Homology Information Research Articles

Computational Removal of Undesired Mass Spectral Features Possessing Repeat Units via a Kendrick Mass Filter.

Polymers are a common component of chemical background which complicates data analysis and can impair interpretation. Undesired chemical background cannot always be addressed via pre-analytical methods, chromatography, or existing data processing methods. The Kendrick mass filter (KMF) is presented for the computational removal of undesired signals present in MS1 spectra. The KMF is analogous to mass defect filtering but utilizes homology information via Kendrick mass scaling in combination with chromatographic retention time and the number of observed signals. The KMF is intended to assist in situations in which current data processing methods to remove background, e.g., blank subtraction, are either not possible or effective. The major parameters affecting KMF were investigated using PEG 400 and NIST standard reference material 1950 (metabolites in human plasma). Further exploration of the KMF performance was tested using an extract of a swab known to contain polymers. An illustrative real-world example of skin analysis with polymeric signal is discussed. The KMF is also able to provide a high-level view of the compositionality of data regarding the presence of signals with repeat units and indicate the presence of different polymers. Graphical Abstract ᅟ.

Structure and Function of Lipins: Key Enzymes in Triglyceride Metabolism

Triglycerides serve as a major reservoir for energy and elevated levels of triglycerides strongly correlate with increased risk for cardiovascular disease and diabetes. Triglyceride biosynthesis is carried out by a series of enzymes, with lipins catalyzing the penultimate step. Lipins are a family of evolutionarily conserved lipid phosphatases that convert phosphatidic acid to diacylglycerol. In mammals, lipins also function in the nucleus as transcriptional co‐activators to upregulate fatty acid oxidation genes. Accordingly, expression levels and mutations in lipins can affect whole‐body energy metabolism, obesity, adipocyte differentiation, and myopathy.Lipins are structurally uncharacterized and contain several unique but highly conserved protein domains. The lack of homology and structural information has hampered our understanding of how lipins can participate as both enzymes in lipid metabolism and in the nucleus to regulate gene expression.We will present new structural information that identifies a novel protein fold/domain in mammalian lipins whose molecular function was not previously determined. This domain forms a tightly associated dimer. The dimer interface is conserved among lipin paralogs that suggests a role in homo and hetero‐dimerization. A large surface patch of this domain is also highly conserved that suggests a potential binding interface for DNA, membranes, or proteins. Functional assays testing these hypotheses will be discussed.Support or Funding InformationAmerican Heart Association Scientist Development Award #17SDG33410860This abstract is from the Experimental Biology 2018 Meeting. There is no full text article associated with this abstract published in The FASEB Journal.

GOLabeler: improving sequence-based large-scale protein function prediction by learning to rank.

Gene Ontology (GO) has been widely used to annotate functions of proteins and understand their biological roles. Currently only <1% of >70 million proteins in UniProtKB have experimental GO annotations, implying the strong necessity of automated function prediction (AFP) of proteins, where AFP is a hard multilabel classification problem due to one protein with a diverse number of GO terms. Most of these proteins have only sequences as input information, indicating the importance of sequence-based AFP (SAFP: sequences are the only input). Furthermore, homology-based SAFP tools are competitive in AFP competitions, while they do not necessarily work well for so-called difficult proteins, which have <60% sequence identity to proteins with annotations already. Thus, the vital and challenging problem now is how to develop a method for SAFP, particularly for difficult proteins. The key of this method is to extract not only homology information but also diverse, deep-rooted information/evidence from sequence inputs and integrate them into a predictor in a both effective and efficient manner. We propose GOLabeler, which integrates five component classifiers, trained from different features, including GO term frequency, sequence alignment, amino acid trigram, domains and motifs, and biophysical properties, etc., in the framework of learning to rank (LTR), a paradigm of machine learning, especially powerful for multilabel classification. The empirical results obtained by examining GOLabeler extensively and thoroughly by using large-scale datasets revealed numerous favorable aspects of GOLabeler, including significant performance advantage over state-of-the-art AFP methods. http://datamining-iip.fudan.edu.cn/golabeler. Supplementary data are available at Bioinformatics online.

MMM: A toolbox for integrative structure modeling.

