The evolution of enzymes through multisite combinations is often constrained by the dynamic correlations among residues, resulting in the challenge of achieving additive effects when combining single-site positive mutants. Nevertheless, the incorporation of distal-site residue mutants offers a promising avenue to unlock synergistic interactions, particularly in enzymes with channels that mediate substrate and product transport. While combinatorial mutagenesis typically requires exhaustive screening of extensive mutant libraries to identify superior variants. In this study, a multisite combination strategy using dynamic cross-correlation matrices (DCCM) iterative analysis, coupled with location orthogonal filtration (DCCM/OL) was established based on single-site mutants enhancing enzyme performance by engineering distal residues using the "Structure, SCANEER, and Sequence (3S)" approach. Thirteen beneficial single mutants of oleate hydratase from Staphylococcus aureus (SaOhy), which catalyzes the hydration of linoleic acid, were targeted using the "3S" approach. By employing the DCCM/OL strategy, the number of candidates in the combination mutation library for experimental screening were reduced from 60 to 5. And a triple-site mutant, SaOhy_L151V/I411L/V135A, was ultimately identified, which increased the catalytic efficiencies with linoleic acid and oleic acid by a 4- and 2.3-fold, respectively. The philosophy of multisite mutation based on distal residues using the DCCM/OL strategy effectively achieves an amplification of enzymatic activity with distinct substrates simultaneously. This study offers a promising strategy for the construction of a mutant smart library and multisite combinations to efficiently increase enzymatic performance.

Antibodies are pivotal molecules in the immune system, playing crucial roles in disease diagnosis, treatment, and research. Among these, shark-derived nanobody (VNAR) antibodies stand out as the smallest known antigen-binding fragments, with a molecular weight of approximately 12 kDa. Their unique size allows for efficient targeting of antigens, making them valuable tools in biomedicine. However, traditional phage display assays for screening shark nanobodies have limitations, as not all nanobodies express effectively in this format. In this study, we innovatively employed Cre recombinase as an antigen and integrated next-generation sequencing, cell sorting, phage display, and mass spectrometry to enhance the screening of antigen-specific shark nanobodies. Through diverse screening strategies, we successfully identified a substantial array of VNARs. Functional assays in mammalian cells confirmed that these VNARs bind specifically to Cre recombinase without inhibiting its enzymatic activity. Our findings not only advance the methodologies for VNAR screening but also open new avenues for the development of targeted therapeutics and immunological research in aquatic fish.

The advancement and utilization of functional foods hold the capacity to efficaciously enhance human nutrition and well-being. However, within the present epoch characterized by recurrent food safety and quality concerns, there exists a paucity of suitable food systems that are capable of concurrently amalgamating the merits of nutritional enrichment and high stability. Therefore, in this study, heteroprotein aggregates were synthesized with modified-soy lipophilic protein (SLP) and lysozyme (LY) as the main components. Subsequently, an interpenetrating network (IPN) was constructed using sugar beet pectin (SBP) and heteroprotein aggregates to form W-O-W emulsion gels. The heteroprotein aggregates formed by SLP and LY provided a stable oil-water interface, allowing the IPN to maximally confine the internal oil droplets and improve the mechanical properties and water retention capacity of the W-O-W emulsion gels. Meanwhile, the W-O-W emulsion gels also possessed pH-responsiveness, endowing them with the ability to modulate their self-morphology and network structure in accordance with pH fluctuations. Moreover, they possessed outstanding protective and slow-release efficacies, which were conducive to the targeted release of the delivered substances. Thus, W-O-W emulsion gels based on heteroprotein aggregation and SBP show great potential as an option for functional food applications.

The volume of protein sequence data has grown exponentially in recent years, driven by advancements in metagenomics. Despite this, a substantial proportion of these sequences remain poorly annotated, underscoring the need for robust bioinformatics tools to facilitate efficient characterisation and annotation for functional studies.

We present PyPropel, a Python-based computational tool developed to streamline the large-scale analysis of protein data, with a particular focus on applications in machine learning. PyPropel integrates sequence and structural data pre-processing, feature generation, and post-processing for model performance evaluation and visualisation, offering a comprehensive solution for handling complex protein datasets.

PyPropel provides added value over existing tools by offering a unified workflow that encompasses the full spectrum of protein research, from raw data pre-processing to functional annotation and model performance analysis, thereby supporting efficient protein function studies.

The interaction between amino acids (AAs) and hydration water is fundamental to protein folding and protein-protein interactions. Here, we proposed a hydrophobicity scale for AAs based on their computed free energetic cost of dewetting. This metric captures both entropic and enthalpic contributions of AA-water interactions and allows a systematic and intuitive classification of AAs. Using indirect umbrella sampling (INDUS), we rank individual AAs based on the relative magnitude of their dewetting free energies, from lowest (most hydrophobic) to highest (most hydrophilic). This new hydrophobicity scale is a starting point to evaluate different elements of water hydration behavior, and we focus here on the water structure and translational diffusivity of the hydration waters. While the latter is commonly used as a proxy for hydrophobicity, we show that its behavior is in fact nonmonotonic: hydrophobic residues show slow water diffusion due to highly structured hydration water networks, while highly hydrophilic residues have slow water diffusion due to strong hydrogen bonds with water despite less structured hydration networks. We extend our analysis of hydration properties to intrinsically disordered peptides with varied sequence patterning (sequences of proline/leucine and arginine/glutamic acid residues). We find that the hydration behavior of these peptides is highly context-dependent, with hydrophobic (hydrophilic) patches cooperatively enhancing hydrophobicity (hydrophilicity). These molecular insights of sequence-dependent hydration behaviors may be particularly impactful for the study of intrinsically disordered proteins implicated in liquid-liquid phase separation and aggregation, processes where AAs' hydration environments are complex and changing.

