Unknown Protein Research Articles

Streamlined analysis of drug targets by proteome integral solubility alteration indicates organ-specific engagement

Proteins are the primary targets of almost all small molecule drugs. However, even the most selectively designed drugs can potentially target several unknown proteins. Identification of potential drug targets can facilitate design of new drugs and repurposing of existing ones. Current state-of-the-art proteomics methodologies enable screening of thousands of proteins against a limited number of drug molecules. Here we report the development of a label-free quantitative proteomics approach that enables proteome-wide screening of small organic molecules in a scalable, reproducible, and rapid manner by streamlining the proteome integral solubility alteration (PISA) assay. We used rat organs ex-vivo to determine organ specific targets of medical drugs and enzyme inhibitors to identify drug targets for common drugs such as Ibuprofen. Finally, global drug profiling revealed overarching trends of how small molecules affect the proteome through either direct or indirect protein interactions.

A Novel Secreted Protein of Fusarium oxysporum Promotes Infection by Inhibiting PR-5 Protein in Plant.

Fusarium oxysporum, an important soilborne fungal pathogen that causes serious Fusarium wilt disease, secretes diverse effectors during the infection. In this study, we identified a novel secreted cysteine-rich protein, FolSCP1, which contains unknown protein functional domain. Here, we characterized FolSCP1 as a secreted virulence factor that promotes the pathogen infection of host plants by inhibiting diverse plant defence responses. FolSCP1 interacted with the pathogenesis-related 5 (PR-5) protein SlPR5, a positive regulator of tomato plant immunity against multiple tomato pathogens, and effectively attenuated the antifungal activity of the tomato PR-5 protein. FoSCP1, a homologue of FolSCP1, was secreted by a F. oxysporum isolate from infected tobacco and targeted the tobacco PR-5 protein NtPR5 to suppress plant defence for further infection. In summary, our study revealed a fungal virulence strategy in which F. oxysporum secrete effectors that interfere with plant immunity by binding to the PR-5 protein of the host plant and inhibiting its biological activity, thereby promoting fungal infection.

Liver proteomics identifies a disconnect between proteins associated with de novo lipogenesis and triglyceride storage

De novo lipogenesis (DNL) has been implicated in the development and progression of liver steatosis. Hepatic DNL is strongly influenced by dietary macronutrient composition with diets high in carbohydrate increasing DNL and while diets high in fat decrease DNL. The enzymes in the core DNL pathway have been well characterised, however less is known about other liver proteins that play accessory or regulatory roles. In the current study, we associate measured rates of hepatic DNL and fat content with liver proteomic analysis in mice to identify known and unknown proteins that may have a role in DNL. Male mice were fed either a standard chow diet, a semi-purified high starch or high fat diet. Both semi-purified diets resulted in increased body weight, fat mass and liver triglyceride content compared to chow controls and hepatic DNL was increased in the high starch and decreased in high fat fed mice. Proteomic analysis identified novel proteins associated with DNL that are involved in taurine metabolism, suggesting a link between these pathways. There was no relationship between proteins that associated with DNL and those associated with liver triglyceride content. Further analysis identified proteins that are differentially regulated when comparing a non-purified chow diet to either of the semi-purified diets which provide a set of proteins that are influenced by dietary complexity. Finally, we compared the liver proteome between 4- and 30-week diet-fed mice and found remarkable similarity suggesting metabolic remodelling of the liver occurs rapidly in response to differing dietary components.

A novel molecular target, superoxide dismutase 1, in ALK inhibitor-resistant lung cancer cells, detected through proteomic analysis

