Protein Design: From the Aspect of Water Solubility and Stability

Interactions between β-lactoglobulin and polyphenols: Mechanisms, properties, characterization, and applications

β-lactoglobulins (βLGs) have a wide range of applications in food because of their ability to emulsify, foam, and gel. This makes them good functional additives. However, their performance depends on temperature, pH, and mineral levels, so their functional qualities are limited in particular applications. How polyphenols (PPs) interact with βLG is crucial for the functional characteristics and quality of dietary compounds. In most food systems, a spontaneous interaction between proteins and PPs results in a "protein-PP conjugate," which is known to affect the sensory, functional, and nutraceutical qualities of food products. The βLG-PP conjugates can be used to enhance the quality of food. This article emphasizes analytical techniques for describing the characteristics of βLG-PP complexes/conjugates. It also goes over the functions of βLG-PP conjugates, including their solubility, thermal stability, emulsifying, and antioxidant qualities. The majority of βLG-PPs interactions is due to non-covalent (H-bonding, electrostatic interactions) or covalent bonds that are mostly caused by βLG or PP oxidation through enzymatic or non-enzymatic mechanisms. Furthermore, the conformation or type of proteins and PPs, as well as environmental factors like pH and temperature, have a significant impact on proteins-PPs interactions. Higher thermal stability, antioxidant activities, and superior emulsifying capabilities of the βLG-PP conjugates make them useful as innovative additives to enhance the quality and functions of food products.

Niallia tiangongensis sp. nov., isolated from the China Space Station

Understanding the characteristics of microbes during long-term space missions is essential for safeguarding the health of astronauts and maintaining the functionality of spacecraft. In this study, a Gram-positive, aerobic, spore-forming, rod-shaped strain JL1B1071T was isolated from the surface of hardware on the China Space Station. This strain belongs to the genus Niallia, with its closest relative being Niallia circulans ATCC 4513T. The genome of JL1B1071T is 5 166 230 bp in size, with a G+C content of 35.6 mol%. The average nucleotide identity and digital DNA-DNA hybridization values between JL1B1071T and N. circulans ATCC 4513T are 83.3 and 27.5%, respectively, both below the recommended thresholds for species delineation. The major cellular fatty acids were anteiso-C15:0 and iso-C15:0. The major quinone was menaquinone-7 (MK-7). Notably, strain JL1B1071T demonstrates a unique ability to hydrolyse gelatin, suggesting that it can utilize gelatin as a substrate in nutrient-limited environments. Genomic analysis of JL1B1071T revealed two conserved signature indels in the GAF domain-containing protein and DNA ligase D protein, which are specific to the genus Niallia. Additionally, structural and functional differences in proteins BshB1 and SplA were identified, which may enhance biofilm formation, oxidative stress response and radiation damage repair, thereby aiding its survival in the space environment. Based on phenotypic, physiological and chemotaxonomic characteristics, as well as genome annotation, strain JL1B1071T was considered a novel species within the genus Niallia and is proposed to be named Niallia tiangongensis sp. nov. The type strain is JL1B1071T (=GDMCC 1.4642=KCTC 43715).

Stress response proteins within biofilm matrixome protect the cell membrane against heavy metals-induced oxidative damage in a marine bacterium Bacillus stercoris GST-03

Biofilm formation is a key adaptive response of marine bacteria towards stress conditions. The protective mechanisms of biofilm matrixome proteins against heavy metals (Pb and Cd) induced oxidative damage in the marine bacterium Bacillus stercoris GST-03 was investigated. Exposure to heavy metals resulted in significant changes in cell morphology, biofilm formation, and matrixome composition. Biofilm-encased cells showed lower oxidative damage. Biofilm matrixome protein exhibited major conformational changes, with 100 % α-helix turned to 62.33 % and 69.64 % of random coil under Pb and Cd stress, respectively. Fluorescence quenching kinetics revealed slow interactions between biofilm matrixome proteins and heavy metals (Kq values < 2.0 × 1010). Thermodynamic analysis showed negative ∆G (-16.02 kJ/mol for Pb and -17.45 kJ/mol for Cd) and binding dissociation constant (KD) (1530 ± 157 μM for Pb and 875 ± 97.4 μM for Cd), indicating a stronger binding affinity of biofilm matrixome to heavy metals. Pb stress led to overproduction of detoxification proteins (YnaI, KhtS, Bacillopeptidase F), competence and sporulation proteins (RapF, CSSF, XkdP), while Cd exposure leads to overproduction of proteins involved in protein misfolding repair (YlxX, cysteine-tRNA ligase, YacP), DNA repair (YfkN), and redox balance (cysteine synthase, YdiK). The findings highlight the resilience of B. stercoris GST-03 to heavy metal stress in biofilm mode.

Exploring a new paradigm for serum-accessible component rules of natural medicines using machine learning and development and validation of a direct predictive model

In the field of pharmaceutical research, Lipinski's Rule of Five (RO5) was once widely regarded as the prevailing standard for the development of novel drugs. Despite the fact that an increasing number of recently approved drugs no longer adhere to this rule, it continues to serve as a valuable guiding principle in the field of drug discovery. The present study aims to establish a set of rules specifically for the serum-accessible components of natural medicines. A comprehensive literature review was conducted to collect data on serum-accessible components of natural medicines, and machine learning methods were then applied to analyse and screen molecular features distinguishing serum-accessible components from non-serum-accessible ones. The most critical rules for serum-accessible components of natural medicines were identified, and these were named the "Natural Medicine's Rule of 5 (NMRO5)." We then compared the molecular property distributions and predictive performance of NMRO5 with RO5. Then, we developed a predictive model capable of directly assessing the possibility of a molecule being serum-accessible. This model was validated using in vivo experiments on multiple natural medicines. Furthermore, we performed molecular modifications on serum-accessible components to "violate" NMRO5, conducting both forward and reverse validations to confirm the reliability of NMRO5. The results obtained revealed that NMRO5 is characterised by the following: higher TPSA, MaxEState, and PEOE VSA1 values, and lower LogP and MinEState values. This indicates that natural medicine components with these properties are more likely to be serum-accessible or remain in plasma rather than being rapidly eliminated. The investigation revealed significant disparities among the five molecular properties of NMRO5, and the predictive performance of eight models based on NMRO5 consistently outperformed those based on RO5. This finding suggests that NMRO5 provides a more reliable framework for determining whether a molecule is serum-accessible compared to RO5. Finally, we developed a direct predictive model for serum-accessible components, achieving an accuracy of 0.7257, an F1 score of 0.7223, and an AUC of 0.7553.

Enhancing Functional Protein Design Using Heuristic Optimization and Deep Learning for Anti-Inflammatory and Gene Therapy Applications

Protein sequence design is a highly challenging task, aimed at discovering new proteins that are more functional and producible under laboratory conditions than their natural counterparts. Deep learning-based approaches developed to address this problem have achieved significant success. However, these approaches often do not adequately emphasize the functional properties of proteins. In this study, we developed a heuristic optimization method to enhance key functionalities such as solubility, flexibility, and stability, while preserving the structural integrity of proteins. This method aims to reduce laboratory demands by enabling a design that is both functional and structurally sound. This approach is particularly valuable for the synthetic production of proteins with anti-inflammatory properties and those used in gene therapy. The designed proteins were initially evaluated for their ability to preserve natural structures using recovery and confidence metrics, followed by assessments with the AlphaFold tool. Additionally, natural protein sequences were mutated using a genetic algorithm and compared with those designed by our method. The results demonstrate that the protein sequences generated by our method exhibit much greater similarity to native protein sequences and structures. The code and sequences for the designed proteins are available at https://github.com/aysenursoyturk/HMHO.

