Identification Of Proteins Research Articles

Systematic identification of structure-specific protein–protein interactions

The physical interactome of a protein can be altered upon perturbation, modulating cell physiology and contributing to disease. Identifying interactome differences of normal and disease states of proteins could help understand disease mechanisms, but current methods do not pinpoint structure-specific PPIs and interaction interfaces proteome-wide. We used limited proteolysis–mass spectrometry (LiP–MS) to screen for structure-specific PPIs by probing for protease susceptibility changes of proteins in cellular extracts upon treatment with specific structural states of a protein. We first demonstrated that LiP–MS detects well-characterized PPIs, including antibody–target protein interactions and interactions with membrane proteins, and that it pinpoints interfaces, including epitopes. We then applied the approach to study conformation-specific interactors of the Parkinson’s disease hallmark protein alpha-synuclein (aSyn). We identified known interactors of aSyn monomer and amyloid fibrils and provide a resource of novel putative conformation-specific aSyn interactors for validation in further studies. We also used our approach on GDP- and GTP-bound forms of two Rab GTPases, showing detection of differential candidate interactors of conformationally similar proteins. This approach is applicable to screen for structure-specific interactomes of any protein, including posttranslationally modified and unmodified, or metabolite-bound and unbound protein states.

Characterization of effective, simple, and low-cost precipitation methods for depleting abundant plasma proteins to enhance the depth and breadth of plasma proteomics.

Plasma is an abundant source of proteins and potential biomarkers to aid in the detection, diagnosis, and prognosis of human diseases. These proteins are often present at low levels in the blood and difficult to identify and measure due to the large dynamic range of proteins. The goal of this work was to characterize and compare various protein precipitation methods related to how they affect the depth and breadth of plasma proteomic studies. Abundant protein precipitation with perchloric acid (PerCA) can increase protein identifications and depth of plasma proteomic studies. Three acid- and four solvent-based precipitation methods were evaluated. All methods tested provided excellent plasma proteomic coverage (>600 identified protein groups) and detected protein in the low pg/mL range. Functional enrichment analysis revealed subtle differences within and larger changes between the precipitant groups. Methanol-based precipitation outperformed the other methods based on identifications and reproducibility. The methods' performance was verified using eight lung cancer patient samples, where >700 protein groups were measured and proteins with an estimated plasma concentration of ∼10pg/mL were detected. Various protein precipitation agents are amenable to extending the depth and breadth of plasma proteomes. These data can guide investigators to implement inexpensive, high-throughput methods for their plasma proteomic workflows.

Recent Progress in the Application of Tau Protein Biosensors for Diagnosis of Neurodegenerative Diseases.

The microtubule-associated Tau protein is found in the central nervous system (CNS) in six major isoforms. Neurodegenerative diseases have been linked to post-translational changes of Tau, most notably phosphorylation. Tau protein's molecular diversity is highly helpful in the identification of neurodegenerative illnesses. Nonetheless, one major obstacle to the early detection of brain illness is the nanoscale identification of tau proteins. The standard methods for identifying tau protein include western blotting, polymerase chain reaction (PCR), and real-time PCR. Enzyme-linked immunosorbent assay (ELISA) is another approach used. The limited sensitivity and specificity of these detections, together with the need for sophisticated equipment, are some of their drawbacks. The development of innovative and complex methods for tau protein screening is necessary to address the aforementioned issues. Biosensors are a cutting-edge instrument that may help identify various neurodegenerative biomarkers as early as feasible. This paper provides an overview of the most recent developments in the detection of neurodegenerative diseases employing biosensors built on nanotechnology and methods for imaging, electrochemical, and optical detection of the Tau protein. Furthermore, we outline the present difficulties and suggest a possible course for biosensor-based detection and intervention in the future.

Identification and Characterization of a Bacterial Periplasmic Solute Binding Protein That Binds l-Amino Acid Amides.

