Domain annotation of trimeric autotransporter adhesins—daTAA

Motivation: Trimeric autotransporter adhesins (TAAs), such as Yersinia YadA, Neisseria NadA, Moraxella UspAs, Haemophilus Hia and Bartonella BadA, are important pathogenicity factors of proteobacteria. Their high sequence diversity and distinct mosaic-like structure lead to difficulties in the annotation of their sequences. These stem from the large number of short repeats, the presence of compositionally unusual coiled-coils, fuzzy domain boundaries and regions of seemingly low sequence complexity

Learning sparse models for a dynamic Bayesian network classifier of protein secondary structure

<p>Abstract</p> <p>Background</p> <p>Protein secondary structure prediction provides insight into protein function and is a valuable preliminary step for predicting the 3D structure of a protein. Dynamic Bayesian networks (DBNs) and support vector machines (SVMs) have been shown to provide state-of-the-art performance in secondary structure prediction. As the size of the protein database grows, it becomes feasible to use a richer model in an effort to capture subtle correlations among the amino acids and the predicted labels. In this context, it is beneficial to derive sparse models that discourage over-fitting and provide biological insight.</p> <p>Results</p> <p>In this paper, we first show that we are able to obtain accurate secondary structure predictions. Our per-residue accuracy on a well established and difficult benchmark (CB513) is 80.3%, which is comparable to the state-of-the-art evaluated on this dataset. We then introduce an algorithm for sparsifying the parameters of a DBN. Using this algorithm, we can automatically remove up to 70-95% of the parameters of a DBN while maintaining the same level of predictive accuracy on the SD576 set. At 90% sparsity, we are able to compute predictions three times faster than a fully dense model evaluated on the SD576 set. We also demonstrate, using simulated data, that the algorithm is able to recover true sparse structures with high accuracy, and using real data, that the sparse model identifies known correlation structure (local and non-local) related to different classes of secondary structure elements.</p> <p>Conclusions</p> <p>We present a secondary structure prediction method that employs dynamic Bayesian networks and support vector machines. We also introduce an algorithm for sparsifying the parameters of the dynamic Bayesian network. The sparsification approach yields a significant speed-up in generating predictions, and we demonstrate that the amino acid correlations identified by the algorithm correspond to several known features of protein secondary structure. Datasets and source code used in this study are available at <url>http://noble.gs.washington.edu/proj/pssp</url>.</p

Automated structural annotation of the malaria proteome and identification of candidate proteins for modelling and crystallization studies

Malaria is the cause of over one million deaths per year, primarily in African children. The parasite responsible for the most virulent form of malaria, is Plasmodium falciparum. Protein structure plays a pivotal role in elucidating mechanisms of parasite functioning and resistance to anti-malarial drugs. Protein structure furthermore aids the determination of protein function, which can together with the structure be used to identify novel drug targets in the parasite. However, various structural features in P. falciparum proteins complicate the experimental determination of protein three dimensional structures. Furthermore, the presence of parasite-specific inserts results in reduced similarity of these proteins to orthologous proteins with experimentally determined structures. The lack of solved structures in the malaria parasite, together with limited similarities to proteins in the Protein Data Bank, necessitate genome-scale structural annotation of P. falciparum proteins. Additionally, the annotation of a range of structural features facilitates the identification of suitable targets for structural studies. An integrated structural annotation system was constructed and applied to all the predicted proteins in P. falciparum, Plasmodium vivax and Plasmodium yoelii. Similarity searches against the PDB, Pfam, Superfamily, PROSITE and PRINTS were included. In addition, the following predictions were made for the P. falciparum proteins: secondary structure, transmembrane helices, protein disorder, low complexity, coiled-coils and small molecule interactions. P. falciparum protein-protein interactions and proteins exported to the RBC were annotated from literature. Finally, a selection of proteins were threaded through a library of SCOP folds. All the results are stored in a relational PostgreSQL database and can be viewed through a web interface (http://deepthought.bi.up.ac.za:8080/Annotation). In order to select groups of proteins which fulfill certain criteria with regard to structural and functional features, a query tool was constructed. Using this tool, criteria regarding the presence or absence of all the predicted features can be specified. Analysis of the results obtained revealed that P. falciparum protein-interacting proteins contain a higher percentage of predicted disordered residues than non-interacting proteins. Proteins interacting with 10 or more proteins have a disordered content concentrated in the range of 60-100%, while the disorder distribution for proteins having only one interacting partner, was more evenly spread. Comparisons of structural and sequence features between the three species, revealed that P. falciparum proteins tend to be longer and vary more in length than the other two species. P. falciparum proteins also contained more predicted low complexity and disorder content than proteins from P. yoelii and P. vivax. P. falciparumprotein targets for experimental structure determination, comparative modeling and in silico docking studies were putatively identified based on structural features. For experimental structure determination, 178 targets were identi_ed. These targets contain limited contents of predicted transmembrane helix, disorder, coiled-coils, low complexity and signal peptide, as these features may complicate steps in the experimental structure determination procedure. In addition, the targets display low similarity to proteins in the PDB. Comparisons of the targets to proteins with crystal structures, revealed that the structures and predicted targets had similar sequence properties and predicted structural features. A group of 373 proteins which displayed high levels of similarity to proteins in the PDB, were identified as targets for comparitive modeling studies. Finally, 197 targets for in silico docking were identified based on predicted small molecule interactions and the availability of a 3D structure.Dissertation (MSc)--University of Pretoria, 2008.Biochemistryunrestricte

