343:), until a certain number of steps have been tried. The assumption is that convergence to the best structure should occur from a large class of initial configurations, only one of which needs to be considered. Initial configurations may be sampled coarsely, and much computation time can be saved. Because of the difficulty of finding a scoring function which is both highly discriminating for the correct configuration and also converges to the correct configuration from a distance, the use of two levels of refinement, with different scoring functions, has been proposed. Torsion can be introduced naturally to Monte Carlo as an additional property of each random move.
221:, the focus moved towards developing generalized techniques which could be applied to an arbitrary set of complexes at acceptable computational cost. The new methods were envisaged to apply even in the absence of phylogenetic or experimental clues; any specific prior knowledge could still be introduced at the stage of choosing between the highest ranking output models, or be framed as input if the algorithm catered for it. 1992 saw the publication of the correlation method, an algorithm which used the
503:
assessed. They are significant in most of the complexes, and large movements or disorder-to-order transitions are frequently observed. The set may be used to benchmark biophysical models aiming to relate affinity to structure in protein–protein interactions, taking into account the reactants and the conformation changes that accompany the association reaction, instead of just the final product.
256:. A subject of speculation is whether or not rigid-body docking is sufficiently good for most docking. When substantial conformational change occurs within the components at the time of complex formation, rigid-body docking is inadequate. However, scoring all possible conformational changes is prohibitively expensive in computer time. Docking procedures which permit conformational change, or
440:(R ~ 0). It was also observed that some components of the scoring algorithms may display better correlation to the experimental binding energies than the full score, suggesting that a significantly better performance might be obtained by combining the appropriate contributions from different scoring algorithms. Experimental methods for the determination of binding affinities are:
306:
clash, the remaining space of possible complexed structures must be sampled exhaustively, evenly and with a sufficient coverage to guarantee a near hit. Each configuration must be scored with a measure that is capable of ranking a nearly correct structure above at least 100,000 alternatives. This is
529:
CAPRI attracts a high level of participation (37 groups participated worldwide in round seven) and a high level of interest from the biological community in general. Although CAPRI results are of little statistical significance owing to the small number of targets in each round, the role of CAPRI in
502:
ranging between 10 and 10 M. Nine pairs of entries represent closely related complexes that have a similar structure, but a very different affinity, each pair comprising a cognate and a noncognate assembly. The unbound structures of the component proteins being available, conformation changes can be
497:
This
Benchmark was post-peer reviewed and significantly expanded. The new set is diverse in terms of the biological functions it represents, with complexes that involve G-proteins and receptor extracellular domains, as well as antigen/antibody, enzyme/inhibitor, and enzyme/substrate complexes. It is
366:
To find a score which forms a consistent basis for selecting the best configuration, studies are carried out on a standard benchmark (see below) of protein–protein interaction cases. Scoring functions are assessed on the rank they assign to the best structure (ideally the best structure should be
205:
complex. Computers discriminated between good and bad models using a scoring function which rewarded large interface area, and pairs of molecules in contact but not occupying the same space. The computer used a simplified representation of the interacting proteins, with one interaction centre for
816:
Strynadka NC, Eisenstein M, Katchalski-Katzir E, Shoichet BK, Kuntz ID, Abagyan R, Totrov M, Janin J, Cherfils J, Zimmerman F, Olson A, Duncan B, Rao M, Jackson R, Sternberg M, James MN (1996). "Molecular
Docking Programs Successfully Predict the Binding of a Beta-lactamase Inhibitory Protein to
326:
Reciprocal space methods have been used extensively for their ability to evaluate enormous numbers of configurations. They lose their speed advantage if torsional changes are introduced. Another drawback is that it is impossible to make efficient use of prior knowledge. The question also remains
57:
but keeping their relative orientations fixed. Later, the relative orientations of the interacting partners in the modelling was allowed to vary, but the internal geometry of each of the partners was held fixed. This type of modelling is sometimes referred to as "rigid docking". With further
468:
A benchmark of 84 protein–protein interactions with known complexed structures has been developed for testing docking methods. The set is chosen to cover a wide range of interaction types, and to avoid repeated features, such as the profile of interactors' structural families according to the
101:
proteins, and there is a desire to understand what, if any, anomalous protein–protein interactions a given mutation can cause. In the distant future, proteins may be designed to perform biological functions, and a determination of the potential interactions of such proteins will be essential.