Periplasmic solute-binding proteins (SBPs) are key ligand recognition components of bacterial ATP-binding cassette (ABC) transporters that allow bacteria to import nutrients and metabolic precursors from the environment. Periplasmic SBPs comprise a large and diverse family of proteins, of which only a small number have been empirically characterized. In this work, we identify a set of 610 unique uncharacterized proteins within the SBP_bac_5 family that are found in conserved operons comprising genes encoding (i) ABC transport systems and (ii) putative amidases from the FmdA_AmdA family. From these uncharacterized SBP_bac_5 proteins, we characterize a representative periplasmic SBP from Mesorhizobium sp. A09 (MeAmi_SBP) and show that MeAmi_SBP binds l-amino acid amides but not the corresponding l-amino acids. An X-ray crystal structure of MeAmi_SBP bound to l-serinamide highlights the residues that impart distinct specificity for l-amino acid amides and reveals a structural Ca2+ binding site within one of the lobes of the protein. We show that the residues involved in ligand and Ca2+ binding are conserved among the 610 SBPs from experimentally uncharacterized FmdA_AmdA amidase-associated ABC transporter systems, suggesting these homologous systems are also likely to be involved in the sensing, uptake, and metabolism of l-amino acid amides across many Gram-negative nitrogen-fixing soil bacteria. We propose that MeAmi_SBP is involved in the uptake of such solutes to supplement pathways such as the citric acid cycle and the glutamine synthetase-glutamate synthase pathway. This work expands our currently limited understanding of microbial interactions with l-amino acid amides and bacterial nitrogen utilization.

Regulatory sequence-based discovery of anti-defense genes in archaeal viruses

In silico identification of viral anti-CRISPR proteins (Acrs) has relied largely on the guilt-by-association method using known Acrs or anti-CRISPR associated proteins (Acas) as the bait. However, the low number and limited spread of the characterized archaeal Acrs and Aca hinders our ability to identify Acrs using guilt-by-association. Here, based on the observation that the few characterized archaeal Acrs and Aca are transcribed immediately post viral infection, we hypothesize that these genes, and many other unidentified anti-defense genes (ADG), are under the control of conserved regulatory sequences including a strong promoter, which can be used to predict anti-defense genes in archaeal viruses. Using this consensus sequence based method, we identify 354 potential ADGs in 57 archaeal viruses and 6 metagenome-assembled genomes. Experimental validation identified a CRISPR subtype I-A inhibitor and the first virally encoded inhibitor of an archaeal toxin-antitoxin based immune system. We also identify regulatory proteins potentially akin to Acas that can facilitate further identification of ADGs combined with the guilt-by-association approach. These results demonstrate the potential of regulatory sequence analysis for extensive identification of ADGs in viruses of archaea and bacteria.

A methodological exploration of distinguishing hair quality based on hair proteomics

Hair is an advantageous biological sample due to its recordable, collectable, and storable nature. Hair's primary components are keratin and keratin-associated proteins. Owing to its abundance of cystine, keratin possesses impressive mechanical strength and chemical stability, formed by creating disulfide bonds as crosslinks within the protein peptide chain. Furthermore, keratin is cross-linked with keratin-associated proteins to create a complex network structure that provides the hair with strength and rigidity. Protein extraction serves as the foundation for hair analysis research. Bleaching hair causes damage to the structure between keratin and keratin-associated proteins, resulting in texture issues and hair breakage. This article outlines various physical treatment methods and lysate analysis that enhance the efficiency of hair protein extraction. The PLEE method achieves a three-fold increase in hair protein extraction efficiency when using a lysis solution containing SDS and combining high temperatures with intense shaking, compared to previous methods found in literature. We utilized the PLEE method to extract hair from both normal and damaged groups. Normal samples identified 156–157 proteins, including 51 keratin and keratin-associated proteins. The damaged group consisted of 155–158 identified proteins, of which 48–50 were keratin and keratin-associated proteins. Bleaching did not cause any notable difference in the protein identification of hair. However, it did reduce coverage of keratin and keratin-associated proteins significantly. Our hair protein extraction method provides extensive coverage of the hair proteome. Our findings indicate that bleaching damage results in subpar hair quality due to reduced coverage of protein primary sequences in keratin and keratin-associated proteins.

