Tag: Simon Olsson

Protein Dynamics Beyond Structure Prediction on ArXiv

Computational advances in protein folding studies. Current approaches address multiple levels of resolution and methodological frameworks, however, none of the existing methods provides quantitative and dynamic information of the relationship between protein sequence and folding mechanism at all-atom resolution and at scale. (Graphics by J. Sacquegno.)
Protein Dynamics Beyond Structure Prediction
Juliette Griffié, Sviatlana Shashkova, Antonio Ciarlo, Sreekanth K. Manikandan, Claes Andréasson, Malin Bäckström, Tristan Bereau, Hjalmar Brismar, Carlos Bustamante, Marta Carroni, Roberto Covino, Andreas Dahlin, Sebastian Deindl, Lucie Delemotte, Arne Elofsson, John Eriksson, Giovanna Fragneto, Anders Gunnarsson, Per Hammarström, Caroline Ingre, Christian Kaiser, Petronella Kettunen, Mark C. Leake, Benjamin Loos, Anna Månberg, Antonia S. J. S. Mey, Richard Neutze, Thomas Nyström, Karl Palmås, Charley Schaefer, Markus J. Tamás, Nicola Ticozzi, Tomás S. Pilvelic, Jacopo Sacquegno, B.M. (Betty)Tijms, Gunnar von Heijne, Björn Wallner, Vitali Zhaunerchyk, Simon Olsson, Joana B. Pereira, Julia Fernandez-Rodriguez, Fredrik Westerlund, Giovanni Volpe
arXiv: 2606.08647

How microorganisms respond to and interact with their environment can vary significantly from individual to individual, which can have important microbiological and ecological implications. However, most microscopy techniques can only observe motile microorganisms for short times because of their limited fields of view. Using Lagrangian tracking, a single microorganism can be followed in 3D, potentially indefinitely, allowing to decipher individual phenotypical traits. Current Lagrangian tracking methods use the fluorescence signal emitted by the microorganism as feedback to keep it in focus. However, over long times, epifluorescent imaging can induce photobleaching and photodamage, and importantly, not all microorganisms can easily be made fluorescent. Additionally, traditional algorithms used in feedback loops to determine microorganism position are prone to errors, especially in optically complex media. Here, we present a faster, more reliable, and versatile Lagrangian tracking method that uses deep learning to determine the 3D position of the microorganism. This new method demonstrates enhanced accuracy and speed in tracking fluorescent bacteria with fluorescence microscopy also in optically complex media. Furthermore, we track bacteria with other microscopy modalities, such as brightfield microscopy — for example, this enables us to track magnetotactic bacteria, which cannot be made fluorescent without degrading their magnetotactic properties. These novel capabilities allow to extract previously inaccessible quantitative information, significantly advancing the study of microorganism behavior — and thus opening new avenues for research in complex biological and ecological systems.

Presentation by S. Olsson, 20 October 2021

Machine Learning for Molecular dynamics — Why bother?
Simon Olsson
Chalmers University of Technology
20 October 2021
Online

With faster compute-infrastructures, molecular simulations play an increasingly important role in the basic sciences and application areas such as drug and materials design. Simultaneously, machine learning and artificial intelligence are receiving increased attention due to increasing volumes of data generated both inside and outside of science. In this talk, I will talk about a few applications of these technologies in molecular simulation, focusing on biomolecular simulations [1,2]

[1] Olsson & Noé ”Dynamic Graphical Models of Molecular Kinetics” Proc. Natl. Acad. Sci. U.S.A. (2019) doi: 10.1073/pnas.1901692116.
[2] Noe†, Olsson, Köhler, Wu ”Boltzmann Generators: Sampling Equilibrium States of Many-Body Systems with Deep Learning” Science (2019). 365, eaaw1147. doi:10.1126/science.aaw1147.

Link: http://www.cse.chalmers.se/~simonols/