Integrating machine learning with
Journal
The European physical journal. E, Soft matter
ISSN: 1292-895X
Titre abrégé: Eur Phys J E Soft Matter
Pays: France
ID NLM: 101126530
Informations de publication
Date de publication:
03 Jun 2024
03 Jun 2024
Historique:
received:
24
01
2024
accepted:
16
05
2024
medline:
4
6
2024
pubmed:
4
6
2024
entrez:
3
6
2024
Statut:
epublish
Résumé
Small-Angle Scattering (SAS), encompassing both X-ray (SAXS) and Neutron (SANS) techniques, is a crucial tool for structural analysis at the nanoscale, particularly in the realm of biological macromolecules. This paper explores the intricacies of SAS, emphasizing its application in studying complex biological systems and the challenges associated with sample preparation and data analysis. We highlight the use of neutron-scattering properties of hydrogen isotopes and isotopic labeling in SANS for probing structures within multi-subunit complexes, employing techniques like contrast variation (CV) for detailed structural analysis. However, traditional SAS analysis methods, such as Guinier and Kratky plots, are limited by their partial use of available data and inability to operate without substantial a priori knowledge of the sample's chemical composition. To overcome these limitations, we introduce a novel approach integrating
Identifiants
pubmed: 38831117
doi: 10.1140/epje/s10189-024-00435-6
pii: 10.1140/epje/s10189-024-00435-6
doi:
Substances chimiques
Macromolecular Substances
0
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
39Informations de copyright
© 2024. The Author(s), under exclusive licence to EDP Sciences, SIF and Springer-Verlag GmbH Germany, part of Springer Nature.
Références
L.A. Feigin, D.I. Svergun, Structure Analysis by Small-Angle X-Ray and Neutron Scattering (Springer, New York, 1987). https://doi.org/10.1007/978-1-4757-6624-0
doi: 10.1007/978-1-4757-6624-0
B. Chaudhuri, I.G. Muñoz, S. Qian, V.S. Urban, Biological Small Angle Scattering: Techniques (Strategies and Tips Springer, Singapore, 2017). https://doi.org/10.1007/978-981-10-6038-0
doi: 10.1007/978-981-10-6038-0
S.M.D.C. Perera, U.S. Chawla, U.R. Shrestha, U. Bhowmik, A.V. Struts, S. Qian, X.Q. Chu, M.F. Brown, Small-angle neutron scattering reveals energy landscape for rhodopsin photoactivation. J. Phys. Chem. Lett. 9, 7064–7071 (2018). https://doi.org/10.1021/acs.jpclett.8b03048
doi: 10.1021/acs.jpclett.8b03048
N.G. Brady, S. Qian, B.D. Bruce, Analysis of styrene maleic acid alternating copolymer supramolecular assemblies in solution by small angle x-ray scattering. Eur. Polym. J. 111, 178–184 (2019). https://doi.org/10.1016/j.eurpolymj.2018.11.034
doi: 10.1016/j.eurpolymj.2018.11.034
L. Ding, Y. Huang, X. Cai, S. Wang, Impact of ph, ionic strength and chitosan charge density on chitosan/casein complexation and phase behavior. Carbohydr. Polym. 208, 133–141 (2019). https://doi.org/10.1016/j.carbpol.2018.12.015
doi: 10.1016/j.carbpol.2018.12.015
C.M. Jeffries, J. Ilavsky, A. Martel, S. Hinrichs, A. Meyer, J.S. Pedersen, A.V. Sokolova, D.I. Svergun, Small-angle x-ray and neutron scattering. Nat. Rev. 1, 70 (2021). https://doi.org/10.1038/s43586-021-00064-9
doi: 10.1038/s43586-021-00064-9
R.G. Kirste, H.B. Stuhrmann, Elimination der intrapartikulären untergrundstreuung bei der röntgenkleinwinkelstreuung an kompakten teilchen [german]. Z. für Physikalische Chem. 56, 338–341 (1967). https://doi.org/10.1524/zpch.1967.56.5_6.338
doi: 10.1524/zpch.1967.56.5_6.338
W.T. Heller, Small-angle neutron scattering and contrast variation: a powerful combination for studying biological structures. Acta Cryst. D 66, 1213–1217 (2010). https://doi.org/10.1107/S0907444910017658
doi: 10.1107/S0907444910017658
R. Lefort, J.L. Duvail, T. Corre, Y. Zhao, D. Morineau, Phase separation of a binary liquid in anodic aluminium oxide templates: a structural study by small angle neutron scattering. Eur. Phys. J. E 34(7), 71 (2011). https://doi.org/10.1140/epje/i2011-11071-2
doi: 10.1140/epje/i2011-11071-2