Structural characterization of proteins and their complexes may require integration of restraints from various experimental techniques. MMM (Multiscale Modeling of Macromolecules) is a Matlab-based open-source modeling toolbox for this purpose with a particular emphasis on distance distribution restraints obtained from electron paramagnetic resonance experiments on spin-labelled proteins and nucleic acids and their combination with atomistic structures of domains or whole protomers, small-angle scattering data, secondary structure information, homology information, and elastic network models. MMM does not only integrate various types of restraints, but also various existing modeling tools by providing a common graphical user interface to them. The types of restraints that can support such modeling and the available model types are illustrated by recent application examples.

The curve cone of almost complex 4-manifolds

In this paper, we study the curve cone of an almost complex $4$-manifold which is tamed by a symplectic form. In particular, we prove the cone theorem as in Mori theory for all such manifolds using the Seiberg-Witten theory. For small rational surfaces and minimal ruled surfaces, we study the configuration of negative curves. We define abstract configuration of negative curves, which records the homological and intersection information of curves. Combinatorial blowdown is the main tool to study these configurations. As an application of our investigation of the curve cone, we prove the Nakai-Moishezon type duality for all almost K\"ahler structures on $\mathbb CP^2\#k\overline{\mathbb CP^2}$ with $k\le 9$ and minimal ruled surfaces with a negative curve. This is proved using a version of Gram-Schmidt orthogonalization process for the $J$-tamed symplectic inflation.

Localisation of topological features using 3D object representations

Holes, tunnels and cavities of two-dimensional (2D) and 3D objects are concise topological features used for object representation and recognition. In this study, the authors are representing any cubical tessellation (regular or not) of 2D and 3D objects and dealing with the extraction and the localisation of these features by using homology-based approach. The cubical tessellation (regular or not) of objects is translated into algebraic language suitable for building a reduced cell complex structure. The extraction of the homology information is equivalent to the estimation of the rank of the homology groups of the reduced complex. The localisation means the reconstruction of the object cycles from the generators of the homology groups. The reduction operation of the cell complex leads to an efficient algorithm. Note that, several objects can be analysed simultaneously by the algorithm conceived in our approach. This algorithm is validated by using 2D and 3D binary images.

DeepLoc: prediction of protein subcellular localization using deep learning.

The prediction of eukaryotic protein subcellular localization is a well-studied topic in bioinformatics due to its relevance in proteomics research. Many machine learning methods have been successfully applied in this task, but in most of them, predictions rely on annotation of homologues from knowledge databases. For novel proteins where no annotated homologues exist, and for predicting the effects of sequence variants, it is desirable to have methods for predicting protein properties from sequence information only. Here, we present a prediction algorithm using deep neural networks to predict protein subcellular localization relying only on sequence information. At its core, the prediction model uses a recurrent neural network that processes the entire protein sequence and an attention mechanism identifying protein regions important for the subcellular localization. The model was trained and tested on a protein dataset extracted from one of the latest UniProt releases, in which experimentally annotated proteins follow more stringent criteria than previously. We demonstrate that our model achieves a good accuracy (78% for 10 categories; 92% for membrane-bound or soluble), outperforming current state-of-the-art algorithms, including those relying on homology information. The method is available as a web server at http://www.cbs.dtu.dk/services/DeepLoc. Example code is available at https://github.com/JJAlmagro/subcellular_localization. The dataset is available at http://www.cbs.dtu.dk/services/DeepLoc/data.php. jjalma@dtu.dk.

Allowing cycles in discrete Morse theory

Discrete gradient vector fields are combinatorial structures that can be used for accelerating the homology computation of CW complexes, such as simplicial or cubical complexes, by reducing their number of cells. Consequently, they provide a bound for the Betti numbers (the most basic homological information). A discrete gradient vector field can eventually reduce the complex to its minimal form, having as many cells of each dimension as its corresponding Betti number, but this is not guaranteed. Moreover, finding an optimal discrete gradient vector field is an NP-hard problem. We describe here a generalization, which we call Homological Discrete Vector Field (HDVF), which can overcome these limitations by allowing cycles under a certain algebraic condition. In this work we define the HDVF and its associated reduction, we study how to efficiently compute a HDVF, we establish the relation between the HDVF and other concepts in computational homology and we estimate the average complexity of its computation. We also introduce five basic operations for modifying a HDVF, which can also be applied to discrete gradient vector fields.

