-
Notifications
You must be signed in to change notification settings - Fork 2
/
validation_help.html
611 lines (476 loc) · 53.9 KB
/
validation_help.html
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
<!-- Template for writing main/home flask page -->
<!-- ganesans - Salilab - UCSF -->
<!-- ganesans@salilab.org -->
<!DOCTYPE html>
<html lang="en">
<head>
<meta charset="utf-8">
<meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no">
<title>PDB-Dev Validation Report</title>
<!-- add bootstrap css file -->
<link rel="stylesheet" href="css/bootstrap.min.css">
<link rel="stylesheet" href="css/main.css">
<!-- Boilerplate from layouts.html -->
<link rel="stylesheet" href="https://maxcdn.bootstrapcdn.com/bootstrap/4.5.2/css/bootstrap.min.css">
<script src="https://ajax.googleapis.com/ajax/libs/jquery/3.5.1/jquery.min.js"></script>
<script src="https://cdnjs.cloudflare.com/ajax/libs/popper.js/1.16.0/umd/popper.min.js"></script>
<script src="https://maxcdn.bootstrapcdn.com/bootstrap/4.5.2/js/bootstrap.min.js"></script>
<style>
@charset "UTF-8";
a {
color: #333399;
font-weight: 450;
}
a:hover {
color:#5353c6;
}
.clearfloat { /* this class can be placed on a <br /> or empty div as the final element following the last floated div (within the #container) if the #footer is removed or taken out of the #container */
clear:both;
height:0;
font-size: 1px;
line-height: 0px;
}
body {
background-color: #669966;
margin: 0;
padding: 0;
}
.home-header {
display: flex;
}
.dropdown-submenu {
position: relative;
}
.dropdown-submenu a::after {
transform: rotate(-90deg);
position: absolute;
right: 6px;
top: .8em;
}
.dropdown-submenu .dropdown-menu {
top: 0;
left: 100%;
margin-left: .1rem;
margin-right: .1rem;
}
</style>
</head>
<body style="background-color: #669966; ">
<!-- header -->
<!-- header -->
<div class="container-fluid">
<div class="card" style="border: none; margin-bottom: 0px; margin-right: 10px; margin-left: 30px; ">
<div class="float-left"><b><span id="siteid"></span></b></div>
<div class="card-body" style="padding: 0px 0px;background-color: rgba(102,153,102, 0.5); ">
<div class="row">
<div class=" col-sm-9" style="margin-left: 0px;">
<a href="/index.html">
<img src="images/logon.png" class="float-left" alt="PDBDEV.org" height="100" width="110" style="margin-top: 0px; margin-bottom: 0px" />
<p>
<h1 style="margin-left: 0px; color: #003366;">PDB-Dev</h1>
<h5 class="float-left" style="color: #003366;">Prototype Archiving System for Integrative Structures</h5>
</p>
</a>
</div>
<div class="col-sm-3 d-flex align-items-end" style="margin-top: 0px;">
<h5 class="home-header ml-auto" style="margin-right:27px; color:#003366"><b><small></small></b></h5>
</div>
</div>
</div>
<!--end panel body-->
<!-- navbar -->
<nav class="navbar navbar-expand-xl navbar-dark">
<div class="dropdown">
<a class="navbar-brand" href="help.html">
<h5>User guide to the PDB-Dev validation reports for integrative structures</h5>
</a>
<button class="navbar-toggler" type="button" data-toggle="collapse" data-target="#navbarSupportedContent" aria-controls="navbarSupportedContent" aria-expanded="false" aria-label="Toggle navigation">
<span class="navbar-toggler-icon"></span>
</button>
</div>
</nav>
<!-- navbar -->
<nav class="navbar navbar-expand-xl navbar-dark p-0">
<div class="collapse navbar-collapse" id="navbarSupportedContent">
<ul class="nav navbar-nav mr-auto">
<li class="nav-item mr-4">
<a class="nav-link " href="/index.html"><span class="home">Home</span></a>
</li>
<li class="nav-item mr-4">
<a class="nav-link " href="/about.html"><span class="about">About</span></a>
</li>
<li class="nav-item mr-4">
<a class="nav-link " href="https://deposit.pdb-dev.wwpdb.org/account/"><span class="deposit">Deposit</span></a>
</li>
<li class="nav-item mr-4">
<a class="nav-link " href="/contact.html"><span class="contact">Contact</span></a>
</li>
<li class="nav-item mr-4">
<a class="nav-link " href="/faq.html"><span class="faq">FAQ</span></a>
</li>
</ul>
</div>
</nav>
<!-- navbar -->
<div class="card-body border-success" style="border-color: solid black;">
<!-- start overall card body for front page -->
<div class="row">
<div class="col-lg-12">
<div id="toc_container">
<p class="toc_title"><h5 style="color: #003366;"> User guide</h5></p>
<ul >
<li><a href="#understanding" style="color: #003366;">1. Understanding the PDB-Dev Validation Report</a>
<li><a href="#overview" style="color: #003366;">2. Overview</a>
<ul>
<li><a href="#overview" style="color: #003366;">2.1 Overall Quality Assessment</a></li>
</ul>
</li>
<li><a href="#ensemble" style="color: #003366;">3. Model Details</a>
<ul>
<li><a href="#ensemble" style="color: #003366;">3.1 Ensemble Information</a></li>
<li><a href="#summary" style="color: #003366;">3.2 Summary </a></li>
<li><a href="#entry" style="color: #003366;">3.3 Entry Composition</a></li>
<li><a href="#datasets" style="color: #003366;">3.4 Datasets Used</a></li>
<li><a href="#representation" style="color: #003366;">3.5 Representation</a></li>
<li><a href="#software" style="color: #003366;">3.6 Methods and Software</a></li>
</ul>
</li>
<li><a href="#dataquality" style="color: #003366;">4. Data Quality Assessment</a>
<ul>
<li><a href="#scattering" style="color: #003366;">4.1 Small Angle Scattering data (SAS): Scattering Profiles</a></li>
<li><a href="#expt" style="color: #003366;">4.2 SAS: Experimental Estimates </a></li>
<li><a href="#flexibility" style="color: #003366;">4.3 SAS: Flexibility Analysis</a></li>
<li><a href="#pofr" style="color: #003366;">4.4 SAS: P(r) Analysis</a></li>
<li><a href="#guinier" style="color: #003366;">4.5 SAS: Guinier Analysis</a></li>
</ul>
</li>
<li><a href="#modelquality" style="color: #003366;">5. Model Quality Assessment</a>
<ul>
<li><a href="#exv" style="color: #003366;">5.1a Excluded Volume Analysis</a></li>