The key structure and self-stabilization mechanism of water-soluble interfacial squalene-hopene cyclase

The water insolubility and structural instability of squalene-hopene cyclase (SHC), a membrane-bound interfacial enzyme, pose significant challenges for their use in industrial applications. The membrane-bound nature results in low enzyme yield, cumbersome processes and increased costs. Here, a novel water-soluble catalyst, SaSHC (EC: 5.4.99.17), was discovered from Streptomyces albolongus. This study clearly elucidates its water-soluble structural basis and develop a model for enhancing the water solubility of SHC. The key region, motif 2, contains poly-positively charged amino acids and outward self-anchored structure. The former enhances the electrostatic interaction with the phospholipid head, which makes SaSHC easily dissociated from the cell membrane. And the later ensures the open conformation of the membrane-bound domain. Base on the that, the "outward self-anchored structure dominated-high electrostatic interaction and hydrophobic interaction" (OSA-HELH) model is proposed and applied to optimization SaSHC and AaSHC (from Alicyclobacillus acidocaldarius, tightly bound to the cell membrane). Excitingly, the catalytic efficiency of the SaSHC-L274K was increased by 34.18 %, and mutant AaSHCm2 (AaSHC's motif 2 is replaced by the SaSHC's motif 2) turned into a water-soluble enzyme. In the 100 mL scale-up experiment, the SaSHC-L274K required only 0.04 % Tween 80 to convert 87.82 % squalene, which is an environment-friendly hopene production mode.

Screening Human IgE Epitopes on α-Lactalbumin and Developing Specific Detection Tools for Enhanced Allergen Detection in Food Matrices

Food proteins undergo significant structural changes during various processing methods; however, key epitopes often exhibit resistance to damage and modification. Recently, we developed a sensitive sandwich enzyme-linked immunosorbent assay (Epi-mAbs-sELISA) based on IgE linear epitope peptides. This method enables the quantitative detection of alpha-lactalbumin (ALA) for the assessment of food allergenicity. We determined that antibodies developed from IgE linear epitopes possess higher affinity. Ultimately, the optimized Epi-mAbs-sELISA presented in this study demonstrated a wide linear detection range (30.52 ng/mL to 125 μg/mL), a low detection limit (1.122 ng/mL), and excellent precision, with an internal determination relative standard deviation of 9.23% and an external determination relative standard deviation of 9.89%. The recoveries across various food matrices ranged from 81.9% to 111.9%, surpassing the sensitivity and detection range of commercial ELISA kits. The application of this testing method to processed food samples confirmed its ability to detect undeclared ALA residues, thereby mitigating potential safety issues for patients with milk allergies.

Integrating synthetic polypeptides with innovative material forming techniques for advanced biomedical applications

Polypeptides are highly valued in biomedical science for their biocompatibility and biodegradability, making them valuable in drug delivery, tissue engineering, and antibacterial dressing. The diverse design of polymer chains and self-assembly techniques allow different side chains and secondary structures, enhancing their biomedical potential. However, the traditional solid powder form of polypeptides presents challenges in skin applications, shipping, and recycling, limiting their practical utility. Recent advancements in material forming methods and polypeptide synthesis have produced biomaterials with uniform, distinct shapes, improving usability. This review outlines the progress in polypeptide synthesis and material-forming methods over the past decade. The main synthesis techniques include solid-phase synthesis and ring-opening polymerization of N-carboxyanhydrides while forming methods like electrospinning, 3D printing, and coating are explored. Integrating structural design with these methods is emphasized, leading to diverse polypeptide materials with unique shapes. The review also identifies research hotspots using VOSviewer software, which are visually presented in circular packing images. It further discusses emerging applications such as drug delivery, wound healing, and tissue engineering, emphasizing the crucial role of material shape in enhancing performance. The review concludes by exploring future trends in developing distinct polypeptide shapes for advanced biomedical applications, encouraging further research.

Engineering the Self-Assembly Pathways of POSS-Peptide Amphiphiles to Form Diverse Cross-β Structures

Cross-β structures are crucial in driving protein folding and aggregation. However, due to their strong aggregating tendency, the precise control of the self-assembly of β-sheet-forming peptides remains a challenge. We propose a molecular geometry strategy to study and control the self-assembly of cross-β structures. We conjugate the peptide with shape-persistent polyhedral oligomeric silsesquioxane (POSS), which acts as a hydrophilic head and senses the solvent environment. The POSS-peptide amphiphiles display two distinct self-assembly pathways: twisted nanoribbons transforming into either nanotubes at low water content or flat nanoribbons at high water content. The peptide packing in flat nanoribbons is predominantly modulated by POSS, diverting the system away from crystal formation, which is the absolute lowest energy state of pure peptide self-assemblies. For the first time, we have demonstrated that POSS can serve as a useful tool to adjust the interactions between cross-β strands, achieving fine-tuning of the pathway complexity (i.e., the kinetic and thermodynamic aspects of peptide self-assembly). With this versatile molecular platform incorporating multiple functionalities of POSS and programable peptide sequences, this study provides a platform to exploit cross-β-based nanomaterials with functional and pathological significance.

An Integrated Modular Vaccination System for Spatiotemporally Separated Perioperative Cancer Immunotherapy

The perioperative period is crucial for determining postoperative tumor recurrence and metastasis. Various factors in postoperative lesions can diminish the therapeutic effect of conventional chemoradiotherapy, while emerging immunotherapy is restricted. The combination use of inflammatory inhibitors during treatment is also controversial. Here, a modular microneedle prepared from engineered keratin proteins is reported, which spatially and temporally differentiates the microenvironment of immune cell activation required for immunotherapy from that of wound healing. The recombinant keratin-84-T-based needle root layer, mainly retained in the epidermis, facilitated dendritic cell recruitment to achieve maximum antigen presentation of loaded vaccines. Meanwhile, the recombinant keratin-81-1Aα-based needle tip layer, located within the dermis, rapidly mitigated inflammatory responses while promoting tissue repair and regeneration. Unlike simply mixing immunotherapy and wound treatment, this spatiotemporal segmentation approach maximized the efficacy of immune therapeutics while promoting wound healing, making it suitable for application throughout the perioperative period.

De novo design protein binders for MBP and GST tags

Maltose-binding protein (MBP) and glutathione S-transferase (GST) are widely used solubility-enhancing protein tags, typically employed to address various issues related to protein expression and purification. The detection of these tags are usually achieved through binding of corresponding antibodies. Designing low-cost binders as alternatives to antibodies is of great significance. This study employed a de novo design approach, starting with a large number of protein scaffolds and screening out 6 candidate binders targeting MBP and 4 candidate binders targeting GST based on scoring functions. Flow cytometry low-affinity selection and biolayer interferometry (BLI) quantitative results showed that MBP and GST can interact strongly with one or several binders, exhibiting nanomolar binding. Among them, LZMB3 has a binding dissociation constant (KD) of 54.05 ± 1.46 nM, while LJGB3 and LJGB4 have KD values of 105.4 ± 1.812 nM and 437.9 ± 17.69 nM, respectively.

Hofmeister ion effects induced by different acidifiers and alkalizers improve the techno-functional properties of complex rapeseed protein during pH-driven self-assembly

pH-driven method is an effective strategy to prepare complex protein. This study provides guidance on how to select acidifiers and alkalizers from view of Hofmeister ion effects. Cations and anions regulated the molecular structure (particle size, surface charge, protein folding/unfolding, structural orderliness) of complex rapeseed proteins (CRPs) mainly via electrostatic and hydrogen bond. No evident changes were found in the molecular weight distribution, but their distribution on oil/air-water interface varied greatly. Various techno-functional properties of CRPs were synergistically improved: Citrate3- and Na+ increased the emulsifying activity index of CRPs from 80 to 102.21 m2/g; Citrate3-, K+ and Na+ made the foaming stability of CRPs close to 80 % after 60 min of storage. Moreover, the oil/water-holding and gel properties of CRPs were regulated effectively. These findings demonstrate the key role of Hofmeister ion effects in improving CRPs properties, contributing to develop, select, and apply novel acidifiers and alkalizers during pH-driven treatment.