Identification of nitrated proteins in human thrombi from ischemic cerebrovascular events

Identification of nitrated proteins in human thrombi from ischemic cerebrovascular events

Fermentation Kinetics, Potability, and Sensory Compounds of Joubidwi: An Ethnic Alcoholic Beverage

Background: Fermentation is a metabolic pathway of the breakdown of carbohydrate molecules like glucose, fructose to ethanol and various volatile and non-volatile congeners. The production of alcoholic beverages from foodstuffs like rice, cereals, fruits, maize etc., is an age-old traditional protocol. In this study, we adapted the traditional protocol of preparation of the ethnic beverage Joubidwi, with a fermentation time of 6 days, i.e., aliquots from the fermented mash (cooked rice) were collected on a daily basis from day 1 to day 6. Objectives: This study aimed to get insight into fermentation kinetics, ethanol content, carbohydrate content, antioxidant activities, bitterness, protein content, pH, colour, and turbidity of the ethnic alcoholic beverage Joubidwi and to identify the sensory compounds and polyphenol molecules. Method: UV-Visible spectroscopic methods, GC-FID and GC-MS analytics methods were used for the estimation and identification of carbohydrates, proteins, ethanol contents, and sensory compounds, respectively. Reverse phase HPLC was adapted for the identification of polyphenol molecules in Joubidwi. Results: A pseudo-first-order fermentation kinetics was observed from day 1 to day 6 fermentation of Joubidwi. Apart from ethanol content (60.48 ± 1.36 mg/mL after 72 h fermentation to 78.80 ± 1.48 mg/mL after 144 h fermentation), the beverage had nutritional values and good antioxidant properties with 22.38 to 35.11% Radical Scavenging Effect (RSE) from 1 to 6th day of fermented Joubidwi. The beverage also contained 12 sensory compounds. Gallic acid was identified as polyphenol in Joubidwi samples. Conclusion: Joubidwi is an alcoholic beverage prepared by Bodos using rice and certain herbs through a traditional fermentation process. The fermentation process follows pseudo-first-order kinetics. The beverage is potable.

Enhanced Proteomic Coverage in Tissue Microenvironment by Immune Cell Subtype Library-Assisted DIA-MS

Immune cells that infiltrate the tumor microenvironment (TME) play crucial roles in shaping cancer development and influencing clinical outcomes and therapeutic responses. However, obtaining a comprehensive proteomic snapshot of tumor-infiltrating immunity in clinical specimens is often hindered by small sample amounts and a low proportion of immune infiltrating cells in the TME. To enable in-depth and highly sensitive profiling of microscale tissues, we established an immune cell-enriched library-assisted strategy for data-independent acquisition mass spectrometry (DIA-MS). Firstly, six immune cell subtype-specific spectral libraries were established from sorted cluster of differentiation markers, CD8+, CD4+ T lymphocytes, B lymphocytes, natural killer cells, dendritic cells, and macrophages in murine mesenteric lymph nodes (MLNs), covering 7815 protein groups with surface markers and immune cell-enriched proteins. The feasibility of microscale immune proteomic profiling was demonstrated on 1 μg tissue protein from the tumor of murine colorectal cancer (CRC) models using single-shot DIA; the immune cell-enriched library increased coverage to quantify 7419 proteins compared to directDIA analysis (6978 proteins). The enhancement enabled the mapping of 841 immune function-related proteins and exclusive identification of many low-abundant immune proteins, such as CD1D1, and CD244, demonstrating high sensitivity for immune landscape profiling. This approach was used to characterize the MLNs in CRC models, aiming to elucidate the mechanism underlying their involvement in cancer development within the TME. Even with a low percentage of immune cell infiltration (0.25–3%) in the tumor, our results illuminate downregulation in the adaptive immune signaling pathways (C-type lectin receptor signaling, chemokine signaling, and so on), T cell receptor signaling, and Th1/Th2/Th17 cell differentiation, suggesting an immunosuppressive status in MLNs of CRC models. The DIA approach using the immune cell-enriched libraries showcased deep coverage and high sensitivity that can facilitate illumination of the immune proteomic landscape for microscale samples.

