Unraveling the influence of CRISPR/Cas13a reaction components on enhancing trans-cleavage activity for ultrasensitive on-chip RNA detection.

The CRISPR/Cas13 nucleases have been widely documented for nucleic acid detection. Understanding the intricacies of CRISPR/Cas13's reaction components is pivotal for harnessing its full potential for biosensing applications. Herein, we report on the influence of CRISPR/Cas13a reaction components on its trans-cleavage activity and the development of an on-chip total internal reflection fluorescence microscopy (TIRFM)-powered RNA sensing system. We used SARS-CoV-2 synthetic RNA and pseudovirus as a model system. Our results show that optimizing Mg2+ concentration, reporter length, and crRNA combination significantly improves the detection sensitivity. Under optimized conditions, we detected 100 fM unamplified SARS-CoV-2 synthetic RNA using a microtiter plate reader. To further improve sensitivity and provide a new amplification-free RNA sensing toolbox, we developed a TIRFM-based amplification-free RNA sensing system. We were able to detect RNA down to 100 aM. Furthermore, the TIRM-based detection system developed in this study is 1000-fold more sensitive than the off-coverslip assay. The possible clinical applicability of the system was demonstrated by detecting SARS-CoV-2 pseudovirus RNA. Our proposed sensing system has the potential to detect any target RNA with slight modifications to the existing setup, providing a universal RNA detection platform.

CRISPR-Cas12-based field-deployable system for rapid detection of synthetic DNA sequence of the monkeypox virus genome.

The global outbreak of the monkeypox virus (MPXV) highlights the need for rapid and cost-effective MPXV detection tools to effectively monitor and control the monkeypox disease. Herein, we demonstrated a portable CRISPR-Cas-based system for naked-eye detection of MPXV. The system harnesses the high selectivity of CRISPR-Cas12 and the isothermal nucleic acid amplification potential of recombinase polymerase amplification. It can detect both the current circulating MPXV clade and the original clades. We reached a limit of detection (LoD) of 22.4 aM (13.5 copies/µl) using a microtiter plate reader, while the visual LoD of the system is 75 aM (45 copies/µl) in a two-step assay, which is further reduced to 25 aM (15 copies/µl) in a one-pot system. We compared our results with quantitative polymerase chain reaction and obtained satisfactory consistency. For clinical application, we demonstrated a sensitive and precise visual detection method with attomolar sensitivity and a sample-to-answer time of 35 min.

Sparse deconvolution for background noise suppression with total variation regularization in light field microscopy.

In this Letter, we present a method aiming at background noise removal in the 3D reconstruction of light field microscopy (LFM). Sparsity and Hessian regularization are taken as two prior knowledges to process the original light field image before 3D deconvolution. Due to the noise suppression function of total variation (TV) regularization, we add the TV regularization term to the 3D Richardson-Lucy (RL) deconvolution. By comparing the light field reconstruction results of our method with another state-of-the-art method that is also based on RL deconvolution, the proposed method shows improved performance in terms of removing background noise and detail enhancement. This method will be beneficial to the application of LFM in biological high-quality imaging.

Overexpression and kinetic analysis of Ideonella sakaiensis PETase for polyethylene terephthalate (PET) degradation.

Ideonella sakaiensis PET hydrolase (IsPETase) is a well-characterized enzyme for effective PET biodegradation. However, the low soluble expression level of the enzyme hampers its practical implementation in the biodegradation of PET. Herein, the expression of IsPETaseMut, one of the most active mutants of IsPETase obtained so far, was systematically explored in E. coli by adopting a series of strategies. A notable improvement of soluble IsPETaseMut was observed by using chaperon co-expression and fusion expression systems. Under the optimized conditions, GroEL/ES co-expression system yielded 75 ± 3.4 mg·L-1 purified soluble IsPETaseMut (GroEL/ES), and NusA fusion expression system yielded 80 ± 3.7 mg·L-1 purified soluble NusA-IsPETaseMut, which are 12.5- and 4.6-fold, respectively, higher than its commonly expression in E. coli. The two purified enzymes were further characterized. The results showed that IsPETaseMut (GroEL/ES) displayed the same catalytic behavior as IsPETaseMut, while the fusion of NusA conferred new enzymatic properties to IsPETaseMut. Although NusA-IsPETaseMut displayed a lower initial hydrolysis capacity than IsPETaseMut, it showed a 1.4-fold higher adsorption constant toward PET. Moreover, the product inhibition effect of terephthalic acid (TPA) on IsPETase was reduced with NusA-IsPETaseMut. Taken together, the latter two catalytic properties of NusA-IsPETaseMut are more likely to contribute to the enhanced product release by NusA-IsPETaseMut PET degradation for two weeks.

