-
Notifications
You must be signed in to change notification settings - Fork 1
/
bmc_article.bib
777 lines (689 loc) · 33 KB
/
bmc_article.bib
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
321
322
323
324
325
326
327
328
329
330
331
332
333
334
335
336
337
338
339
340
341
342
343
344
345
346
347
348
349
350
351
352
353
354
355
356
357
358
359
360
361
362
363
364
365
366
367
368
369
370
371
372
373
374
375
376
377
378
379
380
381
382
383
384
385
386
387
388
389
390
391
392
393
394
395
396
397
398
399
400
401
402
403
404
405
406
407
408
409
410
411
412
413
414
415
416
417
418
419
420
421
422
423
424
425
426
427
428
429
430
431
432
433
434
435
436
437
438
439
440
441
442
443
444
445
446
447
448
449
450
451
452
453
454
455
456
457
458
459
460
461
462
463
464
465
466
467
468
469
470
471
472
473
474
475
476
477
478
479
480
481
482
483
484
485
486
487
488
489
490
491
492
493
494
495
496
497
498
499
500
501
502
503
504
505
506
507
508
509
510
511
512
513
514
515
516
517
518
519
520
521
522
523
524
525
526
527
528
529
530
531
532
533
534
535
536
537
538
539
540
541
542
543
544
545
546
547
548
549
550
551
552
553
554
555
556
557
558
559
560
561
562
563
564
565
566
567
568
569
570
571
572
573
574
575
576
577
578
579
580
581
582
583
584
585
586
587
588
589
590
591
592
593
594
595
596
597
598
599
600
601
602
603
604
605
606
607
608
609
610
611
612
613
614
615
616
617
618
619
620
621
622
623
624
625
626
627
628
629
630
631
632
633
634
635
636
637
638
639
640
641
642
643
644
645
646
647
648
649
650
651
652
653
654
655
656
657
658
659
660
661
662
663
664
665
666
667
668
669
670
671
672
673
674
675
676
677
678
679
680
681
682
683
684
685
686
687
688
689
690
691
692
693
694
695
696
697
698
699
700
701
702
703
704
705
706
707
708
709
710
711
712
713
714
715
716
717
718
719
720
721
722
723
724
725
726
727
728
729
730
731
732
733
734
735
736
737
738
739
740
741
742
743
744
745
746
747
748
749
750
751
752
753
754
755
756
757
758
759
760
761
762
763
764
765
766
767
768
769
770
771
772
773
774
775
776
777
% bmc_article.bib
%
% An example of bibtex entries.
% Entries taken from BMC instructions for authors page.
% uncomment next line to make author-year bibliography
% @settings{label, options="nameyear"}
@article{blank,
author = {},
title = {},
journal = {},
year = {},
month = {},
volume= {},
number= {},
pages = {},
note = {}
}
% Article within a journal
@ARTICLE{koon,
author = {Koonin, E V and Altschul, S F and P Bork},
title = {BRCA1 protein products: functional motifs},
journal = {Nat Genet},
year = {1996},
volume = {13},
pages = {266-267}
}
% Article within a journal supplement
@ARTICLE{oreg,
author = {Orengo, C A and Bray, J E and Hubbard,
T and LoConte, L and Sillitoe, I},
title = {Analysis and assessment of ab initio
three-dimensional prediction, secondary
structure, and contacts prediction},
journal = {Proteins},
year = {1999},
volume = {Suppl 3},
pages = {149-170}
}
% In press article
@inpress{khar,
author = {Kharitonov, S A and Barnes, P J},
title = {Clinical aspects of exhaled nitric oxide},
journal = {Eur Respir J},
note = {in press}
}
%
% Published abstract
%
@ARTICLE{zvai,
author = {Zvaifler, N J and Burger, J A and Marinova-Mutafchieva,
L and Taylor, P and Maini, R N},
title = {Mesenchymal cells, stromal derived factor-1 and
rheumatoid arthritis [abstract]},
journal = {Arthritis Rheum},
year = {1999},
volume = {42},
pages = {s250},
}
%
% Article within conference proceedings
%
@Inproceedings{xjon,
author = {X Jones},
title = {Zeolites and synthetic mechanisms},
booktitle = {Proceedings of the First National Conference on
Porous Sieves: 27-30 June 1996; Baltimore},
year = {1996},
editor = {Y Smith},
pages = {16-27},
organization = {Stoneham: Butterworth-Heinemann}
}
%%%%%%%%
% Book chapter, or article within a book
%
@incollection{schn,
author = {E Schnepf},
title = {From prey via endosymbiont to plastids:
comparative studies in dinoflagellates},
booktitle = {Origins of Plastids},
editor = {R A Lewin},
publisher = {Chapman and Hall},
pages = {53-76},
year = {1993},
address = {New York},
volume = {2},
edition = {2nd}
}
%%%%%%%%
% Whole issue of journal
%
@wholejournal{pond,
editor = {B Ponder and S Johnston and L Chodosh},
title = {Innovative oncology},
journal = {Breast Cancer Res},
year = {1998},
volume= {10},
pages = {1-72}
}
%%%%%%%%
