PubMed Health⌕ Search

PubMed · 11121307

Surface beta-strands in proteins: identification using an hydropathy technique.

Abstract

From a representative set of monomeric globular proteins with known three-dimensional structures, beta-strands with lengths > or = 5 amino acids have been identified and catalogued. By ascertaining the accessible surface areas of the constituent residues in these strands, and by checking whether the exposed/buried pattern is 80% or more similar to that in an idealized surface strand, a subset of structures can be delineated in which the beta-strands are all sited on the surface of the protein. The corresponding sequence data show that about 50% of the residues are apolar (Val, Ile, Leu, Phe, Tyr, Ala) and that the common occurrence of valine (14.3%), isoleucine (9.6%), and threonine (8.1%) is a characteristic feature. The frequencies of occurrence of those amino acids in the strands that face the aqueous environment and the interior have also been determined separately and show that most surface strands have a substructure of the form (apolar-X)(n), where X is approximately equally divided between apolar, charged, and hydrophilic residues. Using the frequency data thus obtained, allied with an algorithm to delineate potential surface beta-strands from characteristic hydropathy profiles, it is now possible to search through the sequences of proteins with unknown tertiary structures and make realistic predictions of the presence of this element of structure on the protein surface. In addition, new data are presented on the distribution of the various types of residues on the surface of proteins and in their interior. Significant differences were observed, not all of which have been identified previously. Furthermore, the distribution of the types of residue in a surface beta-strand was compared to that corresponding to the surfaces of all of the proteins in our database. Again, very characteristic differences were observed. These are helpful in recognizing the presence of surface beta-strands.

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

C C Palliser, M W MacArthur, D A Parry. 2000. Surface beta-strands in proteins: identification using an hydropathy technique.. https://doi.org/10.1006/jsbi.2000.4304

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

KEEP EXPLORING

Related citations

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↗

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↗

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↗