S-layers: from a serendipitous discovery to a toolkit for nanobiotechnology

Prokaryotic microorganisms, comprising Bacteria and Archaea, exhibit a fascinating diversity of cell envelope structures reflecting their adaptations that contribute to their resilience and survival in diverse environments. Among these adaptations, surface layers (S-layers) composed of monomolecular protein or glycoprotein lattices are one of the most observed envelope components. They are the most abundant cellular proteins and represent the simplest biological membranes that have developed during evolution. S-layers provide organisms with a great variety of selective advantages, including acting as an antifouling layer, protective coating, molecular sieve, ion trap, structure involved in cell and molecular adhesion, surface recognition and virulence factor for pathogens. In Archaea that possess S-layers as the exclusive cell wall component, the (glyco)protein lattices function as a cell shape-determining/maintaining scaffold. The wealth of information available on the structure, chemistry, genetics and in vivo and in vitro morphogenesis has revealed a broad application potential for S-layers as patterning elements in a molecular construction kit for bio- and nanotechnology, synthetic biology, biomimetics, biomedicine and diagnostics. In this review, we try to describe the scientifically exciting early days of S-layer research with a special focus on the 'Vienna-S-Layer-Group'. Our presentation is intended to illustrate how our curiosity and joy of discovery motivated us to explore this new structure and to make the scientific community aware of its relevance in the realm of prokaryotes, and moreover, how we developed concepts for exploiting this unique self-assembly structure. We hope that our presentation, with its many personal notes, is also of interest from the perspective of the history of S-layer research.

Interfacial and foaming properties of plant and microbial proteins: Comparison of structure-function behavior of different proteins

Many plant proteins are amphiphilic molecules that can adsorb to air-water interfaces and form protective coatings around gas bubbles. In this study, the composition, structure, physicochemical properties, air-water interfacial properties, and foaming properties of 16 plant and microbial proteins were characterized. We found a correlation between the composition, structure, physicochemical properties, and foaming properties of the proteins. The foaming capacity of them showed a highly significant positive correlation (p ≤ 0.01) with their foaming stability, α-helix content, surface hydrophobicity, and free sulfhydryl content. The foaming capacity and foaming stability showed highly significant negative correlations with disulfide bond content (p ≤ 0.01). We found wheat gluten protein (WGP) and mung bean protein (MBP) had higher foaming capacity (102.67 ± 8.08 % and 89.33 ± 4.72 %), which could be attributed to higher surface hydrophobicity (179.68 ± 1.40 and 130.28 ± 1.41) and larger contact angle (82.369 ± 0.016° and 82.949 ± 0.228°).

Regulation in protein hydrophobicity via whey protein-zein self-assembly for improving the techno-functional properties of protein

This work aims to verify the feasibility of improving protein function by regulating its hydrophobicity and reveal the relationship between structure and function. Whey protein (WP) and zein were the source of hydrophilic and hydrophobic polypeptide chains to prepare complex proteins (CPs) with much different structure and function. The results showed that the water- and oil-holding capacities, emulsifying properties and gel properties of CPs can be significantly improved via changing WP-zein ratio. All these can be attributed to the changes in protein hydrophobicity, which not only regulated the binding strength of protein to water and oil, but also modified their molecular structure (surface characteristics, availability of free thiols, α-helix, β-sheet, random coil and the formation of disulfide bonds). Notably, optimal protein hydrophobicity varies greatly among different functional properties. Overall, the techno-functional properties of protein can be improved via tuning its hydrophobicity, which may provide novel sights in protein modification.

SYNBIP 2.0: epitopes mapping, sequence expansion and scaffolds discovery for synthetic binding protein innovation

Synthetic binding proteins (SBPs) represent a pivotal class of artificially engineered proteins, meticulously crafted to exhibit targeted binding properties and specific functions. Here, the SYNBIP database, a comprehensive resource for SBPs, has been significantly updated. These enhancements include (i) featuring 3D structures of 899 SBP-target complexes to illustrate the binding epitopes of SBPs, (ii) using the structures of SBPs in the monomer or complex forms with target proteins, their sequence space has been expanded five times to 12 025 by integrating a structure-based protein generation framework and a protein property prediction tool, (iii) offering detailed information on 78 473 newly identified SBP-like scaffolds from the RCSB Protein Data Bank, and an additional 16 401 555 ones from the AlphaFold Protein Structure Database, and (iv) the database is regularly updated, incorporating 153 new SBPs. Furthermore, the structural models of all SBPs have been enhanced through the application of the AlphaFold2, with their clinical statuses concurrently refreshed. Additionally, the design methods employed for each SBP are now prominently featured in the database. In sum, SYNBIP 2.0 is designed to provide researchers with essential SBP data, facilitating their innovation in research, diagnosis and therapy. SYNBIP 2.0 is now freely accessible at https://idrblab.org/synbip/.

Investigation on topology-dependent adsorption and aggregation of protein on nanoparticle surface enabled by integrating time-limited proteolysis with cross-linking mass spectrometry

The biological identity of nanomaterials is predominantly dictated by their surface protein corona (PC), yet the topological characteristics of most PCs remain uncharacterized in situ. We employed time-limited proteolysis combined time-segmented cross-linking mass spectrometry at specific intervals (10 min, 1 h, 2 h, 4 h and 18 h) to, for the first time, elucidate the spatial distribution, topological architecture and molecular orientation of multiple proteins within the multi-layered PC on nano-Fe3O4 surfaces. Additional monolinks, intermolecular and intramolecular crosslinks which were previously inaccessible to the crosslinker were unveiled in a layer-by-layer manner. 197 sparse intermolecular crosslinks involving 368 distinct wheat proteins were identified. Notably, charge complementarity and hydrophobic residue pairings, rather than hydrophobic peptide motifs, primarily govern the protein-protein interactions. For the crosslinks bridging the proteolysable and proteolysis-resistant layers, 72 % presented one end in a random coil conformation. Furthermore, the molecular orientation of 16 proteins including Q8L803, P11534 and P93594, etc., in the proteolysis-resistant layer was determined. The observation of violated intramolecular crosslinks between two rigid structural domains (e.g., A0A3B5Y430) suggests that nanoparticle-protein and protein-protein interactions may induce conformational changes in the adsorbed proteins. These findings offer novel insights into the spontaneous formation mechanisms of PC.

Spontaneous Self-Organized Order Emerging From Intrinsically Disordered Protein Polymers

Intrinsically disordered proteins (IDPs) are proteins that, despite lacking a defined 3D structure, are capable of adopting dynamic conformations. This structural adaptability allows them to play not only essential roles in crucial cellular processes, such as subcellular organization or transcriptional control, but also in coordinating the assembly of macromolecules during different stages of development. Thus, in order to artificially replicate the complex processes of morphogenesis and their dynamics, it is crucial to have materials that recapitulate the structural plasticity of IDPs. In this regard, intrinsically disordered protein polymers (IDPPs) emerge as promising materials for engineering synthetic condensates and creating hierarchically self-assembled materials. IDPPs exhibit remarkable properties for their use in biofabrication, such as functional versatility, tunable sequence order-disorder, and the ability to undergo liquid-liquid phase separation (LLPS). Recent research has focused on harnessing the intrinsic disorder of IDPPs to design complex protein architectures with tailored properties. Taking advantage of their stimuli-responsiveness and degree of disorder, researchers have developed innovative strategies to control the self-assembly of IDPPs, resulting in the creation of hierarchically organized structures and intricate morphologies. In this review, we aim to provide an overview of the latest advances in the design and application of IDPP-based materials, shedding light on the fundamental principles that control their supramolecular assembly, and discussing their application in the biomedical and nanobiotechnological fields.

Introducing glutamic acid residues to acyl-ACP reductase to enhance alka(e)ne production in Escherichia coli: Computer-aided design and subsequent experimental validation

Acyl-acyl carrier protein (acyl-ACP) reductase (AAR) is a crucial enzyme in alka(e)ne production by recombinant Escherichia coli (E. coli). Engineered AAR expressed in E. coli holds great promise for the production of alka(e)nes, which are a valuable bio-based alternative to fossil fuels. However, its effectiveness is significantly limited by its low solubility and stability. The aim of this study is to enhance the solubility and stability of AAR to improve the production of alka(e)nes in E. coli. In this study, an integrated computational approach was employed for combining solubility prediction, aggregation propensity prediction, structural modeling, and molecular dynamics (MD) simulations. This multi-faceted approach provides new insights and tools for enzyme engineering. Through this approach, the C-terminus of AAR was identified as the sole significant hydrophobic patch and aggregation-prone regions (APR). Three strategies were evaluated experimentally: direct deletion of these hydrophobic residues; substitution of these residues with negatively charged amino acids, such as glutamic acid (Glu) or aspartic acid (Asp); and the introduction of additional negatively charged amino acids at the C-terminus to shield the hydrophobic patches. The results showed that AAR mutants with additional Glu residues at the C-terminus exhibited improved performance. Specifically, the AAR-E3 mutant, containing three consecutive Glu residues, demonstrated significantly enhanced solubility and stability, with alka(e)ne production (159.25 mg/L) being 6.3 times higher than that of the wild-type AAR (25.37 mg/L). Subsequent computational modeling and molecular dynamics simulations further validated the experimental findings. This study highlights the potential of enzyme engineering to significantly enhance biofuel production efficiency.

