EnzyHTP: A High-Throughput Computational Platform for Enzyme Modeling

Unlocking the potential of enzyme engineering via rational computational design strategies

Enzymes play a pivotal role in various industries by enabling efficient, eco-friendly, and sustainable chemical processes. However, the low turnover rates and poor substrate selectivity of enzymes limit their large-scale applications. Rational computational enzyme design, facilitated by computational algorithms, offers a more targeted and less labor-intensive approach. There has been notable advancement in employing rational computational protein engineering strategies to overcome these issues, it has not been comprehensively reviewed so far. This article reviews recent developments in rational computational enzyme design, categorizing them into three types: structure-based, sequence-based, and data-driven machine learning computational design. Case studies are presented to demonstrate successful enhancements in catalytic activity, stability, and substrate selectivity. Lastly, the article provides a thorough analysis of these approaches, highlights existing challenges and potential solutions, and offers insights into future development directions.

A multifunctional flavoprotein monooxygenase HspB for hydroxylation and C-C cleavage of 6-hydroxy-3-succinoyl-pyridine

Flavoprotein monooxygenases catalyze reactions, including hydroxylation and epoxidation, involved in the catabolism, detoxification, and biosynthesis of natural substrates and industrial contaminants. Among them, the 6-hydroxy-3-succinoyl-pyridine (HSP) monooxygenase (HspB) from Pseudomonas putida S16 facilitates the hydroxylation and C-C bond cleavage of the pyridine ring in nicotine. However, the mechanism for biodegradation remains elusive. Here, we refined the crystal structure of HspB and elucidated the detailed mechanism behind the oxidative hydroxylation and C-C cleavage processes. Leveraging structural information about domains for binding the cofactor flavin adenine dinucleotide (FAD) and HSP substrate, we used molecular dynamics simulations and quantum/molecular mechanics calculations to demonstrate that the transfer of an oxygen atom from the reactive FAD peroxide species (C4a-hydroperoxyflavin) to the C3 atom in the HSP substrate constitutes a rate-limiting step, with a calculated reaction barrier of about 20 kcal/mol. Subsequently, the hydrogen atom was rebounded to the FAD cofactor, forming C4a-hydroxyflavin. The residue Cys218 then catalyzed the subsequent hydrolytic process of C-C cleavage. Our findings contribute to a deeper understanding of the versatile functions of flavoproteins in the natural transformation of pyridine and HspB in nicotine degradation.IMPORTANCEPseudomonas putida S16 plays a pivotal role in degrading nicotine, a toxic pyridine derivative that poses significant environmental challenges. This study highlights a key enzyme, HspB (6-hydroxy-3-succinoyl-pyridine monooxygenase), in breaking down nicotine through the pyrrolidine pathway. Utilizing dioxygen and a flavin adenine dinucleotide cofactor, HspB hydroxylates and cleaves the substrate's side chain. Structural analysis of the refined HspB crystal structure, combined with state-of-the-art computations, reveals its distinctive mechanism. The crucial function of Cys218 was never discovered in its homologous enzymes. Our findings not only deepen our understanding of bacterial nicotine degradation but also open avenues for applications in both environmental cleanup and pharmaceutical development.

Protein3D: Enabling analysis and extraction of metal-containing sites from the Protein Data Bank with molSimplify

Metalloenzymes catalyze a wide range of chemical transformations, with the active site residues playing a key role in modulating chemical reactivity and selectivity. Unlike smaller synthetic catalysts, a metalloenzyme active site is embedded in a larger protein, which makes interrogation of electronic properties and geometric features with quantum mechanical calculations challenging. Here we implement the ability to fetch crystallographic structures from the Protein Data Bank and analyze the metal binding sites in the program molSimplify. We show the usefulness of the newly created protein3D class to extract the local environment around non-heme iron enzymes containing a two histidine motif and prepare 372 structures for quantum mechanical calculations. Our implementation of protein3D serves to expand the range of systems molSimplify can be used to analyze and will enable high-throughput study of metal-containing active sites in proteins.

