44,834 research outputs found
Predicting protein function with hierarchical phylogenetic profiles: The Gene3D phylo-tuner method applied to eukaryotic Genomes
"Phylogenetic profiling'' is based on the hypothesis that during evolution functionally or physically interacting genes are likely to be inherited or eliminated in a codependent manner. Creating presence-absence profiles of orthologous genes is now a common and powerful way of identifying functionally associated genes. In this approach, correctly determining orthology, as a means of identifying functional equivalence between two genes, is a critical and nontrivial step and largely explains why previous work in this area has mainly focused on using presence-absence profiles in prokaryotic species. Here, we demonstrate that eukaryotic genomes have a high proportion of multigene families whose phylogenetic profile distributions are poor in presence-absence information content. This feature makes them prone to orthology mis-assignment and unsuited to standard profile-based prediction methods. Using CATH structural domain assignments from the Gene3D database for 13 complete eukaryotic genomes, we have developed a novel modification of the phylogenetic profiling method that uses genome copy number of each domain superfamily to predict functional relationships. In our approach, superfamilies are subclustered at ten levels of sequence identity from 30% to 100% - and phylogenetic profiles built at each level. All the profiles are compared using normalised Euclidean distances to identify those with correlated changes in their domain copy number. We demonstrate that two protein families will "auto-tune'' with strong co-evolutionary signals when their profiles are compared at the similarity levels that capture their functional relationship. Our method finds functional relationships that are not detectable by the conventional presence - absence profile comparisons, and it does not require a priori any fixed criteria to define orthologous genes
MRFalign: Protein Homology Detection through Alignment of Markov Random Fields
Sequence-based protein homology detection has been extensively studied and so
far the most sensitive method is based upon comparison of protein sequence
profiles, which are derived from multiple sequence alignment (MSA) of sequence
homologs in a protein family. A sequence profile is usually represented as a
position-specific scoring matrix (PSSM) or an HMM (Hidden Markov Model) and
accordingly PSSM-PSSM or HMM-HMM comparison is used for homolog detection. This
paper presents a new homology detection method MRFalign, consisting of three
key components: 1) a Markov Random Fields (MRF) representation of a protein
family; 2) a scoring function measuring similarity of two MRFs; and 3) an
efficient ADMM (Alternating Direction Method of Multipliers) algorithm aligning
two MRFs. Compared to HMM that can only model very short-range residue
correlation, MRFs can model long-range residue interaction pattern and thus,
encode information for the global 3D structure of a protein family.
Consequently, MRF-MRF comparison for remote homology detection shall be much
more sensitive than HMM-HMM or PSSM-PSSM comparison. Experiments confirm that
MRFalign outperforms several popular HMM or PSSM-based methods in terms of both
alignment accuracy and remote homology detection and that MRFalign works
particularly well for mainly beta proteins. For example, tested on the
benchmark SCOP40 (8353 proteins) for homology detection, PSSM-PSSM and HMM-HMM
succeed on 48% and 52% of proteins, respectively, at superfamily level, and on
15% and 27% of proteins, respectively, at fold level. In contrast, MRFalign
succeeds on 57.3% and 42.5% of proteins at superfamily and fold level,
respectively. This study implies that long-range residue interaction patterns
are very helpful for sequence-based homology detection. The software is
available for download at http://raptorx.uchicago.edu/download/.Comment: Accepted by both RECOMB 2014 and PLOS Computational Biolog
Deep learning extends de novo protein modelling coverage of genomes using iteratively predicted structural constraints
The inapplicability of amino acid covariation methods to small protein
families has limited their use for structural annotation of whole genomes.
Recently, deep learning has shown promise in allowing accurate residue-residue
contact prediction even for shallow sequence alignments. Here we introduce
DMPfold, which uses deep learning to predict inter-atomic distance bounds, the
main chain hydrogen bond network, and torsion angles, which it uses to build
models in an iterative fashion. DMPfold produces more accurate models than two
popular methods for a test set of CASP12 domains, and works just as well for
transmembrane proteins. Applied to all Pfam domains without known structures,
confident models for 25% of these so-called dark families were produced in
under a week on a small 200 core cluster. DMPfold provides models for 16% of
human proteome UniProt entries without structures, generates accurate models
with fewer than 100 sequences in some cases, and is freely available.Comment: JGG and SMK contributed equally to the wor
A Systematic Framework for the Construction of Optimal Complete Complementary Codes
The complete complementary code (CCC) is a sequence family with ideal
correlation sums which was proposed by Suehiro and Hatori. Numerous literatures
show its applications to direct-spread code-division multiple access (DS-CDMA)
systems for inter-channel interference (ICI)-free communication with improved
spectral efficiency. In this paper, we propose a systematic framework for the
construction of CCCs based on -shift cross-orthogonal sequence families
(-CO-SFs). We show theoretical bounds on the size of -CO-SFs and CCCs,
and give a set of four algorithms for their generation and extension. The
algorithms are optimal in the sense that the size of resulted sequence families
achieves theoretical bounds and, with the algorithms, we can construct an
optimal CCC consisting of sequences whose lengths are not only almost arbitrary
but even variable between sequence families. We also discuss the family size,
alphabet size, and lengths of constructible CCCs based on the proposed
algorithms
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