Liquid-like movements in crystalline insulin

Caspar, D. L. D.; Clarage, James; Salunke, D.M.; Clarage, M.

doi:10.1038/332659a0

Cited by 134 publications

(151 citation statements)

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“…has previously been observed for liquid-like motions (LLM) models (18)(19)(20)(21), which provide a softer model of the protein than the TLS model. In the LLM model, the atoms in the protein are assumed to move randomly, like in a homogeneous medium; the motions were termed "liquid-like" by Caspar et al (19) because the correlations in the displacements were assumed to fall off exponentially with the distance between atoms.…”

Section: Significancementioning

confidence: 99%

“…In the LLM model, the atoms in the protein are assumed to move randomly, like in a homogeneous medium; the motions were termed "liquid-like" by Caspar et al (19) because the correlations in the displacements were assumed to fall off exponentially with the distance between atoms.…”

Section: Significancementioning

confidence: 99%

“…When each unit cell varies independently, the diffuse intensity is proportional to the variance in the unit cell structure factor (17), which describes correlations in the charge density variations. This assumption is appropriate when analyzing the broadly distributed diffuse intensity that corresponds to small correlation lengths (18)(19)(20)(21), as the contribution of inter-unit cell atom pairs in this case is a small fraction of the total signal.Several approaches have been used to connect macromolecular diffuse scattering data to models of protein motion and lattice disorder. Peter Moore (22) has emphasized the need to validate TLS models using diffuse scattering, as has been performed in a limited number of cases (8,23,24).…”

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Measuring and modeling diffuse scattering in protein X-ray crystallography

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Liu

González

et al. 2016

Proc. Natl. Acad. Sci. U.S.A.

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X-ray diffraction has the potential to provide rich information about the structural dynamics of macromolecules. To realize this potential, both Bragg scattering, which is currently used to derive macromolecular structures, and diffuse scattering, which reports on correlations in charge density variations, must be measured. Until now, measurement of diffuse scattering from protein crystals has been scarce because of the extra effort of collecting diffuse data. Here, we present 3D measurements of diffuse intensity collected from crystals of the enzymes cyclophilin A and trypsin. The measurements were obtained from the same X-ray diffraction images as the Bragg data, using best practices for standard data collection. To model the underlying dynamics in a practical way that could be used during structure refinement, we tested translation-libration-screw (TLS), liquid-like motions (LLM), and coarse-grained normal-modes (NM) models of protein motions. The LLM model provides a global picture of motions and was refined against the diffuse data, whereas the TLS and NM models provide more detailed and distinct descriptions of atom displacements, and only used information from the Bragg data. Whereas different TLS groupings yielded similar Bragg intensities, they yielded different diffuse intensities, none of which agreed well with the data. In contrast, both the LLM and NM models agreed substantially with the diffuse data. These results demonstrate a realistic path to increase the number of diffuse datasets available to the wider biosciences community and indicate that dynamics-inspired NM structural models can simultaneously agree with both Bragg and diffuse scattering.protein dynamics | normal modes | structural biology | diffuse scattering | liquid-like motions X -ray crystallography can be a key tool for elucidating the structural basis of protein motions that play critical roles in enzymatic reactions, protein-protein interactions, and signaling cascades (1). X-ray diffraction yields an ensemble-averaged picture of the protein structure: each photon simultaneously probes multiple unit cells that can vary because of internal rearrangements or changes to the crystal lattice. Bragg analysis of X-ray diffraction only yields the mean charge density of the unit cell, however, which fundamentally limits the information that can be obtained about protein dynamics (2, 3).An inherent limitation in Bragg analysis is that models with different concerted motions can yield the same mean charge density (4). The traditional approach assumes a single structural model with individual atomic displacement parameters (B factors). Given enough data, anisotropic displacement parameters can be modeled. When the data are more limited, translation-libration-screw (TLS) refinement, which models rigid-body motions of subdomains (5), is often used [22% of Protein Data Bank (PDB) depositions (6, 7)]. Variations in the TLS domains can predict very different motions that agree equally well with the Bragg data (8, 9).Bragg analysis can be combined ...

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Section: Significancementioning

confidence: 99%

Section: Significancementioning

confidence: 99%

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confidence: 99%

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confidence: 99%

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Measuring and modeling diffuse scattering in protein X-ray crystallography

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Liu

González

et al. 2016

Proc. Natl. Acad. Sci. U.S.A.