Tailoring CotA Laccase Substrate Specificity by Rationally Reshaping Pocket Edge

CotA is a bacterial multicopper oxidase, capable of oxidizing lots of substrates. In previous work, small size lignin phenol derivates were found to lie only in the partially covered part of pocket. However, big size substate would occupy the whole pocket to react. In this work, five residues sitting at the edge of the pocket were selected to study their roles in regulating activities against different size substrates. All mutants showed impaired activities against small size sinapic acid, however, A227E, G321F and G321P showed around 25 % increase of activities against big size ditaurobilirubin compared to wild type (WT). T262F/G321F showed moderate increased activity to alazin red S. kcat/Kms against ditaurobilirubin of A227E, T262F and G321F are around 1.5, 3 and 1.5 folds of WT's. Unexpectedly, heterologous expression yields of T262F, T262F/G321F and T262F/G321P in Escherichia coli greatly increased by around 5, 7 and 21 folds compared to WT, respectively. It is speculated positive mutants would provide a beneficial orientation for big size substrates. Substituting semi-buried residue T262 by a hydrophobic amino acid might enhance expression yields mainly by increasing van der waals and hydrophobic interaction. This work exemplified rationally regulating specific activities of laccase and is valuable for industrial application.

Accurate Predictions of Molecular Properties of Proteins via Graph Neural Networks and Transfer Learning

Machine learning has emerged as a promising approach for predicting molecular properties of proteins, as it addresses limitations of experimental and traditional computational methods. Here, we introduce GSnet, a graph neural network (GNN) trained to predict physicochemical and geometric properties including solvation free energies, diffusion constants, and hydrodynamic radii, based on three-dimensional protein structures. By leveraging transfer learning, pre-trained GSnet embeddings were adapted to predict solvent-accessible surface area (SASA) and residue-specific pKa values, achieving high accuracy and generalizability. Notably, GSnet outperformed existing protein embeddings for SASA prediction, and a locally charge-aware variant, aLCnet, approached the accuracy of simulation-based and empirical methods for pKa prediction. Our GNN framework demonstrated robustness across diverse datasets, including intrinsically disordered peptides, and scalability for high-throughput applications. These results highlight the potential of GNN-based embeddings and transfer learning to advance protein structure analysis, providing a foundation for integrating predictive models into proteome-wide studies and structural biology pipelines.

Design of a Water-Soluble CD20 Antigen with Computational Epitope Scaffolding

The poor solubility of integral membrane proteins in water frequently hinders studies with these proteins, presenting challenges for structure determination and binding screens. For instance, the transmembrane protein CD20, which is an important target for treating B-cell malignancies, is not soluble in water and cannot be easily screened against potential protein binders with techniques like phage display or yeast display. Here, we use de novo protein design to create a water-soluble mimic of the CD20 dimer ("soluble CD20"). Soluble CD20 replaces the central transmembrane helix of CD20 with a water-soluble helix that dimerizes to form a coiled coil that structurally matches the dimer interface of native CD20 and presents the central extracellular loop of CD20 in a binding competent conformation. Unlike peptides derived from CD20, soluble CD20 binds tightly to monoclonal antibodies that recognize quaternary epitopes on the extracellular face of CD20. We demonstrate that soluble CD20 is easy to produce, remains folded above 60°C, and is compatible with binder screening via yeast display. Our results highlight the ability of computational protein design to scaffold conformational epitopes from membrane proteins for use in binding and protein engineering studies.

Water-Vapor Responsive Metallo-Peptide Nanofibers

Short peptides are versatile molecules for the construction of supramolecular materials. Most reported peptide materials are hydrophobic, stiff, and show limited response to environmental conditions in the solid-state. Herein, we describe a design strategy for minimalistic supramolecular metallo-peptide nanofibers that, depending on their sequence, change stiffness, or reversibly assemble in the solid-state, in response to changes in relative humidity (RH). We tested a series of histidine (H) containing dipeptides with varying hydrophobicity, XH, where X is G, A, L, Y (glycine, alanine, leucine, and tyrosine). The one-dimensional fiber formation is supported by metal coordination and dynamic H-bonds. Solvent conditions were identified where GH/Zn and AH/Zn formed gels that upon air-drying gave rise to nanofibers. Upon exposure of the nanofiber networks to increasing RH, a reduction in stiffness was observed with GH/Zn fibers reversibly (dis-)assembled at 60-70 % RH driven by a rebalancing of hydrogen bonding interactions between peptides and water. When these metallo-peptide nanofibers were deposited on the surface of polyimide films and exposed to varying RH, peptide/water-vapor interactions in the solid-state mechanically transferred to the polymer film, leading to the rapid and reversible folding-unfolding of the films, thus demonstrating RH-responsive actuation.

Computational engineering of water-soluble human potassium ion channels through QTY transformation

Transmembrane potassium ion channels are crucial for ion transport, metabolism, and signaling, and serve as promising targets for anti-cancer therapies. However, their hydrophobic transmembrane nature requires detergents, posing a major bottleneck for experimental handling. In this paper, we present a structural bioinformatics study of six experimentally determined and twelve modeled potassium channel structures, in which hydrophobic amino acids (L, I/V, and F) were systematically replaced with neutral hydrophilic ones (Q, T, and Y), making the proteins more water-soluble. QTY (computationally predicted) and native (experimental and repredicted) variants show remarkable structural similarity (RMSD: ~0.50 Å - ~2.14 Å) despite significant sequence differences. QTY variants, both rigid and refined with MD simulations, maintain comparable to native variants stability, solvent-accessible surface area (SASA), and ionic, aromatic, and van der Waals interactions but differ in the grand average of hydropathy (GRAVY), solubility, and hydrophobic contacts. Overall, our study presents a computational approach for designing hydrophilic potassium ion channels while maintaining the native global structure that could potentially simplify their practical use by eliminating the need for detergents.

Polysaccharide-potato protein coacervates for enhanced anthocyanin bioavailability and stability

Anthocyanins (ACNs) possess strong antioxidants, anti-cancer, anti-obesity, anti-diabetic, and anti-inflammatory properties but are limited use by their susceptibility to environmental factors. This study aims to overcome these limitations by developing and assessing a novel coacervate system, consisting of potato protein isolate (PPI) combined with various polysaccharides, to stabilize and encapsulate anthocyanins from black carrot concentrate The polysaccharides included in this system include inulin, gum Arabic, guar gum, pectin, and soluble fiber. The coacervate system's effectiveness in maintaining stability and increasing the bioavailability of anthocyanins was evaluated compared to conventional soybean protein-based systems. The results show that pH considerably influences potato protein solubility, with maximum solubility at strongly acidic (pH 2) conditions. Hygroscopicity and moisture content analysis of the coacervates showed significant variations, with potato protein-guar gum (PPIGG) microcapsules having the lowest moisture content and potato protein gum Arabic (PPIGA) microcapsules having the highest moisture content. SEM imaging illustrated distinct microcapsule morphologies, while FT-IR measurement verified the successful integration of proteins and polysaccharides. The significance of the research reflects its proof that potato protein isolate (PPI) based coacervate systems consists of potato protein with polysaccharides, particularly those containing gum Arabic and pectin, have significant potential for improving anthocyanin stability and bioavailability. These findings guide future studies to investigate other polysaccharides, improve coacervation processes, and explore applications in the food and nutraceutical sectors. It also offers valuable insights for creating efficient encapsulation techniques for bioactive substances.