BackgroundsTo the best of our knowledge, there are no reports of proteomic analysis for the identification of unknown proteins involved in resistance to anaplastic lymphoma kinase (ALK) inhibitors. In this study, we investigated the proteins involved in resistance to alectinib, a representative ALK inhibitor, through proteomic analysis and the possibility of overcoming resistance. MethodsAn ALK-positive lung adenocarcinoma cell line (ABC-11) and the corresponding alectinib-resistant cell line (ABC-11/CHR2) were used. Two-dimensional difference gel electrophoresis (2D DIGE) was performed; the stained gel was scanned and the spots were analyzed using DeCyder TM2D 7.0. Mass spectrometry (MS) with the UltrafleXtreme matrix-assisted laser desorption ionization-tandem time-of-flight (MALDI-TOF/TOF) MS system was performed. For the MS/MS analysis, the samples were spotted on an AnchorChipTM 600 TF plate. The peptide masses obtained in the reflector positive mode were acquired at m/z of 400−6,000. MS/MS data were searched against the NCBI protein databases. Growth inhibition was measured using an MTT assay. The isobologram and combination index were calculated based on the median-effect analysis. Western blotting was performed using antibodies, including superoxide dismutase (SOD) 1, MET, ERK, PARP, AKT, and BRCA1. ResultsThe 2D DIGE for ABC-11 and ABC-11/CHR2 showed different expression levels in about 2,000 spots. SOD was identified from spots highly expressed in resistant strains. Western blotting also confirmed SOD1 overexpression in ABC-11/CHR2. siSOD1 enhanced the growth inhibitory effects of alectinib, increased cleaved PARP levels, and decreased pERK, pAKT, and BRCA1 levels with a combination of alectinib. In addition, the combination of LCS-1, an SOD1 inhibitor, and alectinib synergistically suppressed the growth in ABC-11/CHR2, but not in ABC-11. ConclusionsSOD1 overexpression is thought to be a mechanism for alectinib resistance, suggesting the possibility of overcoming resistance using SOD1 inhibitors.

HERD-1 mediates multiphase condensate immiscibility to regulate small RNA-driven transgenerational epigenetic inheritance.

Biomolecular condensates, such as the nucleolus, stress granules/processing bodies and germ granules, are multiphase assemblages whose formation mechanisms and significance remain poorly understood. Here we identify protein constituents of the spatiotemporally ordered P, Z and M multiphase condensates in Caenorhabditis elegans germ granules using optimized TurboID-mediated proximity biotin labelling. These include 462, 41 and 86 proteins localizing to P, Z and M condensates, respectively, of which 522 were previously unknown protein constituents. Each condensate's proteins are enriched for distinct classes of structured and intrinsically disordered domains, suggesting divergent functions and assembly mechanisms. Through a functional screen, we identify a germ granule protein, HERD-1, which prevents the mixing of P, Z and M condensates. Mixing in herd-1 mutants correlates with disorganization of germline small RNA pathways and prolonged epigenetic inheritance of RNA interference-induced gene silencing. Forced mixing of these condensate components using a nanobody with specific binding activity against green fluorescent protein also extends epigenetic inheritance. We propose that active maintenance of germ granule immiscibility helps to organize and regulate small RNA-driven transgenerational epigenetic inheritance in C. elegans.

Overlapping Structure Detection in Protein-Protein Interaction Networks Using a Modified Version of Particle Swarm Optimization

In today's world, the science of bioinformatics is developing rapidly, especially with regard to the analysis and study of biological networks. Scientists have used various nature-inspired algorithms to find protein complexes in protein-protein interaction (PPI) networks. These networks help scientists guess the molecular function of unknown proteins and show how cells work regularly. It is very common in PPI networks for a protein to participate in multiple functions and belong to many complexes, and as a result, complexes may overlap in the PPI networks. However, developing an efficient and reliable method to address the problem of detecting overlapping protein complexes remains a challenge since it is considered a complex and hard optimization problem. One of the main difficulties in identifying overlapping protein complexes is the accuracy of the partitioning results. In order to accurately identify the overlapping structure of protein complexes, this paper has proposed an overlapping complex detection algorithm termed OCDPSO-Net, which is based on PSO-Net (a well-known modified version of the particle swarm optimization algorithm). The framework of the OCDPSO-Net method consists of three main steps, including an initialization strategy, a movement strategy for each particle, and enhancing search ability in order to expand the solution space. The proposed algorithm has employed the partition density concept for measuring the partitioning quality in PPI network complexes and tried to optimize the value of this quantity by applying the line graph concept of the original graph representing the protein interaction network. The OCDPSO-Net algorithm is applied to a Collins PPI network and the obtained results are compared with different state-of-the-art algorithms in terms of precision ( ), recall ( ), and F-measure ( ). Experimental results confirm that the proposed algorithm has good clustering performance and has outperformed most of the existing recent overlapping algorithms. .