Substrate Positioning Dynamics Involves a Non-Electrostatic Component to Mediate Catalysis

Substrate positioning dynamics (SPD) orients the substrate in the active site, thereby influencing catalytic efficiency. However, it remains unknown whether SPD effects originate primarily from electrostatic perturbation inside the enzyme or can independently mediate catalysis with a significant non-electrostatic component. In this work, we investigated how the non-electrostatic component of SPD affects transition state (TS) stabilization. Using high-throughput enzyme modeling, we selected Kemp eliminase variants with similar electrostatics inside the enzyme but significantly different SPD. The kinetic parameters of these mutants were experimentally characterized. We observed a valley-shaped, two-segment linear correlation between the TS stabilization free energy (converted from kinetic parameters) and substrate positioning index (a metric to quantify SPD). The energy varies by approximately 2 kcal/mol. Favorable SPD was observed for the distal mutant R154W, increasing the proportion of reactive conformations and leading to the lowest activation free energy. These results indicate the substantial contribution of the non-electrostatic component of SPD to enzyme catalytic efficiency.

Mutexa: A Computational Ecosystem for Intelligent Protein Engineering

Protein engineering holds immense promise in shaping the future of biomedicine and biotechnology. This Review focuses on our ongoing development of Mutexa, a computational ecosystem designed to enable "intelligent protein engineering". In this vision, researchers will seamlessly acquire sequences of protein variants with desired functions as biocatalysts, therapeutic peptides, and diagnostic proteins through a finely-tuned computational machine, akin to Amazon Alexa's role as a versatile virtual assistant. The technical foundation of Mutexa has been established through the development of a database that combines and relates enzyme structures and their respective functions (e.g., IntEnzyDB), workflow software packages that enable high-throughput protein modeling (e.g., EnzyHTP and LassoHTP), and scoring functions that map the sequence-structure-function relationship of proteins (e.g., EnzyKR and DeepLasso). We will showcase the applications of these tools in benchmarking the convergence conditions of enzyme functional descriptors across mutants, investigating protein electrostatics and cavity distributions in SAM-dependent methyltransferases, and understanding the role of nonelectrostatic dynamic effects in enzyme catalysis. Finally, we will conclude by addressing the future steps and fundamental challenges in our endeavor to develop new Mutexa applications that assist the identification of beneficial mutants in protein engineering.

Deciphering the Catalytic Mechanism of Virginiamycin B Lyase with Multiscale Methods and Molecular Dynamics Simulations

Due to the emergence of antibiotic resistance, the need to explore novel antibiotics and/or novel strategies to counter antibiotic resistance is of utmost importance. In this work, we explored the molecular and mechanistic details of the degradation of a streptogramin B antibiotic by virginiamycin B (Vgb) lyase of Staphylococcus aureus using classical molecular dynamics simulations and multiscale quantum mechanics/molecular mechanics methods. Our results were in line with available experimental kinetic information. Although we were able to identify a stepwise mechanism, in the wild-type enzyme, the intermediate is short-lived, showing a small barrier to decay to the product state. The impact of point mutations on the reaction was also assessed, showing not only the importance of active site residues to the reaction catalyzed by Vgb lyase but also of near positive and negative residues surrounding the active site. Using molecular dynamics simulations, we also predicted the most likely protonation state of the 3-hydroxypicolinic moiety of the antibiotic and the impact of mutants on antibiotic binding. All this information will expand our understanding of linearization reactions of cyclic antibiotics, which are crucial for the development of novel strategies that aim to tackle antibiotic resistance.

