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Overflow of GeoBind. ( A ) An illustration of the NBP surface segmentation. GeoBind takes the whole protein surface as input and outputs the segmented surfaces with each point assigned a likelihood of being involved in nucleic acid binding events. ( B ) Definition of binding interface and site. Points located on the protein surface with a distance to nucleic acids less than a cutoff are considered binding interfaces. Binding sites refer to residues that close to nucleic acids according to a similar definition. The mapping from an interface score to a site score is achieved through a max pooling operator. ( C ) Left, an introduction to quasi-geodesic convolution. We assign each point on cloud with a LRF. GeoBind makes use of quasi-geodesic distance and relative position (computed by LRF) for geometric embedding. Middle, the geodesic distance between two points is estimated by their position and orientation. Right, the relative position of a point in the LRF refers to the projection of the point on three axes. ( D ) Each point is initialized with three types of descriptors, namely multiple sequence alignment (MSA), chemical environment and curvature information. ( E ) Basic architecture of GeoBind. GeoBind consists of four blocks which contain the <t>quasi-geodesic</t> <t>convolutional</t> layers. In each block, a Multi-Layer Perceptron <t>(MLP)</t> is set up before and after the quasi-geodesic convolutional layer for the pre- and post-encoder.
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Overflow of GeoBind. ( A ) An illustration of the NBP surface segmentation. GeoBind takes the whole protein surface as input and outputs the segmented surfaces with each point assigned a likelihood of being involved in nucleic acid binding events. ( B ) Definition of binding interface and site. Points located on the protein surface with a distance to nucleic acids less than a cutoff are considered binding interfaces. Binding sites refer to residues that close to nucleic acids according to a similar definition. The mapping from an interface score to a site score is achieved through a max pooling operator. ( C ) Left, an introduction to quasi-geodesic convolution. We assign each point on cloud with a LRF. GeoBind makes use of quasi-geodesic distance and relative position (computed by LRF) for geometric embedding. Middle, the geodesic distance between two points is estimated by their position and orientation. Right, the relative position of a point in the LRF refers to the projection of the point on three axes. ( D ) Each point is initialized with three types of descriptors, namely multiple sequence alignment (MSA), chemical environment and curvature information. ( E ) Basic architecture of GeoBind. GeoBind consists of four blocks which contain the quasi-geodesic convolutional layers. In each block, a Multi-Layer Perceptron (MLP) is set up before and after the quasi-geodesic convolutional layer for the pre- and post-encoder.

Journal: Nucleic Acids Research

Article Title: GeoBind: segmentation of nucleic acid binding interface on protein surface with geometric deep learning

doi: 10.1093/nar/gkad288

Figure Lengend Snippet: Overflow of GeoBind. ( A ) An illustration of the NBP surface segmentation. GeoBind takes the whole protein surface as input and outputs the segmented surfaces with each point assigned a likelihood of being involved in nucleic acid binding events. ( B ) Definition of binding interface and site. Points located on the protein surface with a distance to nucleic acids less than a cutoff are considered binding interfaces. Binding sites refer to residues that close to nucleic acids according to a similar definition. The mapping from an interface score to a site score is achieved through a max pooling operator. ( C ) Left, an introduction to quasi-geodesic convolution. We assign each point on cloud with a LRF. GeoBind makes use of quasi-geodesic distance and relative position (computed by LRF) for geometric embedding. Middle, the geodesic distance between two points is estimated by their position and orientation. Right, the relative position of a point in the LRF refers to the projection of the point on three axes. ( D ) Each point is initialized with three types of descriptors, namely multiple sequence alignment (MSA), chemical environment and curvature information. ( E ) Basic architecture of GeoBind. GeoBind consists of four blocks which contain the quasi-geodesic convolutional layers. In each block, a Multi-Layer Perceptron (MLP) is set up before and after the quasi-geodesic convolutional layer for the pre- and post-encoder.

Article Snippet: Following the fourth convolutional block, an MLP classifier, consisting of two FC layers followed by a Softmax layer, is set up to predict the likelihood \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}$\hat{y}_i$\end{document} that point x i is a binding interface point or not.

Techniques: Binding Assay, Sequencing, Blocking Assay