Molecular Surface Maps

Structure-Based Prediction of lncRNA-Protein Interactions by Deep Learning

The interactions between long noncoding RNA (lncRNA) and protein play crucial roles in various biological processes. Computational methods are essential for predicting lncRNA-protein interactions and deciphering their mechanisms. In this chapter, we aim to introduce the fundamental framework for predicting lncRNA-protein interactions based on three-dimensional structure information. With the increasing availability of lncRNA and protein molecular tertiary structures, the feasibility of using deep learning methods for automatic representation and learning has become evident. This chapter outlines the key steps in predicting lncRNA-protein interactions using deep learning, including three common non-Euclidean data representations for lncRNA and proteins, as well as neural networks tailored to these specific data characteristics. We also highlight the advantages and challenges of structure-based prediction of lncRNA-protein interactions with geometric deep learning methods.

A Survey of Designs for Combined 2D+3D Visual Representations

We examine visual representations of data that make use of combinations of both 2D and 3D data mappings. Combining 2D and 3D representations is a common technique that allows viewers to understand multiple facets of the data with which they are interacting. While 3D representations focus on the spatial character of the data or the dedicated 3D data mapping, 2D representations often show abstract data properties and take advantage of the unique benefits of mapping to a plane. Many systems have used unique combinations of both types of data mappings effectively. Yet there are no systematic reviews of the methods in linking 2D and 3D representations. We systematically survey the relationships between 2D and 3D visual representations in major visualization publications-IEEE VIS, IEEE TVCG, and EuroVis-from 2012 to 2022. We closely examined 105 articles where 2D and 3D representations are connected visually, interactively, or through animation. These approaches are designed based on their visual environment, the relationships between their visual representations, and their possible layouts. Through our analysis, we introduce a design space as well as provide design guidelines for effectively linking 2D and 3D visual representations.

Immersive and interactive visualization of 3D spatio-temporal data using a space time hypercube: Application to cell division and morphogenesis analysis

The analysis of multidimensional time-varying datasets faces challenges, notably regarding the representation of the data and the visualization of temporal variations. We propose an extension of the well-known Space-Time Cube (STC) visualization technique in order to visualize time-varying 3D spatial data, taking advantage of the interaction capabilities of Virtual Reality (VR). First, we propose the Space-Time Hypercube (STH) as an abstraction for 3D temporal data, extended from the STC concept. Second, through the example of embryo development imaging dataset, we detail the construction and visualization of a STC based on a user-driven projection of the spatial and temporal information. This projection yields a 3D STC visualization, which can also encode additional numerical and categorical data. Additionally, we propose a set of tools allowing the user to filter and manipulate the 3D STC which benefits the visualization, exploration and interaction possibilities offered by VR. Finally, we evaluated the proposed visualization method in the context of 3D temporal cell imaging data analysis, through a user study (n = 5) reporting the feedback from five biologists. These domain experts also accompanied the application design as consultants, providing insights on how the STC visualization could be used for the exploration of complex 3D temporal morphogenesis data.

Design - a new way to look at old molecules

We discuss how design enriches molecular science, particularly structural biology and bioinformatics. We present two use cases, one in academic practice and the other to design for outreach. The first case targets the representation of ion channels and their dynamic properties. In the second, we document a transition process from a research environment to general-purpose designs. Several testimonials from practitioners are given. By describing the design process of abstracted shapes, exploded views of molecular structures, motion-averaged slices, 360-degree panoramic projections, and experiments with lit sphere shading, we document how designers help make scientific data accessible without betraying its meaning, and how a creative mind adds value over purely data-driven visualizations. A similar conclusion was drawn for public outreach, as we found that comic-book-style drawings are better suited for communicating science to a broad audience.

PST-PRNA: prediction of RNA-binding sites using protein surface topography and deep learning

MOTIVATION

Protein-RNA interactions play essential roles in many biological processes, including pre-mRNA processing, post-transcriptional gene regulation and RNA degradation. Accurate identification of binding sites on RNA-binding proteins (RBPs) is important for functional annotation and site-directed mutagenesis. Experimental assays to sparse RBPs are precise and convincing but also costly and time consuming. Therefore, flexible and reliable computational methods are required to recognize RNA-binding residues.

