bioRxiv Science⌕ Search

bioRxiv · 10.1101/379768

Multiscale analysis of structurally conserved motifs

Abstract

This work develops a generic framework to perform a multiscale structural analysis of two structures (homologous proteins, conformations) undergoing conformational changes. Practically, given a seed structural alignment, we identify structural motifs with a hierarchical structure, characterized by three unique properties. First, the hierarchical structure sheds light on the trade-off between size and flexibility. Second, motifs can be combined to perform an overall comparison of the input structures in terms of combined RMSD - an improvement over the classical least RMSD. Third, motifs can be used to seed iterative aligners, and to design hybrid sequence-structure profile HMM characterizing protein families.\n\nFrom the methods standpoint, our framework is reminiscent from the bootstrap and combines concepts from rigidity analysis (distance difference matrices), graph theory, computational geometry (space filling diagrams), and topology (topological persistence).\n\nOn challenging cases (class II fusion proteins, flexible molecules) we illustrate the ability of our tools to localize conformational changes, shedding light of commonalities of structures which would otherwise appear as radically different.\n\nOur tools are available within the Structural Bioinformatics Library (http://sbl.inria.fr). We anticipate that they will be of interest to perform structural comparisons at large, and for remote homology detection.

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Cazals, F., Tetley, R.. 2018-07-29. Multiscale analysis of structurally conserved motifs. https://doi.org/10.1101/379768

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

KEEP EXPLORING

Related preprints

VIGAT: VLM-guided gene activity quantification from single-cell ATAC-seq

Summary: Gene activity quantification from single-cell ATAC-seq is challenging because genomic regions informative of cell identity can vary across genes and tissues, while broadly accessible regions may provide limited cell-type-discriminative information. We developed VIGAT, a vision-language model (VLM)-guided tool that automates visual interpretation of ATAC coverage plots for adaptive gene activity quantification. VIGAT jointly examines accessibility patterns across multiple marker genes, integrating genomic annotations and biological context to select gene-specific regions enriched for cluster-discriminative signals. Selected regions are used to generate a gene-by-cell activity matrix. Across 196 paired single-cell RNA-seq and ATAC-seq datasets, VIGAT outperformed five existing methods in recovering marker-associated accessibility enriched in the corresponding cell populations, demonstrating the utility of VLM-guided visual reasoning for scalable gene activity quantification. Availability and implementation: VIGAT is implemented as an open-source R package and is freely available at https://github.com/beijie02/VIGAT.

bioinformatics↗

Improving real-time search through deep learning-enhanced fragment ion indexing

The fragment ion indexed database searching approach enables highly efficient tandem mass spectrometry database searches, including real-time search workflows. However, the index can require tens of gigabytes of memory, often exceeding the free memory available on instrument acquisition computers running the search. This restricts the size of the searchable database, the variable modification space, and the number of searches that can run concurrently. To address this problem, we developed an index-pruning pipeline within the Comet database search tool using Carafe, a deep learning tool that predicts fragment ion intensities based on peptide sequences. In our approach, only fragment ion positions that are predicted to be observable are written in the index, eliminating entries that are unlikely to contribute to spectrum detection. We evaluated this approach on two search spaces in three index configurations to demonstrate that masking significantly reduced the number of fragment ion index entries and, consequently, peak search memory use. Masked searches were also faster than unmasked searches and identified a comparable number of peptide-spectrum matches. Because all prediction and masking steps are performed before searching, their cost is amortized across repeated, future searches of the same database and modification configuration, introducing no per-spectrum search overhead. The complete implementation is open source with an Apache license.

bioinformatics↗

FastaGuard: FASTA preflight QC for modern bioinformatics pipelines

Summary: FastaGuard is a streaming, explainable FASTA preflight QC tool that validates assembly FASTA files, detects structural and composition red flags, and emits a versioned multi-format report with an explicit pipeline gate before expensive downstream analysis. We evaluated v1.1.0 in four analyses whose design was fixed before data collection. On 700 public NCBI assemblies the pipeline gate stopped 19.7% (1.9% weighted to the population), always on its high-N rule and mostly in scaffold-level genomes; the NCBI submission gate stopped 2.0%, and an independent recount confirmed every stop. Among 1,040 faults injected into real assemblies, the NCBI gate flagged 520 of 520 in 13 error classes (table2asn: 280 at error level) and blocked none of 120 benign changes. Across seven downstream tools, 46 of 182 defect cells failed and 12 altered the records without a warning, all in classes the pipeline gate blocks. Stopping a mismatched reference at the gate avoided about 54-62 s of mapping and sorting at a cost of 0.05-0.06 s. Availability and Implementation: Source code and documentation are freely available under the MIT license at https://github.com/ehsanestaji/FastaGuard (release tag v1.1.0: https://github.com/ehsanestaji/FastaGuard/releases/tag/v1.1.0; archived at doi:10.5281/zenodo.23728753). Install via Bioconda (mamba install -c conda-forge -c bioconda fastaguard=1.1.0), BioContainers, or GitHub release binaries. Implemented in Rust; supported on Linux and macOS, including Bioconda builds for linux-64, linux-aarch64, osx-64, and osx-arm64. Contact: ehsan.estaji@umu.se; jianfeng.mao@umu.se Supplementary information: Supplementary Note (designs, all results, deviations from the pre-registered protocol); scripts, manifests and result tables at Zenodo, doi:10.5281/zenodo.23729530.

bioinformatics↗