% Whole conference proceedings
%
@proceedings{smith,
editor = {Y Smith},
title = {Proceedings of the First National Conference
on Porous Sieves: 27-30 June 1996; Baltimore},
year = 1996,
address= {Stoneham},
publisher = {Butterworth-Heinemann},
}
%%%%%%%%
% Complete book
%
@book{marg,
author = {L Margulis},
title = {Origin of Eukaryotic Cells},
publisher = {Yale University Press},
year = {1970},
address = {New Haven}
}
%%%%%%%%
% Monograph or book in series
%
@incollection{hunn,
author = {G W Hunninghake and J E Gadek},
title = {The alveloar macrophage},
booktitle = {Cultured Human Cells and Tissues},
publisher = {Academic Press},
year = {1995},
pages = {54-56},
editor = {T J R Harris},
address = {New York},
note = {Stoner G (Series Editor): Methods and Perspectives in Cell Biology, vol 1}
}
%%%%%%%%
% Book with institutional author
@manual{advi,
title = {Annual Report},
organization = {Advisory Committee on Genetic Modification},
address = {London},
year = {1999}
}
%%%%%%%%
% PHD Thesis
%
@phdthesis{koha,
author = {R Kohavi},
title = {Wrappers for performance enhancement and
obvious decision graphs},
school = {Stanford University, Computer Science Department},
year = {1995}
}
%%%%%%%%
% Webpage Link / URL
%
@article{Batut2018,
doi = {10.1016/j.cels.2018.05.012},
url = {https://doi.org/10.1016/j.cels.2018.05.012},
year = {2018},
month = jun,
publisher = {Elsevier {BV}},
volume = {6},
number = {6},
pages = {752--758.e1},
author = {{Batut et al.}},
title = {Community-Driven Data Analysis Training for Biology},
journal = {Cell Systems}
}
@article{michaudagrawal_mdanalysis_2011,
title = {{MDAnalysis}: {A} toolkit for the analysis of molecular dynamics simulations},
volume = {32},
copyright = {Copyright © 2011 Wiley Periodicals, Inc.},
issn = {1096-987X},
shorttitle = {{MDAnalysis}},
url = {https://onlinelibrary.wiley.com/doi/abs/10.1002/jcc.21787},
doi = {10.1002/jcc.21787},
abstract = {MDAnalysis is an object-oriented library for structural and temporal analysis of molecular dynamics (MD) simulation trajectories and individual protein structures. It is written in the Python language with some performance-critical code in C. It uses the powerful NumPy package to expose trajectory data as fast and efficient NumPy arrays. It has been tested on systems of millions of particles. Many common file formats of simulation packages including CHARMM, Gromacs, Amber, and NAMD and the Protein Data Bank format can be read and written. Atoms can be selected with a syntax similar to CHARMM's powerful selection commands. MDAnalysis enables both novice and experienced programmers to rapidly write their own analytical tools and access data stored in trajectories in an easily accessible manner that facilitates interactive explorative analysis. MDAnalysis has been tested on and works for most Unix-based platforms such as Linux and Mac OS X. It is freely available under the GNU General Public License from http://mdanalysis.googlecode.com. © 2011 Wiley Periodicals, Inc. J Comput Chem 2011},
language = {en},
number = {10},
urldate = {2020-04-17},
journal = {Journal of Computational Chemistry},
author = {Michaud‐Agrawal, Naveen and Denning, Elizabeth J. and Woolf, Thomas B. and Beckstein, Oliver},
year = {2011},
keywords = {analysis, membrane systems, molecular dynamics simulations, object-oriented design, proteins, Python programming language, software},
pages = {2319--2327},
}
@article{mcgibbon_mdtraj_2015,
title = {{MDTraj}: {A} {Modern} {Open} {Library} for the {Analysis} of {Molecular} {Dynamics} {Trajectories}},
volume = {109},
issn = {0006-3495},
shorttitle = {{MDTraj}},
url = {https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4623899/},
doi = {10.1016/j.bpj.2015.08.015},
abstract = {As molecular dynamics (MD) simulations continue to evolve into powerful computational tools for studying complex biomolecular systems, the necessity of flexible and easy-to-use software tools for the analysis of these simulations is growing. We have developed MDTraj, a modern, lightweight, and fast software package for analyzing MD simulations. MDTraj reads and writes trajectory data in a wide variety of commonly used formats. It provides a large number of trajectory analysis capabilities including minimal root-mean-square-deviation calculations, secondary structure assignment, and the extraction of common order parameters. The package has a strong focus on interoperability with the wider scientific Python ecosystem, bridging the gap between MD data and the rapidly growing collection of industry-standard statistical analysis and visualization tools in Python. MDTraj is a powerful and user-friendly software package that simplifies the analysis of MD data and connects these datasets with the modern interactive data science software ecosystem in Python.},
