Identification Of Proteins Research Articles

Construction and analysis of protein-protein interaction network for esophageal squamous cell carcinoma

Esophageal squamous cell carcinoma (ESCC) is a prevalent malignant tumor of the digestive tract. Clinical findings reveal that the five-year survival rate for mid-to late-stage ESCC patients is merely around 20 %, whereas those diagnosed at an early stage can achieve up to a 95 % survival rate. Consequently, early detection is paramount to improving ESCC patient survival. Protein markers are essential for diagnosing diseases, and the identification of new candidate proteins associated with ESCC through the protein-protein interaction (PPI) network is aimed for in this paper. The PPI network related to ESCC was constructed using protein data, comprising 2094 nodes and 19,660 edges. To assess the nodes' importance in the network, three metrics—degree centrality, betweenness centrality, and closeness centrality—were employed, leading to the identification of 81 key proteins. Subsequently, the biological significance of these proteins in the network was explored, combining biomedical knowledge from three perspectives: network, node, and cluster. The results demonstrated that 52 out of 81 key proteins were confirmed to be linked to ESCC. Among the remaining 29 unreported proteins, 18 displayed significant biological significance, indicating their potential as protein markers related to ESCC.

GraphNABP: Identifying nucleic acid-binding proteins with protein graphs and protein language models

The computational identification of nucleic acid-binding proteins (NABP) is of great significance for understanding the mechanisms of these biological activities and drug discovery. Although a bunch of sequence-based methods have been proposed to predict NABP and achieved promising performance, the structure information is often overlooked. On the other hand, the power of popular protein language models (pLM) has seldom been harnessed for predicting NABPs. In this study, we propose a novel framework called GraphNABP, to predict NABP by integrating sequence and predicted 3D structure information. Specifically, sequence embeddings and protein molecular graphs were first obtained from ProtT5 protein language model and predicted 3D structures, respectively. Then, graph attention (GAT) and bidirectional long short-term memory (BiLSTM) neural networks were used to enhance feature representations. Finally, a fully connected layer is used to predict NABPs. To the best of our knowledge, this is the first time to integrate AlphaFold and protein language models for the prediction of NABPs. The performances on multiple independent test sets indicate that GraphNABP outperforms other state-of-the-art methods. Our results demonstrate the effectiveness of pLM embeddings and structural information for NABP prediction. The codes and data used in this study are available at https://github.com/lixiangli01/GraphNABP.

In-Depth Host Cell Protein Analysis and Viral Protein Impurity Monitoring in Adeno-Associated Virus-Based Gene Therapy Products Using Optimized Wide Window Data-Dependent Acquisition Method.

Compared to other protein therapeutics, there is currently limited knowledge about the residual host cell proteins (HCPs) in adeno-associated virus (AAV)-based gene therapy products. This is primarily due to the lack of a robust and sensitive mass spectrometry-based method for HCP analysis in AAV samples. Existing liquid chromatography-mass spectrometry methods used for analyzing HCPs in therapeutic monoclonal antibodies (mAbs) often cannot be directly applied to AAVs, due to some unique characteristics of AAV samples encountered during their development such as limited sample availability/protein concentration and the presence of surfactants. In this study, we have developed a novel workflow for robust and in-depth HCP analysis of AAV samples by combining wide-window data-dependent acquisition for improved low-abundance HCP detection with single-pot, solid-phase-enhanced sample preparation (SP3) for low-input sample preparation. Using this newly developed method, we were able to detect more than 650 HCPs in a commercial AAV1 sample with a high quantitative reproducibility. This represents a greater than 5-fold increase in HCP protein identification compared to an in-solution digestion method followed by traditional data-dependent acquisition. Similar benefits can also be achieved for other AAV serotypes that were produced internally and purified through different processes. The detection limit of this method is as low as 0.06 ng/mL, enabling more comprehensive HCP coverage in AAV samples. Moreover, for the first time, we have identified several process-related viral proteins, such as Rep 78 and E4. These proteins need to be closely monitored during AAV process development as they may present a greater risk for immunogenicity compared to HCPs that are derived from human HEK293 cells.