188:
In the 1970s, complex modelling revolved around manually identifying features on the surfaces of the interactors, and interpreting the consequences for binding, function and activity; any computer programmes were typically used at the end of the modelling process, to discriminate between the
493:
A binding affinity benchmark has been based on the protein–protein docking benchmark. 81 protein–protein complexes with known experimental affinities are included; these complexes span over 11 orders of magnitude in terms of affinity. Each entry of the benchmark includes several biochemical
416:
It is usual to create hybrid scores by combining one or more categories above in a weighted sum whose weights are optimized on cases from the benchmark. To avoid bias, the benchmark cases used to optimize the weights must not overlap with the cases used to make the final test of the score.
517:
The
Critical Assessment of PRediction of Interactions is an ongoing series of events in which researchers throughout the community try to dock the same proteins, as provided by the assessors. Rounds take place approximately every 6 months. Each round contains between one and six target
45:
The ultimate goal of docking is the prediction of the three-dimensional structure of the macromolecular complex of interest as it would occur in a living organism. Docking itself only produces plausible candidate structures. These candidates must be ranked using methods such as
473:
database. Benchmark elements are classified into three levels of difficulty (the most difficult containing the largest change in backbone conformation). The protein–protein docking benchmark contains examples of enzyme-inhibitor, antigen-antibody and homomultimeric complexes.
436:). Several scoring functions have been proposed for binding affinity / free energy prediction. However the correlation between experimentally determined binding affinities and the predictions of nine commonly used scoring functions have been found to be nearly
477:
The latest version of protein-protein docking benchmark consists of 230 complexes. A protein-DNA docking benchmark consists of 47 test cases. A protein-RNA docking benchmark was curated as a dataset of 45 non-redundant test cases with complexes solved by
346:
Monte Carlo methods are not guaranteed to search exhaustively, so that the best configuration may be missed even using a scoring function which would in theory identify it. How severe a problem this is for docking has not been firmly established.
236:(BLIP). The exercise brought into focus the necessity of accommodating conformational change and the difficulty of discriminating between conformers. It also served as the prototype for the CAPRI assessment series, which debuted in 2001.
494:
parameters associated with the experimental data, along with the method used to determine the affinity. This benchmark was used to assess the extent to which scoring functions could also predict affinities of macromolecular complexes.
58:
increases in computational power, it became possible to model changes in internal geometry of the interacting partners that may occur when a complex is formed. This type of modelling is referred to as "flexible docking".
420:
The ultimate goal in protein–protein docking is to select the ideal ranking solution according to a scoring scheme that would also give an insight into the affinity of the complex. Such a development would drive
863:
Gray JJ, Moughon S, Wang C, Schueler-Furman O, Kuhlman B, Rohl CA, Baker D (2003). "Protein–protein docking with simultaneous optimization of rigid-body displacement and side-chain conformations".
53:
The term "docking" originated in the late 1970s, with a more restricted meaning; then, "docking" meant refining a model of a complex structure by optimizing the separation between the
518:
protein–protein complexes whose structures have been recently determined experimentally. The coordinates and are held privately by the assessors, with the cooperation of the
1089:
Kastritis PL, Bonvin AM (May 2010). "Are scoring functions in protein–protein docking ready to predict interactomes? Clues from a novel binding affinity benchmark".
1369:
PĂ©rez-Cano L, JimĂ©nez-GarcĂa B, Fernández-Recio J (July 2012). "A protein-RNA docking benchmark (II): extended set from experimental and homology modeling data".
225:
to give a vastly improved scalability for evaluating coarse shape complementarity on rigid-body models. This was extended in 1997 to cover coarse electrostatics.
1228:
Vreven T, Moal IH, Vangone A, Pierce BG, Kastritis PL, Torchala M, Chaleil R, JimĂ©nez-GarcĂa B, Bates PA, Fernandez-Recio J, Bonvin AM, Weng Z (September 2015).
568:
Yousif, Ragheed Hussam, et al. "Exploring the
Molecular Interactions between Neoculin and the Human Sweet Taste Receptors through Computational Approaches."
1736:
781:
Gabb HA, Jackson RM, Sternberg MJ (September 1997). "Modelling protein docking using shape complementarity, electrostatics and biochemical information".
460:(MST) or spectroscopic measurements and other fluorescence techniques. Textual information from scientific articles can provide useful cues for scoring.
