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Biology and Biochemistry

D-Index
81
Citations
26729
World Ranking
3840
National Ranking
57

Overview

Arne Elofsson is affiliated with the Science for Life Laboratory in Sweden. Their research primarily focuses on biochemistry, genetics, and molecular biology, with 156 publications in these fields. Within this broader area, Elofsson's work extensively covers subfields such as molecular biology, materials chemistry, computational theory and mathematics, spectroscopy, and cell biology.

The main topics addressed in Elofsson's research include:

  • Protein Structure and Dynamics
  • RNA and protein synthesis mechanisms
  • Machine Learning in Bioinformatics
  • Enzyme Structure and Function
  • Computational Drug Discovery Methods
  • Bioinformatics and Genomic Networks
  • COVID-19 epidemiological studies

Elofsson has contributed to several recent scientific papers, including:

  • "Improved prediction of protein-protein interactions using AlphaFold2," published in 2022 in Nature Communications
  • "A structural biology community assessment of AlphaFold2 applications," published in 2022 in Nature Structural & Molecular Biology
  • "Critical assessment of protein intrinsic disorder prediction," published in 2021 in Nature Methods
  • "Towards a structurally resolved human protein interaction network," published in 2023 in Nature Structural & Molecular Biology
  • "Predicting the structure of large protein complexes using AlphaFold and Monte Carlo tree search," published in 2022 in Nature Communications

The frequent co-authors collaborating with Elofsson include Patrick Bryant, Gabriele Pozzati, Aditi Shenoy, Petras J. Kundrotas, and Wensi Zhu. These collaborations reflect a sustained engagement with researchers active in structural biology, computational methods, and protein interaction studies.

Regarding publication venues, Elofsson's work commonly appears in bioRxiv (Cold Spring Harbor Laboratory) with 30 contributions, followed by Bioinformatics, Proteins Structure Function and Bioinformatics, arXiv (Cornell University), and Nature Communications. These venues indicate a blend of preprint repositories and peer-reviewed journals specializing in computational biology and structural bioinformatics.

Best Publications

  • Prediction of transmembrane alpha-helices in prokaryotic membrane proteins: the dense alignment surface method.

    M Cserzö;E Wallin;I Simon;G von Heijne

  • Detecting sequence signals in targeting peptides using deep learning.

    Jose Juan Almagro Armenteros;Marco Salvatore;Marco Salvatore;Olof Emanuelsson;Olof Emanuelsson;Ole Winther;Ole Winther;Ole Winther

  • Can correct protein models be identified

    Björn Wallner;Arne Elofsson

  • The TOPCONS web server for consensus prediction of membrane protein topology and signal peptides

    Konstantinos D. Tsirigos;Christoph Peters;Nanjiang Shu;Lukas Käll

  • 3D-Jury: a simple approach to improve protein structure predictions

    Krzysztof Ginalski;Arne Elofsson;Daniel Fischer;Leszek Rychlewski

  • TOPCONS: consensus prediction of membrane protein topology

    Andreas Bernsel;Håkan Viklund;Aron Hennerdal;Arne Elofsson

  • TransformerCPI: improving compound-protein interaction prediction by sequence-based deep learning with self-attention mechanism and label reversal experiments.

    Lifan Chen;Xiaoqin Tan;Dingyan Wang;Feisheng Zhong

  • Structure is three to ten times more conserved than sequence--a study of structural response in protein cores.

    Kristoffer Illergård;David H. Ardell;David H. Ardell;Arne Elofsson

  • MaxSub: an automated measure for the assessment of protein structure prediction quality.

    Naomi Siew;Arne Elofsson;Leszek Rychlewski;Daniel Fischer

  • OCTOPUS: Improving topology prediction by two-track ANN-based preference scores and an extended topological grammar

    Håkan Viklund;Arne Elofsson

  • What properties characterize the hub proteins of the protein-protein interaction network of Saccharomyces cerevisiae?

    Diana Ekman;Sara Light;Åsa K Björklund;Arne Elofsson

  • Molecular recognition of a single sphingolipid species by a protein’s transmembrane domain

    F.-Xabier Contreras;Andreas M. Ernst;Per Haberkant;Per Haberkant;Patrik Björkholm;Patrik Björkholm

  • Pcons: a neural-network-based consensus predictor that improves fold recognition.

    Jesper Lundström;Leszek Rychlewski;Janusz Bujnicki;Arne Elofsson

  • Prediction of MHC class I binding peptides, using SVMHC

    Pierre Dönnes;Arne Elofsson

  • Prediction of membrane-protein topology from first principles

    Andreas Bernsel;Håkan Viklund;Jenny Falk;Erik Lindahl

  • DisProt 7.0: a major update of the database of disordered proteins.

    Damiano Piovesan;Francesco Tabaro;Francesco Tabaro;Ivan Micetic;Marco Necci

  • Community-wide assessment of GPCR structure modelling and ligand docking: GPCR Dock 2008

    Mayako Michino;Enrique Abola;Charles L. Brooks;J. Scott Dixon

  • Best α‐helical transmembrane protein topology predictions are achieved using hidden Markov models and evolutionary information

    Håkan Viklund;Arne Elofsson

  • Membrane Protein Structure: Prediction versus Reality

    Arne Elofsson;Gunnar von Heijne

  • Expansion of Protein Domain Repeats

    Åsa K Björklund;Diana Ekman;Arne Elofsson

Frequent Co-Authors

Gunnar von Heijne
Gunnar von Heijne Stockholm University
Leszek Rychlewski
Leszek Rychlewski BioInfoBank Institute
Peter Tompa
Peter Tompa Vrije Universiteit Brussel
Erik Lindahl
Erik Lindahl Stockholm University
Lennart Nilsson
Lennart Nilsson Karolinska Institute
Wim F. Vranken
Wim F. Vranken Vrije Universiteit Brussel
Rudolf Rigler
Rudolf Rigler Karolinska Institute
Vladimir N. Uversky
Vladimir N. Uversky University of South Florida
Chris Sander
Chris Sander Harvard University

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