Unified Role of the 145th Residue on the Fluorescence Lifetime of Fluorescent Proteins from the Jellyfish Aequorea victoria.

Finding a unified fluorescence mechanism is essential to develop and utilize fluorescent proteins appropriately. Here, we report the unified role of the 145th residue on the fluorescence efficiency of fluorescent proteins developed from the jellyfish Aequorea victoria by demonstrating the difference and similarity between two representative fluorescent proteins, enhanced green fluorescent protein (eGFP), and enhanced yellow fluorescent protein (eYFP). We determined the fluorescence lifetimes of the 19 different Y145 mutants of eGFP and eYFP by picosecond time-resolved fluorescence spectroscopy. We found that the effect of the 145th mutation on the fluorescence lifetime is significant for eYFP but moderate for eGFP. We compared known crystal structures to clarify the observed difference between eGFP and eYFP. As a result, we conclude that the efficiency of the steric restriction of the chromophore motion by the 145th side chain is essentially the same for both eGFP and eYFP. Meanwhile, the restriction of the chromophore motion by hydrogen bonds is more pronounced for eGFP than for YFP. Balance of the steric effect and hydrogen bonding controls the lifetime of the Y145 mutants for eGFP and eYFP. Furthermore, the steric restriction is induced by the electrostatic effect; the different 145th residue induces a different electrostatic environment around the chromophore. The finding in this study reasonably explains the reported lifetimes of other fluorescent proteins and allows the prediction of the lifetime of unknown fluorescent proteins from jellyfish.

Construction of Aspergillus oryzae strains with organelles labeled with fluorescence and observation of organelle morphology

The organelles in the multi-nucleated filamentous fungus Aspergillus oryzae present polymorphism. To observe the organelle morphology in A. oryzae and provide references for the localization prediction of unknown proteins and the disclosure of biological reaction pathways in A. oryzae, we fused different subcellular localization signals with green fluorescent protein (GFP) to obtain different subcellular localization vectors, which were then transferred into A. oryzae by Agrobacterium tumefaciens-mediated transformation. The A. oryzae reporter strains with fluorescence-labeled nuclei, mitochondria, endoplasmic reticulum, vacuole, lipid droplets, peroxisome, and Golgi apparatus were successfully constructed. Furthermore, staining with small-molecule specific dyes was carried out to validate the co-localization of fluorescence-labeled mitochondria, nuclei, and lipid droplets in the reporter strains, which further confirmed that the reporter strains were successfully constructed. The distribution and morphology of fluorescence-labeled organelles were observed at different growth stages and under different culture conditions. The constructed reporter strains provide basic tools for studying the organelle morphology, localization of unknown target proteins, and subcellular localization in A. oryzae.

Molecular characterization and CRISPR/Cas9 validation of the precursor of egg yolk protein gene, vitellogenin of Leucinodes orbonalis Guenée (Lepidoptera: Crambidae)

Vitellogenin (Vg), a yolk protein precursor, plays an important role in the oocyte development of insects and is an important target of genetic pest management. Vg is synthesized in the fat body, transported through haemolymph and accumulates in developing oocytes. In this regard, the eggplant shoot and fruit borer, Leucinodes orbonalis (Lepidoptera: Crambidae) is the major pest in South and South East Asia and a serious concern for farmers. Therefore, in the present study, we have cloned and characterized Vg from L. orbonalis (LoVg) for further applications. The cloned Vg consisted of 5,370 base pairs encoding 1,790 amino acid residues long protein. Further, sequence alignment revealed that LoVg has three conserved domains: a Vitellogenin N domain (LPD-N), a domain of unknown function protein families (DUF1943), and a von Willebrand factor type D domain (VWD). Using phylogenetic analysis, it was found that LoVg evolved alongside homologous proteins from different insects. The real-time expression levels of LoVg were significantly greater in female adults followed by the pupal stage. This suggests that Vg production and absorption in L. orbonalis occurs in the later pupal stage. Our studies showed that editing LoVg using CRISPR/Cas9 did not affect the total number of eggs laid but affected egg hatchability. These studies help us to design newer approaches in insect pest management through genetic suppression for sustainable pest management.

