| 1. |
- Allison, Timothy M., et al.
(författare)
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Complementing machine learning‐based structure predictions with native mass spectrometry
- 2022
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Ingår i: Protein Science. - : John Wiley & Sons. - 0961-8368 .- 1469-896X. ; 31:6
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Tidskriftsartikel (refereegranskat)abstract
- The advent of machine learning-based structure prediction algorithms such as AlphaFold2 (AF2) and RoseTTa Fold have moved the generation of accurate structural models for the entire cellular protein machinery into the reach of the scientific community. However, structure predictions of protein complexes are based on user-provided input and may require experimental validation. Mass spectrometry (MS) is a versatile, time-effective tool that provides information on post-translational modifications, ligand interactions, conformational changes, and higher-order oligomerization. Using three protein systems, we show that native MS experiments can uncover structural features of ligand interactions, homology models, and point mutations that are undetectable by AF2 alone. We conclude that machine learning can be complemented with MS to yield more accurate structural models on a small and large scale.
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| 2. |
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| 3. |
- Armenteros, Jose Juan Almagro, et al.
(författare)
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Detecting sequence signals in targeting peptides using deep learning
- 2019
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Ingår i: Life Science Alliance. - : LIFE SCIENCE ALLIANCE LLC. - 2575-1077. ; 2:5
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Tidskriftsartikel (refereegranskat)abstract
- In bioinformatics, machine learning methods have been used to predict features embedded in the sequences. In contrast to what is generally assumed, machine learning approaches can also provide new insights into the underlying biology. Here, we demonstrate this by presenting TargetP 2.0, a novel state-of-the-art method to identify N-terminal sorting signals, which direct proteins to the secretory pathway, mitochondria, and chloroplasts or other plastids. By examining the strongest signals from the attention layer in the network, we find that the second residue in the protein, that is, the one following the initial methionine, has a strong influence on the classification. We observe that two-thirds of chloroplast and thylakoid transit peptides have an alanine in position 2, compared with 20% in other plant proteins. We also note that in fungi and single-celled eukaryotes, less than 30% of the targeting peptides have an amino acid that allows the removal of the N-terminal methionine compared with 60% for the proteins without targeting peptide. The importance of this feature for predictions has not been highlighted before.
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| 4. |
- Baldassarre, Federico, et al.
(författare)
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GraphQA: Protein Model Quality Assessment using Graph Convolutional Networks
- 2020
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Ingår i: Bioinformatics. - : Oxford University Press. - 1367-4803 .- 1367-4811 .- 1460-2059. ; 37:3, s. 360-366
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Tidskriftsartikel (refereegranskat)abstract
- MotivationProteins are ubiquitous molecules whose function in biological processes is determined by their 3D structure. Experimental identification of a protein’s structure can be time-consuming, prohibitively expensive, and not always possible. Alternatively, protein folding can be modeled using computational methods, which however are not guaranteed to always produce optimal results.GraphQA is a graph-based method to estimate the quality of protein models, that possesses favorable properties such as representation learning, explicit modeling of both sequential and 3D structure, geometric invariance, and computational efficiency.ResultsGraphQA performs similarly to state-of-the-art methods despite using a relatively low number of input features. In addition, the graph network structure provides an improvement over the architecture used in ProQ4 operating on the same input features. Finally, the individual contributions of GraphQA components are carefully evaluated.Availability and implementationPyTorch implementation, datasets, experiments, and link to an evaluation server are available through this GitHub repository: github.com/baldassarreFe/graphqaSupplementary informationSupplementary material is available at Bioinformatics online.
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| 5. |
- Bano-Polo, Manuel, et al.
(författare)
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Charge Pair Interactions in Transmembrane Helices and Turn Propensity of the Connecting Sequence Promote Helical Hairpin Insertion
- 2013
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Ingår i: Journal of Molecular Biology. - : Elsevier. - 0022-2836 .- 1089-8638. ; 425:4, s. 830-840
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Tidskriftsartikel (refereegranskat)abstract
- alpha-Helical hairpins, consisting of a pair of closely spaced transmembrane (TM) helices that are connected by a short interfacial turn, are the simplest structural motifs found in multi-spanning membrane proteins. In naturally occurring hairpins, the presence of polar residues is common and predicted to complicate membrane insertion. We postulate that the pre-packing process offsets any energetic cost of allocating polar and charged residues within the hydrophobic environment of biological membranes. Consistent with this idea, we provide here experimental evidence demonstrating that helical hairpin insertion into biological membranes can be driven by electrostatic interactions between closely separated, poorly hydrophobic sequences. Additionally, we observe that the integral hairpin can be stabilized by a short loop heavily populated by turn-promoting residues. We conclude that the combined effect of TM-TM electrostatic interactions and tight turns plays an important role in generating the functional architecture of membrane proteins and propose that helical hairpin motifs can be acquired within the context of the Sec61 translocon at the early stages of membrane protein biosynthesis. Taken together, these data further underline the potential complexities involved in accurately predicting TM domains from primary structures.
