Discovery of alkaline laccases from basidiomycete fungi through machine learning-based approach DOI Creative Commons
Xing Wan, Sazzad Shahrear,

Shea Wen Chew

et al.

Biotechnology for Biofuels and Bioproducts, Journal Year: 2024, Volume and Issue: 17(1)

Published: Sept. 11, 2024

Language: Английский

AI-Driven Deep Learning Techniques in Protein Structure Prediction DOI Open Access
Lingtao Chen, Qiaomu Li,

Kazi Fahim Ahmad Nasif

et al.

International Journal of Molecular Sciences, Journal Year: 2024, Volume and Issue: 25(15), P. 8426 - 8426

Published: Aug. 1, 2024

Protein structure prediction is important for understanding their function and behavior. This review study presents a comprehensive of the computational models used in predicting protein structure. It covers progression from established modeling to state-of-the-art artificial intelligence (AI) frameworks. The paper will start with brief introduction structures, modeling, AI. section on discuss homology ab initio threading. next deep learning-based models. introduces some AI models, such as AlphaFold (AlphaFold, AlphaFold2, AlphaFold3), RoseTTAFold, ProteinBERT, etc. also discusses how techniques have been integrated into frameworks like Swiss-Model, Rosetta, I-TASSER. model performance compared using rankings CASP14 (Critical Assessment Structure Prediction) CASP15. CASP16 ongoing, its results are not included this review. Continuous Automated Model EvaluatiOn (CAMEO) complements biennial CASP experiment. Template score (TM-score), global distance test total (GDT_TS), Local Distance Difference Test (lDDT) discussed too. then acknowledges ongoing difficulties emphasizes necessity additional searches dynamic behavior, conformational changes, protein-protein interactions. In application section, applications various fields drug design, industry, education, novel development. summary, provides overview latest advancements predictions. significant achieved by identifies potential areas further investigation.

Language: Английский

Citations

25
Advances, Synergy, and Perspectives of Machine Learning and Biobased Polymers for Energy, Fuels, and Biochemicals for a Sustainable Future DOI Creative Commons
Abu Danish Aiman Bin Abu Sofian, Xun Sun,

Vijai Kumar Gupta

et al.

Energy & Fuels, Journal Year: 2024, Volume and Issue: 38(3), P. 1593 - 1617

Published: Jan. 16, 2024

This review illuminates the pivotal synergy between machine learning (ML) and biopolymers, spotlighting their combined potential to reshape sustainable energy, fuels, biochemicals. Biobased polymers, derived from renewable sources, have garnered attention for roles in energy fuel sectors. These when integrated with ML techniques, exhibit enhanced functionalities, optimizing systems, storage, conversion. Detailed case studies reveal of biobased polymers applications industry, further showcasing how bolsters efficiency innovation. The intersection also marks advancements biochemical production, emphasizing innovations drug delivery medical device development. underscores imperative harnessing convergence future global sustainability endeavors collective evidence presented asserts immense promise this union holds steering a innovative trajectory.

Language: Английский

Citations

19
Precision enzyme discovery through targeted mining of metagenomic data DOI Creative Commons
Shohreh Ariaeenejad, Javad Gharechahi, Mehdi Foroozandeh Shahraki

et al.

Natural Products and Bioprospecting, Journal Year: 2024, Volume and Issue: 14(1)

Published: Jan. 11, 2024

Abstract Metagenomics has opened new avenues for exploring the genetic potential of uncultured microorganisms, which may serve as promising sources enzymes and natural products industrial applications. Identifying with improved catalytic properties from vast amount available metagenomic data poses a significant challenge that demands development novel computational functional screening tools. The all are primarily dictated by their structures, predominantly determined amino acid sequences. However, this aspect not been fully considered in enzyme bioprospecting processes. With accumulating number sequences increasing demand discovering biocatalysts, structural modeling can be employed to identify properties. Recent efforts discover polysaccharide-degrading rumen metagenome using homology-based searches machine learning-based models have shown promise. Here, we will explore various approaches screen shortlist metagenome-derived biocatalyst candidates, conjunction wet lab analytical methods traditionally used characterization.

Language: Английский

Citations

17
Biohydrogen production for sustainable energy transition: A bibliometric and systematic review of the reaction mechanisms, challenges, knowledge gaps and emerging trends DOI

C. Umunnawuike,

S. Q. A. Mahat, Peter Ikechukwu Nwaichi

et al.