Allergic diseases have become a major health issue in the 21st century. The FAO has pinpointed the eight most prevalent allergens worldwide, with shrimp allergy attracting global concern due to its escalating incidence. This review delves into the current knowledge of shrimp allergen types and traits, immune response mechanisms, advancements in cross-reactivity research, and breakthroughs in diagnostic and therapeutic methods. It highlights the variety of shrimp allergens, such as tropomyosin and arginine kinase, and concentrates on IgE-mediated immediate hypersensitivity reactions, involving mast cells and basophils, alongside the role of T cells and cytokines in non-IgE-mediated delayed hypersensitivity reactions. The exploration of cross-reactivity underscores the connection between shrimp allergy and allergies to other animals. Utilizing bioinformatics tools, including homology analysis, epitope prediction, and molecular modeling, has enhanced our comprehension of allergen molecular features. In treatment and diagnosis, innovative approaches like immunotherapy and gene editing technology hold potential to decrease allergic sensitivity, while emerging reduction techniques like heat treatment and enzymatic hydrolysis offer new strategies for the prevention and management of food allergies. The evolution of allergen detection and purification technologies has spurred innovation in testing methodologies, encompassing traditional in vivo tests like SPT and DBPCFC, in addition to a range of other techniques such as immunoassays, biochip technology, PCR, and histamine release experiments, propelling the instantaneous and accurate identification of allergens. These scientific breakthroughs not only expand our understanding of shrimp allergen biology but also lay the foundation for developing more effective allergy prevention and control strategies.

The late embryogenesis abundant proteins (LEAPs) are a class of noncatalytic, intrinsically disordered proteins with a malleable structure. Some LEAPs exhibit a protein and/or membrane binding capacity and LEAP binding to various targets has been positively correlated with abiotic stress tolerance. Regarding the LEAPs' presumptive role in protein protection, identifying client proteins (CtPs) to which LEAPs bind is one practicable means of revealing the mechanism by which they exert their function. To this end, we used phage display affinity selection to screen libraries derived from Arabidopsis thaliana seed mRNA with recombinant orthologous LEAPs from Arabidopsis and soybean (Glycine max). Subsequent high-throughput sequencing of DNA from affinity-purified phage was performed to characterize the entire subpopulation of phage retained by each LEAP ortholog. This entailed cataloging in-frame fusions, elimination of false positives, and aligning the hits on the CtP scaffold to reveal domains of respective CtPs that bound to orthologous LEAPs. This approach (paired-end phage sequencing) revealed a subpopulation of the proteome constituting the CtP repertoire in common between the two dehydrin orthologs (LEA14 and GmPm12) compared to bovine serum albumin (unrelated binding control). The veracity of LEAP:CtP binding for one of the CtPs (LEA14 and GmPM12 self-association) was independently assessed using temperature-related intensity change analysis. Moreover, LEAP:CtP interactions for four other CtPs were confirmed in planta using bimolecular fluorescence complementation assays. The results provide insights into the involvement of the dehydrin Y-segments and K-domains in protein binding.

Significant populations in tropical and sub-tropical locations all over the world are severely impacted by a group of neglected tropical diseases called leishmaniases. This disease is caused by roughly 20 species of the protozoan parasite from the Leishmania genus. Disease prevention strategies that include early detection, vector control, treatment of affected individuals, and vaccination are all essential. The diagnosis is critical for selecting methods of therapy, preventing transmission of the disease, and minimizing symptoms so that the affected individual can have a better quality of life. Nevertheless, the diagnostic methods do eventually have limitations, and there is no established gold standard. Some disadvantages include the existence of cross-reactions with other species, and limited sensitivity and specificity, which are mostly determined by the type of antigen used to perform the tests. A viable alternative for a more precise diagnosis is the application of recombinant antigens, which have been generated using bioinformatics approaches and have shown increased diagnostic accuracy. This approach proves valuable as it spans from epitope selection to predicting the interactions within the antibody-antigen complex through docking analysis. As a result, identifying potential new antigens using bioinformatics resources becomes an effective technique since it may result in an earlier and more accurate diagnosis. Consequently, the primary aim of this review is to conduct a comprehensive overview of the most significant in silico tools developed over time, with a focus on evaluating their efficacy and exploring their potential applications in optimizing the selection of highly specific molecules for a more effective diagnosis of leishmaniasis.

Gastrointestinal cancer is a common malignant tumor with a high incidence worldwide. Despite continuous improvements in diagnosis and treatment strategies, the overall prognosis of gastrointestinal tumors remains poor. Carcinoembryonic antigen (CEA) is highly expressed in various types of cancers, especially in gastrointestinal cancers, making it a potential target for therapeutic intervention. Therefore, the expression of CEA can be used as an indication of the existence of tumors, chosen as a target for molecular imaging diagnosis, and effectively utilized in the targeted therapy of gastrointestinal cancers. In this study, we report the selection and characterization of affibody molecules (ZCEA539, ZCEA546, and ZCEA919) specific to the CEA protein. Their ability to bind to recombinant and native CEA protein has been confirmed by surface plasmon resonance (SPR), immunofluorescence, and immunohistochemistry assays. Furthermore, Dylight755-labeled ZCEA affibody showed accumulation within the tumor site 1 h post injection and was continuously enhanced for 4 h. The Dylight755-labeled ZCEA affibody exhibited high tumor-targeting specificity in CEA+ xenograft-bearing mice and possesses promising characteristics for tumor-targeting imaging. Overall, our results suggest the potential use of ZCEA affibodies as fluorescent molecular imaging probes for detecting CEA expression in gastrointestinal cancer.

A key factor in biomineralization is the use of organic molecules to direct the formation of inorganic materials. However, identification of molecules that can selectively produce the calcium carbonate polymorphs calcite or aragonite has proven extremely challenging. Here, we use a phage display approach to identify proteins - rather than the short peptides typically identified using this method - that can direct calcium carbonate formation. A 1.3 × 1010 library of Affimer proteins was displayed on modified M13 phage, where an Affimer is a ≈13 kDa protein scaffold that displays two variable regions of 9-13 residues. The phage displaying the Affimer library were then screened in binding assays against calcite and aragonite at pH 7.4, and four different strongly-binding proteins were identified. The two aragonite-binding proteins generated aragonite when calcium and magnesium ions were present at a 1 : 1 ratio, while the calcite-binding proteins produce magnesium-calcite under the same conditions. Calcite alone formed in the presence of all four proteins in the absence of magnesium ions. In combination with molecular dynamics simulations to evaluate the conformations of the proteins in solution, this work demonstrates the importance of conformation in polymorph control, and highlights the importance of magnesium ions, which are abundant in seawater, to reduce the energetic barriers associated with aragonite formation.