<li><a href="#molprobity" style="color: #003366;">5.1b Molprobity Analysis </a></li>
</ul>
</li>
<li><a href="#fittodata" style="color: #003366;">6. Fit to Data Used for Modeling Assessment</a>
<ul>
<li><a href="#goodness" style="color: #003366;">6.1 SAS: Χ² Goodness of Fit Assessment </a></li>
<li><a href="#cormap" style="color: #003366;">6.2: SAS: Cormap Analysis </a></li>
</ul>
</li>
<li><a href="#fittoval" style="color: #003366;">7. Fit to Data Used for Validation Assessment</a></li>
</li>
<li><a href="#sumtab" style="color: #003366;">8. Understanding the Summary Table</a></li>
<li><a href="#references1" style="color: #003366;">9. References for the Validation Report </a>
<li><a href="#references2" style="color: #003366;">10. References for Modeling Software </a>
</ul>
</div>
<!-- end card header -->
</div>
</div>
<!--end row-->
</div>
<!-- end card bods-->
<div class="card-body">
<!-- start overall card body for front page -->
<div class="row">
<div class="col-lg-12">
<div class="card-header" style="background-color: #E4E4E4;">
<p class=ex2 align='center' ><b><u> <a name='understanding'>1. Understanding the PDB-Dev Validation Report</a></u> </b></p>
<p class=ex2 align='justify' ><a name='understanding'></a> This validation report was created based on the guidelines and recommendations from IHM TaskForce <a href='https://pubmed.ncbi.nlm.nih.gov/31780431/'>(Berman et al. 2019)</a>. The first version of the PDB-Dev validation report consists of four categories as follows: </p>
<p class=ex2 align='justify'><a name='understanding'><i>1.1 Model composition :</i></a> This section outlines model details and includes information on ensembles deposited, chains and residues of domains, model representation, software, protocol, and methods used. All deposited structures have this section.</p>
<p class=ex2 align='justify'><a name='understanding'><i>1.2. Data quality assessment :</i></a> Data quality assessments are only available for Small Angle Scattering datasets (SAS). This section was developed in collaboration with the SASBDB community. For details on the metrics, guidelines, and recommendations used, refer the 2017 community article <a href='https://pubmed.ncbi.nlm.nih.gov/28876235/'>(Trewhella et al. 2017)</a>. All experimental datasets used to build the model are listed, however, validation criteria for other experimental datasets are currently under development.</p>
<p class=ex2 align='justify'><a name='understanding'><i>1.3. Model quality assessment :</i></a> Model quality for models at atomic resolution is assessed using Molprobity <a href='https://pubmed.ncbi.nlm.nih.gov/29067766/'> (Williams et al. 2018)</a>, consistent with <a href='https://www.wwpdb.org/validation/2017/XrayValidationReportHelp#model_quality'>PDB</a>. Model quality for coarse-grained or multi-resolution structures are assessed by computing excluded volume satisfaction based on reported distances and sizes of beads in the structures.</p>
<p class=ex2 align='justify'><a name='understanding'><i> 1.4. Fit to data used to build the model :</i></a> Fit to data used to build the model is only available for SAS datasets. This section was developed in collaboration with the SASBDB <a href='https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4383894/'>(Valentini et al. 2015)</a>. For details on the metrics, guidelines, and recommendations used, refer the 2017 community article <a href='https://pubmed.ncbi.nlm.nih.gov/28876235/'>(Trewhella et al. 2017)</a>. All experimental datasets used to build the model are listed, however, validation criteria for other experimental datasets are currently under development.</p>
<p class=ex2 >A fifth category, fit to data used to validate the model, is under development.</p>
<p style=margin-bottom:0.5cm;> </p>
</div>
</div>
</div>
</div>
<div class="card-body">
<!-- start overall card body for front page -->
<div class="row">
<div class="col-lg-12">
<div class="card-header" style="background-color: #E4E4E4;">
<p class=ex2 align='center' ><b><u> <a name='overview'>2. Overview</a></u> </b></p>
<p class=ex2 align='justify' ><a name='overview'><i>2.1 Overall Quality Assessment :</i></a> This is a set of plots that represent a snapshot view of the validation results. There are four tabs, one for each validation criterion: (i) model quality, (ii) data quality, (iii) fit to data used for modeling, and (iv) fit to data used for validation. </p>
<p style=margin-bottom:0.5cm;> </p>
<ul style="list-style-type:none;">
<li><p class=ex2 > <i> <a name='overview'>2.1.1. Model quality :</a></i> For atomic structures, MolProbity is used for evaluation. We evaluate bond outliers, side chain outliers, clash score, rotamer satisfaction, and ramachandran dihedral satisfaction <a href='https://pubmed.ncbi.nlm.nih.gov/29067766/'>(Williams et al. 2018)</a> . Details on MolProbity evaluation and tables can be found <a href='https://www.wwpdb.org/validation/2017/XrayValidationReportHelp#model_quality'>here</a>. For coarse-grained structures of beads, we evaluate excluded volume satisfaction. An excluded volume violation or overlap between two beads occurs if the distance between the two beads is less than the sum of their radii <a href='https://pubmed.ncbi.nlm.nih.gov/29539637/'>(S. J. Kim et al. 2018)</a>. Excluded volume satisfaction is the percentage of pair distances in a structure that are not violated (higher values are better).</p></li>
<li><p class=ex2 ><i> <a name='overview'>2.1.2. Data quality :</a></i> Data quality assessments are only available for SAS datasets. The current plot displays radius of gyration (R<sub>g</sub>) for each dataset used to build the model. R<sub>g</sub> is obtained from both a P(r) analysis (see more <a href='#pofr'>here</a>), and a Guinier analysis (see more <a href='#guinier'>here</a>). </p></li>