number = {8},
urldate = {2020-04-17},
journal = {Biophysical Journal},
author = {McGibbon, Robert T. and Beauchamp, Kyle A. and Harrigan, Matthew P. and Klein, Christoph and Swails, Jason M. and Hernández, Carlos X. and Schwantes, Christian R. and Wang, Lee-Ping and Lane, Thomas J. and Pande, Vijay S.},
month = oct,
year = {2015},
pmcid = {PMC4623899},
pages = {1528--1532},
}
@article{skjaerven_integrating_2014,
title = {Integrating protein structural dynamics and evolutionary analysis with {Bio3D}},
volume = {15},
issn = {1471-2105},
url = {https://doi.org/10.1186/s12859-014-0399-6},
doi = {10.1186/s12859-014-0399-6},
abstract = {Popular bioinformatics approaches for studying protein functional dynamics include comparisons of crystallographic structures, molecular dynamics simulations and normal mode analysis. However, determining how observed displacements and predicted motions from these traditionally separate analyses relate to each other, as well as to the evolution of sequence, structure and function within large protein families, remains a considerable challenge. This is in part due to the general lack of tools that integrate information of molecular structure, dynamics and evolution.},
number = {1},
urldate = {2020-04-17},
journal = {BMC Bioinformatics},
author = {Skjærven, Lars and Yao, Xin-Qiu and Scarabelli, Guido and Grant, Barry J.},
month = dec,
year = {2014},
pages = {399},
}
@article{senapathi_biomolecular_2019,
title = {Biomolecular {Reaction} and {Interaction} {Dynamics} {Global} {Environment} ({BRIDGE})},
volume = {35},
issn = {1367-4803},
url = {https://academic.oup.com/bioinformatics/article/35/18/3508/5317160},
doi = {10.1093/bioinformatics/btz107},
abstract = {AbstractMotivation. The pathway from genomics through proteomics and onto a molecular description of biochemical processes makes the discovery of drugs and bio},
language = {en},
number = {18},
urldate = {2020-04-17},
journal = {Bioinformatics},
author = {Senapathi, Tharindu and Bray, Simon and Barnett, Christopher B. and Grüning, Björn and Naidoo, Kevin J.},
month = sep,
year = {2019},
note = {Publisher: Oxford Academic},
pages = {3508--3509},
}
@article{abraham_gromacs_2015,
title = {{GROMACS}: {High} performance molecular simulations through multi-level parallelism from laptops to supercomputers},
volume = {1-2},
issn = {2352-7110},
shorttitle = {{GROMACS}},
url = {http://www.sciencedirect.com/science/article/pii/S2352711015000059},
doi = {10.1016/j.softx.2015.06.001},
abstract = {GROMACS is one of the most widely used open-source and free software codes in chemistry, used primarily for dynamical simulations of biomolecules. It provides a rich set of calculation types, preparation and analysis tools. Several advanced techniques for free-energy calculations are supported. In version 5, it reaches new performance heights, through several new and enhanced parallelization algorithms. These work on every level; SIMD registers inside cores, multithreading, heterogeneous CPU–GPU acceleration, state-of-the-art 3D domain decomposition, and ensemble-level parallelization through built-in replica exchange and the separate Copernicus framework. The latest best-in-class compressed trajectory storage format is supported.},
language = {en},
urldate = {2020-04-17},
journal = {SoftwareX},
author = {Abraham, Mark James and Murtola, Teemu and Schulz, Roland and Páll, Szilárd and Smith, Jeremy C. and Hess, Berk and Lindahl, Erik},
month = sep,
year = {2015},
keywords = {Free energy, GPU, Molecular dynamics, SIMD},
pages = {19--25},
}
@article{afgan_galaxy_2018,
title = {The {Galaxy} platform for accessible, reproducible and collaborative biomedical analyses: 2018 update},
volume = {46},
issn = {0305-1048},
shorttitle = {The {Galaxy} platform for accessible, reproducible and collaborative biomedical analyses},
