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An innovative integrated paper-based microdevice was developed for protein separation by isoelectric focusing (IEF), allowing for robust design thanks to a 3D-printed holder integrating separation channel, reservoirs, and electrodes. To reach robustness and precision, the optimization focused on the holder geometry, the paper nature, the reservoir design, the IEF medium, and various focusing parameters. A well-established and stable pH gradient was obtained on a glass-fiber paper substrate with simple sponge reservoirs, and the integration of the electrodes in the holder led to a straightforward system. The separation medium composed of water/glycerol (85/15, v/v) allowed for reducing medium evaporation while being an efficient medium for most hydrophobic and hydrophilic proteins, compatible with mass spectrometry detection for further proteomics developments. To our knowledge, this is the first report of the use of glycerol solutions as a separation medium in a paper-based microdevice. Analytical performances regarding pH gradient generation, pI determination, separation efficiency, and resolution were estimated while varying the IEF experimental parameters. The overall process led to an efficient separation within 25 min. Then, this methodology was applied to a sample composed of saliva doped with proteins. A minimal matrix effect was evidenced, underscoring the practical viability of our platform. This low-cost, versatile and robust paper-based IEF microdevice opens the way to various applications, ranging from sample pre-treatment to integration in an overall proteomic-on-a-chip device., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier B.V. All rights reserved.)

This study describes the development of a paper microzone colorimetric assay embedded on a 3D printed support for quantifying total protein content in different biological matrices and foods. The aim was to develop an accurate and reliable method, ensuring at the same time the possibility of customizability, facility of use, wide applicability, and reduced analysis for both time and costs. The device consists of a 3D printed thermoplastic polyurethane support housing the detection substrate (GF/F glass microfiber). The bromophenol blue (BPB) assay was optimized in this substrate to quantify total protein content. The analytical performance, assessed through image analysis, indicated that the hue factor of the HSV colour space represents the best analytical signal ( r 2 ), and an accuracy between 92% and 95%. The bioanalytical feasibility was demonstrated through total protein concentration measurement in different biological matrices (bee venom and mouse brain tissue), and foods (soya milk, cow's milk and protein supplements). The obtained values showed a strong agreement with those derived from a standard spectrophotometric analysis. Overall, the paper microzone BPB assay may represent an important contribution to protein quantification technology and could significantly impact many areas, such as quality control analysis and pre-clinical laboratory analysis.-1 ), and an accuracy between 92% and 95%. The bioanalytical feasibility was demonstrated through total protein concentration measurement in different biological matrices (bee venom and mouse brain tissue), and foods (soya milk, cow's milk and protein supplements). The obtained values showed a strong agreement with those derived from a standard spectrophotometric analysis. Overall, the paper microzone BPB assay may represent an important contribution to protein quantification technology and could significantly impact many areas, such as quality control analysis and pre-clinical laboratory analysis.

Background noise due to nonspecific binding of biomolecules on the assay substrates is one of the most common challenges that limits the sensitivity of microarray-based immunoassays. Background signal intensity usually increases when complex biological fluids are used because they have a combination of molecules and vesicles that can adsorb onto substrate surfaces. Blocking strategies coupled with surface chemistries can reduce such nonspecific binding and improve assay sensitivity. In this paper, we conducted a systematic optimization of blocking strategies on a variety of commonly used substrates for protein measurement in complex biofluids. Four blocking strategies (BSA, non-fat milk, PEG, and a protein-free solution) coupled with four surface chemistries (3-glycidoxypropyltrimethoxysilane (GPS), poly-L-lysine (PLL), aminoalkylsilane (AAS), and nitrocellulose (NC)) were studied for their effect on background, microspot, and net signal intensities. We have also explored the effect that these blocking strategies have when proteins in complex samples (plasma, serum, cell culture media, and EV lysate) are measured. Irregular spot morphology could affect signal extraction using automated software. We found that the microspots with the best morphology were the ones printed on GPS glass surfaces for all immunoassays. On NC membrane, the protein-based blocking strategies yielded the highest net fluorescent intensity with the antigen contained in PBS, plasma, serum, and serum-free cell culture media. Differently, with EV lysate samples, Pierce™ protein-free blocker yielded the best net signal intensity on both GPS and NC surfaces. The choice of blocking strategies highly depends on the substrate. Moreover, the findings discovered in this study are not limited to microarray-based immunoassays but can provide insights for other assay formats., (© 2023. Springer-Verlag GmbH Germany, part of Springer Nature.)

