ABSTRACT
Maize (Zea mays) is a major staple crop in Africa, where its yield and the livelihood of millions are compromised by the parasitic witchweed Striga. Germination of Striga is induced by strigolactones exuded from maize roots into the rhizosphere. In a maize germplasm collection, we identified two strigolactones, zealactol and zealactonoic acid, which stimulate less Striga germination than the major maize strigolactone, zealactone. We then showed that a single cytochrome P450, ZmCYP706C37, catalyzes a series of oxidative steps in the maize-strigolactone biosynthetic pathway. Reduction in activity of this enzyme and two others involved in the pathway, ZmMAX1b and ZmCLAMT1, can change strigolactone composition and reduce Striga germination and infection. These results offer prospects for breeding Striga-resistant maize.
Subject(s)
Lactones , Striga , Zea mays , Germination , Lactones/metabolism , Plant Breeding , Striga/growth & development , Zea mays/genetics , Zea mays/metabolismABSTRACT
This chapter presents a use case illustrating the search for homologues of a known protein in species-specific genome sequence databases. The results from different species-specific resources are compared to each other and to results obtained from a more general genome sequence database (Phytozome). Various options and settings relevant when searching these databases are discussed. For example, it is shown how the choice of reference sequence set in a given database influences the results one obtains. The provided examples illustrate some problems and pitfalls related to interpreting results obtained from species-specific genome sequence databases.
Subject(s)
Computational Biology/methods , Databases, Nucleic Acid , Genome , Genomics , Web Browser , Genomics/methods , Search Engine , Species SpecificityABSTRACT
MOTIVATION: Recent research underlines the importance of finegrained knowledge on protein localization. In particular, subcompartmental localization in the Golgi apparatus is important, for example, for the order of reactions performed in glycosylation pathways or the sorting functions of SNAREs, but is currently poorly understood. RESULTS: We assemble a dataset of type II transmembrane proteins with experimentally determined sub-Golgi localizations and use this information to develop a predictor based on the transmembrane domain of these proteins, making use of a dedicated proteinstructure based kernel in an SVM. Various applications demonstrate the power of our approach. In particular, comparison with a large set of glycan structures illustrates the applicability of our predictions on a 'glycomic' scale and demonstrates a significant correlation between sub-Golgi localization and the ordering of different steps in glycan biosynthesis. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
Subject(s)
Golgi Apparatus/metabolism , Models, Biological , Models, Chemical , Pattern Recognition, Automated/methods , SNARE Proteins/chemistry , SNARE Proteins/metabolism , Sequence Analysis, Protein/methods , Amino Acid Sequence , Artificial Intelligence , Computer Simulation , Molecular Sequence Data , Structure-Activity RelationshipABSTRACT
MOTIVATION: Transcription factor interactions are the cornerstone of combinatorial control, which is a crucial aspect of the gene regulatory system. Understanding and predicting transcription factor interactions based on their sequence alone is difficult since they are often part of families of factors sharing high sequence identity. Given the scarcity of experimental data on interactions compared to available sequence data, however, it would be most useful to have accurate methods for the prediction of such interactions. RESULTS: We present a method consisting of a Random Forest-based feature-selection procedure that selects relevant motifs out of a set found using a correlated motif search algorithm. Prediction accuracy for several transcription factor families (bZIP, MADS, homeobox and forkhead) reaches 60-90%. In addition, we identified those parts of the sequence that are important for the interaction specificity, and show that these are in agreement with available data. We also used the predictors to perform genome-wide scans for interaction partners and recovered both known and putative new interaction partners.
Subject(s)
Models, Chemical , Pattern Recognition, Automated/methods , Protein Interaction Mapping/methods , Sequence Analysis, Protein/methods , Transcription Factors/chemistry , Amino Acid Sequence , Binding Sites , Combinatorial Chemistry Techniques/methods , Computer Simulation , Data Interpretation, Statistical , Molecular Sequence Data , Protein BindingABSTRACT
We have shown previously that given high-resolution structures of the unbound molecules, structure determination of protein complexes is possible by including biochemical and/or biophysical data as highly ambiguous distance restraints in a docking approach. We applied this method, implemented in the HADDOCK (High Ambiguity Driven DOCKing) package (Dominguez et al., J Am Chem Soc 2003;125:1731-1737), to the targets in the fourth and fifth rounds of CAPRI. Here we describe our results and analyze them in detail. Special attention is given to the role of flexibility in our docking method and the way in which this improves the docking results. We describe extensions to our approach that were developed as a direct result of our participation in CAPRI. In addition to experimental information, we also included interface residue predictions from PPISP (Protein-Protein Interaction Site Predictor; Zhou and Shan, Proteins 2001;44:336-343), a neural network method. Using HADDOCK we were able to generate acceptable structures for 6 of the 8 targets, and to submit at least 1 acceptable structure for 5 of them. Of these 5 submissions, 3 were of medium quality (Targets 10, 11, and 15) and 2 of high quality (Targets 13 and 14). In all cases, predictions were obtained containing at least 40% of the correct epitope at the interface for both ligand and receptor simultaneously.