EnzyHTP Computational Directed Evolution with Adaptive Resource Allocation

Directed evolution facilitates enzyme engineering via iterative rounds of mutagenesis. Despite the wide applications of high-throughput screening, building "smart libraries" to effectively identify beneficial variants remains a major challenge in the community. Here, we developed a new computational directed evolution protocol based on EnzyHTP, a software that we have previously reported to automate enzyme modeling. To enhance the throughput efficiency, we implemented an adaptive resource allocation strategy that dynamically allocates different types of computing resources (e.g., GPU/CPU) based on the specific need of an enzyme modeling subtask in the workflow. We implemented the strategy as a Python library and tested the library using fluoroacetate dehalogenase as a model enzyme. The results show that compared to fixed resource allocation where both CPU and GPU are on-call for use during the entire workflow, applying adaptive resource allocation can save 87% CPU hours and 14% GPU hours. Furthermore, we constructed a computational directed evolution protocol under the framework of adaptive resource allocation. The workflow was tested against two rounds of mutational screening in the directed evolution experiments of Kemp eliminase (KE07) with a total of 184 mutants. Using folding stability and electrostatic stabilization energy as computational readout, we identified all four experimentally observed target variants. Enabled by the workflow, the entire computation task (i.e., 18.4 μs MD and 18,400 QM single-point calculations) completes in 3 days of wall-clock time using ∼30 GPUs and ∼1000 CPUs.

High-throughput computational investigation of protein electrostatics and cavity for SAM-dependent methyltransferases

S-adenosyl methionine (SAM)-dependent methyl transferases (MTases) are a ubiquitous class of enzymes catalyzing dozens of essential life processes. Despite targeting a large space of substrates with diverse intrinsic reactivity, SAM MTases have similar catalytic efficiency. While understanding of MTase mechanism has grown tremendously through the integration of structural characterization, kinetic assays, and multiscale simulations, it remains elusive how these enzymes have evolved to fit the diverse chemical needs of their respective substrates. In this work, we performed a high-throughput molecular modeling analysis of 91 SAM MTases to better understand how their properties (i.e., electric field [EF] strength and active site volumes) help achieve similar catalytic efficiency toward substrates of different reactivity. We found that EF strengths have largely adjusted to make the target atom a better methyl acceptor. For MTases that target RNA/DNA and histone proteins, our results suggest that EF strength accommodates formal hybridization state and variation in cavity volume trends with diversity of substrate classes. Metal ions in SAM MTases contribute negatively to EF strength for methyl donation and enzyme scaffolds tend to offset these contributions.

MoSDeF-GOMC: Python Software for the Creation of Scientific Workflows for the Monte Carlo Simulation Engine GOMC

MoSDeF-GOMC is a python interface for the Monte Carlo software GOMC to the Molecular Simulation Design Framework (MoSDeF) ecosystem. MoSDeF-GOMC automates the process of generating initial coordinates, assigning force field parameters, and writing coordinate (PDB), connectivity (PSF), force field parameter, and simulation control files. The software lowers entry barriers for novice users while allowing advanced users to create complex workflows that encapsulate simulation setup, execution, and data analysis in a single script. All relevant simulation parameters are encoded within the workflow, ensuring reproducible simulations. MoSDeF-GOMC's capabilities are illustrated through a number of examples, including prediction of the adsorption isotherm for CO₂ in IRMOF-1, free energies of hydration for neon and radon over a broad temperature range, and the vapor-liquid coexistence curve of a four-component surrogate for the jet fuel S-8. The MoSDeF-GOMC software is available on GitHub at https://github.com/GOMC-WSU/MoSDeF-GOMC.

LassoHTP: A High-Throughput Computational Tool for Lasso Peptide Structure Construction and Modeling

Lasso peptides are a subclass of ribosomally synthesized and post-translationally modified peptides with a slipknot conformation. With superior thermal stability, protease resistance, and antimicrobial activity, lasso peptides are promising candidates for bioengineering and pharmaceutical applications. To enable high-throughput computational prediction and design of lasso peptides, we developed a software, LassoHTP, for automatic lasso peptide structure construction and modeling. LassoHTP consists of three modules, including the scaffold constructor, mutant generator, and molecular dynamics (MD) simulator. With a user-provided sequence and conformational annotation, LassoHTP can either generate the structure and conformational ensemble as is or conduct random mutagenesis. We used LassoHTP to construct eight known lasso peptide structures de novo and to simulate their conformational ensembles for 100 ns MD simulations. For benchmarking, we calculated the root mean square deviation (RMSD) of these ensembles with reference to their experimental crystal or NMR PDB structures; we also compared these RMSD values against those of the MD ensembles that are initiated from the PDB structures. Dihedral principal component analysis was also conducted. The results show that the LassoHTP-initiated ensembles are similar to those of the PDB-initiated ensembles. LassoHTP offers a computational platform to develop strategies for lasso peptide prediction and design.