Integration and storage of bioactive reagents is an important and challenging task in microfluidic paper-based analytical devices (μPADs). Here, we developed a convenient and universal method to store proteins and preserve their activities in μPADs by using aqueous two-phase systems (ATPs) evolved film. A polyethylene glycol (PEG)-dextran (DEX) double-layer film was formed through dehydration of ATPs. Functional biomolecules were stored in the bottom DEX layer on the basis of the biased partitioning and rehydrated conveniently by simple addition of buffer solution at usage. As a demonstration, enzyme immunoassay (EIA) of carcinoembryonic antigen was performed successfully on μPAD integrated with antibodies. Even after 104 days of storage at 4 °C and ambient conditions, the EIA signal just lost less than 10% and 30%, which meet the storage requirements of invitro diagnosis reagents. The ATPs evolved double-layer film has double functions of stabilization and insulation, and provide a high efficiency of biomolecule preservation, thereby promoting the applications of μPADs in POC diagnostic assay., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2022 Elsevier B.V. All rights reserved.)

Paper-based platforms are ideal for on-site surveillance of infectious diseases in low-resource settings due to their simplicity, self-containment, and low cost. The two most popular materials used in paper-based platforms are nitrocellulose and cellulose. The nitrocellulose membrane has a high protein binding affinity, but its high price is an issue. Cellulose paper is inexpensive and allows intricate fluidic control for more sophisticated biochemical reactions, but it has a low protein binding affinity. By examining the microstructure of cellulose paper, we discover that cellulose fibers in the paper matrix are covered by thin films, which possibly result from the additives used in the paper-making process. Our finding suggests that the thin films are inert to protein adsorption. By selectively depleting the inert films with reactive plasma, we were able to enhance the protein adsorption to the cellulose paper and improve the performance of lateral flow assays. The performance of certain lateral flow assays on the plasma-treated cellulose paper is equivalent to or better than that on the nitrocellulose membrane. This leads us to believe that cellulose paper with a microstructure exclusively designed for protein binding, either by refined paper manufacturing process or by post-manufacture modification such as the plasma treatment presented herein, can potentially replace nitrocellulose as a less expensive paper substrate for point-of-care rapid test kits.

Proteinuria is a condition in which an excessive amount of protein is excreted in urine. It is, among others, an indicator of kidney disease or risk of cardiovascular disease. Rapid and reliable diagnosis and monitoring of proteinuria is of great importance for both patients and their physicians. For that reason, a paper-based sensor for proteinuria diagnosis was designed, optimized, and validated utilizing smartphone-assisted signal acquisition. In the first step, a few commonly employed protein assays were optimized and compared in terms of analytical performance on paper matrix. The tetrabromophenol blue method was selected as the one providing a sufficiently low limit of detection (39 mg·L -1 ) on the one hand and appropriate long-term stability (up to 3 months) on the other hand. The optimized assay was employed for protein-to-creatinine ratio (PCR) determination on a single paper-based sensor. For both analytes the linear ranges were within the clinically relevant range. The analytical usefulness of the developed sensors was demonstrated by a PCR recovery study in artificial urine. The obtained PCR recoveries were from ca. 80 to 150%.

Proteinoids - thermal proteins - are produced by heating amino acids to their melting point and initiation of polymerisation to produce polymeric chains. Proteinoids swell in aqueous solution into hollow microspheres. The proteinoid microspheres produce endogenous burst of electrical potential spikes and change patterns of their electrical activity in response to illumination. The microspheres can interconnect by pores and tubes and form networks with a programmable growth. We speculate on how ensembles of the proteinoid microspheres can be developed into unconventional computing devices., (Copyright © 2021 Elsevier B.V. All rights reserved.)

There is current interest in replacing petroleum-based additives in consumer paper products with abundantly available, renewable and sustainable biopolymers such as lignin-containing cellulose nanofibers (LCNFs) and cottonseed protein. This research characterized the performance of cottonseed protein isolate with/without LCNFs to increase the dry strength of filter paper. The application of 10% protein solution with 2% LCNFs as an additive improved the elongation at break, tensile strength and modulus of treated paper products compared to the improved performance of cottonseed protein alone. Improvements in tensile modulus and tensile strength were greatest for samples containing larger amounts of lignin and a greater degree of polymerization than for those with less lignin from the same biomass sources. [ABSTRACT FROM AUTHOR]

Determination of proteins from dried matrix spots using MS is an expanding research area. Mainly, the collected dried matrix sample is whole blood from a finger or heal prick, resulting in dried blood spots. However as other matrices such as plasma, serum, urine, and tear fluid also can be collected in this way, the term dried matrix spot is used as an overarching term. In this review, the focus is on advancements in the field made from 2017 up to 2023. In the first part reviews concerning the subject are discussed. After this, advancements made for clinical purposes are highlighted. Both targeted protein analyses, with and without the use of affinity extractions, as well as untargeted, global proteomic approaches are discussed. In the last part, both methodological advancements are being reviewed as well as the possibility to integrate sample preparation steps during the sample handling. The focus, of this so-called smart sampling, is on the incorporation of cell separation, proteolysis, and antibody-based affinity capture., (© 2024 The Authors. Journal of Separation Science published by Wiley‐VCH GmbH.)