Understanding molecular differences in endocrine and exocrine pancreatic tissue in healthy and unhealthy metabolic states

Type II diabetes (T2D) is a metabolic disorder characterized by hyperglycemia largely due the development of insulin-resistance, and in some cases, due to insuffcient insulin secretion. The endocrine portion of the pancreas, the islets of Langerhans, is composed of a diverse population of cells (beta, alpha, delta) that play vital roles in glucose homeostasis. Intercellular signaling events that occur within this diverse cell population play an active role in T2D progression. Therefore, to properly understand the progression of T2D it is critical to understand the how both the cellular neighborhoods and cell-cell communications within the islet of Langerhans and the surrounding exocrine (non-Islet) tissue contribute to the development of T2D. As the pancreas is a heterogenous organ, techniques allowing for the identification of cellular subpopulations within the tissue, such as single-cell RNA sequencing (scRNAseq), have been critical to answering questions about the pathogenesis of T2D. However, these techniques provide no insight into the anatomical and spatial context of cells, and therefore lack the ability to determine the influence of cellular neighborhoods in T2D pathogenesis. To begin to fill the gap in our understanding of how cellular neighborhoods and cell-cell communication impacts the progression of T2D, we utilized cutting edge technology that allows for the identification of RNA and protein within tissue at cellular and subcellular level, the Nanostring CosMx Spatial Molecular Imager. In this study, we aimed to determine if the location of individual cells within the islet (exocrine adjacent versus endocrine adjacent) contributes to gene expression changes that direct disease progression. To answer this question, we collected 200 human donor pancreases and analyzed them for 1000 RNA targets using the CosMX Spatial Molecular Imager. Ig. We then performed an analysis of RNA expression of exocrine cells bordering the islets of Langerhans and endocrine cells surrounding pancreatic vasculature was compared between healthy, metabolically heathy obese (MHO), and obese T2D groups. Preliminary analysis of these data yield novel insights into the transcriptomic profile of metabolically unhealthy tissues that may play a role in the progression of T2D. In particular, we observed an upregulation of mRNA transcripts associated with pro-inflammatory IL-21 and IL-22 signaling in the alpha cells from MHO donor pancreases. Furthermore, this upregulation is primarily observed in alpha cells in close proximity to acinar cells. Overall, these data will help pave the way to addressing previously unanswered questions in the context of T2D pathogenesis and unexplored cell-cell interactions that occur between the endocrine and exocrine spaces of the pancreas. Funding: Saint Louis University. This is the full abstract presented at the American Physiology Summit 2024 meeting and is only available in HTML format. There are no additional versions or additional content available for this abstract. Physiology was not involved in the peer review process.

Identification of proteins differentially associated with Npt2a in the presence and absence of NHERF1

Background: NHERF1 deficient mice are hypophosphatemic and phosphaturic due to failure of functional apical membrane localization of Npt2a. Wild type (WT) and NHERF1 knock out (KO) mice show similar proximal tubule protein expression of Npt2a. Previous studies have shown that dissociation of Npt2a and NHERF1 stimulated by PTH and FGF23 results in lysosomal degradation of Npt2a. The persistence of Npt2a expression in NHERF1 deficiency suggests a defect in Npt2a forward traffcking. Hypothesis: We hypothesized that comparison of Npt2a-associated proteins from WT and KO mouse kidney cortex would identify novel proteins critical for forward traffcking of Npt2a. Methods: We immunoprecipitated homogenized kidney cortex from WT and NHERF1 KO C57Bl6 mice using a polyclonal Npt2a antibody followed by proteomic analysis using liquid chromatography-mass spectrometry and the STRING database of known and predicted protein-protein interactions. Results: We identified 31 proteins that showed statistically significantly greater association with Npt2a in WT kidney cortex and 50 proteins that showed statistically significantly greater association with Npt2a in KO kidney cortex. We did not identify greater association of Npt2a from KO kidney cortex with any of the other known PDZ proteins known to associate with Npt2a including PDZK1 and PIST. Among the proteins more highly associated with Npt2a in KO were 14-3-3 and valacyclovir hydrolase. STRING analysis of the WT-associated proteins identified membrane transport, phosphate ion homeostasis, and oxidative phosphorylation as important biological processes. Analysis of the KO-associated proteins identified DNA unwinding, regulation of cytoplasmic translation, and clathrin-dependent endocytosis were identified as important biological processes in the absence of NHERF1. Conclusions: We conclude that the aberrantly-traffcked Npt2a in KO has successfully traversed the Golgi and is correctly folded but may interact with 14-3-3 through its PDZ binding motif, preventing forward traffcking. We also conclude that the function of Npt2a may significantly change when the cotransporter does not properly forward traffc in the absence of NHERF1 expression. Support for EDL from Dept Veterans Affairs CX0001614 and the Pak Mineral Metabolism Center. This is the full abstract presented at the American Physiology Summit 2024 meeting and is only available in HTML format. There are no additional versions or additional content available for this abstract. Physiology was not involved in the peer review process.