Biomass and Bioenergy, Journal Year: 2024, Volume and Issue: 188, P. 107345 - 107345

Published: Aug. 14, 2024

Language: Английский

Citations

10
Latest Trends in Lipase-Catalyzed Synthesis of Ester Carbohydrate Surfactants: From Key Parameters to Opportunities and Future Development DOI Open Access
Alexis Spalletta, Nicolas Joly, Patrick Martin

et al.

International Journal of Molecular Sciences, Journal Year: 2024, Volume and Issue: 25(7), P. 3727 - 3727

Published: March 27, 2024

Carbohydrate-based surfactants are amphiphilic compounds containing hydrophilic moieties linked to hydrophobic aglycones. More specifically, carbohydrate esters biosourced and biocompatible derived from inexpensive renewable raw materials (sugars fatty acids). Their unique properties allow them be used in various areas, such as the cosmetic, food, medicine industries. These multi-applications have created a worldwide market for biobased consequently expectations their production. Biobased can obtained processes, chemical synthesis or microorganism culture surfactant purification. In accordance with need more sustainable greener of these molecules by enzymatic pathways is an opportunity. This work presents state-of-the-art lipase action mode, focus on active sites proteins, then four essential parameters optimizing reaction: type lipase, reaction medium, temperature, ratio substrates. Finally, this review discusses latest trends recent developments, showing unlimited potential optimization syntheses.

Language: Английский

Citations

6
Revolutionizing biocatalysis: A review on innovative design and applications of enzyme-immobilized microfluidic devices DOI
Pravin D. Patil,

Niharika Gargate,

Khushi Dongarsane

et al.

International Journal of Biological Macromolecules, Journal Year: 2024, Volume and Issue: unknown, P. 136193 - 136193

Published: Oct. 1, 2024

Language: Английский

Citations

6
Perspectives on flow biocatalysis: the engine propelling enzymatic reactions DOI Creative Commons
Ana I. Benítez‐Mateos, Francesca Paradisi

Journal of Flow Chemistry, Journal Year: 2023, Volume and Issue: 14(1), P. 211 - 218

Published: Oct. 23, 2023

Abstract Flow biocatalysis has emerged as an empowering tool to boost the potential of enzymatic reactions towards more automatized, sustainable, and generally efficient synthetic processes. In last fifteen years, increasing number biocatalytic transformations carried out in continuous flow exemplified benefits that this technology can bring incorporate into industrial operations. This perspective aims capture a nutshell available methodologies for well discuss current limitations future directions field. Graphical abstract

Language: Английский

Citations

13
Recent advances in the biological depolymerization and upcycling of polyethylene terephthalate DOI

Lita Amalia,

Chia-Yu Chang,

Steven S-S Wang

et al.

Current Opinion in Biotechnology, Journal Year: 2023, Volume and Issue: 85, P. 103053 - 103053

Published: Dec. 20, 2023

Language: Английский

Citations

12
Machine learning meets enzyme engineering: examples in the design of polyethylene terephthalate hydrolases DOI

Rohan Ali,

Yifei Zhang

Frontiers of Chemical Science and Engineering, Journal Year: 2024, Volume and Issue: 18(12)

Published: Sept. 10, 2024

Language: Английский

Citations

3
Interpretable and explainable predictive machine learning models for data-driven protein engineering DOI Creative Commons
David Medina-Ortiz, Ashkan Khalifeh, Hoda Anvari-Kazemabad

et al.

Biotechnology Advances, Journal Year: 2024, Volume and Issue: 79, P. 108495 - 108495

Published: Dec. 5, 2024

Protein engineering through directed evolution and (semi)rational design has become a powerful approach for optimizing enhancing proteins with desired properties. The integration of artificial intelligence methods further accelerated protein process by enabling the development predictive models based on data-driven strategies. However, lack interpretability transparency in these limits their trustworthiness applicability real-world scenarios. Explainable Artificial Intelligence addresses challenges providing insights into decision-making processes machine learning models, reliability interpretability. strategies been successfully applied various biotechnology fields, including drug discovery, genomics, medicine, yet its application remains underexplored. incorporation explainable holds significant potential, as it can guide revealing how function, benefiting approaches such learning-assisted evolution. This perspective work explores principles methodologies intelligence, highlighting relevance potential to enhance design. Additionally, three theoretical pipelines integrating are proposed, focusing advantages, disadvantages, technical requirements. Finally, remaining future directions support tool traditional discussed.

Language: Английский

Citations

3