url = {https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6030816/},
doi = {10.1093/nar/gky379},
abstract = {Galaxy (homepage: https://galaxyproject.org, main public server: https://usegalaxy.org) is a web-based scientific analysis platform used by tens of thousands of scientists across the world to analyze large biomedical datasets such as those found in genomics, proteomics, metabolomics and imaging. Started in 2005, Galaxy continues to focus on three key challenges of data-driven biomedical science: making analyses accessible to all researchers, ensuring analyses are completely reproducible, and making it simple to communicate analyses so that they can be reused and extended. During the last two years, the Galaxy team and the open-source community around Galaxy have made substantial improvements to Galaxy's core framework, user interface, tools, and training materials. Framework and user interface improvements now enable Galaxy to be used for analyzing tens of thousands of datasets, and {\textgreater}5500 tools are now available from the Galaxy ToolShed. The Galaxy community has led an effort to create numerous high-quality tutorials focused on common types of genomic analyses. The Galaxy developer and user communities continue to grow and be integral to Galaxy's development. The number of Galaxy public servers, developers contributing to the Galaxy framework and its tools, and users of the main Galaxy server have all increased substantially.},
number = {Web Server issue},
urldate = {2020-04-17},
journal = {Nucleic Acids Research},
author = {Afgan, Enis and Baker, Dannon and Batut, Bérénice and van den Beek, Marius and Bouvier, Dave and Čech, Martin and Chilton, John and Clements, Dave and Coraor, Nate and Grüning, Björn A and Guerler, Aysam and Hillman-Jackson, Jennifer and Hiltemann, Saskia and Jalili, Vahid and Rasche, Helena and Soranzo, Nicola and Goecks, Jeremy and Taylor, James and Nekrutenko, Anton and Blankenberg, Daniel},
month = jul,
year = {2018},
pmcid = {PMC6030816},
pages = {W537--W544},
}
@article{hump_vmd_1996,
author={William Humphrey and Andrew Dalke and Klaus Schulten},
title={{VMD} -- {V}isual {M}olecular {D}ynamics},
journal={Journal of Molecular Graphics},
year=1996,
volume=14,
pages={33-38},
note={},
tbstatus={Published.},
techrep={},
tbreference={222}
}
@article{miller_mmpbsa,
author = {Miller, Bill R. and McGee, T. Dwight and Swails, Jason M. and Homeyer, Nadine and Gohlke, Holger and Roitberg, Adrian E.},
title = {MMPBSA.py: An Efficient Program for End-State Free Energy Calculations},
journal = {Journal of Chemical Theory and Computation},
volume = {8},
number = {9},
pages = {3314-3321},
year = {2012},
doi = {10.1021/ct300418h},
URL = {
https://doi.org/10.1021/ct300418h
},
eprint = {
https://doi.org/10.1021/ct300418h
}
}
@article{sloggett_bioblend,
author = {Sloggett, Clare and Goonasekera, Nuwan and Afgan, Enis},
title = "{BioBlend: automating pipeline analyses within Galaxy and CloudMan}",
journal = {Bioinformatics},
volume = {29},
number = {13},
pages = {1685-1686},
year = {2013},
month = {04},
abstract = "{Summary: We present BioBlend, a unified API in a high-level language (python) that wraps the functionality of Galaxy and CloudMan APIs. BioBlend makes it easy for bioinformaticians to automate end-to-end large data analysis, from scratch, in a way that is highly accessible to collaborators, by allowing them to both provide the required infrastructure and automate complex analyses over large datasets within the familiar Galaxy environment.Availability and implementation:http://bioblend.readthedocs.org/. Automated installation of BioBlend is available via PyPI (e.g. pip install bioblend). Alternatively, the source code is available from the GitHub repository (https://github.com/afgane/bioblend) under the MIT open source license. The library has been tested and is working on Linux, Macintosh and Windows-based systems.Contact:[email protected]}",
issn = {1367-4803},
doi = {10.1093/bioinformatics/btt199},
url = {https://doi.org/10.1093/bioinformatics/btt199},
}
@article{Schuetz2018,
doi = {10.1021/acs.jcim.8b00614},
url = {https://doi.org/10.1021/acs.jcim.8b00614},
year = {2018},
month = nov,
publisher = {American Chemical Society ({ACS})},
volume = {59},
number = {1},
pages = {535--549},