DTI-LM: language model powered drug-target interaction prediction.

The identification and understanding of drug-target interactions (DTIs) play a pivotal role in the drug discovery and development process. Sequence representations of drugs and proteins in computational model offer advantages such as their widespread availability, easier input quality control, and reduced computational resource requirements. These make them an efficient and accessible tools for various computational biology and drug discovery applications. Many sequence-based DTI prediction methods have been developed over the years. Despite the advancement in methodology, cold start DTI prediction involving unknown drug or protein remains a challenging task, particularly for sequence-based models. Introducing DTI-LM, a novel framework leveraging advanced pretrained language models, we harness their exceptional context-capturing abilities along with neighborhood information to predict DTIs. DTI-LM is specifically designed to rely solely on sequence representations for drugs and proteins, aiming to bridge the gap between warm start and cold start predictions. Large-scale experiments on four datasets show that DTI-LM can achieve state-of-the-art performance on DTI predictions. Notably, it excels in overcoming the common challenges faced by sequence-based models in cold start predictions for proteins, yielding impressive results. The incorporation of neighborhood information through a graph attention network further enhances prediction accuracy. Nevertheless, a disparity persists between cold start predictions for proteins and drugs. A detailed examination of DTI-LM reveals that language models exhibit contrasting capabilities in capturing similarities between drugs and proteins. Source code is available at: https://github.com/compbiolabucf/DTI-LM.

Proteome-Scale Tissue Mapping Using Mass Spectrometry Based on Label-Free and Multiplexed Workflows

Multiplexed bimolecular profiling of tissue microenvironment, or spatial omics, can provide deep insight into cellular compositions and interactions in healthy and diseased tissues. Proteome-scale tissue mapping, which aims to unbiasedly visualize all the proteins in a whole tissue section or region of interest, has attracted significant interest because it holds great potential to directly reveal diagnostic biomarkers and therapeutic targets. While many approaches are available, however, proteome mapping still exhibits significant technical challenges in both protein coverage and analytical throughput. Since many of these existing challenges are associated with mass spectrometry-based protein identification and quantification, we performed a detailed benchmarking study of three protein quantification methods for spatial proteome mapping, including label-free, TMT-MS2, and TMT-MS3. Our study indicates label-free method provided the deepest coverages of ∼3500 proteins at a spatial resolution of 50 μm and the highest quantification dynamic range, while TMT-MS2 method holds great benefit in mapping throughput at >125 pixels per day. The evaluation also indicates both label-free and TMT-MS2 provide robust protein quantifications in identifying differentially abundant proteins and spatially co-variable clusters. In the study of pancreatic islet microenvironment, we demonstrated deep proteome mapping not only enables the identification of protein markers specific to different cell types, but more importantly, it also reveals unknown or hidden protein patterns by spatial co-expression analysis.

Deep Learning-Based Self-Adaptive Evolution of Enzymes

AbstractBiocatalysis has been widely used to prepare drug leads and intermediates. Enzymatic synthesis has advantages, mainly in terms of strict chirality and regional selectivity compared with chemical methods. However, the enzymatic properties of wild-type enzymes may or may not meet the requirements for biopharmaceutical applications. Therefore, protein engineering is required to improve their catalytic activities. Thanks to advances in algorithmic models and the accumulation of immense biological data, artificial intelligence can provide novel approaches for the functional evolution of enzymes. Deep learning has the advantage of learning functions that can predict the properties of previously unknown protein sequences. Deep learning-based computational algorithms can intelligently navigate the sequence space and reduce the screening burden during evolution. Thus, intelligent computational design combined with laboratory evolution is a powerful and potentially versatile strategy for developing enzymes with novel functions. Herein, we introduce and summarize deep-learning-assisted enzyme functional adaptive evolution strategies based on recent studies on the application of deep learning in enzyme design and evolution. Altogether, with the developments of technology and the accumulation of data for the characterization of enzyme functions, artificial intelligence may become a powerful tool for the design and evolution of intelligent enzymes in the future.

