Publications associées à Protein Data Bank

A Geometric Transformer for Structural Biology: Development and Applications of the Protein Structure Transformer

Proteins, the central building blocks of life, play pivotal roles in nearly every biological function. To do so, these macromolecular structures interact with their surrounding environment in complex ways, leading to diverse functional behaviors. The predi ...

EPFL2023

De novo design of protein interactions with learned surface fingerprints

Didier Trono, Henning Paul-Julius Stahlberg, Beat Fierz, Priscilla Turelli, Bruno Emanuel Ferreira De Sousa Correia, Elisa Oricchio, Sandrine Madeleine Suzanne Georgeon, Dongchun Ni, Andreas Scheck, Michael Bronstein, Charlène Mireille Raymonde Raclot, Anthony Marchand, Alexandra Teslenko, Casper Alexander Goverde, Aaron Simone Petruzzella, Stephen Michael Buckley, Martin Pacesa, Pablo Gainza Cirauqui, Stéphane Rosset, Zander Harteveld, Sarah Wehrle, Freyr Sverrisson, Alexandra Krina Van Hall-Beauvais, Jane Marsden

Physical interactions between proteins are essential for most biological processes governing life. However, the molecular determinants of such interactions have been challenging to understand, even as genomic, proteomic and structural data increase. This k ...

2023

Vanadium complexes with N-heterocyclic vinylidene ligands

Rosario Scopelliti, Kay Severin, Farzaneh Fadaei Tirani, Laura Piveteau, Bastiaan Kooij, Zhaowen Dong, Paul Varava

The preparation and the structural characterization of vanadium complexes with terminal and bridging N-heterocyclic vinylidene ligands is reported. The synthesis of the complexes was enabled by utilization of diazoolefins as ligand precursors. Structural d ...

Royal Society of Chemistry2022

Statistical potentials from the Gaussian scaling behaviour of chain fragments buried within protein globules

Stefano Zamuner

Knowledge-based approaches use the statistics collected from protein data-bank structures to estimate effective interaction potentials between amino acid pairs. Empirical relations are typically employed that are based on the crucial choice of a reference ...

PUBLIC LIBRARY SCIENCE2022

Amplitude and phase reconstruction for Low-Energy Electron Holography of individual proteins

Hannah Julia Ochner

Single-molecule imaging methods are of importance in structural biology, and specifically in the imaging of proteins, since they can elucidate conformational variability and structural changes that might be lost in imaging methods relying on averaging proc ...

EPFL2022

cell2mol: encoding chemistry to interpret crystallographic data

Rubén Laplaza Solanas, Anne-Clémence Corminboeuf, Sergio Vela Llausi, Yuri Cho

The creation and maintenance of crystallographic data repositories is one of the greatest data-related achievements in chemistry. Platforms such as the Cambridge Structural Database host what is likely the most diverse collection of synthesizable molecules ...

NATURE PORTFOLIO2022

A long-term study on structural changes in calcium aluminate silicate hydrates

Christian Ludwig, Barbara Lothenbach, Sonya Barzgar, Yu Yan, Mohamed Tarik

Production of blended cements in which Portland cement is combined with supplementary cementitious materials (SCM) is an effective strategy for reducing the CO2 emissions during cement manufacturing and achieving sustainable concrete production. However, t ...

SPRINGER2022

Targeting molecular surfaces to engineer novel protein-based immunogens and inhibitors

Sarah Wehrle

Proteins are the basic building blocks necessary for the operation and regulation of virtually all functions in living organisms. Over millions of years, evolution has created a vast repertoire of proteins finely tuned to execute their biological functions ...

EPFL2022

Assessing normalization methods in mass spectrometry-based proteome profiling of clinical samples

Loïc Dayon

Background: Large-scale proteomic studies have to deal with unwanted variability, especially when samples originate from different centers and multiple analytical batches are needed. Such variability is typically added throughout all the steps of a clinica ...

ELSEVIER SCI LTD2022

Azole-Based Indoleamine 2,3-Dioxygenase 1 (IDO1) Inhibitors

Pierre Vogel, Florence Pojer, Somi Reddy Majjigapu, George Coukos, Olivier Michielin

The heme enzyme indoleamine 2,3-dioxygenase 1 (IDO1) plays an essential role in immunity, neuronal function, and aging through catalysis of the rate-limiting step in the kynurenine pathway of tryptophan metabolism. Many IDO1 inhibitors with different chemo ...

AMER CHEMICAL SOC2021

Three-membered cyclic digermylenes stabilised by an N-heterocyclic carbene dagger

Zhaowen Dong

Treatment of potassium salts of silole dianions with donor stabilised germanium dichlorides gave the anticipated silagermafulvenylidenes R2Si = Ge(Do) (R2Si = 1-silacyclopentadiendiyl, Do = N-heterocyclic carbene (NHC)) only as transient intermediates in a ...

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

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

New Methods for the Integrative Dynamic Modeling of Biomolecular Structures

Sylvain Träger

The true understanding of most cellular functions is only really achievable through the structural determination of their underlying macromolecular assemblies. However, their size, large number of individual components and metastable states make their eluc ...

EPFL2021

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

Protein Structural Information and Evolutionary Landscape by In Vitro Evolution

Paolo De Los Rios

Protein structure is tightly intertwined with function according to the laws of evolution. Understanding how structure determines function has been the aim of structural biology for decades. Here, we have wondered instead whether it is possible to exploit ...

OXFORD UNIV PRESS2020

Tailoring Chiral Cyclopentadienyl Rhodium(III) Catalysts for Enantioselective Alkenyl C-H functionalizations

Coralie Danielle Annick Duchemin

Chiral cyclopentadienyl (Cpx) metal complexes have empowered the developement of challenging asymmetric C(sp2)-H activation transformations. However the preparation of competent atropchiral Cpx ligands is long and tedious. Moreover the range of application ...

EPFL2020

Atomic Motif Recognition in (Bio)Polymers: Benchmarks From the Protein Data Bank

Michele Ceriotti, Piero Gasparotto, Benjamin Aaron Helfrecht, Federico Giberti

Rationalizing the structure and structure–property relations for complex materials such as polymers or biomolecules relies heavily on the identification of local atomic motifs, e.g., hydrogen bonds and secondary structure patterns, that are seen as buildin ...

2019

Enhanced Student Learning in Proteomics - an Interactive Tool Support for Teaching Workflows

Kalpani Anuradha Welivita

Teaching and learning support for bioinformatics has become a popular research area. Proteomics education can be bridged with technology enhancements such that tool support for protein structures experiments. Generally, students in proteomics require diffe ...

IEEE2018

Error Tolerance Analysis of Deep Learning Hardware Using a Restricted Boltzmann Machine Toward Low-Power Memory Implementation

Alexandre Schmid, Takao Marukame, Kodai Ueyoshi

Remarkable hardware robustness of deep learning (DL) is revealed by error injection analyses performed using a custom hardware model implementing parallelized restricted Boltzmann machines (RBMs). RBMs in deep belief networks demonstrate robustness against ...

Ieee-Inst Electrical Electronics Engineers Inc2017