Laponite nanoclays for the sustained delivery of therapeutic proteins

Protein therapeutics hold immense promise for treating a wide array of diseases. However, their efficacy is often compromised by rapid degradation and clearance. The synthetic smectite clay Laponite emerges as a promising candidate for their sustained delivery. Despite its unique properties allow to load and release proteins mitigating burst release and extending their effects, precise control over Laponite-protein interactions remains challenging since it depends on a complex interplay of factors whose implication is not fully understood yet. The aim of this review article is to shed light on this issue, providing a comprehensive discussion of the factors influencing protein loading and release, including the physicochemical properties of the nanoclay and proteins, pH, dispersion buffer, clay/protein concentration and Laponite degradation. Furthermore, we thoroughly revise the array of bioactive proteins that have been delivered from formulations containing the nanoclay, highlighting Laponite-polymer nanocomposite hydrogels, a promising avenue currently under extensive investigation.

AAontology: An Ontology of Amino Acid Scales for Interpretable Machine Learning

Amino acid scales are crucial for protein prediction tasks, many of them being curated in the AAindex database. Despite various clustering attempts to organize them and to better understand their relationships, these approaches lack the fine-grained classification necessary for satisfactory interpretability in many protein prediction problems. To address this issue, we developed AAontology-a two-level classification for 586 amino acid scales (mainly from AAindex) together with an in-depth analysis of their relations-using bag-of-word-based classification, clustering, and manual refinement over multiple iterations. AAontology organizes physicochemical scales into 8 categories and 67 subcategories, enhancing the interpretability of scale-based machine learning methods in protein bioinformatics. Thereby it enables researchers to gain a deeper biological insight. We anticipate that AAontology will be a building block to link amino acid properties with protein function and dysfunctions as well as aid informed decision-making in mutation analysis or protein drug design.

Rational design approach to improve the solubility of the β-sandwich domain 1 of a thermophilic protein

The β-sandwich domain 1 (SD1) of islandisin is a stable thermophilic protein with surface loops that can be redesigned for specific target binding, architecturally comparable to the variable domain of immunoglobulin (IgG). SD1's propensity to aggregate due to incorrect folding and subsequent accumulation in Escherichia coli inclusion bodies limits its use in biotechnological applications. We rationally designed SD1 for improved variants that were expressed in soluble forms in E. coli while maintaining the intrinsic thermal stability of the protein (melting temperature (Tm) = 73). We used FoldX's ΔΔG predictions to find beneficial mutations and aggregation-prone regions (APRs) using Tango. The S26K substitution within protein core residues did not affect protein stability. Among the soluble mutants studied, the S26K/Q91P combination significantly improved the expression and solubility of SD1. We also examined the effects of the surface residue, pH, and concentration on the solubility of SD1. We showed that the surface polarity of proteins had little or no effect on solubility, whereas surface charges played a substantial role. The storage stability of several SD1 variants was impaired at pH values near their isoelectric point, and pH levels resulting in highly charged groups. We observed that mutations that create an uneven distribution of charged groups on the SD1 surface could enhance protein solubility by eliminating favorable protein-protein surface charge interactions. Our findings suggest that SD1 is mutationally tolerant to new functionalities, thus providing a novel perspective for the application of rational design to improve the solubility of targeted proteins.

Multienzyme Immobilization on PVDF Membrane via One-Step Mussel-Inspired Method: Enhancing Fouling Resistance and Self-Cleaning Efficiency

Immobilizing different enzymes on membranes can result in biocatalytic active membranes with a self-cleaning capacity toward a complex mixture of foulants. The membrane modification can reduce fouling and enhance filtration performance. Protease, lipase, and amylase were immobilized on poly(vinylidene fluoride) (PVDF) microfiltration membranes using a polydopamine coating in a one-step method. The concentrations of polydopamine precursor and enzymes were optimized during the immobilization. The higher hydrolytic activities were obtained using 0.2 mg/mL of dopamine hydrochloride and 4 mg/mL of enzymes: 0.90 mgstarch/min·cm2 for amylase, 10.16 nmoltyrosine/min·cm2 for protease, and 20.48 µmolp-nitrophenol/min·cm2 for lipase. Filtration tests using a protein, lipid, and carbohydrate mixture showed that the modified membrane retained 41%, 29%, and 28% of its initial water permeance (1808 ± 39 L/m2·h·bar) after three consecutive filtration cycles, respectively. In contrast, the pristine membrane (initial water permeance of 2016 ± 40 L/m2·h·bar) retained only 23%, 12%, and 8%. Filtrations of milk powder solution were also performed to simulate dairy industry wastewater: the modified membrane maintained 28%, 26%, and 26% of its initial water permeance after three consecutive filtration cycles, respectively, and the pristine membrane retained 34%, 21%, and 7%. The modified membrane showed increased fouling resistance against a mixture of foulants and presented a similar water permeance after three cycles of simulated dairy wastewater filtration. Membrane fouling is reduced by the immobilized enzymes through two mechanisms: increased membrane hydrophilicity (evidenced by the reduced water contact angle after modification) and the enzymatic hydrolysis of foulants as they accumulate on the membrane surface.

Precise redesign for improving enzyme robustness based on coevolutionary analysis and multidimensional virtual screening

Natural enzymes are able to function effectively under optimal physiological conditions, but the intrinsic performance often fails to meet the demands of industrial production. Existing strategies are based mainly on the evaluation and subsequent combination of single-point mutations; however, this approach often suffers from a limited number of designable residues and from low accuracy. Here, we propose a strategy (Co-MdVS) based on coevolutionary analysis and multidimensional virtual screening for precise design to improve enzyme robustness, employing nattokinase as a model. Using this strategy, we efficiently screened 8 dual mutants with enhanced thermostability from a virtual mutation library containing 7980 mutants. After further iterative combination, the optimal mutant M6 exhibited a 31-fold increase in half-life at 55 °C, significantly enhanced acid resistance, and improved catalytic efficiency with different substrates. Molecular dynamics simulations indicated that the reduced flexibility of thermal and acid-sensitive regions resulted in a significantly increased robustness of M6. Furthermore, the potential of multidimensional virtual screening in enhancing design precision has been validated on l-rhamnose isomerase and PETase. Therefore, the Co-MdVS strategy introduced in this research may offer a viable approach for developing enzymes with enhanced robustness.

A hyperstable, low-salt adapted protease from halophilic archaeon with potential applications in salt-fermented foods

Salt-tolerant proteases with remarkable stability are highly desirable biocatalysts in the salt-fermented food industry. In this study, the undigested autocleavage product of HlyA (halolysin A), a low-salt adapted halolysin from halophilic archaeon Halococcus salifodinae, was investigated. HlyA underwent autocleavage of its C-terminal extension (CTE) at temperatures over 40 °C or NaCl concentrations below 2 M to yield HlyAΔCTE. HlyAΔCTE demonstrated robust stability over a wide range of -20-60 °C, 0.5-4 M NaCl, and pH 6.0-10.0 for at least 72 h. Notably, HlyAΔCTE is the first reported halolysin with such exceptional stability. Compared with HlyA, HlyAΔCTE preferred high temperatures (50-75 °C), low salinities (0.5-2.5 M NaCl), and near-neutral (pH 6.5-8.0) conditions to achieve high activity, consistently with its production conditions. HlyAΔCTE displayed a higher Vmax value against azocasein than HlyA. During fish sauce fermentation, HlyAΔCTE significantly enhanced fish protein hydrolysis, indicating its potential as a robust biocatalyst in the salt-fermented food industry.