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“…Covariances can be used to estimate the entropy of the system (Karplus & Kushick, 1981;Schlitter, 1993), in the interpretation of diffuse scattering experiments (Caspar et al, 1988;Caspar & Badger, 1991;Clarage et al, 1992), in quasiharmonic mode determination (see Case, 1994) and in ''essential'' mode determination (Amadei et al, 1993). The cross-correlation (or normalized covariance) matrix elements, Cij , are defined by: …”

Section: %mentioning

confidence: 99%

Fluctuation and Cross-correlation Analysis of Protein Motions Observed in Nanosecond Molecular Dynamics Simulations

Hünenberger

Mark

Gunsteren

1995

Journal of Molecular Biology

396

326

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Nanosecond molecular dynamics simulations of bovine pancreatic trypsin inhibitor and lysozyme in water are analyzed in terms of backbone atomic Physikalische Chemie positional fluctuations and dynamical cross-correlations. It is found that although the molecular systems are stable, B-factors calculated over a time Zü rich, Switzerland period as long as 500 ps are not representative for the motions within the proteins. This is especially true for the most mobile residues. On a nanosecond time-scale, the B-factors calculated from the simulations of the proteins in solution are considerably larger than those obtained by structure refinement of the proteins in crystals, based on X-ray data. The time evolution of the atomic fluctuations shows that for large portions of the proteins under study, atomic positional fluctuations are not yet converged after a nanosecond. Cross-correlations do not converge faster than the fluctuations themselves. Most display very erratic behavior if the sampling covers less than about 200 ps. It is also shown that inclusion of mobile atoms into the procedure used to remove rigid-body motion from the simulation can lead to spurious correlations between the motions of the atoms at the surface of the protein.

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Ansätze zur Beschreibung und Vorhersage der Bindungsaffinität niedermolekularer Liganden an makromolekulare Rezeptoren

Gohlke

Klebe

2002

Angew. Chem.

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Der Einfluss einer körperfremden Verbindung auf einen Organismus wird in aller Regel an dem Begriff der biologischen Aktivität festgemacht. Liegt eine gezielte, therapeutisch relevante und regulierende Wirkung vor, so hat die Verbindung das Potenzial zu einem Arzneistoff, andernfalls betrachtet man ihren toxikologischen Einfluss auf das biologische System. Doch was versteht man unter dem Begriff der biologischen Aktivität? Natürlich ist an dieser Stelle der gesamte Effekt auf einen Organismus zu betrachten, doch wegen der Komplexität der ineinandergreifenden Vorgänge betrachtet man zunächst nur die „Hauptwirkung“ auf den Organismus. Auf die molekulare Ebene übertragen, bedeutet biologische Aktivität die Bindung einer (niedermolekularen) Verbindung an einen makromolekularen Rezeptor, in aller Regel ein Protein. Dadurch werden enzymatische Reaktionen oder Signaltransduktionskaskaden in ihrer Funktion für den Organismus beeinflusst. Wir betrachten diese Bindung als einen Vorgang unter Gleichgewichtsbedingungen, sodass sich die Bindung als Assoziations‐ oder Dissoziationsvorgang beschreiben lässt. Somit wird biologische Aktivität als Affinität der beiden Partner zueinander in Form einer thermodynamischen Gleichgewichtsgröße ausgedrückt. Wie gut sind diese Begriffe heute verstanden und theoretisch vorhersagbar? Kern des rationalen Wirkstoffdesigns ist die Vorhersage der biologischen Aktivität einer Verbindung. Die Vorgänge bei der Ligandenbindung sind äußerst komplex, sowohl Ligand als auch Protein sind flexible Moleküle, und die Energiebilanz muss zwischen getrenntem und gebundenem Zustand in wässriger Umgebung betrachtet werden. Wie ausgereift und aussagekräftig sind unsere experimentellen Verfahren, um hier die notwendigen Einblicke zu gewinnen? Der vorliegende Aufsatz fasst den derzeitigen Kenntnisstand zur Bindungsaffinität eines niedermolekularen Liganden zu einem Protein zusammen. Es werden theoretische wie empirische Ansätze beschrieben und gegenübergestellt, die eine Vorhersage der Bindungsaffinität aus der Raumstruktur eines Protein‐Ligand‐Komplexes zum Ziel haben. Experimentelle Ansätze, vor allem die Mikrokalorimetrie, werden diskutiert. Als Ausblick werden ein eigenes wissensbasiertes Verfahren zur Affinitätsvorhersage und experimentelle Daten zur Faktorisierung von Bindungsbeiträgen zur Ligandenbindung an Proteine vorgestellt.

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Liquid-like movements in crystalline insulin

Cited by 134 publications

References 19 publications

Measuring and modeling diffuse scattering in protein X-ray crystallography

Measuring and modeling diffuse scattering in protein X-ray crystallography

Fluctuation and Cross-correlation Analysis of Protein Motions Observed in Nanosecond Molecular Dynamics Simulations

Ansätze zur Beschreibung und Vorhersage der Bindungsaffinität niedermolekularer Liganden an makromolekulare Rezeptoren

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