323:. It is possible to construct reasonable, if approximate, convolution-like scoring functions representing both stereochemical and electrostatic fitness.
319:, configurations related to each other by translation of one protein by an exact lattice vector can all be scored almost simultaneously by applying the
367:
ranked 1), and on their coverage (the proportion of the benchmark cases for which they achieve an acceptable result). Types of scores studied include:
217:
In the early 1990s, more structures of complexes were determined, and available computational power had increased substantially. With the emergence of
512:
1558:
Janin J, Henrick K, Moult J, Eyck LT, Sternberg MJ, Vajda S, Vakser I, Wodak SJ (2003). "CAPRI: a
Critical Assessment of PRedicted Interactions".
339:, an initial configuration is refined by taking random steps which are accepted or rejected based on their induced improvement in score (see the
189:
relatively few configurations which remained after all the heuristic constraints had been imposed. The first use of computers was in a study on
146:
principles, even proteins of unknown function (or which have been studied relatively little) may be docked. The only prerequisite is that their
180:
they interact with is composed of nucleic acids. Modeling protein–nucleic acid complexes presents some unique challenges, as described below.
1000:
Zhang C, Liu S, Zhu Q, Zhou Y (2005). "A knowledge-based energy function for protein–ligand, protein–protein, and protein–DNA complexes".
228:
In 1996 the results of the first blind trial were published, in which six research groups attempted to predict the complexed structure of
1789:
142:
Protein–protein docking is ultimately envisaged to address all these issues. Furthermore, since docking methods can be based on purely
1628:
105:
For any given set of proteins, the following questions may be of interest, from the point of view of technology or natural history:
1876:
1230:"Updates to the Integrated Protein-Protein Interaction Benchmarks: Docking Benchmark Version 5 and Affinity Benchmark Version 2"
1185:
Mintseris J, Wiehe K, Pierce B, Anderson R, Chen R, Janin J, Weng Z (2005). "Protein-Protein
Docking Benchmark 2.0: an update".
1871:
470:
724:"Molecular surface recognition: determination of geometric fit between proteins and their ligands by correlation techniques"
445:
1455:
Nithin, Chandran; Ghosh, Pritha; Bujnicki, Janusz; Nithin, Chandran; Ghosh, Pritha; Bujnicki, Janusz M. (2018-08-25).
1457:"Bioinformatics Tools and Benchmarks for Computational Docking and 3D Structure Prediction of RNA-Protein Complexes"
315:
Each of the proteins may be represented as a simple cubic lattice. Then, for the class of scores which are discrete
1726:
1706:
453:
279:
For many interactions, the binding site is known on one or more of the proteins to be docked. This is the case for
1687:
71:
1412:
Nithin C, Mukherjee S, Bahadur RP (November 2016). "A non-redundant protein-RNA docking benchmark version 2.0".
1716:
1621:
361:
340:
229:
47:
1746:
1912:
1764:
643:
151:
23:
951:"In silico screening of mutational effects on enzyme-proteic inhibitor affinity: a docking-based approach"
1863:
1769:
1697:
260:
procedures, must intelligently select small subset of possible conformational changes for consideration.
327:
whether convolutions are too limited a class of scoring function to identify the best complex reliably.
1907:
1897:
429:
1326:
Barik A, C N, P M, Bahadur RP (July 2012). "A protein-RNA docking benchmark (I): nonredundant cases".
1721:
1711:
1701:
1663:
457:
441:
1572:
584:"Hemoglobin Interactions in Sickle Cell Fibers: I. Theoretical Approaches to the Molecular Contacts"
1741:
1614:
38:–protein complexes are the most commonly attempted targets of such modelling, followed by protein–
1902:
392:
382:
490:
and now it consists of 126 test cases. The benchmarks have a combined dataset of 209 complexes.
1812:
1567:
222:
1731:
487:
479:
284:
158:
1779:
1658:
735:
595:
543:
272:
Generating a set of configurations which reliably includes at least one nearly correct one.
