Machine Learning Enables Prediction of Pyrrolysyl-tRNA Synthetase Substrate Specificity

CMDmpnn: Combining Comparative Molecular Dynamics and ProteinMPNN to Rapidly Expand Enzyme Substrate Spectrum

Expanding enzyme substrate spectra enhances industrial applications and drives sustainable biocatalysis. Despite advances, challenges in modification efficiency and high-throughput screening persist. Here, we developed a virtual screening method called CMDmpnn that combines comparative molecular dynamics (MD) simulations and ProteinMPNN to broaden enzyme substrate spectra without compromising other industrially important properties of enzymes, such as thermostability. Using glycosyltransferase as a model, we first established a dynamic model library of the wild-type enzyme through MD simulations and performed clustering. Subsequently, we utilized ProteinMPNN to generate a comprehensive set of new sequences for the entire library, enabling rapid identification of all possible enzyme variants. Short MD simulations were then conducted on variant-substrate complex models, with results compared to those of the wild-type enzyme. By analyzing catalytically relevant information such as substrate binding modes and key atomic distances, we identified multiple variants capable of catalyzing a broad spectrum of phenolic compounds, all within a timeframe of less than 2 weeks. The CMDmpnn method offers a powerful and efficient tool for rapidly expanding enzyme substrate spectra.

Strategies to Expand the Genetic Code of Mammalian Cells

Genetic code expansion (GCE) in mammalian cells has emerged as a powerful technology for investigating and engineering protein function. This method allows for the precise incorporation of a rapidly growing toolbox of noncanonical amino acids (ncAAs) into predefined sites of target proteins expressed in living cells. Due to the minimal size of these genetically encoded ncAAs, the wide range of functionalities they provide, and the ability to introduce them freely at virtually any site of any protein by simple mutagenesis, this technology holds immense potential for probing the complex biology of mammalian cells and engineering next-generation biotherapeutics. In this review, we provide an overview of the underlying machinery that enables ncAA mutagenesis in mammalian cells and how these are developed. We have also compiled an updated list of ncAAs that have been successfully incorporated into proteins in mammalian cells. Finally, we provide our perspectives on the current challenges that need to be addressed to fully harness the potential of this technology.

Machine-Learning-Aided Engineering Hemoglobin as Carbene Transferase for Catalyzing Enantioselective Olefin Cyclopropanation

In this study, we developed a machine-learning-aided protein design strategy for engineering Vitreoscilla hemoglobin (VHb) as carbene transferase. A Natural Language Processing (NLP) model was used for the first time to construct an algorithm (EESP, enzyme enantioselectivity score predictor) and predict the enantioselectivity of VHb. We identified critical amino acid residue sites by molecular docking and established a simplified mutation library by site-saturated mutagenesis. Based on the simplified mutant library, the trianed EESP scored 160,000 virtual mutants, and 15 predicted high-score mutants were chosen for experimental validation. Among these mutants, VHb-WK (Y29W/P54K) demonstrated the highest diastereoselectivity and enantioselectivity of carbene transferase for the olefin cyclopropanation in aqueous conditions. Subsequently, molecular dynamics simulations were performed to explore the interaction between protein and substrates, finding that the high enantioselectivity of VHb-WK stems from the interactions of R47, Q53, and K84, which narrows the entrance of the enzyme's pocket, favoring the restriction of the formation of reaction intermediates. Integrating the NLP model and enzyme modification offers significant advantages by reducing economic costs and workloads associated with the protein engineering process.

1.3 Å Crystal Structure of E. coli Peptidyl-Prolyl Isomerase B with Uniform Substitution of Valine by (2S,3S)-4-Fluorovaline Reveals Structure Conservation and Multiple Staggered Rotamers of CH2F Groups

(2S,3S)-4-Fluorovaline (FVal) is an analogue of valine, where a single CH3 group is substituted by a CH2F group. In the absence of valine, E. coli valyl-tRNA synthetase uses FVal as a substitute, enabling the production of proteins uniformly labeled with FVal. Here, we describe the production and analysis of E. coli peptidyl-prolyl isomerase B where all 16 valine residues have been replaced by FVal synthesized with a 13C-labeled CH2F group. Although the melting temperature is lower by about 11 °C relative to the wild-type protein, the three-dimensional protein structure is almost completely conserved, as shown by X-ray crystallography. The CH2F groups invariably populate staggered rotamers. Most CH2F groups populate two different rotamers. The increased space requirement of fluorine versus hydrogen does not prohibit rotamers that position fluorine next to a backbone carbonyl carbon. 19F NMR spectra show a signal dispersion over 25 ppm. The most high-field shifted 19F resonances correlate with large 3JHF coupling constants, confirming the impact of the γ-gauche effect on the signal dispersion. The present work is the second experimental verification of the effect and extends its validity to fluorovaline. The abundance of valine in proteins and structural conservation with FVal renders this valine analogue attractive for probing proteins by 19F NMR spectroscopy.

