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    Unknown Protein Identification Service

      Unknown protein identification refers to the process of identifying, studying, and annotating proteins that are not fully understood in terms of function and structure. This process mainly includes protein molecular weight determination, amino acid sequence determination, structure prediction, functional analysis, and interaction network analysis. Mass spectrometry technology is an important method for identifying unknown proteins. Through the identification of unknown proteins, we can discover new biomarkers, drug targets, and treatment strategies, providing important information for disease prevention, diagnosis, and treatment.

       

      MtoZ Biolabs offers unknown protein identification services using the latest Thermo's Orbitrap Exploris 240 mass spectrometer combined with Nano-LC chromatography. This service enables efficient and precise mass spectrometric identification of unknown proteins in samples such as protein extracts, SDS-PAGE protein bands, 2D protein gel spots, pull-downs, and Co-IPs. The identification includes molecular weight, amino acid composition and arrangement, as well as post-translational modification information of unknown proteins.

       

      Analysis Workflow

      1. Protein Sample Preparation

      2. Protein Separation and Purification

      3. Protein Mass Spectrometric Analysis

      4. Protein Sequence Analysis

      5. Structure Prediction

      6. Functional Analysis

      7. Interaction Network Analysis

       

      Service Advantages

      1. Advanced Mass Spectrometric Equipment

      We have state-of-the-art high-resolution mass spectrometers, including the Q-Exactive series and Orbitrap series, to provide you with high-quality, high-throughput mass spectrometric data, ensuring the accuracy of amino acid sequence information for accurate identification of unknown proteins.

       

      2. High Sensitivity and Throughput

      We can simultaneously analyze multiple proteins or peptides and detect proteins at very low abundances, meeting the identification needs of rare or low-abundance proteins.

       

      3. Diverse Protein Separation Techniques

      Our team has extensive experience in protein separation and can choose appropriate separation methods based on sample characteristics, such as 1D or 2D-PAGE, HPLC, etc., to reduce sample complexity and improve protein identification coverage.

       

      4. Efficient Enzymatic Digestion and Peptide Separation

      We use optimized enzymatic digestion conditions and multidimensional liquid chromatography techniques to effectively improve the accuracy and coverage of peptide identification.

       

      5. Wide Applicability

      We can analyze proteins from different sources and types, including but not limited to bacteria, viruses, eukaryotes, and complex biological samples.

       

      Applications

      1. Discovery of New Proteins

      Unknown protein identification technology can identify new protein sequences.

       

      2. Protein Function Research

      Revealing the sequence information of unknown proteins helps to understand their functions and roles in cells and organisms.

       

      3. Study of Protein Modifications

      Identifying the types and locations of protein modifications, such as phosphorylation and methylation, helps to understand the regulatory mechanisms of protein function.

       

      4. Drug Development

      It can be used to identify target proteins for drug action, accelerating the development and optimization of new drugs.

       

      5. Diagnosis and Treatment

      It can help identify new biomarkers, which is crucial for early diagnosis and treatment of diseases.

       

      Deliverables

      In the technical report, MtoZ Biolabs will provide you with detailed technical information, including:

      1. Experimental Procedures

      2. Relevant Mass Spectrometric Parameters

      3. Detailed Information on Unknown Protein Identification

      4. Mass Spectrometric Images

      5. Raw Data

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