295:
evidence. Configurations where the proteins interpenetrate severely may also be ruled out
8:
1692:
1683:
1136:"Natural language processing in text mining for structural modeling of protein complexes"
549:
486:
as well. The protein-RNA benchmark has been updated to include more structures solved by
425:
389:
320:
147:
74:, are known at best incompletely. Even those proteins that participate in a well-studied
27:
739:
599:
1678:
1593:
1535:
1510:
1483:
1456:
1437:
1394:
1351:
1303:
1278:
1254:
1229:
1210:
1162:
1135:
1063:
1036:
977:
950:
842:
519:
336:
249:
83:
75:
876:
618:
583:
546:– any biological complex of protein, RNA, DNA (sometimes has lipids and carbohydrates)
252:
of the components are not modified at any stage of complex generation, it is known as
1833:
1585:
1540:
1488:
1429:
1386:
1343:
1308:
1259:
1202:
1167:
1116:
1068:
1017:
982:
931:
926:
899:
880:
834:
798:
763:
758:
723:
722:
Katchalski-Katzir E, Shariv I, Eisenstein M, Friesem AA, Aflalo C, Vakser IA (1992).
701:
696:
679:
660:
656:
623:
483:
233:
1597:
1441:
1398:
1214:
846:
1848:
1577:
1530:
1522:
1509:
Kastritis PL, Moal IH, Hwang H, Weng Z, Bates PA, Bonvin AM, Janin J (March 2011).
1478:
1468:
1421:
1378:
1335:
1298:
1290:
1249:
1241:
1194:
1157:
1147:
1106:
1098:
1058:
1048:
1009:
972:
962:
921:
911:
872:
826:
790:
753:
743:
691:
652:
613:
603:
307:
a computationally intensive task, and a variety of strategies have been developed.
177:
1355:
482:
only as well as an extended dataset of 71 test cases with structures derived from
1817:
1641:
1606:
303:
173:
162:
94:
90:
534:
assessment is a similar exercise in the field of protein structure prediction).
1756:
1673:
641:
Wodak SJ, Janin J (1978). "Computer
Analysis of Protein-Protein Interactions".
432:
and/or high-throughput annotation of which proteins bind or not (annotation of
218:
211:
1245:
1152:
1891:
1853:
1843:
1784:
1053:
207:
31:
815:
1838:
1807:
1668:
1589:
1544:
1492:
1433:
1390:
1347:
1312:
1263:
1206:
1171:
1120:
1072:
1021:
986:
967:
935:
916:
884:
794:
748:
608:
523:
39:
838:
802:
767:
705:
627:
157:
Protein–nucleic acid interactions feature prominently in the living cell.
1473:
1294:
664:
449:
433:
316:
288:
194:
124:
79:
830:
721:
1650:
1581:
1425:
1382:
1368:
1339:
1198:
1111:
437:
375:
190:
166:
89:
In cases of known protein–protein interactions, other questions arise.
54:
1102:
1013:
498:
also diverse in terms of the partners' affinity for each other, with K
275:
Reliably distinguishing nearly correct configurations from the others.
371:
292:
170:
1526:
150:
has been either determined experimentally, or can be estimated by a
280:
98:
1511:"A structure-based benchmark for protein-protein binding affinity"
1037:"Scoring docking conformations using predicted protein interfaces"
1637:
198:
143:
111:
67:
35:
50:
to identify structures that are most likely to occur in nature.
396:
862:
287:. In other cases, a binding site may be strongly suggested by
1799:
400:
1184:
581:
123:
What is the spatial configuration which they adopt in their
531:
202:
239:
1454:
680:"Computer Studies of Interactions between Macromolecules"
677:
70:
roles of most proteins, as characterized by which other
1227:
1508:
1411:
522:
who determined them. The assessment of submissions is
1557:
582:
Levinthal C, Wodak SJ, Kahn P, Dadivanian AK (1975).
406:
Phylogenetic desirability of the interacting regions.
780:
302:
After making exclusions based on prior knowledge or
900:"Protein docking along smooth association pathways"
1636:
1133:
1034:
513:Critical Assessment of PRediction of Interactions
197:fibres. This was followed in 1978 by work on the
1889:
138:Can they be made to bind by inducing a mutation?
1504:
1502:
1088:
1084:
1082:
999:
904:Proceedings of the National Academy of Sciences
588:Proceedings of the National Academy of Sciences
388:Free energies, estimated using parameters from
16:Computational modeling of molecular interaction
1325:
1276:
858:
856:
82:) may have unexpected interaction partners or
1622:
1134:Badal, VD, Kundrotas, PJ, Vakser, IA (2018).
717:
715:
1551:
1499:
1178:
1127:
1079:
993:
948:
942:
897:
684:Progress in Biophysics and Molecular Biology
678:Wodak SJ, De Crombrugghe M, Janin J (1987).