<li><p class=ex2 > <i> <a name='overview'>2.1.3. Fit to data used for modeling :</a></i> Fit to data used for modeling assessments are available for SAS datasets. The current plot displays Χ² Goodness of Fit Assessment for SAS-model fits (see more <a href='#goodness'>here</a>).</p></li>
<li><p class=ex2 > <i> <a name='overview'>2.1.4. Fit to data used for validation :</a></i> Fit to data used for validation is currently under development.</p></li>
</ul>
</div>
</div>
</div>
</div>
<div class="card-body">
<!-- start overall card body for front page -->
<div class="row">
<div class="col-lg-12">
<div class="card-header" style="background-color: #E4E4E4;">
<p class=ex2 align='center' ><b><u> <a name='ensemble'>3. Model Details </a></u> </b></p>
<p class=ex2 align='justify' ><a name='ensemble'><i>3.1. Ensemble Information :</i></a> Number of ensembles deposited, where each ensemble consists of two or more structures. </p>
<p class=ex2 align='justify' ><a name='summary'> <i>3.2. Summary :</i></a> Summary of the structure, including number of models deposited, datasets used to build the models and information on model representation. </p>
<p class=ex2 align='justify' ><a name='entry'><i>3.3. Entry Composition :</i></a> Number of chains present in the integrative structure. </p>
<p class=ex2 align='justify' ><a name='datasets'><i>3.4. Datasets Used :</i></a> Number and type of experimental datasets used to build the model. </p>
<p class=ex2 align='justify' ><a name='representation'><i>3.5. Representation :</i></a> Number and details on rigid and non-rigid elements of the structure. </p>
<p class=ex2 align='justify' ><a name='software'><i>3.6. Methods and Software :</i></a> Methods, protocols, and softwares used to build the integrative structure. </p>
<p style=margin-bottom:0.5cm;> </p>
</div>
</div>
</div>
</div>
<div class="card-body">
<!-- start overall card body for front page -->
<div class="row">
<div class="col-lg-12">
<div class="card-header" style="background-color: #E4E4E4;">
<p class=ex2 align='center' ><b><u> <a name='dataquality'>4. Data Quality </a></u> </b></p>
<p class=ex2 align='justify' ><a name='scattering'><i>4.1. SAS: Scattering Profiles :</i></a> Data from solutions of biological macromolecules are presented as both log I(q) vs q and log I(q) vs log (q) based on SAS validation task force (SASvtf) recommendations <a href='https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5586245/'>(Trewhella et al. 2017)</a>. I(q) is the intensity (in arbitrary units) and q is the modulus of the scattering vector. </p>
<p class=ex2 align='justify' ><a name='expt'> <i>4.2. SAS: Experimental Estimates :</i></a> Molecular weight (MW) and volume data are displayed. True MW can be compared to Porod estimate from scattering profiles, estimated volume can be compared to Porod volume obtained from scattering profiles <a href='https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5586245/'>(Trewhella et al. 2017)</a>. </p>
<p class=ex2 align='justify' ><a name='flexibility'><i>4.3. SAS: Flexibility Analysis :</i></a> Flexibility of chains are assessed by inferring Porod-Debye and Kratky plots. In a Porod-Debye plot, a clear plateau is observed for globular (partial or fully folded) domains, whereas fully unfolded domains are devoid of any discernible plateau. For details, refer to Figure 5 in Rambo and Tainer, 2011 <a href='https://pubmed.ncbi.nlm.nih.gov/21509745/'> (Rambo and Tainer 2011)</a>. In a Kratky plot, a parabolic shape is observed for globular (partial or fully folded) domains and a hyperbolic shape is observed for fully unfolded domains.</p>
<p class=ex2 align='justify' ><a name='pofr'><i>4.4. SAS: P(r) Analysis :</i></a> P(r) represents the distribution of distances between all pairs of atoms within the particle weighted by the respective electron densities <a href='https://onlinelibrary.wiley.com/doi/abs/10.1107/S002188988001179X'> (Moore 1980) </a>. P(r) is the Fourier transform of I(s) (and vice versa). R<sub>g</sub> can be estimated from integrating the P(r) function. Agreement between the P(r) and Guinier-determined R<sub>g</sub> (table below) is a good measure of the self-consistency of the SAS profile. R<sub>g</sub> is a measure for the overall size of a macromolecule; e.g. a protein with a smaller R<sub>g</sub> is more compact than a protein with a larger R<sub>g</sub>, provided both have the same molecular weight (Mw). The point where P(r) is decaying to zero is called D<sub>max</sub> and represents the maximum size of the particle. The value of P(r) should be zero beyond r=D<sub>max</sub>.</p>
<p class=ex2 align='justify' ><a name='guinier'><i>4.5. SAS: Guinier Analysis :</i></a> Agreement between the P(r) and Guinier-determined R<sub>g</sub> (table below) is a good measure of the self-consistency of the SAS profile. The linearity of the Guinier plot is a sensitive indicator of the quality of the experimental SAS data; a linear Guinier plot is a necessary but not sufficient demonstration that a solution contains monodisperse particles of the same size. Deviations from linearity usually point to strong interference effects, polydispersity of the samples or improper background subtraction <a href='https://link.springer.com/book/10.1007/978-1-4757-6624-0'> (Feigin and Svergun 1987)</a>. Residual value plot and coefficient of determination (R<sup>2</sup>) are measures to assess linear fit to the data. A perfect fit has an R<sup>2</sup> value of 1. Residual values should be equally and randomly spaced around the horizontal axis.</p>
<p style=margin-bottom:0.5cm;> </p>
</div>
</div>
</div>
</div>
<div class="card-body">
<!-- start overall card body for front page -->
<div class="row">
<div class="col-lg-12">
<div class="card-header" style="background-color: #E4E4E4;">
