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Concept

Protein function prediction

Related publications (31)

OccuVAE: Integrating unsupervised occupancy inference in data-driven energy modeling for human-centric operation

Andrew James Sonta, Yufei Zhang

Data-driven building energy modeling is an emerging solution to facilitate the implementation of energy-flexible buildings. However, its black-box nature hinders interpretation, including with respect to human-building interaction. This drawback may bring ...

Association for Computing Machinery2023

Impact of phylogeny on structural contact inference from protein sequence data

Anne-Florence Raphaëlle Bitbol, Umberto Lupo, Nicola Dietler

Local and global inference methods have been developed to infer structural contacts from multiple sequence alignments of homologous proteins. They rely on correlations in amino acid usage at contacting sites. Because homologous proteins share a common ance ...

ROYAL SOC2023

Thermo-hydro-mechanical analysis of the complete lifetime of the bentonite barrier in the FEBEX in-situ test

Lyesse Laloui, Alessio Ferrari, Jose Antonio Bosch Llufriu, Yafei Qiao

The FEBEX test was a large-scale demonstration project for the deep geological disposal concept of nuclear waste involving bentonite seals that lasted 18 years. One of the objectives of the test was to evaluate the capabilities of numerical methods to prov ...

2023

Systems and methods for de novo design of protein interactions with learned surface fingerprints

Pablo Gainza Cirauqui, Michael Bronstein, Zander Harteveld, Andreas Scheck, Sarah Wehrle, Anthony Marchand, Alexandra Krina Van Hall-Beauvais, Freyr Sverrisson

The present application relates to a computer-implemented systems and methods for protein interaction design using surface fingerprints. The method comprises predicting at least one target interface site with high binding propensity, wherein, optionally, t ...

2023

De Novo Crystal Structure Determination from Machine Learned Chemical Shifts

David Lyndon Emsley, Manuel Cordova, Albert Hofstetter, Martins Balodis

Determination of the three-dimensional atomic-level structure of powdered solids is one of the key goals in current chemistry. Solid-state NMR chemical shifts can be used to solve this problem, but they are limited by the high computational cost associated ...

2022

Correlations from structure and phylogeny combine constructively in the inference of protein partners from sequences

Anne-Florence Raphaëlle Bitbol, Nicola Dietler

Inferring protein-protein interactions from sequences is an important task in computational biology. Recent methods based on Direct Coupling Analysis (DCA) or Mutual Information (MI) allow to find interaction partners among paralogs of two protein families ...

Public Library Science2022

Pre-bcc: A novel integrated machine learning framework for predicting mechanical and durability properties of blended cement concrete

Hisham Tarek Mohamed Hafez

Partially replacing ordinary Portland cement (OPC) with low-carbon supplementary cementitious materials (SCMs) in blended cement concrete (BCC) is perceived as the most promising route for sustainable concrete production. Despite having a lower environment ...

ELSEVIER SCI LTD2022

Deep learning approaches for conformational flexibility and switching properties in protein design

Patrick Daniel Barth, Lucas Sebastian Powell-Rudden, Mahdi Hijazi

Following the hugely successful application of deep learning methods to protein structure prediction, an increasing number of design methods seek to leverage generative models to design proteins with improved functionality over native proteins or novel str ...

2022

Towards automating de novo protein design for novel functionalities: controlling protein folds and protein-protein interactions

Zander Harteveld

The sheer size of the protein sequence space is massive: a protein of 100 residues can have 20^100 possible sequence combinations; and knowing that this exceeds the number of atoms in the universe, the chance of randomly discovering a stable new sequence w ...

EPFL2022

Extracting phylogenetic dimensions of coevolution reveals hidden functional signals

Anne-Florence Raphaëlle Bitbol

Despite the structural and functional information contained in the statistical coupling between pairs of residues in a protein, coevolution associated with function is often obscured by artifactual signals such as genetic drift, which shapes a protein's ph ...

Nature Portfolio2022

Beyond potentials: Integrated machine learning models for materials

Michele Ceriotti

Over the past decade, interatomic potentials based on machine learning (ML) techniques have become an indispensable tool in the atomic-scale modeling of materials. Trained on energies and forces obtained from electronic-structure calculations, they inherit ...

SPRINGER HEIDELBERG2022