Endogenous osteoprotegerin (OPG) represses ERα and promotes stemness and chemoresistance in breast cancer cells

Breast cancer (BC) is the most prevalent cancer and the leading cause of death among women worldwide. The osteoprotegerin (OPG) cytokine, a decoy receptor for RANKL and a key player in bone homeostasis, has pro-and anti-carcinogenic effects in various types of cancer, including breast neoplasms. In the present study, we have shown that ectopic expression of OPG in breast epithelial/cancer cells promotes the pro-metastatic processes epithelial-to-mesenchymal transition (EMT), stemness, angiogenesis as well as the activation of breast stromal fibroblasts. Furthermore, proteomics analysis, which allows the identification and quantification of a plethora of known and unknown proteins, has shown a strong and significant correlation between OPG upregulation and the expression of proteins with functions in EMT and stemness. On the other hand, OPG knockdown in triple-negative breast cancer (TNBC) cells inhibited the formation of cancer stem cells. Importantly, while OPG upregulation significantly enhanced the resistance of luminal BC cells to cisplatin and docetaxel, OPG downregulation sensitized TNBC cells to these chemotherapeutic drugs. We have also shown that OPG negatively controls estrogen receptor α (ERα), and OPG upregulation correlated well with the expression of genes related to ER-negative claudin low cells. Collectively, these results show that OPG promotes stemness and the consequent chemoresistance of breast cancer cells.

Variants in NLRP2 and ZFP36L2, non-core components of the human subcortical maternal complex, cause female infertility with embryonic development arrest.

The subcortical maternal complex (SCMC), which is vital in oocyte maturation and embryogenesis, consists of core proteins (NLRP5, TLE6, OOEP), non-core proteins (PADI6, KHDC3L, NLRP2, NLRP7), and other unknown proteins that are encoded by maternal effect genes. Some variants of SCMC genes have been linked to female infertility characterized by embryonic development arrest. However, so far, the candidate non-core SCMC components associated with embryonic development need further exploration and the pathogenic variants that have been identified are still limited. In this study, we discovered two novel variants [p.(Ala131Val) and p.(Met326Val)] of NLRP2 in patients with primary infertility displaying embryonic development arrest from large families. In vitro studies using 293T cells and mouse oocytes, respectively, showed that these variants significantly decreased protein expression and caused the phenotype of embryonic development arrest. Additionally, we combined the 'DevOmics' database with the whole exome sequence data of our cohort and screened out a new candidate non-core SCMC gene ZFP36L2. Its variants [p.(Ala241Pro) and p.(Pro291dup)] were found to be responsible for embryonic development arrest. Co-immunoprecipitation experiments in 293T cells, used to demonstrate the interaction between proteins, verified that ZFP36L2 is one of the human SCMC components, and microinjection of ZFP36L2 complementary RNA variants into mouse oocytes affected embryonic development. Furthermore, the ZFP36L2 variants were associated with disrupted stability of its target mRNAs, which resulted in aberrant H3K4me3 and H3K9me3 levels. These disruptions decreased oocyte quality and further developmental potential. Overall, this is the first report of ZFP36L2 as a non-core component of the human SCMC and we found four novel pathogenic variants in the NLRP2 and ZFP36L2 genes in 4 of 161 patients that caused human embryonic development arrest. These findings contribute to the genetic diagnosis of female infertility and provide new insights into the physiological function of SCMC in female reproduction.

Utilizing Thermal Shift Assay to Probe Substrate Binding to Selenoprotein O.

