Re: [ccp4bb] new section in IUCrData
Dear Loes, This is very welcome news !! With best wishes, Gerard. -- On Wed, Aug 04, 2021 at 10:34:38AM +, Kroon-Batenburg, L.M.J. (Loes) wrote: > Dear all, > > > I would like to attract you attention to the following. > > > Best wishes, > > Loes Kroon-Batenburg > > --- > > IUCrData to publish "Raw" Data Letters > > > IUCrData, the peer-reviewed open-access data publication from the > International Union of Crystallography (IUCr), is launching a new section for > authors to describe their unprocessed or "raw" diffraction images. This is a > collaborative innovation of IUCr Journals with the IUCr Committee on Data. > The new section will publish short descriptions of crystallographic raw data > sets in the biological, chemical or materials science fields and provide a > persistent link to the location of the raw data. This will allow researchers > to attract attention to particular features of the data that could be of > interest to methods and software developers or may be relevant to the > structural interpretation. The IUCr will adhere to the FAIR principles for > which the correctness and completeness of the metadata are crucial, and these > will be central to the reviewing process. The new section will be accepting > submissions from the autumn and anyone wanting to know more should contact > the IUCr Editorial Office (m...@iucr.org). > > > > > ___ > > Dr. Loes Kroon-Batenburg > > Dept. of Crystal and Structural Chemistry > Bijvoet Center for Biomolecular Research > Utrecht University > Padualaan 8, 3584 CH Utrecht > The Netherlands > > E-mail : l.m.j.kroon-batenb...@uu.nl > phone : +31-30-2532865 > fax: +31-30-2533940 > > > > To unsubscribe from the CCP4BB list, click the following link: > https://www.jiscmail.ac.uk/cgi-bin/WA-JISC.exe?SUBED1=CCP4BB=1 > > This message was issued to members of www.jiscmail.ac.uk/CCP4BB, a mailing > list hosted by www.jiscmail.ac.uk, terms & conditions are available at > https://www.jiscmail.ac.uk/policyandsecurity/ To unsubscribe from the CCP4BB list, click the following link: https://www.jiscmail.ac.uk/cgi-bin/WA-JISC.exe?SUBED1=CCP4BB=1 This message was issued to members of www.jiscmail.ac.uk/CCP4BB, a mailing list hosted by www.jiscmail.ac.uk, terms & conditions are available at https://www.jiscmail.ac.uk/policyandsecurity/
[ccp4bb] new section in IUCrData
Dear all, I would like to attract you attention to the following. Best wishes, Loes Kroon-Batenburg --- IUCrData to publish "Raw" Data Letters IUCrData, the peer-reviewed open-access data publication from the International Union of Crystallography (IUCr), is launching a new section for authors to describe their unprocessed or "raw" diffraction images. This is a collaborative innovation of IUCr Journals with the IUCr Committee on Data. The new section will publish short descriptions of crystallographic raw data sets in the biological, chemical or materials science fields and provide a persistent link to the location of the raw data. This will allow researchers to attract attention to particular features of the data that could be of interest to methods and software developers or may be relevant to the structural interpretation. The IUCr will adhere to the FAIR principles for which the correctness and completeness of the metadata are crucial, and these will be central to the reviewing process. The new section will be accepting submissions from the autumn and anyone wanting to know more should contact the IUCr Editorial Office (m...@iucr.org). ___ Dr. Loes Kroon-Batenburg Dept. of Crystal and Structural Chemistry Bijvoet Center for Biomolecular Research Utrecht University Padualaan 8, 3584 CH Utrecht The Netherlands E-mail : l.m.j.kroon-batenb...@uu.nl phone : +31-30-2532865 fax: +31-30-2533940 To unsubscribe from the CCP4BB list, click the following link: https://www.jiscmail.ac.uk/cgi-bin/WA-JISC.exe?SUBED1=CCP4BB=1 This message was issued to members of www.jiscmail.ac.uk/CCP4BB, a mailing list hosted by www.jiscmail.ac.uk, terms & conditions are available at https://www.jiscmail.ac.uk/policyandsecurity/
Re: [ccp4bb] AI papers in experimental macromolecular structure determination
