By Andrzej Kolinski (auth.), Andrzej Kolinski (eds.)
Multiscale techniques to Protein Modeling is a finished overview of the main complex multiscale equipment for protein constitution prediction, computational reviews of protein dynamics, folding mechanisms and macromolecular interactions. The techniques span a variety of the degrees of coarse-grained representations, a variety of sampling suggestions and diversity of purposes to biomedical and biophysical difficulties. because of huge, immense growth in sequencing of genomic information, we almost immediately be aware of thousands of protein sequences. whilst, the variety of experimentally solved protein buildings is way smaller, ca. 60,000. it is because of the massive expense of constitution decision. therefore, theoretical, in silico, prediction of protein constructions and dynamics is vital for realizing the molecular foundation of drug motion, metabolic and signaling pathways in dwelling cells, designing new applied sciences within the lifestyles technological know-how and fabric sciences. regrettably, a “brute strength” procedure is still impractical. Folding of a regular protein (in vivo or in vitro) takes milliseconds to mins, whereas state of the art all-atom molecular mechanics simulations of protein structures can conceal just a period of time diversity of nanosecond to microseconds. that is why for the large development in improvement of varied mutiscale modeling thoughts, utilized to protein constitution prediction, modeling of protein dynamics and folding pathways, in silico protein engineering, model-aided interpretation of experimental information, modeling of macromolecular assemblies and theoretical reports of protein thermodynamics. Coarse-graining of the proteins’ conformational area is a typical characteristic of these kind of methods, even supposing the main points and the underlying actual versions span a truly wide spectrum.
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Extra info for Multiscale Approaches to Protein Modeling: Structure Prediction, Dynamics, Thermodynamics and Macromolecular Assemblies
Sample text
2009). Another way to increase recognition specificity of the interfaces could be achieved by use of multibody knowledge-based potentials. For instance, four-body statistical pseudo-potentials proven to be useful in protein–peptide docking, allowing for full flexibility of the peptide moieties (Aita et al. 2010). Before the applications in protein docking, four-body potentials proven to be very effective efficient in scoring protein decoys (Krishnamoorthy and Tropsha 2003; Feng et al. 2010). In summary, while small, local conformational changes accompanying protein docking are relatively well handled by a variety of docking algorithms, large deformations of components are more difficult to predict.
For instance, four-body statistical pseudo-potentials proven to be useful in protein–peptide docking, allowing for full flexibility of the peptide moieties (Aita et al. 2010). Before the applications in protein docking, four-body potentials proven to be very effective efficient in scoring protein decoys (Krishnamoorthy and Tropsha 2003; Feng et al. 2010). In summary, while small, local conformational changes accompanying protein docking are relatively well handled by a variety of docking algorithms, large deformations of components are more difficult to predict.
Biophys J 94:726–736 Koehl P (2006) Electrostatics calculations: latest methodological advances. Curr Opin Struct Biol 16:142–151 Kolinski A (2004) Protein modeling and structure prediction with a reduced representation. Acta Biochim Pol 51:349–371 Kozakov D, Brenke R, Comeau SR, Vajda S (2006) PIPER: an FFT-based protein docking program with pairwise potentials. Proteins 65:392–406 Krishnamoorthy B, Tropsha A (2003) Development of a four-body statistical pseudo-potential to discriminate native from non-native protein conformations.