Liquid–liquid phase separation of proteins underpins the formation of membraneless compartments in living cells. Elucidating the molecular driving forces underlying protein phase transitions is therefore a key objective for understanding biological function and malfunction. Here we show that cellular proteins, which form condensates at low salt concentrations, including FUS, TDP-43, Brd4, Sox2, and Annexin A11, can reenter a phase-separated regime at high salt concentrations. By bringing together experiments and simulations, we demonstrate that this reentrant phase transition in the high-salt regime is driven by hydrophobic and non-ionic interactions, and is mechanistically distinct from the low-salt regime, where condensates are additionally stabilized by electrostatic forces. Our work thus sheds light on the cooperation of hydrophobic and non-ionic interactions as general driving forces in the condensation process, with important implications for aberrant function, druggability, and material properties of biomolecular condensates.

Rationale and Objectives To develop a computerized data integration framework (MaWERiC) for quantitatively combining structural and metabolic information from different Magnetic Resonance (MR) imaging modalities. Materials and Methods In this paper, we present a novel computerized support system that we call Multimodal Wavelet Embedding Representation for data Combination (MaWERiC) which (1) employs wavelet theory and dimensionality reduction for providing a common, uniform representation of the different imaging (T2-w) and non-imaging (spectroscopy) MRI channels, and (2) leverages a random forest classifier for automated prostate cancer detection on a per voxel basis from combined 1.5 Tesla in vivo MRI and MRS. Results A total of 36 1.5 T endorectal in vivo T2-w MRI, MRS patient studies were evaluated on a per-voxel via MaWERiC, using a three-fold cross validation scheme across 25 iterations. Ground truth for evaluation of the results was obtained via ex-vivo whole-mount histology sections which served as the gold standard for expert radiologist annotations of prostate cancer on a per-voxel basis. The results suggest that MaWERiC based MRS-T2-w meta-classifier (mean AUC, μ = 0.89 ± 0.02) significantly outperformed (i) a T2-w MRI (employing wavelet texture features) classifier (μ = 0.55± 0.02), (ii) a MRS (employing metabolite ratios) classifier (μ= 0.77 ± 0.03), (iii) a decision-fusion classifier, obtained by combining individual T2-w MRI and MRS classifier outputs (μ = 0.85 ± 0.03) and (iv) a data combination scheme involving combination of metabolic MRS and MR signal intensity features (μ = 0.66± 0.02). Conclusion A novel data integration framework, MaWERiC, for combining imaging and non-imaging MRI channels was presented. Application to prostate cancer detection via combination of T2-w MRI and MRS data demonstrated significantly higher AUC and accuracy values compared to the individual T2-w MRI, MRS modalities and other data integration strategies.

Liquid–liquid phase separation underlies the formation of biological condensates. Physically, such systems are microemulsions that in general have a propensity to fuse and coalesce; however, many condensates persist as independent droplets in the test tube and inside cells. This stability is crucial for their function, but the physicochemical mechanisms that control the emulsion stability of condensates remain poorly understood. Here, by combining single-condensate zeta potential measurements, optical microscopy, tweezer experiments, and multiscale molecular modeling, we investigate how the nanoscale forces that sustain condensates impact their stability against fusion. By comparing peptide–RNA (PR25:PolyU) and proteinaceous (FUS) condensates, we show that a higher condensate surface charge correlates with a lower fusion propensity. Moreover, measurements of single condensate zeta potentials reveal that such systems can constitute classically stable emulsions. Taken together, these results highlight the role of passive stabilization mechanisms in protecting biomolecular condensates against coalescence.

Many cellular proteins have the ability to demix spontaneously from solution to form liquid condensates. These phase-separated structures form membraneless compartments in living cells and have wide-ranging roles in health and disease. Elucidating the molecular driving forces underlying liquid-liquid phase separation (LLPS) of proteins has thus become a key objective for understanding biological function and malfunction. Here we show that proteins implicated in cellular phase separation, such as FUS, TDP-43, and Annexin A11, which form condensates at low salt concentrations via homotypic multivalent interactions, also have the ability to undergo LLPS at high salt concentrations by reentering into a phase-separated regime. Through a combination of experiments and simulations, we demonstrate that phase separation in the high-salt regime is mainly driven by hydrophobic and non-ionic interactions. As such, it is mechanistically distinct from the low-salt regime, where condensates are stabilized by a broad mix of electrostatic, hydrophobic, and non-ionic forces. Our work thus expands the molecular grammar of interactions governing LLPS of cellular proteins and provides a new view on hydrophobicity and non-ionic interactions as non-specific driving forces for the condensation process, with important implications for the aberrant function, druggability, and material properties of biomolecular condensates. One Sentence SummaryProteins implicated in cellular phase separation can undergo a salt-mediated reentrant liquid-liquid phase transition.

Water is often found to mediate interactions between a ligand and a protein. It can play a significant role in orientating the ligand within a binding pocket and contribute to the free energy of binding. It would thus be extremely useful to be able to accurately predict the position and orientation of water molecules within a binding pocket. Recently, we developed the WaterDock protocol that was able to predict 97% of the water molecules in a test set. However, this approach generated false positives at a rate of over 20% in most cases and whilst this might be acceptable for some applications, in high throughput scenarios this is not desirable. Here we tackle this problem via the inclusion of knowledge regarding the solvation structure of ligand functional groups. We call this new protocol WaterDock2 and demonstrate that this protocol maintains a similar true positive rate to the original implementation but is capable of reducing the false-positive rate by over 50%. To improve the usability of the method, we have also developed a plugin for the popular graphics program PyMOL. The plugin also contains an implementation of the original WaterDock.