<p class=ex2 align='center' ><b><u> <a name='modelquality'>5. Model Quality Assessment </a></u> </b></p>
<p class=ex2 align='justify' > Excluded volume assessments are performed for coarse-grained structures and MolProbity analysis is performed for atomic structures.</p>
<p class=ex2 align='justify' ><a name='exv'><i>5.1a. Excluded Volume Analysis :</i></a> Excluded volume violation is defined as percentage of overlaps between coarse-grained beads in a structure. This percentage is obtained by dividing the number of overlaps/violations by the total number of pair distances in a structure. An overlap or violation between two beads occurs if the distance between the two beads is less than the sum of their radii <a href='https://pubmed.ncbi.nlm.nih.gov/29539637/'>(S. J. Kim et al. 2018)</a>. </p>
<p class=ex2 align='justify' ><a name='molprobity'> <i>5.1b. Molprobity Analysis :</i></a> Molprobity analysis for atomic structures reported is consistent with PDB standards for X-ray structures <a href='https://pubmed.ncbi.nlm.nih.gov/29067766/'>(Williams et al. 2018)</a>. Summarized information is available in both the HTML and PDF reports. Detailed information is available for download as csv files, both from the HTML and the PDF reports. Please refer to the <a href='https://www.wwpdb.org/validation/2017/XrayValidationReportHelp#model_quality'>PDB user guide</a> for details. </p>
<p style=margin-bottom:0.5cm;> </p>
</div>
</div>
</div>
</div>
<div class="card-body">
<!-- start overall card body for front page -->
<div class="row">
<div class="col-lg-12">
<div class="card-header" style="background-color: #E4E4E4;">
<p class=ex2 align='center' ><b><u> <a name='fittodata'>6. Fit to Data Used for Modeling Assessment</a></u> </b></p>
<p>Recommendations from <a href='https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5586245/'>SAS validation task force (SASvtf) for model fit assessment</a> include: </p>
<figure class="quote">
<blockquote>
<ol>
<li><p class=ex2> All software, including version numbers, used for modelling; three-dimensional shape, bead or atomistic modelling. </p></li>
<li><p class=ex2>All modelling assumptions clearly stated, including adjustable parameter values. In the case of imposed symmetry, especially in the case of shape models, comparison with results obtained in the absence of symmetry restraints.</p></li>
<li><p class=ex2> For atomistic modelling, a description of how the starting models were obtained (e.g. crystal or NMR structure of a domain, homology model etc.), connectivity or distance restraints used and flexible regions specified and the basis for their selection.</p></li>
<li><p class=ex2> Any additional experimental or bioinformatics-based evidence supporting modelling assumptions and therefore enabling modelling restraints or independent model validation.</p></li>
<li><p class=ex2> For three-dimensional models, values for adjustable parameters, constant adjustments to intensity, χ² and associated p-values and a clear representation of the model fit to the experimental I(q) versus q including a residual plot that clearly identifies systematic deviations.</p></li>
<li><p class=ex2> Analysis of the ambiguity and precision of models, e.g. based on cluster analysis of results from multiple independent optimizations of the model against the SAS profile or profiles, with examples of any distinct clusters in addition to any final averaged model.</p></li>
</ol>
</blockquote>
</figure>
<p class=ex2 align='justify' ><a name='goodness'><i>6.1. SAS: Χ² Goodness of Fit Assessment :</i></a> Model and fits displayed below were obtained from SASBDB. χ² values are a measure of fit of the model to data. A perfect fit has a χ² value of zero. (<a href='https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5586245/#bb95'>Trewhella et al. 2013</a>,<a href='https://pubmed.ncbi.nlm.nih.gov/22800408/'>Schneidman-Duhovny, Kim, and Sali 2012</a>, and <a href='https://pubmed.ncbi.nlm.nih.gov/23495971/'> Rambo and Tainer 2013</a>)</p>
<p class=ex2 align='justify' ><a name='cormap'> <i>6.2. SAS: Cormap Analysis :</i></a> ATSAS datcmp <a href='https://pubmed.ncbi.nlm.nih.gov/33833657/'>(Manalastas-Cantos et al. 2021)</a> was used for hypothesis testing, using the null hypothesis that all data sets (i.e. the fit and the data collected) are similar. The reported p-value is a measure of evidence against the null hypothesis; the smaller the value, the stronger the evidence that the null hypothesis should be rejected.</p>
<p style=margin-bottom:0.5cm;> </p>
</div>
</div>
</div>
</div>
<div class="card-body">
<!-- start overall card body for front page -->
<div class="row">
<div class="col-lg-12">
<div class="card-header" style="background-color: #E4E4E4;">
<p class=ex2 align='center' ><b><u> <a name='fittodata'>7. Fit to Data Used for Validation Assessment</a></u> </b></p>
<p> This includes assessing model fit to data that was not used explicitly or implicitly in modeling. This section is currently under development. </p>
</div>
</div>
</div>
</div>
<div class="card-body">
<!-- start overall card body for front page -->
<div class="row">
<div class="col-lg-12">
<div class="card-header" style="background-color: #E4E4E4;">
<p class=ex2 align='center' ><b><u> <a name='sumtab'>8. Understanding the Summary Table</a></u> </b></p>
<p class=ex2 > <i><a name='sumtabentry'> 8.1. Entry composition :</a></i> List of unique molecules that are present in the entry</p>
<p lcass=ex2 ><i> <a name='sumtabdata'>8.2. Datasets used for modeling :</a></i> List of input experimental datasets used for modeling.</p>
<p class=ex2 ><i> <a name='sumtabrep'>8.3. Representation :</a></i> Representation of modeled structure.</p>
<ul style="list-style-type:none;">
