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Conserved residues in protein homolog sequence alignments are structurally or functionally important. For intrinsically disordered proteins or proteins with intrinsically disordered regions (IDRs), however, alignment often fails because they lack a steric structure to constrain evolution. Although sequences vary, the physicochemical features of IDRs may be preserved in maintaining function. Therefore, a method to retrieve common IDR features may help identify functionally important residues. We applied unsupervised contrastive learning to train a model with self‐attention neuronal networks on human IDR orthologs. Parameters in the model were trained to match sequences in ortholog pairs but not in other IDRs. The trained model successfully identifies previously reported critical residues from experimental studies, especially those with an overall pattern (e.g., multiple aromatic residues or charged blocks) rather than short motifs. This predictive model can be used to identify potentially important residues in other proteins, improving our understanding of their functions. The trained model can be run directly from the Jupyter Notebook in the GitHub repository using Binder (mybinder.org). The only required input is the primary sequence. The training scripts are available on GitHub (https://github.com/allmwh/IFF). The training datasets have been deposited in an Open Science Framework repository (https://osf.io/jk29b). [ABSTRACT FROM AUTHOR]

Aromatic residues appeared relatively late in the evolution of protein sequences to stabilize the globular proteins' folding core and are less in the intrinsically disordered regions (IDRs). Recent advances in protein liquid–liquid phase separation (LLPS) studies have also shown that aromatic residues in IDRs often act as "stickers" to promote multivalent interactions in forming higher‐order oligomers. To study how general these structure‐promoting residues are in IDRs, we compared levels of sequence disorder in RNA binding proteins (RBPs), which are often found to undergo LLPS, and the human proteome. We found that aromatic residues appear more frequently than expected in the IDRs of RBPs and, through multiple sequence alignment analysis, those aromatic residues are often conserved among chordates. Using TDP‐43, FUS, and some other well‐studied LLPS proteins as examples, the conserved aromatic residues are important to their LLPS‐related functions. These analyses suggest that aromatic residues may have contributed twice to evolution: stabilizing structured proteins and assembling biomolecular condensates. [ABSTRACT FROM AUTHOR]

Conformational analysis: An approach to the prediction of RDCs from disordered protein chains, integrating the effect of nearest neighbors and the alignment characteristics of the statistical coil, is reported. NMR residual dipolar couplings (RDC) are sensitive probes of conformational sampling in unfolded proteins (see picture). [ABSTRACT FROM AUTHOR]

NMR spectroscopy is a powerful method for studying disordered proteins, providing atomic resolution and ensemble‐averaged information. In their Communication on page 687 ff., M. Blackledge et al. show that by analyzing local and long‐range effects, residual dipolar couplings can be determined up to six orders of magnitude faster than by existing techniques. [ABSTRACT FROM AUTHOR]

NMR ‐ Spektroskopie ist eine leistungsstarke Methode zur Untersuchung ungeordneter Proteine, die atomare Auflösung und über das Ensemble gemittelte Information liefert. In ihrer Zuschrift auf S. 715 ff. zeigen M. Blackledge et al., dass durch Analyse von lokalen und weitreichenden Effekten dipolare Restkopplungen um bis zu sechs Größenordnungen schneller bestimmt werden können als mit existierenden Methoden. [ABSTRACT FROM AUTHOR]