Porcine sapelovirus (PSV) is a new pathogen that negatively impacts the pig industry in China. Affected pigs experience severe diarrhea and even death. Vaccination is used to control disease outbreaks, and sensitive diagnostic methods that can distinguish infected animals from vaccinated animals (DIVA) are essential for monitoring the effectiveness of disease control programs. Tests based on the detection of the nonstructural protein (NSP) 3AB are reliable indicators of viral replication in infected and vaccinated animals. In this study, the recombinant PSV 3AB protein was expressed by a prokaryotic expression system, and an indirect ELISA method was established. Serum samples from healthy animals, immunized animals, and infected animals were evaluated. The ELISA method identified 3AB with high sensitivity (99.78%) and specificity (100.0%), and no cross-reaction was observed with serum antibodies against porcine reproductive and respiratory syndrome virus (PRRSV), infection with classical swine fever virus (CSFV), pseudorabies virus (PRV), bovine viral diarrhea virus (BVDV), porcine epidemic diarrhea virus (PEDV), or foot-and-mouth disease virus (FMDV). The ELISA method described here can effectively distinguish infected and vaccinated animals and is an important inexpensive tool for monitoring serum and controlling PSV.

While computational epitope prediction methods have found broad application, their use, specifically in allergy-related contexts, remains relatively less explored. This study benchmarks several publicly available epitope prediction tools, focusing on the allergenic IgE and T-cell epitopes of Fel d 1, an extensively studied allergen. Using a variety of tools accessible via the Immune Epitope Database (IEDB) and other resources, we evaluate their ability to identify the known linear IgE and T-cell epitopes of Fel d 1. Our results show a limited effectiveness for B-cell epitope prediction methods, with most performing only marginally better than random selection. We also explored the general predictive abilities on other allergens, and the results were largely random. When predicting T-cell epitopes, ProPred successfully identified all known Fel d 1 T-cell epitopes, whereas the IEDB approach missed two known epitopes and demonstrated a tendency to over-predict. However, when applied to a larger test set, both methods performed only slightly better than random selection. Our findings show the limitations of current computational epitope prediction methods in accurately identifying allergenic epitopes, emphasizing the need for methodological advancements in allergen research.

The plerocercoid larva of Spirometra mansoni can cause a parasitic zoonosis-sparganosis. Malate dehydrogenase (MDH) plays a very important role in the life activities of parasites. However, little is known about the MDH family in S. mansoni. We identified eight new MDH members in S. mansoni in this study. Clustering analysis divided SmMDHs into two groups and revealed patterns similar to the conserved motif organization. RT-qPCR suggested that five MDHs were highly expressed in the mature proglottid and that three MDHs were highly expressed in the gravid proglottid. Phylogenetic analysis revealed that SmMDHs contain both conserved family members and members in the process of further diversification. rSmMDH has an NAD binding domain, a dimer interface and a substrate binding domain. Natural SmMDH was immunolocalized in the tissues and follicles around the uterus in the mature or gravid proglottid and eggshells. The maximum forward and reverse reaction activities of rSmMDH were observed at pH 8.5 and 9.0, respectively. The optimum temperature for enzyme activity was 37 °C in the forward reaction and 40 °C in the reverse reaction. These results lay the foundation for studying the molecular functions and mechanisms of MDHs in S. mansoni and related taxa.

Accurate computational prediction of B-cell epitopes can greatly enhance biomedical research and rapidly advance efforts to develop therapeutics, monoclonal antibodies, vaccines, and immunodiagnostic reagents. Previous research efforts have primarily focused on the development of computational methods to predict linear epitopes rather than conformational epitopes; however, the latter is much more biologically predominant. Several conformational B-cell epitope prediction methods have recently been published, but their predictive performances are weak. Here, we present a review of the latest computational methods and assess their performances on a diverse test set of 29 non-redundant unbound antigen structures. Our results demonstrate that ISPIPab performs better than most methods and compares favorably with other recent antigen-specific methods. Finally, we suggest new strategies and opportunities to improve computational predictions of conformational B-cell epitopes.

Understanding protein structures is crucial for various bioinformatics research, including drug discovery, disease diagnosis, and evolutionary studies. Protein structure classification is a critical aspect of structural biology, where supervised machine learning algorithms classify structures based on data from databases such as Protein Data Bank (PDB). However, the challenge lies in designing numerical embeddings for protein structures without losing essential information. Although some effort has been made in the literature, researchers have not effectively and rigorously combined the structural and sequence-based features for efficient protein classification to the best of our knowledge. To this end, we propose numerical embeddings that extract relevant features for protein sequences fetched from PDB structures from popular datasets such as PDB Bind and STCRDAB. The features are physicochemical properties such as aromaticity, instability index, flexibility, Grand Average of Hydropathy (GRAVY), isoelectric point, charge at pH, secondary structure fracture, molar extinction coefficient, and molecular weight. We also incorporate scaling features for the sliding windows (e.g., k-mers), which include Kyte and Doolittle (KD) hydropathy scale, Eisenberg hydrophobicity scale, Hydrophilicity scale, Flexibility of the amino acids, and Hydropathy scale. Multiple-feature selection aims to improve the accuracy of protein classification models. The results showed that the selected features significantly improved the predictive performance of existing embeddings.

Lipidic nanocarriers are a broad class of lipid-based vectors with proven potential for packaging and delivering emerging nucleic acid therapeutics. An important early step in the clinical development cycle is large-scale screening of diverse formulation libraries to assess particle quality and payload delivery efficiency. Due to the size of the screening space, this process can be both costly and time-consuming. To address this, computational models capable of predicting clinically relevant physio-chemical properties of dendrimer-lipid nanocarriers, along with their mRNA payload delivery efficiency in human cells are developed. The models are then deployed on a large theoretical nanocarrier pool consisting of over 4.5 million formulations. Top predictions are synthesised for validation using cell-based assays, leading to the discovery of a high quality, high performing, candidate. The methods reported here enable rapid, high-throughput, in silico pre-screening for high-quality candidates, and have great potential to reduce the cost and time required to bring mRNA therapies to the clinic.

