EnzyKR: a chirality-aware deep learning model for predicting the outcomes of the hydrolase-catalyzed kinetic resolution.

Hydrolase-catalyzed kinetic resolution is a well-established biocatalytic process. However, the computational tools that predict favorable enzyme scaffolds for separating a racemic substrate mixture are underdeveloped. To address this challenge, we trained a deep learning framework, EnzyKR, to automate the selection of hydrolases for stereoselective biocatalysis. EnzyKR adopts a classifier-regressor architecture that first identifies the reactive binding conformer of a substrate-hydrolase complex, and then predicts its activation free energy. A structure-based encoding strategy was used to depict the chiral interactions between hydrolases and enantiomers. Different from existing models trained on protein sequences and substrate SMILES strings, EnzyKR was trained using 204 substrate-hydrolase complexes, which were constructed by docking. EnzyKR was tested using a held-out dataset of 20 complexes on the task of predicting activation free energy. EnzyKR achieved a Pearson correlation coefficient (R) of 0.72, a Spearman rank correlation coefficient (Spearman R) of 0.72, and a mean absolute error (MAE) of 1.54 kcal mol-1 in this task. Furthermore, EnzyKR was tested on the task of predicting enantiomeric excess ratios for 28 hydrolytic kinetic resolution reactions catalyzed by fluoroacetate dehalogenase RPA1163, halohydrin HheC, A. mediolanus epoxide hydrolase, and P. fluorescens esterase. The performance of EnzyKR was compared against that of a recently developed kinetic predictor, DLKcat. EnzyKR correctly predicts the favored enantiomer and outperforms DLKcat in 18 out of 28 reactions, occupying 64% of the test cases. These results demonstrate EnzyKR to be a new approach for prediction of enantiomeric outcomes in hydrolase-catalyzed kinetic resolution reactions.

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.

Iron acquisition by a commensal bacterium modifies host nutritional immunity during Salmonella infection.

During intestinal inflammation, host nutritional immunity starves microbes of essential micronutrients, such as iron. Pathogens scavenge iron using siderophores, including enterobactin; however, this strategy is counteracted by host protein lipocalin-2, which sequesters iron-laden enterobactin. Although this iron competition occurs in the presence of gut bacteria, the roles of commensals in nutritional immunity involving iron remain unexplored. Here, we report that the gut commensal Bacteroides thetaiotaomicron acquires iron and sustains its resilience in the inflamed gut by utilizing siderophores produced by other bacteria, including Salmonella, via a secreted siderophore-binding lipoprotein XusB. Notably, XusB-bound enterobactin is less accessible to host sequestration by lipocalin-2 but can be "re-acquired" by Salmonella, allowing the pathogen to evade nutritional immunity. Because the host and pathogen have been the focus of studies of nutritional immunity, this work adds commensal iron metabolism as a previously unrecognized mechanism modulating the host-pathogen interactions and nutritional immunity.

Iron acquisition by a commensal bacterium modifies host nutritional immunity during Salmonella infection.

During intestinal inflammation, host nutritional immunity starves microbes of essential micronutrients such as iron. Pathogens scavenge iron using siderophores, which is counteracted by the host using lipocalin-2, a protein that sequesters iron-laden siderophores, including enterobactin. Although the host and pathogens compete for iron in the presence of gut commensal bacteria, the roles of commensals in nutritional immunity involving iron remain unexplored. Here, we report that the gut commensal Bacteroides thetaiotaomicron acquires iron in the inflamed gut by utilizing siderophores produced by other bacteria including Salmonella, via a secreted siderophore-binding lipoprotein termed XusB. Notably, XusB-bound siderophores are less accessible to host sequestration by lipocalin-2 but can be "re-acquired" by Salmonella , allowing the pathogen to evade nutritional immunity. As the host and pathogen have been the focus of studies of nutritional immunity, this work adds commensal iron metabolism as a previously unrecognized mechanism modulating the interactions between pathogen and host nutritional immunity.

Accelerated Entropic Path Sampling with a Bidirectional Generative Adversarial Network.