DomainFit: Identification of protein domains in cryo-EM maps at intermediate resolution using AlphaFold2-predicted models.

Cryoelectron microscopy (cryo-EM) has revolutionized the structural determination of macromolecular complexes. With the paradigm shift to structure determination of highly complex endogenous macromolecular complexes exvivo and in situ structural biology, there are an increasing number of structures of native complexes. These complexes often contain unidentified proteins, related to different cellular states or processes. Identifying proteins at resolutions lower than 4Å remains challenging because side chains cannot be visualized reliably. Here, we present DomainFit, a program for semi-automated domain-level protein identification from cryo-EM maps, particularly at resolutions lower than 4Å. By fitting domains from AlphaFold2-predicted models into cryo-EM maps, the program performs statistical analyses and attempts to identify the domains and protein candidates forming the density. Using DomainFit, we identified two microtubule inner proteins, one of which contains a CCDC81 domain and is exclusively localized in the proximal region of the doublet microtubule in Tetrahymena thermophila.

Exploring Lung Cancer Protein Network: Understanding Structure and Function through Metric Space Modeling

Lung cancer remains a significant health threat with high mortality rates. Using graph theory, we modeled the protein-protein interaction (PPI) network in lung cancer to explore its complex structure. This approach allows for the analysis of network properties and the identification of key proteins driving biological processes. Our analysis revealed RPS27A as a central proteinwithin the network, associated with diverse functions related to ribosome biogenesis, translation, cell growth, apoptosis, and cancer progression. This suggests that RPS27A may have multiple functions in cancer development and progression, including in MAPK signaling pathways. Importantly, our study uniquely identifies RPS27A as a central hub in lung cancer PPI networks, shedding light on its pivotal role in disease pathogenesis. Additionally, we identified a central network zone enriched with proteins involved in key signaling pathways, presenting novel insights into potential therapeutic targets for lung cancer treatment. Pathway enrichment analysis further highlighted functional specialization across network zones, providing a comprehensive understanding of the intricate interplay between biological pathways in lung cancer progression. This study underscores the multifaceted roles of central proteins like RPS27A within lung cancer’s PPI network and the network’s potential for pinpointing therapeutic targets, presenting a novel perspective on the intricate network of molecular interactions driving lung cancer pathogenesis.

A stacking-based algorithm for antifreeze protein identification using combined physicochemical, pseudo amino acid composition, and reduction property features

Antifreeze proteins have wide applications in the medical and food industries. In this study, we propose a stacking-based classifier that can effectively identify antifreeze proteins. Initially, feature extraction was performed in three aspects: reduction properties, scalable pseudo amino acid composition, and physicochemical properties. A hybrid feature set comprised of the combined information from these three categories was obtained. Subsequently, we trained the training set based on LightGBM, XGBoost, and RandomForest algorithms, and the training outcomes were passed to the Logistic algorithm for matching, thereby establishing a stacking algorithm. The proposed algorithm was tested on the test set and an independent validation set. Experimental data indicates that the algorithm achieved a recognition accuracy of 98.3 %, and an accuracy of 98.5 % on the validation set. Lastly, we analyzed the reasons why numerical features achieved high recognition capabilities from multiple aspects. Data dimensionality reduction and the analysis from two-dimensional and three-dimensional views revealed separability between positive and negative samples, and the protein three-dimensional structure further demonstrated significant differences in related features between the two samples. Analysis of the classifier revealed that Hr*Hr, HrHr, and Sc-PseAAC_1, 188D(152,116,57,183) were among the seven most important numerical features affecting algorithm recognition. For Hr*Hr and HrHr, supportive sequence level evidence for the reduction dictionary was found in terms of conservation area analysis, multiple sequence alignment, and amino acid conservative substitution. Moreover, the importance of the reduction dictionary was recognized through a comparative analysis of importance before and after the reduction, realizing the effectiveness of the dictionary in improving feature importance. A decision tree model has been utilized to discern the distinctions between dipeptides associated with the physical and chemical properties of His(H), Iso(I), Leu(L), and Lys(K) and other dipeptides. We finally analyzed the other seven features of importance, and data analysis confirmed that hydrophobicity, secondary structure, charge properties, van der Waals forces, and solvent accessibility are also factors affecting the antifreeze capability of proteins.