Designing multi-epitope vaccine against human cytomegalovirus integrating pan-genome and reverse vaccinology pipelines

Human cytomegalovirus (HCMV) is accountable for high morbidity in neonates and immunosuppressed individuals. Due to the high genetic variability of HCMV, current prophylactic measures are insufficient. In this study, we employed a pan-genome and reverse vaccinology approach to screen the target for efficient vaccine candidates. Four proteins, envelope glycoprotein M, UL41A, US23, and US28, were shortlisted based on cellular localization, high solubility, antigenicity, and immunogenicity. A total of 29 B-cell and 44 T-cell highly immunogenic and antigenic epitopes with high global population coverage were finalized using immunoinformatics tools and algorithms. Further, the epitopes that were overlapping among the finalized B-cell and T-cell epitopes were linked with suitable linkers to form various combinations of multi-epitopic vaccine constructs. Among 16 vaccine constructs, Vc12 was selected based on physicochemical and structural properties. The docking and molecular simulations of VC12 were performed, which showed its high binding affinity (-23.35 kcal/mol) towards TLR4 due to intermolecular hydrogen bonds, salt bridges, and hydrophobic interactions, and there were only minimal fluctuations. Furthermore, Vc12 eliciting a good response was checked for its expression in Escherichia coli through in silico cloning and codon optimization, suggesting it to be a potent vaccine candidate.

Molecular regulation of rapeseed protein for improving its techno-functional properties

To improve the techno-functional properties of rapeseed protein (RP), this work tried to regulate the molecular structure of RP via inducing the co-assembly of RP with zein and whey protein (WP). The results showed that WP and zein mainly regulate the folding process of RP through hydrophobic and disulfide bonds, thereby altering the structural conformation and forming stable complex RP (CRP). WP addition not only increased the number of surface charges and hydrophilicity of proteins, but also decreased their sizes, improved the water solubility, as well as the availability of active groups. These changes significantly increased the foaming capacity (from 60 % to 147 %) and in vitro gastric digestion rate (from 10 % to 60 %) of CRP. Besides, WP also contributed to the formation of gels and the regulation of their textural profiles. Comparatively, zein improved the hydrophobicity of CRP and balanced degree of intermolecular forces, which effectively increased the emulsifying activity index of CRP from 22 m2/g to 90 m2/g. Zein decreased the hardness, springiness and water-holding capacity of gel, but increased its gumminess and chewiness. Overall, both WP and zein effectively changed the structural conformation of RP, and improved its techno-functional properties, which provides an effective strategy to modify protein.

Design of a water-soluble transmembrane receptor kinase with intact molecular function by QTY code

Membrane proteins are critical to biological processes and central to life sciences and modern medicine. However, membrane proteins are notoriously challenging to study, mainly owing to difficulties dictated by their highly hydrophobic nature. Previously, we reported QTY code, which is a simple method for designing water-soluble membrane proteins. Here, we apply QTY code to a transmembrane receptor, histidine kinase CpxA, to render it completely water-soluble. The designed CpxAQTY exhibits expected biophysical properties and highly preserved native molecular function, including the activities of (i) autokinase, (ii) phosphotransferase, (iii) phosphatase, and (iv) signaling receptor, involving a water-solubilized transmembrane domain. We probe the principles underlying the balance of structural stability and activity in the water-solubilized transmembrane domain. Computational approaches suggest that an extensive and dynamic hydrogen-bond network introduced by QTY code and its flexibility may play an important role. Our successful functional preservation further substantiates the robustness and comprehensiveness of QTY code.

Hydrophobic core evolution of major histocompatibility complex class I chain-related protein A for dramatic enhancing binding affinity

Interface residues at sites of protein-protein interaction (PPI) are the focus for affinity optimisation. However, protein hydrophobic cores (HCs) play critical roles and shape the protein surface. We hypothesise that manipulating protein HCs can enhance PPI interaction affinities. A cell stress molecule, major histocompatibility complex class I chain-related protein A (MICA), binds to the natural killer group 2D (NKG2D) homodimer to form three molecule interactions. MICA was used as a study subject to support our hypothesis. We redesigned MICA HCs by directed mutagenesis and isolated high-affinity variants through a newly designed partial-denature panning (PDP) method. A few mutations in MICA HCs increased the NKG2D-MICA interaction affinity by 325-5613-fold. Crystal structures of the NKG2D-MICA variant complexes indicated that mutagenesis of MICA HCs stabilised helical elements for decreasing intermolecular interactive free energy (ΔG) of the NKG2D-MICA heterotrimer. The repacking of MICA HC mutants maintained overall surface residues and the authentic binding specificity of MICA. In conclusion, this study provides a new method for MICA redesign and affinity optimisation through HC manipulation without mutating PPI interface residues. Our study introduces a novel approach to protein manipulation, potentially expanding the toolkit for protein affinity optimisation.

Druggability properties of a L309K mutation in the antibody CH2 domain

In the early stages of antibody drug development, it is imperative to conduct a comprehensive assessment and enhancement of the druggability attributes of potential molecules by considering their fundamental physicochemical properties. This study specifically concentrates on the surface-exposed hydrophobic region of the candidate antibody aPDL1-WT and explores the effectiveness of the L309K mutation strategy. The resulting aPDL1-LK variant demonstrates a notable enhancement over the original antibody in addressing the issue of aggregation and formation of large molecular impurities under accelerated high-temperature conditions. The mutated molecule, aPDL1-LK, exhibits excellent physicochemical properties such as hydrophilicity, conformational stability, charge variant stability, post-translational modifications, and serum stability. In terms of biological function, aPDL1-LK maintains the same glycosylation pattern as the original antibody and shows no significant difference in affinity for antigen hPDL1 protein, CD16a-F158, CD64, CD32a-H131, and complement C1q, compared to aPDL1-WT. The L309K mutation results in an approximately twofold reduction in its affinity for CD16a-V158 and CD32a-R131. In vitro biological assays, including antibody-dependent cell-mediated cytotoxicity (ADCC), antibody-dependent cellular phagocytosis (ADCP), and complement-dependent cytotoxicity (CDC), reveal that the L309K mutation may decrease CD16a-V158-mediated ADCC activity due to the mutation-induced decrease in ligand affinity, while not affect CD32a-R131-mediated ADCP activity. In conclusion, the L309K mutation offers a promising strategy to enhance the druggability properties of candidate antibodies.

Linguistics-based formalization of the antibody language as a basis for antibody language models

Apparent parallels between natural language and antibody sequences have led to a surge in deep language models applied to antibody sequences for predicting cognate antigen recognition. However, a linguistic formal definition of antibody language does not exist, and insight into how antibody language models capture antibody-specific binding features remains largely uninterpretable. Here we describe how a linguistic formalization of the antibody language, by characterizing its tokens and grammar, could address current challenges in antibody language model rule mining.

GATSol, an enhanced predictor of protein solubility through the synergy of 3D structure graph and large language modeling

BACKGROUND

Protein solubility is a critically important physicochemical property closely related to protein expression. For example, it is one of the main factors to be considered in the design and production of antibody drugs and a prerequisite for realizing various protein functions. Although several solubility prediction models have emerged in recent years, many of these models are limited to capturing information embedded in one-dimensional amino acid sequences, resulting in unsatisfactory predictive performance.

RESULTS

In this study, we introduce a novel Graph Attention network-based protein Solubility model, GATSol, which represents the 3D structure of proteins as a protein graph. In addition to the node features of amino acids extracted by the state-of-the-art protein large language model, GATSol utilizes amino acid distance maps generated using the latest AlphaFold technology. Rigorous testing on independent eSOL and the Saccharomyces cerevisiae test datasets has shown that GATSol outperforms most recently introduced models, especially with respect to the coefficient of determination R2, which reaches 0.517 and 0.424, respectively. It outperforms the current state-of-the-art GraphSol by 18.4% on the S. cerevisiae_test set.

CONCLUSIONS

GATSol captures 3D dimensional features of proteins by building protein graphs, which significantly improves the accuracy of protein solubility prediction. Recent advances in protein structure modeling allow our method to incorporate spatial structure features extracted from predicted structures into the model by relying only on the input of protein sequences, which simplifies the entire graph neural network prediction process, making it more user-friendly and efficient. As a result, GATSol may help prioritize highly soluble proteins, ultimately reducing the cost and effort of experimental work. The source code and data of the GATSol model are freely available at https://github.com/binbinbinv/GATSol .

