Prot2Prot: a deep learning model for rapid, photorealistic macromolecular visualization

Abstract

Molecular visualization is a cornerstone of structural biology, providing insights into the form and function of biomolecules that are difficult to achieve any other way. Scientific analysis, publication, education, and outreach often benefit from photorealistic molecular depictions rendered using advanced computer-graphics programs such as Maya, 3ds Max, and Blender. However, setting up molecular scenes in these programs is laborious even for expert users, and rendering often requires substantial time and computer resources. We have created a deep-learning model called Prot2Prot that quickly imitates photorealistic visualization styles, given a much simpler, easy-to-generate molecular representation. The resulting images are often indistinguishable from images rendered using industry-standard 3D graphics programs, but they can be created in a fraction of the time, even when running in a web browser. To the best of our knowledge, Prot2Prot is the first example of image-to-image translation applied to macromolecular visualization. Prot2Prot is available free of charge, released under the terms of the Apache License, Version 2.0. Users can access a Prot2Prot-powered web app without registration at http://durrantlab.com/prot2prot.

FPocketWeb: protein pocket hunting in a web browser

“…The FPocketWeb graphical user interface (GUI) allows users to quickly load protein structures into the browser's memory, specify FPocketWeb parameters, run calculations using the Wasm-compiled fpocket module, and visualize/download the results. To create the interface, we followed the same approach we have used previously [9][10][11][12]. The FPocketWeb interface is written in the Microsoft TypeScript programming language, which compiles to JavaScript.…”

Copper(ii) complexes of sterically hindered phenolic Schiff bases: synthesis, characterization, interaction with biomolecules, and antioxidant and antimicrobial activity

“…81 BINANA software was used to analyse the results. 82 Graphics was created using BIOVIA software. 3D-structures of ligands were calculated using Gaussian software.…”

From complex data to clear insights: visualizing molecular dynamics trajectories

“…To visualize such data, ( Chae et al, 2019 ), presented an interactive visual analysis system for embeddings of MD simulations, aiming to assess and elucidate an embedding model while also analyzing various characteristics of the simulations. Recently, the integration of deep learning algorithms into molecular dynamics visualization programs has been observed, enabling the rapid emulation of photorealistic visualization styles from simpler, more accessible molecular representations ( Durrant, 2022 ). This development has made it easier and faster to create animations that represent molecular dynamics simulations.…”