Self-powered microfluidics presents a revolutionary approach to address the challenges of healthcare in decentralized and point-of-care settings where limited access to resources and infrastructure prevails or rapid clinical decision-making is critical. These microfluidic systems exploit physical and chemical phenomena, such as capillary forces and surface tension, to manipulate tiny volumes of fluids without the need for external power sources, making them cost-effective and highly portable. Recent technological advancements have demonstrated the ability to preprogram complex multistep liquid operations within the microfluidic circuit of these standalone systems, which enabled the integration of sensitive detection and readout principles. This chapter first addresses how the accessibility to in vitro diagnostics can be improved by shifting toward decentralized approaches like remote microsampling and point-of-care testing. Next, the crucial role of self-powered microfluidic technologies to enable this patient-centric healthcare transition is emphasized using various state-of-the-art examples, with a primary focus on applications related to biofluid collection and the detection of either proteins or nucleic acids. This chapter concludes with a summary of the main findings and our vision of the future perspectives in the field of self-powered microfluidic technologies and their use for in vitro diagnostics applications., (© 2024. The Author(s), under exclusive license to Springer Science+Business Media, LLC, part of Springer Nature.)

The Hofmeister series is a series of ions ordered according to their ability to precipitate proteins. It has also been linked to a host of (bio)chemical phenomena. Several attempts over the years to correlate the series to the varying success of different salts in crystallising proteins have been largely inconclusive. A correlation, based on published data and crystallisation conditions for several proteins, is proposed here between some thermodynamic properties of proteins and the position in the Hofmeister series of the salts from which they preferentially crystallise. Namely, a high ratio between the entropic or enthalpic protein-solvent interactions contribution to thermodynamic stability and the total thermodynamic stability of a given protein, indicate the protein's high propensity to crystallise in solutions of highly kosmotropic salts. Low such ratios on the other hand, indicate that chaotropic salts can be equally successful, i. e. that the protein in question is rather indifferent to the Hofmeister character of the salt. Testing various model proteins for crystallisation against screens containing salts found at different points on the Hofmeister series, as well as further bibliographic analysis, have yielded results that appear to largely corroborate this hypothesis. These conclusions may conceivably be used as a crystallisation predictive tool., (© 2024 The Authors. ChemPlusChem published by Wiley-VCH GmbH.)

Protein Protein low complexity regions (LCRs) are compositionally biased amino acid sequences, many of which have significant evolutionary impacts on the proteins which contain them. They are mutationally unstable experiencing higher rates of indels and substitutions than higher complexity regions. LCRs also impact the expression of their proteins, likely through multiple effects along the path from gene transcription, through translation, and eventual protein degradation. It has been observed that proteins which contain LCRs are associated with elevated transcript abundance (TAb), despite having lower protein abundance. We have gathered and integrated human data to investigate the co-evolution of TAb and LCRs through ancestral reconstructions and model inference using an approximate Bayesian calculation based method. We observe that on short evolutionary timescales TAb evolution is significantly impacted by changes in LCR length, with insertions driving TAb down. But in contrast, the observed data is best explained by indel rates in LCRs which are unaffected by shifts in TAb. Our work demonstrates a coupling between LCR and TAb evolution, and the utility of incorporating multiple responses into evolutionary analyses., (© 2024. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.)

Autophagy is a process that targets various intracellular elements for degradation. Autophagy can be non-selective - associated with the indiscriminate engulfment of cytosolic components - occurring in response to nutrient starvation and is commonly referred to as bulk autophagy. By contrast, selective autophagy degrades specific targets, such as damaged organelles (mitophagy, lysophagy, ER-phagy, ribophagy), aggregated proteins (aggrephagy) or invading bacteria (xenophagy), thereby being importantly involved in cellular quality control. Hence, not surprisingly, aberrant selective autophagy has been associated with various human pathologies, prominently including neurodegeneration and infection. In recent years, considerable progress has been made in understanding mechanisms governing selective cargo engulfment in mammals, including the identification of ubiquitin-dependent selective autophagy receptors such as p62, NBR1, OPTN and NDP52, which can bind cargo and ubiquitin simultaneously to initiate pathways leading to autophagy initiation and membrane recruitment. This progress opens the prospects for enhancing selective autophagy pathways to boost cellular quality control capabilities and alleviate pathology., (© 2022. Springer Nature Limited.)

