Discovery, design, and engineering of enzymes based on molecular retrobiosynthesis

mLife, Journal Year: 2025, Volume and Issue: 4(2), P. 107 - 125

Published: March 28, 2025

Abstract Biosynthesis—a process utilizing biological systems to synthesize chemical compounds—has emerged as a revolutionary solution 21st‐century challenges due its environmental sustainability, scalability, and high stereoselectivity regioselectivity. Recent advancements in artificial intelligence (AI) are accelerating biosynthesis by enabling intelligent design, construction, optimization of enzymatic reactions systems. We first introduce the molecular retrosynthesis route planning biochemical pathway including single‐step algorithms AI‐based design tools. highlight advantages large language models addressing sparsity data. Furthermore, we review enzyme discovery methods based on sequence structure alignment techniques. Breakthroughs structural prediction expected significantly improve accuracy discovery. also summarize for de novo generation nonnatural or orphan reactions, focusing functional annotation techniques reaction small molecule similarity. Turning engineering, discuss strategies thermostability, solubility, activity, well applications AI these fields. The shift from traditional experiment‐driven data‐driven computationally driven is already underway. Finally, present potential provide perspective future research directions. envision expanded biocatalysis drug development, green chemistry, complex synthesis.

Language: Английский

---

Plants lipases: challenges, recent advances, and future prospects - a review

Bioprocess and Biosystems Engineering, Journal Year: 2025, Volume and Issue: unknown

Published: April 12, 2025

Language: Английский

---

Enhanced Microbial Strategies to Mitigate Microplastic Transfer via Composting to Agricultural Ecosystems - A Short Review

Current Opinion in Environmental Science & Health, Journal Year: 2025, Volume and Issue: unknown, P. 100625 - 100625

Published: April 1, 2025

Language: Английский

---

Exploring molecular catalysis in the enzymatic synthesis of biolubricants: A comprehensive review and bibliometric assessment

Molecular Catalysis, Journal Year: 2025, Volume and Issue: 583, P. 115191 - 115191

Published: May 21, 2025

Language: Английский

---

Advancing high-throughput screening systems for synthetic biology and biofoundry

Current Opinion in Systems Biology, Journal Year: 2023, Volume and Issue: 37, P. 100487 - 100487

Published: Nov. 21, 2023

High-throughput (HT) methodologies are extensively applied in synthetic biology for the rapid enrichment and selection of desired properties from a wide range genetic diversity. In order to effectively analyze these vast variants, HT tools must offer parallel experiments compact reaction capabilities enhance overall throughput. Here, we discuss about various aspects three representative high-throughput screening (HTS) systems: microwell-, droplet-, single cell-based screening. These systems can be categorized based on their volume, which turn determines associated technology, machinery, supporting applications. Furthermore, techniques that rapidly connects numerous genotypes phenotypes, have evolved precision predictions through integration digital technologies like machine learning artificial intelligence. The use advanced within biofoundry will enable analysis extensive diversity, making it driving force advancement biology.

Language: Английский

---

Use of genomics & proteomics in studying lipase producing microorganisms & its application

Food Chemistry Molecular Sciences, Journal Year: 2024, Volume and Issue: 9, P. 100218 - 100218

Published: Aug. 23, 2024

In biotechnological applications, lipases are recognized as the most widely utilized and versatile enzymes, pivotal in biocatalytic processes, predominantly produced by various microbial species. Utilizing omics technology, natural sources can be meticulously screened to find flora which responsible for oil production. Lipases biocatalysts. They used a variety of bioconversion reactions receiving lot attention because quick development enzyme technology its usefulness industrial operations. This article offers recent insights into lipase sources, including fungi, bacteria, yeast, alongside traditional modern methods purification such precipitation, immunopurification chromatographic separation. Additionally, it explores innovative like reversed micellar system, aqueous two-phase system (ATPS), flotation (ATPF). The deals with use sectors, food, textile, leather, cosmetics, paper, detergent, while also critically analyzing lipase-producing microbes. Moreover, highlights role biosensors, biodiesel production, tea processing, bioremediation, racemization. review provides concept technique mechanism screening species those capable producing potential applications.

Language: Английский

---

Enzyme catalytic efficiency prediction: employing convolutional neural networks and XGBoost

Frontiers in Artificial Intelligence, Journal Year: 2024, Volume and Issue: 7

Published: Oct. 21, 2024

Introduction In the intricate realm of enzymology, precise quantification enzyme efficiency, epitomized by turnover number ( k cat ), is a paramount yet elusive objective. Existing methodologies, though sophisticated, often grapple with inherent stochasticity and multifaceted nature enzymatic reactions. Thus, there arises necessity to explore avant-garde computational paradigms. Methods this context, we introduce “enzyme catalytic efficiency prediction (ECEP),” leveraging advanced deep learning techniques enhance previous implementation, TurNuP, for predicting catalase . Our approach significantly outperforms prior incorporating new features derived from sequences chemical reaction dynamics. Through ECEP, unravel enzyme-substrate interactions, capturing nuanced interplay molecular determinants. Results Preliminary assessments, compared against established models like TurNuP DLKcat, underscore superior predictive capabilities marking pivotal shift in silico estimation. This study enriches toolkit available enzymologists lays groundwork future explorations burgeoning field bioinformatics. paper suggested multi-feature ensemble learning-based predict kinetic parameters using an convolution neural network XGBoost calculating weighted-average each feature-based model’s output outperform traditional machine methods. The proposed “ECEP” model outperformed existing achieving mean squared error (MSE) reduction 0.35 0.81 0.46 R -squared score 0.44 0.54, thereby demonstrating its accuracy effectiveness prediction. Discussion improvement underscores potential bioinformatics, setting benchmark performance.

Language: Английский

---

Glycoside hydrolases in the biodegradation of lignocellulosic biomass

3 Biotech, Journal Year: 2023, Volume and Issue: 13(12)

Published: Nov. 16, 2023

Language: Английский

---

Enhanced catalytic performance of methanol-tolerant Rhizopus stolonifera immobilized on polyurethane foam with hydrophobic coating for biodiesel production

Industrial Crops and Products, Journal Year: 2024, Volume and Issue: 222, P. 119857 - 119857

Published: Oct. 16, 2024

Language: Английский

---

Rational design of lanosterol 14α-demethylase for ergosterol biosynthesis in Saccharomyces cerevisiae

3 Biotech, Journal Year: 2024, Volume and Issue: 14(12)

Published: Nov. 15, 2024

Language: Английский

---