Journal club

Journal club meetings are on Mondays at 2-3 pm through video conference.

March 9, 2022
Learning the molecular grammar of protein condensates from sequence determinants and embeddings

Saar, K. L., et al. (2021) PNAS, 118(15). https://doi.org/10.1073/pnas.2019053118

Abstract PDF | SI

presented by Gilberto

February 23, 2022
Engineering the protein dynamics of an ancestral luciferase

Schenkmayerova, A., Pinto, G.P., Toul, M. et al. Engineering the protein dynamics of an ancestral luciferase. Nat Commun 12, 3616 (2021). https://doi-org.proxy1.cl.msu.edu/10.1038/s41467-021-23450-z

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presented by Lim

February 9, 2022
Deciphering how naturally occurring sequence features impact the phase behaviours of disordered prion-like domains

Bremer, A., et al. (2022). Nature Chemistry14(2), 196–207. https://doi.org/10.1038/s41557-021-00840-w

Abstract PDF | SI

presented by Gilberto

January 12, 2022
RNA length has a non-trivial effect in the stability of biomolecular condensates formed by RNA-binding proteins

Sanchez-burgos, I., Espinosa, J. R., & Joseph, J. A. (2021). https://www.biorxiv.org/content/10.1101/2021.10.07.463486v1

Abstract PDF

presented by Gilberto

November 3, 2021
A Data-Driven Hydrophobicity Scale for Predicting Liquid–Liquid Phase Separation of Proteins.

Dannenhoffer-Lafage, T., & Best, R. B. (2021). JPC B125(16), 4046–4056. https://doi.org/10.1021/acs.jpcb.0c11479

Abstract PDF | SI

presented by Gilberto

September 15, 2021
Geometric deep learning of RNA structure

Townshend, R. J. L.;  Eismann, S.;  Watkins, A. M.;  Rangan, R.;  Karelina, M.;  Das, R.; Dror, R. O., Geometric deep learning of RNA structure. Science 2021, 373 (6558), 1047-1051.

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presented by Lim

August 25, 2021
Comparative roles of charge, π , and hydrophobic interactions in sequence-dependent phase separation of intrinsically disordered proteins.

Das, S., Lin, Y.-H., Vernon, R. M., Forman-Kay, J. D., & Chan, H. S. (2020) PNAS117(46), 28795–28805. https://doi.org/10.1073/pnas.2008122117

Abstract PDF | SI

presented by Gilberto

July 21, 2021
Highly accurate protein structure prediction with AlphaFold

Jumper, J.;  Evans, R.;  Pritzel, A.;  Green, T.;  Figurnov, M.;  Ronneberger, O.;  Tunyasuvunakool, K.;  Bates, R.;  Žídek, A.;  Potapenko, A.;  Bridgland, A.;  Meyer, C.;  Kohl, S. A. A.;  Ballard, A. J.;  Cowie, A.;  Romera-Paredes, B.;  Nikolov, S.;  Jain, R.;  Adler, J.;  Back, T.;  Petersen, S.;  Reiman, D.;  Clancy, E.;  Zielinski, M.;  Steinegger, M.;  Pacholska, M.;  Berghammer, T.;  Bodenstein, S.;  Silver, D.;  Vinyals, O.;  Senior, A. W.;  Kavukcuoglu, K.;  Kohli, P.; Hassabis, D., Highly accurate protein structure prediction with AlphaFold. Nature 2021.

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presented by Lim

July 7, 2021
A Generative Model for Molecular Distance Geometry

Simm and Hernández-Lobato (2020) Proceedings of the 37th International Conference on Machine Learning

Abstract | PDF

Presented by Giacomo

June 23, 2021
Thermodynamics and kinetics of phase separation of protein-RNA mixtures by a minimal model

Joseph, J. A., Espinosa, J. R., Sanchez-Burgos, I., Garaizar, A., Frenkel, D., & Collepardo-Guevara, R. (2021). Biophysical Journal120(7), 1219–1230. https://doi.org/10.1016/j.bpj.2021.01.031

Abstract PDF

presented by Gilberto

June 9, 2021
Learning from Protein Structure with Geometric Vector Perceptrons

Jing et al. (2021) Ninth International Conference on Learning Representations

Abstract | PDF

Presented by Giacomo

May 19, 2021
Arginine multivalency stabilizes protein/RNA condensates

Paloni, M., Bussi, G., & Barducci, A. (2021). Protein Science, pro.4109. https://doi.org/10.1002/pro.4109

Abstract PDF

presented by Gilberto

May 12, 2021
Efficient sampling of high-dimensional free energy landscapes using adaptive reinforced dynamics

Wang D, Zhang L, Wang H, E W. arXiv. 2021.

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presented by Lim

May 5, 2021
Differentiable molecular simulation can learn all the parameters in a coarse-grained force field for proteins

Greener and Jones, (2021) bioRxiv

Abstract | PDF

Presented by Giacomo

April 28, 2021
Sequence-encoded and composition-dependent protein-RNA interactions control multiphasic condensate morphologies

Kaur, T., Raju, M., Alshareedah, I. et al. Nat Commun 12, 872 (2021). https://doi.org/10.1038/s41467-021-21089-4

Abstract PDF | SI

presented by Gilberto

April 21, 2021
Predicting new protein conformations from molecular dynamics simulation conformational landscapes and machine learning

Yiming Jin, Linux O. Johannissen, Sam Hay: Prediction new protein conformations from molecular dynamics simulation conformational landscapes and machine learning. Proteins (2021)

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presented by Michael

April 21, 2021
MSA Transformer

Rao R, Liu J, Verkuil R, Meier J, Canny JF, Abbeel P, et al. MSA Transformer. bioRxiv. 2021.

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presented by Lim

April 14, 2021
Protein sequence design by conformational landscape optimization

Norn et al., (2021) PNAS

Abstract | PDF | SI

Presented by Giacomo

April 7, 2021
FtsZ-Induced shape transformation of coacervates

Fanalista, F., Deshpande, S., Lau, A., Pawlik, G., & Dekker, C. (2018). Advanced Biosystems2(9), 1800136. https://doi.org/10.1002/adbi.201800136

Abstract PDF | SI

presented by Gilberto

March 24, 2021
Mechanistic basis for ubiquitin modulation of a protein energy landscape

Carroll, E.C., Latorraca, N.R., Lindner, J.M., Maguire, B.C., Pelton, J.G., and Marqusee, S. Proc. Natl. Acad. Sci. U.S.A. (2021).

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presented by Lim