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) > <64fe7ccc6b4d-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 ) 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 , 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 on behalf of Nave, Colin > (DLSLtd,RAL,LSCI) <64fdcfc6624b-dmarc-requ...@jiscmail.ac.uk> > Sent: 04 August 2021 09:34 > To: 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 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 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 + > "Thorn, Dr. Andrea" 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, 1705–1716. > > - Morris, R. J. (2004). Acta Cryst D. 60, 2133–2143. > > > > Micrograph preparation: > > - (2020). Journal of Structural Biology. 210, 107498. > >
Re: [ccp4bb] AI papers in experimental macromolecular structure determination
I don't have a list to add here, as my review on the topic awaits feedback on the corrections (self-advertisement ) 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 , 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 on behalf of Nave, Colin (DLSLtd,RAL,LSCI) <64fdcfc6624b-dmarc-requ...@jiscmail.ac.uk> Sent: 04 August 2021 09:34 To: 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 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 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 + "Thorn, Dr. Andrea" 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, 1705–1716. > - Morris, R. J. (2004). Acta Cryst D. 60, 2133–2143. > > 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, 854–865. > - Al-Azzawi, A., Ouadou, A., Tanner, J. J. & Cheng, J. (2019). BMC > Bioinformatics. 20, 1–26. > - George, B., Assaiya, A., Roy, R. J., Kembhavi, A., Chauhan, R., > Paul, G., Kumar, J. & Philip, N. S. (2021). Commun Biol. 4, 1–12. > - Lata, K. R., Penczek, P. & Frank, J. (1995). Ultramicroscopy. 58, > 381–391. > - Nguyen, N. P., Ersoy, I., Gotberg, J., Bunyak, F. & White, T. A. > (2021). BMC Bioinformatics. 22, 1–28. > - Wang, F., Gong, H., Liu, G., Li, M., Yan, C., Xia, T., Li, X. & > Zeng, J. (2016). Journal of Structural Biology. 195, 325–336. > - Wong, H.
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 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 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 + "Thorn, Dr. Andrea" 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, 1705–1716. > - Morris, R. J. (2004). Acta Cryst D. 60, 2133–2143. > > 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, 854–865. > - Al-Azzawi, A., Ouadou, A., Tanner, J. J. & Cheng, J. (2019). BMC > Bioinformatics. 20, 1–26. > - George, B., Assaiya, A., Roy, R. J., Kembhavi, A., Chauhan, R., > Paul, G., Kumar, J. & Philip, N. S. (2021). Commun Biol. 4, 1–12. > - Lata, K. R., Penczek, P. & Frank, J. (1995). Ultramicroscopy. 58, > 381–391. > - Nguyen, N. P., Ersoy, I., Gotberg, J., Bunyak, F. & White, T. A. > (2021). BMC Bioinformatics. 22, 1–28. > - Wang, F., Gong, H., Liu, G., Li, M., Yan, C., Xia, T., Li, X. & > Zeng, J. (2016). Journal of Structural Biology. 195, 325–336. > - Wong, H. C., Chen, J., Mouche, F., Rouiller, I. & Bern, M. (2004). > Journal of Structural Biology. 145, 157–167. > > Motion description in Cryo-EM: > - Matsumoto, S., Ishida, S., Araki, M., Kato, T., Terayama, K. & > Okuno, Y. (2021). Nat Mach Intell. 3, 153–160. > - Zhong, E. D., Bepler, T., Berger, B. & Davis, J. H. (2021). Nat > Methods. 18, 176–185. > > Local resolution: > - Avramov, T. K., Vyenielo, D., Gomez-Blanco, J., Adinarayanan, S., > Vargas, J. & Si, D. (2019). Molecules. 24, 1181. > - Ramírez-Aportela, E., Mota, J., Conesa, P., Carazo, J. M. & Sorzano, > C. O. S. (2019). IUCrJ. 6, 1054–1063. > - (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, 911–917. > - Li, R., Si, D., Zeng, T., Ji, S. & He, J. (2016). 2016 IEEE > International Conference on Bioinformatics and Biomedicine (BIBM), > Vol. pp. 41–46. > - Si, D., Ji, S., Nasr, K. A. & He, J. (2012). Biopolymers. 97, > 698–708. > - 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, 1–22. > - 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, 753–763. > >
[ccp4bb] CCPBioSim Industry Talk