Genome-wide identification of CaWD40 proteins reveals the involvement of a novel complex (CaAN1-CaDYT1-CaWD40-91) in anthocyanin biosynthesis and genic male sterility in Capsicum annuum

BackgroundThe WD40 domain, one of the most abundant in eukaryotic genomes, is widely involved in plant growth and development, secondary metabolic biosynthesis, and mediating responses to biotic and abiotic stresses. WD40 repeat (WD40) protein has been systematically studied in several model plants but has not been reported in the Capsicum annuum (pepper) genome.ResultsHerein, 269, 237, and 257 CaWD40 genes were identified in the Zunla, CM334, and Zhangshugang genomes, respectively. CaWD40 sequences from the Zunla genome were selected for subsequent analysis, including chromosomal localization, phylogenetic relationships, sequence characteristics, motif compositions, and expression profiling. CaWD40 proteins were unevenly distributed on 12 chromosomes, encompassing 19 tandem duplicate gene pairs. The 269 CaWD40s were divided into six main branches (A to F) with 17 different types of domain distribution. The CaWD40 gene family exhibited diverse expression patterns, and several genes were specifically expressed in flowers and seeds. Yeast two-hybrid (Y2H) and dual-luciferase assay indicated that CaWD40-91 could interact with CaAN1 and CaDYT1, suggesting its involvement in anthocyanin biosynthesis and male sterility in pepper.ConclusionsIn summary, we systematically characterized the phylogeny, classification, structure, and expression of the CaWD40 gene family in pepper. Our findings provide a valuable foundation for further functional investigations on WD40 genes in pepper.

Unraveling the Mysteries of PCOS: A Comprehensive Investigation into Causes, Mechanisms, and Molecular Docking Analysis with Dolichos biflorus Linn.

Abstract: Polycystic ovary syndrome (PCOS) presents as a complex endocrine and metabolic disorder characterized by anovulation, infertility, obesity, insulin resistance, and polycystic ovaries. Various factors contribute to PCOS risk, including lifestyle choices, diet, environmental pollutants, genetics, gut dysbiosis, neuroendocrine changes, and obesity. These factors can impact hyperinsulinemia, oxidative stress, hyperandrogenism, decreased folliculogenesis, and irregular menstrual cycles, ultimately contributing to metabolic syndrome. Conventional treatments for PCOS often carry significant side effects, prompting exploration into Ayurvedic alternatives. This review concentrates on molecular docking studies of phytochemicals sourced from Dolichos Biflorus Linn. against key proteins implicated in PCOS pathogenesis. Identification, modeling, and evaluation of target proteins were conducted, followed by the assessment of their interactions with selected phytochemicals and additional ADMET analysis. Molecular docking studies utilizing Auto Dock Vina software were then performed to forecast the binding affinities of these phytochemicals to the active sites of the specified targets.

Profiling sorghum-microbe interactions with a specialized photoaffinity probe identifies key sorgoleone binders in Acinetobacter pittii.

Interactions between plants and soil microbial communities that benefit plant growth and enhance nutrient acquisition are driven by the selective release of metabolites from plant roots, or root exudation. To investigate these plant-microbe interactions, we developed a photoaffinity probe based on sorgoleone (sorgoleone diazirine alkyne for photoaffinity labeling, SoDA-PAL), a hydrophobic secondary metabolite and allelochemical produced in Sorghum bicolor root exudates. We applied SoDA-PAL to the identification of sorgoleone-binding proteins in Acinetobacter pittii SO1, a potential plant growth-promoting microbe isolated from sorghum rhizosphere soil. Competitive photoaffinity labeling of A. pittii whole cell lysates with SoDA-PAL identified 137 statistically enriched proteins, including putative transporters, transcriptional regulators, and a subset of proteins with predicted enzymatic functions. We performed computational protein modeling and docking with sorgoleone to prioritize candidates for experimental validation and then confirmed binding of sorgoleone to four of these proteins in vitro: the α/β fold hydrolase SrgB (OH685_09420), a fumarylacetoacetase (OH685_02300), a lysophospholipase (OH685_14215), and an unannotated hypothetical protein (OH685_18625). Our application of this specialized sorgoleone-based probe coupled with structural bioinformatics streamlines the identification of microbial proteins involved in metabolite recognition, metabolism, and toxicity, widening our understanding of the range of cellular pathways that can be affected by a plant secondary metabolite.IMPORTANCEHere, we demonstrate that a photoaffinity-based chemical probe modeled after sorgoleone, an important secondary metabolite released by sorghum roots, can be used to identify microbial proteins that directly interact with sorgoleone. We applied this probe to the sorghum-associated bacterium Acinetobacter pittii and showed that probe labeling is dose-dependent and sensitive to competition with purified sorgoleone. Coupling the probe with proteomics and computational analysis facilitated the identification of putative sorgoleone binders, including a protein implicated in a conserved pathway essential for sorgoleone catabolism. We anticipate that discoveries seeded by this workflow will expand our understanding of the molecular mechanisms by which specific metabolites in root exudates shape the sorghum rhizosphere microbiome.

