Mutexa: A Computational Ecosystem for Intelligent Protein Engineering

Computational Studies of Enzymes for C-F Bond Degradation and Functionalization

Organofluorine compounds have revolutionized chemical and pharmaceutical industries, serving as essential components in numerous applications and aspects of modern life. However, their bioaccumulation and resistance to degradation have resulted in environmental pollution, posing significant risks to human and animal health. The exceptionally strong C-F bond in these compounds makes their degradation challenging, with current methods often requiring extreme experimental conditions. Therefore, the development of eco-friendly approaches that operate under milder conditions is crucial, with enzyme-mediated C-F bond cleavage strategies emerging as a particularly promising solution. In this review, we present an overview of how computational approaches, including molecular docking, molecular dynamics simulations, quantum mechanics/molecular mechanics calculations, and bioinformatics, have been utilized to investigate the mechanisms underlying enzymatic C-F bond degradation and functionalization. This review highlights how these computational approaches provide critical insights into the atomic-level interactions and energetics underlying enzymatic processes, offering a foundation for the rational design and engineering of enzymes capable of addressing the challenges posed by fluorinated compounds. This review covers several types of enzymes including: fluoroacetate dehalogenases, cysteine dioxygenase, L-2-haloacid dehalogenase, cytochrome P450, fluorinase and tyrosine hydroxylase.

Digging out the Molecular Connections between the Catalytic Mechanism of Human Lysosomal α-Mannosidase and Its Pathophysiology

Human lysosomal α-mannosidase (hLAMAN) is a paradigmatic example of how a few missense mutations can critically affect normal catabolism in the lysosome and cause the severe condition named α-mannosidosis. Here, using extensive quantum mechanical/molecular mechanical metadynamics calculations, we show how four reported pathological orthosteric and allosteric single-point mutations alter substrate puckering in the Michaelis complex and how the D74E mutation doubles the energy barrier of the rate-limiting step compared to the wild-type enzyme.

SYNBIP 2.0: epitopes mapping, sequence expansion and scaffolds discovery for synthetic binding protein innovation

Synthetic binding proteins (SBPs) represent a pivotal class of artificially engineered proteins, meticulously crafted to exhibit targeted binding properties and specific functions. Here, the SYNBIP database, a comprehensive resource for SBPs, has been significantly updated. These enhancements include (i) featuring 3D structures of 899 SBP-target complexes to illustrate the binding epitopes of SBPs, (ii) using the structures of SBPs in the monomer or complex forms with target proteins, their sequence space has been expanded five times to 12 025 by integrating a structure-based protein generation framework and a protein property prediction tool, (iii) offering detailed information on 78 473 newly identified SBP-like scaffolds from the RCSB Protein Data Bank, and an additional 16 401 555 ones from the AlphaFold Protein Structure Database, and (iv) the database is regularly updated, incorporating 153 new SBPs. Furthermore, the structural models of all SBPs have been enhanced through the application of the AlphaFold2, with their clinical statuses concurrently refreshed. Additionally, the design methods employed for each SBP are now prominently featured in the database. In sum, SYNBIP 2.0 is designed to provide researchers with essential SBP data, facilitating their innovation in research, diagnosis and therapy. SYNBIP 2.0 is now freely accessible at https://idrblab.org/synbip/.

Revolutionizing Molecular Design for Innovative Therapeutic Applications through Artificial Intelligence

The field of computational protein engineering has been transformed by recent advancements in machine learning, artificial intelligence, and molecular modeling, enabling the design of proteins with unprecedented precision and functionality. Computational methods now play a crucial role in enhancing the stability, activity, and specificity of proteins for diverse applications in biotechnology and medicine. Techniques such as deep learning, reinforcement learning, and transfer learning have dramatically improved protein structure prediction, optimization of binding affinities, and enzyme design. These innovations have streamlined the process of protein engineering by allowing the rapid generation of targeted libraries, reducing experimental sampling, and enabling the rational design of proteins with tailored properties. Furthermore, the integration of computational approaches with high-throughput experimental techniques has facilitated the development of multifunctional proteins and novel therapeutics. However, challenges remain in bridging the gap between computational predictions and experimental validation and in addressing ethical concerns related to AI-driven protein design. This review provides a comprehensive overview of the current state and future directions of computational methods in protein engineering, emphasizing their transformative potential in creating next-generation biologics and advancing synthetic biology.

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.

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.