Autoinhibition plays a significant role in the regulation of many proteins. By analyzing autoinhibited proteins, we demonstrate that these proteins are enriched in intrinsic disorder because of the properties of their inhibitory modules (IMs). A comparison of autoinhibited proteins with structured and intrinsically disordered IMs revealed that in the latter group (1) multiple phosphorylation sites are highly abundant; (2) splice variants occur in greater number than in their structured cousins; and (3) activation is often associated with changes in secondary structure in the IM. Analyses of families of autoinhibited proteins revealed that the levels of disorder in IMs can vary significantly throughout homologous proteins, whereas residues located at the interfaces between the IMs and inhibited domains are conserved. Our findings suggest that intrinsically disordered IMs provide advantages over structured ones that are likely to be exploited in the fine-tuning of the equilibrium between active and inactive states of autoinhibited proteins.

Amyloids are fibrillar nanostructures of proteins that are assembled in several physiological processes in human cells (e.g., hormone storage) but also during the course of infectious (prion) and noninfectious (nonprion) diseases such as Creutzfeldt-Jakob and Alzheimer's diseases, respectively. How the amyloid state, a state accessible to all proteins and peptides, can be exploited for functional purposes but also have detrimental effects remains to be determined. Here, we measure the nanomechanical properties of different amyloids and link them to features found in their structure models. Specifically, we use shape fluctuation analysis and sonication-induced scission in combination with full-atom molecular dynamics simulations to reveal that the amyloid fibrils of the mammalian prion protein PrP are mechanically unstable, most likely due to a very low hydrogen bond density in the fibril structure. Interestingly, amyloid fibrils formed by HET-s, a fungal protein that can confer functional prion behavior, have a much higher Young's modulus and tensile strength than those of PrP, i.e., they are much stiffer and stronger due to a tighter packing in the fibril structure. By contrast, amyloids of the proteins RIP1/RIP3 that have been shown to be of functional use in human cells are significantly stiffer than PrP fibrils but have comparable tensile strength. Our study demonstrates that amyloids are biomaterials with a broad range of nanomechanical properties, and we provide further support for the strong link between nanomechanics and b-sheet characteristics in the amyloid core.

MotivationIntrinsically disordered regions of proteins play an essential role in the regulation of various biological processes. Key to their regulatory function is often the binding to globular protein domains via sequence elements known as molecular recognition features (MoRFs). Development of computational tools for the identification of candidate MoRF locations in amino acid sequences is an important task and an area of growing interest. Given the relative sparseness of MoRFs in protein sequences, the accuracy of the available MoRF predictors is often inadequate for practical usage, which leaves a significant need and room for improvement. In this work, we introduce MoRFCHiBi_Web, which predicts MoRF locations in protein sequences with higher accuracy compared to current MoRF predictors.MethodsThree distinct and largely independent property scores are computed with component predictors and then combined to generate the final MoRF propensity scores. The first score reflects the likelihood of sequence windows to harbour MoRFs and is based on amino acid composition and sequence similarity information. It is generated by MoRFCHiBi using small windows of up to 40 residues in size. The second score identifies long stretches of protein disorder and is generated by ESpritz with the DisProt option. Lastly, the third score reflects residue conservation and is assembled from PSSM files generated by PSI-BLAST. These propensity scores are processed and then hierarchically combined using Bayes rule to generate the final MoRFCHiBi_Web predictions.ResultsMoRFCHiBi_Web was tested on three datasets. Results show that MoRFCHiBi_Web outperforms previously developed predictors by generating less than half the false positive rate for the same true positive rate at practical threshold values. This level of accuracy paired with its relatively high processing speed makes MoRFCHiBi_Web a practical tool for MoRF prediction.Availability http://morf.chibi.ubc.ca:8080/morf/.

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