MiLoPYP: self-supervised molecular pattern mining and particle localization in situ

Cryo-electron tomography allows the routine visualization of cellular landscapes in three dimensions at nanometer-range resolutions. When combined with single-particle tomography, it is possible to obtain near-atomic resolution structures of frequently occurring macromolecules within their native environment. Two outstanding challenges associated with cryo-electron tomography/single-particle tomography are the automatic identification and localization of proteins, tasks that are hindered by the molecular crowding inside cells, imaging distortions characteristic of cryo-electron tomography tomograms and the sheer size of tomographic datasets. Current methods suffer from low accuracy, demand extensive and time-consuming manual labeling or are limited to the detection of specific types of proteins. Here, we present MiLoPYP, a two-step dataset-specific contrastive learning-based framework that enables fast molecular pattern mining followed by accurate protein localization. MiLoPYP’s ability to effectively detect and localize a wide range of targets including globular and tubular complexes as well as large membrane proteins, will contribute to streamline and broaden the applicability of high-resolution workflows for in situ structure determination.

Deep Learning Powers Protein Identification from Precursor MS Information.

Proteome analysis currently heavily relies on tandem mass spectrometry (MS/MS), which does not fully utilize MS1 features, as many precursors remain unselected for MS/MS fragmentation, especially in the cases of low abundance samples and wide abundance dynamic range samples. Therefore, leveraging MS1 features as a complement to MS/MS has become an attractive option to improve the coverage of feature identification. Herein, we propose MonoMS1, an approach combining deep learning-based retention time, ion mobility, detectability prediction, and logistic regression-based scoring for MS1 feature identification. The approach achieved a significant increase in MS1 feature identification based on an E. coli data set. Application of MonoMS1 to data sets with wide dynamic range, such as human serum proteome samples, and with low sample abundance, such as single-cell proteome samples, enabled substantial complementation of MS/MS-based peptide and protein identification. This method opens a new avenue for proteomic analysis and can boost proteomic research on complex samples.

Identification of STAM-binding protein as a target for the treatment of gemcitabine resistance pancreatic cancer in a nutrient-poor microenvironment

Pancreatic cancer (PC) is a highly malignant solid tumor whose resistance to gemcitabine (GEM) chemotherapy is a major cause of poor patient prognosis. Although PC is known to thrive on malnutrition, the mechanism underlying its chemotherapy resistance remains unclear. The current study analyzed clinical tissue sample databases using bioinformatics tools and observed significantly upregulated expression of the deubiquitinase STAMBP in PC tissues. Functional experiments revealed that STAMBP knockdown remarkably increases GEM sensitivity in PC cells. Multiple omics analyses suggested that STAMBP enhances aerobic glycolysis and suppresses mitochondrial respiration to increase GEM resistance in PC both in vitro and in vivo. STAMBP knockdown decreased PDK1 levels, an essential regulator of the aerobic glycolytic process, in several cancers. Mechanistically, STAMBP promoted the PDK1-mediated Warburg effect and chemotherapy resistance by modulating E2F1 via direct binding to E2F1 and suppressing its degradation and ubiquitination. High-throughput compound library screening using three-dimensional protein structure analysis and drug screening identified the FDA drug entrectinib as a potent GEM sensitizer and STAMBP inhibitor, augmenting the antitumor effect of GEM in a patient-derived xenograft (PDX) model. Overall, we established a novel mechanism, via the STAMBP–E2F1–PDK1 axis, by which PC cells become chemoresistant in a nutrient-poor tumor microenvironment.

