Hi folks
This is probably a good time to mention that both Melanie and Andrea will be
giving presentations at the IUCr in Prague in a couple of weeks or so in the
Commission for Crystallographic Computing session chaired by Rita Giordano -
MS-73 Machine learning in biological and structural sciences
Friday 20th August 2021 10:20 - 12:45
Harry
> On 4 Aug 2021, at 10:11, Vollmar, Melanie (DLSLtd,RAL,LSCI)
> <000064fe7ccc6b4d-dmarc-requ...@jiscmail.ac.uk> wrote:
>
> I don't have a list to add here, as my review on the topic awaits feedback on
> the corrections (self-advertisement π) but perhaps we should consider that
> machine learning and AI are two different beasts. Admittingly, I don't always
> make a proper distinction either.
>
> Surely, many of us get their heads around machine learning, which usually
> covers so called shallow learners that firmly sit in well-known concepts of
> statistics. This type of machine learning doesn't require many resources and
> is accessible to almost anyone with an average laptop. Plenty of software in
> MX and EM use these tools and no-one every thinks about them.
>
> I think, Andrea, perhaps, was looking more into the direction of AI (based on
> so many cryo-EM references listed π, where this is a standard tool), which
> requires a lot more understanding and thought as well as resources and would
> appear to many as a magic black box. This type of machine learning has only
> recently taken off due to huge leaps in hardware development, which many of
> us can't afford to buy, unless it is provided through some shared resource.
> Having said that, an average graphics card GPU is often a good start. And if
> one isn't the book reading kind (usually due to lack of time), there are lots
> of good blogs, videos and other online resources to get one into the basics.
>
> The papers that should clearly be added, are those for protein structure
> prediction, as, in a way, they determine a structure, albeit with a different
> kind of experiment:
>
> https://science.sciencemag.org/content/early/2021/07/19/science.abj8754
> https://www.nature.com/articles/s41586-021-03819-2
>
> Cheers
>
> M
> From: CCP4 bulletin board <CCP4BB@JISCMAIL.AC.UK> on behalf of Nave, Colin
> (DLSLtd,RAL,LSCI) <000064fdcfc6624b-dmarc-requ...@jiscmail.ac.uk>
> Sent: 04 August 2021 09:34
> To: CCP4BB@JISCMAIL.AC.UK <CCP4BB@JISCMAIL.AC.UK>
> Subject: Re: [ccp4bb] AI papers in experimental macromolecular structure
> determination
>
> Bernhard
> What qualifies? Good question.
> There are plenty of books on AI/machine learning but, as always, it is more
> efficient/lazier to read reviews than the books themselves. I think the
> London Review of Books allows limited access to its articles so most should
> be able to read this
> https://www.lrb.co.uk/the-paper/v43/n02/paul-taylor/insanely-complicated-hopelessly-inadequate?referrer=https%3A%2F%2Fwww.google.com%2F
> It might be interesting (though perhaps not useful) to classify the examples
> for macromolecular structure determination in to categories such as GOFAI
> etc. However, this particular term is rather pejorative as it would mean
> describing the developers as old fashioned!
>
> Colin
>
>
>
>
> -----Original Message-----
> From: CCP4 bulletin board <CCP4BB@JISCMAIL.AC.UK> On Behalf Of Bernhard Rupp
> Sent: 03 August 2021 21:00
> To: CCP4BB@JISCMAIL.AC.UK
> Subject: Re: [ccp4bb] AI papers in experimental macromolecular structure
> determination
>
> Maybe we should get to the root of this - what qualifies as machine learning
> and what not?
>
> Do nonparametric predictors such as KDE qualify?
>
> https://www.ruppweb.org/mattprob/default.html
>
> Happy toa dd to the confusion.
>
> -----Original Message-----
> From: CCP4 bulletin board <CCP4BB@JISCMAIL.AC.UK> On Behalf Of Tim Gruene
> Sent: Tuesday, August 3, 2021 11:59
> To: CCP4BB@JISCMAIL.AC.UK
> Subject: Re: [ccp4bb] AI papers in experimental macromolecular structure
> determination
>
> Hello Andrea,
>
> profile fitting, like it is done in mosflm
> (https://doi.org/10.1107/S090744499900846X) or evalccd, or ... probably also
> qualify as AI/machine learning.