Defining the biological importance of proteins with unknown functions poses a significant obstacle in understanding cellular processes. Although bioinformatic and structural predictions have contributed to the study of unknown proteins, in vitro experimental validations are often hampered by the optimal conditions and cofactors required for biochemical activity. Cofactor binding is not only essential for the activity of some enzymes but may also enhance the thermal stability of the protein. One practical application of this phenomenon lies in utilizing the change in thermal stability, as measured by alterations in the protein's melting temperature, to probe ligand binding. Thermal shift assay (TSA) can be used to analyze the binding of different ligands to the protein of interest or find a stabilizing condition to perform experiments such as X-ray crystallography. Here we will describe a protocol for TSA utilizing the pseudokinase, Selenoprotein O (SelO), for a simple and high-throughput method for testing metal and nucleotide binding. In contrast to canonical kinases, SelO binds ATP in an inverted orientation to catalyze the transfer of AMP to the hydroxyl side chains of proteins in a posttranslational modification known as protein AMPylation. By leveraging the shift in the melting temperatures, we provide crucial insights into the molecular interactions underlying SelO function.

Proximity analysis of native proteomes reveals phenotypic modifiers in a mouse model of autism and related neurodevelopmental conditions

One of the main drivers of autism spectrum disorder is risk alleles within hundreds of genes, which may interact within shared but unknown protein complexes. Here we develop a scalable genome-editing-mediated approach to target 14 high-confidence autism risk genes within the mouse brain for proximity-based endogenous proteomics, achieving the identification of high-specificity spatial proteomes. The resulting native proximity proteomes are enriched for human genes dysregulated in the brain of autistic individuals, and reveal proximity interactions between proteins from high-confidence risk genes with those of lower-confidence that may provide new avenues to prioritize genetic risk. Importantly, the datasets are enriched for shared cellular functions and genetic interactions that may underlie the condition. We test this notion by spatial proteomics and CRISPR-based regulation of expression in two autism models, demonstrating functional interactions that modulate mechanisms of their dysregulation. Together, these results reveal native proteome networks in vivo relevant to autism, providing new inroads for understanding and manipulating the cellular drivers underpinning its etiology.

Death-Associated LIM-Only Protein Reduces Cry1Ac Toxicity by Sequestration of Cry1Ac Protoxin and Activated Toxin in Helicoverpa armigera.

The extensive use of Bacillus thuringiensis (Bt) in pest management has driven the evolution of pest resistance to Bt toxins, particularly Cry1Ac. Effective management of Bt resistance necessitates a good understanding of which pest proteins interact with Bt toxins. In this study, we screened a Helicoverpa armigera larval midgut cDNA library and captured 208 potential Cry1Ac-interacting proteins. Among these, we further examined the interaction between Cry1Ac and a previously unknown Cry1Ac-interacting protein, HaDALP (H. armigera death-associated LIM-only protein), as well as its role in toxicology. The results revealed that HaDALP specifically binds to both the Cry1Ac protoxin and activated toxin, significantly enhancing cell and larval tolerance to Cry1Ac. Additionally, HaDALP was overexpressed in a Cry1Ac-resistant H. armigera strain. These findings reveal a greater number of Cry1Ac-interacting proteins than previously known and demonstrate, for the first time, that HaDALP reduces Cry1Ac toxicity by sequestering both the protoxin and activated toxin.

Artificial intelligence in fusion protein three-dimensional structure prediction: Review and perspective.

Recent advancements in artificial intelligence (AI) have accelerated the prediction of unknown protein structures. However, accurately predicting the three-dimensional (3D) structures of fusion proteins remains a difficult task because the current AI-based protein structure predictions are focused on the WT proteins rather than on the newly fused proteins in nature. Following the central dogma of biology, fusion proteins are translated from fusion transcripts, which are made by transcribing the fusion genes between two different loci through the chromosomal rearrangements in cancer. Accurately predicting the 3D structures of fusion proteins is important for understanding the functional roles and mechanisms of action of new chimeric proteins. However, predicting their 3D structure using a template-based model is challenging because known template structures are often unavailable in databases. Deep learning (DL) models that utilize multi-level protein information have revolutionized the prediction of protein 3D structures. In this review paper, we highlighted the latest advancements and ongoing challenges in predicting the 3D structure of fusion proteins using DL models. We aim to explore both the advantages and challenges of employing AlphaFold2, RoseTTAFold, tr-Rosetta and D-I-TASSER for modelling the 3D structures. HIGHLIGHTS: This review provides the overall pipeline and landscape of the prediction of the 3D structure of fusion protein. This review provides the factors that should be considered in predicting the 3D structures of fusion proteins using AI approaches in each step. This review highlights the latest advancements and ongoing challenges in predicting the 3D structure of fusion proteins using deep learning models. This review explores the advantages and challenges of employing AlphaFold2, RoseTTAFold, tr-Rosetta, and D-I-TASSER to model 3D structures.