Component-resolved diagnosis: enhancing precision diagnosis and clinical management of shellfish allergies

Shellfish, being one of the eight major food allergens, affects approximately 3% of the global population. The occurrence of shellfish allergy is not only related to the individual's immune system sensitivity but is also influenced by geographical environment, food availability, and dietary habits. Although crustaceans (such as shrimp, crab, and lobster) and mollusks (such as oysters, mussels, and squid) are collectively referred to as shellfish, they exhibit significant differences in biological evolution and the spectrum of allergenic molecules they contain, leading to various allergic reactions. Accurate identification of allergenic proteins is crucial for the diagnosis and management of shellfish allergies, with key allergenic protein families including tropomyosin, arginine kinase, and hemocyanin. Furthermore, due to the diversity of shellfish allergens and their cross-reactivity with dust mite and insect allergens, diagnosing and managing shellfish allergies is complex, especially concerning tropomyosin and arginine kinase protein families. Currently, there are no specific immunotherapy treatments for shellfish allergies, and clinical management primarily relies on avoiding allergens and using anti-allergy medications. This article thoroughly interprets the " Molecular Allergology User's Guide 2.0 (MAUG 2.0)" published by the European Academy of Allergy and Clinical Immunology (EAACI) and the latest research on shellfish allergies both domestically and internationally. It highlights the significant role of allergen component diagnostics in optimizing the diagnostic and treatment processes for shellfish allergies, effectively assisting clinicians in accurately identifying common allergens and cross-reactions, thereby providing patients with more personalized diagnosis and treatment plans.

Slot Blot- and Electrospray Ionization-Mass Spectrometry/Matrix-Assisted Laser Desorption/Ionization-Mass Spectrometry-Based Novel Analysis Methods for the Identification and Quantification of Advanced Glycation End-Products in the Urine.

Proteins, saccharides, and low molecular organic compounds in the blood, urine, and saliva could potentially serve as biomarkers for diseases related to diet, lifestyle, and the use of illegal drugs. Lifestyle-related diseases (LSRDs) such as diabetes mellitus (DM), non-alcoholic steatohepatitis, cardiovascular disease, hypertension, kidney disease, and osteoporosis could develop into life-threatening conditions. Therefore, there is an urgent need to develop biomarkers for their early diagnosis. Advanced glycation end-products (AGEs) are associated with LSRDs and may induce/promote LSRDs. The presence of AGEs in body fluids could represent a biomarker of LSRDs. Urine samples could potentially be used for detecting AGEs, as urine collection is convenient and non-invasive. However, the detection and identification of AGE-modified proteins in the urine could be challenging, as their concentrations in the urine might be extremely low. To address this issue, we propose a new analytical approach. This strategy employs a method previously introduced by us, which combines slot blotting, our unique lysis buffer named Takata's lysis buffer, and a polyvinylidene difluoride membrane, in conjunction with electrospray ionization-mass spectrometry (ESI)/matrix-assisted laser desorption/ionization-mass spectrometry (MALDI-MS). This novel strategy could be used to detect AGE-modified proteins, AGE-modified peptides, and free-type AGEs in urine samples.

Accurate Identification of Periplasmic Urea-binding Proteins by Structure- and Genome Context-assisted Functional Analysis

ABC transporters are ancient and ubiquitous nutrient transport systems in bacteria and play a central role in defining lifestyles. Periplasmic solute-binding proteins (SBPs) are components that deliver ligands to their translocation machinery. SBPs have diversified to bind a wide range of ligands with high specificity and affinity. However, accurate assignment of cognate ligands remains a challenging problem in SBPs. Urea metabolism plays an important role in the nitrogen cycle; anthropogenic sources account for more than half of global nitrogen fertilizer. We report identification of urea-binding proteins within a large SBP sequence family that encodes diverse functions. By combining genetic linkage between SBPs, ABC transporter components, enzymes or transcription factors, we accurately identified cognate ligands, as we verified experimentally by biophysical characterization of ligand binding and crystallographic determination of the urea complex of a thermostable urea-binding homolog. Using three-dimensional structure information, these functional assignments were extrapolated to other members in the sequence family lacking genetic linkage information, which revealed that only a fraction bind urea. Using the same combined approaches, we also inferred that other family members bind various short-chain amides, aliphatic amino acids (leucine, isoleucine, valine), γ-aminobutyrate, and as yet unknown ligands. Comparative structural analysis revealed structural adaptations that encode diversification in these SBPs. Systematic assignment of ligands to SBP sequence families is key to understanding bacterial lifestyles, and also provides a rich source of biosensors for clinical and environmental analysis, such as the thermostable urea-binding protein identified here.

Genome-Wide Mapping of RNA-Protein Associations via Sequencing.

