Machine learning applications in macromolecular X-ray crystallography

被引：12
作者：
Vollmar, Melanie [1 ]
Evans, Gwyndaf [1 ,2 ]
机构：
[1] Diamond Light Source Ltd, Harwell Sci & Innovat Campus, Harwell, Berks, England
[2] Rosalind Franklin Inst, Harwell Sci & Innovat Campus, Harwell, Berks, England
来源：
CRYSTALLOGRAPHY REVIEWS | 2021年 / 27卷 / 02期
基金：
英国生物技术与生命科学研究理事会;
关键词：
Machine learning; big data; automation; macromolecular X-ray crystallography; synchrotron; structural biology; PROTEIN-STRUCTURE DETERMINATION; STRUCTURE PREDICTION; STRUCTURAL GENOMICS; PATTERN-RECOGNITION; NEURAL-NETWORKS; AUTOMATED CLASSIFICATION; SECONDARY STRUCTURE; RECEPTIVE FIELDS; DATA-COLLECTION; WEB SERVER;
D O I：
10.1080/0889311X.2021.1982914
中图分类号：
O7 [晶体学];
学科分类号：
0702 ; 070205 ; 0703 ; 080501 ;
摘要：
After more than half a century of evolution, machine learning and artificial intelligence, in general, are entering a truly exciting era of broad application in commercial and research sectors. In X-ray crystallography, and its application to structural biology, machine learning is finding a home within expert and automated systems, is forecasting experiment and data analysis outcomes, is predicting whether crystals can be grown and even generating macromolecular structures. This review provides a historical perspective on AI and machine learning, offers an introduction and guide to its application in crystallography and concludes with topical examples of how it is currently influencing macromolecular crystallography.
引用
收藏
页码：54 / 101
页数：48
相关论文
共 50 条
[31]   Machine learning control of an elliptically bent hard X-ray mirror [J].
Mashrafi, Sheikh ;
Harder, Ross ;
Shi, Xianbo ;
Shu, Deming ;
Qiao, Zhi ;
Wyman, Max ;
Mooney, Tim ;
Anton, Jayson ;
Kearney, Steven ;
Rebuffi, Luca ;
Qian, Jun ;
Shi, Bing ;
Assoufid, Lahsen .
ADVANCES IN X-RAY/EUV OPTICS AND COMPONENTS XV, 2020, 11491
[32]   Reducing the background in X-ray imaging detectors via machine learning [J].
Wilkins, D. R. ;
Allen, S. W. ;
Miller, E. D. ;
Bautz, M. ;
Chattopadhyay, T. ;
Foster, R. ;
Grant, C. E. ;
Herrmann, S. ;
Kraft, R. ;
Morris, R. G. ;
Nulsen, P. ;
Schellenberger, G. .
SPACE TELESCOPES AND INSTRUMENTATION 2022: ULTRAVIOLET TO GAMMA RAY, 2022, 12181
[33]   Analyzing Chest X-Ray Lung Images Using Machine Learning [J].
Somasundaram, K. ;
Raman, Ramakrishnan ;
Meenakshi, R. ;
Chirputkar, Abhijit .
CARDIOMETRY, 2022, (25) :145-148
[34]   Evaluation of a machine learning based model observer for x-ray CT [J].
Kopp, Felix K. ;
Catalano, Marco ;
Pfeiffer, Daniela ;
Rummeny, Ernst J. ;
Noel, Peter B. .
MEDICAL IMAGING 2018: IMAGE PERCEPTION, OBSERVER PERFORMANCE, AND TECHNOLOGY ASSESSMENT, 2018, 10577
[35]   Machine learning deciphers structural features of RNA duplexes measured with solution X-ray scattering [J].
Chen, Yen-Lin ;
Pollack, Lois .
IUCRJ, 2020, 7 :870-880
[36]   Comparison of different machine learning approaches to detect femoral neck fractures in x-ray images [J].
Acici, Koray ;
Sumer, Emre ;
Beyaz, Salih .
HEALTH AND TECHNOLOGY, 2021, 11 (03) :643-653
[37]   Machine learning in crystallography and structural science [J].
Billinge, Simon J. L. ;
Proffen, Thomas .
ACTA CRYSTALLOGRAPHICA A-FOUNDATION AND ADVANCES, 2024, 80 :139-145
[38]   The X-ray crystallography phase problem solved thanks to AlphaFold and RoseTTAFold models: a case-study report [J].
Barbarin-Bocahu, Irene ;
Graille, Marc .
ACTA CRYSTALLOGRAPHICA SECTION D-STRUCTURAL BIOLOGY, 2022, 78 :517-531
[39]   Multipurpose diffractometer for in situ X-ray crystallography of functional materials [J].
Gorfman, Semen ;
Spirito, David ;
Cohen, Netanela ;
Siffalovic, Peter ;
Nadazdy, Peter ;
Li, Youli .
JOURNAL OF APPLIED CRYSTALLOGRAPHY, 2021, 54 :914-923
[40]   Measuring and modeling diffuse scattering in protein X-ray crystallography [J].
Van Benschoten, Andrew H. ;
Liu, Lin ;
Gonzalez, Ana ;
Brewster, Aaron S. ;
Sauter, Nicholas K. ;
Fraser, James S. ;
Wall, Michael E. .
PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA, 2016, 113 (15) :4069-4074
12345