Optimized antibody reagents are important in research, and erratic antibody performance leads to variability in immunoassays. Specificity of antibodies binding the protein of interest is vital to obtain accurate results. Recommendations for validation and use of primary antibodies are unique to each type of immunoassay as the antibodies' performance is greatly affected by the assay context. Immunoblotting procedures have been used along with other important antibody-based detection methods like enzyme-linked immunosorbent assay and immunohistochemistry to confirm results in research and diagnostic testing. Specificity of antibodies employed for immunohistochemical studies is of critical importance. Therefore, the use of western blotting is imperative to address the specificity of antibodies with/without siRNA knockdown of proteins of interest or with the use of peptide inhibitors to inhibit the binding of specific antibodies to the target protein. In spite of its overall simplicity, western blotting or protein blotting is a powerful procedure for immunodetection of proteins, especially those that are of low abundance, following electrophoretic separation. The usefulness of this procedure stems from its ability to provide simultaneous resolution of multiple immunogenic antigens within a sample for detection by specific antibodies. Protein blotting has evolved greatly over the last few decades, and researchers have a variety of ways and means to carry out this procedure to validate antibodies for immunohistochemistry., (© 2023. The Author(s), under exclusive license to Springer Science+Business Media, LLC, part of Springer Nature.)

Background: Autohydrolysis is an extensively investigated pretreatment method due to its environmental friendliness. During autohydrolysis, most xylan from hemicellulose can be converted into xylooligosaccharides (XOS), and cellulose in the autohydrolyzed residues can be transformed into glucose after enzymatic hydrolysis. Both of these are value-added biochemicals in the biorefining process. In this work, paper mulberry (PM), which contains abundant protein, was utilized as a raw material to coproduce XOS and single-cell protein (SCP) through autohydrolysis and fermentation technologies. Results: The results showed that 8.3 g of XOS and 1.8 g of amino acids could be recovered in the autohydrolysate (based on 100 g raw material) after autohydrolysis (170 °C, 1 h). Moreover, 5.7 g of low-DP XOS along with 1.8 g of amino acids could be further obtained from the autohydrolysate after hydrolysis with endo-β-1-4-xylanase. In addition, 20.1 g of fermentable monosaccharides was recovered after hydrolyzing the autohydrolyzed PM with cellulase, which can be used to produce 4.8 g of SCP after fermentation with Candida utilis. Conclusion: As a valuable application of PM, a novel process is proposed to coproduce amino acid-rich XOS and SCP through autohydrolysis. The carbohydrate of PM is effectively converted to high value-added products. [ABSTRACT FROM AUTHOR]

In recent years, investigations on molecular interaction mechanisms between food proteins and ligands have attracted much interest. The interaction mechanisms can supply much useful information for many fields in the food industry, including nutrient delivery, food processing, auxiliary detection, and others. Molecular simulation has offered extraordinary insights into the interaction mechanisms. It can reflect binding conformation, interaction forces, binding affinity, key residues, and other information that physicochemical experiments cannot reveal in a fast and detailed manner. The simulation results have proven to be consistent with the results of physicochemical experiments. Molecular simulation holds great potential for future applications in the field of food protein-ligand interactions. This review elaborates on the principles of molecular docking and molecular dynamics simulation. Besides, their applications in food protein-ligand interactions are summarized. Furthermore, challenges, perspectives, and trends in molecular simulation of food protein-ligand interactions are proposed. Based on the results of molecular simulation, the mechanisms of interfacial behavior, enzyme-substrate binding, and structural changes during food processing can be reflected, and strategies for hazardous substance detection and food flavor adjustment can be generated. Moreover, molecular simulation can accelerate food development and reduce animal experiments. However, there are still several challenges to applying molecular simulation to food protein-ligand interaction research. The future trends will be a combination of international cooperation and data sharing, quantum mechanics/molecular mechanics, advanced computational techniques, and machine learning, which contribute to promoting food protein-ligand interaction simulation. Overall, the use of molecular simulation to study food protein-ligand interactions has a promising prospect., (© 2023 Institute of Food Technologists®.)