Enzyme immobilization on glass fiber membrane for detection of halogenated compounds.

Enzyme immobilization using inorganic membranes has enticed increased attention as they not only improve enzyme stability, but also furnish user-friendly biodevices that can be tailored to different applications. Herein, we explored the suitability of the glass fiber membrane for enzyme immobilization and its application for halocarbon detection. For this, halohydrin dehalogenase (HheC) and bovine serum albumin were crosslinked and immobilized on a glass fiber membrane without membrane functionalization. Immobilized HheC exhibited higher storage stability than its free counterpart over 60 days at 4 °C (67% immobilized vs. 8.1% free) and 30 °C (77% immobilized vs. 57% free). Similarly, the thermal endurance of the immobilized HheC was significantly improved. The practical utility of the membrane-immobilized enzyme was demonstrated by colorimetric detection of 1,3-dichloro-2-propanol (1,3-DCP) and 2,3-dibromo-1-propanol (2,3-DBP) as model analytes. Under optimized conditions, the detection limits of 0.06 mM and 0.09 mM were achieved for 1,3-DCP and 2,3-DBP, respectively. The satisfactory recoveries were observed with spiked river and lake water samples, which demonstrate the application potential of immobilized HheC for screening contaminants in water samples. Our results revealed that the proposed frugal and facile approach could be useful for enzyme stabilization, and mitigation of halocarbon pollution.

A paper-based whole-cell screening assay for directed evolution-driven enzyme engineering.

Directed evolution has become an important method to unleash the latent potential of enzymes to make them uniquely suited for human purposes. However, the need for a large reagent volume and sophisticated instrumentation hampers its broad implementation. In an attempt to address this problem, here we report a paper-based high-throughput screening approach that should find broad application in generating desired enzymes. As an example case, the dehalogenation reaction of the halohydrin dehalogenase was adopted for assay development. In addition to visual detection, quantitative measurements were performed by measuring the color intensity of an image that was photographed by a smartphone and processed using ImageJ free software. The proposed method was first validated using a gold standard method and then applied to mutagenesis library screening with reduced consumption of reagents (i.e., ≤ 10 µl per assay) and a shorter assay time. We identified two active mutants (P135A and G137A) with improved activities toward four tested substrates. The assay not only consumes less reagents but also eliminates the need for expensive instrumentation. The proposed method demonstrates the potential of paper-based whole-cell screening coupled with digital image colorimetry as a promising approach for the discovery of industrially important enzymes.Key Points• A frugal method was developed for directed enzyme evolution.• Mutagenesis libraries were successfully screened on a paper platform.• Smartphone imaging was efficiently used to measure enzyme activities.

Enzyme-based detection of epoxides using colorimetric assay integrated with smartphone imaging.

Epoxides are widely used chemicals, the determination of which is of paramount importance. Herein, we present an enzyme-based approach for noninstrumental detection of epoxides in standard solution and environmental samples. Halohydrin dehalogenase (HheC) as a biological recognition element and epichlorohydrin as a model analyte were evaluated for sensing. The detection is based on the color change of the pH indicator dye bromothymol blue caused by the HheC-catalyzed ring-opening of the epoxide substrate. The color change is then exploited for the determination of epoxide using a smartphone as an image acquisition and data processing device, eliminating the need for computer-based image analysis software. The color parameters were systematically evaluated to determine the optimum quantitative analytical parameter. Under optimal conditions, the proposed enzyme-based detection system showed a linear range of 0.13-2 mM with a detection limit of 0.07 mM and an assay time of 8 Min. In addition, the repeatability expressed as relative standard deviation was found to be below 5% (n = 6). Validation with gas chromatographic analyses showed that the proposed enzyme-based epoxide detection could be an alternative way in the quantitative determination of epoxides, and particularly useful in resource-limited settings.

Mitochondrial UCP1: Potential thermogenic mechanistic switch for the treatment of obesity and neurodegenerative diseases using natural and epigenetic drug candidates.

