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Using the RosettaSurface algorithm to predict protein structure at mineral surfaces.

ABSTRACT: Determination of protein structure on mineral surfaces is necessary to understand biomineralization processes toward better treatment of biomineralization diseases and design of novel protein-synthesized materials. To date, limited atomic-resolution data have hindered experimental structure determination for proteins on mineral surfaces. Molecular simulation represents a complementary approach. In this chapter, we review RosettaSurface, a computational structure prediction-based algorithm designed to broadly sample conformational space to identify low-energy structures. We summarize the computational approaches, the published applications, and the new releases of the code in the Rosetta 3 framework. In addition, we provide a protocol capture to demonstrate the practical steps to employ RosettaSurface. As an example, we provide input files and output data analysis for a previously unstudied mineralization protein, osteocalcin. Finally, we summarize ongoing challenges in energy function optimization and conformational searching and suggest that the fusion between experiment and calculation is the best route forward.

SUBMITTER: Pacella MS

PROVIDER: S-EPMC4020438 | biostudies-literature | 2013

REPOSITORIES: biostudies-literature

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Using the RosettaSurface algorithm to predict protein structure at mineral surfaces.

Pacella Michael S MS Koo Da Chen Emily da CE Thottungal Robin A RA Gray Jeffrey J JJ

Methods in enzymology 20130101

Determination of protein structure on mineral surfaces is necessary to understand biomineralization processes toward better treatment of biomineralization diseases and design of novel protein-synthesized materials. To date, limited atomic-resolution data have hindered experimental structure determination for proteins on mineral surfaces. Molecular simulation represents a complementary approach. In this chapter, we review RosettaSurface, a computational structure prediction-based algorithm design ...[more]

PMID: 24188775

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Project description:BACKGROUND:Protein structure comparison play important role in in silico functional prediction of a new protein. It is also used for understanding the evolutionary relationships among proteins. A variety of methods have been proposed in literature for comparing protein structures but they have their own limitations in terms of accuracy and complexity with respect to computational time and space. There is a need to improve the computational complexity in comparison/alignment of proteins through incorporation of important biological and structural properties in the existing techniques. RESULTS:An efficient algorithm has been developed for comparing protein structures using elastic shape analysis in which the sequence of 3D coordinates atoms of protein structures supplemented by additional auxiliary information from side-chain properties are incorporated. The protein structure is represented by a special function called square-root velocity function. Furthermore, singular value decomposition and dynamic programming have been employed for optimal rotation and optimal matching of the proteins, respectively. Also, geodesic distance has been calculated and used as the dissimilarity score between two protein structures. The performance of the developed algorithm is tested and found to be more efficient, i.e., running time reduced by 80-90 % without compromising accuracy of comparison when compared with the existing methods. Source codes for different functions have been developed in R. Also, user friendly web-based application called ProtSComp has been developed using above algorithm for comparing protein 3D structures and is accessible free. CONCLUSIONS:The methodology and algorithm developed in this study is taking considerably less computational time without loss of accuracy (Table 2). The proposed algorithm is considering different criteria of representing protein structures using 3D coordinates of atoms and inclusion of residue wise molecular properties as auxiliary information.

Project description:BackgroundThe iconic Pilbara in northwestern Australia is an ancient geological and biophysical region that is an important zone of biodiversity, endemism and refugia. It also is overlain by some of the oldest erosion surfaces on Earth, but very little is known about the patterns of biotic diversity within the Pilbara or how they relate to the landscape. We combined phylogenetic and spatial-autocorrelation genetic analyses of mitochondrial DNA data on populations of the gekkotan lizard Lucasium stenodactylum within the Pilbara with geological, distributional and habitat data to test the hypothesis that ancient surface geology predicts current clade-habitat associations in saxicoline animals.ResultsThis is the first detailed phylogenetic examination of a vertebrate organism across the Pilbara region. Our phylogeny provides strong support for a deep and ancient phylogenetic split within L. stenodactylum that distinguishes populations within the Pilbara region from those outside the Pilbara. Within the Pilbara region itself, our phylogeny has identified five major clades whose distribution closely matches different surface geologies of this ancient landscape. Each clade shows strong affinities with particular terrain types and topographic regions, which are directly related to different geological bedrock.ConclusionTogether our phylogenetic, distributional, geological and habitat data provide a clear example of ecological diversification across an ancient and heterogeneous landscape. Our favoured hypothesis is that ancestors of the Pilbara lineages radiated into the region at the onset of aridity in Australia approximately 5 mya and locally adapted to the various ancient and highly stable terrain types and the micro-habitats derived from them. In terms of specimen recovery and analysis, we are only beginning to reconstruct the biotic history of this ancient landscape. Our results show the geological history and the habitats derived from them will form an important part of this emerging story.

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Using the RosettaSurface algorithm to predict protein structure at mineral surfaces.

Publications

Using the RosettaSurface algorithm to predict protein structure at mineral surfaces.

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