Comparison of Xrn1 and Rat1 5' → 3' exoribonucleases in budding yeast supports the specific role of Xrn1 in cotranslational mRNA decay.

The yeast Saccharomyces cerevisiae and most eukaryotes carry two 5' → 3' exoribonuclease paralogs. In yeast, they are called Xrn1, which shuttles between the nucleus and the cytoplasm, and executes major cytoplasmic messenger RNA (mRNA) decay, and Rat1, which carries a strong nuclear localization sequence (NLS) and localizes to the nucleus. Xrn1 is 30% identical to Rat1 but has an extra ~500 amino acids C-terminal extension. In the cytoplasm, Xrn1 can degrade decapped mRNAs during the last round of translation by ribosomes, a process referred to as "cotranslational mRNA decay." The division of labor between the two enzymes is still enigmatic and serves as a paradigm for the subfunctionalization of many other paralogs. Here we show that Rat1 is capable of functioning in cytoplasmic mRNA decay, provided that Rat1 remains cytoplasmic due to its NLS disruption (cRat1). This indicates that the physical segregation of the two paralogs plays roles in their specific functions. However, reversing segregation is not sufficient to fully complement the Xrn1 function. Specifically, cRat1 can partially restore the cell volume, mRNA stability, the proliferation rate, and 5' → 3' decay alterations that characterize xrn1Δ cells. Nevertheless, cotranslational decay is only slightly complemented by cRat1. The use of the AlphaFold prediction for cRat1 and its subsequent docking with the ribosome complex and the sequence conservation between cRat1 and Xrn1 suggest that the tight interaction with the ribosome observed for Xrn1 is not maintained in cRat1. Adding the Xrn1 C-terminal domain to Rat1 does not improve phenotypes, which indicates that lack of the C-terminal is not responsible for partial complementation. Overall, during evolution, it appears that the two paralogs have acquired specific characteristics to make functional partitioning beneficial.

DisProt in 2022: improved quality and accessibility of protein intrinsic disorder annotation.

The Database of Intrinsically Disordered Proteins (DisProt, URL: https://disprot.org) is the major repository of manually curated annotations of intrinsically disordered proteins and regions from the literature. We report here recent updates of DisProt version 9, including a restyled web interface, refactored Intrinsically Disordered Proteins Ontology (IDPO), improvements in the curation process and significant content growth of around 30%. Higher quality and consistency of annotations is provided by a newly implemented reviewing process and training of curators. The increased curation capacity is fostered by the integration of DisProt with APICURON, a dedicated resource for the proper attribution and recognition of biocuration efforts. Better interoperability is provided through the adoption of the Minimum Information About Disorder (MIADE) standard, an active collaboration with the Gene Ontology (GO) and Evidence and Conclusion Ontology (ECO) consortia and the support of the ELIXIR infrastructure.

Limits and potential of combined folding and docking.

MOTIVATION: In the last decade, de novo protein structure prediction accuracy for individual proteins has improved significantly by utilising deep learning (DL) methods for harvesting the co-evolution information from large multiple sequence alignments (MSAs). The same approach can, in principle, also be used to extract information about evolutionary-based contacts across protein-protein interfaces. However, most earlier studies have not used the latest DL methods for inter-chain contact distance prediction. This article introduces a fold-and-dock method based on predicted residue-residue distances with trRosetta. RESULTS: The method can simultaneously predict the tertiary and quaternary structure of a protein pair, even when the structures of the monomers are not known. The straightforward application of this method to a standard dataset for protein-protein docking yielded limited success. However, using alternative methods for generating MSAs allowed us to dock accurately significantly more proteins. We also introduced a novel scoring function, PconsDock, that accurately separates 98% of correctly and incorrectly folded and docked proteins. The average performance of the method is comparable to the use of traditional, template-based or ab initio shape-complementarity-only docking methods. Moreover, the results of conventional and fold-and-dock approaches are complementary, and thus a combined docking pipeline could increase overall docking success significantly. This methodology contributed to the best model for one of the CASP14 oligomeric targets, H1065. AVAILABILITY AND IMPLEMENTATION: All scripts for predictions and analysis are available from https://github.com/ElofssonLab/bioinfo-toolbox/ and https://gitlab.com/ElofssonLab/benchmark5/. All models joined alignments, and evaluation results are available from the following figshare repository https://doi.org/10.6084/m9.figshare.14654886.v2. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Accurate contact-based modelling of repeat proteins predicts the structure of new repeats protein families.

