Sequence-Based Prediction of Fuzzy Protein Interactions

It is becoming increasingly recognised that disordered proteins may be fuzzy, in that they can exhibit a wide variety of binding modes. In addition to the well-known process of folding upon binding (disorder-to-order transition), many examples are emerging of interacting proteins that remain disordered in their bound states (disorder-to-disorder transitions). Furthermore, disordered proteins may populate ordered and disordered states to different extents depending on their partners (context-dependent binding). Here we assemble three datasets comprising disorder-to-order, context-dependent, and disorder-to-disorder transitions of 828 protein regions represented in 2157 complexes and elucidate the sequence-determinants of the different interaction modes. We found that fuzzy interactions originate from local sequence compositions that promote the sampling of a wide range of different structures. Based on this observation, we developed the FuzPred method (http://protdyn-fuzpred.org) of predicting the binding modes of disordered proteins based on their amino acid sequences, without specifying their partners. We thus illustrate how the amino acid sequences of proteins can encode a wide range of conformational changes upon binding, including transitions from disordered to ordered and from disordered to disordered states

Sequence-based prediction of protein binding mode landscapes.

Interactions between disordered proteins involve a wide range of changes in the structure and dynamics of the partners involved. These changes can be classified in terms of binding modes, which include disorder-to-order (DO) transitions, when proteins fold upon binding, as well as disorder-to-disorder (DD) transitions, when the conformational heterogeneity is maintained in the bound states. Furthermore, systematic studies of these interactions are revealing that proteins may exhibit different binding modes with different partners. Proteins that exhibit this context-dependent binding can be referred to as fuzzy proteins. Here we investigate amino acid code for fuzzy binding in terms of the entropy of the probability distribution of transitions towards decreasing order. We implement these entropy calculations into the FuzPred (http://protdyn-fuzpred.org) algorithm to predict the range of context-dependent binding modes of proteins from their amino acid sequences. As we illustrate through a variety of examples, this method identifies those binding sites that are sensitive to the cellular context or post-translational modifications, and may serve as regulatory points of cellular pathways

Sequence-based prediction of protein binding mode landscapes

Interactions between disordered proteins involve a wide range of changes in the structure and dynamics of the partners involved. These changes can be classified in terms of binding modes, which include disorder-to-order (DO) transitions, when proteins fold upon binding, as well as disorder-to-disorder (DD) transitions, when the conformational heterogeneity is maintained in the bound states. Furthermore, systematic studies of these interactions are revealing that proteins may exhibit different binding modes with different partners. Proteins that exhibit this context-dependent binding can be referred to as fuzzy proteins. Here we investigate amino acid code for fuzzy binding in terms of the entropy of the probability distribution of transitions towards decreasing order. We implement these entropy calculations into the FuzPred (http://protdyn-fuzpred.org) algorithm to predict the range of context-dependent binding modes of proteins from their amino acid sequences. As we illustrate through a variety of examples, this method identifies those binding sites that are sensitive to the cellular context or post-translational modifications, and may serve as regulatory points of cellular pathways

Genome-wide mapping of binding sites of the transposase-derived SETMAR protein in the human genome

Throughout evolution, DNA transposons provide a recurrent supply of genetic information to give rise to novel gene functions by fusion of their transposase domain to various domains of host-encoded proteins. One of these “domesticated”, transposase-derived factors is SETMAR/Metnase which is a naturally occurring fusion protein that consists of a histone-lysine methyltransferase domain and an HsMar1 transposase. To elucidate the biological role of SETMAR, it is crucial to identify genomic targets to which SETMAR specifically binds and link these sites to the regulation of gene expression. Herein, we mapped the genomic landscape of SETMAR binding in a near-haploid human leukemia cell line (HAP1) in order to identify on-target and off-target binding sites at high resolution and to elucidate their role in terms of gene expression. Our analysis revealed a perfect correlation between SETMAR and inverted terminal repeats (ITRs) of HsMar1 transposon remnants, which are considered as natural target sites for SETMAR binding. However, we did not detect any untargeted events at non-ITR sequences, calling into question previously proposed off-target binding sites. We identified sequence fidelity of the ITR motif as a key factor for determining the binding affinity of SETMAR for chromosomes, as higher conservation of ITR sequences resulted in increased affinity for chromatin and stronger repression of SETMAR-bound gene loci. These associations highlight how SETMAR’s chromatin binding fine-tune gene regulatory networks in human tumour cells

Conformational Characterization of Intrinsically Disordered Proteins and Its Biological Significance

International audienc

The 2008 update of the Aspergillus nidulans genome annotation : a community effort

The identification and annotation of protein-coding genes is one of the primary goals of whole-genome sequencing projects, and the accuracy of predicting the primary protein products of gene expression is vital to the interpretation of the available data and the design of downstream functional applications. Nevertheless, the comprehensive annotation of eukaryotic genomes remains a considerable challenge. Many genomes submitted to public databases, including those of major model organisms, contain significant numbers of wrong and incomplete gene predictions. We present a community-based reannotation of the Aspergillus nidulans genome with the primary goal of increasing the number and quality of protein functional assignments through the careful review of experts in the field of fungal biology