The first invertebrate NFIL3 transcription factor with role in immune defense identified from the Hong Kong oyster, Crassostrea hongkongensis

NFIL3 (nuclear factor interleukin 3-regulated) is a basic leucine zipper type transcription factor that mediates a variety of immune responses in vertebrates. However, the sequence information and function of NFIL3 homologs in invertebrates, especially mollusks, remains unknown. In the present study, the first NFIL3 homolog was identified in a marine mollusk, Crassostrea hongkongensis (designated as ChNFIL3), followed by its functional characterization. The full-length cDNA of ChNFIL3 is 2221 bp and consists of an open reading frame (ORF) of 1536 bp that encodes a polypeptide of 551 amino acids. Simple Modular Architecture Research Tool (SMART) analysis indicated that ChNFIL3 has two basic leucin zipper domains, similar to the other known NFIL3 family proteins. Tissue distribution analysis of NFIL3 in this mollusk revealed high expression in digestive glands and hemocytes. A significant induction in the mRNA level of ChNFIL3 was observed following bacterial stimulation. ChNFIL3 was found to be localized in the nucleus and over expression of ChNIFL3 led to upregulation of transcriptional activity of an NF-κB reporter gene in HEK 293T cells, indicating its role in innate immunity. Furthermore, addition of exogenous recombinant ChNFIL3 proteins resulted in enhanced mRNA level of hemocyte interleukin 17 in vitro. In conclusion, our findings revealed that NFIL3 in molluscs, plays a conserved role in host defense, similar to its mammalian homolog.

EigenTHREADER: analogous protein fold recognition by efficient contact map threading.

MotivationProtein fold recognition when appropriate, evolutionarily-related, structural templates can be identified is often trivial and may even be viewed as a solved problem. However in cases where no homologous structural templates can be detected, fold recognition is a notoriously difficult problem (Moult et al., 2014). Here we present EigenTHREADER, a novel fold recognition method capable of identifying folds where no homologous structures can be identified. EigenTHREADER takes a query amino acid sequence, generates a map of intra-residue contacts, and then searches a library of contact maps of known structures. To allow the contact maps to be compared, we use eigenvector decomposition to resolve the principal eigenvectors these can then be aligned using standard dynamic programming algorithms. The approach is similar to the Al-Eigen approach of Di Lena et al. (2010), but with improvements made both to speed and accuracy. With this search strategy, EigenTHREADER does not depend directly on sequence homology between the target protein and entries in the fold library to generate models. This in turn enables EigenTHREADER to correctly identify analogous folds where little or no sequence homology information is.ResultsEigenTHREADER outperforms well-established fold recognition methods such as pGenTHREADER and HHSearch in terms of True Positive Rate in the difficult task of analogous fold recognition. This should allow template-based modelling to be extended to many new protein families that were previously intractable to homology based fold recognition methods.Availability and implementationAll code used to generate these results and the computational protocol can be downloaded from https://github.com/DanBuchan/eigen_scripts. EigenTHREADER, the benchmark code and the data this paper is based on can be downloaded from: http://bioinfadmin.cs.ucl.ac.uk/downloads/eigenTHREADER/.

Homology-Aware Phylogenomics at Gigabase Scales.

Obstacles to inferring species trees from whole genome data sets range from algorithmic and data management challenges to the wholesale discordance in evolutionary history found in different parts of a genome. Recent work that builds trees directly from genomes by parsing them into sets of small n}{}k-mer strings holds promise to streamline and simplify these efforts, but existing approaches do not account well for gene tree discordance. We describe a “seed and extend” protocol that finds nearly exact matching sets of orthologous n}{}k-mers and extends them to construct data sets that can properly account for genomic heterogeneity. Exploiting an efficient suffix array data structure, sets of whole genomes can be parsed and converted into phylogenetic data matrices rapidly, with contiguous blocks of n}{}k-mers from the same chromosome, gene, or scaffold concatenated as needed. Phylogenetic trees constructed from highly curated rice genome data and a diverse set of six other eukaryotic whole genome, transcriptome, and organellar genome data sets recovered trees nearly identical to published phylogenomic analyses, in a small fraction of the time, and requiring many fewer parameter choices. Our method’s ability to retain local homology information was demonstrated by using it to characterize gene tree discordance across the rice genome, and by its robustness to the high rate of interchromosomal gene transfer found in several rice species.