Proteins typically exert their biological functions by interacting with other biomolecules or ligands. The study of ligand-protein interactions is crucial in elucidating the biological mechanisms of proteins. Most existing studies have focused on analyzing ligand-protein interactions, and they ignore the additional situational of inserted and modified residues. Besides, the resources often support only a single ligand type and cannot obtain satisfied results in analyzing novel complexes. Therefore, it is important to develop a general analytical tool to extract the binding residues of ligand-protein interactions in complexes fully. In this study, we propose a ligand-protein interaction binding residue extractor (PDB-BRE), which can be used to automatically extract interacting ligand or protein-binding residues from complex three-dimensional (3D) structures based on the RCSB Protein Data Bank (RCSB PDB). PDB-BRE offers a notable advantage in its comprehensive support for analyzing six distinct types of ligands, including proteins, peptides, DNA, RNA, mixed DNA and RNA entities, and non-polymeric entities. Moreover, it takes into account the consideration of inserted and modified residues within complexes. Compared to other state-of-the-art methods, PDB-BRE is more suitable for massively parallel batch analysis, and can be directly applied for downstream tasks, such as predicting binding residues of novel complexes. PDB-BRE is freely available at http://bliulab.net/PDB-BRE., (© 2023 Wiley Periodicals LLC.)

Improved understanding of cellulose swelling mechanism is beneficial for increasing the hydrolysis efficiency of cellulosic substrates. Here, we report a family 5 glycoside hydrolase ArCel5 isolated from the cellulose-gelatinizing fungus Arthrobotrys sp. CX1. ArCel5 exhibited low specific hydrolysis activity and high cellulose swelling capability, which suggested that this protein might function as an accessory protein. Homology modeling glycosylation detection revealed that ArCel5 is a multi-domain protein including a family 1 carbohydrate-binding module, a glycosylation linker, and a catalytic domain. The adsorption capacity, structural changes and hydrature index of filter paper treated by different ArCel5 mutants demonstrated that CBM1 and linker played an essential role in recognizing, binding and decrystallizing cellulosic substrates, which further encouraged the synergistic action between ArCel5 and cellulases. Notably, glycosylation modification further strengthened the function of the linker region. Overall, our study provides insight into the cellulose decrystallization mechanism by a novel accessory protein ArCel5 that will benefit future applications. [ABSTRACT FROM AUTHOR]

Exposure to natural rubber latex (NRL) can result in sensitivity to NRL protein with resulting allergic reactions. Low-cost, portable, simple, sensitive analytical tools for NRL protein measurements are needed for rapid and accurate assessments of allergenic risks at the point-of-care (POC) instead of using traditional methods that require large and expensive instruments, long-time analysis, and complex sample preparation steps. Here, an electrochemical paper-based analytical device (ePAD) is presented by combining sample preparation and electrochemical detection within a single device to offer a one-step NRL analysis. The lack of antibodies and/or enzymes against NRL makes POC analysis difficult. In this work, detection is based on electrochemical measurement of the remaining Cu after in situ protein complexation instead of more complex biological assays for the first time. Graphene screen-printed electrodes modified with 1,10-phenanthroline and Nafion were used in the ePAD to improve Cu signal 18-fold relative to unmodified carbon screen-printed electrodes. The optimum parameters including 1,10-phenanthroline concentration, reaction time between Cu and protein, and the starting Cu concentration were 5% w/v, 1 min, and 600 μg mL−1, respectively. In addition to short analysis time (4 min), the system selectivity indicated no other interfering species affecting protein detection. The proposed ePAD achieved an LOD of 3.0 mg dL−1 and a linear range of 10.0–200.0 mg dL−1. Finally, the proposed sensors were applied for NRL protein detection and the results were not significantly different from the traditional Lowry method at 95% confidence. [ABSTRACT FROM AUTHOR]

Condensed states of proteins, including liquid-like membraneless organelles and solid-like aggregates, contribute in fundamental ways to the organisation and function of the cell. Perturbations of these states can lead to a variety of diseases through mechanisms that we are now beginning to understand. We define protein condensation diseases as conditions caused by the disruption of the normal behaviour of the condensed states of proteins. We analyze the problem of the identification of targets for pharmacological interventions for these diseases and explore opportunities for the regulation of the formation and organisation of aberrant condensed states of proteins., (© 2022. The Author(s).)