RESULTS

In this work, we propose PST-PRNA, a novel model for predicting RNA-binding sites (PRNA) based on protein surface topography (PST). Taking full advantage of the 3D structural information of protein, PST-PRNA creates representative topography images of the entire protein surface by mapping it onto a unit spherical surface. Four kinds of descriptors are encoded to represent residues on the surface. Then, the potential features are integrated and optimized by using deep learning models. We compile a comprehensive non-redundant RBP dataset to train and test PST-PRNA using 10-fold cross-validation. Numerous experiments demonstrate PST-PRNA learns successfully the latent structural information of protein surface. On the non-redundant dataset with sequence identity of 0.3, PST-PRNA achieves area under the receiver operating characteristic curves (AUC) value of 0.860 and Matthew's correlation coefficient value of 0.420. Furthermore, we construct a completely independent test dataset for justification and comparison. PST-PRNA achieves AUC value of 0.913 on the independent dataset, which is superior to the other state-of-the-art methods.

AVAILABILITY AND IMPLEMENTATION

The code and data are available at https://www.github.com/zpliulab/PST-PRNA. A web server is freely available at http://www.zpliulab.cn/PSTPRNA.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

LMap: Shape-Preserving Local Mappings for Biomedical Visualization

Visualization of medical organs and biological structures is a challenging task because of their complex geometry and the resultant occlusions. Global spherical and planar mapping techniques simplify the complex geometry and resolve the occlusions to aid in visualization. However, while resolving the occlusions these techniques do not preserve the geometric context, making them less suitable for mission-critical biomedical visualization tasks. In this paper, we present a shape-preserving local mapping technique for resolving occlusions locally while preserving the overall geometric context. More specifically, we present a novel visualization algorithm, LMap, for conformally parameterizing and deforming a selected local region-of-interest (ROI) on an arbitrary surface. The resultant shape-preserving local mappings help to visualize complex surfaces while preserving the overall geometric context. The algorithm is based on the robust and efficient extrinsic Ricci flow technique, and uses the dynamic Ricci flow algorithm to guarantee the existence of a local map for a selected ROI on an arbitrary surface. We show the effectiveness and efficacy of our method in three challenging use cases: (1) multimodal brain visualization, (2) optimal coverage of virtual colonoscopy centerline flythrough, and (3) molecular surface visualization.

Challenges for visualizing three-dimensional data in genomic browsers

Genomic interactions reveal the spatial organization of genomes and genomic domains, which is known to play key roles in cell function. Physical proximity can be represented as two-dimensional heat maps or matrices. From these, three-dimensional (3D) conformations of chromatin can be computed revealing coherent structures that highlight the importance of nonsequential relationships across genomic features. Mainstream genomic browsers have been classically developed to display compact, stacked tracks based on a linear, sequential, per-chromosome coordinate system. Genome-wide comparative analysis demands new approaches to data access and new layouts for analysis. The legibility can be compromised when displaying track-aligned second dimension matrices, which require greater screen space. Moreover, 3D representations of genomes defy vertical alignment in track-based genome browsers. Furthermore, investigation at previously unattainable levels of detail is revealing multiscale, multistate, time-dependent complexity. This article outlines how these challenges are currently handled in mainstream browsers as well as how novel techniques in visualization are being explored to address them. A set of requirements for coherent visualization of novel spatial genomic data is defined and the resulting potential for whole genome visualization is described.

Placenta Maps: In Utero Placental Health Assessment of the Human Fetus

The human placenta is essential for the supply of the fetus. To monitor the fetal development, imaging data is acquired using (US). Although it is currently the gold-standard in fetal imaging, it might not capture certain abnormalities of the placenta. (MRI) is a safe alternative for the in utero examination while acquiring the fetus data in higher detail. Nevertheless, there is currently no established procedure for assessing the condition of the placenta and consequently the fetal health. Due to maternal respiration and inherent movements of the fetus during examination, a quantitative assessment of the placenta requires fetal motion compensation, precise placenta segmentation and a standardized visualization, which are challenging tasks. Utilizing advanced motion compensation and automatic segmentation methods to extract the highly versatile shape of the placenta, we introduce a novel visualization technique that presents the fetal and maternal side of the placenta in a standardized way. Our approach enables physicians to explore the placenta even in utero. This establishes the basis for a comparative assessment of multiple placentas to analyze possible pathologic arrangements and to support the research and understanding of this vital organ. Additionally, we propose a three-dimensional structure-aware surface slicing technique in order to explore relevant regions inside the placenta. Finally, to survey the applicability of our approach, we consulted clinical experts in prenatal diagnostics and imaging. We received mainly positive feedback, especially the applicability of our technique for research purposes was appreciated.