BACKGROUND: Brown fat is known to provide non-shivering thermogenesis through mitochondrial uncoupling mediated by uncoupling protein 1 (UCP1). Non-shivering is not dependent on UCP2, UCP4, and BMCP1/UCP5 genes, which are distinct from UCP1 in a way that they are not constitutive uncouplers. Although they are susceptible to free fatty acid and free radical activation, their functioning has a significant impact on the performance of neurons. METHODOLOGY: Using subject-specific keywords (Adipose tissue; Adipocytes; Mitochondria; Obesity; Thermogenesis; UCP's in Neurodegeneration; Alzheimer's disease; Parkinson's disease), research articles and reviews were retrieved from Web of Science, ScienceDirect, Google Scholar, and PubMed. This article includespublications published between 2018 and 2023. The drugs that upregulate UCP1 are included in the study while the drugs that do not impact UCP1 are were not included. RESULTS: Neuronal UCPs have a direct impact on synaptic plasticity, neurodegenerative processes, and neurotransmission, by modulating calcium flux, mitochondrial biogenesis, local temperature, and free radical generation. Numerous significant advances in the study of neuronal UCPs and neuroprotection are still to be made. Identification of the tissue-dependent effects of UCPs is essential first. Pharmacologically targeting neuronal UCPs is a key strategy for preventing both neurodegenerative diseases and physiological aging. Given that UCP2 has activities that are tissue-specific, it will be essential to develop treatments without harmful side effects. The triggering of UCPs by CoQ, an essential cofactor, produces nigral mitochondrial uncoupling, reduces MPTP-induced toxicity, and may even decrease the course of Parkinson's disease, according to early indications. CONCLUSION: Herein, we explore the potential of UCP1 as a therapeutic target for treating obesity, neurodegenerative diseases as well as a potential activator of both synthetic and natural drugs. A deeper knowledge of synaptic signaling and neurodegeneration may pave the way to new discoveries regarding the functioning and controlling of these genes.

Exploring enantioselective recognition of dTMP-Co-bpe coordination polymer for natural amino acids using molecular simulations and circular dichroism.

The 1D homochiral coordination polymer (CP-1) {[Co(dTMP)(bpe)2(H2O)3]·9H2O}n was constructed by using 2'-deoxy thymidine 5'-monophosphate disodium salt (dTMP·2Na), and auxiliary ligand bpe (1,2-bis(4-pyridyl)ethene) and characterized by single-crystal XRD, PXRD, IR, UV-visible, CD and TGA analyses. Molecular simulations revealed the selective chiral behaviour of CP-1 towards phenylalanine and histidine, as indicated by their higher binding free energies compared to other amino acids. Theoretical parameters were also compared with experimental UV-visible verdicts. Notably, the D-enantiomers of phenylalanine and histidine demonstrated strong bonding abilities and optimal configurations for probing and distinguishing them from their L-counterparts. These findings led to propositions suggesting that the dissimilarities between these D and L amino acid forms and their binding orientations with CP-1 may contribute to alterations in the CD signal. CP-1 exhibited a robust inherent circular dichroism (CD) signal in aqueous solutions, modulated by the presence of specific amino acids, namely D/L phenylalanine and D/L histidine. Leveraging the measurement of CD signal intensity, a sensor capable of detecting unmodified amino acids has been developed. Unlike previously reported approaches that relied on complex chemical reactions between initially CD-silent molecules and probed amino acids, this new method offers a more straightforward means of amplifying the CD signal. Consequently, this change facilitates a more accurate differentiation between the enantiomers of these specific amino acids compared to others.

Practical guidelines for cell segmentation models under optical aberrations in microscopy.

Cell segmentation is essential in biomedical research for analyzing cellular morphology and behavior. Deep learning methods, particularly convolutional neural networks (CNNs), have revolutionized cell segmentation by extracting intricate features from images. However, the robustness of these methods under microscope optical aberrations remains a critical challenge. This study evaluates cell image segmentation models under optical aberrations from fluorescence and bright field microscopy. By simulating different types of aberrations, including astigmatism, coma, spherical aberration, trefoil, and mixed aberrations, we conduct a thorough evaluation of various cell instance segmentation models using the DynamicNuclearNet (DNN) and LIVECell datasets, representing fluorescence and bright field microscopy cell datasets, respectively. We train and test several segmentation models, including the Otsu threshold method and Mask R-CNN with different network heads (FPN, C3) and backbones (ResNet, VGG, Swin Transformer), under aberrated conditions. Additionally, we provide usage recommendations for the Cellpose 2.0 Toolbox on complex cell degradation images. The results indicate that the combination of FPN and SwinS demonstrates superior robustness in handling simple cell images affected by minor aberrations. In contrast, Cellpose 2.0 proves effective for complex cell images under similar conditions. Furthermore, we innovatively propose the Point Spread Function Image Label Classification Model (PLCM). This model can quickly and accurately identify aberration types and amplitudes from PSF images, assisting researchers without optical training. Through PLCM, researchers can better apply our proposed cell segmentation guidelines. This study aims to provide guidance for the effective utilization of cell segmentation models in the presence of minor optical aberrations and pave the way for future research directions.