C.M. Jeffries, M.A. Graewert, C.E. Blanchet, D.B. Langley, A.E. Whitten, D.I. Svergun, Preparing monodisperse macromolecular samples for successful biological small-angle x-ray and neutron-scattering experiments. Nat. Protoc. 11, 2122–2153 (2016). https://doi.org/10.1038/nprot.2016.113
doi: 10.1038/nprot.2016.113
E.M. Anitas, α-sas: an integrative approach for structural modeling of biological macromolecules in solution. Acta Cryst. D 78, 1046–1063 (2022). https://doi.org/10.1107/S2059798322006349
doi: 10.1107/S2059798322006349
R.S. Morais, O. Delalande, J. Pérez, L. Mouret, A. Bondon, A. Martel, M.S. Appavou, E. Le Rumeur, J.F. Hubert, S. Combet, Contrast-matched isotropic bicelles: a versatile tool to specifically probe the solution structure of peripheral membrane proteins using sans. Langmuir 33, 6572–6580 (2017). https://doi.org/10.1021/acs.langmuir.7b01369
doi: 10.1021/acs.langmuir.7b01369
R.S. Morais, O. Delalande, J. Pérez, D. Mias-Lucquin, M. Lagarrique, A. Martel, A.E. Molza, A. Chéron, C. Raguénès-Nicol, T. Chenuel, A. Bondon, M.S. Appavou, E. Le Rumeur, S. Combet, J.F. Hubert, Human dystrophin structural changes upon binding to anionic membrane lipids. Biophys. J . 115, 1231–1239 (2018). https://doi.org/10.1016/j.bpj.2018.07.039
doi: 10.1016/j.bpj.2018.07.039
S.J. Henderson, Monte carlo modeling of small-angle scattering data from non-interacting homogeneous and heterogeneous particles in solution. Biophys. J . 70, 1618–1627 (1996). https://doi.org/10.1016/S0006-3495(96)79725-4
doi: 10.1016/S0006-3495(96)79725-4
E.G. Iashina, M.V. Filatov, R.A. Pantina, E.Y. Varfolomeeva, W.G. Bouwman, C.P. Duif, D. Honecker, V. Pipich, S.V. Grigoriev, Small-angle neutron scattering (sans) and spin-echo sans measurements reveal the logarithmic fractal structure of the large-scale chromatin organization in hela nuclei. J. Appl. Cryst. 52, 844–853 (2019). https://doi.org/10.1107/S160057671900921X
doi: 10.1107/S160057671900921X
A.C. Genix, J. Oberdisse, On the absence of structure factors in concentrated colloidal suspensions and nanocomposites. Eur. Phys. J. E 46(6), 46 (2023). https://doi.org/10.1140/epje/s10189-023-00306-6
doi: 10.1140/epje/s10189-023-00306-6
O. Glatter, A new method for the evaluation of small-angle scattering data. J. Appl. Cryst. 10, 415–421 (1977). https://doi.org/10.1107/S0021889877013879
doi: 10.1107/S0021889877013879
A.Y. Cherny, E.M. Anitas, V.A. Osipov, A.I. Kuklin, Small-angle scattering from multiphase fractals. J. Appl. Cryst. 47, 198–206 (2014). https://doi.org/10.1107/S1600576713029956
doi: 10.1107/S1600576713029956
K. Manalastas-Cantos, P.V. Konarev, N.R. Hajizadeh, A.G. Kikhney, M.V. Petoukhov, D.S. Molodenskiy, A. Panjkovich, H.D.T. Mertens, A. Gruzinov, C. Borges, C.M. Jeffries, D.I. Svergun, D. Franke, ATSAS 3.0: expanded functionality and new tools for small-angle scattering data analysis. J. Appl. Cryst. 54(1), 343–355 (2021). https://doi.org/10.1107/S1600576720013412
doi: 10.1107/S1600576720013412
J. Kohlbrecher, I. Breßler, Updates in SASfit for fitting analytical expressions and numerical models to small-angle scattering patterns. J. Appl. Cryst. 55(6), 1677–1688 (2022). https://doi.org/10.1107/S1600576722009037
doi: 10.1107/S1600576722009037
E.M. Anitas, Structural characterization of janus nanoparticles with tunable geometric and chemical asymmetries by small-angle scattering. Phys. Chem. Chem. Phys. 22, 536–548 (2020). https://doi.org/10.1039/C9CP05521E
doi: 10.1039/C9CP05521E
S. Krueger, Designing and performing biological solution small-angle neutron scattering contrast variation experiments on multi-component assemblies. Adv. Exp. Med. Biol. 1009, 65–85 (2017). https://doi.org/10.1007/978-981-10-6038-0_5
doi: 10.1007/978-981-10-6038-0_5
T. Hastie, R. Tibshirani, J. Friedman, Biological Small Angle Scattering: Techniques Strategies and Tips (Springer, 2009). https://doi.org/10.1007/978-0-387-84858-7
doi: 10.1007/978-0-387-84858-7
Lightgbm framework. https://lightgbm.readthedocs.io/en/v3.3.2/#
G. Ke, Q. Meng, T. Finley, T. Wang, W. Chen, W. Ma, Q. Ye, T.Y. Liu, Lightgbm: a highly efficient gradient boosting decision tree. Adv. Neural Inf. Process. Syst. 30, 3149–3157 (2017)