Examining the toxicity of peptides is essential for therapeutic peptide-based drug design. Machine learning approaches are frequently used to develop highly accurate predictors for peptide toxicity prediction. In this paper, we present ToxTeller, which provides four predictors using logistic regression, support vector machines, random forests, and XGBoost, respectively. For prediction model development, we construct a data set of toxic and nontoxic peptides from SwissProt and ConoServer databases with existence evidence levels checked. We also fully utilize the protein annotation in SwissProt to collect more toxic peptides than using keyword search alone. From this data set, we construct an independent test data set that shares at most 40% sequence similarity within itself and with the training data set. From a quite comprehensive list of 28 feature combinations, we conduct 10-fold cross-validation on the training data set to determine the optimized feature combination for model development. ToxTeller's performance is evaluated and compared with existing predictors on the independent test data set. Since toxic peptides must be avoided for drug design, we analyze strategies for reducing false-negative predictions of toxic peptides and suggest selecting models by top sensitivity instead of the widely used Matthews correlation coefficient, and also suggest using a meta-predictor approach with multiple predictors.

Malaria remains a public health challenge. Since many control strategies have proven ineffective in eradicating this disease, new strategies are required, among which the design of a multivalent vaccine stands out. However, the effectiveness of this strategy has been hindered, among other reasons, by the genetic diversity observed in parasite antigens. In Plasmodium vivax, the Erythrocyte Binding Protein (PvEBP, also known as DBP2) is an alternate ligand to Duffy Binding Protein (DBP); given its structural resemblance to DBP, EBP/DBP2 is proposed as a promising antigen for inclusion in vaccine design. However, the extent of genetic diversity within the locus encoding this protein has not been comprehensively assessed. Thus, this study aimed to characterize the genetic diversity of the locus encoding the P. vivax EBP/DBP2 protein and to determine the evolutionary mechanisms modulating this diversity. Several intrapopulation genetic variation parameters were estimated using 36 gene sequences of PvEBP/DBP2 from Colombian P. vivax clinical isolates and 186 sequences available in databases. The study then evaluated the worldwide genetic structure and the evolutionary forces that may influence the observed patterns of genetic variation. It was found that the PvEBP/DBP2 gene exhibits one of the lowest levels of genetic diversity compared to other vaccine-candidate antigens. Four major haplotypes were shared worldwide. Analysis of the protein's 3D structure and epitope prediction identified five regions with potential antigenic properties. The results suggest that the PvEBP/DBP2 protein possesses ideal characteristics to be considered when designing a multivalent effective antimalarial vaccine against P. vivax.

Accurate atomistic modeling of the interactions of a chromatography resin with a solute can inform the selection of purification conditions for a product, an important problem in the biotech and pharmaceutical industries. We present a molecular dynamics simulation-based approach for the qualitative prediction of interaction sites (specificity) and retention times (affinity) of a protein for a given chromatography resin. We mimicked the resin with an unrestrained ligand composed of the resin headgroup coupled with successively larger fragments of the agarose backbone. The interactions of the ligand with the protein are simulated in an explicit solvent using the Replica Exchange Molecular Dynamics enhanced sampling approach in conjunction with Hydrogen Mass Repartitioning (REMD-HMR). We computed the ligand interaction surface from the simulation trajectories and correlated the features of the interaction surface with experimentally determined retention times. The simulation and analysis protocol were first applied to a series of ubiquitin mutants for which retention times on Capto MMC resin are available. The ubiquitin simulations helped identify the optimal ligand that was used in subsequent simulations on six proteins for which Capto MMC elution times are available. For each of the six proteins, we computed the interaction surface and characterized it in terms of a range of simulation-averaged residue-level physicochemical descriptors. Modeling of the salt concentrations required for elution with respect to the descriptors resulted in a linear fit in terms of aromaphilicity and Kyte-Doolittle hydrophobicity that was robust to outliers, showed high correlation, and correctly ranked the protein elution order. The physics-based model building approach described here does not require a large experimental data set and can be readily applied to different resins and diverse biomolecules.

Bordetella bronchiseptica is a pathogen causing respiratory infections in mammals. With the improving understanding of companion animals' welfare, addressing the side effects of bordetella vaccine gains importance in dogs. Studies on diverse subunit vaccines are actively pursued in humans to safely and effectively control bordetellosis. Therefore, our objective was to develop a canine bordetella vaccine inspired by human vaccine development. We evaluated the immunogenicity of the two bacterial components: the outer membrane proteins (OMPs) and the dermonecrotic toxin (DNT) from a canine isolate of B. bronchiseptica. In-silico analysis identified eight domains of DNT, and Domain 3 was selected as the most promising antigen candidate. Additionally, the OMPs were extracted and examined using SDS-PAGE and Western blot analysis. The distinct immunological characteristic of OMPs and DNT-3 were examined individually and in combination. Gene expression and cytokine production were also evaluated in DH82 cells after stimulation with those antigens. Treatment with OMPs resulted in higher level of Th1 related cytokines, while DNT-3 induced a predominant response associated with Th17 and Th2 in the cytokine production. Synergistic effects were observed exclusively on IL-23, indicating increase of a potential risk of side effects when OMPs and DNT act together. These findings provide valuable insights into the reactogenicity of conventional Bordetella vaccines. Further, the presented preclinical data in this study offer an alternative method of the development for an optimal next-generation Bordetella vaccine for companion animals and humans, replacing the acellular vaccines containing both toxin and protein components.

The emergence and spread of antibiotic-resistant bacteria pose a significant public health threat, necessitating the exploration of alternative antibacterial strategies. Antibacterial peptide (ABP) is a kind of antimicrobial peptide (AMP) that has the potential ability to fight against bacteria infection, offering a promising avenue for developing novel therapeutic interventions. This study introduces AMPActiPred, a three-stage computational framework designed to identify ABPs, characterize their activity against diverse bacterial species, and predict their activity levels. AMPActiPred employed multiple effective peptide descriptors to effectively capture the compositional features and physicochemical properties of peptides. AMPActiPred utilized deep forest architecture, a cascading architecture similar to deep neural networks, capable of effectively processing and exploring original features to enhance predictive performance. In the first stage, AMPActiPred focuses on ABP identification, achieving an Accuracy of 87.6% and an MCC of 0.742 on an elaborate dataset, demonstrating state-of-the-art performance. In the second stage, AMPActiPred achieved an average GMean at 82.8% in identifying ABPs targeting 10 bacterial species, indicating AMPActiPred can achieve balanced predictions regarding the functional activity of ABP across this set of species. In the third stage, AMPActiPred demonstrates robust predictive capabilities for ABP activity levels with an average PCC of 0.722. Furthermore, AMPActiPred exhibits excellent interpretability, elucidating crucial features associated with antibacterial activity. AMPActiPred is the first computational framework capable of predicting targets and activity levels of ABPs. Finally, to facilitate the utilization of AMPActiPred, we have established a user-friendly web interface deployed at https://awi.cuhk.edu.cn/∼AMPActiPred/.