COVID-19: Post infection implications in different age groups, mechanism, diagnosis, effective prevention, treatment, and recommendations.

SARS-CoV-2 is a highly contagious pathogen that predominantly caused the COVID-19 pandemic. The persistent effects of COVID-19 are defined as an inflammatory or host response to the virus that begins four weeks after initial infection and persists for an undetermined length of time. Chronic effects are more harmful than acute ones thus, this review explored the long-term effects of the virus on various human organs, including the pulmonary, cardiovascular, and neurological, reproductive, gastrointestinal, musculoskeletal, endocrine, and lymphoid systems and found that SARS-CoV-2 adversely affects these organs of older adults. Regarding diagnosis, the RT-PCR is a gold standard method of diagnosing COVID-19; however, it requires specialized equipment and personnel for performing assays and a long time for results production. Therefore, to overcome these limitations, artificial intelligence employed in imaging and microfluidics technologies is the most promising in diagnosing COVID-19. Pharmacological and non-pharmacological strategies are the most effective treatment for reducing the persistent impacts of COVID-19 by providing immunity to post-COVID-19 patients by reducing cytokine release syndrome, improving the T cell response, and increasing the circulation of activated natural killer and CD8 T cells in blood and tissues, which ultimately reduces fever, nausea, fatigue, and muscle weakness and pain. Vaccines such as inactivated viral, live attenuated viral, protein subunit, viral vectored, mRNA, DNA, or nanoparticle vaccines significantly reduce the adverse long-term virus effects in post-COVID-19 patients; however, no vaccine was reported to provide lifetime protection against COVID-19; consequently, protective measures such as physical separation, mask use, and hand cleansing are promising strategies. This review provides a comprehensive knowledge of the persistent effects of COVID-19 on people of varying ages, as well as diagnosis, treatment, vaccination, and future preventative measures against the spread of SARS-CoV-2.

Emergence, phylogeography, and adaptive evolution of mpox virus.

Mpox (Monkeypox) is a zoonotic disease caused by mpox virus (MPXV). A multi-country MPXV outbreak in non-endemic demographics was identified in May 2022. A systematic evaluation of MPXV evolutionary trajectory and genetic diversity could be a timely addition to the MPXV diagnostics and prophylaxis. Herein, we integrated a systematic evolution analysis including phylogenomic and phylogeographic, followed by an in-depth analysis of the adaptive evolution and amino acid variations in type I interferon binding protein (IFNα/ßBP). Mutations in IFNα/ßBP protein may impair its binding capacity, affecting the MPXV immune evasion strategy. Based on the equilibrated data, we found an evolutionary rate of 7.75 × 10 - 5 substitutions/site/year, and an earlier original time (2021.25) of the clade IIb. We further discovered significant genetic variations in MPXV genomes from different regions and obtained six plausible spread trajectories from its intricate viral flow network, implying that North America might have acted as a bridge for the spread of MPXV from Africa to other continents. We identified two amino acids under positive selection in the Rifampicin resistance protein and extracellular enveloped virus (EEV) type-I membrane glycoprotein, indicating a role in adaptive evolution. Our research sheds light on the emergence, dispersal, and adaptive evolution of MPXV, providing theoretical support for mitigating and containing its expansion.

Detection of Frog Virus 3 by Integrating RPA-CRISPR/Cas12a-SPM with Deep Learning.

