Prediction of order parameters based on protein NMR structure ensemble and machine learning

Alzubaidi L et al (2021) Review of deep learning: concepts, CNN architectures, challenges, applications, future directions. Journal of Big Data 8:53

Article  Google Scholar 

Andersen CAF, Palmer AG, Brunak S, Rost B (2002) Continuum secondary structure captures protein flexibility. Structure (London) 10:175–184

Article  Google Scholar 

Berjanskii M, Wishart DS (2006) NMR: prediction of protein flexibility. Nat Protoc 1:683–688

Article  Google Scholar 

Biau G, Scornet E (2016) A random forest guided tour. TEST 25:197–227

Article  MathSciNet  Google Scholar 

Biological Magnetic Resonance Data Bank. https://bmrb.io/

Bockting CL, van Dis EAM, Bollen J, van Rooij R, Zuidema W (2023) ChatGPT: five priorities for research. Nature 614:224–226

Article  ADS  Google Scholar 

Breiman L (2001) Random forests. Mach Learn 45:5–32

Article  Google Scholar 

Cala O, Guilliere F, Krimm I (2014) NMR-based analysis of protein-ligand interactions. Anal Bioanal Chem 406:943–956

Article  Google Scholar 

Chao F-A, Zhang Y, Byrd RA (2022) Facilitating spectral analyses, simplification, and new tools through deep neural networks. Magnet Reson Lett 2:56–58

Article  Google Scholar 

Cilia E, Pancsa R, Tompa P, Lenaerts T, Vranken WF (2013) From protein sequence to dynamics and disorder with DynaMine. Nat Commun 4:2741

Article  ADS  Google Scholar 

Clore GM et al (1990) Deviations from the simple two-parameter model-free approach to the interpretation of nitrogen-15 nuclear magnetic relaxation of proteins. J Am Chem Soc 112:4989–4991

Article  Google Scholar 

Fowler NJ, Sljoka A, Williamson MP (2020) A method for validating the accuracy of NMR protein structures. Nature Communications 11:6321

Article  ADS  Google Scholar 

Gáspári Z, Perczel A (2010) Chapter 2 – Protein dynamics as reported by NMR. In: Webb GA (eds) Annual reports on NMR spectroscopy, vol 71. Academic Press, pp 35–75

Gobl C, Madl T, Simon B, Sattler M (2014) NMR approaches for structural analysis of multidomain proteins and complexes in solution. Prog Nucl Magnet Reson Spectrosc 80:26–63

Article  Google Scholar 

Grimaldo M, Roosen-Runge F, Zhang F, Schreiber F, Seydel T (2019) Dynamics of proteins in solution. Quart Rev Biophys 52:e7

Article  Google Scholar 

Hu Y, Jin C (2022) Conformational dynamics in GPCR signaling by NMR. Magnet Reson Lett 2:139–146

Article  Google Scholar 

Huang S-W, Shih C-H, Lin C-P, Hwang J-K (2008) Prediction of NMR order parameters in proteins using weighted protein contact-number model. Theor Chem Acc 121:197–200

Article  Google Scholar 

Jarymowycz VA, Stone MJ (2006) Fast time scale dynamics of protein backbones: NMR relaxation methods, applications, and functional consequences. Chem Rev 106:1624–1671

Article  Google Scholar 

Jumper J et al (2021) Highly accurate protein structure prediction with AlphaFold. Nature 596:583–589

Article  ADS  Google Scholar 

Kabsch W, Sander C (1983) Dictionary of protein secondary structure: pattern recognition of hydrogen-bonded and geometrical features. Biopolymers: Orig Res Biomol 22:2577–2637

Article  Google Scholar 

Kleckner IR, Foster MP (2011) An introduction to NMR-based approaches for measuring protein dynamics. Biochimica Et Biophysica Acta-Prot Proteomics 1814:942–968

Article  Google Scholar 

Kovermann M, Rogne P, Wolf-Watz M (2016) Protein dynamics and function from solution state NMR spectroscopy. Quart Rev Biophys 49:e6

Article  Google Scholar 

Lipari G, Szabo A (1982) Model-free approach to the interpretation of nuclear magnetic resonance relaxation in macromolecules. 1. Theory and range of validity. J Am Chem Soc 104:4546–4559

Article  Google Scholar 

Lipari G, Szabo A (1982) Model-free approach to the interpretation of nuclear magnetic resonance relaxation in macromolecules 2. Analysis of experimental results. J Am Chem Soc 104:4559–4570

Article  Google Scholar 

Ma PY, Li DW, Bruschweiler R (2023) Predicting protein flexibility with AlphaFold. Prot Struct Funct Bioinformatics 91:847–855

Article  Google Scholar 

Ming DM, Bruschweiler R (2006) Reorientational contact-weighted elastic network model for the prediction of protein dynamics: comparison with NMR relaxation. Biophys J 90:3382–3388

Article  Google Scholar 

Ortega G, Pons M, Millet O (2013) Chapter Six – Protein functional dynamics in multiple timescales as studied by NMR spectroscopy. In: T Karabencheva-Christova (ed) Advances in protein chemistry and structural biology, vol 92. Academic Press, pp 219–251

Peti W, Meiler J, Brüschweiler R, Griesinger C (2002) Model-free analysis of protein backbone motion from residual dipolar couplings. J Am Chem Soc 124:5822–5833

Article  Google Scholar 

RCSB Protein Data Bank. https://www.rcsb.org/

Sekhar A, Kay LE (2019) An NMR View of protein dynamics in health and disease. In: Dill KA (eds) Annual review of biophysics, vol 48, pp 297–319

Trott O, Siggers K, Rost B, Palmer AG III (2008) Protein conformational flexibility prediction using machine learning. J Magnet Reson 192:37–47

Article  ADS  Google Scholar 

Tzeng SR, Kalodimos CG (2011) Protein dynamics and allostery: an NMR view. CurrOpin Struct Biol 21:62–67

Article  Google Scholar 

Vera R, Synsmir-Zizzamia M, Ojinnaka S, Snyder DA (2018) Prediction of protein flexibility using a conformationally restrained contact map. Prot Struct Funct Bioinformatics 86:1111–1116

Article  Google Scholar 

Zhang FL, Bruschweiler R (2002) Contact model for the prediction of NMR N-H order parameters in globular proteins. J Am Chem Soc 124:12654–12655

Article  Google Scholar 

Zhang H et al (2009) On the relation between residue flexibility and local solvent accessibility in proteins. Prot: Struct Funct Bioinformatics 76:617–636

Google Scholar 

Zhou ZH (2021) Machine learning. Springer Nature

留言 (0)

沒有登入
gif