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1. Macromolecular condensation organizes nucleolar sub-phases to set up a pH gradient

   https://doi.org/10.1016/j.cell.2024.02.029  

   King, Matthew R; Ruff, Kiersten M; Lin, Andrew Z; Pant, Avnika; Farag, Mina; Lalmansingh, Jared M; Wu, Tingting; Fossat, Martin J; Ouyang, Wei; Lew, Matthew D; et al (April 2024, Cell)

   Nucleoli are multicomponent condensates defined by coexisting sub-phases. We identified distinct intrinsically disordered regions (IDRs), including acidic (D/E) tracts and K-blocks interspersed by E-rich regions, as defining features of nucleolar proteins. We show that the localization preferences of nucleolar proteins are determined by their IDRs and the types of RNA or DNA binding domains they encompass. In vitro reconstitutions and studies in cells showed how condensation, which combines binding and complex coacervation of nucleolar components, contributes to nucleolar organization. D/E tracts of nucleolar proteins contribute to lowering the pH of co-condensates formed with nucleolar RNAs in vitro. In cells, this sets up a pH gradient between nucleoli and the nucleoplasm. By contrast, juxta-nucleolar bodies, which have different macromolecular compositions, featuring protein IDRs with very different charge profiles, have pH values that are equivalent to or higher than the nucleoplasm. Our findings show that distinct compositional specificities generate distinct physicochemical properties for condensates. 

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2. Compositional Bias of Intrinsically Disordered Proteins and Regions and Their Predictions

   https://doi.org/10.3390/biom12070888  

   Zhao, Bi; Kurgan, Lukasz (July 2022, Biomolecules)

   Intrinsically disordered regions (IDRs) carry out many cellular functions and vary in length and placement in protein sequences. This diversity leads to variations in the underlying compositional biases, which were demonstrated for the short vs. long IDRs. We analyze compositional biases across four classes of disorder: fully disordered proteins; short IDRs; long IDRs; and binding IDRs. We identify three distinct biases: for the fully disordered proteins, the short IDRs and the long and binding IDRs combined. We also investigate compositional bias for putative disorder produced by leading disorder predictors and find that it is similar to the bias of the native disorder. Interestingly, the accuracy of disorder predictions across different methods is correlated with the correctness of the compositional bias of their predictions highlighting the importance of the compositional bias. The predictive quality is relatively low for the disorder classes with compositional bias that is the most different from the “generic” disorder bias, while being much higher for the classes with the most similar bias. We discover that different predictors perform best across different classes of disorder. This suggests that no single predictor is universally best and motivates the development of new architectures that combine models that target specific disorder classes. 

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3. CLIP: accurate prediction of disordered linear interacting peptides from protein sequences using co-evolutionary information

   https://doi.org/10.1093/bib/bbac502  

   Peng, Zhenling; Li, Zixia; Meng, Qiaozhen; Zhao, Bi; Kurgan, Lukasz (December 2022, Briefings in Bioinformatics)

   Abstract One of key features of intrinsically disordered regions (IDRs) is facilitation of protein–protein and protein–nucleic acids interactions. These disordered binding regions include molecular recognition features (MoRFs), short linear motifs (SLiMs) and longer binding domains. Vast majority of current predictors of disordered binding regions target MoRFs, with a handful of methods that predict SLiMs and disordered protein-binding domains. A new and broader class of disordered binding regions, linear interacting peptides (LIPs), was introduced recently and applied in the MobiDB resource. LIPs are segments in protein sequences that undergo disorder-to-order transition upon binding to a protein or a nucleic acid, and they cover MoRFs, SLiMs and disordered protein-binding domains. Although current predictors of MoRFs and disordered protein-binding regions could be used to identify some LIPs, there are no dedicated sequence-based predictors of LIPs. To this end, we introduce CLIP, a new predictor of LIPs that utilizes robust logistic regression model to combine three complementary types of inputs: co-evolutionary information derived from multiple sequence alignments, physicochemical profiles and disorder predictions. Ablation analysis suggests that the co-evolutionary information is particularly useful for this prediction and that combining the three inputs provides substantial improvements when compared to using these inputs individually. Comparative empirical assessments using low-similarity test datasets reveal that CLIP secures area under receiver operating characteristic curve (AUC) of 0.8 and substantially improves over the results produced by the closest current tools that predict MoRFs and disordered protein-binding regions. The webserver of CLIP is freely available at http://biomine.cs.vcu.edu/servers/CLIP/ and the standalone code can be downloaded from http://yanglab.qd.sdu.edu.cn/download/CLIP/. 