<li><p class=ex2 > <i> <a name='sumtabatom'>8.3.1. Atomic structural coverage :</a></i> Percentage of modeled structure or residues for which atomic structures are available. These structures can include X-ray, NMR, EM, and other comparative models.</p></li>
<li><p class=ex2 ><i> <a name='sumtabrigid'>8.3.2. Rigid bodies :</a></i> A rigid body consists of multiple coarse-grained (CG) beads or atomic residues. In a rigid body, the beads (or residues) have their relative distances constrained during conformational sampling.</p></li>
<li><p class=ex2 > <i> <a name='sumtabflex'>8.3.3. Flexible units :</a></i> Flexible units consist of strings of beads that are restrained by the sequence connectivity.</p></li>
<li><p class=ex2 > <i> <a name='sumtabint'>8.3.4. Interface units :</a></i> An automatic definition based on identified interface for each model. Applicable to models built with HADDOCK.</p></li>
<li><p class=ex2 > <i> <a name='sumtabres'>8.3.5. Resolution :</a></i> An automatic definition based on identified interface for each model. Applicable to models built with HADDOCK.</p></li>
</ul>
<p class=ex2 > <i> <a name='sumtabres'>8.4. Restraints :</a></i> A set of restraints used to compute modeled structure.</p>
<ul style="list-style-type:none;">
<li><p class=ex2 > <i> <a name='sumtabresphys'>8.4.1. Physical restraints :</a></i> A list of restraints derived from physical principles to compute modeled structure.</p></li>
<li><p lcass=ex2 > <i> <a name='sumtabresexp'>8.4.2. Experimental information :</a></i> A list of restraints derived from experimental datasets to compute modeled structure. </p></li>
</ul>
<p class=ex2 ><i> <a name='sumtabval'>8.5. Validation :</a></i> Assessment of models based on validation criteria set by IHM task force (<a href='https://pubmed.ncbi.nlm.nih.gov/26095030/'>Sali et al. 2021</a> and <a href='https://pubmed.ncbi.nlm.nih.gov/31780431/'>Berman et al. 2019</a>)</p>
<ul style="list-style-type:none;">
<li><p class=ex2 > <i> <a name='sumtabvalsamp'>8.5.1. Sampling validation :</a></i> Validation metrics used to assess sampling convergence for stochastic sampling. Sampling precision is defined as the largest allowed Root-mean-square deviation (RMSD) between the cluster centroid and a model within any cluster in the finest clustering for which each sample contributes structures proportionally to its size (considering both the significance and magnitude of the difference) and for which a sufficient proportion of all structures occur in sufficiently large clusters (<a href='https://pubmed.ncbi.nlm.nih.gov/29211988/'>Viswanath et al. 2017</a>).</p></li>
<li><p class=ex2 > <i> <a name='sumtabclus1'>8.5.2. Clustering algorithm :</a></i> Clustering algorithm used to analyze resulting solution.</p></li>
<li><p class=ex2 > <i> <a name='sumtabclus2'>8.5.3. Clustering feature :</a></i> Feature or reaction co-ordinate used to cluster solution.</p></li>
<li><p class=ex2 > <i> <a name='sumtabnum1'>8.5.4. Number of ensembles :</a></i> Number of solutions or ensembles of modeled structure.</p></li>
<li><p class=ex2 > <i> <a name='sumtabnum2'>8.4.5. Number of models in ensemble(s) :</a></i> Number of structures in the solution ensemble(s).</p></li>
<li><p class=ex2 > <i> <a name='sumtabprec'>8.5.6. Model precision :</a></i> Measurement of variation among the models in the ensemble upon a global least-squares superposition.</p></li>
<li><p class=ex2 > <i> <a name='sumtabdata'>8.5.7. Data quality :</a></i>Assessment of data on which modeled structures are based. See section 4 for more details. </p></li>
<li><p class=ex2 ><i> <a name='sumtabdata'>8.5.8. Model quality :</a></i>Assessment of modeled structures based on physical principles.See section 5 for more details.</p></li>
<li><p class=ex2 > <i> <a name='sumtabatom'>8.5.9. Assessment of atomic segments :</a></i>Assessment of atomic segments in the integrative structure. See section 5 for more details. </p></li>
<li><p class=ex2 > <i> <a name='sumtabexv'>8.5.10. Excluded volume satisfaction :</a></i>Assessment of excluded volume satisfaction of coarse-grained beads in the modeled structure. Excluded volume between two beads not connected in sequence are satisfied if the distance between them is greater than that of the sum of their radii. See section 5 for more details.</p></li>
<li><p class=ex2 > <i> <a name='sumtabfit1'>8.5.11. Fit to data used for modeling :</a></i>Assessment of modeled structure based on data used for modeling. See section 6 for more details.</p></li>
<li><p class=ex2 > <i> <a name='sumtabfit2'>8.5.12. Fit to data used for validation :</a></i>Assessment of modeled structure based on data not used for modeling. See section 7 for more details. </p></li>
</ul>
<p class=ex2 ><i> <a name='sumtabmet'>8.6. Methodology and software :</a></i> List of methods on which modeled structures are based and software used to obtain structures.</p>
<ul style="list-style-type:none;">
<li><p class=ex2 > <i> <a name='sumtabmet1'>8.6.1. Method name :</a></i> Name(s) of method(s) used to generate modeled structures.</p></li>
<li><p class=ex2 ><i> <a name='sumtabmet2'>8.6.2. Method details :</a></i> Details of method(s) used to generate modeled structures.</p></li>
<li><p class=ex2 > <i> <a name='sumtabsoft'> 8.6.3. Software details:</a></i> Software used to compute modeled structure, also includes scripts used to generate and analyze models.</p></li>
</ul>
</div>
</div>
</div>
</div>
<div class="card-body">
<!-- start overall card body for front page -->
<div class="row">
<div class="col-lg-12">
<div class="card-header" style="background-color: #E4E4E4;">
<p class=ex2 align='center' ><b><u> <a name='references1'>9. References for Validation Report</a></u> </b></p>
<figure class="quote">
<blockquote>
<ol>