Repeat proteins are abundant in eukaryotic proteomes. They are involved in many eukaryotic specific functions, including signalling. For many of these proteins, the structure is not known, as they are difficult to crystallise. Today, using direct coupling analysis and deep learning it is often possible to predict a protein's structure. However, the unique sequence features present in repeat proteins have been a challenge to use direct coupling analysis for predicting contacts. Here, we show that deep learning-based methods (trRosetta, DeepMetaPsicov (DMP) and PconsC4) overcomes this problem and can predict intra- and inter-unit contacts in repeat proteins. In a benchmark dataset of 815 repeat proteins, about 90% can be correctly modelled. Further, among 48 PFAM families lacking a protein structure, we produce models of forty-one families with estimated high accuracy.

The evolutionary history of topological variations in the CPA/AT transporters.

CPA/AT transporters are made up of scaffold and a core domain. The core domain contains two non-canonical helices (broken or reentrant) that mediate the transport of ions, amino acids or other charged compounds. During evolution, these transporters have undergone substantial changes in structure, topology and function. To shed light on these structural transitions, we create models for all families using an integrated topology annotation method. We find that the CPA/AT transporters can be classified into four fold-types based on their structure; (1) the CPA-broken fold-type, (2) the CPA-reentrant fold-type, (3) the BART fold-type, and (4) a previously not described fold-type, the Reentrant-Helix-Reentrant fold-type. Several topological transitions are identified, including the transition between a broken and reentrant helix, one transition between a loop and a reentrant helix, complete changes of orientation, and changes in the number of scaffold helices. These transitions are mainly caused by gene duplication and shuffling events. Structural models, topology information and other details are presented in a searchable database, CPAfold (cpafold.bioinfo.se).

DisProt: intrinsic protein disorder annotation in 2020.

The Database of Protein Disorder (DisProt, URL: https://disprot.org) provides manually curated annotations of intrinsically disordered proteins from the literature. Here we report recent developments with DisProt (version 8), including the doubling of protein entries, a new disorder ontology, improvements of the annotation format and a completely new website. The website includes a redesigned graphical interface, a better search engine, a clearer API for programmatic access and a new annotation interface that integrates text mining technologies. The new entry format provides a greater flexibility, simplifies maintenance and allows the capture of more information from the literature. The new disorder ontology has been formalized and made interoperable by adopting the OWL format, as well as its structure and term definitions have been improved. The new annotation interface has made the curation process faster and more effective. We recently showed that new DisProt annotations can be effectively used to train and validate disorder predictors. We believe the growth of DisProt will accelerate, contributing to the improvement of function and disorder predictors and therefore to illuminate the 'dark' proteome.

Why do eukaryotic proteins contain more intrinsically disordered regions?

Intrinsic disorder is more abundant in eukaryotic than prokaryotic proteins. Methods predicting intrinsic disorder are based on the amino acid sequence of a protein. Therefore, there must exist an underlying difference in the sequences between eukaryotic and prokaryotic proteins causing the (predicted) difference in intrinsic disorder. By comparing proteins, from complete eukaryotic and prokaryotic proteomes, we show that the difference in intrinsic disorder emerges from the linker regions connecting Pfam domains. Eukaryotic proteins have more extended linker regions, and in addition, the eukaryotic linkers are significantly more disordered, 38% vs. 12-16% disordered residues. Next, we examined the underlying reason for the increase in disorder in eukaryotic linkers, and we found that the changes in abundance of only three amino acids cause the increase. Eukaryotic proteins contain 8.6% serine; while prokaryotic proteins have 6.5%, eukaryotic proteins also contain 5.4% proline and 5.3% isoleucine compared with 4.0% proline and ≈ 7.5% isoleucine in the prokaryotes. All these three differences contribute to the increased disorder in eukaryotic proteins. It is tempting to speculate that the increase in serine frequencies in eukaryotes is related to regulation by kinases, but direct evidence for this is lacking. The differences are observed in all phyla, protein families, structural regions and type of protein but are most pronounced in disordered and linker regions. The observation that differences in the abundance of three amino acids cause the difference in disorder between eukaryotic and prokaryotic proteins raises the question: Are amino acid frequencies different in eukaryotic linkers because the linkers are more disordered or do the differences cause the increased disorder?