RNA-protein interactions are crucial for regulating gene expression and cellular functions, with their dysregulation potentially impacting disease progression. Systematically mapping these interactions is resource-intensive due to the vast number of potential RNA and protein interactions. Here, we introduce PRIM-seq (Protein-RNA Interaction Mapping by sequencing), a method for the concurrent de novo identification of RNA-binding proteins (RBPs) and the elucidation of their associated RNAs. PRIM-seq works by converting each RNA-protein pair into a unique chimeric DNA sequence, which is then decoded through DNA sequencing. Applied to two human cell types, PRIM-seq generated a comprehensive human RNA-protein association network (HuRPA), consisting of more than 350,000 RNA-proteins pairs involving approximately 7,000 RNAs and 11,000 proteins. The data revealed an enrichment of previously reported RBPs and RNA-protein interactions within HuRPA. We also identified LINC00339 as a protein-associating non-coding RNA and PHGDH as an RNA-associating protein. Notably, PHGDH interacts with BECN1 and ATF4 mRNAs, suppressing their protein expression and consequently inhibiting autophagy, apoptosis, and neurite outgrowth while promoting cell proliferation. PRIM-seq offers a powerful tool for discovering RBPs and RNA-protein associations, contributing to more comprehensive functional genome annotations.

Antiviral Activity of Angelica Tenuissima Nakai against Influenza A Virus.

The influenza A virus poses a serious threat to human health and is an important global public health issue. The drugs currently used for treatment are becoming increasingly ineffective against influenza A viruses and require the development of new antiviral drugs. Angelica tenuissima Nakai (ATN), a traditional herbal medicine belonging to the Umbelliferae family, exhibits a broad range of pharmacological activities, including inflammation, headache, and cold symptoms. In the present study, based on target protein identification, functional enrichment analysis, and gene set comparisons, we first suggested that ATN has potential therapeutic effects against influenza A virus infection. Next, methylthiazol tetrazolium (MTT) and sulforhodamine B colorimetric (SRB) assay results revealed that ATN exhibited low cytotoxicity in Madin-Darby canine kidney (MDCK) cells. The antiviral properties of ATN were observed against H1N1 and H3N2 virus strains. Microscopy confirmed the increased survival rate of the host cells. Further time-of-addition experiments revealed that the addition of ATN before virus adsorption showed similar results to the whole period of treatment. The pre- and co-treated groups showed lower levels of viral RNA (M1 protein). The results of this study suggest that ATN exhibits antiviral properties against the influenza A virus. These therapeutic properties of ATN can serve as a theoretical basis for further research on the applicability of ATN in the development of antiviral agents.

Identification, characterization, and anti-inflammatory activity of a lipocalin-like protein cloned from Oenanthe javanica

Identification, characterization, and anti-inflammatory activity of a lipocalin-like protein cloned from Oenanthe javanica

PE/PPE Proteome and ESX-5 Substrate Spectrum in Mycobacterium marinum.

PE/PPE proteins secreted by the ESX-5 type VII secretion system constitute a major protein repertoire in pathogenic mycobacteria and are essential for bacterial survival, pathogenicity, and host-pathogen interaction; however, little is known about their expression and secretion. The scarcity of arginine and lysine residues in PE/PPE protein sequences and the high homology of their N-terminal domains limit protein identification using classical trypsin-based proteomic methods. This study used endoproteinase AspN and trypsin to characterize the proteome of Mycobacterium marinum. Twenty-seven PE/PPE proteins were uniquely identified in AspN digests, especially PE_PGRS proteins. These treatments allowed the identification of approximately 50% of the PE/PPE pool encoded in the genome. Moreover, EspG5 pulldown assays retrieved 44 ESX-5-associated PPE proteins, covering 85% of the PPE pool in the identified proteome. The identification of PE/PE_PGRS proteins in the EspG5 interactome suggested the presence of PE-PPE pairs. The correlation analysis between protein abundance and phylogenetic relationships found potential PE/PPE pairs, indicating the presence of multiple PE/PE_PGRS partners in one PPE. We validated that EspG5 interacted with PPE31 and PPE32 and mapped critical residues for complex formation. The modified proteomic platform increases the coverage of PE/PPE proteins and elucidates the expression and localization of these proteins.

Unraveling the Mystery of Energy-Sensing Enzymes and Signaling Pathways in Tumorigenesis and Their Potential as Therapeutic Targets for Cancer.