<li><p class=ex2> Berman, Helen M., Paul D. Adams, Alexandre A. Bonvin, Stephen K. Burley, Bridget Carragher, Wah Chiu, Frank DiMaio, et al. 2019. “Federating Structural Models and Data: Outcomes from A Workshop on Archiving Integrative Structures.” Structure 27 (12): 1745–59. </p></li>
<li><p class=ex2> Manalastas-Cantos, Karen, Petr V. Konarev, Nelly R. Hajizadeh, Alexey G. Kikhney, Maxim V. Petoukhov, Dmitry S. Molodenskiy, Alejandro Panjkovich, et al. 2021. “ATSAS 3.0: Expanded Functionality and New Tools for Small-Angle Scattering Data Analysis.” Journal of Applied Crystallography 54 (Pt 1): 343–55. </p></li>
<li><p class=ex2> Rambo, Robert P., and John A. Tainer. 2011. “Characterizing Flexible and Intrinsically Unstructured Biological Macromolecules by SAS Using the Porod-Debye Law.” Biopolymers 95 (8): 559–71.</p></li>
<li><p class=ex2> Sali, Andrej, Helen M. Berman, Torsten Schwede, Jill Trewhella, Gerard Kleywegt, Stephen K. Burley, John Markley, et al. 2015. “Outcome of the First wwPDB Hybrid/Integrative Methods Task Force Workshop.” Structure 23 (7): 1156–67.</p></li>
<li><p class=ex2> Trewhella, Jill, Anthony P. Duff, Dominique Durand, Frank Gabel, J. Mitchell Guss, Wayne A. Hendrickson, Greg L. Hura, et al. 2017. “2017 Publication Guidelines for Structural Modelling of Small-Angle Scattering Data from Biomolecules in Solution: An Update.” Acta Crystallographica. Section D, Structural Biology 73 (Pt 9): 710–28</p></li>
<li><p class=ex2> Valentini, Erica, Alexey G. Kikhney, Gianpietro Previtali, Cy M. Jeffries, and Dmitri I. Svergun. 2015. “SASBDB, a Repository for Biological Small-Angle Scattering Data.” Nucleic Acids Research 43 (Database issue): D357–63. </p></li>
<li><p class=ex2> Viswanath, Shruthi, Ilan E. Chemmama, Peter Cimermancic, and Andrej Sali. 2017. “Assessing Exhaustiveness of Stochastic Sampling for Integrative Modeling of Macromolecular Structures.” Biophysical Journal 113 (11): 2344–53. </p></li>
<li><p class=ex2> Williams, Christopher J., Jeffrey J. Headd, Nigel W. Moriarty, Michael G. Prisant, Lizbeth L. Videau, Lindsay N. Deis, Vishal Verma, et al. 2018. “MolProbity: More and Better Reference Data for Improved All-Atom Structure Validation.” Protein Science: A Publication of the Protein Society 27 (1): 293–315. </p></li>
</ol>
</blockquote>
</figure>
</div>
</div>
</div>
</div>
<div class="card-body">
<!-- start overall card body for front page -->
<div class="row">
<div class="col-lg-12">
<div class="card-header" style="background-color: #E4E4E4;">
<p class=ex2 align='center' ><b><u> <a name='references2'>10. References for Modeling Software</a></u> </b></p>
<figure class="quote">
<blockquote>
<ol>
<li><p class=ex2> Ahmed, Aqeel, Friedrich Rippmann, Gerhard Barnickel, and Holger Gohlke. 2011. “A Normal Mode-Based Geometric Simulation Approach for Exploring Biologically Relevant Conformational Transitions in Proteins.” Journal of Chemical Information and Modeling 51 (7): 1604–22.</p></li>
<li><p class=ex2> Berjanskii, Mark, Yongjie Liang, Jianjun Zhou, Peter Tang, Paul Stothard, You Zhou, Joseph Cruz, et al. 2010. “PROSESS: A Protein Structure Evaluation Suite and Server.” Nucleic Acids Research 38 (Web Server issue): W633–40.</p></li>
<li><p class=ex2>Brannetti, B., A. Zanzoni, L. Montecchi-Palazzi, G. Cesareni, and M. Helmer-Citterich. 2001. “iSPOT: A Web Tool for the Analysis and Recognition of Protein Domain Specificity.” Comparative and Functional Genomics 2 (5): 314–18.</p></li>
<li><p class=ex2>Brunger, Axel T. 2007. “Version 1.2 of the Crystallography and NMR System.” Nature Protocols 2 (11): 2728–33.</p></li>
<li><p class=ex2>Bryson, Kevin, Liam J. McGuffin, Russell L. Marsden, Jonathan J. Ward, Jaspreet S. Sodhi, and David T. Jones. 2005. “Protein Structure Prediction Servers at University College London.” Nucleic Acids Research 33 (Web Server issue): W36–38.</p></li>
<li><p class=ex2>Buchan, Daniel W. A., and David T. Jones. 2019. “The PSIPRED Protein Analysis Workbench: 20 Years on.” Nucleic Acids Research 47 (W1): W402–7.</p></li>
<li><p class=ex2>Chaudhury, Sidhartha, Sergey Lyskov, and Jeffrey J. Gray. 2010. “PyRosetta: A Script-Based Interface for Implementing Molecular Modeling Algorithms Using Rosetta.” Bioinformatics 26 (5): 689–91.</p></li>
<li><p class=ex2>Cherry, J. Michael, Eurie L. Hong, Craig Amundsen, Rama Balakrishnan, Gail Binkley, Esther T. Chan, Karen R. Christie, et al. 2012. “Saccharomyces Genome Database: The Genomics Resource of Budding Yeast.” Nucleic Acids Research 40 (Database issue): D700–705.</p></li>
<li><p class=ex2>Dimura, Mykola, Thomas-Otavio Peulen, Hugo Sanabria, Dmitro Rodnin, Katherina Hemmen, Christian A. Hanke, Claus A. M. Seidel, and Holger Gohlke. 2020. “Automated and Optimally FRET-Assisted Structural Modeling.” Nature Communications 11 (1): 5394.</p></li>
<li><p class=ex2>Ding, Feng, Douglas Tsao, Huifen Nie, and Nikolay V. Dokholyan. 2008. “Ab Initio Folding of Proteins with All-Atom Discrete Molecular Dynamics.” Structure 16 (7): 1010–18.</p></li>
<li><p class=ex2>Dominguez, C., R. Boelens, and A. M. Bonvin. 2003. “HADDOCK: A Protein-Protein Docking Approach Based on Biochemical or Biophysical Information.” Journal of the American Chemical Society 125 (7): 1731–37.</p></li>
<li><p class=ex2>Feigin, L. A., and D. I. Svergun. 1987. Structure Analysis by Small-Angle X-Ray and Neutron Scattering. Edited by George W. Taylor. Springer, Boston, MA.</p></li>
<li><p class=ex2>Finn, Robert D., Jody Clements, and Sean R. Eddy. 2011. “HMMER Web Server: Interactive Sequence Similarity Searching.” Nucleic Acids Research 39 (Web Server issue): W29–37.</p></li>
<li><p class=ex2>Gautier, Romain, Dominique Douguet, Bruno Antonny, and Guillaume Drin. 2008. “HELIQUEST: A Web Server to Screen Sequences with Specific Alpha-Helical Properties.” Bioinformatics 24 (18): 2101–2.</p></li>
