Publications related to IMPACT: Interval-based Multi-pass Proteomic Alignment with Constant Traceback

Related publications (32)

Benchmarking informatics approaches for virus discovery: caution is needed when combining in silico identification methods

Understanding the ecological impacts of viruses on natural and engineered ecosystems relies on the accurate identification of viral sequences from community sequencing data. To maximize viral recovery from metagenomes, researchers frequently combine viral ...

Amer Soc Microbiology2024

Opportunities and challenges in design and optimization of protein function

Bruno Emanuel Ferreira De Sousa Correia, Casper Alexander Goverde

The field of protein design has made remarkable progress over the past decade. Historically, the low reliability of purely structure-based design methods limited their application, but recent strategies that combine structure-based and sequence-based calcu ...

Nature Portfolio2024

Investigating the intra-molecular and inter-molecular effects of post-translational modifications on intrinsically disordered protein regions and structured protein regions

Zhidian Zhang

Post-translational modifications (PTMs) play a pivotal role in regulating protein structure, interaction, and function. Aberrant PTM patterns are associated with diseases. Moreover, individual PTMs have a complex interaction with each other, known as PTM c ...

EPFL2024

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

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

Genetic Algorithms for the Discovery of Homogeneous Catalysts

Anne-Clémence Corminboeuf, Rubén Laplaza Solanas, Simone Gallarati, Puck Elisabeth van Gerwen, Alexandre Alain Schöpfer

In this account, we discuss the use of genetic algorithms in the inverse design process of homogeneous catalysts for chemical transformations. We describe the main components of evolutionary experiments, specifically the nature of the fitness function to o ...

2023

A combined genetic algorithm and active learning approach to build and test surrogate models in Process Systems Engineering

François Maréchal, Julia Granacher

In Process Systems Engineering, computationally-demanding models are frequent and plentiful. Handling such complexity in an optimization framework in a fast and reliable way is essential, not only for generating meaningful solutions but also for providing ...

Pergamon-Elsevier Science Ltd2023

Combining phylogeny and coevolution improves the inference of interaction partners among paralogous proteins

Anne-Florence Raphaëlle Bitbol

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

Supplementary dataset for Enzyme promiscuous profiles for protein sequence and reaction annotation

Vassily Hatzimanikatis, Ljubisa Miskovic, Anastasia Sveshnikova, Homa Mohammadi Peyhani

The data and scripts used to produce, analyze, and visualize the results of the manuscript Enzyme promiscuous profiles for protein sequence and reaction annotation by Homa MohammadiPeyhani, Anastasia Sveshnikova, Ljubisa Miskovic, and Vassily Hatzimanikati ...

Zenodo2023

Wind farm power density optimization according to the area size using a novel self-adaptive genetic algorithm

Fernando Porté Agel, Nicolas Otto Kirchner Bossi

This work studies the power density (PD) optimization in wind farms, and its sensitivity to the available area size. A novel genetic algorithm (PDGA) is introduced, which optimizes PD and the turbine layout, by self-adapting to the PD and to the solutions ...

Pergamon-Elsevier Science Ltd2023

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

Lucien Fabrice Krapp

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

A new age in protein design empowered by deep learning

Bruno Emanuel Ferreira De Sousa Correia, Michael Bronstein, Casper Alexander Goverde, Arne Schneuing, Hamed Khakzad, Ilia Igashov

The rapid progress in the field of deep learning has had a significant impact on protein design. Deep learning methods have recently produced a breakthrough in protein structure prediction, leading to the availability of high-quality models for millions of ...

Cell Press2023

De novo designed proteins: a study in engineering novel folds and functions

Alexandra Krina Van Hall-Beauvais

Proteins control nearly every facet of life on a molecular level. Proteins are formed from linear strings of amino acids, which fold into three-dimensional structures that can enact functions. Evolution has created highly efficient proteins in diverse fold ...

EPFL2023

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

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

Policy-based Exploration of Equilibrium Representations (PEER): A topology grammar for generative conceptual structural design

Ioannis Mirtsopoulos

Design exploration is a creative process that consists of the incremental generation of design candidates. Supported by digital means or not, the process handles the ill-structured nature of design and allows creativity to flourish through diversity of des ...

EPFL2022

Exploration of static equilibrium representations; policies and genetic algorithms

Corentin Jean Dominique Fivet, Ioannis Mirtsopoulos

Design exploration consists in browsing the design space and through the incremental generation of diverse design candidates increases creativity and diversity. When integrated into the early stages of the structural design process, the task is expected to ...

CRC Press2022

Towards mouse genetic-specific RNA-sequencing read mapping

Maxime Jan

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

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

A generic framework for hierarchical de novo protein design

Bruno Emanuel Ferreira De Sousa Correia, Jaume Bonet Martinez, Zander Harteveld, Fabian Sesterhenn, Stéphane Rosset, Che Yang

De novo protein design enables the exploration of novel sequences and structures absent from the natural protein universe. De novo design also stands as a stringent test for our understanding of the underlying physical principles of protein folding and may ...

National Academy of Sciences2022