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Splicing-accessible coding 3'UTRs control protein stability and interaction networks.

ABSTRACT: BACKGROUND:3'-Untranslated regions (3'UTRs) play crucial roles in mRNA metabolism, such as by controlling mRNA stability, translation efficiency, and localization. Intriguingly, in some genes the 3'UTR is longer than their coding regions, pointing to additional, unknown functions. Here, we describe a protein-coding function of 3'UTRs upon frameshift-inducing alternative splicing in more than 10% of human and mouse protein-coding genes. RESULTS:3'UTR-encoded amino acid sequences show an enrichment of PxxP motifs and lead to interactome rewiring. Furthermore, an elevated proline content increases protein disorder and reduces protein stability, thus allowing splicing-controlled regulation of protein half-life. This could also act as a surveillance mechanism for erroneous skipping of penultimate exons resulting in transcripts that escape nonsense mediated decay. The impact of frameshift-inducing alternative splicing on disease development is emphasized by a retinitis pigmentosa-causing mutation leading to translation of a 3'UTR-encoded, proline-rich, destabilized frameshift-protein with altered protein-protein interactions. CONCLUSIONS:We describe a widespread, evolutionarily conserved mechanism that enriches the mammalian proteome, controls protein expression and protein-protein interactions, and has important implications for the discovery of novel, potentially disease-relevant protein variants.

SUBMITTER: Preussner M

PROVIDER: S-EPMC7392665 | biostudies-literature | 2020 Jul

REPOSITORIES: biostudies-literature

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Splicing-accessible coding 3'UTRs control protein stability and interaction networks.

Preussner Marco M Gao Qingsong Q Morrison Eliot E Herdt Olga O Finkernagel Florian F Schumann Michael M Krause Eberhard E Freund Christian C Chen Wei W Heyd Florian F

Genome biology 20200729 1

<h4>Background</h4>3'-Untranslated regions (3'UTRs) play crucial roles in mRNA metabolism, such as by controlling mRNA stability, translation efficiency, and localization. Intriguingly, in some genes the 3'UTR is longer than their coding regions, pointing to additional, unknown functions. Here, we describe a protein-coding function of 3'UTRs upon frameshift-inducing alternative splicing in more than 10% of human and mouse protein-coding genes.<h4>Results</h4>3'UTR-encoded amino acid sequences sh ...[more]

PMID: 32727563

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Project description:BackgroundThe existence of negative correlations between degrees of interacting proteins is being discussed since such negative degree correlations were found for the large-scale yeast protein-protein interaction (PPI) network of Ito et al. More recent studies observed no such negative correlations for high-confidence interaction sets. In this article, we analyzed a range of experimentally derived interaction networks to understand the role and prevalence of degree correlations in PPI networks. We investigated how degree correlations influence the structure of networks and their tolerance against perturbations such as the targeted deletion of hubs.ResultsFor each PPI network, we simulated uncorrelated, positively and negatively correlated reference networks. Here, a simple model was developed which can create different types of degree correlations in a network without changing the degree distribution. Differences in static properties associated with degree correlations were compared by analyzing the network characteristics of the original PPI and reference networks. Dynamics were compared by simulating the effect of a selective deletion of hubs in all networks.ConclusionConsiderable differences between the network types were found for the number of components in the original networks. Negatively correlated networks are fragmented into significantly less components than observed for positively correlated networks. On the other hand, the selective deletion of hubs showed an increased structural tolerance to these deletions for the positively correlated networks. This results in a lower rate of interaction loss in these networks compared to the negatively correlated networks and a decreased disintegration rate. Interestingly, real PPI networks are most similar to the randomly correlated references with respect to all properties analyzed. Thus, although structural properties of networks can be modified considerably by degree correlations, biological PPI networks do not actually seem to make use of this possibility.

Project description:BACKGROUND: Protein structure prediction and computational protein design require efficient yet sufficiently accurate descriptions of aqueous solvent. We continue to evaluate the performance of the Coulomb/Accessible Surface Area (CASA) implicit solvent model, in combination with the Charmm19 molecular mechanics force field. We test a set of model parameters optimized earlier, and we also carry out a new optimization in this work, using as a target a set of experimental stability changes for single point mutations of various proteins and peptides. The optimization procedure is general, and could be used with other force fields. The computation of stability changes requires a model for the unfolded state of the protein. In our approach, this state is represented by tripeptide structures of the sequence Ala-X-Ala for each amino acid type X. We followed an iterative optimization scheme which, at each cycle, optimizes the solvation parameters and a set of tripeptide structures for the unfolded state. This protocol uses a set of 140 experimental stability mutations and a large set of tripeptide conformations to find the best tripeptide structures and solvation parameters. RESULTS: Using the optimized parameters, we obtain a mean unsigned error of 2.28 kcal/mol for the stability mutations. The performance of the CASA model is assessed by two further applications: (i) calculation of protein-ligand binding affinities and (ii) computational protein design. For these two applications, the previous parameters and the ones optimized here give a similar performance. For ligand binding, we obtain reasonable agreement with a set of 55 experimental mutation data, with a mean unsigned error of 1.76 kcal/mol with the new parameters and 1.47 kcal/mol with the earlier ones. We show that the optimized CASA model is not inferior to the Generalized Born/Surface Area (GB/SA) model for the prediction of these binding affinities. Likewise, the new parameters perform well for the design of 8 SH3 domain proteins where an average of 32.8% sequence identity relative to the native sequences was achieved. Further, it was shown that the computed sequences have the character of naturally-occuring homologues of the native sequences. CONCLUSION: Overall, the two CASA variants explored here perform very well for a wide variety of applications. Both variants provide an efficient solvent treatment for the computational engineering of ligands and proteins.

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Splicing-accessible coding 3'UTRs control protein stability and interaction networks.

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Splicing-accessible coding 3'UTRs control protein stability and interaction networks.

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