<li><p class=ex2>Hummer, Gerhard, and Jürgen Köfinger. 2015. “Bayesian Ensemble Refinement by Replica Simulations and Reweighting.” The Journal of Chemical Physics 143 (24): 243150.</p></li>
<li><p class=ex2>Jones, David T., and Domenico Cozzetto. 2015. “DISOPRED3: Precise Disordered Region Predictions with Annotated Protein-Binding Activity.” Bioinformatics 31 (6): 857–63.</p></li>
<li><p class=ex2>Källberg, Morten, Gohar Margaryan, Sheng Wang, Jianzhu Ma, and Jinbo Xu. 2014. “RaptorX Server: A Resource for Template-Based Protein Structure Modeling.” Methods in Molecular Biology 1137: 17–27.</p></li>
<li><p class=ex2>Kelley, Lawrence A., Stefans Mezulis, Christopher M. Yates, Mark N. Wass, and Michael J. E. Sternberg. 2015. “The Phyre2 Web Portal for Protein Modeling, Prediction and Analysis.” Nature Protocols 10 (6): 845–58.</p></li>
<li><p class=ex2>Kim, David E., Dylan Chivian, and David Baker. 2004. “Protein Structure Prediction and Analysis Using the Robetta Server.” Nucleic Acids Research 32 (Web Server issue): W526–31.</p></li>
<li><p class=ex2>Kim, Seung Joong, Javier Fernandez-Martinez, Ilona Nudelman, Yi Shi, Wenzhu Zhang, Barak Raveh, Thurston Herricks, et al. 2018. “Integrative Structure and Functional Anatomy of a Nuclear Pore Complex.” Nature 555 (7697): 475–82.</p></li>
<li><p class=ex2>Li, Yunqi, and Yang Zhang. 2009. “REMO: A New Protocol to Refine Full Atomic Protein Models from C-Alpha Traces by Optimizing Hydrogen-Bonding Networks.” Proteins 76 (3): 665–76.</p></li>
<li><p class=ex2>Ludtke, S. J. 2016. “Single-Particle Refinement and Variability Analysis in EMAN2.1.” Methods in Enzymology 579 (July): 159–89.</p></li>
<li><p class=ex2>Lupas, A., M. Van Dyke, and J. Stock. 1991. “Predicting Coiled Coils from Protein Sequences.” Science 252 (5009): 1162–64.</p></li>
<li><p class=ex2>Manalastas-Cantos, Karen, Petr V. Konarev, Nelly R. Hajizadeh, Alexey G. Kikhney, Maxim V. Petoukhov, Dmitry S. Molodenskiy, Alejandro Panjkovich, et al. 2021. “ATSAS 3.0: Expanded Functionality and New Tools for Small-Angle Scattering Data Analysis.” Journal of Applied Crystallography 54 (Pt 1): 343–55.</p></li>
<li><p class=ex2>Matthew Allen Bullock, Joshua, Jannik Schwab, Konstantinos Thalassinos, and Maya Topf. 2016. “The Importance of Non-Accessible Crosslinks and Solvent Accessible Surface Distance in Modeling Proteins with Restraints From Crosslinking Mass Spectrometry.” Molecular & Cellular Proteomics: MCP 15 (7): 2491–2500.</p></li>
<li><p class=ex2> Moore, P. B. 1980. “Small-Angle Scattering. Information Content and Error Analysis.” Journal of Applied Crystallography 13 (2): 168–75.</p></li>
<li><p class=ex2>Ovchinnikov, Sergey, Hetunandan Kamisetty, and David Baker. 2014. “Robust and Accurate Prediction of Residue-Residue Interactions across Protein Interfaces Using Evolutionary Information.” eLife 3 (May): e02030.</p></li>
<li><p class=ex2>Pettersen, Eric F., Thomas D. Goddard, Conrad C. Huang, Gregory S. Couch, Daniel M. Greenblatt, Elaine C. Meng, and Thomas E. Ferrin. 2004. “UCSF Chimera--a Visualization System for Exploratory Research and Analysis.” Journal of Computational Chemistry 25 (13): 1605–12.</p></li>
<li><p class=ex2>Pires, Douglas E. V., David B. Ascher, and Tom L. Blundell. 2014. “mCSM: Predicting the Effects of Mutations in Proteins Using Graph-Based Signatures.” Bioinformatics 30 (3): 335–42.</p></li>
<li><p class=ex2>Pronk, Sander, Szilárd Páll, Roland Schulz, Per Larsson, Pär Bjelkmar, Rossen Apostolov, Michael R. Shirts, et al. 2013. “GROMACS 4.5: A High-Throughput and Highly Parallel Open Source Molecular Simulation Toolkit.” Bioinformatics 29 (7): 845–54.</p></li>
<li><p class=ex2>Rambo, Robert P., and John A. Tainer. 2013. “Super-Resolution in Solution X-Ray Scattering and Its Applications to Structural Systems Biology.” Annual Review of Biophysics 42 (March): 415–41.</p></li>
<li><p class=ex2>Rohl, Carol A., Charlie E. M. Strauss, Kira M. S. Misura, and David Baker. 2004. “Protein Structure Prediction Using Rosetta.” Methods in Enzymology 383: 66–93.</p></li>
<li><p class=ex2>Russel, Daniel, Keren Lasker, Ben Webb, Javier Velázquez-Muriel, Elina Tjioe, Dina Schneidman-Duhovny, Bret Peterson, and Andrej Sali. 2012. “Putting the Pieces Together: Integrative Modeling Platform Software for Structure Determination of Macromolecular Assemblies.” PLoS Biology 10 (1): e1001244.</p></li>
<li><p class=ex2>Scheres, Sjors H. W. 2012. “RELION: Implementation of a Bayesian Approach to Cryo-EM Structure Determination.” Journal of Structural Biology 180 (3): 519–30.</p></li>
<li><p class=ex2>Schneider, Michael, and Oliver Brock. 2014. “Combining Physicochemical and Evolutionary Information for Protein Contact Prediction.” PloS One 9 (10): e108438.</p></li>
<li><p class=ex2>Schneidman, D., M. Hammel, J. Tainer, and A. Sali. 2016. “FoXS, FoXSDock, and MultiFoXS: Single-State and Multi-State Structural Modeling of Proteins and Their Complexes Based on SAXS Profiles.” Nucleic Acids Research 44 (W1): W424–29.</p></li>
<li><p class=ex2>Schneidman-Duhovny, Dina, Seung Joong Kim, and Andrej Sali. 2012. “Integrative Structural Modeling with Small Angle X-Ray Scattering Profiles.” BMC Structural Biology 12 (July): 17.</p></li>
<li><p class=ex2>Serra, F., D. Bau, M. Goodstadt, D. Castillo, G. J. Filion, and M. A. Marti-Renom. 2017. “Automatic Analysis and 3D-Modelling of Hi-C Data Using TADbit Reveals Structural Features of the Fly Chromatin Colors.” PLoS Computational Biology 13 (7): e1005665.</p></li>
<li><p class=ex2>Shen, Yang, Oliver Lange, Frank Delaglio, Paolo Rossi, James M. Aramini, Gaohua Liu, Alexander Eletsky, et al. 2008. “Consistent Blind Protein Structure Generation from NMR Chemical Shift Data.” Proceedings of the National Academy of Sciences of the United States of America 105 (12): 4685–90.</p></li>
