Evidence map›Paper›PMID 42056697›Full record

ArticleMethods in molecular biology (Clifton, N.J.)2026

Phylogeny Building for Structural Phylogenomic Research.

Arshan Nasir, Gustavo Caetano-Anollés

Abstract read
PubMed Publisher
In one paragraph

Article in Methods in molecular biology (Clifton, N.J.), 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

2 authors.

Arshan NasirDepartment of Crop Sciences, University of Illinois at Urbana-Champaign, Urbana, IL, USA. arshan.nasir@gmail.com.
Gustavo Caetano-AnollésDepartment of Crop Sciences, University of Illinois at Urbana-Champaign, Urbana, IL, USA. gca@illinois.edu.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Over the last two decades, we have routinely published phylogenies describing the evolution of organisms and viruses using protein structure information. The structure-based trees offer several advantages over traditional approaches, including improved resolution of basal branches of the trees, a more realistic representation of evolutionary events such as gene duplication, loss, gain, and horizontal gene transfer, better handling of fast-evolving (micro)-organisms and organisms with parasitic tendencies (viruses), and reduced susceptibility to artifacts arising from sequence alignment and reconstruction in complex genomic datasets. Here, we present a generic protocol for phylogenomic analysis of molecular structure, which is timely considering the recent revolution in AI-driven models of protein structure prediction. While the protocol is illustrated with viral protein structures, the procedure is generic in nature and can be easily adapted for other structures (e.g., RNA) and molecular characteristics (e.g., molecular functions, pathways), thereby enriching the phylogenetic toolkit available to molecular biologists.

Indexed as

Computational BiologyGenomicsPhylogenyProtein ConformationProteinsSoftwareProteinsHMMPhylogeneticsProtein structurePythonSCOPStructural domainSUPERFAMILYTree of Life

Identifiers

PMID42056697

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

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.