Protein structure characters in the light of phylogenetic systematics

Protein structure characters have great potential for improving phylogenetic inference, especially for deep nodes where amino acid sequences are highly diverged. The combination of AlphaFold structure predictions and Foldseek's "3Di" structural alphabet makes it relatively easy to conduct model-based phylogenetic inference that includes a partition of slow-evolving 3Di characters. However, we show that even identical amino acid sequences can produce substantially different 3Di characters, depending on the source of structural model and whether inter-chain interactions are considered. We argue that such variability can be addressed with key concepts from traditional organism-based phylogenetic systematics: semaphoront, hypodigm, and character ascertainment method. To illustrate this, we develop an analogy between organismal development, taphonomy, and subsequent description and character coding by a systematist, and the process of protein synthesis, folding, and interaction and subsequent extraction, experimentation, and structural modeling by a biochemist. We conclude that differences in 3Di characters between semaphoronts are not intrinsically a problem, but they do require that the researcher uses the same replicable method on all proteins in the phylogenetic analysis. The guiding principle should be to maximize the chance that character differences in the data matrix are the results of underlying evolutionary changes, rather than artefacts due to differences in the methods used for obtaining semaphoronts and coding characters.

Authors

Institutions

Publication Details

Journal
Genome Biology and Evolution
Published
2026-07-30
DOI
https://doi.org/10.1093/gbe/evag168
Primary Topic
Genomics and Phylogenetic Studies
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Protein structure characters in the light of phylogenetic systematics

Nicholas J. Matzke, Changhao Li
Genome Biology and Evolution
Genomics and Phylogenetic Studies
article

Protein structure characters in the light of phylogenetic systematics

Nicholas J. Matzke, Changhao Li
article en

Abstract

Protein structure characters have great potential for improving phylogenetic inference, especially for deep nodes where amino acid sequences are highly diverged. The combination of AlphaFold structure predictions and Foldseek's "3Di" structural alphabet makes it relatively easy to conduct model-based phylogenetic inference that includes a partition of slow-evolving 3Di characters. However, we show that even identical amino acid sequences can produce substantially different 3Di characters, depending on the source of structural model and whether inter-chain interactions are considered. We argue that such variability can be addressed with key concepts from traditional organism-based phylogenetic systematics: semaphoront, hypodigm, and character ascertainment method. To illustrate this, we develop an analogy between organismal development, taphonomy, and subsequent description and character coding by a systematist, and the process of protein synthesis, folding, and interaction and subsequent extraction, experimentation, and structural modeling by a biochemist. We conclude that differences in 3Di characters between semaphoronts are not intrinsically a problem, but they do require that the researcher uses the same replicable method on all proteins in the phylogenetic analysis. The guiding principle should be to maximize the chance that character differences in the data matrix are the results of underlying evolutionary changes, rather than artefacts due to differences in the methods used for obtaining semaphoronts and coding characters.

Genome Biology and Evolution
University of Auckland (NZ)
Openalex Percentile: Top 14%
Genomics and Phylogenetic Studies
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.