250:bond angles, bond lengths and torsion angles
891:
853:
530:stimulating discourse is significant. (The
310:
1629:
1615:
809:
774:
712:
671:
640:
575:
268:Successful docking requires two criteria:
1571:
1534:
1482:
1472:
1302:
1253:
1161:
1151:
1110:
1062:
1052:
976:
966:
925:
915:
819:Nature Structural & Molecular Biology
757:
747:
695:
634:
617:
607:
97:) are known to be caused by misfolded or
130:How strong or weak is their interaction?
506:
1890:
1872:Photoactivated localization microscopy
1790:Protein–protein interaction prediction
330:
22:is the computational modelling of the
1610:
1277:van Dijk M, Bonvin AM (August 2008).
86:which are unrelated to that process.
552:– small molecule docking to proteins
355:
176:, are composed of proteins, and the
1747:Freeze-fracture electron microscopy
13:
30:formed by two or more interacting
14:
1924:
1279:"A protein-DNA docking benchmark"
1035:Esmaielbeiki R, Nebel JC (2014).
446:Förster resonance energy transfer
72:macromolecules they interact with
1727:Isothermal titration calorimetry
1707:Dual-polarization interferometry
454:isothermal titration calorimetry
1448:
1405:
1362:
1319:
1270:
1221:
1028:
1002:Journal of Medicinal Chemistry
562:
1:
1717:Chromatin immunoprecipitation
877:10.1016/S0022-2836(03)00670-3
556:
463:
362:Scoring functions for docking
350:
61:
1780:Protein structural alignment
1765:Protein structure prediction
1234:Journal of Molecular Biology
949:Camacho CJ, Vajda S (2007).
898:Camacho CJ, Vajda S (2008).
728:Proc. Natl. Acad. Sci. U.S.A
697:10.1016/0079-6107(87)90008-3
657:10.1016/0022-2836(78)90302-9
644:Journal of Molecular Biology
152:protein structure prediction
7:
1864:Super-resolution microscopy
1770:Protein function prediction
1698:Peptide mass fingerprinting
1693:Protein immunoprecipitation
537:
214:, were identified by hand.
10:
1929:
510:
430:computer-aided drug design
359:
263:
183:
1862:
1826:
1798:
1755:
1722:Surface plasmon resonance
1712:Microscale thermophoresis
1702:Protein mass spectrometry
1664:Green fluorescent protein
1649:
1246:10.1016/j.jmb.2015.07.016
1153:10.1186/s12859-018-2079-4
458:microscale thermophoresis
442:surface plasmon resonance
381:Shape complementarity of
32:biological macromolecules
1742:Cryo-electron microscopy
1054:10.1186/1471-2105-15-171
409:Clustering coefficients.
311:Reciprocal space methods
210:interactions, including
206:each residue. Favorable
1775:Protein–protein docking
1688:Protein electrophoresis
817:TEM-1 Beta-Lactamase".
412:Information based cues.
109:Do these proteins bind
1674:Protein immunostaining
1283:Nucleic Acids Research
968:10.1186/1472-6807-7-37
955:BMC Structural Biology
917:10.1073/pnas.181147798
795:10.1006/jmbi.1997.1203
749:10.1073/pnas.89.6.2195
609:10.1073/pnas.72.4.1330
285:competitive inhibitors
223:fast Fourier transform
20:Macromolecular docking
1732:X-ray crystallography
572:49.3 (2020): 517-525.
520:structural biologists
488:X-ray crystallography
480:X-ray crystallography
159:Transcription factors
134:If they do not bind,
1659:Protein purification
1474:10.3390/genes9090432
544:Biomolecular complex
507:The CAPRI assessment
385:("stereochemistry").
341:Metropolis criterion
232:with Beta-lactamase
230:TEM-1 Beta-lactamase
24:quaternary structure
1913:Molecular modelling
1684:Gel electrophoresis
910:(19): 10636–10641.
