The Height of Chitinous Ridges Alone Produces the Entire Structural Color Palette

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Computationally designed GPCR quaternary structures bias signaling pathway activation

Patrick Daniel Barth, Robert Everett Jefferson

Communication across membranes controls critical cellular processes and is achieved by receptors translating extracellular signals into selective cytoplasmic responses. While receptor tertiary structures can be readily characterized, receptor associations ...

Nature Research2022

Identifying the origin of local flexibility in a carbohydrate polymer

Klaus Kern, Stephan Rauschenbach, Sabine Abb

Correlating the structures and properties of a polymer to its monomer sequence is key to understanding how its higher hierarchy structures are formed and how its macroscopic material properties emerge. Carbohydrate polymers, such as cellulose and chitin, a ...

National Academy of Sciences2021

SPServer: split-statistical potentials for the analysis of protein structures and protein-protein interactions

Jaume Bonet Martinez

BackgroundStatistical potentials, also named knowledge-based potentials, are scoring functions derived from empirical data that can be used to evaluate the quality of protein folds and protein-protein interaction (PPI) structures. In previous works we deco ...

2021

Assessment of transferable forcefields for protein simulations attests improved description of disordered states and secondary structure propensities, and hints at multi-protein systems as the next challenge for optimization

Matteo Dal Peraro, Luciano Andres Abriata

Continuous assessment of transferable forcefields for molecular simulations is essential to identify their weaknesses and direct improvement efforts. The latest efforts focused on better describing disordered proteins while retaining proper description of ...

2021

On the use of direct-coupling analysis with a reduced alphabet of amino acids combined with super-secondary structure motifs for protein fold prediction

Jaume Bonet Martinez

Direct-coupling analysis (DCA) for studying the coevolution of residues in proteins has been widely used to predict the three-dimensional structure of a protein from its sequence. We present RADI/raDIMod, a variation of the original DCA algorithm that grou ...

OXFORD UNIV PRESS2021

Silk granular hydrogels self-reinforced with regenerated silk fibroin fibers

Dominique Pioletti, Pierre-Etienne Bourban, Céline Samira Wyss, Peyman Karami, Adrien Julien Demongeot

Granular hydrogels with high stability, strength, and toughness are laborious to develop. Post-curing is often employed to bind microgels chemically and enhance mechanical properties. Here a unique strategy was investigated to maintain microgels together w ...

2021

3D architecture and structural flexibility revealed in the subfamily of large glutamate dehydrogenases by a mycobacterial enzyme

Luciano Andres Abriata

Glutamate dehydrogenases (GDHs) are widespread metabolic enzymes that play key roles in nitrogen homeostasis. Large glutamate dehydrogenases composed of 180kDa subunits (L-GDHs(180)) contain long N- and C-terminal segments flanking the catalytic core. Desp ...

2021

Structure-Property Relationships in Complex Materials by Combining Supervised and Unsupervised Machine Learning

Benjamin Aaron Helfrecht

The work presented in this thesis combines supervised and unsupervised machine learning to examine structure-property relationships in databases of materials. While either supervised learning or unsupervised learning alone can be a powerful tool for assess ...

EPFL2021

Modeling Immunity with Rosetta: Methods for Antibody and Antigen Design

Bruno Emanuel Ferreira De Sousa Correia, Jaume Bonet Martinez

Structure-based antibody and antigen design has advanced greatly in recent years, due not only to the increasing availability of experimentally determined structures but also to improved computational methods for both prediction and design. Constant improv ...

AMER CHEMICAL SOC2021

Fast end-to-end learning on protein surfaces

Bruno Emanuel Ferreira De Sousa Correia, Freyr Sverrisson

Proteins' biological functions are defined by the geometric and chemical structure of their 3D molecular surfaces. Recent works have shown that geometric deep learning can be used on mesh-based representations of proteins to identify potential functional s ...

IEEE COMPUTER SOC2021

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