Data-driven enzyme engineering to identify function-enhancing enzymes

Identifying function-enhancing enzyme variants is a 'holy grail' challenge in protein science because it will allow researchers to expand the biocatalytic toolbox for late-stage functionalization of drug-like molecules, environmental degradation of plastics and other pollutants, and medical treatment of food allergies. Data-driven strategies, including statistical modeling, machine learning, and deep learning, have largely advanced the understanding of the sequence-structure-function relationships for enzymes. They have also enhanced the capability of predicting and designing new enzymes and enzyme variants for catalyzing the transformation of new-to-nature reactions. Here, we reviewed the recent progresses of data-driven models that were applied in identifying efficiency-enhancing mutants for catalytic reactions. We also discussed existing challenges and obstacles faced by the community. Although the review is by no means comprehensive, we hope that the discussion can inform the readers about the state-of-the-art in data-driven enzyme engineering, inspiring more joint experimental-computational efforts to develop and apply data-driven modeling to innovate biocatalysts for synthetic and pharmaceutical applications.

Convergence in determining enzyme functional descriptors across Kemp eliminase variants

Molecular simulations have been extensively employed to accelerate biocatalytic discoveries. Enzyme functional descriptors derived from molecular simulations have been leveraged to guide the search for beneficial enzyme mutants. However, the ideal active-site region size for computing the descriptors over multiple enzyme variants remains untested. Here, we conducted convergence tests for dynamics-derived and electrostatic descriptors on 18 Kemp eliminase variants across six active-site regions with various boundary distances to the substrate. The tested descriptors include the root-mean-square deviation of the active-site region, the solvent accessible surface area ratio between the substrate and active site, and the projection of the electric field (EF) on the breaking C-H bond. All descriptors were evaluated using molecular mechanics methods. To understand the effects of electronic structure, the EF was also evaluated using quantum mechanics/molecular mechanics methods. The descriptor values were computed for 18 Kemp eliminase variants. Spearman correlation matrices were used to determine the region size condition under which further expansion of the region boundary does not substantially change the ranking of descriptor values. We observed that protein dynamics-derived descriptors, including RMSD_{active_site} and SASA_ratio, converge at a distance cutoff of 5 Å from the substrate. The electrostatic descriptor, EF_C-H, converges at 6 Å using molecular mechanics methods with truncated enzyme models and 4 Å using quantum mechanics/molecular mechanics methods with whole enzyme model. This study serves as a future reference to determine descriptors for predictive modeling of enzyme engineering.

IntEnzyDB: an Integrated Structure-Kinetics Enzymology Database

Data-driven modeling has emerged as a new paradigm for biocatalyst design and discovery. Biocatalytic databases that integrate enzyme structure and function data are in urgent need. Here we describe IntEnzyDB as an integrated structure-kinetics database for facile statistical modeling and machine learning. IntEnzyDB employs a relational database architecture with a flattened data structure, which allows rapid data operation. This architecture also makes it easy for IntEnzyDB to incorporate more types of enzyme function data. IntEnzyDB contains enzyme kinetics and structure data from six enzyme commission classes. Using 1050 enzyme structure-kinetics pairs, we investigated the efficiency-perturbing propensities of mutations that are close or distal to the active site. The statistical results show that efficiency-enhancing mutations are globally encoded and that deleterious mutations are much more likely to occur in close mutations than in distal mutations. Finally, we describe a web interface that allows public users to access enzymology data stored in IntEnzyDB. IntEnzyDB will provide a computational facility for data-driven modeling in biocatalysis and molecular evolution.