author = {Doris A. Schuetz and Mattia Bernetti and Martina Bertazzo and Djordje Musil and Hans-Michael Eggenweiler and Maurizio Recanatini and Matteo Masetti and Gerhard F. Ecker and Andrea Cavalli},
title = {Predicting Residence Time and Drug Unbinding Pathway through Scaled Molecular Dynamics},
journal = {Journal of Chemical Information and Modeling}
}
@article{Pearl2006,
doi = {10.1146/annurev.biochem.75.103004.142738},
url = {https://doi.org/10.1146/annurev.biochem.75.103004.142738},
year = {2006},
month = jun,
publisher = {Annual Reviews},
volume = {75},
number = {1},
pages = {271--294},
author = {Laurence H. Pearl and Chrisostomos Prodromou},
title = {Structure and Mechanism of the {Hsp90} Molecular Chaperone Machinery},
journal = {Annual Review of Biochemistry}
}
@article{Vanommeslaeghe2009,
doi = {10.1002/jcc.21367},
url = {https://doi.org/10.1002/jcc.21367},
year = {2009},
publisher = {Wiley},
pages = {NA--NA},
author = {K. Vanommeslaeghe and E. Hatcher and C. Acharya and S. Kundu and S. Zhong and J. Shim and E. Darian and O. Guvench and P. Lopes and I. Vorobyov and A. D. Mackerell},
title = {{CHARMM} general force field: A force field for drug-like molecules compatible with the {CHARMM} all-atom additive biological force fields},
journal = {Journal of Computational Chemistry}
}
@article{Maier2015,
doi = {10.1021/acs.jctc.5b00255},
url = {https://doi.org/10.1021/acs.jctc.5b00255},
year = {2015},
month = jul,
publisher = {American Chemical Society ({ACS})},
volume = {11},
number = {8},
pages = {3696--3713},
author = {James A. Maier and Carmenza Martinez and Koushik Kasavajhala and Lauren Wickstrom and Kevin E. Hauser and Carlos Simmerling},
title = {ff14SB: Improving the Accuracy of Protein Side Chain and Backbone Parameters from ff99SB},
journal = {Journal of Chemical Theory and Computation}
}
@article{reif2012,
author = {Reif, Maria M. and Hünenberger, Philippe H. and Oostenbrink, Chris},
title = {New Interaction Parameters for Charged Amino Acid Side Chains in the {GROMOS} Force Field},
journal = {Journal of Chemical Theory and Computation},
volume = {8},
number = {10},
pages = {3705-3723},
year = {2012},
doi = {10.1021/ct300156h},
URL = {
https://doi.org/10.1021/ct300156h
},
eprint = {
https://doi.org/10.1021/ct300156h
}
}
@article{Mobley2018,
author = {Mobley, David L. and Bannan, Caitlin C. and Rizzi, Andrea and Bayly, Christopher I. and Chodera, John D. and Lim, Victoria T. and Lim, Nathan M. and Beauchamp, Kyle A. and Slochower, David R. and Shirts, Michael R. and Gilson, Michael K. and Eastman, Peter
K.},
title = {Escaping Atom Types in Force Fields Using Direct Chemical Perception},
journal = {Journal of Chemical Theory and Computation},
volume = {14},
number = {11},
pages = {6076-6092},
year = {2018},
doi = {10.1021/acs.jctc.8b00640}
}
@misc{Swails2016,
title={{ParmEd}: Cross-program parameter and topology file editor and molecular mechanical simulator engine},
author={Swails, J and Hernandez, CX and Mobley, DL and Nguyen, H and Wang, LP and Janowski, P},
url={https://parmed.github.io/ParmEd/html/index.html},
year={2016},
note = {Accessed: 23.01.20}
}
@book{berendsen01,
title={Simulating the Physical World: Hierarchical Modeling from Quantum Mechanics to Fluid Dynamics.},
DOI={10.1017/CBO9780511815348},
publisher={Cambridge University Press},
address={Cambridge, United Kingdom},
author={Berendsen, Herman J. C.},
year={2007}
}
@incollection{Lemkul2020,
doi = {10.1016/bs.pmbts.2019.12.009},
year = {2020},
publisher = {Elsevier},
pages = {1--71},
author = {Justin A. Lemkul},
title = {Pairwise-additive and polarizable atomistic force fields for molecular dynamics simulations of proteins},
address = {New York},
booktitle = {Computational Approaches for Understanding Dynamical Systems: Protein Folding and Assembly}
}
@article{Wang2004,
doi = {10.1002/jcc.20035},
year = {2004},
publisher = {Wiley},
volume = {25},
number = {9},
pages = {1157--1174},
author = {Junmei Wang and Romain M. Wolf and James W. Caldwell and Peter A. Kollman and David A. Case},
title = {Development and testing of a general {AMBER} force field},
journal = {Journal of Computational Chemistry}
}
@article{Onufriev2017,
doi = {10.1002/wcms.1347},
year = {2017},
month = nov,
publisher = {Wiley},
volume = {8},
number = {2},
pages = {e1347},