The role of entropy in mediating the dynamic outcomes of chemical reactions remains largely unknown. To evaluate the change of entropy along post-transition state paths, we have previously developed entropic path sampling that computes configurational entropy from an ensemble of reaction trajectories. However, one major caveat of this approach lies in its high computational demand: about 2000 trajectories are needed to converge the computation of an entropic profile. Here, by leveraging a deep generative model, we developed an accelerated entropic path sampling approach that evaluates entropic profiles using merely a few hundred reaction dynamic trajectories. The new method, called bidirectional generative adversarial network-entropic path sampling, can enhance the estimation of probability density functions of molecular configurations by generating pseudo-molecular configurations that are statistically indistinguishable from the true data. The method was established using cyclopentadiene dimerization, in which we reproduced the reference entropic profiles (derived from 2480 trajectories) using merely 124 trajectories. The method was further benchmarked using three reactions with symmetric post-transition-state bifurcation, including endo-butadiene dimerization, 5-fluoro-1,3-cyclopentadiene dimerization, and 5-methyl-1,3-cyclopentadiene dimerization. The results indicate the existence of a "hidden entropic intermediate", which is a dynamic species that binds to a local entropic maximum where no free energy minimum is formed.

High-Throughput Screen Reveals the Structure-Activity Relationship of the Antimicrobial Lasso Peptide Ubonodin.

The Burkholderia cepacia complex (Bcc) is a group of bacteria including opportunistic human pathogens. Immunocompromised individuals and cystic fibrosis patients are especially vulnerable to serious infections by these bacteria, motivating the search for compounds with antimicrobial activity against the Bcc. Ubonodin is a lasso peptide with promising activity against Bcc species, working by inhibiting RNA polymerase in susceptible bacteria. We constructed a library of over 90 000 ubonodin variants with 2 amino acid substitutions and used a high-throughput screen and next-generation sequencing to examine the fitness of the entire library, generating the most comprehensive data set on lasso peptide activity so far. This screen revealed information regarding the structure-activity relationship of ubonodin over a large sequence space. Remarkably, the screen identified one variant with not only improved activity compared to wild-type ubonodin but also a submicromolar minimum inhibitory concentration (MIC) against a clinical isolate of the Bcc member Burkholderia cenocepacia. Ubonodin and several of the variants identified in this study had lower MICs against certain Bcc strains than those of many clinically approved antibiotics. Finally, the large library size enabled us to develop DeepLasso, a deep learning model that can predict the RNAP inhibitory activity of an ubonodin variant.

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.

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.

Rate-Perturbing Single Amino Acid Mutation for Hydrolases: A Statistical Profiling.

Hydrolases are a critical component for modern chemical, pharmaceutical, and environmental sciences. Identifying mutations that enhance catalytic efficiency presents a roadblock to design and to discover new hydrolases for broad academic and industrial uses. Here, we report the statistical profiling for rate-perturbing mutant hydrolases with a single amino acid substitution. We constructed an integrated structure-kinetics database for hydrolases, IntEnzyDB, which contains 3907 kcats, 4175 KMs, and 2715 Protein Data Bank IDs. IntEnzyDB adopts a relational architecture with a flattened data structure, enabling facile and efficient access to clean and tabulated data for machine learning uses. We conducted statistical analyses on how single amino acids mutations influence the turnover number (i.e., kcat) and efficiency (i.e., kcat/KM), with a particular emphasis on profiling the features for rate-enhancing mutations. The results show that mutation to bulky nonpolar residues with a hydrocarbon chain involves a higher likelihood for rate acceleration than to other types of residues. Linear regression models reveal geometric descriptors of substrate and mutation residues that mediate rate-perturbing outcomes for hydrolases with bulky nonpolar mutations. On the basis of the analyses of the structure-kinetics relationship, we observe that the propensity for rate enhancement is independent of protein sizes. In addition, we observe that distal mutations (i.e., >10 Å from the active site) in hydrolases are significantly more prone to induce efficiency neutrality and avoid efficiency deletion but involve similar propensity for rate enhancement. The studies reveal the statistical features for identifying rate-enhancing mutations in hydrolases, which will potentially guide hydrolase discovery in biocatalysis.