Lazar, T. | Search bioRxiv

bioRxiv2021

Concentration and dosage sensitivity of proteins driving liquid-liquid phase separation

Liquid-liquid phase separation (LLPS) is a molecular process that leads to the formation of membraneless organelles (MLOs), i.e. functionally specialized liquid-like cellular condensates formed by proteins and nucleic acids. Integration of data on LLPS-associated proteins from dedicated databases revealed only modest overlap between them and resulted in a confident set of 89 human LLPS driver proteins. Since LLPS is highly concentration-sensitive, the underlying experiments are often criticized for applying higher-than-physiological protein concentrations. To clarify this issue, we performed a naive comparison of in vitro applied and quantitative proteomics-derived protein concentrations and discuss a number of considerations that rationalize the choice of apparently high in vitro concentrations in most LLPS studies. The validity of in vitro LLPS experiments is further supported by in vivo phase-separation experiments and by the observation that the corresponding genes show a strong propensity for dosage sensitivity. This observation implies that the availability of the respective proteins is tightly regulated in cells to avoid erroneous condensate formation. In all, we propose that although local protein concentrations are practically impossible to determine in cells, proteomics-derived cellular concentrations should rather be considered as lower limits of protein concentrations, than strict upper bounds, to be respected by in vitro experiments.

molecular biology↗

bioRxiv2020

Distance-based metrics for comparing conformational ensembles of intrinsically disordered proteins

Intrinsically disordered proteins (IDPs) are proteins whose native functional states represent ensembles of highly diverse conformations. Such ensembles are a challenge for quantitative structure comparisons as their conformational diversity precludes optimal superimposition of the atomic coordinates, necessary for deriving common similarity measures such as the root-mean-square deviation (RMSD) of these coordinates. Here we introduce superimposition-free metrics, which are based on computing matrices of C-C distance distributions within ensembles and comparing these matrices between ensembles. Differences between two matrices yield information on the similarity between specific regions of the polypeptide, whereas the global structural similarity is captured by the ens_dRMS, defined as the root-mean-square difference between the medians of the C-Cdistance distributions of two ensembles. Together, our metrics enable rigorous investigations of structure-function relationships in conformational ensembles of IDPs derived using experimental restraints or by molecular simulations, and for proteins containing both structured and disordered regions. Statement of SignificanceImportant biological insight is obtained from comparing the high-resolution structures of proteins. Such comparisons commonly involve superimposing two protein structures and computing the residual root-mean-square deviation of the atomic positions. This approach cannot be applied to intrinsically disordered proteins (IDPs) because IDPs do not adopt well-defined 3D structures, rather, their native functional state is defined by ensembles of heterogeneous conformations that cannot be meaningfully superimposed. We report new measures that quantify the local and global similarity between different conformational ensembles by evaluating differences between the distributions of residue-residue distances and their statistical significance. Applying these measures to IDP ensembles and to a protein containing both structured and intrinsically disordered domains provides deeper insights into how structural features relate to function.

bioinformatics↗