Structural Property Prediction

While many good textbooks are available on Protein Structure, Molecular Simulations, Thermodynamics and Bioinformatics methods in general, there is no good introductory level book for the field of Structural Bioinformatics. This book aims to give an introduction into Structural Bioinformatics, which is where the previous topics meet to explore three dimensional protein structures through computational analysis. We provide an overview of existing computational techniques, to validate, simulate, predict and analyse protein structures. More importantly, it will aim to provide practical knowledge about how and when to use such techniques. We will consider proteins from three major vantage points: Protein structure quantification, Protein structure prediction, and Protein simulation & dynamics. Some structural properties of proteins that are closely linked to their function may be easier (or much faster) to predict from sequence than the complete tertiary structure; for example, secondary structure, surface accessibility, flexibility, disorder, interface regions or hydrophobic patches. Serving as building blocks for the native protein fold, these structural properties also contain important structural and functional information not apparent from the amino acid sequence. Here, we will first give an introduction into the application of machine learning for structural property prediction, and explain the concepts of cross-validation and benchmarking. Next, we will review various methods that incorporate knowledge of these concepts to predict those structural properties, such as secondary structure, surface accessibility, disorder and flexibility, and aggregation.Comment: editorial responsability: Juami H. M. van Gils, K. Anton Feenstra, Sanne Abeln. This chapter is part of the book "Introduction to Protein Structural Bioinformatics". The Preface arXiv:1801.09442 contains links to all the (published) chapter

Multicoil2: predicting coiled coils and their oligomerization States from sequence in the twilight zone

The alpha-helical coiled coil can adopt a variety of topologies, among the most common of which are parallel and antiparallel dimers and trimers. We present Multicoil2, an algorithm that predicts both the location and oligomerization state (two versus three helices) of coiled coils in protein sequences. Multicoil2 combines the pairwise correlations of the previous Multicoil method with the flexibility of Hidden Markov Models (HMMs) in a Markov Random Field (MRF). The resulting algorithm integrates sequence features, including pairwise interactions, through multinomial logistic regression to devise an optimized scoring function for distinguishing dimer, trimer and non-coiled-coil oligomerization states; this scoring function is used to produce Markov Random Field potentials that incorporate pairwise correlations localized in sequence. Multicoil2 significantly improves both coiled-coil detection and dimer versus trimer state prediction over the original Multicoil algorithm retrained on a newly-constructed database of coiled-coil sequences. The new database, comprised of 2,105 sequences containing 124,088 residues, includes reliable structural annotations based on experimental data in the literature. Notably, the enhanced performance of Multicoil2 is evident when tested in stringent leave-family-out cross-validation on the new database, reflecting expected performance on challenging new prediction targets that have minimal sequence similarity to known coiled-coil families. The Multicoil2 program and training database are available for download from http://multicoil2.csail.mit.edu.National Institutes of Health (U.S.) (Grant 1R01GM081871)National Science Foundation (U.S.) (Grant MCB-0347203)National Science Foundation (U.S.) (Grant 0821391