<li><p class=ex2>Söding, Johannes, Andreas Biegert, and Andrei N. Lupas. 2005. “The HHpred Interactive Server for Protein Homology Detection and Structure Prediction.” Nucleic Acids Research 33 (Web Server issue): W244–48.</p></li>
<li><p class=ex2>Steinegger, Martin, Markus Meier, Milot Mirdita, Harald Vöhringer, Stephan J. Haunsberger, and Johannes Söding. 2019. “HH-suite3 for Fast Remote Homology Detection and Deep Protein Annotation.” BMC Bioinformatics 20 (1): 473.</p></li>
<li><p class=ex2>Trigg, Jason, Karl Gutwin, Amy E. Keating, and Bonnie Berger. 2011. “Multicoil2: Predicting Coiled Coils and Their Oligomerization States from Sequence in the Twilight Zone.” PloS One 6 (8): e23519.</p></li>
<li><p class=ex2>Trnka, Michael J., Peter R. Baker, Philip J. J. Robinson, A. L. Burlingame, and Robert J. Chalkley. 2014. “Matching Cross-Linked Peptide Spectra: Only as Good as the Worse Identification.” Molecular & Cellular Proteomics: MCP 13 (2): 420–34.</p></li>
<li><p class=ex2>Tubiana, Thibault, Jean-Charles Carvaillo, Yves Boulard, and Stéphane Bressanelli. 2018. “TTClust: A Versatile Molecular Simulation Trajectory Clustering Program with Graphical Summaries.” Journal of Chemical Information and Modeling 58 (11): 2178–82.</p></li>
<li><p class=ex2>Vries, Sjoerd J. de, and Alexandre M. J. J. Bonvin. 2011. “CPORT: A Consensus Interface Predictor and Its Performance in Prediction-Driven Docking with HADDOCK.” PloS One 6 (3): e17695.</p></li>
<li><p class=ex2>Wang, Yan, Jian Wang, Ruiming Li, Qiang Shi, Zhidong Xue, and Yang Zhang. 2017. “ThreaDomEx: A Unified Platform for Predicting Continuous and Discontinuous Protein Domains by Multiple-Threading and Segment Assembly.” Nucleic Acids Research 45 (W1): W400–407.</p></li>
<li><p class=ex2>Waterhouse, Andrew, Martino Bertoni, Stefan Bienert, Gabriel Studer, Gerardo Tauriello, Rafal Gumienny, Florian T. Heer, et al. 2018. “SWISS-MODEL: Homology Modelling of Protein Structures and Complexes.” Nucleic Acids Research 46 (W1): W296–303.</p></li>
<li><p class=ex2>Webb, B., and A. Sali. 2014. “Comparative Protein Structure Modeling Using Modeller.” In Current Protocols in Bioinformatics. John Wiley and Sons.</p></li>
<li><p class=ex2>Weinkam, Patrick, Jaume Pons, and Andrej Sali. 2012. “Structure-Based Model of Allostery Predicts Coupling between Distant Sites.” Proceedings of the National Academy of Sciences of the United States of America 109 (13): 4875–80.</p></li>
<li><p class=ex2>Williams, Christopher J., Jeffrey J. Headd, Nigel W. Moriarty, Michael G. Prisant, Lizbeth L. Videau, Lindsay N. Deis, Vishal Verma, et al. 2018. “MolProbity: More and Better Reference Data for Improved All-Atom Structure Validation.” Protein Science: A Publication of the Protein Society 27 (1): 293–315.</p></li>
<li><p class=ex2>Wriggers, Willy. 2012. “Conventions and Workflows for Using Situs.” Acta Crystallographica. Section D, Biological Crystallography 68 (Pt 4): 344–51.</p></li>
<li><p class=ex2>Yang, Jianyi, Ivan Anishchenko, Hahnbeom Park, Zhenling Peng, Sergey Ovchinnikov, and David Baker. 2020. “Improved Protein Structure Prediction Using Predicted Interresidue Orientations.” Proceedings of the National Academy of Sciences of the United States of America 117 (3): 1496–1503.</p></li>
<li><p class=ex2>Yang, Jianyi, Renxiang Yan, Ambrish Roy, Dong Xu, Jonathan Poisson, and Yang Zhang. 2015. “The I-TASSER Suite: Protein Structure and Function Prediction.” Nature Methods 12 (1): 7–8.</p></li>
<li><p class=ex2>Yu, Jinchao, Geraldine Picord, Pierre Tuffery, and Raphael Guerois. 2015. “HHalign-Kbest: Exploring Sub-Optimal Alignments for Remote Homology Comparative Modeling.” Bioinformatics 31 (23): 3850–52.</p></li>
<li><p class=ex2>Zundert, G. C. P. van, and A. M. J. J. Bonvin. 2015. “DisVis: Quantifying and Visualizing Accessible Interaction Space of Distance-Restrained Biomolecular Complexes.” Bioinformatics 31 (19): 3222–24./p></li>
<li><p class=ex2>Zundert, Gydo C. P. van, Adrien S. J. Melquiond, and Alexandre M. J. J. Bonvin. 2015. “Integrative Modeling of Biomolecular Complexes: HADDOCKing with Cryo-Electron Microscopy Data.” Structure 23 (5): 949–60.</p></li>
</ol>
</blockquote>
</figure>
</div>
</div>
</div>
</div>
<!-- start card body2 -->
<div class="card-body">
<!-- start overall card body for front page -->
<div class="row">
<div class="col-lg-12">
<!-- end card-header -->
<div class="mt-2" />
</div>
<p align="justify"><em>Integrative Modeling Validation Package : Version 1.0</em></p>
</div>
<!-- end column -->
</div>
<!--end row-->
</div>
<!--end card body-->
<!-- start footer -->
<footer class="page-footer">
<div class="container-fluid">
<div class="row" style="/color:#FFF; background-color: rgba(102,153,102, 0.5); padding-top: 10px; padding-bottom: 2px;">
<div class="col-sm-6" style="padding-top: 10px; padding-bottom: 10px;">
<a href="http://www.wwpdb.org/" target="_blank">
<img src="images/wwpdb-logo11.png" alt="wwpdb" height="35px" width="160px"></a>
</div>
<div class="col-sm-6">
<div class="float-right text-right">
<p style="margin-bottom: 0px; margin-right: 0px;color: #000">Supported by<br />National Science Foundation</p>
</div>
</div>
</div>
<div class="row" style="color:#FFF; background-color: #669966; padding-top: 3px; padding-bottom: 8px;">
</div>
</div>
</footer>
<!-- add Javasscript file from js file -->
<script type="text/javascript" src="js/jquery.min.js"></script>
<script type="text/javascript" src="js/bootstrap.min.js"></script>
<script type="text/javascript" src="js/main.js"></script>
<script type="text/javascript" src="js/jquery-3.3.1.min.js"></script>
<script type="text/javascript" src="js/popper1.12.9.min.js"></script>
<script type="text/javascript" src=".js/bootstrap4.1.3.min.js"></script>
<script type="text/javascript" src="js/bootstrap3-typeahead.min.js"></script>
</div>
</body>
</html>