831:10.1038/nsb0396-233
740:1992PNAS...89.2195K
600:1975PNAS...72.1330L
550:Docking (molecular)
452:-based techniques,
426:protein engineering
390:molecular mechanics
331:Monte Carlo methods
321:convolution theorem
240:Rigid-body docking
148:molecular structure
1827:Display techniques
1679:Protein sequencing
1582:10.1002/prot.10381
1426:10.1002/prot.25211
1383:10.1002/prot.24075
1340:10.1002/prot.24083
1295:10.1093/nar/gkn386
1199:10.1002/prot.20560
1140:BMC Bioinformatics
1041:BMC Bioinformatics
484:homology modelling
383:molecular surfaces
254:rigid body docking
244:. flexible docking
76:biological process
1908:Molecular physics
1898:Protein structure
1885:
1884:
1834:Bacterial display
1103:10.1021/pr9009854
1014:10.1021/jm049314d
356:Scoring functions
234:inhibitor protein
161:, which regulate
119:If they do bind,
48:scoring functions
1920:
1849:Ribosome display
1785:Protein ontology
1631:
1624:
1617:
1608:
1607:
1602:
1601:
1575:
1555:
1549:
1548:
1538:
1506:
1497:
1496:
1486:
1476:
1452:
1446:
1445:
1409:
1403:
1402:
1366:
1360:
1359:
1323:
1317:
1316:
1306:
1274:
1268:
1267:
1257:
1225:
1219:
1218:
1182:
1176:
1175:
1165:
1155:
1131:
1125:
1124:
1114:
1097:(5): 2216–2225.
1086:
1077:
1076:
1066:
1056:
1032:
1026:
1025:
1008:(7): 2325–2335.
997:
991:
990:
980:
970:
946:
940:
939:
929:
919:
895:
889:
888:
860:
851:
850:
813:
807:
806:
778:
772:
771:
761:
751:
734:(6): 2195–2199.
719:
710:
709:
699:
675:
669:
668:
638:
632:
631:
621:
611:
594:(4): 1330–1334.
579:
573:
570:Sains Malaysiana
566:
374:scores based on
258:flexible docking
178:genetic material
91:Genetic diseases
1928:
1927:
1923:
1922:
1921:
1919:
1918:
1917:
1888:
1887:
1886:
1881:
1858:
1822:
1818:Secretion assay
1794:
1751:
1645:
1635:
1605:
1573:10.1.1.461.3355
1556:
1552:
1527:10.1002/pro.580
1515:Protein Science
1507:
1500:
1453:
1449:
1410:
1406:
1367:
1363:
1324:
1320:
1275:
1271:
1240:(19): 3031–41.
1226:
1222:
1183:
1179:
1132:
1128:
1091:J. Proteome Res
1087:
1080:
1033:
1029:
998:
994:
947:
943:
896:
892:
861:
854:
814:
810:
779:
775:
720:
713:
676:
672:
639:
635:
580:
576:
567:
563:
559:
540:
515:
509:
501:
466:
364:
358:
353:
333:
313:
266:
246:
193:interaction in
186:
163:gene expression
95:cystic fibrosis
64:
17:
12:
11:
5:
1926:
1916:
1915:
1910:
1905:
1903:Bioinformatics
1900:
1883:
1882:
1880:
1879:
1874:
1868:
1866:
1860:
1859:
1857:
1856:
1851:
1846:
1841:
1836:
1830:
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1824:
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1796:
1795:
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1777:
1772:
1767:
1761:
1759:
1757:Bioinformatics
1753:
1752:
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1749:
1744:
1739:
1734:
1729:
1724:
1719:
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1653:
1647:
1646:
1634:
1633:
1626:
1619:
1611:
1604:
1603:
1550:
1521:(3): 482–491.
1498:
1447:
1420:(2): 256–267.
1404:
1377:(7): 1872–82.
1361:
1334:(7): 1866–71.
1318:
1269:
1220:
1193:(2): 214–216.
1177:
1126:
1078:
1027:
992:
941:
890:
871:(1): 281–299.
852:
825:(3): 233–239.
808:
789:(1): 106–120.
773:
711:
670:
651:(2): 323–342.
