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DANIELE MONTEPIETRA


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2023 - The beauty and complexity of the small heat shock proteins: a report on the proceedings of the fourth workshop on small heat shock proteins [Articolo su rivista]
Ecroyd, Heath; Bartelt-Kirbach, Britta; Ben-Zvi, Anat; Bonavita, Raffaella; Bushman, Yevheniia; Casarotto, Elena; Cecconi, Ciro; Lau, Wilson Chun Yu; Hibshman, Jonathan D; Joosten, Joep; Kimonis, Virginia; Klevit, Rachel; Liberek, Krzysztof; Mcmenimen, Kathryn A; Miwa, Tsukumi; Mogk, Axel; Montepietra, Daniele; Peters, Carsten; Rocchetti, Maria Teresa; Saman, Dominik; Sisto, Angela; Secco, Valentina; Strauch, Annika; Taguchi, Hideki; Tanguay, Morgan; Tedesco, Barbara; Toth, Melinda E; Wang, Zihao; Benesch, Justin L P; Carra, Serena
abstract

: The Fourth Cell Stress Society International workshop on small heat shock proteins (sHSPs), a follow-up to successful workshops held in 2014, 2016 and 2018, took place as a virtual meeting on the 17-18 November 2022. The meeting was designed to provide an opportunity for those working on sHSPs to reconnect and discuss their latest work. The diversity of research in the sHSP field is reflected in the breadth of topics covered in the talks presented at this meeting. Here we summarise the presentations at this meeting and provide some perspectives on exciting future topics to be addressed in the field.


2022 - Combining enhanced sampling and deep learning dimensionality reduction for the study of the heat shock protein B8 and its pathological mutant K141E [Articolo su rivista]
Montepietra, Daniele; Cecconi, Ciro; Brancolini, Giorgia
abstract

The biological functions of proteins closely depend on their conformational dynamics. This aspect is especially relevant for intrinsically disordered proteins (IDP) for which structural ensembles often offer more useful representations than individual conformations. Here we employ extensive enhanced sampling temperature replica-exchange atomistic simulations (TREMD) and deep learning dimensionality reduction to study the conformational ensembles of the human heat shock protein B8 and its pathological mutant K141E, for which no experimental 3D structures are available. First, we combined homology modelling with TREMD to generate high-dimensional data sets of 3D structures. Then, we employed a recently developed machine learning based post-processing algorithm, EncoderMap, to project the large conformational data sets into meaningful two-dimensional maps that helped us interpret the data and extract the most significant conformations adopted by both proteins during TREMD. These studies provide the first 3D structural characterization of HSPB8 and reveal the effects of the pathogenic K141E mutation on its conformational ensembles. In particular, this missense mutation appears to increase the compactness of the protein and its structural variability, at the same time rearranging the hydrophobic patches exposed on the protein surface. These results offer the possibility of rationalizing the pathogenic effects of the K141E mutation in terms of conformational changes.