Tumor-associated antigens and their derived peptides constitute an opportunity to design off-the-shelf mainline or adjuvant anti-cancer immunotherapies for a broad array of patients. A performant and rational antigen selection pipeline would lay the foundation for immunotherapy trials with the potential to enhance treatment, tremendously benefiting patients suffering from rare, understudied cancers.

We present an experimentally validated, data-driven computational pipeline that selects and ranks antigens in a multipronged approach. In addition to minimizing the risk of immune-related adverse events by selecting antigens based on their expression profile in tumor biopsies and healthy tissues, we incorporated a network analysis-derived antigen indispensability index based on computational modeling results, and candidate immunogenicity predictions from a machine learning ensemble model relying on peptide physicochemical characteristics.

In a model study of uveal melanoma, Human Leukocyte Antigen (HLA) docking simulations and experimental quantification of the peptide-major histocompatibility complex binding affinities confirmed that our approach discriminates between high-binding and low-binding affinity peptides with a performance similar to that of established methodologies. Blinded validation experiments with autologous T-cells yielded peptide stimulation-induced interferon-γ secretion and cytotoxic activity despite high interdonor variability. Dissecting the score contribution of the tested antigens revealed that peptides with the potential to induce cytotoxicity but unsuitable due to potential tissue damage or instability of expression were properly discarded by the computational pipeline.

In this study, we demonstrate the feasibility of the de novo computational selection of antigens with the capacity to induce an anti-tumor immune response and a predicted low risk of tissue damage. On translation to the clinic, our pipeline supports fast turn-around validation, for example, for adoptive T-cell transfer preparations, in both generalized and personalized antigen-directed immunotherapy settings.

Lecithin:cholesterol acyltransferase (LCAT) exhibits α-activity on high-density and β-activity on low-density lipoproteins. However, the molecular determinants governing LCAT activation by different apolipoproteins remain elusive. Uncovering these determinants would offer the opportunity to design and explore advanced therapies against dyslipidemias. Here, we have conducted coarse-grained and all-atom molecular dynamics simulations of LCAT with nanodiscs made with α-helical amphiphilic peptides either derived from apolipoproteins A1 and E (apoA1 and apoE) or apoA1 mimetic peptide 22A that was optimized to activate LCAT. This study aims to explore what drives the binding of peptides to our previously identified interaction site in LCAT. We hypothesized that this approach could be used to screen for binding sites of LCAT in different apolipoproteins and would provide insights to differently localized LCAT activities. Our screening approach was able to discriminate apoA1 helixes 4, 6, and 7 as key contributors to the interaction with LCAT supporting the previous research data. The simulations provided detailed molecular determinants driving the interaction with LCAT: the formation of hydrogen bonds or salt bridges between peptides E4 or D4 and LCAT S236 or K238 residues. Additionally, salt bridging between R7 and D73 was observed, depending on the availability of R7. Expanding our investigation to diverse plasma proteins, we detected novel LCAT binding helixes in apoL1, apoB100, and serum amyloid A. Our findings suggest that the same binding determinants, involving E4 or D4 -S236 and R7-D73 interactions, influence LCAT β-activity on low-density lipoproteins, where apoE and or apoB100 are hypothesized to interact with LCAT.

Noncoding RNAs (ncRNAs) play crucial roles in many cellular life activities by interacting with proteins. Identification of ncRNA-protein interactions (ncRPIs) is key to understanding the function of ncRNAs. Although a number of computational methods for predicting ncRPIs have been developed, the problem of predicting ncRPIs remains challenging. It has always been the focus of ncRPIs research to select suitable feature extraction methods and develop a deep learning architecture with better recognition performance. In this work, we proposed an ensemble deep learning framework, RPI-EDLCN, based on a capsule network (CapsuleNet) to predict ncRPIs. In terms of feature input, we extracted the sequence features, secondary structure sequence features, motif information, and physicochemical properties of ncRNA/protein. The sequence and secondary structure sequence features of ncRNA/protein are encoded by the conjoint k-mer method and then input into an ensemble deep learning model based on CapsuleNet by combining the motif information and physicochemical properties. In this model, the encoding features are processed by convolution neural network (CNN), deep neural network (DNN), and stacked autoencoder (SAE). Then the advanced features obtained from the processing are input into the CapsuleNet for further feature learning. Compared with other state-of-the-art methods under 5-fold cross-validation, the performance of RPI-EDLCN is the best, and the accuracy of RPI-EDLCN on RPI1807, RPI2241, and NPInter v2.0 data sets was 93.8%, 88.2%, and 91.9%, respectively. The results of the independent test indicated that RPI-EDLCN can effectively predict potential ncRPIs in different organisms. In addition, RPI-EDLCN successfully predicted hub ncRNAs and proteins in Mus musculus ncRNA-protein networks. Overall, our model can be used as an effective tool to predict ncRPIs and provides some useful guidance for future biological studies.