Prokaryotic Expression, Purification, and Biological Properties of a Novel Bioactive Protein (PFAP-1) from Pinctada fucata.

Pinctada fucata meat is the main by-product of the pearl harvesting industry. It is rich in nutrition, containing a lot of protein and peptides, and holds significant value for both medicine and food. In this study, a new active protein was discovered and expressed heterogeneously through bioinformatics analysis. It was then identified using Western blot, molecular weight, and mass spectrometry. The antibacterial activity, hemolysis activity, antioxidant activity, and Angiotensin-Converting Enzyme II (ACE2) inhibitory activity were investigated. An unknown functional protein was screened through the Uniprot protein database, and its primary structure did not resemble existing proteins. It was an α-helical cationic polypeptide we named PFAP-1. The codon-optimized full-length PFAP-1 gene was synthesized and inserted into the prokaryotic expression vector pET-30a. The induced expression conditions were determined with a final isopropyl-β-d-thiogalactoside (IPTG) concentration of 0.2 mM, an induction temperature of 15 °C, and an induction time of 16 h. The recombinant PFAP-1 protein, with low endotoxin and sterility, was successfully prepared. The recombinant PFAP-1 protein exhibited strong antibacterial activity against methicillin-resistant Staphylococcus aureus (MRSA) in vitro, and the diameter of the inhibition zone was 15.99 ± 0.02 mm. Its minimum inhibitory concentration (MIC) and minimum bactericidal concentration (MBC) were 37.5 μg/mL and 150 μg/mL, respectively, and its hemolytic activity was low (11.21%) at the bactericidal concentration. The recombinant PFAP-1 protein significantly inhibited the formation of MRSA biofilm and eradicated MRSA biofilm. It also demonstrated potent 1,1-diphenyl-2-picryl-hydrazyl radical (DPPH) scavenging activity with a half-maximal inhibitory concentration (IC50) of 40.83 μg/mL. The IC50 of ACE2 inhibition was 5.66 μg/mL. Molecular docking results revealed that the optimal docking fraction of PFAP-1 protein and ACE2 protein was -267.78 kcal/mol, with a confidence level of 0.913. The stable binding complex was primarily formed through nine groups of hydrogen bonds, three groups of salt bridges, and numerous hydrophobic interactions. In conclusion, recombinant PFAP-1 can serve as a promising active protein in food, cosmetics, or medicine.

A modular inquiry-based semester theme that integrates data science education and bioinformatics in protein structure function courses.

With an exponential growth in biological data and computing power, familiarity with bioinformatics has become a demanding and popular skill set both in academia and industry. There is a need to increase students' competencies to be able to take on bioinformatic careers, to get them familiarized with scientific professions in data science and the academic training required to pursue them, in a field where demand outweighs the supply. Here we implemented a set of bioinformatic activities into a protein structure and function course of a graduate program. Concisely, students were given hands-on opportunities to explore the bioinformatics-based analyses of biomolecular data and structural biology via a semester-long case study structured as inquiry-based bioinformatics exercises. Towards the end of the term, the students also designed and presented an assignment project that allowed them to document the unknown protein that they identified using bioinformatic knowledge during the term. The post-module survey responses and students' performances in the lab module imply that it furthered an in-depth knowledge of bioinformatics. Despite having not much prior knowledge of bioinformatics prior to taking this module students indicated positive feedback. The students got familiar with cross-indexed databases that interlink important data about proteins, enzymes as well as genes. The essential skillsets honed by this research-based bioinformatic pedagogical approach will empower students to be able to leverage this knowledge for their future endeavours in the bioinformatics field.