Recombinant multiepitope proteins expressed in Escherichia coli cells and their potential for immunodiagnosis

Recombinant multiepitope proteins (RMPs) are a promising alternative for application in diagnostic tests and, given their wide application in the most diverse diseases, this review article aims to survey the use of these antigens for diagnosis, as well as discuss the main points surrounding these antigens. RMPs usually consisting of linear, immunodominant, and phylogenetically conserved epitopes, has been applied in the experimental diagnosis of various human and animal diseases, such as leishmaniasis, brucellosis, cysticercosis, Chagas disease, hepatitis, leptospirosis, leprosy, filariasis, schistosomiasis, dengue, and COVID-19. The synthetic genes for these epitopes are joined to code a single RMP, either with spacers or fused, with different biochemical properties. The epitopes' high density within the RMPs contributes to a high degree of sensitivity and specificity. The RMPs can also sidestep the need for multiple peptide synthesis or multiple recombinant proteins, reducing costs and enhancing the standardization conditions for immunoassays. Methods such as bioinformatics and circular dichroism have been widely applied in the development of new RMPs, helping to guide their construction and better understand their structure. Several RMPs have been expressed, mainly using the Escherichia coli expression system, highlighting the importance of these cells in the biotechnological field. In fact, technological advances in this area, offering a wide range of different strains to be used, make these cells the most widely used expression platform. RMPs have been experimentally used to diagnose a broad range of illnesses in the laboratory, suggesting they could also be useful for accurate diagnoses commercially. On this point, the RMP method offers a tempting substitute for the production of promising antigens used to assemble commercial diagnostic kits.

Structure-Based De Novo Design for the Discovery of Miniprotein Inhibitors Targeting Oncogenic Mutant BRAF

Being a component of the Ras/Raf/MEK/ERK signaling pathway crucial for cellular responses, the VRAF murine sarcoma viral oncogene homologue B1 (BRAF) kinase has emerged as a promising target for anticancer drug discovery due to oncogenic mutations that lead to pathway hyperactivation. Despite the discovery of several small-molecule BRAF kinase inhibitors targeting oncogenic mutants, their clinical utility has been limited by challenges such as off-target effects and suboptimal pharmacological properties. This study focuses on identifying miniprotein inhibitors for the oncogenic V600E mutant BRAF, leveraging their potential as versatile drug candidates. Using a structure-based de novo design approach based on binding affinity to V600E mutant BRAF and hydration energy, 39 candidate miniprotein inhibitors comprising three helices and 69 amino acids were generated from the substructure of the endogenous ligand protein (14-3-3). Through in vitro binding and kinase inhibition assays, two miniproteins (63 and 76) were discovered as novel inhibitors of V600E mutant BRAF with low-micromolar activity, with miniprotein 76 demonstrating a specific impediment to MEK1 phosphorylation in mammalian cells. These findings highlight miniprotein 76 as a potential lead compound for developing new cancer therapeutics, and the structural features contributing to its biochemical potency against V600E mutant BRAF are discussed in detail.

Enhancing Protein Solubility via Glycosylation: From Chemical Synthesis to Machine Learning Predictions

Glycosylation is a valuable tool for modulating protein solubility; however, the lack of reliable research strategies has impeded efficient progress in understanding and applying this modification. This study aimed to bridge this gap by investigating the solubility of a model glycoprotein molecule, the carbohydrate-binding module (CBM), through a two-stage process. In the first stage, an approach involving chemical synthesis, comparative analysis, and molecular dynamics simulations of a library of glycoforms was employed to elucidate the effect of different glycosylation patterns on solubility and the key factors responsible for the effect. In the second stage, a predictive mathematical formula, innovatively harnessing machine learning algorithms, was derived to relate solubility to the identified key factors and accurately predict the solubility of the newly designed glycoforms. Demonstrating feasibility and effectiveness, this two-stage approach offers a valuable strategy for advancing glycosylation research, especially for the discovery of glycoforms with increased solubility.

DeepKa Web Server: High-Throughput Protein pKa Prediction

DeepKa is a deep-learning-based protein pKa predictor proposed in our previous work. In this study, a web server was developed that enables online protein pKa prediction driven by DeepKa. The web server provides a user-friendly interface where a single step of entering a valid PDB code or uploading a PDB format file is required to submit a job. Two case studies have been attached in order to explain how pKa's calculated by the web server could be utilized by users. Finally, combining the web server with post processing as described in case studies, this work suggests a quick workflow of investigating the relationship between protein structure and function that are pH dependent. The web server of DeepKa is freely available at http://www.computbiophys.com/DeepKa/main.

Pathways of amyloid fibril formation and protein aggregation

The main cause of many neurodegenerative diseases and systemic amyloidoses is protein and peptide aggregation and the formation of amyloid fibrils. The study of aggregation mechanisms, the discovery and description of aggregate structures, and a comprehensive understanding of the molecular mechanisms of amyloid formation are of great importance for the diagnostic processes at the molecular level and for the development of therapeutic strategies to counter aggregation-associated disorders. Given that understanding protein misfolding phenomena is directly related to the protein folding process, we will briefly explain the protein folding mechanism and then discuss the important factors involved in protein aggregation. In the following, we review different mechanisms of amyloid formation and finally represent the current knowledge on how amyloid fibrils are formed based on kinetic and thermodynamic factors.

Changing Cinnamaldehyde Skeleton Achieves Antibacterial Nanoswitch

Changeable substituent groups of organic molecules can provide an opportunity to clarify the antibacterial mechanism of organic molecules by tuning the electron cloud density of their skeleton. However, understanding the antibacterial mechanism of organic molecules is challenging. Herein, we reported a molecular view strategy for clarifying the antibacterial switch mechanism by tuning electron cloud density of cinnamaldehyde molecule skeleton. The cinnamaldehyde and its derivatives were self-assembled into nanosheets with excellent water solubility, respectively. The experimental results show that α-bromocinnamaldehyde (BCA) nanosheets exhibits unprecedented antibacterial activity, but there is no antibacterial activity for α-methylcinnamaldehyde nanosheets. Therefore, the BCA nanosheets and α-methylcinnamaldehyde nanosheets achieve an antibacterial switch. Theoretical calculations further confirmed that the electron-withdrawing substituent of the bromine atom leads to a lower electron cloud density of the aldehyde group than that of the electron-donor substituent of the methyl group at the α-position of the cinnamaldehyde skeleton, which is a key point in elucidating the antimicrobial switch mechanism. The excellent biocompatibility of BCA nanosheets was confirmed by CCK-8. The mouse wound infection model, H&E staining, and the crawling ability of drosophila larvae show that as-prepared BCA nanosheets are safe and promising for wound healing. This study provides a new strategy for the synthesis of low-cost organic nanomaterials with good biocompatibility. It is expected to expand the application of natural organic small molecule materials in antimicrobial agents.

Structural bioinformatics studies of glutamate transporters and their AlphaFold2 predicted water-soluble QTY variants and uncovering the natural mutations of L->Q, I->T, F->Y and Q->L, T->I and Y->F

Glutamate transporters play key roles in nervous physiology by modulating excitatory neurotransmitter levels, when malfunctioning, involving in a wide range of neurological and physiological disorders. However, integral transmembrane proteins including the glutamate transporters remain notoriously difficult to study, due to their localization within the cell membrane. Here we present the structural bioinformatics studies of glutamate transporters and their water-soluble variants generated through QTY-code, a protein design strategy based on systematic amino acid substitutions. These include 2 structures determined by X-ray crystallography, cryo-EM, and 6 predicted by AlphaFold2, and their predicted water-soluble QTY variants. In the native structures of glutamate transporters, transmembrane helices contain hydrophobic amino acids such as leucine (L), isoleucine (I), and phenylalanine (F). To design water-soluble variants, these hydrophobic amino acids are systematically replaced by hydrophilic amino acids, namely glutamine (Q), threonine (T) and tyrosine (Y). The QTY variants exhibited water-solubility, with four having identical isoelectric focusing points (pI) and the other four having very similar pI. We present the superposed structures of the native glutamate transporters and their water-soluble QTY variants. The superposed structures displayed remarkable similarity with RMSD 0.528Å-2.456Å, despite significant protein transmembrane sequence differences (41.1%->53.8%). Additionally, we examined the differences of hydrophobicity patches between the native glutamate transporters and their QTY variants. Upon closer inspection, we discovered multiple natural variations of L->Q, I->T, F->Y and Q->L, T->I, Y->F in these transporters. Some of these natural variations were benign and the remaining were reported in specific neurological disorders. We further investigated the characteristics of hydrophobic to hydrophilic substitutions in glutamate transporters, utilizing variant analysis and evolutionary profiling. Our structural bioinformatics studies not only provided insight into the differences between the hydrophobic helices and hydrophilic helices in the glutamate transporters, but they are also expected to stimulate further study of other water-soluble transmembrane proteins.

