Pub Date : 2026-01-23DOI: 10.1016/j.biotechadv.2026.108811
Rodrigo Andler , Daisuke Kasai
Rubber waste is one of the most persistent solid wastes of our times, mostly represented by end-of-life tires. While the biological origin of natural rubber makes it biodegradable, many tire components are not, and they make enzymatic attack by microorganisms extremely difficult. Despite the great multi-enzymatic catabolic capacity of various bacteria and fungi, there are currently no organisms or enzymes capable of effectively degrading vulcanized tire waste. However, biotechnological advances in enzymatic rubber degradation processes are opening new opportunities. The diversity of rubber oxygenases, the transcriptional regulation of their corresponding genes, and the downstream oxidation of oligo-isoprene aldehydes are also discussed in this review. This biotransformation is positioned as a potential enzymatic upcycling of rubber wastes. Although there have been significant advances at the molecular and bioprocess levels, there are several obstacles that must be solved to propose an efficient and scalable process.
{"title":"Advances and challenges in enzymatic rubber degradation: Exploring genetic, molecular, and biotechnological aspects","authors":"Rodrigo Andler , Daisuke Kasai","doi":"10.1016/j.biotechadv.2026.108811","DOIUrl":"10.1016/j.biotechadv.2026.108811","url":null,"abstract":"<div><div>Rubber waste is one of the most persistent solid wastes of our times, mostly represented by end-of-life tires. While the biological origin of natural rubber makes it biodegradable, many tire components are not, and they make enzymatic attack by microorganisms extremely difficult. Despite the great multi-enzymatic catabolic capacity of various bacteria and fungi, there are currently no organisms or enzymes capable of effectively degrading vulcanized tire waste. However, biotechnological advances in enzymatic rubber degradation processes are opening new opportunities. The diversity of rubber oxygenases, the transcriptional regulation of their corresponding genes, and the downstream oxidation of oligo-isoprene aldehydes are also discussed in this review. This biotransformation is positioned as a potential enzymatic upcycling of rubber wastes. Although there have been significant advances at the molecular and bioprocess levels, there are several obstacles that must be solved to propose an efficient and scalable process.</div></div>","PeriodicalId":8946,"journal":{"name":"Biotechnology advances","volume":"88 ","pages":"Article 108811"},"PeriodicalIF":12.5,"publicationDate":"2026-01-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146033200","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-01-21DOI: 10.1016/j.biotechadv.2026.108809
Adam A. Aboalroub
Nuclear Magnetic Resonance (NMR) spectroscopy is a crucial tool in structural biology, uniquely capable of revealing protein structure, dynamics, and interactions at atomic resolution in environments that closely resemble native conditions. The combination of key methodological breakthroughs—including strategic isotopic labeling, stronger magnetic fields, cryogenic probes, and advanced pulse sequences—has established NMR as the definitive method for gaining atomic-level insights into complex biomolecules, especially pathogenic proteins involved in disease. These advances enable various NMR techniques, from high-resolution solution and solid-state NMR (ssNMR) for insoluble assemblies to in-cell NMR. Beyond structural analysis, NMR provides robust quantitative performance, high reproducibility, and rich structural information, making it a valuable platform for biomolecular analysis and metabolomics. This review aims to provide a comprehensive overview of these critical roles, with a particular emphasis on the transformative influence of integrating Artificial Intelligence (AI) into NMR techniques to accelerate metabolomics-based biomarker discovery for various diseases and conditions.