Dear all, We are pleased to announce that our next industrial speaker will be Nicolas Foloppe from Vernalis. The talk will be on 26 August 2021 at 2pm British time (registration is free but required by 24 August 2021). Details and registration can be found at https://www.ccpbiosim.ac.uk/binding2021. Title: Characterising the unbound state of drug-like compounds: implications for molecular recognition Abstract: The unbound state of drug compounds is important to better understand their binding to proteins, including conformational preorganization and the intramolecular reorganization energy of compounds upon binding (ΔEReorg). These questions were addressed with molecular dynamics (MD) simulations of diverse compounds, unbound or complexed to their protein target. Analysis of those systems involved observations regarding their electrostatics. The unbound compounds simulated with MD were compared to conformers generated with implicit generalized Born (GB) aqueous solvation models. The notion of conformational pre-organization for binding was investigated by comparing the simulated compounds to their bioactive X-ray structure. The study yielded low to moderate values of ΔEReorg for most, but not all, compounds. For three particularly polar compounds, ΔEReorg was substantial (≥ 15 kcal/mol). Those large ΔEReorg values may be interpreted as a redistribution of electrostatic interactions upon binding. Best wishes, Sarah This email and any attachments are intended solely for the use of the named recipients. If you are not the intended recipient you must not use, disclose, copy or distribute this email or any of its attachments and should notify the sender immediately and delete this email from your system. UK Research and Innovation (UKRI) has taken every reasonable precaution to minimise risk of this email or any attachments containing viruses or malware but the recipient should carry out its own virus and malware checks before opening the attachments. UKRI does not accept any liability for any losses or damages which the recipient may sustain due to presence of any viruses. To unsubscribe from the CCP4BB list, click the following link: https://www.jiscmail.ac.uk/cgi-bin/WA-JISC.exe?SUBED1=CCP4BB=1 This message was issued to members of www.jiscmail.ac.uk/CCP4BB, a mailing list hosted by www.jiscmail.ac.uk, terms & conditions are available at https://www.jiscmail.ac.uk/policyandsecurity/
Re: [ccp4bb] AI papers in experimental macromolecular structure determination
Dear Andrea, https://journals.iucr.org/s/issues/2000/06/00/hi2016/index.html Greetings, John Emeritus Professor John R Helliwell DSc > On 3 Aug 2021, at 12:53, Thorn, Dr. Andrea > 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, 1705–1716. > - Morris, R. J. (2004). Acta Cryst D. 60, 2133–2143. > > 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, 854–865. > - Al-Azzawi, A., Ouadou, A., Tanner, J. J. & Cheng, J. (2019). BMC > Bioinformatics. 20, 1–26. > - George, B., Assaiya, A., Roy, R. J., Kembhavi, A., Chauhan, R., Paul, G., > Kumar, J. & Philip, N. S. (2021). Commun Biol. 4, 1–12. > - Lata, K. R., Penczek, P. & Frank, J. (1995). Ultramicroscopy. 58, 381–391. > - Nguyen, N. P., Ersoy, I., Gotberg, J., Bunyak, F. & White, T. A. (2021). > BMC Bioinformatics. 22, 1–28. > - Wang, F., Gong, H., Liu, G., Li, M., Yan, C., Xia, T., Li, X. & Zeng, J. > (2016). Journal of Structural Biology. 195, 325–336. > - Wong, H. C., Chen, J., Mouche, F., Rouiller, I. & Bern, M. (2004). Journal > of Structural Biology. 145, 157–167. > > Motion description in Cryo-EM: > - Matsumoto, S., Ishida, S., Araki, M., Kato, T., Terayama, K. & Okuno, Y. > (2021). Nat Mach Intell. 3, 153–160. > - Zhong, E. D., Bepler, T., Berger, B. & Davis, J. H. (2021). Nat Methods. > 18, 176–185. > > Local resolution: > - Avramov, T. K., Vyenielo, D., Gomez-Blanco, J., Adinarayanan, S., Vargas, > J. & Si, D. (2019). Molecules. 24, 1181. > - Ramírez-Aportela, E., Mota, J., Conesa, P., Carazo, J. M. & Sorzano, C. O. > S. (2019). IUCrJ. 6, 1054–1063. > - (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, > 911–917. > - Li, R., Si, D., Zeng, T., Ji, S. & He, J. (2016). 2016 IEEE International > Conference on Bioinformatics and Biomedicine (BIBM), Vol. pp. 41–46. > - Si, D., Ji, S., Nasr, K. A. & He, J. (2012). Biopolymers. 97, 698–708. > - 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, 1–22. > - 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, > 753–763. > > Crystallization: > - Liu, R., Freund, Y. & Spraggon, G. (2008). Acta Cryst D. 64, 1187–1195. > - (2004). Methods. 34, 390–407. > - 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, 1361–1366. > - 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, 2216–2225. > > 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, 655–670. > > 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., Baslé, A., Murshudov, G. N., > Waterman, D. G. & Evans, G. (2020). IUCrJ. 7, 342–354. > > Ligand recognition: > Kowiel, M., Brzezinski, D., Porebski, P. J., Shabalin, I. G., Jaskolski, M. & > Minor, W. (2019).