A fast, easy-to-implement, highly sensitive, and point-of-care (POC) detection system for frog virus 3 (FV3) is proposed. Combining recombinase polymerase amplification (RPA) and CRISPR/Cas12a, a limit of detection (LoD) of 100 aM (60.2 copies/µL) is achieved by optimizing RPA primers and CRISPR RNAs (crRNAs). For POC detection, smartphone microscopy is implemented, and an LoD of 10 aM is achieved in 40 min. The proposed system detects four positive animal-derived samples with a quantitation cycle (Cq) value of quantitative PCR (qPCR) in the range of 13 to 32. In addition, deep learning models are deployed for binary classification (positive or negative samples) and multiclass classification (different concentrations of FV3 and negative samples), achieving 100 and 98.75% accuracy, respectively. Without temperature regulation and expensive equipment, the proposed RPA-CRISPR/Cas12a combined with smartphone readouts and artificial-intelligence-assisted classification showcases the great potential for FV3 detection, specifically POC detection of DNA virus.

Machine Learning for Endometrial Cancer Prediction and Prognostication.

Endometrial cancer (EC) is a prevalent uterine cancer that remains a major contributor to cancer-associated morbidity and mortality. EC diagnosed at advanced stages shows a poor therapeutic response. The clinically utilized EC diagnostic approaches are costly, time-consuming, and are not readily available to all patients. The rapid growth in computational biology has enticed substantial research attention from both data scientists and oncologists, leading to the development of rapid and cost-effective computer-aided cancer surveillance systems. Machine learning (ML), a subcategory of artificial intelligence, provides opportunities for drug discovery, early cancer diagnosis, effective treatment, and choice of treatment modalities. The application of ML approaches in EC diagnosis, therapies, and prognosis may be particularly relevant. Considering the significance of customized treatment and the growing trend of using ML approaches in cancer prediction and monitoring, a critical survey of ML utility in EC may provide impetus research in EC and assist oncologists, molecular biologists, biomedical engineers, and bioinformaticians to further collaborative research in EC. In this review, an overview of EC along with risk factors and diagnostic methods is discussed, followed by a comprehensive analysis of the potential ML modalities for prevention, screening, detection, and prognosis of EC patients.

iThermo: A Sequence-Based Model for Identifying Thermophilic Proteins Using a Multi-Feature Fusion Strategy.

Thermophilic proteins have important application value in biotechnology and industrial processes. The correct identification of thermophilic proteins provides important information for the application of these proteins in engineering. The identification method of thermophilic proteins based on biochemistry is laborious, time-consuming, and high cost. Therefore, there is an urgent need for a fast and accurate method to identify thermophilic proteins. Considering this urgency, we constructed a reliable benchmark dataset containing 1,368 thermophilic and 1,443 non-thermophilic proteins. A multi-layer perceptron (MLP) model based on a multi-feature fusion strategy was proposed to discriminate thermophilic proteins from non-thermophilic proteins. On independent data set, the proposed model could achieve an accuracy of 96.26%, which demonstrates that the model has a good application prospect. In order to use the model conveniently, a user-friendly software package called iThermo was established and can be freely accessed at http://lin-group.cn/server/iThermo/index.html. The high accuracy of the model and the practicability of the developed software package indicate that this study can accelerate the discovery and engineering application of thermally stable proteins.

Angiotensin-Converting Enzyme 2-Based Biosensing Modalities and Devices for Coronavirus Detection.

Rapid and cost-effective diagnostic tests for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) are a critical and valuable weapon for the coronavirus disease 2019 (COVID-19) pandemic response. SARS-CoV-2 invasion is primarily mediated by human angiotensin-converting enzyme 2 (hACE2). Recent developments in ACE2-based SARS-CoV-2 detection modalities accentuate the potential of this natural host-virus interaction for developing point-of-care (POC) COVID-19 diagnostic systems. Although research on harnessing ACE2 for SARS-CoV-2 detection is in its infancy, some interesting biosensing devices have been developed, showing the commercial viability of this intriguing new approach. The exquisite performance of the reported ACE2-based COVID-19 biosensors provides opportunities for researchers to develop rapid detection tools suitable for virus detection at points of entry, workplaces, or congregate scenarios in order to effectively implement pandemic control and management plans. However, to be considered as an emerging approach, the rationale for ACE2-based biosensing needs to be critically and comprehensively surveyed and discussed. Herein, we review the recent status of ACE2-based detection methods, the signal transduction principles in ACE2 biosensors and the development trend in the future. We discuss the challenges to development of ACE2-biosensors and delineate prospects for their use, along with recommended solutions and suggestions.

Current and Perspective Sensing Methods for Monkeypox Virus.