Transmucosal Delivery of Peptides and Proteins Through Nanofibers: Current Status and Emerging Developments

Advancements in recombinant DNA technology have made proteins and peptides available for diagnostic and therapeutic applications, but their effectiveness when taken orally leads to poor patient compliance, requiring clinical administration. Among the alternative routes, transmucosal delivery has the advantage of being noninvasive and bypassing hepato-gastrointestinal clearance. Various mucosal routes-buccal, nasal, pulmonary, rectal, and vaginal-have been explored for delivering these macromolecules. Nanofibers, due to their unique properties like high surface-area-to-volume ratio, mechanical strength, and improved encapsulation efficiency, serve as promising carriers for proteins and peptides. These nanofibers can be tailored for quick dissolution, controlled release, enhanced encapsulation, targeted delivery, and improved bioavailability, offering superior pharmaceutical and pharmacokinetic performance compared to conventional methods. This leads to reduced dosages, fewer side effects, and enhanced patient compliance. Hence, nanofibers hold tremendous potential for protein/peptide delivery, especially through mucosal routes. This review focuses on the therapeutic application of proteins and peptides, challenges faced in their conventional delivery, techniques for fabricating different types of nanofibers and, various nanofiber-based dosage forms, and factors influencing nanofiber generation. Insights pertaining to the precise selection of materials used for fabricating nanofibers and regulatory aspects have been covered. Case studies wherein the use of specific protein/peptide-loaded nanofibers and delivered via oral/vaginal/nasal mucosa for diagnostic/therapeutic use and related preclinical and clinical studies conducted have been included in this review.

Synthesis of bioengineered heparin chemically and biologically similar to porcine-derived products and convertible to low MW heparin

Heparins have been invaluable therapeutic anticoagulant polysaccharides for over a century, whether used as unfractionated heparin or as low molecular weight heparin (LMWH) derivatives. However, heparin production by extraction from animal tissues presents multiple challenges, including the risk of adulteration, contamination, prion and viral impurities, limited supply, insecure supply chain, and significant batch-to-batch variability. The use of animal-derived heparin also raises ethical and religious concerns, as well as carries the risk of transmitting zoonotic diseases. Chemoenzymatic synthesis of animal-free heparin products would offer several advantages, including reliable and scalable production processes, improved purity and consistency, and the ability to produce heparin polysaccharides with molecular weight, structural, and functional properties equivalent to those of the United States Pharmacopeia (USP) heparin, currently only sourced from porcine intestinal mucosa. We report a scalable process for the production of bioengineered heparin that is biologically and compositionally similar to USP heparin. This process relies on enzymes from the heparin biosynthetic pathway, immobilized on an inert support and requires a tailored N-sulfoheparosan with N-sulfo levels similar to those of porcine heparins. We also report the conversion of our bioengineered heparin into a LMWH that is biologically and compositionally similar to USP enoxaparin. Ultimately, we demonstrate major advances to a process to provide a potential clinical and sustainable alternative to porcine-derived heparin products.

Assessing the performance of MM/PBSA and MM/GBSA methods. 10. Prediction reliability of binding affinities and binding poses for RNA-ligand complexes

Ribonucleic acid (RNA)-ligand interactions play a pivotal role in a wide spectrum of biological processes, ranging from protein biosynthesis to cellular reproduction. This recognition has prompted the broader acceptance of RNA as a viable candidate for drug targets. Delving into the atomic-scale understanding of RNA-ligand interactions holds paramount importance in unraveling intricate molecular mechanisms and further contributing to RNA-based drug discovery. Computational approaches, particularly molecular docking, offer an efficient way of predicting the interactions between RNA and small molecules. However, the accuracy and reliability of these predictions heavily depend on the performance of scoring functions (SFs). In contrast to the majority of SFs used in RNA-ligand docking, the end-point binding free energy calculation methods, such as molecular mechanics/generalized Born surface area (MM/GBSA) and molecular mechanics/Poisson Boltzmann surface area (MM/PBSA), stand as theoretically more rigorous approaches. Yet, the evaluation of their effectiveness in predicting both binding affinities and binding poses within RNA-ligand systems remains unexplored. This study first reported the performance of MM/PBSA and MM/GBSA with diverse solvation models, interior dielectric constants (εin) and force fields in the context of binding affinity prediction for 29 RNA-ligand complexes. MM/GBSA is based on short (5 ns) molecular dynamics (MD) simulations in an explicit solvent with the YIL force field; the GBGBn2 model with higher interior dielectric constant (εin = 12, 16 or 20) yields the best correlation (Rp = -0.513), which outperforms the best correlation (Rp = -0.317, rDock) offered by various docking programs. Then, the efficacy of MM/GBSA in identifying the near-native binding poses from the decoys was assessed based on 56 RNA-ligand complexes. However, it is evident that MM/GBSA has limitations in accurately predicting binding poses for RNA-ligand systems, particularly compared with notably proficient docking programs like rDock and PLANTS. The best top-1 success rate achieved by MM/GBSA rescoring is 39.3%, which falls below the best results given by docking programs (50%, PLNATS). This study represents the first evaluation of MM/PBSA and MM/GBSA for RNA-ligand systems and is expected to provide valuable insights into their successful application to RNA targets.

Enhancing bezlotoxumab binding to C. difficile toxin B2: insights from computational simulations and mutational analyses for antibody design

Clostridioides difficile infection (CDI) is a significant concern caused by widespread antibiotic use, resulting in diarrhea and inflammation from the gram-positive anaerobic bacterium C. difficile. Although bezlotoxumab (Bez), a monoclonal antibody (mAb), was developed to address CDI recurrences, the recurrence rate remains high, partly due to reduced neutralization efficiency against toxin B2. In this study, we aimed to enhance the binding of Bez to C. difficile toxin B2 by combining computational simulations and mutational analyses. We identified specific mutations in Bez, including S28R, S31W/K, Y32R, S56W and G103D/S in the heavy chain (Hc), and S32F/H/R/W/Y in the light chain (Lc), which significantly improved binding to toxin B2 and formed critical protein-protein interactions. Through molecular dynamics simulations, several single mutations, such as HcS28R, LcS32H, LcS32R, LcS32W and LcS32Y, exhibited superior binding affinities to toxin B2 compared to Bez wild-type (WT), primarily attributed to Coulombic interactions. Combining the HcS28R mutation with four different mutations at residue LcS32 led to even greater binding affinities in double mutants (MTs), particularly HcS28R/LcS32H, HcS28R/LcS32R and HcS28R/LcS32Y, reinforcing protein-protein binding. Analysis of per-residue decomposition free energy highlighted key residues contributing significantly to enhanced binding interactions, emphasizing the role of electrostatic interactions. These findings offer insights into rational Bez MT design for improved toxin B2 binding, providing a foundation for developing more effective antibodies to neutralize toxin B2 and combat-related infections.Communicated by Ramaswamy H. Sarma.

Computational design of soluble functional analogues of integral membrane proteins

De novo design of complex protein folds using solely computational means remains a significant challenge. Here, we use a robust deep learning pipeline to design complex folds and soluble analogues of integral membrane proteins. Unique membrane topologies, such as those from GPCRs, are not found in the soluble proteome and we demonstrate that their structural features can be recapitulated in solution. Biophysical analyses reveal high thermal stability of the designs and experimental structures show remarkable design accuracy. The soluble analogues were functionalized with native structural motifs, standing as a proof-of-concept for bringing membrane protein functions to the soluble proteome, potentially enabling new approaches in drug discovery. In summary, we designed complex protein topologies and enriched them with functionalities from membrane proteins, with high experimental success rates, leading to a de facto expansion of the functional soluble fold space.