PROSNET: INTEGRATING HOMOLOGY WITH MOLECULAR NETWORKS FOR PROTEIN FUNCTION PREDICTION.

Automated annotation of protein function has become a critical task in the post-genomic era. Network-based approaches and homology-based approaches have been widely used and recently tested in large-scale community-wide assessment experiments. It is natural to integrate network data with homology information to further improve the predictive performance. However, integrating these two heterogeneous, high-dimensional and noisy datasets is non-trivial. In this work, we introduce a novel protein function prediction algorithm ProSNet. An integrated heterogeneous network is first built to include molecular networks of multiple species and link together homologous proteins across multiple species. Based on this integrated network, a dimensionality reduction algorithm is introduced to obtain compact low-dimensional vectors to encode proteins in the network. Finally, we develop machine learning classification algorithms that take the vectors as input and make predictions by transferring annotations both within each species and across different species. Extensive experiments on five major species demonstrate that our integration of homology with molecular networks substantially improves the predictive performance over existing approaches.

단백질 서열의 상동 관계를 가중 조합한 단백질 이차 구조 예측

단백질 서열의 상동 관계를 가중 조합한 단백질 이차 구조 예측

Index-Based Network Aligner of Protein-Protein Interaction Networks.

Network Alignment over graph-structured data has received considerable attention in many recent applications. Global network alignment tries to uniquely find the best mapping for a node in one network to only one node in another network. The mapping is performed according to some matching criteria that depend on the nature of data. In molecular biology, functional orthologs, protein complexes, and evolutionary conserved pathways are some examples of information uncovered by global network alignment. Current techniques for global network alignment suffer from several drawbacks, e.g., poor performance and high memory requirements. We address these problems by proposing IBNAL, Indexes-Based Network ALigner, for better alignment quality and faster results. To accelerate the alignment step, IBNAL makes use of a novel clique-based index and is able to align large networks in seconds. IBNAL produces a higher topological quality alignment and comparable biological match in alignment relative to other state-of-the-art aligners even though topological fit is primarily used to match nodes. IBNAL's results confirm and give another evidence that homology information is more likely to be encoded in network topology than sequence information.

Genome-wide analysis of the DNA-binding with one zinc finger (Dof) transcription factor family in bananas.

DNA-binding with one finger (Dof) domain proteins are a multigene family of plant-specific transcription factors involved in numerous aspects of plant growth and development. In this study, we report a genome-wide search for Musa acuminata Dof (MaDof) genes and their expression profiles at different developmental stages and in response to various abiotic stresses. In addition, a complete overview of the Dof gene family in bananas is presented, including the gene structures, chromosomal locations, cis-regulatory elements, conserved protein domains, and phylogenetic inferences. Based on the genome-wide analysis, we identified 74 full-length protein-coding MaDof genes unevenly distributed on 11 chromosomes. Phylogenetic analysis with Dof members from diverse plant species showed that MaDof genes can be classified into four subgroups (StDof I, II, III, and IV). The detailed genomic information of the MaDof gene homologs in the present study provides opportunities for functional analyses to unravel the exact role of the genes in plant growth and development.

PRED-TMBB2: improved topology prediction and detection of beta-barrel outer membrane proteins.

The PRED-TMBB method is based on Hidden Markov Models and is capable of predicting the topology of beta-barrel outer membrane proteins and discriminate them from water-soluble ones. Here, we present an updated version of the method, PRED-TMBB2, with several newly developed features that improve its performance. The inclusion of a properly defined end state allows for better modeling of the beta-barrel domain, while different emission probabilities for the adjacent residues in strands are used to incorporate knowledge concerning the asymmetric amino acid distribution occurring there. Furthermore, the training was performed using newly developed algorithms in order to optimize the labels of the training sequences. Moreover, the method is retrained on a larger, non-redundant dataset which includes recently solved structures, and a newly developed decoding method was added to the already available options. Finally, the method now allows the incorporation of evolutionary information in the form of multiple sequence alignments. The results of a strict cross-validation procedure show that PRED-TMBB2 with homology information performs significantly better compared to other available prediction methods. It yields 76% in correct topology predictions and outperforms the best available predictor by 7%, with an overall SOV of 0.9. Regarding detection of beta-barrel proteins, PRED-TMBB2, using just the query sequence as input, achieves an MCC value of 0.92, outperforming even predictors designed for this task and are much slower. The method, along with all datasets used, is freely available for academic users at http://www.compgen.org/tools/PRED-TMBB2 CONTACT: pbagos@compgen.org.