Ca2+ binding to F-ATP synthase ß subunit triggers the mitochondrial permeability transition.

F-ATP synthases convert the electrochemical energy of the H+ gradient into the chemical energy of ATP with remarkable efficiency. Mitochondrial F-ATP synthases can also undergo a Ca2+-dependent transformation to form channels with properties matching those of the permeability transition pore (PTP), a key player in cell death. The Ca2+ binding site and the mechanism(s) through which Ca2+ can transform the energy-conserving enzyme into a dissipative structure promoting cell death remain unknown. Through in vitro, in vivo and in silico studies we (i) pinpoint the "Ca2+-trigger site" of the PTP to the catalytic site of the F-ATP synthase ß subunit and (ii) define a conformational change that propagates from the catalytic site through OSCP and the lateral stalk to the inner membrane. T163S mutants of the ß subunit, which show a selective decrease in Ca2+-ATP hydrolysis, confer resistance to Ca2+-induced, PTP-dependent death in cells and developing zebrafish embryos. These findings are a major advance in the molecular definition of the transition of F-ATP synthase to a channel and of its role in cell death.

Using PconsC4 and PconsFold2 to Predict Protein Structure.

In spite of the fact that there has been a significant increase in the number of solved protein structures, structural information is missing for many proteins. Although structural information is codified in the amino acid sequence, computational prediction using only this information is still an unsolved problem. However, one successful method to model a protein's structure starting from the primary sequence is to use contact prediction derived from multiple sequence alignment (MSA). Here we use our contact predictor PconsC4 to generate a list of probable contacts between residues in the primary sequences. These contacts are then used together with the secondary structure prediction as constraints for the CONFOLD folding method. In this way, a 3D protein model can be built starting directly from the primary sequence. © 2019 by John Wiley & Sons, Inc.

Calcium and regulation of the mitochondrial permeability transition.

Recent years have seen renewed interest in the permeability transition pore, a high conductance channel responsible for permeabilization of the inner mitochondrial membrane, a process that leads to depolarization and Ca2+ release. Transient openings may be involved in physiological Ca2+ homeostasis while long-lasting openings may trigger and/or execute cell death. In this review we specifically focus (i) on the hypothesis that the PTP forms from the F-ATP synthase and (ii) on the mechanisms through which Ca2+ can reversibly switch this energy-conserving nanomachine into an energy-dissipating device.

Topology of membrane proteins-predictions, limitations and variations.

Transmembrane proteins perform a variety of important biological functions necessary for the survival and growth of the cells. Membrane proteins are built up by transmembrane segments that span the lipid bilayer. The segments can either be in the form of hydrophobic alpha-helices or beta-sheets which create a barrel. A fundamental aspect of the structure of transmembrane proteins is the membrane topology, that is, the number of transmembrane segments, their position in the protein sequence and their orientation in the membrane. Along these lines, many predictive algorithms for the prediction of the topology of alpha-helical and beta-barrel transmembrane proteins exist. The newest algorithms obtain an accuracy close to 80% both for alpha-helical and beta-barrel transmembrane proteins. However, lately it has been shown that the simplified picture presented when describing a protein family by its topology is limited. To demonstrate this, we highlight examples where the topology is either not conserved in a protein superfamily or where the structure cannot be described solely by the topology of a protein. The prediction of these non-standard features from sequence alone was not successful until the recent revolutionary progress in 3D-structure prediction of proteins.

Mapping pathogenic mutations suggests an innovative structural model for the pendrin (SLC26A4) transmembrane domain.

Human pendrin (SLC26A4) is an anion transporter mostly expressed in the inner ear, thyroid and kidney. SLC26A4 gene mutations are associated with a broad phenotypic spectrum, including Pendred Syndrome and non-syndromic hearing loss with enlarged vestibular aqueduct (ns-EVA). No experimental structure of pendrin is currently available, making phenotype-genotype correlations difficult as predictions of transmembrane (TM) segments vary in number. Here, we propose a novel three-dimensional (3D) pendrin transmembrane domain model based on the SLC26Dg transporter. The resulting 14 TM topology was found to include two non-canonical transmembrane segments crucial for pendrin activity. Mutation mapping of 147 clinically validated pathological mutations shows that most affect two previously undescribed TM regions.