author = {Alexey V. Onufriev and Saeed Izadi},
title = {Water models for biomolecular simulations},
journal = {Wiley Interdisciplinary Reviews: Computational Molecular Science}
}
@article{Rose2018ngl,
author = {Rose, Alexander S and Bradley, Anthony R and Valasatava, Yana and Duarte, Jose M and Prlić, Andreas and Rose, Peter W},
title = "{NGL viewer: web-based molecular graphics for large complexes}",
journal = {Bioinformatics},
volume = {34},
number = {21},
pages = {3755-3758},
year = {2018},
month = {05},
abstract = "{The interactive visualization of very large macromolecular complexes on the web is becoming a challenging problem as experimental techniques advance at an unprecedented rate and deliver structures of increasing size.We have tackled this problem by developing highly memory-efficient and scalable extensions for the NGL WebGL-based molecular viewer and by using Macromolecular Transmission Format (MMTF), a binary and compressed MMTF. These enable NGL to download and render molecular complexes with millions of atoms interactively on desktop computers and smartphones alike, making it a tool of choice for web-based molecular visualization in research and education.The source code is freely available under the MIT license at github.com/arose/ngl and distributed on NPM (npmjs.com/package/ngl). MMTF-JavaScript encoders and decoders are available at github.com/rcsb/mmtf-javascript.}",
issn = {1367-4803},
doi = {10.1093/bioinformatics/bty419},
}
@article{Trott2009,
year = {2009},
publisher = {Wiley},
pages = {NA--NA},
author = {Oleg Trott and Arthur J. Olson},
title = {{AutoDock Vina}: Improving the speed and accuracy of docking with a new scoring function, efficient optimization, and multithreading},
journal = {Journal of Computational Chemistry},
doi={10.1002/jcc.21334},
volume={31},
number={2},
}
@article{Ruiz2014,
title={{rDock}: a fast, versatile and open source program for docking ligands to proteins and nucleic acids},
author={Ruiz-Carmona, Sergio and Alvarez-Garcia, Daniel and Foloppe, Nicolas and Garmendia-Doval, A Beatriz and Juhos, Szilveszter and Schmidtke, Peter and Barril, Xavier and Hubbard, Roderick E and Morley, S David},
journal={PLoS Computational Biology},
volume={10},
number={4},
pages={e1003571},
year={2014},
doi={10.1371/journal.pcbi.1003571},
publisher={Public Library of Science}
}
@misc{gtn_comp,
key = {Galaxy {Training}: {Computational} chemistry},
title = {Galaxy {Training}: {Computational} chemistry},
shorttitle = {Galaxy {Training}},
url = {https://training.galaxyproject.org/training-material/topics/computational-chemistry/tutorials/htmd-analysis/tutorial.html},
abstract = {Modelling, simulation and analysis of biomolecular systems},
language = {en},
}
@misc{gtn_collections,
key = {Galaxy Training: Collections: Using dataset collection},
title = {Galaxy Training: Collections: Using dataset collection},
shorttitle = {Galaxy {Training}},
url = {https://galaxyproject.github.io/training-material/topics/galaxy-data-manipulation/tutorials/collections/tutorial.html},
language = {en},
urldate = {2020-04-29},
}
@misc{gtn_toworkflow,
key = {Workflows: Extracting Workflows from Histories},
title = {Workflows: Extracting Workflows from Histories},
shorttitle = {Galaxy {Training}},
url = {https://galaxyproject.github.io/training-material/topics/galaxy-ui/tutorials/history-to-workflow/tutorial.html},
language = {en},
urldate = {2020-04-29},
}
@misc{gtn_multiple,
key = {Histories: Understanding Galaxy history system},
title = {Galaxy Training: Histories: Understanding Galaxy history system},
shorttitle = {Galaxy {Training}},
url = {https://galaxyproject.github.io/training-material/topics/galaxy-ui/tutorials/history/tutorial.html},
language = {en},
urldate = {2020-04-29},
}
@misc{gtn_api,
key = {Scripting Galaxy using the API and BioBlend},
title = {Galaxy Training: Scripting Galaxy using the API and BioBlend},
shorttitle = {Galaxy {Training}},
url = {https://training.galaxyproject.org/training-material/topics/dev/tutorials/bioblend-api/slides.html},
language = {en},
urldate = {2020-04-29},
}
@misc{eu_6hhr,
key = {Protein-ligand docking (6hhr)},
title = {Galaxy | Europe | Accessible History | Protein-ligand docking (6hhr)},
url = {https://cheminformatics.usegalaxy.eu/u/sbray/h/protein-ligand-docking-6hhr},
language = {en},
urldate = {2020-04-29}