Recognition of the inducible, secretory small protein OsSSP1 by the membrane receptor OsSSR1 and the co-receptor OsBAK1 confers rice resistance to the blast fungus

The plant apoplast, which serves as the frontline battleground for long-term host–pathogen interactions, harbors a wealth of disease resistance resources. However, the identification of the disease resistance proteins in the apoplast is relatively lacking. In this study, we identified the rice secretory protein OsSSP1 (Oryza sativa secretory small protein 1). OsSSP1 can be secreted into the plant apoplast, and both in vitro treatment of recombinant OsSSP1 and overexpression of OsSSP1 in rice can trigger plant immune response. The expression of OsSSP1 is suppressed significantly during Magnaporthe oryzae infection in susceptible rice Taibei 309, and OsSSP1-overexpressing lines all show strong resistance to M. oryzae. Combining the knockout and overexpression results, we found that OsSSP1 positively regulates plant immunity in response to fungal infection. Moreover, the recognition and immune response triggered by OsSSP1 depend on an uncharacterized transmembrane OsSSR1 (secretory small protein receptor 1) and the key co-receptor OsBAK1, since most of the induced immune response and resistance are lost in the absence of OsSSR1 or OsBAK1. Intriguingly, the OsSSP1 protein is relatively stable and can still induce plant resistance after 1 week of storage in the open environment, and exogenous OsSSP1 treatment for a 2-week period did not affect rice yield. Collectively, our study reveals that OsSSP1 can be secreted into the apoplast and percepted by the plasma membrane receptors OsSSR1 and OsBAK1 during fungal infection, thereby triggering the immune response to enhance plant resistance to M. oryzae. These findings providing novel resources and potential strategies for crop breeding and disease control.

Gut symbionts alleviate MASH through a secondary bile acid biosynthetic pathway

The gut microbiota has been found to play an important role in the progression of metabolic dysfunction-associated steatohepatitis (MASH), but the mechanisms have not been established. Here, by developing a click-chemistry-based enrichment strategy, we identified several microbial-derived bile acids, including the previously uncharacterized 3-succinylated cholic acid (3-sucCA), which is negatively correlated with liver damage in patients with liver-tissue-biopsy-proven metabolic dysfunction-associated fatty liver disease (MAFLD). By screening human bacterial isolates, we identified Bacteroides uniformis strains as effective producers of 3-sucCA both invitro and invivo. By activity-based protein purification and identification, we identified an enzyme annotated as β-lactamase in B.uniformis responsible for 3-sucCA biosynthesis. Furthermore, we found that 3-sucCA is a lumen-restricted metabolite and alleviates MASH by promoting the growth of Akkermansia muciniphila. Together, our data offer new insights into the gut microbiota-liver axis that may be leveraged to augment the management of MASH.

Model fusion for predicting unconventional proteins secreted by exosomes using deep learning.

Unconventional secretory proteins (USPs) are vital for cell-to-cell communication and are necessary for proper physiological processes. Unlike classical proteins that follow the conventional secretory pathway via the Golgi apparatus, these proteins are released using unconventional pathways. The primary modes of secretion for USPs are exosomes and ectosomes, which originate from the endoplasmic reticulum. Accurate and rapid identification of exosome-mediated secretory proteins is crucial for gaining valuable insights into the regulation of non-classical protein secretion and intercellular communication, as well as for the advancement of novel therapeutic approaches. Although computational methods based on amino acid sequence prediction exist for predicting unconventional proteins secreted by exosomes (UPSEs), they suffer from significant limitations in terms of algorithmic accuracy. In this study, we propose a novel approach to predict UPSEs by combining multiple deep learning models that incorporate both protein sequences and evolutionary information. Our approach utilizes a convolutional neural network (CNN) to extract protein sequence information, while various densely connected neural networks (DNNs) are employed to capture evolutionary conservation patterns.By combining six distinct deep learning models, we have created a superior framework that surpasses previous approaches, achieving an ACC score of 77.46% and an MCC score of 0.5406 on an independent test dataset.