{"title":"Advances in NMR Spectroscopy for biological systems: Principles, techniques, and their growing scope","authors":"Adam A. Aboalroub","doi":"10.1016/j.biotechadv.2026.108809","DOIUrl":"10.1016/j.biotechadv.2026.108809","url":null,"abstract":"<div><div>Nuclear Magnetic Resonance (NMR) spectroscopy is a crucial tool in structural biology, uniquely capable of revealing protein structure, dynamics, and interactions at atomic resolution in environments that closely resemble native conditions. The combination of key methodological breakthroughs—including strategic isotopic labeling, stronger magnetic fields, cryogenic probes, and advanced pulse sequences—has established NMR as the definitive method for gaining atomic-level insights into complex biomolecules, especially pathogenic proteins involved in disease. These advances enable various NMR techniques, from high-resolution solution and solid-state NMR (ssNMR) for insoluble assemblies to in-cell NMR. Beyond structural analysis, NMR provides robust quantitative performance, high reproducibility, and rich structural information, making it a valuable platform for biomolecular analysis and metabolomics. This review aims to provide a comprehensive overview of these critical roles, with a particular emphasis on the transformative influence of integrating Artificial Intelligence (AI) into NMR techniques to accelerate metabolomics-based biomarker discovery for various diseases and conditions.</div></div>","PeriodicalId":8946,"journal":{"name":"Biotechnology advances","volume":"88 ","pages":"Article 108809"},"PeriodicalIF":12.5,"publicationDate":"2026-01-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146033202","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-01-19DOI: 10.1016/j.biotechadv.2026.108807
Aoyun Geng , Chunyan Cui , Zhenjie Luo , Junlin Xu , Yajie Meng , Feifei Cui , Leyi Wei , Quan Zou , Zilong Zhang
The rapid development of spatial multi-omics technologies has enabled the simultaneous acquisition of transcriptomic, proteomic, and epigenomic information from the same tissue section. However, substantial differences in distributional properties, data dimensionality, and noise levels across modalities, together with the inherent sparsity and incompleteness of spatial information, pose major challenges for data integration and modeling. In recent years, deep learning–based spatial multi-omics integration algorithms have emerged rapidly, offering new approaches for constructing unified latent representations and achieving cross-modal fusion. In this review, we systematically summarize existing spatial multi-omics integration methods for the first time, categorizing and comparing them from two perspectives. We not only systematically surveyed the datasets employed by these methods, but also highlighted the key downstream analytical tasks they support, and further summarized the major challenges currently faced in spatial multi-omics integration research. Furthermore, we compare the strengths and limitations of different approaches to assist researchers in selecting appropriate methods more efficiently, thereby advancing the application of spatial multi-omics in uncovering multilayer regulatory mechanisms of tissue microenvironments and disease processes.
{"title":"Computational methods for spatial multi-omics integration","authors":"Aoyun Geng , Chunyan Cui , Zhenjie Luo , Junlin Xu , Yajie Meng , Feifei Cui , Leyi Wei , Quan Zou , Zilong Zhang","doi":"10.1016/j.biotechadv.2026.108807","DOIUrl":"10.1016/j.biotechadv.2026.108807","url":null,"abstract":"<div><div>The rapid development of spatial multi-omics technologies has enabled the simultaneous acquisition of transcriptomic, proteomic, and epigenomic information from the same tissue section. However, substantial differences in distributional properties, data dimensionality, and noise levels across modalities, together with the inherent sparsity and incompleteness of spatial information, pose major challenges for data integration and modeling. In recent years, deep learning–based spatial multi-omics integration algorithms have emerged rapidly, offering new approaches for constructing unified latent representations and achieving cross-modal fusion. In this review, we systematically summarize existing spatial multi-omics integration methods for the first time, categorizing and comparing them from two perspectives. We not only systematically surveyed the datasets employed by these methods, but also highlighted the key downstream analytical tasks they support, and further summarized the major challenges currently faced in spatial multi-omics integration research. Furthermore, we compare the strengths and limitations of different approaches to assist researchers in selecting appropriate methods more efficiently, thereby advancing the application of spatial multi-omics in uncovering multilayer regulatory mechanisms of tissue microenvironments and disease processes.</div></div>","PeriodicalId":8946,"journal":{"name":"Biotechnology advances","volume":"87 ","pages":"Article 108807"},"PeriodicalIF":12.5,"publicationDate":"2026-01-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146000872","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-01-19DOI: 10.1016/j.biotechadv.2026.108806
Yi Shi , Lefei Wang , Yao Chen , Ling Jiang
Covalent bond–forming peptide tagging systems have emerged as powerful and versatile tools across a broad spectrum of biological and biotechnological applications. This review systematically summarizes the origins, molecular mechanisms of intramolecular covalent bond formation, major classes, and design strategies of peptide tagging systems. Based on their underlying chemistry, current systems are primarily categorized into isopeptide-bond-based and ester-bond-based platforms, both of which have demonstrated prominent utility in protein cyclization as well as in vivo and in vitro multi-enzyme assembly. Beyond these applications, isopeptide-bond-forming systems have been widely adopted as robust purification tags, whereas ester-bond-based systems offer unique opportunities for pH-responsive modulation of enzyme activity. Collectively, peptide tagging systems based on either isopeptide or ester bond formation represent an expanding and highly efficient toolkit for biotechnology. Continued advances in their design and application are expected to further broaden their functional scope and provide innovative solutions for future developments in protein engineering and related fields.