}
@misc{eu_htmd_simulation_workflow,
key = {Protein-ligand HTMD simulation workflow},
title = {Galaxy | Europe | Accessible History | Protein-ligand HTMD simulation},
url = {https://cheminformatics.usegalaxy.eu/u/sbray/w/protein-ligand-htmd-sim},
language = {en},
urldate = {2020-04-29}
}
@misc{eu_htmd_analysis_workflow,
key = {Protein-ligand HTMD analysis workflow},
title = {Galaxy | Europe | Accessible History | Protein-ligand HTMD analysis},
url = {https://cheminformatics.usegalaxy.eu/u/sbray/w/protein-ligand-htmd-analysis},
language = {en},
urldate = {2020-04-29}
}
@misc{za_htmd_simulation_workflow,
key = {Protein-ligand HTMD simulation workflow},
title = {Galaxy | South Africa | Accessible History | Protein-ligand HTMD analysis},
url = {https://galaxy-compchem.ilifu.ac.za/u/sbray/w/protein-ligand-htmd-sim},
language = {en},
urldate = {2020-04-29}
}
@misc{za_htmd_analysis_workflow,
key = {Protein-ligand HTMD analysis workflow},
title = {Galaxy | South Africa | Accessible History | Protein-ligand HTMD analysis},
url = {https://galaxy-compchem.ilifu.ac.za/u/sbray/w/protein-ligand-htmd-analysis},
language = {en},
urldate = {2020-04-29}
}
@article{Stebbins1997,
doi = {10.1016/s0092-8674(00)80203-2},
year = {1997},
month = apr,
publisher = {Elsevier {BV}},
volume = {89},
number = {2},
pages = {239--250},
author = {Charles E Stebbins and Alicia A Russo and Christine Schneider and Neal Rosen and F.Ulrich Hartl and Nikola P Pavletich},
title = {Crystal Structure of an {Hsp90}{\textendash}Geldanamycin Complex: Targeting of a Protein Chaperone by an Antitumor Agent},
journal = {Cell}
}
@article{Hermane2019,
doi = {10.1039/c9ob00892f},
year = {2019},
publisher = {Royal Society of Chemistry ({RSC})},
volume = {17},
number = {21},
pages = {5269--5278},
author = {Jekaterina Hermane and Simone Eichner and Lena Mancuso and Benjamin Schr\"{o}der and Florenz Sasse and Carsten Zeilinger and Andreas Kirschning},
title = {New geldanamycin derivatives with anti {Hsp} properties by mutasynthesis},
journal = {Organic {\&} Biomolecular Chemistry}
}
@article{Schopf2017,
doi = {10.1038/nrm.2017.20},
year = {2017},
month = apr,
publisher = {Springer Science and Business Media {LLC}},
volume = {18},
number = {6},
pages = {345--360},
author = {Florian H. Schopf and Maximilian M. Biebl and Johannes Buchner},
title = {The {HSP}90 chaperone machinery},
journal = {Nature Reviews Molecular Cell Biology}
}
@misc{ligand_resorcinol,
key = {3-(2,4-{Dihydroxyphenyl})-4-(2-fluorophenyl)-{1H}-1,2,4-triazole-5-thione},
title = {3-(2,4-{Dihydroxyphenyl})-4-(2-fluorophenyl)-{1H}-1,2,4-triazole-5-thione},
url = {https://pubchem.ncbi.nlm.nih.gov/compound/135508238},
abstract = {3-(2,4-Dihydroxyphenyl)-4-(2-fluorophenyl)-1H-1,2,4-triazole-5-thione {\textbar} C14H10FN3O2S {\textbar} CID 135508238 - structure, chemical names, physical and chemical properties, classification, patents, literature, biological activities, safety/hazards/toxicity information, supplier lists, and more.},
language = {en},
urldate = {2020-04-29},
author = {PubChem},
note = {Library Catalog: pubchem.ncbi.nlm.nih.gov},
}
@article{kuzmanic_determination_2010,
title = {Determination of {Ensemble}-{Average} {Pairwise} {Root} {Mean}-{Square} {Deviation} from {Experimental} {B}-{Factors}},
volume = {98},
issn = {0006-3495},
doi = {10.1016/j.bpj.2009.11.011},
abstract = {Root mean-square deviation (RMSD) after roto-translational least-squares fitting is a measure of global structural similarity of macromolecules used commonly. On the other hand, experimental x-ray B-factors are used frequently to study local structural heterogeneity and dynamics in macromolecules by providing direct information about root mean-square fluctuations (RMSF) that can also be calculated from molecular dynamics simulations. We provide a mathematical derivation showing that, given a set of conservative assumptions, a root mean-square ensemble-average of an all-against-all distribution of pairwise RMSD for a single molecular species, {\textless}RMSD2{\textgreater}1/2, is directly related to average B-factors ({\textless}B{\textgreater}) and {\textless}RMSF2{\textgreater}1/2. We show this relationship and explore its limits of validity on a heterogeneous ensemble of structures taken from molecular dynamics simulations of villin headpiece generated using distributed-computing techniques and the Folding@Home cluster. Our results provide a basis for quantifying global structural diversity of macromolecules in crystals directly from x-ray experiments, and we show this on a large set of structures taken from the Protein Data Bank. In particular, we show that the ensemble-average pairwise backbone RMSD for a microscopic ensemble underlying a typical protein x-ray structure is ∼1.1 Å, under the assumption that the principal contribution to experimental B-factors is conformational variability.},