Tuberculosis (TB) is a severe disease caused by Mycobacterium tuberculosis that poses a significant threat to human health. The emergence of drug-resistant strains has made the global fight against TB even more challenging. Antituberculosis peptides (ATPs) have shown promising results as a potential treatment for TB. However, conventional wet lab-based approaches to ATP discovery are time-consuming and costly and often fail to discover peptides with desired properties. To address these challenges, we propose a novel machine learning-based framework called ATPfinder that can significantly accelerate the discovery of ATP. Our approach integrates various efficient peptide descriptors and utilizes the deep forest algorithm to construct the model. This neural network-like cascading structure can effectively process and mine features without complex hyperparameter tuning. Our experimental results show that ATPfinder outperforms existing ATP prediction tools, achieving state-of-the-art performance with an accuracy of 89.3% and an MCC of 0.70. Moreover, our framework exhibits better robustness than baseline algorithms commonly used for other sequence analysis tasks. Additionally, the excellent interpretability of our model can assist researchers in understanding the critical features of ATP. Finally, we developed a downloadable desktop application to simplify the use of our framework for researchers. Therefore, ATPfinder can facilitate the discovery of peptide drugs and provide potential solutions for TB treatment. Our framework is freely available at https://github.com/lantianyao/ATPfinder/ (data sets and code) and https://awi.cuhk.edu.cn/dbAMP/ATPfinder.html (software).

Identifying antigens within a pathogen is a critical task to develop effective vaccines and diagnostic methods, as well as understanding the evolution and adaptation to host immune responses. Historically, antigenicity was studied with experiments that evaluate the immune response against selected fragments of pathogens. Using this approach, the scientific community has gathered abundant information regarding which pathogenic fragments are immunogenic. The systematic collection of this data has enabled unraveling many of the fundamental rules underlying the properties defining epitopes and immunogenicity, and has resulted in the creation of a large panel of immunologically relevant predictive (in silico) tools. The development and application of such tools have proven to accelerate the identification of novel epitopes within biomedical applications reducing experimental costs. This chapter introduces some basic concepts about MHC presentation, T cell and B cell epitopes, the experimental efforts to determine those, and focuses on state-of-the-art methods for epitope prediction, highlighting their strengths and limitations, and catering instructions for their rational use.

Hybridoma technology is a well-established and indispensable tool for generating high-quality monoclonal antibodies and has become one of the most common methods for monoclonal antibody production. In this process, antibody-producing B cells are isolated from mice following immunization of mice with a specific immunogen and fused with an immortal myeloma cell line to form antibody-producing hybridoma cell lines. Hybridoma-derived monoclonal antibodies not only serve as powerful research and diagnostic reagents but have also emerged as the most rapidly expanding class of therapeutic biologicals. In spite of the development of new high-throughput monoclonal antibody generation technologies, hybridoma technology still is applied for antibody production due to its ability to preserve innate functions of immune cells and to preserve natural cognate antibody paring information. In this chapter, an overview of hybridoma technology and the laboratory procedures used for hybridoma production and antibody screening of peptide-specific antibodies are presented.

CD4 and LAG-3 are related molecules that are receptors for MHC class II molecules. Their major functional differences are situated in their cytoplasmic tails, in which CD4 has an activation motif and LAG-3 an inhibitory motif. Here, we identify shark LAG-3 and show that a previously identified shark CD4-like gene has a genomic location, expression pattern, and motifs similar to CD4 in other vertebrates. In nurse shark (Ginglymostoma cirratum) and cloudy catshark (Scyliorhinus torazame), the highest CD4 expression was consistently found in the thymus whereas such was not the case for LAG-3. Throughout jawed vertebrates, the CD4 cytoplasmic tail possesses a Cx(C/H) motif for binding kinase LCK, and the LAG-3 cytoplasmic tail possesses (F/Y)xxL(D/E) including the previously determined FxxL inhibitory motif resembling an immunoreceptor tyrosine-based inhibition motif (ITIM). On the other hand, the acidic end of the mammalian LAG-3 cytoplasmic tail, which is believed to have an inhibitory function as well, was acquired later in evolution. The present study also identified CD4-1, CD4-2, and LAG-3 in the primitive ray-finned fishes bichirs, sturgeons, and gars, and experimentally determined these sequences for sterlet sturgeon (Acipenser ruthenus). Therefore, with CD4-1 and CD4-2 already known in teleosts (modern ray-finned fish), these two CD4 lineages have now been found within all major clades of ray-finned fish. Although different from each other, the cytoplasmic tails of ray-finned fish CD4-1 and chondrichthyan CD4 not only contain the Cx(C/H) motif but also an additional highly conserved motif which we expect to confer a function. Thus, although restricted to some species and gene copies, in evolution both CD4 and LAG-3 molecules appear to have acquired functional motifs besides their canonical Cx(C/H) and ITIM-like motifs, respectively. The presence of CD4 and LAG-3 molecules with seemingly opposing functions from the level of sharks, the oldest living vertebrates with a human-like adaptive immune system, underlines their importance for the jawed vertebrate immune system. It also emphasizes the general need of the immune system to always find a balance, leading to trade-offs, between activating and inhibiting processes.

The identification of B-cell epitopes (BCEs) in antigens is a crucial step in developing recombinant vaccines or immunotherapies for various diseases. Over the past four decades, numerous in silico methods have been developed for predicting BCEs. However, existing reviews have only covered specific aspects, such as the progress in predicting conformational or linear BCEs. Therefore, in this paper, we have undertaken a systematic approach to provide a comprehensive review covering all aspects associated with the identification of BCEs. First, we have covered the experimental techniques developed over the years for identifying linear and conformational epitopes, including the limitations and challenges associated with these techniques. Second, we have briefly described the historical perspectives and resources that maintain experimentally validated information on BCEs. Third, we have extensively reviewed the computational methods developed for predicting conformational BCEs from the structure of the antigen, as well as the methods for predicting conformational epitopes from the sequence. Fourth, we have systematically reviewed the in silico methods developed in the last four decades for predicting linear or continuous BCEs. Finally, we have discussed the overall challenge of identifying continuous or conformational BCEs. In this review, we only listed major computational resources; a complete list with the URL is available from the BCinfo website (https://webs.iiitd.edu.in/raghava/bcinfo/).

