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Analysis of Three-Dimensional Protein Images

Journal of Artificial Intelligence Research

A fundamental goal of research in molecular biology is to understand protein structure. Protein crystallography is currently the most successful method for determining the three-dimensional (3D) conformation of a protein, yet it remains labor intensive and relies on an expert's ability to derive and evaluate a protein scene model. In this paper, the problem of protein structure determination is formulated as an exercise in scene analysis. A computational methodology is presented in which a 3D image of a protein is segmented into a graph of critical points. Bayesian and certainty factor approaches are described and used to analyze critical point graphs and identify meaningful substructures, such as alpha-helices and beta-sheets. Results of applying the methodologies to protein images at low and medium resolution are reported. The research is related to approaches to representation, segmentation and classification in vision, as well as to top-down approaches to protein structure prediction.



[Research Article] Molecular architecture of the human U4/U6.U5 tri-snRNP

Science

The U4/U6.U5 triple small nuclear ribonucleoprotein (tri-snRNP) is a major spliceosome building block. We obtained a three-dimensional structure of the 1.8-megadalton human tri-snRNP at a resolution of 7 angstroms using single-particle cryo–electron microscopy (cryo-EM). We fit all known high-resolution structures of tri-snRNP components into the EM density map and validated them by protein cross-linking. Our model reveals how the spatial organization of Brr2 RNA helicase prevents premature U4/U6 RNA unwinding in isolated human tri-snRNPs and how the ubiquitin C-terminal hydrolase–like protein Sad1 likely tethers the helicase Brr2 to its preactivation position. Comparison of our model with cryo-EM three-dimensional structures of the Saccharomyces cerevisiae tri-snRNP and Schizosaccharomyces pombe spliceosome indicates that Brr2 undergoes a marked conformational change during spliceosome activation, and that the scaffolding protein Prp8 is also rearranged to accommodate the spliceosome's catalytic RNA network.


Three-dimensional mechanical metamaterials with a twist

Science

Rationally designed artificial materials enable mechanical properties that are inaccessible with ordinary materials. Pushing on an ordinary linearly elastic bar can cause it to be deformed in many ways. However, a twist, the counterpart of optical activity in the static case, is strictly zero. The unavailability of this degree of freedom hinders applications in terms of mode conversion and the realization of advanced mechanical designs using coordinate transformations. Here, we aim at realizing microstructured three-dimensional elastic chiral mechanical metamaterials that overcome this limitation.


Analysis of Three-Dimensional Protein Images

Journal of Artificial Intelligence Research

A fundamental goal of research in molecular biology is to understand protein structure. Protein crystallography is currently the most successful method for determining the three-dimensional (3D) conformation of a protein, yet it remains labor intensive and relies on an expert's ability to derive and evaluate a protein scene model. In this paper, the problem of protein structure determination is formulated as an exercise in scene analysis. A computational methodology is presented in which a 3D image of a protein is segmented into a graph of critical points. Bayesian and certainty factor approaches are described and used to analyze critical point graphs and identify meaningful substructures, such as alpha-helices and beta-sheets. Results of applying the methodologies to protein images at low and medium resolution are reported. The research is related to approaches to representation, segmentation and classification in vision, as well as to top-down approaches to protein structure prediction.