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Expanding the space of protein geometries by computational design of de novo fold families

Science

Protein design typically selects a protein topology and then identifies the geometries (secondary-structure lengths and orientations) that give the most stable structures. A challenge for this approach is that functional sites in natural proteins often adopt nonideal geometries. Pan et al. addressed this issue by exploring the diversity of geometries that can be sampled by a given topology. They developed a computational method called LUCS that systematically samples geometric variation in loop-helix-loop elements and applied it to two different topologies. This method generated families of well-folded proteins that include structures with non-native geometries. The ability to tune protein geometry may enable the custom design of new functions. Science , this issue p. [1132][1] Naturally occurring proteins vary the precise geometries of structural elements to create distinct shapes optimal for function. We present a computational design method, loop-helix-loop unit combinatorial sampling (LUCS), that mimics nature’s ability to create families of proteins with the same overall fold but precisely tunable geometries. Through near-exhaustive sampling of loop-helix-loop elements, LUCS generates highly diverse geometries encompassing those found in nature but also surpassing known structure space. Biophysical characterization showed that 17 (38%) of 45 tested LUCS designs encompassing two different structural topologies were well folded, including 16 with designed non-native geometries. Four experimentally solved structures closely matched the designs. LUCS greatly expands the designable structure space and offers a new paradigm for designing proteins with tunable geometries that may be customizable for novel functions. [1]: /lookup/doi/10.1126/science.abc0881


In pictures: Sacred geometries

BBC News

A small group of photographers have turned their lenses on the urban landscape, seeking to capture the beauty of the architecture around us. The images explore the idea of sacred geometries, the perfect mix of proportion and mathematical ratios that are pleasing to the eye and a reflection of those found in nature. The pictures can be seen at the Anise Gallery in London until 15 April 2017.


Spatial Data Science with PostgreSQL: Geometries

#artificialintelligence

Geometries are the glues that hold together geospatial data. They form an integral part of any spatial data processing. In this tutorial, I will go through some of the different types of geometries available in Postgis. We also touch on some of the most used functions with real-world data examples. In my last article, I explained how to install PostgreSQL and activate Postgis extensions.


AI system solves SAT geometry questions as well as an eleven year old

AITopics Original Links

Scientists have revealed an artificial intelligence (AI) system that can solve SAT geometry questions as well as the average American 11th-grade student. Called GeoS, it uses a combination of computer vision to interpret diagrams, natural language processing to read and understand text and a geometric solver to achieve 49 percent accuracy on official SAT test questions. If these results were extrapolated to the entire Math SAT test, the computer achieved an SAT score of 500 (out of 800), the average test score for 2015, the team behind it say. The system uses a combination of computer vision, natural language processing and a geometric solver to achieve 49 percent accuracy on official SAT test questions. GeoS is the first end-to-end system that solves SAT plane geometry problems.


Elasticity, Geometry and Buckling

VideoLectures.NET

Andrej Košmrlj from the Department of Mechanical and Aerospace Engineering at Princeton University presents how geometrical shape affects the mechanical properties of thin solid shells and how buckling instabilities change the geometry of periodic microstructures in materials.