{"title":"Recent advances in the covalent-bond-based peptide tagging systems and their applications","authors":"Yi Shi , Lefei Wang , Yao Chen , Ling Jiang","doi":"10.1016/j.biotechadv.2026.108806","DOIUrl":"10.1016/j.biotechadv.2026.108806","url":null,"abstract":"<div><div>Covalent bond–forming peptide tagging systems have emerged as powerful and versatile tools across a broad spectrum of biological and biotechnological applications. This review systematically summarizes the origins, molecular mechanisms of intramolecular covalent bond formation, major classes, and design strategies of peptide tagging systems. Based on their underlying chemistry, current systems are primarily categorized into isopeptide-bond-based and ester-bond-based platforms, both of which have demonstrated prominent utility in protein cyclization as well as <em>in vivo</em> and <em>in vitro</em> multi-enzyme assembly. Beyond these applications, isopeptide-bond-forming systems have been widely adopted as robust purification tags, whereas ester-bond-based systems offer unique opportunities for pH-responsive modulation of enzyme activity. Collectively, peptide tagging systems based on either isopeptide or ester bond formation represent an expanding and highly efficient toolkit for biotechnology. Continued advances in their design and application are expected to further broaden their functional scope and provide innovative solutions for future developments in protein engineering and related fields.</div></div>","PeriodicalId":8946,"journal":{"name":"Biotechnology advances","volume":"88 ","pages":"Article 108806"},"PeriodicalIF":12.5,"publicationDate":"2026-01-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146000541","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-01-19DOI: 10.1016/j.biotechadv.2026.108808
Ran You , Xueqin Lv , Yan Zhang , Jian Chen , Long Liu
Glucose-6-phosphate (G-6-P) and fructose-6-phosphate (F-6-P), which are located upstream of glycolysis, are crucial node compounds that provide carbon skeletons and supply energy for cell growth. De novo microbial synthesis of functional carbohydrates involves the derivatization of G-6-P and F-6-P. It is often associated with negative growth effects, creating challenges for efficient production. In this review, the main derivatization reactions with G-6-P and F-6-P as precursors were divided into three categories: the IAD (Isomerization And Dephosphorylation) module, the FGF (F-6-P to GDP-Fucose) module, and the FAA (F-6-P transAcetylation and transAmination) module. The representative functional carbohydrates of these pathways were briefly introduced, and pathway reconstruction and optimization for these carbohydrates were summarized. In addition, advances in central carbon metabolism regulation for G-6-P and F-6-P redirection were classified and summarized. Finally, the synthesis of functional carbohydrates by microbial redirection of G-6-P and F-6-P was investigated. This review facilitates the understanding of strategies and core principles involved in glycolytic node G-6-P and F-6-P redirection and the de novo biosynthesis of functional carbohydrate derivatives. It has significant implications for constructing efficient microbial cell factories that redirect G-6-P and F-6-P to derivatives and enable their industrial production.
{"title":"Biosynthesis of functional carbohydrates from glycolytic node precursors glucose-6-phosphate and fructose-6-phosphate: Advances and prospects","authors":"Ran You , Xueqin Lv , Yan Zhang , Jian Chen , Long Liu","doi":"10.1016/j.biotechadv.2026.108808","DOIUrl":"10.1016/j.biotechadv.2026.108808","url":null,"abstract":"<div><div>Glucose-6-phosphate (G-6-P) and fructose-6-phosphate (F-6-P), which are located upstream of glycolysis, are crucial node compounds that provide carbon skeletons and supply energy for cell growth. <em>De novo</em> microbial synthesis of functional carbohydrates involves the derivatization of G-6-P and F-6-P. It is often associated with negative growth effects, creating challenges for efficient production. In this review, the main derivatization reactions with G-6-P and F-6-P as precursors were divided into three categories: the IAD (<strong>I</strong>somerization <strong>A</strong>nd <strong>D</strong>ephosphorylation) module, the FGF (<strong>F</strong>-6-P to <strong>G</strong>DP-<strong>F</strong>ucose) module, and the FAA (<strong>F</strong>-6-P trans<strong>A</strong>cetylation and trans<strong>A</strong>mination) module. The representative functional carbohydrates of these pathways were briefly introduced, and pathway reconstruction and optimization for these carbohydrates were summarized. In addition, advances in central carbon metabolism regulation for G-6-P and F-6-P redirection were classified and summarized. Finally, the synthesis of functional carbohydrates by microbial redirection of G-6-P and F-6-P was investigated. This review facilitates the understanding of strategies and core principles involved in glycolytic node G-6-P and F-6-P redirection and the <em>de novo</em> biosynthesis of functional carbohydrate derivatives. It has significant implications for constructing efficient microbial cell factories that redirect G-6-P and F-6-P to derivatives and enable their industrial production.</div></div>","PeriodicalId":8946,"journal":{"name":"Biotechnology advances","volume":"87 ","pages":"Article 108808"},"PeriodicalIF":12.5,"publicationDate":"2026-01-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146001550","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-01-18DOI: 10.1016/j.biotechadv.2026.108805