Machine learning-based models of protein fitness typically learn from either unlabeled, evolutionarily related sequences or variant sequences with experimentally measured labels. For regimes where only limited experimental data are available, recent work has suggested methods for combining both sources of information. Toward that goal, we propose a simple combination approach that is competitive with, and on average outperforms more sophisticated methods. Our approach uses ridge regression on site-specific amino acid features combined with one probability density feature from modeling the evolutionary data. Within this approach, we find that a variational autoencoder-based probability density model showed the best overall performance, although any evolutionary density model can be used. Moreover, our analysis highlights the importance of systematic evaluations and sufficient baselines., (© 2022. The Author(s), under exclusive licence to Springer Nature America, Inc.)

Gathering information from multi-perspective graphs is an essential issue for many applications especially for protein-ligand-binding affinity prediction. Most of traditional approaches obtained such information individually with low interpretability. In this paper, we harness the rich information from multi-perspective graphs with a general model, which abstractly represents protein-ligand complexes with better interpretability while achieving excellent predictive performance. In addition, we specially analyze the protein-ligand-binding affinity problem, taking into account the heterogeneity of proteins and ligands. Experimental evaluations demonstrate the effectiveness of our data representation strategy on public datasets by fusing information from different perspectives. All codes are available in the https://github.com/Jthy-af/HaPPy ., (© 2023. International Association of Scientists in the Interdisciplinary Areas.)

Nuclear magnetic resonance is a crucial technique for studying biological complexes, as it provides precise structural and dynamic information at the atomic level. However, the process of assigning resonances can be time-consuming and challenging, particularly in cases where peaks overlap, or the data quality is poor. In this paper, we present TINTO (Two and three-dimensional Imaging for NMR sTrip Operation via CV/ML), an advanced semiautomatic toolset for NMR resonance assignment. TINTO comprises two separate tools, each tailored for either two-dimensional or three-dimensional imaging. The toolset utilizes a computer-vision approach and a machine learning approach, specifically structural similarity index and principal components analysis, to perform visual similarity searches of resonances and quickly locate similar strips, and in that way overcome the challenges associated with peak overlap without requiring peak picking. Our tool offers a user-friendly interface and has the potential to enhance the efficiency and accuracy of NMR resonance assignment, particularly in complex cases. This advancement holds promising implications for furthering our understanding of biological systems at the molecular level. TINTO is pre-installed in the POKY suite, which is available at https://poky.clas.ucdenver.edu ., (© 2023. The Author(s), under exclusive licence to Springer Nature B.V.)

Stability of proteins at high temperature has been a topic of interest for many years, as this attribute is favourable for applications ranging from therapeutics to industrial chemical manufacturing. Our current understanding and methods for designing high-temperature stability into target proteins are inadequate. To drive innovation in this space, we have curated a large dataset, learn2thermDB, of protein-temperature examples, totalling 24 million instances, and paired proteins across temperatures based on homology, yielding 69 million protein pairs - orders of magnitude larger than the current largest. This important step of pairing allows for study of high-temperature stability in a sequence-dependent manner in the big data era. The data pipeline is parameterized and open, allowing it to be tuned by downstream users. We further show that the data contains signal for deep learning. This data offers a new doorway towards thermal stability design models., (© 2023. Springer Nature Limited.)

Plant resistance (R) proteins play a significant role in the detection of pathogen invasion. Accurately predicting plant R proteins is a key task in phytopathology. Most plant R protein predictors are dependent on traditional feature extraction methods. Recently, deep representation learning methods have been successfully applied in solving protein classification problems. Motivated by this, we propose a new computational approach, called prPred-DRLF, which uses deep representation learning feature models to encode the amino acids as numerical vectors. The results show that the fused features of bidirectional long short-term memory (BiLSTM) embedding and unified representation (UniRep) embedding have a better performance than other features for plant R protein identification using a light gradient boosting machine (LGBM) classifier. The model was evaluated using an independent test achieving an accuracy of 0.956, F1-score of 0.933, and area under the receiver operating characteristic (ROC) curve (AUC) of 0.997. Meanwhile, compared with the state-of-the-art prPred and HMMER method, prPred-DRLF shows an overall improvement in accuracy, F1-score, AUC, and recall. prPred-DRLF is a higher-performance plant R protein prediction tool based on two kinds of deep representation learning technologies and offers a user-friendly interface for inspecting possible plant R proteins. We hope that prPred-DRLF will become a useful tool for biological research. A user-friendly webserver for prPred-DRLF is freely accessible at http://lab.malab.cn/soft/prPred-DRLF. The Python script can be downloaded from https://github.com/Wangys-prog/prPred-DRLF., (© 2021 Wiley-VCH GmbH.)