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4. Competition between Nucleic Acids and Intrinsically Disordered Regions within Proteins

   https://doi.org/10.1021/acs.accounts.5c00261  

   Wang, Xi; Levy, Yaakov; Iwahara, Junji (July 2025, Accounts of Chemical Research)

   Intrinsically disordered regions (IDRs) are important components of protein functionality, with their charge distribution serving as a key factor in determining their roles. Notably, many proteins possess IDRs that are highly negatively charged, characterized by sequences rich in aspartate (D) or glutamate (E) residues. Bioinformatic analyses indicate that negatively charged low-complexity IDRs are significantly more common than their positively charged counterparts rich in arginine (R) or lysine (K). For instance, sequences of 10 or more consecutive negatively charged residues (D or E) are present in 268 human proteins. In contrast, corresponding sequences of 10 or more consecutive positively charged residues (K or R) are present in only 12 human proteins. Interestingly, about 50% of proteins containing D/E tracts function as DNA-binding or RNA-binding proteins. Negatively charged IDRs can electrostatically mimic nucleic acids and dynamically compete with them for the DNA-binding domains (DBDs) or RNA-binding domains (RBDs) that are positively charged. This leads to a phenomenon known as autoinhibition, in which the negatively charged IDRs inhibit binding to nucleic acids by occupying the binding interfaces within the proteins through intramolecular interactions. Rather than merely reducing binding activity, negatively charged IDRs offer significant advantages for the functions of DNA/RNA-binding proteins. The dynamic competition between negatively charged IDRs and nucleic acids can accelerate the target search processes for these proteins. When a protein encounters DNA or RNA, the electrostatic repulsion force between the nucleic acids and the negatively charged IDRs can trigger conformational changes that allow the nucleic acids to access DBDs or RBDs. Additionally, when proteins are trapped at high-affinity non-target sites on DNA or RNA ("decoys"), the electrostatic repulsion from the negatively charged IDRs can rescue the proteins from these traps. Negatively charged IDRs act as gatekeepers, rejecting nonspecific ligands while allowing the target to access the molecular interfaces of DBDs or RBDs, which increases binding specificity. These IDRs can also promote proper protein folding, facilitate chromatin remodeling by displacing other proteins bound to DNA, and influence phase separation, affecting local pH. The functions of negatively charged IDRs can be regulated through protein-protein interactions, post-translational modifications, and proteolytic processing. These characteristics can be harnessed as tools for protein engineering. Some frame-shift mutations that convert negatively charged IDRs into positively charged ones are linked to human diseases. Therefore, it is crucial to understand the physicochemical properties and functional roles of negatively charged IDRs that compete with nucleic acids. 

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5. Deciphering the Mystery in p300 Taz2–p53 TAD2 Recognition

   https://doi.org/10.1021/acs.jctc.4c00541  

   Li, Tongtong; Motta, Stefano; He, Yi (August 2024, Journal of Chemical Theory and Computation)

   Intrinsically disordered proteins (IDPs) engage in various fundamental biological activities, and their behavior is of particular importance for a better understanding of the verbose but well-organized signal transduction in cells. IDPs exhibit uniquely paradoxical features with low affinity but simultaneously high specificity in recognizing their binding targets. The transcription factor p53 plays a crucial role in cancer suppression, carrying out some of its biological functions using its disordered regions, such as N-terminal transactivation domain 2 (TAD2). Exploration of the binding and unbinding processes between proteins is challenging, and the inherently disordered properties of these regions further complicate the issue. Computer simulations are a powerful tool to complement the experiments to fill gaps to explore the binding/unbinding processes between proteins. Here, we investigated the binding mechanism between p300 Taz2 and p53 TAD2 through extensive molecular dynamics (MD) simulations using the physics- based UNited RESidue (UNRES) force field with additional Go̅-like potentials. Distance restraints extracted from the NMR- resolved structures were imposed on intermolecular residue pairs to accelerate binding simulations, in which Taz2 was immobilized in a native-like conformation and disordered TAD2 was fully free. Starting from six structures with TAD2 placed at different positions around Taz2, we observed a metastable intermediate state in which the middle helical segment of TAD2 is anchored in the binding pocket, highlighting the significance of the TAD2 helix in directing protein recognition. Physics-based binding simulations show that successful binding is achieved after a series of stages, including (1) protein collisions to initiate the formation of encounter complexes, (2) partial attachment of TAD2, and finally (3) full attachment of TAD2 to the correct binding pocket of Taz2. Furthermore, machine-learning-based PathDetect-SOM was used to identify two binding pathways, the encounter complexes, and the intermediate states. 

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