By targeting the membrane (M) proteins of monkeypox virus (MPXV) strain VEROE6, we analyzed its evolutionary hierarchy and predicted its dominant antigenic B-cell epitope to provide a theoretical basis for the development of MPXV epitope vaccines and related monoclonal antibodies. In this study, phylogenetic trees were constructed based on the nucleic acid sequences of MPXV and the amino acid sequences of M proteins. The 3D structure of the MPXV_VEROE6 M proteins was predicted with AlphaFold v2.0 and the dominant antigenic B-cell epitopes were comprehensively predicted by analyzing parameters such as flexible segments, the hydrophilic index, the antigenic index, and the protein surface probability. The results showed that the M protein of MPXV_VEROE6 contained 377 amino acids, and their spatial configuration was relatively regular with a turning and random coil structure. The results of a comprehensive multiparameter analysis indicated that possible B-cell epitopes were located in the 23-28, 57-63, 67-78, 80-93, 98-105, 125-131, 143-149, 201-206, 231-237, 261-270, 291-303, and 346-362 amino acid segments. This study elucidated the structural and evolutionary characteristics of MPXV membrane proteins with the aim of providing theoretical information for the development of epitope vaccines, rapid diagnostic reagents, and monoclonal antibodies for monkeypox virus.

The demand for thermophilic protein has been increasing in protein engineering recently. Many machine-learning methods for identifying thermophilic proteins have emerged during this period. However, most machine learning-based thermophilic protein identification studies have only focused on accuracy. The relationship between the features' meaning and the proteins' physicochemical properties has yet to be studied in depth. In this article, we focused on the relationship between the features and the thermal stability of thermophilic proteins. This method used 2-D general series correlation pseudo amino acid (SC-PseAAC-General) features and realized accuracy of 82.76% using the J48 classifier. In addition, this research found the presence of higher frequencies of glutamic acid in thermophilic proteins, which help thermophilic proteins maintain their thermal stability by forming hydrogen bonds and salt bridges that prevent denaturation at high temperatures.

The present study aimed to generate antibodies against predicted B cell epitopic peptides encoding bAMH for developing different ELISA models. Sandwich ELISA was determined to be an excellent technique for assessing bAMH in bovine plasma based on sensitivity tests. The assay's specificity, sensitivity, inter- and intra-assay CV, recovery %, Lower limit of quantification (LLOQ), and Upper limit of quantification (ULOQ) were determined. The test was selective since it did not bind to AMH-related growth and differentiation factors (LH and FSH) or non-related components (BSA, progesterone). The intra-assay CV was 5.67%, 3.12%, 4.94%, 3.61% and 4.27% for 72.44, 183.11, 368.24, 522.24 and 732.25 pg/ml AMH levels, respectively. At the same time, the inter-assay CV was 8.77%, 7.87%, 4.53%, 5.76% and 6.70% for 79.30, 161.27, 356.30, 569.33 and 798.19 pg/ml AMH levels, respectively. The average (Mean ± SEM) recovery percentages were 88-100%. LLOQ was 5 pg/ml and ULOQ at 50 µg/ml (CV < 20%). In conclusion, we developed a new highly sensitive ELISA against bAMH using epitope specific antibodies.

Antibodies play an important role in the immune system by binding to molecules called antigens at their respective epitopes. These interfaces or epitopes are structural entities determined by the interactions between an antibody and an antigen, making them ideal systems to analyze by using docking programs. Since the advent of high-throughput antibody sequencing, the ability to perform epitope mapping using only the sequence of the antibody has become a high priority. ClusPro, a leading protein-protein docking server, together with its template-based modeling version, ClusPro-TBM, have been re-purposed to map epitopes for specific antibody-antigen interactions by using the Antibody Epitope Mapping server (AbEMap). ClusPro-AbEMap offers three different modes for users depending on the information available on the antibody as follows: (i) X-ray structure, (ii) computational/predicted model of the structure or (iii) only the amino acid sequence. The AbEMap server presents a likelihood score for each antigen residue of being part of the epitope. We provide detailed information on the server's capabilities for the three options and discuss how to obtain the best results. In light of the recent introduction of AlphaFold2 (AF2), we also show how one of the modes allows users to use their AF2-generated antibody models as input. The protocol describes the relative advantages of the server compared to other epitope-mapping tools, its limitations and potential areas of improvement. The server may take 45-90 min depending on the size of the proteins.

Listeria adhesion protein (LAP) is a secreted acetaldehyde alcohol dehydrogenase (AdhE) that anchors to an unknown molecule on the Listeria monocytogenes (Lm) surface, which is critical for its intestinal epithelium crossing. In the present work, immunoprecipitation and mass spectrometry identify internalin B (InlB) as the primary ligand of LAP (KD ∼ 42 nM). InlB-deleted and naturally InlB-deficient Lm strains show reduced LAP-InlB interaction and LAP-mediated pathology in the murine intestine and brain invasion. InlB-overexpressing non-pathogenic Listeria innocua also displays LAP-InlB interplay. In silico predictions reveal that a pocket region in the C-terminal domain of tetrameric LAP is the binding site for InlB. LAP variants containing mutations in negatively charged (E523S, E621S) amino acids in the C terminus confirm altered binding conformations and weaker affinity for InlB. InlB transforms the housekeeping enzyme, AdhE (LAP), into a moonlighting pathogenic factor by fastening on the cell surface.

In the last few years, the importance of neoantigens in the development of personalized antitumor vaccines has increased remarkably. In order to study whether bioinformatic tools are effective in detecting neoantigens that generate an immune response, DNA samples from patients with cutaneous melanoma in different stages were obtained, resulting in a total of 6048 potential neoantigens gathered. Thereafter, the immunological responses generated by some of those neoantigens ex vivo were tested, using a vaccine designed by a new optimization approach and encapsulated in nanoparticles. Our bioinformatic analysis indicated that no differences were found between the number of neoantigens and that of non-mutated sequences detected as potential binders by IEDB tools. However, those tools were able to highlight neoantigens over non-mutated peptides in HLA-II recognition (p-value 0.03). However, neither HLA-I binding affinity (p-value 0.08) nor Class I immunogenicity values (p-value 0.96) indicated significant differences for the latter parameters. Subsequently, the new vaccine, using aggregative functions and combinatorial optimization, was designed. The six best neoantigens were selected and formulated into two nanoparticles, with which the immune response ex vivo was evaluated, demonstrating a specific activation of the immune response. This study reinforces the use of bioinformatic tools in vaccine development, as their usefulness is proven both in silico and ex vivo.