The fragility of biological systems during storage, transport, and utilization necessitates reliable cold-chain infrastructure and limits the potential of biotechnological applications. In order to unlock the broad applications of existing and emerging biological technologies, we report the development of a novel solid-state storage platform for complex biologics. The resulting solid-state biologics (SSB) platform meets four key requirements: facile rehydration of solid materials, activation of biochemical activity, ability to support complex downstream applications and functionalities, and compatibility for deployment in a variety of reaction formats and environments. As a model system of biochemical complexity, we utilized crude Escherichia coli cell extracts that retain active cellular metabolism and support robust levels of in vitro transcription and translation. We demonstrate broad versatility and utility of SSB through proof-of-concepts for on-demand in vitro biomanufacturing of proteins at a milliliter scale, the activation of downstream CRISPR activity, as well as deployment on paper-based devices. SSBs unlock a breadth of applications in biomanufacturing, discovery, diagnostics, and education in resource-limited environments on Earth and in space.

Food allergy remains a public health, business, and regulatory challenge. Risk analysis (RA) and risk management (RM) of food allergens are of great importance and analysis for food allergens is necessary for both. The current workhorse techniques for allergen analysis (enzyme linked immunosorbent assay [ELISA] and real-time polymerase chain reaction) exhibit recognized challenges including variable and antibody specific responses and detection of species DNA rather than allergen protein, respectively. Liquid chromatography-tandem mass spectrometry (LC-MS/MS) enables protein identification, with potential for multiplex analysis and traceability to the System of International units (SI), aiding global measurement standardization. In this review, recent literature has been systematically reviewed to assess progress in LC-MS/MS and define the potential and benefits of matrix-assisted laser desorption/ionization-time-of-flight MS (MALDI-ToF-MS) technology for allergen analysis. MALDI-ToF-MS of initially intact protein is already applied to verify in silico-derived peptide sequences for LC-MS/MS analysis. We describe the origins of MALDI and its future perspectives, including affinity bead-assisted assays coupled to MALDI. Based on the proliferation of reliable and reproducible MALDI-based clinical applications, the technique should emulate the detection capability (sensitivity) of established allergen detection techniques, whilst reducing technical support and having equivalent multiplexing potential to competing techniques, for example, LC-MS/MS and ELISA. Although unlikely to offer inherent SI traceability, MALDI-based allergen analysis will complement existing MS approaches for allergens. Affinity bead-MALDI appears capable of higher throughput at lower cost per sample than almost any existing technique, enabling repeated sub-sampling as a way to reduce representative sampling issues., (© 2023 The Authors. Comprehensive Reviews in Food Science and Food Safety published by Wiley Periodicals LLC on behalf of Institute of Food Technologists.)

There has been considerable recent progress in designing new proteins using deep-learning methods 1-9 . Despite this progress, a general deep-learning framework for protein design that enables solution of a wide range of design challenges, including de novo binder design and design of higher-order symmetric architectures, has yet to be described. Diffusion models 10,11 have had considerable success in image and language generative modelling but limited success when applied to protein modelling, probably due to the complexity of protein backbone geometry and sequence-structure relationships. Here we show that by fine-tuning the RoseTTAFold structure prediction network on protein structure denoising tasks, we obtain a generative model of protein backbones that achieves outstanding performance on unconditional and topology-constrained protein monomer design, protein binder design, symmetric oligomer design, enzyme active site scaffolding and symmetric motif scaffolding for therapeutic and metal-binding protein design. We demonstrate the power and generality of the method, called RoseTTAFold diffusion (RFdiffusion), by experimentally characterizing the structures and functions of hundreds of designed symmetric assemblies, metal-binding proteins and protein binders. The accuracy of RFdiffusion is confirmed by the cryogenic electron microscopy structure of a designed binder in complex with influenza haemagglutinin that is nearly identical to the design model. In a manner analogous to networks that produce images from user-specified inputs, RFdiffusion enables the design of diverse functional proteins from simple molecular specifications., (© 2023. The Author(s).)

The search and design for the better use of bioactive compounds are used in many experiments to best mimic compounds' functions in the human body. However, finding a cost-effective and timesaving approach is a top priority in different disciplines. Nowadays, artificial intelligence (AI) and particularly deep learning (DL) methods are widely applied to improve the precision and accuracy of models used in the drug discovery process. DL approaches have been used to provide more opportunities for a faster, efficient, cost-effective, and reliable computer-aided drug discovery. Moreover, the increasing biomedical data volume in areas, like genome sequences, medical images, protein structures, etc., has made data mining algorithms very important in finding novel compounds that could be drugs, uncovering or repurposing drugs and improving the area of genetic markers-based personalized medicine. Furthermore, deep neural networks (DNNs) have been demonstrated to outperform other techniques such as random forests and SVMs for QSAR studies and ligand-based virtual screening. Despite this, in QSAR studies, the quality of different data sources and potential experimental errors has greatly affected the accuracy of QSAR predictions. Therefore, further researches are still needed to improve the accuracy, selectivity, and sensitivity of the DL approach in building the best models of drug discovery., (© 2021 John Wiley & Sons A/S.)