A protein network refinement method based on module discovery and biological information

BackgroundThe identification of essential proteins can help in understanding the minimum requirements for cell survival and development to discover drug targets and prevent disease. Nowadays, node ranking methods are a common way to identify essential proteins, but the poor data quality of the underlying PIN has somewhat hindered the identification accuracy of essential proteins for these methods in the PIN. Therefore, researchers constructed refinement networks by considering certain biological properties of interacting protein pairs to improve the performance of node ranking methods in the PIN. Studies show that proteins in a complex are more likely to be essential than proteins not present in the complex. However, the modularity is usually ignored for the refinement methods of the PINs.MethodsBased on this, we proposed a network refinement method based on module discovery and biological information. The idea is, first, to extract the maximal connected subgraph in the PIN, and to divide it into different modules by using Fast-unfolding algorithm; then, to detect critical modules according to the orthologous information, subcellular localization information and topology information within each module; finally, to construct a more refined network (CM-PIN) by using the identified critical modules.ResultsTo evaluate the effectiveness of the proposed method, we used 12 typical node ranking methods (LAC, DC, DMNC, NC, TP, LID, CC, BC, PR, LR, PeC, WDC) to compare the overall performance of the CM-PIN with those on the S-PIN, D-PIN and RD-PIN. The experimental results showed that the CM-PIN was optimal in terms of the identification number of essential proteins, precision-recall curve, Jackknifing method and other criteria, and can help to identify essential proteins more accurately.

Comprehensive Research on Druggable Proteins: From PSSM to Pre-Trained Language Models.

Identification of druggable proteins can greatly reduce the cost of discovering new potential drugs. Traditional experimental approaches to exploring these proteins are often costly, slow, and labor-intensive, making them impractical for large-scale research. In response, recent decades have seen a rise in computational methods. These alternatives support drug discovery by creating advanced predictive models. In this study, we proposed a fast and precise classifier for the identification of druggable proteins using a protein language model (PLM) with fine-tuned evolutionary scale modeling 2 (ESM-2) embeddings, achieving 95.11% accuracy on the benchmark dataset. Furthermore, we made a careful comparison to examine the predictive abilities of ESM-2 embeddings and position-specific scoring matrix (PSSM) features by using the same classifiers. The results suggest that ESM-2 embeddings outperformed PSSM features in terms of accuracy and efficiency. Recognizing the potential of language models, we also developed an end-to-end model based on the generative pre-trained transformers 2 (GPT-2) with modifications. To our knowledge, this is the first time a large language model (LLM) GPT-2 has been deployed for the recognition of druggable proteins. Additionally, a more up-to-date dataset, known as Pharos, was adopted to further validate the performance of the proposed model.

Genome-Wide Identification and Expression Analysis of Kiwifruit Leucine-Rich Repeat Receptor-Like Proteins Reveal Their Roles in Biotic and Abiotic Stress Responses.

Leucine-rich repeat receptor-like proteins (LRR-RLPs), a major group of receptor-like proteins in plants, have diverse functions in plant physiology, including growth, development, signal transduction, and stress responses. Despite their importance, the specific roles of kiwifruit LRR-RLPs in response to biotic and abiotic stresses remain poorly understood. In this study, we performed family identification, characterization, transcriptome data analysis, and differential gene expression analysis of kiwifruit LRR-RLPs. We identified totals of 101, 164, and 105 LRR-RLPs in Actinidia chinensis 'Hongyang', Actinidia eriantha 'Huate', and Actinidia chinensis 'Red5', respectively. Synteny analysis revealed that the expansion of kiwifruit LRR-RLPs was primarily attributed to segmental duplication events. Based on RNA-seq data from pathogen-infected kiwifruits, we identified specific LRR-RLP genes potentially involved in different stages of pathogen infection. Additionally, we observed the potential involvement of kiwifruit LRR-RLPs in abiotic stress responses, with upstream transcription factors possibly regulating their expression. Furthermore, protein interaction network analysis unveiled the participation of kiwifruit LRR-RLP in the regulatory network of abiotic stress responses. These findings highlight the crucial roles of LRR-RLPs in mediating both biotic and abiotic stress responses in kiwifruit, offering valuable insights for the breeding of stress-resistant kiwifruit varieties.