633:
574:
560:
558:
555:
554:
553:
547:
539:
536:
511:Main article:
508:
505:
499:
465:
462:
414:
413:
410:
407:
404:
386:
379:
360:Main article:
357:
354:
352:
349:
332:
329:
312:
309:
304:stereochemical
277:
276:
273:
265:
262:
245:
238:
219:bioinformatics
212:hydrogen bonds
185:
182:
140:
139:
132:
131:
128:
117:
116:
63:
60:
15:
9:
6:
4:
3:
2:
1925:
1914:
1911:
1909:
1906:
1904:
1901:
1899:
1896:
1895:
1893:
1878:
1875:
1873:
1870:
1869:
1867:
1865:
1861:
1855:
1854:Yeast display
1852:
1850:
1847:
1845:
1844:Phage display
1842:
1840:
1837:
1835:
1832:
1831:
1829:
1825:
1819:
1816:
1814:
1813:Protein assay
1811:
1809:
1806:
1805:
1803:
1801:
1797:
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1778:
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1396:
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1365:
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1349:
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1314:
1310:
1305:
1300:
1296:
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1273:
1265:
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1256:
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1235:
1231:
1224:
1216:
1212:
1208:
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1200:
1196:
1192:
1188:
1181:
1173:
1169:
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1159:
1154:
1149:
1145:
1141:
1137:
1130:
1122:
1118:
1113:
1108:
1104:
1100:
1096:
1092:
1085:
1083:
1074:
1070:
1065:
1060:
1055:
1050:
1046:
1042:
1038:
1031:
1023:
1019:
1015:
1011:
1007:
1003:
996:
988:
984:
979:
974:
969:
964:
960:
956:
952:
945:
937:
933:
928:
923:
918:
913:
909:
905:
901:
894:
886:
882:
878:
874:
870:
866:
859:
857:
848:
844:
840:
836:
832:
828:
824:
820:
812:
804:
800:
796:
792:
788:
784:
777:
769:
765:
760:
755:
750:
745:
741:
737:
733:
729:
725:
718:
716:
707:
703:
698:
693:
689:
685:
681:
674:
666:
662:
658:
654:
650:
646:
645:
637:
629:
625:
620:
615:
610:
605:
601:
597:
593:
589:
585:
578:
571:
565:
561:
551:
548:
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542:
541:
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533:
527:
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521:
514:
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491:
489:
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481:
475:
472:
461:
459:
455:
451:
447:
443:
439:
435:
431:
427:
424:
418:
411:
408:
405:
402:
398:
394:
391:
387:
384:
380:
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373:
370:
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368:
363:
348:
344:
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338:
328:
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318:
308:
305:
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298:
294:
290:
286:
282:
274:
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269:
261:
259:
255:
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243:
237:
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231:
226:
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220:
215:
213:
209:
208:electrostatic
204:
200:
196:
192:
181:
179:
175:
172:
168:
164:
160:
155:
153:
149:
145:
137:
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120:
114:
113:
108:
107:
106:
103:
100:
96:
92:
87:
85:
81:
77:
73:
69:
59:
56:
51:
49:
43:
41:
37:
33:
29:
25:
21:
1839:mRNA display
1808:Enzyme assay
1774:
1669:Western blot
1651:Experimental
1563:
1559:
1553:
1518:
1514:
1464:
1460:
1450:
1417:
1413:
1407:
1374:
1370:
1364:
1331:
1327:
1321:
1286:
1282:
1272:
1237:
1233:
1223:
1190:
1186:
1180:
1143:
1139:
1129:
1094:
1090:
1044:
1040:
1030:
1005:
1001:
995:
958:
954:
944:
907:
903:
893:
868:
865:J. Mol. Biol
864:
822:
818:
811:
786:
783:J. Mol. Biol
782:
776:
731:
727:
690:(1): 29–63.
687:
683:
673:
648:
642:
636:
591:
587:
577:
569:
564:
528:
524:double blind
516:
496:
492:
476:
467:
422:
419:
415:
393:force fields
365:
345:
334:
325:
317:convolutions
314:
301:
296:
293:phylogenetic
278:
267:
257:
253:
247:
241:
227:
216:
187:
156:
141:
133:
118:
110:
104:
88:
65:
52:
44:
40:nucleic acid
19:
18:
1877:Vertico SMI
1737:Protein NMR
1289:(14): e88.
1112:1874/202590
450:radioligand
434:interactome
337:Monte Carlo
195:sickle-cell
174:replication
167:polymerases
154:technique.
125:bound state
80:Krebs cycle
78:(e.g., the
55:interactors
42:complexes.
1892:Categories
1566:(1): 2–9.
1467:(9): 432.
557:References
464:Benchmarks
438:orthogonal
351:Evaluation
281:antibodies
191:hemoglobin
68:biological
62:Background
1568:CiteSeerX
1146:(1): 84.
423:in silico
378:contacts.