There is a close relationship between the tertiary structure and the function of a protein. One of the important steps to determine the tertiary structure is protein secondary structure prediction (PSSP). For this reason, predicting secondary structure with higher accuracy will give valuable information about the tertiary structure. Recently, deep learning techniques have obtained promising improvements in several machine learning applications including PSSP. In this article, a novel deep learning model, based on convolutional neural network and graph convolutional network is proposed. PSIBLAST PSSM, HHMAKE PSSM, physico-chemical properties of amino acids are combined with structural profiles to generate a rich feature set. Furthermore, the hyper-parameters of the proposed network are optimized using Bayesian optimization. The proposed model IGPRED obtained 89.19%, 86.34%, 87.87%, 85.76%, and 86.54% Q3 accuracies for CullPDB, EVAset, CASP10, CASP11, and CASP12 datasets, respectively., (© 2021 Wiley Periodicals LLC.)

Human body fluids contain biomarkers which are used extensively for prognostication, diagnosis, monitoring, and evaluation of different treatments for a variety of diseases and disorders. The application of biosensors based on cell-free protein synthesis (CFPS) offers numerous advantages including on-demand and at-home use for fast, accurate detection of a variety of biomarkers in human fluids at an affordable price. However, current CFPS-based biosensors use commercial RNase inhibitors to inhibit different RNases present in human fluids and this reagent is approximately 90% of the expense of these biosensors. Here the flexible nature of Escherichia coli-lysate-based CFPS was used for the first time to produce murine RNase Inhibitor (m-RI) and to optimize its soluble and active production by tuning reaction temperature, reaction time, reduced potential, and addition of GroEL/ES folding chaperons. Furthermore, RNase inhibition activity of m-RI with the highest activity and stability was determined against increasing amounts of three human fluids of serum, saliva, and urine (0%-100% v/v) in lyophilized CFPS reactions. To further demonstrate the utility of the CFPS-produced m-RI, a lyophilized saliva-based glutamine biosensor was demonstrated to effectively work with saliva samples. Overall, the use of CFPS-produced m-RI reduces the total reagent costs of CFPS-based biosensors used in human body fluids approximately 90%., (© 2021 Wiley Periodicals LLC.)

Coarse-grained normal mode analyses of protein dynamics rely on the idea that the geometry of a protein structure contains enough information for computing its fluctuations around its equilibrium conformation. This geometry is captured in the form of an elastic network (EN), namely a network of edges between its residues. The normal modes of a protein are then identified with the normal modes of its EN. Different approaches have been proposed to construct ENs, focusing on the choice of the edges that they are comprised of, and on their parameterizations by the force constants associated with those edges. Here we propose new tools to guide choices on these two facets of EN. We study first different geometric models for ENs. We compare cutoff-based ENs, whose edges have lengths that are smaller than a cutoff distance, with Delaunay-based ENs and find that the latter provide better representations of the geometry of protein structures. We then derive an analytical method for the parameterization of the EN such that its dynamics leads to atomic fluctuations that agree with experimental B-factors. To limit overfitting, we attach a parameter referred to as flexibility constant to each atom instead of to each edge in the EN. The parameterization is expressed as a non-linear optimization problem whose parameters describe both rigid-body and internal motions. We show that this parameterization leads to improved ENs, whose dynamics mimic MD simulations better than ENs with uniform force constants, and reduces the number of normal modes needed to reproduce functional conformational changes., (© 2021 Wiley Periodicals LLC.)

Genetically encoded tools for the regulation of endogenous molecules (RNA, DNA elements and protein) are needed to study and control biological processes with minimal interference caused by protein overexpression and overactivation of signaling pathways. Here we focus on light-controlled optogenetic tools (OTs) that allow spatiotemporally precise regulation of gene expression and protein function. To control endogenous molecules, OTs combine light-sensing modules from natural photoreceptors with specific protein or nucleic acid binders. We discuss OT designs and group OTs according to the principles of their regulation. We outline characteristics of OT performance, discuss considerations for their use in vivo and review available OTs and their applications in cells and in vivo. Finally, we provide a brief outlook on the development of OTs., (© 2021. Springer Nature America, Inc.)