Protein allostery, signal trans- mission and dynamics: a classification scheme of allosteric mechanisms. Molecular Biosystems, 5(3), 207-216

Basic Research Program, SAIC-Frederick, Inc., Center for Cancer Research Nanobiology Program, NCI-Frederick, Frederick, MD 21702, USA.
Molecular BioSystems (Impact Factor: 3.21). 04/2009; 5(3):207-16. DOI: 10.1039/b819720b
Source: PubMed


Allostery has come of age; the number, breadth and functional roles of documented protein allostery cases are rising quickly. Since all dynamic proteins are potentially allosteric and allostery plays crucial roles in all cellular pathways, sorting and classifying allosteric mechanisms in proteins should be extremely useful in understanding and predicting how the signals are regulated and transmitted through the dynamic multi-molecular cellular organizations. Classification organizes the complex information thereby unraveling relationships and patterns in molecular activation and repression. In signaling, current classification schemes consider classes of molecules according to their functions; for example, epinephrine and norepinephrine secreted by the central nervous system are classified as neurotransmitters. Other schemes would account for epinephrine when secreted by the adrenal medulla to be hormone-like. Yet, such classifications account for the global function of the molecule; not for the molecular mechanism of how the signal transmission initiates and how it is transmitted. Here we provide a unified view of allostery and the first classification framework. We expect that a classification scheme would assist in comprehension of allosteric mechanisms, in prediction of signaling on the molecular level, in better comprehension of pathways and regulation of the complex signals, in translating them to the cascading events, and in allosteric drug design. We further provide a range of examples illustrating mechanisms in protein allostery and their classification from the cellular functional standpoint.

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    • "Early definitions of allostery can be based on the conformational change with the binding of ligands [2], [3] and displacement of the equilibrium between conformational states [4]. The newly emerging definition emphasizes the importance of dynamics in allosteric regulation [5] with the identification of residues responsible for the dynamics and combine this with the evolutionary information [6], [7], [8], [9], [10], [11]. "
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    ABSTRACT: It is of significant interest to understand how proteins interact, which holds the key phenomenon in biological functions. Using dynamic fluctuations in high frequency modes, we show that the Gaussian Network Model (GNM) predicts hot spot residues with success rates ranging between S 8-58%, C 84-95%, P 5-19% and A 81-92% on unbound structures and S 8-51%, C 97-99%, P 14-50%, A 94-97% on complex structures for sensitivity, specificity, precision and accuracy, respectively. High specificity and accuracy rates with a single property on unbound protein structures suggest that hot spots are predefined in the dynamics of unbound structures and forming the binding core of interfaces, whereas the prediction of other functional residues with similar dynamic behavior explains the lower precision values. The latter is demonstrated with the case studies; ubiquitin, hen egg-white lysozyme and M2 proton channel. The dynamic fluctuations suggest a pseudo network of residues with high frequency fluctuations, which could be plausible for the mechanism of biological interactions and allosteric regulation.
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    • "In addition, allostery may occur without a conformational change [30] [31], which led to the new concept of dynamically driven allostery [32] and the notion that all proteins may be capable of being allosterically regulated. A recent classification of allosteric mechanisms considers the extent of conformational change, and whether the allostery is driven by enthalpy, entropy, or both [33] "
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    • "Because the strategy described here does not involve any changes to the current PSI-MI XML schema, backward compatibility is maintained. Another advantage is that it provides flexibility, as new CV terms can be defined to describe additional features of cooperative interactions, for instance, based on the classification scheme for allosteric mechanisms that has been defined previously (81) or information captured in the AlloSteric Database (ASD) (82). "
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    ABSTRACT: The complex biological processes that control cellular function are mediated by intricate networks of molecular interactions. Accumulating evidence indicates that these interactions are often interdependent, thus acting cooperatively. Cooperative interactions are prevalent in and indispensible for reliable and robust control of cell regulation, as they underlie the conditional decision-making capability of large regulatory complexes. Despite an increased focus on experimental elucidation of the molecular details of cooperative binding events, as evidenced by their growing occurrence in literature, they are currently lacking from the main bioinformatics resources. One of the contributing factors to this deficiency is the lack of a computer-readable standard representation and exchange format for cooperative interaction data. To tackle this shortcoming, we added functionality to the widely used PSI-MI interchange format for molecular interaction data by defining new controlled vocabulary terms that allow annotation of different aspects of cooperativity without making structural changes to the underlying XML schema. As a result, we are able to capture cooperative interaction data in a structured format that is backward compatible with PSI-MI–based data and applications. This will facilitate the storage, exchange and analysis of cooperative interaction data, which in turn will advance experimental research on this fundamental principle in biology.Database URL:
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