Systematic discovery of circular permutations across the protein universe using CIRPIN
Unknown authors
Sep 2026· Proceedings of the National Academy of Sciences of the United States of America· Vol 123· 0 citations· 56 references
Medicine
TL;DR
A circular permutation invariant graph neural network (CIRPIN) that identifies proteins related by a circular permutation across databases of solved and predicted structures and reveals previously unrecognized topological diversity within PDZ domains.
Abstract
Significance Circular permutation—a phenomenon where proteins fold into a similar 3D shape, yet differ in their termini positioning—presents a fascinating evolutionary puzzle. However, identifying such relationships has remained challenging due to search tools that rely on sequential alignment. We develop a circular permutation invariant graph neural network (CIRPIN) to overcome this problem. Our method identifies proteins related by a circular permutation across databases of solved and predicted structures. CIRPIN reveals previously unrecognized topological diversity within PDZ domains and identifies previously uncharacterized circular permutations across diverse protein families. We anticipate CIRPIN and our accompanying database will serve as an invaluable resource for investigating the evolution of proteins related by circular permutation.
How solid-phase peptide synthesis, genetically encoded libraries, and high-throughput selection and screening enable systematic exploration of vast, noncanonical landscapes largely inaccessible to traditional engineering is discussed.
Filip Buchel, V. G. Giacobelli, K. Hlouchová· TIBS -Trends in Biochemical...· 0 citations
The known universe of protein families represents only a small fraction of nature’s molecular diversity. From 40.3 billion sequences across 40,446 metagenomes, 9,540 metatranscriptomes, and 539 million proteins from 167,415 reference genomes, we identified 608,258 previously uncharacterized protein families with ≥100 m...
Eleni Aplakidou, Fotis A. Baltoumas, Maria N. Chasapi et al.· bioRxiv· 0 citations
BENDER, a dataset of 11,533 IDP sequences spanning 13 taxonomic groups, each simulated under CALVADOS-2 molecular dynamics and annotated with ensemble-level geometric and novel contact-network properties, suggests that hub topology in disordered protein contact networks is a conserved physical feature of sequence-encod...
LoGoPPI is presented, which infers PPIs from sequence by combining bi-encoder global protein representation with local residue-level late interaction, and provides a scalable framework for comparative and functional analysis of protein networks across diverse taxa.
H. Lee, Junyeong Ma, Han-June Kim et al.· bioRxiv· 0 citations
Significance Understanding how protein sequences relate to their functions is critical for discovering enzymes with applications in biotechnology and medicine. Traditional tools, such as phylogenetic trees and sequence similarity networks, are commonly used but struggle to handle large protein datasets and fail to capt...
Chang-Hwa Chiang, Daniel Ong, A. Narayan et al.· Proceedings of the National...· 0 citations
The answer lies in geometry: proteins with denser cores, larger size, and higher-order oligomeric assembly tolerate mutations more readily, occupy larger structural families, and support more versatile biological roles, reveals that protein size, shape, and self-assembly, not just sequence, are fundamental drivers of e...
S. Acharya, Sucharita Dey· bioRxiv· 0 citations
Related blog posts
MIT News · Artificial Intelligence· news.mit.eduJul 15, 2026
Assistant Professor Pat Pataranutaporn describes a new interface that lets everyday users glimpse inside an AI's neural network before their chatbot ever says a word.
Microsoft Research Blog· microsoft.comJul 13, 2026
Cryptographic code supports vital protections in modern computing systems. Learn how a new method helps verify code as developers write it while preserving speed and adaptability as it gets implemented and evolves. The post Verifying Rust cryptography in SymCrypt, from standards to code appeared first on Microsoft Research.
MIT News · Artificial Intelligence· news.mit.eduJul 6, 2026
PhD student Rachel Sava, winner of the Envisioning the Future of Computing Prize, explores transformative improvements and dystopian risks of neural technology.
