Motivation: Intrinsically disordered proteins (IDPs) represent a significant fraction of the human proteome. The classical structure function paradigm that has successfully underpinned our understanding of molecular biology breaks down when considering proteins that have no stable tertiary structure in their functional form. One convenient approach is to describe the protein in terms of an equilibrium of rapidly inter-converting conformers. Currently, tools to generate such ensemble descriptions are extremely rare, and poorly adapted to the prediction of experimental data. Results: We present flexible-meccano—a highly efficient algorithm that generates ensembles of molecules, on the basis of amino acid-specific conformational potentials and volume exclusion. Conformational sampling depends uniquely on the primary sequence, with the possibility of introducing additional local or long-range conformational propensities at an amino acid-specific resolution. The algorithm can also be used to calculate expected values of experimental parameters measured at atomic or molecular resolution, such as nuclear magnetic resonance (NMR) and small angle scattering, respectively. We envisage that flexible-meccano will be useful for researchers who wish to compare experimental data with those expected from a fully disordered protein, researchers who see experimental evidence of deviation from ‘random coil’ behaviour in their protein, or researchers who are interested in working with a broad ensemble of conformers representing the flexibility of the IDP of interest. Availability: A fully documented multi-platform executable is provided, with examples, at http://www.ibs.fr/science-213/scientific-output/software/flexible-meccano/ Contact:martin.blackledge@ibs.fr

Eukaryotic cells contain distinct organelles, but not all of these compartments are enclosed by membranes. Some intrinsically disordered proteins mediate membraneless organelle formation through liquid–liquid phase separation (LLPS). LLPS facilitates many biological functions such as regulating RNA stability and ribonucleoprotein assembly, and disruption of LLPS pathways has been implicated in several diseases. Proteins exhibiting LLPS typically have low sequence complexity and specific repeat motifs. These motifs promote multivalent connections with other molecules and the formation of higher-order oligomers, and their removal usually prevents LLPS. The intrinsically disordered C-terminal domain of TAR DNA-binding protein 43 (TDP-43), a protein involved in motor neuron disease and dementia lacks a dominant LLPS motif, however, and how this domain forms condensates is unclear. Using extensive mutagenesis of TDP-43, we demonstrate here that three tryptophan residues and, to a lesser extent, four other aromatic residues are most important for TDP-43 to undergo LLPS. Our results also suggested that only a few residues may be required for TDP-43 LLPS because the α-helical segment (spanning ∼20 residues) in the middle part of the C-terminal domain tends to self-assemble, reducing the number of motifs required for forming a multivalent connection. Our results indicating that a self-associating α-helical element with a few key residues regulates condensate formation highlight a different type of LLPS involving intrinsically disordered regions. The C-terminal domain of TDP-43 contains ∼50 disease-related mutations, with no clear physicochemical link between them. We propose that they may disrupt LLPS indirectly by interfering with the key residues identified here.

Significance: Proteins are the most versatile components of the molecular machinery of life. Synthesized as linear polymers of amino acids, proteins start out in their unfolded state and perform their function either in well-defined folded conformations or as intrinsically disordered proteins (IDPs) lacking tertiary structure. Both for the folding process and the properties of IDPs, a quantitative understanding of the conformational distributions and dynamics of unfolded proteins is thus essential. However, reaching this goal has been challenging owing to the large conformational heterogeneity and rapid dynamics of these systems. Here we combine three of the most powerful biophysical methods available to obtain a comprehensive view of an unfolded protein that would not be available from any of the individual methods.