372:Heuristic
289:mutagenic
84:functions
28:complexes
1644:of study
1638:Proteins
1598:31489448
1590:12784359
1560:Proteins
1545:21213247
1493:30149645
1442:26814049
1434:27862282
1414:Proteins
1399:20322388
1391:22488990
1371:Proteins
1348:22488669
1328:Proteins
1313:18583363
1264:26231283
1215:24049376
1207:15981264
1187:Proteins
1172:29506465
1121:20329755
1073:24906633
1022:15801826
987:17559675
936:11517309
885:12875852
847:40212654
538:See also
444:(SPR),
395:such as
297:a priori
283:and for
171:catalyse
169:, which
144:physical
1642:methods
1536:3064828
1484:6162694
1304:2504314
1255:4677049
1163:5838950
1064:4057934
1047:: 171.
978:1913526
839:8605624
803:9299341
768:1549581
736:Bibcode
706:3310103
628:1055409
596:Bibcode
456:(ITC),
376:residue
264:Methods
248:If the
199:trypsin
184:History
112:in vivo
99:mutated
93:(e.g.,
36:Protein
1640:: key
1596:
1588:
1570:
1543:
1533:
1491:
1481:
1440:
1432:
1397:
1389:
1356:437472
1354:
1346:
1311:
1301:
1262:
1252:
1213:
1205:
1170:
1160:
1119:
1071:
1061:
1020:
985:
975:
961:: 37.
934:
924:
883:
845:
837:
801:
766:
756:
704:
665:712840
663:
626:
619:432527
616:
397:CHARMM
165:, and
1800:Assay
1594:S2CID
1461:Genes
1438:S2CID
1395:S2CID
1352:S2CID
1211:S2CID
927:58518
843:S2CID
759:48623
401:AMBER
1586:PMID
1541:PMID
1489:PMID
1430:PMID
1387:PMID
1344:PMID
1309:PMID
1260:PMID
1203:PMID
1168:PMID
1117:PMID
1069:PMID
1018:PMID
983:PMID
932:PMID
881:PMID
835:PMID
799:PMID
764:PMID
702:PMID
661:PMID
624:PMID
532:CASP
471:SCOP
203:BPTI
66:The
1578:doi
1531:PMC
1523:doi
1479:PMC
1469:doi
1422:doi
1379:doi
1336:doi
1299:PMC
1291:doi
1250:PMC
1242:doi
1238:427
1195:doi
1158:PMC
1148:doi
1107:hdl
1099:doi
1059:PMC
1049:doi
1010:doi
973:PMC
963:doi
922:PMC
912:doi
873:doi
869:331
827:doi
791:doi
787:272
754:PMC
744:doi
692:doi
653:doi
649:124
614:PMC
604:doi
399:or
335:In
291:or
26:of
1894::
1592:.
1584:.
1576:.
1564:52
1562:.
1539:.
1529:.
1519:20
1517:.
1513:.
1501:^
1487:.
1477:.
1463:.
1459:.
1436:.
1428:.
1418:85
1416:.
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1385:.
1375:80
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1350:.
1342:.
1332:80
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1297:.
1287:36
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1281:.
1258:.
1248:.
1236:.
1232:.
1209:.
1201:.
1191:60
1189:.
1166:.
1156:.
1144:19
1142:.
1138:.
1115:.
1105:.
1093:.
1081:^
1067:.
1057:.
1045:15
1043:.
1039:.
1016:.
1006:48
1004:.
981:.
971:.
957:.
953:.
930:.
920:.
908:98
906:.
902:.
879:.
867:.
855:^
841:.
833:.
821:.
797:.
785:.
762:.
752:.
742:.
732:89
730:.
726:.
714:^
700:.
688:49
686:.
682:.
659:.
647:.
622:.
612:.
602:.
592:72
590:.
586:.
526:.
448:,
428:,
299:.
242:vs
34:.
1700:/
1686:/
1630:e
1623:t
1616:v
1600:.
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1547:.
1525::
1495:.
1471::
1465:9
1444:.
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1401:.
1381::
1358:.
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1315:.
1293::
1266:.
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1217:.
1197::
1174:.
1150::
1123:.
1109::
1101::
1095:9
1075:.
1051::
1024:.
1012::
989:.
965::
959:7
938:.
914::
887:.
875::
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823:3
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770:.
746::
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655::
630:.
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201:-
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115:?
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