Nuclear speckles are subcellular structures originally characterized by punctate immunofluorescence staining of the monoclonal antibody SC35, which recognizes an epitope on SRRM2 (serine/arginine repetitive matrix protein 2) and Sfrs2, a member of the SR (serine/arginine-rich) family of splicing factors. Galectin-3 co-localizes with SC35 in nuclear speckles, which represent one group of nuclear bodies that include the nucleolus, Cajal bodies and gems, paraspeckles, etc. Although they appear to have well-delineated physical boundaries, these nuclear bodies are not membrane-bound structures but represent macromolecular assemblies arising from a phenomenon called liquid-liquid phase separation. There has been much recent interest in liquid phase condensation as a newly recognized mechanism by which a cell can organize and compartmentalize subcellular structures with distinct composition. The punctate/speckled staining of galectin-3 with SC3 demonstrates their co-localization in a phase-separated body in vivo, under conditions endogenous to the cell. The purpose of the present review is to summarize the studies that document three key features of galectin-3 for its localization in liquid phase condensates: (a) an intrinsically disordered domain; (b) oligomer formation for multivalent binding; and (c) association with RNA and ribonucleoprotein complexes.

Parvalbumin (PV) can be subdivided into two phylogenetic lineages, αPV and βPV. The bony fish βPV is considered a major fish allergen. However, there is no available report on the immunological property and epitope mapping of bony fish αPV.

To characterize the allergenic property of bony fish αPV and investigate the difference in allergenic property of bony fish αPV and βPV, turbot (Scophthalmus maximus) αPV and βPV were identified by mass spectrometry and were expressed in Escherichia coli system in this study. Spectra analysis and three-dimensional (3D) modeling showed the similar structure between αPV and βPV. However, αPV exhibited lower immunoglobulin E/immunoglobulin G (IgE/IgG) binding capacity than βPV. Three identified βPV epitopes possessed higher IgE reactivity and more hydrophobic residues than three identified αPV epitopes. In addition, less similarity in sequence homology of αPV epitopes was observed with allergen sequences in database.

These finding expanded information on fish PV epitopes and substantiated the difference in allergenicity and epitope mapping between fish αPV and βPV, which will improve the epitope-based detection tools of PV and diagnostic of PV induced fish allergy. © 2023 Society of Chemical Industry.

Marine elasmobranch fish contain urea, a protein denaturant, in their bodies. The urea-trimethylamine N-oxide (TMAO) counteraction mechanism contributes to urea-resistibility, where TMAO compensates for protein denaturation by urea. However, previous studies revealed that shark fast skeletal muscle myosin exhibits native activity at physiological urea concentrations in the absence of TMAO, suggesting that shark myosin has urea-resistibility. In this study, we compared the urea-resistibility of myosin alkali light chains (A1-LC and A2-LC) from banded houndshark and carp by examining the α-helical content at various urea concentrations. The α-helical content of carp myosin A1-LC and A2-LC gradually decreased as urea concentrations increased to 2 M. In contrast, the α-helical content of banded houndshark A1-LC increased between 0 and 0.5 M urea, and the α-helical content of A2-LC remained constant until 0.5 M urea. We determined the full-length sequences of the banded houndshark myosin light chains (A1-LC, A2-LC and DTNB-LC). Hydrophilicity analysis revealed that the N-terminal region (residues 28-34) of A1-LC from banded houndshark is more hydrophilic than the corresponding region of A1-LC from carp. These findings support the notion that shark myosin exhibits urea-resistibility independent of the urea-TMAO counteraction mechanism.

The papillomavirus L2 capsid protein protrudes through the endosome membrane into the cytoplasm during virus entry to bind cellular factors required for intracellular virus trafficking. Cytoplasmic protrusion of HPV16 L2, virus trafficking, and infectivity are inhibited by large deletions in an ∼110 amino acid segment of L2 that is predicted to be disordered. The activity of these mutants can be restored by inserting protein segments with diverse compositions and chemical properties into this region, including scrambled sequences, a tandem array of a short sequence, and the intrinsically disordered region of a cellular protein. The infectivity of mutants with small in-frame insertions and deletions in this segment directly correlates with the size of the segment. These results indicate that the length of the disordered segment, not its sequence or its composition, determines its activity during virus entry. Sequence independent but length dependent activity has important implications for protein function and evolution.

The insertion of proteins into membranes is crucial for understanding their function in many biological processes. In this work, we present UNILIPID, a universal implicit lipid-protein description as a methodology for dealing with implicit membranes. UNILIPID is independent of the scale of representation and can be applied at the level of all atoms, coarse-grained particles down to the level of a single bead per amino acid. We provide example implementations for these scales and demonstrate the versatility of our approach by accurately reflecting the free energy of transfer for each amino acid. In addition to single membranes, we describe the analytical implementation of double membranes and show that UNILIPID is well suited for modeling at multiple scales. We generalize to membranes of arbitrary shape. With UNILIPID, we provide a methodological framework for a simple and general parameterization tuned to reproduce a selected reference hydrophobicity scale. The software we provide along with the methodological description is optimized for specific user features such as real-time response, live visual analysis, and virtual reality experiences.

Bitter peptides are short peptides with potential medical applications. The huge potential behind its bitter taste remains to be tapped. To better explore the value of bitter peptides in practice, we need a more effective classification method for identifying bitter peptides.

In this study, we developed a Random forest (RF)-based model, called Bitter-RF, using sequence information of the bitter peptide. Bitter-RF covers more comprehensive and extensive information by integrating 10 features extracted from the bitter peptides and achieves better results than the latest generation model on independent validation set.

The proposed model can improve the accurate classification of bitter peptides (AUROC = 0.98 on independent set test) and enrich the practical application of RF method in protein classification tasks which has not been used to build a prediction model for bitter peptides.

We hope the Bitter-RF could provide more conveniences to scholars for bitter peptide research.

This chapter describes the principles for selection of antigenic peptides for the development of anti-peptide antibodies suitable for microarray-based multiplex affinity assays and optional mass spectrometry detection. The methods described here are mostly applicable to small- and medium-scale multiplex affinity assay and microarrays. Although the same principles of peptide selection may also be applied to larger-scale arrays (with 100+ features), informatics software and printing methods may well differ. Due to the sheer number of proteins/peptides to be processed and analyzed, dedicated software with high processing capacity and enterprise-level array robotics may be required for larger-scale efforts. This report aims to provide practical advice to those seeking to develop or use arrays with up to ~100 different peptide or protein features.