>
> Best wishes,
> Tim
>
> On Tue, 3 Aug 2021 11:43:06 +0000
> "Thorn, Dr. Andrea" <andrea.th...@uni-hamburg.de> wrote:
>
> > Dear colleagues,
> > I have compiled a list of papers that cover the application of
> > AI/machine learning methods in single-crystal structure determination
> > (mostly macromolecular crystallography) and single-particle Cryo-EM.
> > The draft list is attached below.
> >
> > If I missed any papers, please let me know. I will send the final list
> > back here, for the benefit of all who are interested in the topic.
> >
> > Best wishes,
> >
> >
> > Andrea.
> >
> >
> > __
> > General:
> > - Gopalakrishnan, V., Livingston, G., Hennessy, D., Buchanan, B. &
> > Rosenberg, J. M. (2004). Acta Cryst D. 60, 1705β1716.
> > - Morris, R. J. (2004). Acta Cryst D. 60, 2133β2143.
> >
> > Micrograph preparation:
> > - (2020). Journal of Structural Biology. 210, 107498.
> >
> > Particle Picking:
> > - Sanchez-Garcia, R., Segura, J., Maluenda, D., Carazo, J. M. &
> > Sorzano, C. O. S. (2018). IUCrJ. 5, 854β865.
> > - Al-Azzawi, A., Ouadou, A., Tanner, J. J. & Cheng, J. (2019). BMC
> > Bioinformatics. 20, 1β26.
> > - George, B., Assaiya, A., Roy, R. J., Kembhavi, A., Chauhan, R.,
> > Paul, G., Kumar, J. & Philip, N. S. (2021). Commun Biol. 4, 1β12.
> > - Lata, K. R., Penczek, P. & Frank, J. (1995). Ultramicroscopy. 58,
> > 381β391.
> > - Nguyen, N. P., Ersoy, I., Gotberg, J., Bunyak, F. & White, T. A.
> > (2021). BMC Bioinformatics. 22, 1β28.
> > - Wang, F., Gong, H., Liu, G., Li, M., Yan, C., Xia, T., Li, X. &
> > Zeng, J. (2016). Journal of Structural Biology. 195, 325β336.
> > - Wong, H. C., Chen, J., Mouche, F., Rouiller, I. & Bern, M. (2004).
> > Journal of Structural Biology. 145, 157β167.
> >
> > Motion description in Cryo-EM:
> > - Matsumoto, S., Ishida, S., Araki, M., Kato, T., Terayama, K. &
> > Okuno, Y. (2021). Nat Mach Intell. 3, 153β160.
> > - Zhong, E. D., Bepler, T., Berger, B. & Davis, J. H. (2021). Nat
> > Methods. 18, 176β185.
> >
> > Local resolution:
> > - Avramov, T. K., Vyenielo, D., Gomez-Blanco, J., Adinarayanan, S.,
> > Vargas, J. & Si, D. (2019). Molecules. 24, 1181.
> > - RamΓrez-Aportela, E., Mota, J., Conesa, P., Carazo, J. M. & Sorzano,
> > C. O. S. (2019). IUCrJ. 6, 1054β1063.
> > - (2021). QAEmap: A Novel Local Quality Assessment Method for Protein
> > Crystal Structures Using Machine Learning.
> >
> > Map post-processing:
> > - Sanchez-Garcia, R., Gomez-Blanco, J., Cuervo, A., Carazo, J. M.,
> > Sorzano, C. O. S. & Vargas, J. (2020). BioRxiv. 2020.06.12.148296.
> >
> > Secondary structure assignment in map:
> > - Subramaniya, S. R. M. V., Terashi, G. & Kihara, D. (2019). Nat
> > Methods. 16, 911β917.
> > - Li, R., Si, D., Zeng, T., Ji, S. & He, J. (2016). 2016 IEEE
> > International Conference on Bioinformatics and Biomedicine (BIBM),
> > Vol. pp. 41β46.
> > - Si, D., Ji, S., Nasr, K. A. & He, J. (2012). Biopolymers. 97,
> > 698β708.
