Showing papers by "Aalt D. J. van Dijk published in 2019"

Comprehensive phenotyping reveals interactions and functions of Arabidopsis thaliana TCP genes in yield determination

Abstract: Members of the Arabidopsis thaliana TCP transcription factor (TF) family affect plant growth and development. We systematically quantified the effect of mutagenizing single or multiple TCP TFs and how altered vegetative growth or branching influences final seed yield. We monitored rosette growth over time and branching patterns and seed yield characteristics at the end of the lifecycle. Subsequently, an approach was developed to disentangle vegetative growth and to determine possible effects on seed yield. Analysis of growth parameters showed all investigated tcp mutants to be affected in certain growth aspects compared with wild-type plants, highlighting the importance of TCP TFs in plant development. Furthermore, we found evidence that all class II TCPs are involved in axillary branch outgrowth, either as inhibitors (BRANCHED-like genes) or enhancers (JAW- and TCP5-like genes). Comprehensive phenotyping of plants mutant for single or multiple TCP TFs reveals that the proposed opposite functions of class I and class II TCPs in plant growth needs revision and shows complex interactions between closely related TCP genes instead of full genetic redundancy. In various instances, the alterations in vegetative growth or in branching patterns result into negative trade-off effects on seed yield that were missed in previous studies, showing the importance of comprehensive and quantitative phenotyping.

Improved inference of intermolecular contacts through protein–protein interaction prediction using coevolutionary analysis

Abstract: MOTIVATION Predicting residue-residue contacts between interacting proteins is an important problem in bioinformatics. The growing wealth of sequence data can be used to infer these contacts through correlated mutation analysis on multiple sequence alignments of interacting homologs of the proteins of interest. This requires correct identification of pairs of interacting proteins for many species, in order to avoid introducing noise (i.e. non-interacting sequences) in the analysis that will decrease predictive performance. RESULTS We have designed Ouroboros, a novel algorithm to reduce such noise in intermolecular contact prediction. Our method iterates between weighting proteins according to how likely they are to interact based on the correlated mutations signal, and predicting correlated mutations based on the weighted sequence alignment. We show that this approach accurately discriminates between protein interaction versus non-interaction and simultaneously improves the prediction of intermolecular contact residues compared to a naive application of correlated mutation analysis. This requires no training labels concerning interactions or contacts. Furthermore, the method relaxes the assumption of one-to-one interaction of previous approaches, allowing for the study of many-to-many interactions. AVAILABILITY AND IMPLEMENTATION Source code and test data are available at www.bif.wur.nl/. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.

Coevolution-based prediction of protein-protein interactions in polyketide biosynthetic assembly lines

Abstract: Polyketide synthases are multimodular enzymes that generate diverse molecules of great pharmaceutical importance, including a range of clinically used antimicrobials and antitumor agents. Many polyketides are synthesized by type I polyketide synthases (PKSs), which are organized in assembly lines, in which multiple enzymes line up in a specific order. This order is defined by specific protein-protein interactions. The unique modular structure and catalyzing mechanism of these assembly lines makes their products predictable and also spurred combinatorial biosynthesis studies to produce novel polyketides using synthetic biology. However, predicting the interactions of PKSs, and thereby inferring the order of their assembly line, is still challenging, especially for cases in which this order is not reflected by the ordering of the PKS-encoding genes in the genome. Here, we introduce PKSpop, which uses a coevolution-based protein-protein interaction prediction algorithm to infer protein order in PKS assembly lines. Our method accurately predicts protein orders (80% accuracy). Additionally, we identify new residue pairs that are key in determining interaction specificity, and show that coevolution of N- and C-terminal docking domains of PKSs is significantly more predictive for protein-protein interactions than coevolution between ketosynthase and acyl carrier protein domains.

CAPICE: a computational method for Consequence-Agnostic Pathogenicity Interpretation of Clinical Exome variations

Abstract: Exome sequencing is now mainstream in clinical practice, however, identification of pathogenic Mendelian variants remains time consuming, partly because limited accuracy of current computational prediction methods leaves much manual classification. Here we introduce CAPICE, a new machine-learning based method for prioritizing pathogenic variants, including SNVs and short InDels, that outperforms best general (CADD, GAVIN) and consequence-type-specific (REVEL, ClinPred) computational prediction methods, for both rare and ultra-rare variants. CAPICE is easily integrated into diagnostic pipelines and is available as free and open source command-line software, file of pre-computed scores, and as a web application with web service API.