number = {5},
urldate = {2020-04-30},
journal = {Biophysical Journal},
author = {Kuzmanic, Antonija and Zagrovic, Bojan},
month = mar,
year = {2010},
pmcid = {PMC2830444},
pages = {861--871},
}
@article{berjanskii_nmr_2006,
title = {{NMR}: prediction of protein flexibility},
volume = {1},
copyright = {2006 Nature Publishing Group},
issn = {1750-2799},
shorttitle = {{NMR}},
doi = {10.1038/nprot.2006.108},
abstract = {We present a protocol for predicting protein flexibility from NMR chemical shifts. The protocol consists of (i) ensuring that the chemical shift assignments are correctly referenced or, if not, performing a reference correction using information derived from the chemical shift index, (ii) calculating the random coil index (RCI), and (iii) predicting the expected root mean square fluctuations (RMSFs) and order parameters (S2) of the protein from the RCI. The key advantages of this protocol over existing methods for studying protein dynamics are that (i) it does not require prior knowledge of a protein's tertiary structure, (ii) it is not sensitive to the protein's overall tumbling and (iii) it does not require additional NMR measurements beyond the standard experiments for backbone assignments. When chemical shift assignments are available, protein flexibility parameters, such as S2 and RMSF, can be calculated within 1–2 h using a spreadsheet program.},
language = {en},
number = {2},
urldate = {2020-04-30},
journal = {Nature Protocols},
author = {Berjanskii, Mark and Wishart, David S.},
month = aug,
year = {2006},
note = {Number: 2
Publisher: Nature Publishing Group},
pages = {683--688},
}
@article{Lemkul2019,
doi = {10.33011/livecoms.1.1.5068},
year = {2019},
publisher = {University of Colorado at Boulder},
volume = {1},
number = {1},
author = {Justin Lemkul},
title = {From Proteins to Perturbed {H}amiltonians: A Suite of Tutorials for the {GROMACS}-2018 Molecular Simulation Package [Article v1.0]},
journal = {Living Journal of Computational Molecular Science}
}
@article{HARVEY20121059,
title = "High-throughput molecular dynamics: the powerful new tool for drug discovery",
journal = "Drug Discovery Today",
volume = "17",
number = "19",
pages = "1059 - 1062",
year = "2012",
issn = "1359-6446",
doi = "https://doi.org/10.1016/j.drudis.2012.03.017",
author = "Matthew J. Harvey and Gianni [De Fabritiis]",
}
@article{Guterres2020,
doi = {10.1021/acs.jcim.0c00057},
year = {2020},
month = mar,
publisher = {American Chemical Society ({ACS})},
volume = {60},
number = {4},
pages = {2189--2198},
author = {Hugo Guterres and Wonpil Im},
title = {Improving Protein-Ligand Docking Results with High-Throughput Molecular Dynamics Simulations},
journal = {Journal of Chemical Information and Modeling}
}
@article{SousadaSilva2012,
doi = {10.1186/1756-0500-5-367},
year = {2012},
publisher = {Springer Science and Business Media {LLC}},
volume = {5},
number = {1},
pages = {367},
author = {Alan W Sousa da Silva and Wim F Vranken},
title = {{ACPYPE} - {AnteChamber} {PYthon} Parser {interfacE}},
journal = {{BMC} Research Notes}
}
@misc{zenodo_sm_2020,
title = {{galaxycomputationalchemistry/htmd-paper-sm: Data
and workflows - Intuitive, reproducible high-
throughput molecular dynamics in Galaxy: a
tutorial}},
month = may,
year = 2020,
publisher = {Zenodo},
version = {2020.05.07},
doi = {10.5281/zenodo.3813283},
url = {https://doi.org/10.5281/zenodo.3813283}
}
@article{OBoyle2011,
year = {2011},
month = oct,
publisher = {Springer Science and Business Media {LLC}},
volume = {3},
number = {1},
author = {Noel M O'Boyle and Michael Banck and Craig A James and Chris Morley and Tim Vandermeersch and Geoffrey R Hutchison},
title = {{OpenBabel}: An open chemical toolbox},
journal = {Journal of Cheminformatics}
}