Noncanonical Amino Acids: Bringing New-to-Nature Functionalities to Biocatalysis

Biocatalysis has become an important component of modern organic chemistry, presenting an efficient and environmentally friendly approach to synthetic transformations. Advances in molecular biology, computational modeling, and protein engineering have unlocked the full potential of enzymes in various industrial applications. However, the inherent limitations of the natural building blocks have sparked a revolutionary shift. In vivo genetic incorporation of noncanonical amino acids exceeds the conventional 20 amino acids, opening new avenues for innovation. This review provides a comprehensive overview of applications of noncanonical amino acids in biocatalysis. We aim to examine the field from multiple perspectives, ranging from their impact on enzymatic reactions to the creation of novel active sites, and subsequent catalysis of new-to-nature reactions. Finally, we discuss the challenges, limitations, and promising opportunities within this dynamic research domain.

Non-canonical amino acids uncover the significant impact of Tyr671 on Taq DNA polymerase catalytic activity

Responsible for synthesizing the complementary strand of the DNA template, DNA polymerase is a crucial enzyme in DNA replication, recombination and repair. A highly conserved tyrosine (Tyr), located at the C-terminus of the O-helix in family A DNA polymerases, plays a critical role in enzyme activity and fidelity. Here, we combined the technology of genetic code extension to incorporate non-canonical amino acids and molecular dynamics (MD) simulations to uncover the mechanisms by which Tyr671 impacts substrate binding and conformation transitions in a DNA polymerase from Thermus aquaticus. Five non-canonical amino acids, namely l-3,4-dihydroxyphenylalanine (l-DOPA), p-aminophenylalanine (pAF), p-acetylphenylalanine (pAcF), p-cyanophenylalanine (pCNF) and p-nitrophenylalanine (pNTF), were individually incorporated at position 671. Strikingly, Y671pAF and Y671DOPA were active, but with lower activity compared to Y671F and wild-type. Y671pAF showed a higher fidelity than the Y671F, despite both possessing lower fidelity than the wild-type. Metadynamics and long-timescale MD simulations were carried out to probe the role of mutations in affecting protein structure, including open conformation, open-to-closed conformation transition, closed conformation, and closed-to-open conformation transition. The MD simulations clearly revealed that the size of the 671 amino acid residue and interactions with substrate or nearby residues were critical for Tyr671 to determine enzyme activity and fidelity.

Data-Driven Protein Engineering for Improving Catalytic Activity and Selectivity

Protein engineering is essential for altering the substrate scope, catalytic activity and selectivity of enzymes for applications in biocatalysis. However, traditional approaches, such as directed evolution and rational design, encounter the challenge in dealing with the experimental screening process of a large protein mutation space. Machine learning methods allow the approximation of protein fitness landscapes and the identification of catalytic patterns using limited experimental data, thus providing a new avenue to guide protein engineering campaigns. In this concept article, we review machine learning models that have been developed to assess enzyme-substrate-catalysis performance relationships aiming to improve enzymes through data-driven protein engineering. Furthermore, we prospect the future development of this field to provide additional strategies and tools for achieving desired activities and selectivities.

Enzyme redesign and genetic code expansion

Enzyme design is an important application of computational protein design (CPD). It can benefit enormously from the additional chemistries provided by noncanonical amino acids (ncAAs). These can be incorporated into an 'expanded' genetic code, and introduced in vivo into target proteins. The key step for genetic code expansion is to engineer an aminoacyl-transfer RNA (tRNA) synthetase (aaRS) and an associated tRNA that handles the ncAA. Experimental directed evolution has been successfully used to engineer aaRSs and incorporate over 200 ncAAs into expanded codes. But directed evolution has severe limits, and is not yet applicable to noncanonical AA backbones. CPD can help address several of its limitations, and has begun to be applied to this problem. We review efforts to redesign aaRSs, studies that designed new proteins and functionalities with the help of ncAAs, and some of the method developments that have been used, such as adaptive landscape flattening Monte Carlo, which allows an enzyme to be redesigned with substrate or transition state binding as the design target.