Origin of metabolites diversity and selectivity of P450 catalyzed benzo[a]pyrene metabolic activation

Polycyclic Aromatic Hydrocarbon (PAHs) presents one of the most abundant class of environmental pollutants. Recent study shows a lab-synthesized PAHs derivative, helicenium, can selectively kill cancer cells rather than normal cells, calling for the in-depth understanding of the metabolic process. However, the origin of metabolites diversity and selectivity of P450 catalyzed PAHs metabolic activation is still unclear to a great extent. Here we systematically investigated P450 enzymes catalyzed activation mechanism of a representative PAHs, benzo[a]pyrene (BaP), and found the corresponding activation process mainly involves two elementary steps: electrophilic addition and epoxidation. Electrophilic addition step is evidenced to be rate determining step. Two representative binding modes of BaP with P450 were found, which enables the electrophilic addition of Heme (FeO) to almost all the carbons of BaP. This electrophilic addition was proposed to be accelerated by the P450 enzyme environment when compared with the gas phase and water solvent. To dig deeper on the origin of metabolites diversity, we built several linear regression models to explore the structural-energy relationships. The selectivity was eventually attributed to the integrated effects of structural (e.g. O-C distance and O-C-Fe angle) and electrostatic parameters (e.g. charge of C and O) from both BaP and P450.

Endocrine-disrupting metabolic activation of 2-nitrofluorene catalyzed by human cytochrome P450 1A1: A QM/MM approach

Nitropolycyclic aromatic hydrocarbons (NPAHs) present one of the most important airborne pollutants. Recent studies have shown that one of the most abundant NPAHs, 2-Nitrofluorene (NF), was supposed to be converted to endocrine-disrupting metabolites by cytochrome P450 1A1 (CYP1A1) in human cells. However, the mechanism is still largely unexplored. Here the metabolic activation and transformation mechanism of NF catalyzed by CYP1A1 were systematically studied with the aid of Molecular Dynamics, Density Functional Theory and Quantum Mechanics/Molecular Mechanics techniques. We evidence that CYP1A1 can activate NF through two elementary processes: (i) electrophilic addition (12.4 kcal·mol-1) or hydrogen abstraction (38.2 kcal·mol-1) and (ii) epoxidation (5.9 and 8.7 kcal·mol-1) or NIH shift (12.5 and 14.9 kcal·mol-1) or proton shuttle (12.1 kcal·mol-1). Electrophilic addition was found to be the rate-determining step while epoxidation rather than NIH shift or proton shuttle is the more feasible pathway after electrophilic addition. Metabolites 6,7-epoxide-2-nitrofluorene and 7,8-epoxide-2-nitrofluorene were identified as the major epoxidation products. Epoxides are unstable and easy to react with hydrated hydrogen ions and hydroxyls to produce endocrine disrupter 7-hydroxy-2-nitrofluorene. Toxic analysis shows that some of the metabolites are more toxic to model aquatic organisms (e.g. Green algea) than NF. Binding affinity analysis to human sex hormone binding globulin reveals that NF metabolites all have endocrine-disrupting potential. This study provides a comprehensive understanding on the biotransformation process of NF and may aid future studies on various NPAHs activation catalyzed by human P450 enzyme.

Molecular Dynamics-Derived Descriptor Informs the Impact of Mutation on the Catalytic Turnover Number in Lactonase Across Substrates

Molecular dynamics simulations have been extensively employed to reveal the roles of protein dynamics in mediating enzyme catalysis. However, simulation-derived predictive descriptors that inform the impacts of mutations on catalytic turnover numbers remain largely unexplored. In this work, we report the identification of molecular modeling-derived descriptors to predict mutation effect on the turnover number of lactonase SsoPox with both native and non-native substrates. The study consists of 10 enzyme-substrate complexes resulting from a combination of five enzyme variants with two substrates. For each complex, we derived 15 descriptors from molecular dynamics simulations and applied principal component analysis to rank the predictive capability of the descriptors. A top-ranked descriptor was identified, which is the solvent-accessible surface area (SASA) ratio of the substrate to the active site pocket. A uniform volcano-shaped plot was observed in the distribution of experimental activation free energy against the SASA ratio. To achieve efficient lactonase hydrolysis, a non-native substrate-bound enzyme variant needs to involve a similar range of the SASA ratio to the native substrate-bound wild-type enzyme. The descriptor reflects how well the enzyme active site pocket accommodates a substrate for reaction, which has the potential of guiding optimization of enzyme reaction turnover for non-native chemical transformations.