A Phasin with Many Faces: Structural Insights on PhaP from Azotobacter sp. FA8

Phasins are a group of proteins associated to granules of polyhydroxyalkanoates (PHAs). Apart from their structural role as part of the PHA granule cover, different structural and regulatory functions have been found associated to many of them, and several biotechnological applications have been developed using phasin protein fusions. Despite their remarkable functional diversity, the structure of these proteins has not been analyzed except in very few studies. PhaP from Azotobacter sp. FA8 (PhaPAz) is a representative of the prevailing type in the multifunctional phasin protein family. Previous work performed in our laboratory using this protein have demonstrated that it has some very peculiar characteristics, such as its stress protecting effects in recombinant Escherichia coli, both in the presence and absence of PHA. The aim of the present work was to perform a structural characterization of this protein, to shed light on its properties. Its aminoacid composition revealed that it lacks clear hydrophobic domains, a characteristic that appears to be common to most phasins, despite their lipid granule binding capacity. The secondary structure of this protein, consisting of α-helices and disordered regions, has a remarkable capacity to change according to its environment. Several experimental data support that it is a tetramer, probably due to interactions between coiled-coil regions. These structural features have also been detected in other phasins, and may be related to their functional diversity.Fil: Mezzina, Mariela Paula. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Instituto de Química Biológica de la Facultad de Ciencias Exactas y Naturales. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Instituto de Química Biológica de la Facultad de Ciencias Exactas y Naturales; ArgentinaFil: Wetzler, Diana Elena. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Instituto de Química Biológica de la Facultad de Ciencias Exactas y Naturales. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Instituto de Química Biológica de la Facultad de Ciencias Exactas y Naturales; ArgentinaFil: Catone, Mariela Verónica. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Instituto de Química Biológica de la Facultad de Ciencias Exactas y Naturales. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Instituto de Química Biológica de la Facultad de Ciencias Exactas y Naturales; ArgentinaFil: Bucci, Hernán Andrés. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Instituto de Química Biológica de la Facultad de Ciencias Exactas y Naturales. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Instituto de Química Biológica de la Facultad de Ciencias Exactas y Naturales; ArgentinaFil: Di Paola, Matías Ezequiel. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Instituto de Química Biológica de la Facultad de Ciencias Exactas y Naturales. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Instituto de Química Biológica de la Facultad de Ciencias Exactas y Naturales; ArgentinaFil: Pettinari, María Julia. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Instituto de Química Biológica de la Facultad de Ciencias Exactas y Naturales. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Instituto de Química Biológica de la Facultad de Ciencias Exactas y Naturales; Argentin

The MPI bioinformatics Toolkit as an integrative platform for advanced protein sequence and structure analysis.

The MPI Bioinformatics Toolkit (http://toolkit.tuebingen.mpg.de) is an open, interactive web service for comprehensive and collaborative protein bioinformatic analysis. It offers a wide array of interconnected, state-of-the-art bioinformatics tools to experts and non-experts alike, developed both externally (e.g. BLAST+, HMMER3, MUSCLE) and internally (e.g. HHpred, HHblits, PCOILS). While a beta version of the Toolkit was released 10 years ago, the current production-level release has been available since 2008 and has serviced more than 1.6 million external user queries. The usage of the Toolkit has continued to increase linearly over the years, reaching more than 400 000 queries in 2015. In fact, through the breadth of its tools and their tight interconnection, the Toolkit has become an excellent platform for experimental scientists as well as a useful resource for teaching bioinformatic inquiry to students in the life sciences. In this article, we report on the evolution of the Toolkit over the last ten years, focusing on the expansion of the tool repertoire (e.g. CS-BLAST, HHblits) and on infrastructural work needed to remain operative in a changing web environment