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1.
Nat Commun ; 14(1): 2175, 2023 04 18.
Article in English | MEDLINE | ID: mdl-37072397

ABSTRACT

Proteins are essential molecular building blocks of life, responsible for most biological functions as a result of their specific molecular interactions. However, predicting their  binding  interfaces remains a challenge. In this study, we present a geometric transformer that acts directly on atomic coordinates labeled only with element names. The resulting model-the Protein Structure Transformer, PeSTo-surpasses the current state of the art in predicting protein-protein interfaces and can also predict and differentiate between interfaces involving nucleic acids, lipids, ions, and small molecules with high confidence. Its low computational cost enables processing high volumes of structural data, such as molecular dynamics ensembles allowing for the discovery of interfaces that remain otherwise inconspicuous in static experimentally solved structures. Moreover, the growing foldome provided by de novo structural predictions can be easily analyzed, providing new opportunities to uncover unexplored biology.


Subject(s)
Deep Learning , Protein Binding , Proteins/metabolism , Molecular Dynamics Simulation , Computational Biology/methods
2.
J Mol Graph Model ; 114: 108164, 2022 07.
Article in English | MEDLINE | ID: mdl-35325844

ABSTRACT

Several groups developed in the last years augmented and virtual reality (AR/VR) software to visualize 3D molecules, most rather static, limited in content, and requiring software installs, some even requiring expensive hardware. We launched in 2020 moleculARweb (https://molecularweb.epfl.ch), a website that offers interactive content for chemistry and structural biology education through commodity web-based AR that works on consumer devices like smartphones, tablets and laptops. Among thousands of users, teachers increasingly request more biological macromolecules to be available, a demand that we cannot address individually. Therefore, to allow users to build their own material, we built a web interface where they can create online AR experiences in few steps starting from Protein Data Bank, AlphaFold or custom uploaded structures, or from virtual objects/scenes exported from the Visual Molecular Dynamics program, without any programming knowledge. The web tool also returns WebXR sessions for viewing and manipulating the models in WebXR-compatible devices including smartphones, tablets, and also immersive VR headsets with WebXR-capable browsers, where models can be manipulated even with bare hands when supported by the device. The tool is accessible for free at https://molecularweb.epfl.ch/pages/pdb2ar.html.


Subject(s)
Virtual Reality , Models, Molecular , Software
3.
Chimia (Aarau) ; 76(1-2): 145-150, 2022 Feb 23.
Article in English | MEDLINE | ID: mdl-38069760

ABSTRACT

moleculARweb (https://molecularweb.epfl.ch) began as a website for education and outreach in chemistry and structural biology through augmented reality (AR) content that runs in the web browsers of regular devices like smartphones, tablets, and computers. Here we present two evolutions of moleculARweb's Virtual Modeling Kits (VMK), tools where users can build and view molecules, and explore their mechanics, in 3D AR by handling the molecules in full 3D with custom-printed cube markers (VMK 2.0) or by moving around a simulated scene with mouse or touch gestures (VMK 3.0). Upon simulation the molecules experience visually realistic torsions, clashes, and hydrogen-bonding interactions that the user can manually switch on and off to explore their effects. Moreover, by manually tuning a fictitious temperature the users can accelerate conformational transitions or 'freeze' specific conformations for careful inspection in 3D. Even some phase transitions and separations can be simulated. We here showcase these and other features of the new VMKs connecting them to possible specific applications to teaching and self-learning of concepts from general, organic, biological and physical chemistry; and in assisting with small tasks in molecular modelling for research. Last, in a short discussion section we overview what future developments are needed for the 'dream tool' for the future of chemistry education and work.

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