Applications of NMR in Protein Structure Prediction.pptx

This presentation explores the pivotal role of Nuclear Magnetic Resonance (NMR) spectroscopy in predicting protein structures. It delves into the methodologies, advancements, and applications of NMR in determining the three-dimensional configurations of proteins, which is crucial for understanding their function and interactions.

1. Applications of NMR in Protein Structure Prediction Anagha r anil M.Pharm pharmacology

2. contents 2 Protein structure prediction Different methods of protein prediction Nmr spectroscopy Determining protein structure using NMR spectroscopy Why nmr spectroscopy? Research article Applications of NMR in Protein Structure Prediction

3. Protein structure prediction 3 - Refers to finding the exact orientations and arrangements of different amino acids present in protein. - Biological system depend on structure & function of protein. - To understand protein functions at molecular level ,determine the 3D structure. Applications of NMR in Protein Structure Prediction

4. 4 Applications of NMR in Protein Structure Prediction

5. Nmr spectroscopy The NMR spectroscopy works on the principle of change in the energy state and orientation of an atomic nuclei in a magnetic field. The change depends on the presence or absence of electrons around the atomic nuclei of a molecule. 5 Applications of NMR in Protein Structure Prediction

6. determining protein structure using NMR spectroscopy 6 Applications of NMR in Protein Structure Prediction

7. 7 Applications of NMR in Protein Structure Prediction The target protein is expressed in a suitable system (e.g., E. coli) and purified to homogeneity.
Proteins are often labeled with isotopes like 15 N and 13 C to enhance sensitivity and simplify the interpretation of NMR spectra. This involves growing the cells in media containing these isotopes.
The protein is dissolved in an appropriate buffer solution, typically at a concentration of 0.5-1 mM . Conditions such as pH, temperature, and salt concentration are optimized to ensure the protein is in a stable, soluble form. SAMPLE PREPARATION

8. 8 Applications of NMR in Protein Structure Prediction 1D NMR - assess the overall quality, purity, and stability of the sample. 2D NMR - HSQC (Heteronuclear Single Quantum Coherence): correlates the resonance frequencies of hydrogen ( 1 H) and nitrogen ( 15 N) or carbon ( 13 C) atoms and makes it easier to assign the NMR signals to specific nuclei in the protein. On theHSQC results, each cross peak indicates a pair of hydrogen and nitrogen or carbon atoms that are close to each other in the protein. NMR DATA COLLECTION

9. 9 Applications of NMR in Protein Structure Prediction 3D NMR HNCA (Heteronuclear Single Quantum Coherence - Alpha Carbon: Correlates the amide proton ( 1 H) with the directly bonded nitrogen ( 15 N) and the alpha carbon ( 13 C). This helps in sequentially assigning the backbone atoms. HNCO (Heteronuclear Single Quantum Coherence - Carbonyl: Correlates the amide proton ( 1 H) with the directly bonded nitrogen ( 15 N) and the carbonyl carbon ( 13 C). It provides complementary information to HNCA and helps confirm assignments.

10. 10 Applications of NMR in Protein Structure Prediction 4D NMR NOESY-HSQC: Combines NOESY (Nuclear Overhauser Effect Spectroscopy) with HSQC to provide distance constraints between hydrogen atoms through space This is crucial for determining the 3D structure of the protein. NOESY measures the distances between hydrogen atoms (protons) in a protein by observing the Nuclear Overhauser Effect (NOE). Distance constraints obtained from NOESY-HSQC help in determining the spatial arrangement of both the backbone and side chains of the protein .

11. 11 Applications of NMR in Protein Structure Prediction Resonance assignments refer to the process of identifying and matching the observed NMR signals (resonances) to specific atoms within a protein. Sequential Assignment The first step involves assigning the NMR signals to specific nuclei within the protein. This is typically done using triple-resonance experiments (e.g., HNCA, HNCO) to connect the backbone amide signals sequentially along the polypeptide chain . Side-Chain Assignment Once the backbone assignments are complete, side-chain resonances are assigned using additional experiments like HCCH-TOCSY. RESONANCE ASSIGNMENTS

12. 12 Applications of NMR in Protein Structure Prediction Distance Restraints: Derived from NOESY experiments, where cross peaks indicate spatial proximity between nuclei. The intensity of these peaks is inversely proportional to the sixth power of the distance. Stronger peaks correspond to shorter distances, providing constraints that help in modeling the protein's 3D structure. Angle Restraints: Derived from torsion angles (Φ and Ψ) of the protein backbone, and defines the protein's secondary structure elements, such as alpha-helices and beta-sheets . STRUCTURE DETERMINATION FROM THE NMR DATA

13. 13 Applications of NMR in Protein Structure Prediction J-Coupling Constants: J-couplings (scalar couplings) are used to determine the angles between atoms connected by bonds , contributing to the understanding of the protein’s conformation. Residual Dipolar Couplings (RDCs): provide information on bond vector orientations relative to a common reference frame and are useful for defining the overall fold. Chemical Shifts: Chemical shifts can be used to predict secondary structure elements (e.g., alpha-helices, beta-sheets).

14. 14 Applications of NMR in Protein Structure Prediction Initial Structure Generation: Software programs (e.g., CYANA, ARIA) use the experimental constraints to generate initial structural models. Energy Minimization: Energy minimization aims to find the lowest energy conformation of the protein ensuring it is physically realistic and stable . Validation: The final structures are validated using various criteria, such as agreement with experimental data, Ramachandran plot analysis, and comparison with known structures. STRUCTURE CALCULATION AND REFINEMENT

15. 15 Applications of NMR in Protein Structure Prediction The quality of the NMR structure is assessed using parameters like RMSD (Root Mean Square Deviation) between the calculated structures and experimental data. RMSD assess the quality of NMR structures by measuring the deviation between the experimentally determined structures and the calculated models . The structural information is analyzed to gain insights into the protein's function, dynamics, and interactions with other molecules. STRUCTURE ANALYSIS AND INTERPRETATION

16. Why Nmr spectroscopy? It allows proteins to be studied in solution, mimicking their natural environment . It provides insights into protein dynamics, including conformational changes and folding processes . It does not require protein crystallization , overcoming a major bottleneck in X-ray crystallography. It offers detailed information on protein-ligand and protein-protein interactions , critical for drug design. It utilizes isotopic labeling to simplify spectra and focus on specific molecule parts , aiding the study of large proteins. It requires small amounts of protein sample , beneficial for proteins difficult to produce in large quantities. 16 Applications of NMR in Protein Structure Prediction

17. 17 Applications of NMR in Protein Structure Prediction APPLICATIONS

18. 18 NMR data ambiguity and limitations with larger proteins (>50-70 kDa ) hinder accurate structure determination. MELD ( Modeling Employing Limited Data) integrates noisy NMR data into molecular dynamics simulations ( MELDxMD ), using a Bayesian approach (a probabilistic graphical model) to enhance accuracy of protein structure predictions. Advantages: Improves NMR data interpretation, enhances structural prediction accuracy, and excels in complex scenarios like the CASP13 blind test. Applications of NMR in Protein Structure Prediction

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