The outbreak of the monkeypox virus (MPXV) in non-endemic countries is an emerging global health threat and may have an economic impact if proactive actions are not taken. As shown by the COVID-19 pandemic, rapid, accurate, and cost-effective virus detection techniques play a pivotal role in disease diagnosis and control. Considering the sudden multicountry MPXV outbreak, a critical evaluation of the MPXV detection approaches would be a timely addition to the endeavors in progress for MPXV control and prevention. Herein, we evaluate the current MPXV detection methods, discuss their pros and cons, and provide recommended solutions to the problems. We review the traditional and emerging nucleic acid detection approaches, immunodiagnostics, whole-particle detection, and imaging-based MPXV detection techniques. The insights provided in this article will help researchers to develop novel techniques for the diagnosis of MPXV.

RCMNet: A deep learning model assists CAR-T therapy for leukemia.

Acute leukemia is a type of blood cancer with a high mortality rate. Current therapeutic methods include bone marrow transplantation, supportive therapy, and chemotherapy. Although a satisfactory remission of the disease can be achieved, the risk of recurrence is still high. Therefore, novel treatments are demanding. Chimeric antigen receptor-T (CAR-T) therapy has emerged as a promising approach to treating and curing acute leukemia. To harness the therapeutic potential of CAR-T cell therapy for blood diseases, reliable cell morphological identification is crucial. Nevertheless, the identification of CAR-T cells is a big challenge posed by their phenotypic similarity with other blood cells. To address this substantial clinical challenge, herein we first construct a CAR-T dataset with 500 original microscopy images after staining. Following that, we create a novel integrated model called RCMNet (ResNet18 with Convolutional Block Attention Module and Multi-Head Self-Attention) that combines the convolutional neural network (CNN) and Transformer. The model shows 99.63% top-1 accuracy on the public dataset. Compared with previous reports, our model obtains satisfactory results for image classification. Although testing on the CAR-T cell dataset, a decent performance is observed, which is attributed to the limited size of the dataset. Transfer learning is adapted for RCMNet and a maximum of 83.36% accuracy is achieved, which is higher than that of other state-of-the-art models. This study evaluates the effectiveness of RCMNet on a big public dataset and translates it to a clinical dataset for diagnostic applications.

Polysaccharides-based bio-nanostructures and their potential food applications.

Polysaccharides are omnipresent biomolecules that hold great potential as promising biomaterials for a myriad of applications in various biotechnological and industrial sectors. The presence of diverse functional groups renders them tailorable functionalities for preparing a multitude of novel bio-nanostructures. Further, they are biocompatible and biodegradable, hence, considered as environmentally friendly biopolymers. Application of nanotechnology in food science has shown many advantages in improving food quality and enhancing its shelf life. Recently, considerable efforts have been made to develop polysaccharide-based nanostructures for possible food applications. Therefore, it is of immense importance to explore literature on polysaccharide-based nanostructures delineating their food application potentialities. Herein, we reviewed the developments in polysaccharide-based bio-nanostructures and highlighted their potential applications in food preservation and bioactive "smart" food packaging. We categorized these bio-nanostructures into polysaccharide-based nanoparticles, nanocapsules, nanocomposites, dendrimeric nanostructures, and metallo-polysaccharide hybrids. This review demonstrates that the polysaccharides are emerging biopolymers, gaining much attention as robust biomaterials with excellent tuneable properties.

Smart chemistry of enzyme immobilization using various support matrices - A review.

The surface chemistry, pendent functional entities, and ease in tunability of various materials play a central role in properly coordinating with enzymes for immobilization purposes. Due to the interplay between the new wave of support matrices and enzymes, the development of robust biocatalytic constructs via protein engineering expands the practical scope and tunable catalysis functions. The concept of stabilization via functional entities manipulation, the surface that comprises functional groups, such as thiol, aldehyde, carboxylic, amine, and epoxy have been the important driving force for immobilizing purposes. Enzyme immobilization using multi-functional supports has become a powerful norm and presents noteworthy characteristics, such as selectivity, specificity, stability, resistivity, induce activity, reaction efficacy, multi-usability, high catalytic turnover, optimal yield, ease in recovery, and cost-effectiveness. There is a plethora of literature on traditional immobilization approaches, e.g., intramolecular chemical (covalent) attachment, adsorption, encapsulation, entrapment, and cross-linking. However, the existing literature is lacking state-of-the-art smart chemistry of immobilization. This review is a focused attempt to cover the literature gap of surface functional entities that interplay between support materials at large and enzyme of interest, in particular, to tailor robust biocatalysts to fulfill the growing and contemporary needs of several industrial sectors.