> > - He, J. & Huang, S.-Y. Brief Bioinform.
> > - Lyu, Z., Wang, Z., Luo, F., Shuai, J. & Huang, Y. (2021). Frontiers
> > in Bioengineering and Biotechnology. 9,.
> > - Mostosi, P., Schindelin, H., Kollmannsberger, P. & Thorn, A.
> > (2020). Angewandte Chemie International Edition.
> >
> > Automatic structure building:
> > - Alnabati, E. & Kihara, D. (2020). Molecules. 25, 82.
> > - Si, D., Moritz, S. A., Pfab, J., Hou, J., Cao, R., Wang, L., Wu, T.
> > & Cheng, J. (2020). Sci Rep. 10, 1β22.
> > - Moritz, S. A., Pfab, J., Wu, T., Hou, J., Cheng, J., Cao, R., Wang,
> > L. & Si, D. (2019).
> > - Chojnowski, G., Pereira, J. & Lamzin, V. S. (2019). Acta Cryst D.
> > 75, 753β763.
> >
> > Crystallization:
> > - Liu, R., Freund, Y. & Spraggon, G. (2008). Acta Cryst D. 64,
> > 1187β1195.
> > - (2004). Methods. 34, 390β407.
> > - Bruno, A. E., Charbonneau, P., Newman, J., Snell, E. H., So, D. R.,
> > Vanhoucke, V., Watkins, C. J., Williams, S. & Wilson, J. (2018). PLOS
> > ONE. 13, e0198883.
> >
> > Crystal centering:
> > - Ito, S., Ueno, G. & Yamamoto, M. (2019). J Synchrotron Rad. 26,
> > 1361β1366.
> > - Crystal centering using deep learning in X-ray crystallography.
> > - Elbasir, A., Moovarkumudalvan, B., Kunji, K., Kolatkar, P. R., Mall,
> > R. & Bensmail, H. (2019). Bioinformatics. 35, 2216β2225.
> >
> > Diffraction image analysis:
> > - Czyzewski, A., Krawiec, F., Brzezinski, D., Porebski, P. J. & Minor,
> > W. (2021). Expert Systems with Applications. 174, 114740.
> >
> > Peak search in serial crystallography:
> > Ke, T.-W., Brewster, A. S., Yu, S. X., Ushizima, D., Yang, C. &
> > Sauter, N. K. (2018). J Synchrotron Rad. 25, 655β670.
> >
> > Space group assignment from diffraction image (small molecules):
> > Aguiar, J. A., Gong, M. L., Unocic, R. R., Tasdizen, T. & Miller, B.
> > D. (2019). Science Advances. 5, eaaw1949.
> >
> > Data quality assessment in MX:
> > - Vollmar, M., Parkhurst, J. M., Jaques, D., BaslΓ©, A., Murshudov, G.
> > N., Waterman, D. G. & Evans, G. (2020). IUCrJ. 7, 342β354.
> >
> > Ligand recognition:
> > Kowiel, M., Brzezinski, D., Porebski, P. J., Shabalin, I. G.,
> > Jaskolski, M. & Minor, W. (2019). Bioinformatics. 35, 452β461.
> >
> > Prediction of missing atoms in small molecular structures:
> > Thomas, N., Smidt, T., Kearnes, S., Yang, L., Li, L., Kohlhoff, K. &
> > Riley, P. (2018).
> >
> > ADP estimation (small molecules):
> > Gagner, V. A., Jensen, M. & Katona, G. (2021). Mach. Learn.: Sci.
> > Technol. 2, 035033.
> >
> >
> > --
> > Dr. Andrea Thorn | group leader
> > andrea.th...@uni-hamburg.de
> >
> > Institute for Nanostructure and Solid State Physics, UniversitΓ€t
> > Hamburg Luruper Chaussee 149 / Bldg. 610 (HARBOR) | 22761 Hamburg |
> > Germany Tel. +49 (0)40 42838 3651
> > www.thorn-lab.de<http://www.thorn-lab.de> | www.insidecorona.net
> >
> >
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>
> --
> --
> Tim Gruene
> Head of the Centre for X-ray Structure Analysis Faculty of Chemistry
> University of Vienna
>
> Phone: +43-1-4277-70202
>
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