Cancer research has advanced tremendously with the identification of causative genes, proteins, and signaling pathways. Numerous antitumor drugs have been designed and screened for cancer therapeutics; however, designing target-specific drugs for malignant cells with minimal side effects is challenging. Recently, energy-sensing- and homeostasis-associated molecules and signaling pathways playing a role in proliferation, apoptosis, autophagy, and angiogenesis have received increasing attention. Energy-metabolism-based studies have shown the contribution of energetics to cancer development, where tumor cells show increased glycolytic activity and decreased oxidative phosphorylation (the Warburg effect) in order to obtain the required additional energy for rapid division. The role of energy homeostasis in the survival of normal as well as malignant cells is critical; therefore, fuel intake and expenditure must be balanced within acceptable limits. Thus, energy-sensing enzymes detecting the disruption of glycolysis, AMP, ATP, or GTP levels are promising anticancer therapeutic targets. Here, we review the common energy mediators and energy sensors and their metabolic properties, mechanisms, and associated signaling pathways involved in carcinogenesis, and explore the possibility of identifying drugs for inhibiting the energy metabolism of tumor cells. Furthermore, to corroborate our hypothesis, we performed meta-analysis based on transcriptomic profiling to search for energy-associated biomarkers and canonical pathways.

A Novel Method for Targeted Identification of Essential Proteins by Integrating Chemical Reaction Optimization and Naive Bayes Model.

Targeted identification of essential proteins is of great significance for species identification, drug manufacturing, and disease treatment. It is a challenge to analyze the binding mechanism between essential proteins and improve the identification speed while ensuring the accuracy of the identification. This paper proposes a novel method called EPCRO for identifying essential proteins, which incorporates the chemical reaction optimization (CRO) algorithm and the naive Bayes model to effectively detect essential proteins. In EPCRO, the naive Bayes model is employed to analyze the homogeneity between proteins. In order to improve the identification rate and speed of essential proteins, the protein homogeneity rate is integrated into the CRO algorithm to balance between local and global searches. EPCRO is experimentally compared with 17 existing methods (including, DC, SC, IC, EC, LAC, NC, PeC, WDC, EPD-RW, RWHN, TEGS, CFMM, BSPM, AFSO-EP, CVIM, RWEP, and EPPSO-DC) based on biological datasets. The results show that EPCRO is superior to the above methods in identification accuracy and speed.

From Sequence to Solution: Intelligent Learning Engine Optimization in Drug Discovery and Protein Analysis.

This study introduces the intelligent learning engine (ILE) optimization technology, a novel approach designed to revolutionize screening processes in bioinformatics, cheminformatics, and a range of other scientific fields. By focusing on the efficient and precise identification of candidates with desirable characteristics, the ILE technology marks a significant leap forward in addressing the complexities of candidate selection in drug discovery, protein classification, and beyond. The study's primary objective is to address the challenges associated with optimizing screening processes to efficiently select candidates across various fields, including drug discovery and protein classification. The methodology employed involves a detailed algorithmic process that includes dataset preparation, encoding of protein sequences, sensor nucleation, and optimization, culminating in the empirical evaluation of molecular activity indexing, homology-based modeling, and classification of proteins such as G-protein-coupled receptors. This process showcases the method's success in multiple sequence alignment, protein identification, and classification. Key results demonstrate the ILE's superior accuracy in protein classification and virtual high-throughput screening, with a notable breakthrough in drug development for assessing drug-induced long QT syndrome risks through hERG potassium channel interaction analysis. The technology showcased exceptional results in the formulation and evaluation of novel cancer drug candidates, highlighting its potential for significant advancements in pharmaceutical innovations. The findings underline the ILE optimization technology as a transformative tool in screening processes due to its proven effectiveness and broad applicability across various domains. This breakthrough contributes substantially to the fields of systems optimization and holds promise for diverse applications, enhancing the process of selecting candidate molecules with target properties and advancing drug discovery, protein classification, and modeling.

CLL-001 Identification of Isoform p53 Protein by ELISA Method of Failed Cases of Chronic Lymphocytic Leukemia Resistant to Conventional Treatment

CLL-001 Identification of Isoform p53 Protein by ELISA Method of Failed Cases of Chronic Lymphocytic Leukemia Resistant to Conventional Treatment