bioRxiv Science⌕ Search
Home/Archive/bioRxiv

bioRxiv · 10.64898/2026.09.28.754887

Characterization of cross-family conserved fragments

Chen, X.·Tian, P.
View original source ↗Export metadata ↓

Abstract

In a companion study, we introduced ProDive and used it to identify 318,289 high-confidence cross-family segment correspondences across all 25,545 Pfam families. Here we characterize the pervasiveness and biophysical context of the resulting mapped fragment resource. Graph-based community analysis shows that segment reuse is broadly distributed across the protein family universe, with thousands of mostly small modules rather than concentration in a small number of family pairs. Comparison between Pfam families and de novo designed proteins reveals an approximately 2.1-fold enrichment of cross-family segment correspondences relative to the Pfam-Pfam background. Mapped fragments are strongly enriched in helix-dominant secondary structures with intermediate solvent exposure, occupy a narrowed sequence-context entropy regime, and show a significant shift toward lower fragment-associated contact order relative to random control windows, directly supporting a folding-compatible local structural regime. Matched annotation analyses show depletion from known domain-based interaction interfaces and mild depletion from strict DisProt disorder. Direct Start2Fold/HDX and{varphi} -value comparisons are limited in scale but connect the same fragment class to folding-related protection: stability annotations support local structural stabilization that may facilitate folding, whereas folding annotations and transition-state enrichments provide more specific evidence for folding initiation or pathway involvement. Together, these results support a physical-constraint interpretation of local structural conservation, in which reusable compact fragments contribute prominently to local stability and folding-relevant local structure, with folding initiation supported in specific subsets rather than as an exclusive mechanism.

Explore related subjects

bioinformaticsPreprint

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Chen, X., Tian, P.. 2026-10-06. Characterization of cross-family conserved fragments. https://doi.org/10.64898/2026.09.28.754887

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related preprints

bioRxiv2026

Cross-modal transfer enables zero-shot prediction of drug responses across cell types

Genetic perturbation responses capture the functional consequences of gene-level interventions, providing functional information for drug-response prediction beyond baseline gene expression. Within the same cellular context, genetic and chemical perturbations engage shared regulatory networks, providing a biological basis for transferring response information between perturbation modalities. Here we introduce GRAFT, a framework that combines genetic-to-chemical transfer with cross-context prediction to infer drug-induced transcriptional responses without drug measurements in the target context. Across benchmarked LINCS and external contexts, GRAFT improved response correlation, directional agreement and magnitude reconstruction over competing methods, particularly for drugs with context-dependent responses. GRAFT-Adapt further refines predictions of unmeasured responses using a small number of drug measurements from the target context. GRAFT-Atlas spans 13 cellular contexts and 7,856 drug catalog entries, comprising 102,128 predictions for drug-context combinations. The atlas captured recognizable pharmacological neighborhoods, shared functional programs, context-dependent transcriptional changes and structural-functional associations supported by molecular docking. Across ten iPSC lines from distinct donors, GRAFT identified candidate inter-donor differences, with functional support for selected candidates from measured genetic perturbation responses. Together, GRAFT enables perturbation-informed prediction and comparison of drug responses across cellular contexts.

bioinformatics↗
bioRxiv2026

Pycea: a scverse toolkit for single-cell lineage tracing analysis

Lineage tracing enables reconstruction of cellular histories in development, regeneration, and disease, but integrating lineage data into single-cell analysis workflows remains challenging. We introduce Pycea, a Python toolkit for lineage analysis and visualization, and TreeData, an extension of AnnData that stores lineage trees alongside cellular measurements. TreeData is compatible with Scanpy and other scverse tools, enabling integrated analysis without converting between formats. Pycea provides scalable methods for tree visualization, clade identification, distance comparisons, ancestral state inference, and lineage statistics. This software is open source and available at https://github.com/YosefLab/pycea.

bioinformatics↗
bioRxiv2026

Transcript quantification using context-aware MAPQ modeling

Long-read RNA-sequencing enables accurate transcript-level quantification, but prior static filtering of MAPping Quality (MAPQ) can ignore transcriptome-intrinsic ambiguity, and discarding reads on this basis distorts abundance estimates. We present CALMeR (Context-Aware Long-read MAPQ-based Rescuer), which conditions MAPQ filtering on transcript properties rather than a fixed cutoff. CALMeR fits a zero-one inflated beta regression on various transcript and read-level features to model the expected MAPQ distribution for each alignment. Reads whose MAPQ fits this context, such as low-MAPQ reads from multi-isoform genes and paralog families are retained; reads with anomalously low MAPQ are removed as likely artifacts. Retained reads can be summed per transcript or passed to any downstream quantifier. Compared with a MAPQ [≥] 1 filter and expectation-maximization-based transcript quantification methods, CALMeR recovers more spike-in transcripts and better matches known spike-in concentrations. CALMeR is available at https://github.com/sparthib/CALMeR.

bioinformatics↗