Predictive characterization of hypothetical proteins in Staphylococcus aureus NCTC 8325.

Staphylococcus aureus is one of the most common hospital acquired infections. It colonizes immunocompromised patients and with the number of antibiotic resistant strains increasing, medicine needs new treatment options. Understanding more about the proteins this organism uses would further this goal. Hypothetical proteins are sequences thought to encode a functional protein but for which little to no evidence of that function exists. About half of the genomic proteins in reference strain S. aureus NCTC 8325 are hypothetical. Since annotation of these proteins can lead to new therapeutic targets, a high demand to characterize hypothetical proteins is present. This work examines 35 hypothetical proteins from the chromosome of S. aureus NCTC 8325. Examination includes physiochemical characterization; sequence homology; structural homology; domain recognition; structure modeling; active site depiction; predicted protein-protein interactions; protein-chemical interactions; protein localization; protein stability; and protein solubility. The examination revealed some hypothetical proteins related to virulent domains and protein-protein interactions including superoxide dismutase, O-antigen, bacterial ferric iron reductase and siderophore synthesis. Yet other hypothetical proteins appear to be metabolic or transport proteins including ABC transporters, major facilitator superfamily, S-adenosylmethionine decarboxylase, and GTPases. Progress evaluating some hypothetical proteins, particularly the smaller ones, was incomplete due to limited homology and structural information in public repositories. These data characterizing hypothetical proteins will contribute to the scientific understanding of S. aureus by identifying potential drug targets and aiding in future drug discovery.

Effective homology of k-D digital objects (partially) calculated in parallel

In [18], a membrane parallel theoretical framework for computing (co)homology information of foreground or background of binary digital images is developed. Starting from this work, we progress here in two senses: (a) providing advanced topological information, such as (co)homology torsion and efficiently answering to any decision or classification problem for sum of k-xels related to be a (co)cycle or a (co)boundary; (b) optimizing the previous framework to be implemented in using GPGPU computing. Discrete Morse theory, Effective Homology Theory and parallel computing techniques are suitably combined for obtaining a homological encoding, called algebraic minimal model, of a Region-Of-Interest (seen as cubical complex) of a presegmented k-D digital image.

Global graph matching using diffusion maps

We present a new algorithm, global positioning graph matching (GPGM), to perform global network alignments between pairs of undirected graphs by minimizing a dissimilarity score over matched vertices. We define structural dissimilarities based on a random walk over each graph to provide a robust me asure of the global graph topology using a nonlinear manifold learning algorithm known as diffusion maps. Measures of vertex-vertex dissimilarity are straightforwardly incorporated in a convex combination. We have tested our approach in pairwise alignments of protein-protein interaction networks of Xenopus laevis (frog), Rattus norvegicus (rat), Caenorhabditis elegans (worm), Mus musculus (mouse), and Drosophila melanogaster (fly). When vertex-vertex dissimilarities are incorporated using homology scores between protein sequences, the performance of GPGM is comparable to that of the IsoRank algorithm (Singh et al. Proc. Natl. Acad. Sci. USA 105 35 12763 (2008)). When homology information is not included, GPGM discovers superior alignments, making it well suited to graph matching applications where vertex labels are unknown or undefined.

Structural homology guided alignment of cysteine rich proteins.

BackgroundCysteine rich protein families are notoriously difficult to align due to low sequence identity and frequent insertions and deletions.ResultsHere we present an alignment method that ensures homologous cysteines align by assigning a unique 10 amino acid barcode to those identified as structurally homologous by the DALI webserver. The free inter-cysteine regions of the barcoded sequences can then be aligned using any standard algorithm. Finally the barcodes are replaced with the original columns to yield an alignment which requires the minimum of manual refinement.ConclusionsUsing structural homology information to constrain sequence alignments allows the alignment of highly divergent, repetitive sequences that are poorly dealt with by existing algorithms. Tools are provided to perform this method online using the CysBar web-tool (http://CysBar.science.latrobe.edu.au) and offline (python script available from http://github.com/ts404/CysBar).Electronic supplementary materialThe online version of this article (doi:10.1186/s40064-015-1609-z) contains supplementary material, which is available to authorized users.