Jiahao Wang , Guangjie Liang , Zixuan Wang , Cong Gao , Guipeng Hu , Liming Liu , Jing Wu
Methanol is a highly promising feedstock for biomanufacturing owing to its broad availability, low cost, and high energy density. Methylotrophic fermentations have been exploited to produce diverse fuels, chemicals, and materials. However, although such processes have been practiced for decades, their applications have been constrained by low methanol assimilation efficiency, insufficient cellular energy and reducing equivalents supply, the cytotoxicity of methanol and its intermediates, and inadequate robustness of chassis strains. In this review, progress is synthesized along four pillars for constructing high-performance methanol bio-converting cell factories: methanol assimilation pathways, energy-supply strategies, tolerance-enhancement approaches, and metabolic engineering for chemical synthesis, with the aim of informing the rational design and construction of efficient methanol bio-converting cell factories.
{"title":"Construction and applications of methanol bio-converting cell factories","authors":"Jiahao Wang , Guangjie Liang , Zixuan Wang , Cong Gao , Guipeng Hu , Liming Liu , Jing Wu","doi":"10.1016/j.biotechadv.2026.108805","DOIUrl":"10.1016/j.biotechadv.2026.108805","url":null,"abstract":"<div><div>Methanol is a highly promising feedstock for biomanufacturing owing to its broad availability, low cost, and high energy density. Methylotrophic fermentations have been exploited to produce diverse fuels, chemicals, and materials. However, although such processes have been practiced for decades, their applications have been constrained by low methanol assimilation efficiency, insufficient cellular energy and reducing equivalents supply, the cytotoxicity of methanol and its intermediates, and inadequate robustness of chassis strains. In this review, progress is synthesized along four pillars for constructing high-performance methanol bio-converting cell factories: methanol assimilation pathways, energy-supply strategies, tolerance-enhancement approaches, and metabolic engineering for chemical synthesis, with the aim of informing the rational design and construction of efficient methanol bio-converting cell factories.</div></div>","PeriodicalId":8946,"journal":{"name":"Biotechnology advances","volume":"87 ","pages":"Article 108805"},"PeriodicalIF":12.5,"publicationDate":"2026-01-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145995160","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-01-18DOI: 10.1016/j.biotechadv.2026.108802
Yan Xia , Yi-Xin Huo
Cis-regulatory elements (CREs) play a crucial role in regulating gene expression by controlling transcription, making the understanding and design of these elements essential for the advancement of biology. Traditional approaches often rely on empirical rules and iterative experimentation, which can be time-consuming and labor-intensive. Recent advances in deep learning have begun to influence this field by improving the accuracy of predictions for existing elements and offering preliminary strategies for designing synthetic CREs. Specialized design models can incorporate high-throughput experimental data, and DNA foundation models draw on pre-trained genomic representations to inform the design process. These approaches have shown encouraging progress in generating promoters, enhancers and more complex regulatory architectures. Nonetheless, substantial challenges remain, including limited data availability, gaps between computational predictions and experimental outcomes, and limited model interpretability. Moreover, although AI-driven methods hold considerable promise for CRE prediction and design, their generative capabilities are still constrained by data quality and by the tendency of current models to rely predominantly on sequence-level features without fully capturing broader regulatory context. In this review, we examine how emerging AI technologies may support more systematic and targeted design of synthetic CREs, and we discuss key challenges and future directions, including multimodal modeling, reinforcement learning (RL), and system-level regulatory network design.
