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Enhancing Luciferase Activity and Stability through Generative Modeling of Natural Enzyme Sequences.

ABSTRACT: The availability of natural protein sequences synergized with generative artificial intelligence (AI) provides new paradigms to create enzymes. Although active enzyme variants with numerous mutations have been produced using generative models, their performance often falls short compared to their wild-type counterparts. Additionally, in practical applications, choosing fewer mutations that can rival the efficacy of extensive sequence alterations is usually more advantageous. Pinpointing beneficial single mutations continues to be a formidable task. In this study, using the generative maximum entropy model to analyze Renilla luciferase homologs, and in conjunction with biochemistry experiments, we demonstrated that natural evolutionary information could be used to predictively improve enzyme activity and stability by engineering the active center and protein scaffold, respectively. The success rate of designed single mutants is ~50% to improve either luciferase activity or stability. These finding highlights nature's ingenious approach to evolving proficient enzymes, wherein diverse evolutionary pressures are preferentially applied to distinct regions of the enzyme, ultimately culminating in an overall high performance. We also reveal an evolutionary preference in Renilla luciferase towards emitting blue light that holds advantages in terms of water penetration compared to other light spectrum. Taken together, our approach facilitates navigation through enzyme sequence space and offers effective strategies for computer-aided rational enzyme engineering.

SUBMITTER: Xie WJ

PROVIDER: S-EPMC10541610 | biostudies-literature | 2023 Oct

REPOSITORIES: biostudies-literature

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Enhancing Luciferase Activity and Stability through Generative Modeling of Natural Enzyme Sequences.

Xie Wen Jun WJ Liu Dangliang D Wang Xiaoya X Zhang Aoxuan A Wei Qijia Q Nandi Ashim A Dong Suwei S Warshel Arieh A

bioRxiv : the preprint server for biology 20231005

The availability of natural protein sequences synergized with generative artificial intelligence (AI) provides new paradigms to create enzymes. Although active enzyme variants with numerous mutations have been produced using generative models, their performance often falls short compared to their wild-type counterparts. Additionally, in practical applications, choosing fewer mutations that can rival the efficacy of extensive sequence alterations is usually more advantageous. Pinpointing benefici ...[more]

PMID: 37786693

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Project description:In the field of chiral amine synthesis, ω-amine transaminase (ω-ATA) is one of the most established enzymes capable of asymmetric amination under optimal conditions. However, the applicability of ω-ATA toward more non-natural complex molecules remains limited due to its low transamination activity, thermostability, and narrow substrate scope. Here, by employing a combined approach of computational virtual screening strategy and combinatorial active-site saturation test/iterative saturation mutagenesis strategy, we have constructed the best variant M14C3-V5 (M14C3-V62A-V116S-E117I-L118I-V147F) with improved ω-ATA from Aspergillus terreus (AtATA) activity and thermostability toward non-natural substrate 1-acetylnaphthalene, which is the ketone precursor for producing the intermediate (R)-(+)-1-(1-naphthyl)ethylamine [(R)-NEA] of cinacalcet hydrochloride, showing activity enhancement of up to 3.4-fold compared to parent enzyme M14C3 (AtATA-F115L-M150C-H210N-M280C-V149A-L182F-L187F). The computational tools YASARA, Discovery Studio, Amber, and FoldX were applied for predicting mutation hotspots based on substrate-enzyme binding free energies and to show the possible mechanism with features related to AtATA structure, catalytic activity, and stability in silico analyses. M14C3-V5 achieved 71.8% conversion toward 50 mM 1-acetylnaphthalene in a 50 mL preparative-scale reaction for preparing (R)-NEA. Moreover, M14C3-V5 expanded the substrate scope toward aromatic ketone compounds. The generated virtual screening strategy based on the changes in binding free energies has successfully predicted the AtATA activity toward 1-acetylnaphthalene and related substrates. Together with experimental data, these approaches can serve as a gateway to explore desirable performances, expand enzyme-substrate scope, and accelerate biocatalysis.IMPORTANCEChiral amine is a crucial compound with many valuable applications. Their asymmetric synthesis employing ω-amine transaminases (ω-ATAs) is considered an attractive method. However, most ω-ATAs exhibit low activity and stability toward various non-natural substrates, which limits their industrial application. In this work, protein engineering strategy and computer-aided design are performed to evolve the activity and stability of ω-ATA from Aspergillus terreus toward non-natural substrates. After five rounds of mutations, the best variant, M14C3-V5, is obtained, showing better catalytic efficiency toward 1-acetylnaphthalene and higher thermostability than the original enzyme, M14C3. The robust combinational variant acquired displayed significant application value for pushing the asymmetric synthesis of aromatic chiral amines to a higher level.

Project description:BackgroundMedical students often struggle to engage with and retain complex pharmacology topics during their preclinical education. Traditional teaching methods can lead to passive learning and poor long-term retention of critical concepts.ObjectiveThis study aims to enhance the teaching of clinical pharmacology in medical school by using a multimodal generative artificial intelligence (genAI) approach to create compelling, cinematic clinical narratives (CCNs).MethodsWe transformed a standard clinical case into an engaging, interactive multimedia experience called "Shattered Slippers." This CCN used various genAI tools for content creation: GPT-4 for developing the storyline, Leonardo.ai and Stable Diffusion for generating images, Eleven Labs for creating audio narrations, and Suno for composing a theme song. The CCN integrated narrative styles and pop culture references to enhance student engagement. It was applied in teaching first-year medical students about immune system pharmacology. Student responses were assessed through the Situational Interest Survey for Multimedia and examination performance. The target audience comprised first-year medical students (n=40), with 18 responding to the Situational Interest Survey for Multimedia survey (n=18).ResultsThe study revealed a marked preference for the genAI-enhanced CCNs over traditional teaching methods. Key findings include the majority of surveyed students preferring the CCN over traditional clinical cases (14/18), as well as high average scores for triggered situational interest (mean 4.58, SD 0.53), maintained interest (mean 4.40, SD 0.53), maintained-feeling interest (mean 4.38, SD 0.51), and maintained-value interest (mean 4.42, SD 0.54). Students achieved an average score of 88% on examination questions related to the CCN material, indicating successful learning and retention. Qualitative feedback highlighted increased engagement, improved recall, and appreciation for the narrative style and pop culture references.ConclusionsThis study demonstrates the potential of using a multimodal genAI-driven approach to create CCNs in medical education. The "Shattered Slippers" case effectively enhanced student engagement and promoted knowledge retention in complex pharmacological topics. This innovative method suggests a novel direction for curriculum development that could improve learning outcomes and student satisfaction in medical education. Future research should explore the long-term retention of knowledge and the applicability of learned material in clinical settings, as well as the potential for broader implementation of this approach across various medical education contexts.

Dataset Information

Enhancing Luciferase Activity and Stability through Generative Modeling of Natural Enzyme Sequences.

Publications

Enhancing Luciferase Activity and Stability through Generative Modeling of Natural Enzyme Sequences.

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