A. Patrykiejew, Phase transitions in two-dimensional systems of janus-like particles on a triangular lattice. Int. J. Mol. Sci. 22, 10484 (2021). https://doi.org/10.3390/ijms221910484
doi: 10.3390/ijms221910484
T. Sato, K. Esashika, E. Yamamoto, T. Saiki, N. Arai, Theoretical design of a janus-nanoparticle-based sandwich assay for nucleic acids. Int. J. Mol. Sci. 33, 8807 (2022). https://doi.org/10.3390/ijms23158807
doi: 10.3390/ijms23158807
O. Jasnovidova, T. Klumpler, K. Kubicek, S. Kalynych, P. Plevka, R. Stefl, Structure and dynamics of the rnapii ctdsome with rtt103. PNAS 114, 11133–11138 (2017). https://doi.org/10.1073/pnas.1712450114
doi: 10.1073/pnas.1712450114
J. Jumper, R. Evans, A. Pritzel, T. Green, M. Figurnov, O. Ronnenberger, K. Tunyasuvunakool, R. Bates, A. Žídek, A. Potapenko et al., Highly accurate protein structure prediction with alphafold. Nature 596, 583–589 (2021). https://doi.org/10.1038/s41586-021-03819-2
doi: 10.1038/s41586-021-03819-2
A.E. Whitten, D.A. Jacques, B. Hammouda, T. Hanleu, G.F. King, J.M. Guss, J. Trewhella, D.B. Langley, The structure of the kina-sda complex suggests an allosteric mechanism of histidine kinase inhibition. J. Mol. Biol. 368, 407–420 (2007). https://doi.org/10.1016/j.jmb.2007.01.064
doi: 10.1016/j.jmb.2007.01.064
K. Ibel, H.B. Stuhrmann, Comparison of neutron and x-ray scattering of dilute myoglobin solutions. J. Mol. Biol. 93, 255–265 (1975). https://doi.org/10.1016/0022-2836(75)90131-X
doi: 10.1016/0022-2836(75)90131-X
A.E. Whitten, S. Cai, J. Trewhella, Mulch: modules for the analysis of small-angle neutron contrast variation data from biomolecular assemblies. J. Appl. Cryst. 41, 222–226 (2008). https://doi.org/10.1107/S0021889807055136
doi: 10.1107/S0021889807055136
P. Cover, T. Hart, Nearest neighbor pattern classification. IEEE Trans. Inf. Theory 13, 21–27 (1967). https://doi.org/10.1109/TIT.1967.1053964
doi: 10.1109/TIT.1967.1053964
J. Schmidhuber, Deep learning in neural networks: an overview. Neural Netw. 61, 85–117 (2015). https://doi.org/10.1016/j.neunet.2014.09.003
G.A. Olah, S.E. Rokop, C.L. Albert Wang, S.L. Blechner, J. Trewhella, Troponin i encompasses an extended troponin c in the ca2+-bound complex: a small-angle x-ray and neutron scattering study. Biochemistry 33, 8233–8239 (1994). https://doi.org/10.1021/bi00193a009
doi: 10.1021/bi00193a009
D. Franke, C.M. Jeffries, D.I. Svergun, Machine learning methods for x-ray scattering data analysis from biomacromolecular solutions. Biophys. J . 114(11), 2485–2492 (2020). https://doi.org/10.1016/j.isci.2020.100906
doi: 10.1016/j.isci.2020.100906
H. He, C. Liu, H. Liu, Model reconstruction from small-angle x-ray scattering data using deep learning methods. iScience 23(3), 100906 (2020). https://doi.org/10.1016/j.isci.2020.100906
doi: 10.1016/j.isci.2020.100906
R.K. Archibald, M. Doucet, T. Johnston, S.R. Young, E. Yang, W.T. Heller, Classifying and analyzing small-angle scattering data using weighted k nearest neighbors machine learning techniques. J. Appl. Cryst. 53(2), 326–334 (2020). https://doi.org/10.1107/S1600576720000552
doi: 10.1107/S1600576720000552
C. Do, W.R. Chen, S. Lee, Small angle scattering data analysis assisted by machine learning methods. MRS Adv. 5, 1577–1584 (2020). https://doi.org/10.1557/adv.2020.130
doi: 10.1557/adv.2020.130
L. Breiman, J. Friedman, R. Olshen, C. Stone, Classification and Regression Trees (Chapman and Hall/CRC, 1984). https://doi.org/10.1201/9781315139470
doi: 10.1201/9781315139470
A practical guide to tree based learning algorithms (2017). https://reckoning.dev/blog/tree-based-models/
J.H. Friedman, Greedy function approximation: a gradient boosting machine. Ann. Stat. 29(5), 1189–1232 (2001). https://doi.org/10.1214/aos/101320345
doi: 10.1214/aos/101320345
D. Liu, J. Nocedal, On the limited memory BFGS method for large scale optimization. Math. Program. 45, 503–528 (1989). https://doi.org/10.1007/BF01589116
doi: 10.1007/BF01589116