{"title":"Controlling gene expression using AI designed Cis-regulatory elements","authors":"Yan Xia , Yi-Xin Huo","doi":"10.1016/j.biotechadv.2026.108802","DOIUrl":"10.1016/j.biotechadv.2026.108802","url":null,"abstract":"<div><div><em>Cis</em>-regulatory elements (CREs) play a crucial role in regulating gene expression by controlling transcription, making the understanding and design of these elements essential for the advancement of biology. Traditional approaches often rely on empirical rules and iterative experimentation, which can be time-consuming and labor-intensive. Recent advances in deep learning have begun to influence this field by improving the accuracy of predictions for existing elements and offering preliminary strategies for designing synthetic CREs. Specialized design models can incorporate high-throughput experimental data, and DNA foundation models draw on pre-trained genomic representations to inform the design process. These approaches have shown encouraging progress in generating promoters, enhancers and more complex regulatory architectures. Nonetheless, substantial challenges remain, including limited data availability, gaps between computational predictions and experimental outcomes, and limited model interpretability. Moreover, although AI-driven methods hold considerable promise for CRE prediction and design, their generative capabilities are still constrained by data quality and by the tendency of current models to rely predominantly on sequence-level features without fully capturing broader regulatory context. In this review, we examine how emerging AI technologies may support more systematic and targeted design of synthetic CREs, and we discuss key challenges and future directions, including multimodal modeling, reinforcement learning (RL), and system-level regulatory network design.</div></div>","PeriodicalId":8946,"journal":{"name":"Biotechnology advances","volume":"87 ","pages":"Article 108802"},"PeriodicalIF":12.5,"publicationDate":"2026-01-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145995163","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-01-14DOI: 10.1016/j.biotechadv.2026.108803
Qiang Liu , Tiantian Wang , Binbin Nian , Feiyang Ma , Siqi Zhao , Andrés F. Vásquez , Liping Guo , Chao Ding , Mehdi D. Davari
Polyphenols and proteins are essential biomolecules that influence food functionality and, by extension, human health. Their interactions—hereafter referred to as PhPIs (Polyphenol–Protein Interactions)— affect key processes such as nutrient bioavailability, antioxidant activity, and therapeutic efficacy. However, these interactions remain challenging due to the structural diversity of polyphenols and the dynamic nature of protein binding. Traditional experimental techniques like nuclear magnetic resonance (NMR) and mass spectrometry (MS), along with computational tools such as molecular docking and molecular dynamics (MD) have offered important insights but face constraints in scalability, throughput, and reproducibility. This review explores how deep learning (DL) is reshaping the study of PhPIs by enabling efficient prediction of binding sites, interaction affinities, and MD using high-dimensional bio- and cheminformatics data. While DL enhances prediction accuracy and reduces experimental redundancy, its effectiveness remains limited by data availability, quality, and representativeness—particularly in the context of natural products. We critically assess current DL frameworks for PhPIs analysis and outline future directions, including multimodal data integration, improved model generalizability, and development of domain-specific benchmark datasets. This synthesis offers guidance for researchers aiming to apply DL in unraveling structure–function relationships of polyphenols, accelerating discovery in nutritional science and therapeutic development.
{"title":"Decoding polyphenol–protein interactions with deep learning: From molecular mechanisms to food applications","authors":"Qiang Liu , Tiantian Wang , Binbin Nian , Feiyang Ma , Siqi Zhao , Andrés F. Vásquez , Liping Guo , Chao Ding , Mehdi D. Davari","doi":"10.1016/j.biotechadv.2026.108803","DOIUrl":"10.1016/j.biotechadv.2026.108803","url":null,"abstract":"<div><div>Polyphenols and proteins are essential biomolecules that influence food functionality and, by extension, human health. Their interactions—hereafter referred to as <em>PhPIs</em> (Polyphenol–Protein Interactions)— affect key processes such as nutrient bioavailability, antioxidant activity, and therapeutic efficacy. However, these interactions remain challenging due to the structural diversity of polyphenols and the dynamic nature of protein binding. Traditional experimental techniques like nuclear magnetic resonance (NMR) and mass spectrometry (MS), along with computational tools such as molecular docking and molecular dynamics (MD) have offered important insights but face constraints in scalability, throughput, and reproducibility. This review explores how deep learning (DL) is reshaping the study of PhPIs by enabling efficient prediction of binding sites, interaction affinities, and MD using high-dimensional bio- and cheminformatics data. While DL enhances prediction accuracy and reduces experimental redundancy, its effectiveness remains limited by data availability, quality, and representativeness—particularly in the context of natural products. We critically assess current DL frameworks for PhPIs analysis and outline future directions, including multimodal data integration, improved model generalizability, and development of domain-specific benchmark datasets. This synthesis offers guidance for researchers aiming to apply DL in unraveling structure–function relationships of polyphenols, accelerating discovery in nutritional science and therapeutic development.</div></div>","PeriodicalId":8946,"journal":{"name":"Biotechnology advances","volume":"87 ","pages":"Article 108803"},"PeriodicalIF":12.5,"publicationDate":"2026-01-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145987994","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-01-14DOI: 10.1016/j.biotechadv.2026.108804
