Evidence map›Paper›PMID 25534034›Full record

ArticleMolecular biology and evolution2015

Antibody-specific model of amino acid substitution for immunological inferences from alignments of antibody sequences.

Alexander Mirsky, Linda Kazandjian, Maria Anisimova

Open access · hybridAbstract read
In one paragraph

Article in Molecular biology and evolution, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.

0numbers the graph read from it
0cells of the map it votes in
15citing papers in PubMed
2.4field-weighted citation impact, top 12% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

15 citing papers in PubMed, 31 citations in OpenAlex.

  1. Trial
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  5. Stochasticity in genetics and gene regulation.Philosophical transactions of the Royal Society of London. Series B, Biological sciences · 2024
    Review
  6. AbRSA: A robust tool for antibody numbering.Protein science : a publication of the Protein Society · 2019
    Article
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  9. Review
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  13. Quantifying evolutionary constraints on B-cell affinity maturation.Philosophical transactions of the Royal Society of London. Series B, Biological sciences · 2015
    Article
  14. Article
  15. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

3 authors at 2 institutions in 2 countries.

Alexander MirskySystems Biophysics and Functional Nanosystems, Ludwig-Maximilians-Universität München, München, Germany Department of Computer Science, Swiss Federal Institute of Technology (ETH Zürich), Zürich, Switzerland alexander@mirsky.de.
Linda KazandjianMAB Discovery GmbH, Neuried, Germany.
Maria AnisimovaDepartment of Computer Science, Swiss Federal Institute of Technology (ETH Zürich), Zürich, Switzerland Institute of Applied Simulation (IAS), School of Life Sciences and Facility Management, Zürich University of Applied Sciences (ZHAW), Wädenswil, Switzerland.
École Polytechnique Fédérale de Lausanne · CHLudwig-Maximilians-Universität München · DE

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Antibodies are glycoproteins produced by the immune system as a dynamically adaptive line of defense against invading pathogens. Very elegant and specific mutational mechanisms allow B lymphocytes to produce a large and diversified repertoire of antibodies, which is modified and enhanced throughout all adulthood. One of these mechanisms is somatic hypermutation, which stochastically mutates nucleotides in the antibody genes, forming new sequences with different properties and, eventually, higher affinity and selectivity to the pathogenic target. As somatic hypermutation involves fast mutation of antibody sequences, this process can be described using a Markov substitution model of molecular evolution. Here, using large sets of antibody sequences from mice and humans, we infer an empirical amino acid substitution model AB, which is specific to antibody sequences. Compared with existing general amino acid models, we show that the AB model provides significantly better description for the somatic evolution of mice and human antibody sequences, as demonstrated on large next generation sequencing (NGS) antibody data. General amino acid models are reflective of conservation at the protein level due to functional constraints, with most frequent amino acids exchanges taking place between residues with the same or similar physicochemical properties. In contrast, within the variable part of antibody sequences we observed an elevated frequency of exchanges between amino acids with distinct physicochemical properties. This is indicative of a sui generis mutational mechanism, specific to antibody somatic hypermutation. We illustrate this property of antibody sequences by a comparative analysis of the network modularity implied by the AB model and general amino acid substitution models. We recommend using the new model for computational studies of antibody sequence maturation, including inference of alignments and phylogenetic trees describing antibody somatic hypermutation in large NGS data sets. The AB model is implemented in the open-source software CodonPhyML (http://sourceforge.net/projects/codonphyml) and can be downloaded and supplied by the user to ProGraphMSA (http://sourceforge.net/projects/prographmsa) or other alignment and phylogeny reconstruction programs that allow for user-defined substitution models.

Indexed as

Evolution, MolecularAmino Acid SequenceAmino Acid SubstitutionAnimalsAntibodiesDatabases, ProteinHumansMarkov ChainsMiceMolecular Sequence DataMutationSequence AlignmentAntibodiesalignmentamino acid substitutionantibodyantibody genealogyevolutionMarkov modelsomatic hypermutation

Identifiers

PMID25534034
PMCPMC4327158
OpenAlexW2113199469

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.