Concept

Sequence analysis

Related publications (256)

Author summaryWhen two protein families interact, their sequences feature statistical dependencies. First, interacting proteins tend to share a common evolutionary history. Second, maintaining structure and interactions through the course of evolution yiel ...

PUBLIC LIBRARY SCIENCE2023

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

Generative power of a protein language model trained on multiple sequence alignments

Anne-Florence Raphaëlle Bitbol, Damiano Sgarbossa, Umberto Lupo

Computational models starting from large ensembles of evolutionarily related protein sequences capture a representation of protein families and learn constraints associated to protein structure and function. They thus open the possibility for generating no ...

eLIFE SCIENCES PUBL LTD2023

Single-Molecule Peptide Identification Using Fluorescence Blinking Fingerprints

Pablo Rivera Fuentes, Salome Riccarda Püntener

The ability to identify peptides with single-molecule sensitivity would lead to next-generation proteomics methods for basic research and clinical applications. Existing single-molecule peptide sequencing methods can read some amino acid sequences, but the ...

AMER CHEMICAL SOC2023

Protein target highlights in CASP15: Analysis of models by structure providers

Bruno Lemaitre, Luciano Andres Abriata, Samuel Rommelaere, Kuan-Lin Wu, Han Xiao

We present an in-depth analysis of selected CASP15 targets, focusing on their biological and functional significance. The authors of the structures identify and discuss key protein features and evaluate how effectively these aspects were captured in the su ...

WILEY2023

Protein language models trained on multiple sequence alignments learn phylogenetic relationships

Anne-Florence Raphaëlle Bitbol, Damiano Sgarbossa, Umberto Lupo

Self-supervised neural language models with attention have recently been applied to biological sequence data, advancing structure, function and mutational effect prediction. Some protein language models, including MSA Transformer and AlphaFold's EvoFormer, ...

NATURE PORTFOLIO2022

Towards mouse genetic-specific RNA-sequencing read mapping

Maxime Jan, Ioannis Xenarios

Genetic variations affect behavior and cause disease but understanding how these variants drive complex traits is still an open question. A common approach is to link the genetic variants to intermediate molecular phenotypes such as the transcriptome using ...

2022

Plasma confinement mode classification using a sequence-to-sequence neural network with attention

Alessandro Pau

In a typical fusion experiment, the plasma can have several possible confinement modes. At the tokamak a configuration variable, aside from the low (L) and high (H) confinement modes, an additional mode, dithering (D), is frequently observed. Developing me ...

2021

Ultrafast homomorphic encryption models enable secure outsourcing of genotype imputation

Jean-Pierre Hubaux, Juan Ramón Troncoso-Pastoriza, Jean-Philippe Léonard Bossuat, David Jules Froelicher, Yiping Ma

Genotype imputation is a fundamental step in genomic data analysis, where missing variant genotypes are predicted using the existing genotypes of nearby "tag"variants. Although researchers can outsource genotype imputation, privacy concerns may prohibit ge ...

CELL PRESS2021

A Rosetta-based protein design protocol converging to natural sequences

Bruno Emanuel Ferreira De Sousa Correia, Zander Harteveld, Stéphane Rosset, Giulia Sormani

Computational protein design has emerged as a powerful tool capable of identifying sequences compatible with pre-defined protein structures. The sequence design protocols, implemented in the Rosetta suite, have become widely used in the protein engineering ...

AMER INST PHYSICS2021

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