PubMed Health⌕ Search
Home/Archive/PubMed

PubMed · 3599076

Pf1 Inovirus. Electron density distribution calculated by a maximum entropy algorithm from native fibre diffraction data to 3 A resolution and single isomorphous replacement data to 5 A resolution.

D A Marvin·R K Bryan·C Nave
View original source ↗Export metadata ↓

Abstract

We have calculated the electron density distribution of the Pf1 strain of filamentous bacteriophage by a maximum entropy method. In the calculation we included native X-ray fibre diffraction data extending to 3 A resolution in the meridional direction on 60 layerlines that are resolved to 4 A in the equatorial direction, and lower resolution data from a single isomorphous derivative iodinated on the Tyr25 residue. The electron density map indicates that the 46-residue protein subunit is a single, gently curved stretch of alpha-helix with its axis at an angle of about 20 degrees to the axis of the virion. The alpha-helix subunit curves around the virion axis by about 1/6 turn, and decreases from about 27 A radius to about 13 A radius in the virion as the amino acid sequence of the subunit runs from the N terminus to the C terminus. Nearest-neighbour alpha-helical subunits are about 10 A apart along their length, and the axis of each subunit makes an unexpected negative angle with its nearest neighbours in the virion. To confirm the validity of the maximum entropy calculation, we have varied the constraints on the calculation. All variations result in either a map that is close to the original map or a map that cannot be interpreted in terms of secondary structure: we find only one map that makes structural sense.

Explore related subjects

AlgorithmsBacteriophagesCapsidMacromolecular SubstancesModels, BiologicalProtein ConformationX-Ray DiffractionJournal Article

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

D A Marvin, R K Bryan, C Nave. 1987-01-20. Pf1 Inovirus. Electron density distribution calculated by a maximum entropy algorithm from native fibre diffraction data to 3 A resolution and single isomorphous replacement data to 5 A resolution.. https://doi.org/10.1016/0022-2836(87)90222-1

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

KEEP EXPLORING

Related citations

PubMed2026

A note on a generalized single step theory for any number of hierarchical genomic matrices.

BACKGROUND: The Single Step algorithm allows combining information from genotyped and un-genotyped individuals, provided they are connected by a pedigree. However, current single step theory is limited to a single list of markers. RESULTS: We present a generalized single step (GSS) method that can accommodate any number of hierarchical molecular datasets (e.g. sequence, high and low density arrays) and pedigree, avoiding imputation. We prove that a similar efficient inversion algorithm exists. The method is recursive, starting with the highest marker density scenario. We illustrate the method with simulation and show that GSS can increase predictive accuracy compared to standard single step. R code is provided so that custom scenarios can be easily compared, either with simulated or real data. CONCLUSION: The method developed generalizes extant single step theory to any number of hierarchical molecular relationship matrices, broadening the scenarios where single step can be applied. A topic of particular interest can be ecology field data or human populations where pedigree is not available, but where samples sequenced and genotyped at different densities can exist. GSS can also be a useful tool to optimize allocation of genotyping and / or sequencing resources.

Algorithms↗
PubMed2026

cgDist: Nucleotide-level distance calculation from cgMLST allelic profiles.

Bacterial genomic surveillance requires balancing computational efficiency with genetic resolution for effective cluster investigation. cgMLST distance calculations treat all allelic differences as equivalent units, obscuring nucleotide-level variation. Furthermore, single nucleotide polymorphism-based pipelines provide finer resolution at substantially higher computational cost, which limits their routine deployment in surveillance laboratories. We present cgDist, an algorithm that calculates nucleotide-level distances directly from cgMLST allelic profiles, providing finer resolution than allele-count distances by leveraging within-allele nucleotide variation. The cache architecture stores alignment statistics, enabling distance calculation modes without computation and supporting both dataset-specific and schema-complete cache generation. This design enables incremental surveillance analysis, with performance benefits as laboratories accumulate alignment data. cgDist functions as a precision 'zoom lens' for the investigation of clusters identified through initial cgMLST screening. Rather than restructuring population relationships, this targeted approach concentrates enhanced resolution where it is most informative. The algorithm ensures that cgDist distances are greater than or equal to corresponding cgMLST distances, preserving epidemiological interpretability while adding genetic discrimination. By increasing resolution within identified clusters, cgDist may also support outbreak investigation, a potential application that remains to be evaluated on outbreak-derived data.

Algorithms↗
PubMed2026

Theseus: fast and optimal affine-gap sequence-to-graph alignment.

MOTIVATION: Sequence-to-graph alignment is a central problem in bioinformatics, with applications in multiple sequence alignment (MSA) and pangenome analysis, among others. However, current algorithms for optimal affine-gap alignment impose high memory and computational requirements, limiting their scalability to aligning long sequences to complex graphs. Practical solutions partially address this problem using heuristic strategies that ultimately trade off optimality for speed. RESULTS: This work presents Theseus, a novel, fast, and optimal affine-gap sequence-to-graph alignment algorithm. Theseus leverages similarities between genomic sequences to accelerate the alignment computation and reduces the overall memory requirements without compromising optimality. To that end, Theseus processes only a subset of the dynamic programming cells, using a sparse-data strategy that enables efficient sequence-to-graph alignment. Moreover, our algorithm supports optimal affine-gap alignment on arbitrary directed graphs, including those with cycles. We evaluate Theseus on two key problems: MSA and pangenome read mapping. For MSA, we compare it against SPOA, abPOA, and POASTA. Theseus is 1.6× to 17.6× faster than POASTA, and 7.3× faster, on average, than SPOA, both optimal aligners. Compared with abPOA, Theseus ensures optimality and scales to the largest problems. For pangenome read mapping, we benchmark Theseus against the alignment stage of the mapping tool vg map, along with the alignment kernels of SPOA, abPOA, and POASTA. Theseus outperforms the other methods, showing a 1.9× to 16.9× speedup on short reads. Moreover, Theseus is 1.5× to 36.3× faster than vg when aligning against synthetic cyclic graphs. AVAILABILITY AND IMPLEMENTATION: Theseus code and documentation are publicly available at https://github.com/albertjimenezbl/theseus-lib.

Algorithms↗