Bianca Costa Bernardo Port , Paula Rogovski , Gislaine Fongaro , Thiago Caon
Bacteriophage(phage)-based interventions have been considered for environmental and biomedical applications as well as during food processing, representing a promising alternative when multidrug-resistant bacteria are found. Although liquid and semi-solid formulations are easier to prepare as few unit operations are required, stability issues or short-term effects have led to the prioritization of solid formulations. Polymeric films have gained prominence as a strict control of phage release or improved phage stability can be achieved. During film preparation, phages are deposited onto a pre-ready solid support or incorporated in a film-forming solution. Advantages and disadvantages of each preparation method as well as the impact of different processing conditions (temperature, pH, ionic strength and agitation) on phage viability/stability are discussed in detail in this review. High viral titer broadens the spectrum of materials and film preparation methods that can be considered. The orientation of some phages during immobilization into solid supports, in turn, has proven to be a key aspect for phage infectivity, particularly for tailed phages. The points raised in this review are certainly an important direction for future technological developments in this field, contributing to the development of films with longer-lasting action.
{"title":"Immobilization/incorporation methods of bacteriophages into polymeric films: Technological challenges & perspectives","authors":"Bianca Costa Bernardo Port , Paula Rogovski , Gislaine Fongaro , Thiago Caon","doi":"10.1016/j.biotechadv.2026.108804","DOIUrl":"10.1016/j.biotechadv.2026.108804","url":null,"abstract":"<div><div>Bacteriophage(phage)-based interventions have been considered for environmental and biomedical applications as well as during food processing, representing a promising alternative when multidrug-resistant bacteria are found. Although liquid and semi-solid formulations are easier to prepare as few unit operations are required, stability issues or short-term effects have led to the prioritization of solid formulations. Polymeric films have gained prominence as a strict control of phage release or improved phage stability can be achieved. During film preparation, phages are deposited onto a pre-ready solid support or incorporated in a film-forming solution. Advantages and disadvantages of each preparation method as well as the impact of different processing conditions (temperature, pH, ionic strength and agitation) on phage viability/stability are discussed in detail in this review. High viral titer broadens the spectrum of materials and film preparation methods that can be considered. The orientation of some phages during immobilization into solid supports, in turn, has proven to be a key aspect for phage infectivity, particularly for tailed phages. The points raised in this review are certainly an important direction for future technological developments in this field, contributing to the development of films with longer-lasting action.</div></div>","PeriodicalId":8946,"journal":{"name":"Biotechnology advances","volume":"87 ","pages":"Article 108804"},"PeriodicalIF":12.5,"publicationDate":"2026-01-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145987937","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-01-13DOI: 10.1016/j.biotechadv.2026.108801
Andrea Luaces , Andrea Rodríguez-Sanz , Concepción Pérez-Lamela , Clara Fuciños , Ana Torrado , Maria Luisa Rúa
The growing interest in functional foods and prebiotics has encouraged the search for novel bioactive ingredients. Macroalgae (seaweeds) represent an underexplored source of unique xylans with β-1,3 and β-1,3/1,4 glycosidic linkages, that can be enzymatically hydrolysed into novel xylooligosaccharides (XOS). These oligosaccharides, characterized by linkage patterns different from those in terrestrial biomass, may exert distinct biological effects with potential biotechnological applications in food, nutraceuticals, and health. However, current knowledge regarding these xylans and their hydrolytic enzymes remains limited. This review therefore provides a comprehensive and up-to-date overview of macroalgal species containing β-1,3-xylan and β-1,3/1,4-mixed-linkage xylan (MLX), together with the extraction methodologies currently employed to obtain these xylans as substrates for enzymatic studies. It also highlights the specific enzymes involved in their hydrolysis —endo-1,3-β-xylanases and β-1,3/1,4-mixed-linkage xylanases (MLXases)— and discuss their biochemical, kinetic and structural features as well as the current insights into substrate specificity and catalytic mechanisms. Finally, the review examines current advances in the enzymatic production of XOS from macroalgal xylans and summarizes the bioactive properties of β-1,3-XOS and β-1,3/1,4-mixed-linkage XOS (MLXOS) reported to date.
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