehalogenases Show Robustness and Unique Substrate Specificity
The quest to predict and understand protein evolution has been hindered by limitations on both the theoretical and the experimental fronts. Most existing theoretical models of evolution are descriptive, rather than predictive, leaving the final modifications in the hands of researchers. Existing experimental techniques to help probe the evolutionary sequence space of proteins, such as directed evolution, are resource-intensive and require specialised skills. We present the successor sequence predictor (SSP) as an innovative solution. Successor sequence predictor is an in silico protein design method that mimics laboratory-based protein evolution by reconstructing a protein's evolutionary history and suggesting future amino acid substitutions based on trends observed in that history through carefully selected physicochemical descriptors. This approach enhances specialised proteins by predicting mutations that improve desired properties, such as thermostability, activity, and solubility. Successor Sequence Predictor can thus be used as a general protein engineering tool to develop practically useful proteins. The code of the Successor Sequence Predictor is provided at https://github.com/loschmidt/successor-sequence-predictor , and the design of mutations will be also possible via an easy-to-use web server https://loschmidt.chemi.muni.cz/fireprotasr/ . SCIENTIFIC CONTRIBUTION: The Successor Sequence Predictor advances protein evolution prediction at the amino acid level by integrating ancestral sequence reconstruction with a novel in silico approach that models evolutionary trends through selected physicochemical descriptors. Unlike prior work, SSP can forecast future amino acid substitutions that enhance protein properties such as thermostability, activity, and solubility. This method reduces reliance on resource-intensive directed evolution techniques while providing a generalizable, predictive tool for protein engineering.
- Keywords
- Activity, Adaptation, Evolution, Evolutionary trajectory, Protein design, Solubility, Thermostability,
- Publication type
- Journal Article MeSH
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/.
- Publication type
- Journal Article 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.
- Publication type
- Journal Article MeSH
- Review 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.
- Keywords
- DhmeA, Haloferax mediterranei, catalysis, cryo-EM, haloalkane dehalogenase, multimerization, x-ray crystallography,
- MeSH
- Cryoelectron Microscopy methods MeSH
- Hydrolases * chemistry MeSH
- Crystallography, X-Ray MeSH
- Substrate Specificity MeSH
- Publication type
- Journal Article MeSH
- Research Support, Non-U.S. Gov't MeSH
- Names of Substances
- haloalkane dehalogenase MeSH Browser
- Hydrolases * 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.
- Keywords
- ancestral sequence reconstruction, haloalkane dehalogenase, halogenated pollutants, structural analysis,
- MeSH
- Hydrolases chemistry metabolism MeSH
- Hydrolysis MeSH
- Catalytic Domain MeSH
- Crystallography, X-Ray methods MeSH
- Evolution, Molecular MeSH
- Models, Molecular MeSH
- Mycobacterium bovis enzymology MeSH
- Protein Engineering methods MeSH
- Sequence Analysis, Protein methods MeSH
- Sphingomonadaceae enzymology MeSH
- Binding Sites MeSH
- Publication type
- Journal Article MeSH
- Names of Substances
- haloalkane dehalogenase MeSH Browser
- Hydrolases 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.