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| 6. |
- Basile, Walter, et al.
(författare)
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High GC content causes orphan proteins to be intrinsically disordered
- 2017
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Ingår i: PloS Computational Biology. - : Public Library of Science (PLoS). - 1553-734X .- 1553-7358. ; 13:3
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Tidskriftsartikel (refereegranskat)abstract
- De novo creation of protein coding genes involves the formation of short ORFs from noncoding regions; some of these ORFs might then become fixed in the population These orphan proteins need to, at the bare minimum, not cause serious harm to the organism, meaning that they should for instance not aggregate. Therefore, although the creation of short ORFs could be truly random, the fixation should be subjected to some selective pressure. The selective forces acting on orphan proteins have been elusive, and contradictory results have been reported. In Drosophila young proteins are more disordered than ancient ones, while the opposite trend is present in yeast. To the best of our knowledge no valid explanation for this difference has been proposed. To solve this riddle we studied structural properties and age of proteins in 187 eukaryotic organisms. We find that, with the exception of length, there are only small differences in the properties between proteins of different ages. However, when we take the GC content into account we noted that it could explain the opposite trends observed for orphans in yeast (low GC) and Drosophila (high GC). GC content is correlated with codons coding for disorder promoting amino acids. This leads us to propose that intrinsic disorder is not a strong determining factor for fixation of orphan proteins. Instead these proteins largely resemble random proteins given a particular GC level. During evolution the properties of a protein change faster than the GC level causing the relationship between disorder and GC to gradually weaken.
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| 7. |
- Basile, Walter, et al.
(författare)
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Why do eukaryotic proteins contain more intrinsically disordered regions?
- 2019
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Ingår i: PloS Computational Biology. - : Public Library of Science (PLoS). - 1553-734X .- 1553-7358. ; 15:7
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Tidskriftsartikel (refereegranskat)abstract
- 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?
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| 8. |
- Basmarke-Wehelie, Rahma, et al.
(författare)
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The complement regulator CD46 is bactericidal to Helicobacter pylori and blocks urease activity
- 2011
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Ingår i: Gastroenterology. - Baltimore : Elsevier BV. - 0016-5085 .- 1528-0012. ; 141:3, s. 918-928
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Tidskriftsartikel (refereegranskat)abstract
- BACKGROUND & AIMS: CD46 is a C3b/C4b binding complement regulator and a receptor for several human pathogens. We examined the interaction between CD46 and Helicobacter pylori (a bacterium that colonizes the human gastric mucosa and causes gastritis), peptic ulcers, and cancer.METHODS: Using gastric epithelial cells, we analyzed a set of H pylori strains and mutants for their ability to interact with CD46 and/or influence CD46 expression. Bacterial interaction with full-length CD46 and small CD46 peptides was evaluated by flow cytometry, fluorescence microscopy, enzyme-linked immunosorbent assay, and bacterial survival analyses.RESULTS: H pylori infection caused shedding of CD46 into the extracellular environment. A soluble form of CD46 bound to H pylori and inhibited growth, in a dose- and time-dependent manner, by interacting with urease and alkyl hydroperoxide reductase, which are essential bacterial pathogenicity-associated factors. Binding of CD46 or CD46-derived synthetic peptides blocked the urease activity and ability of bacteria to survive in acidic environments. Oral administration of one CD46 peptide eradicated H pylori from infected mice.CONCLUSIONS: CD46 is an antimicrobial agent that can eradicate H pylori. CD46 peptides might be developed to treat H pylori infection.
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| 9. |
- Bassot, Claudio, et al.
(författare)
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Accurate contact-based modelling of repeat proteins predicts the structure of new repeats protein families
- 2021
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Ingår i: PloS Computational Biology. - : Public Library of Science (PLoS). - 1553-734X .- 1553-7358. ; 17:4
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Tidskriftsartikel (refereegranskat)abstract
- Repeat proteins are widespread among organisms and particularly abundant in eukaryotic proteomes. Their primary sequence presents repetition in the amino acid sequences that origin structures with repeated folds/domains. Although the repeated units often can be recognised from the sequence alone, often structural information is missing. Here, we used contact prediction for predicting the structure of repeats protein directly from their primary sequences. We benchmark the methods on a dataset comprehensive of all the known repeated structures. We evaluate the contact predictions and the obtained models for different classes of repeat proteins. Further, we develop and benchmark a quality assessment (QA) method specific for repeat proteins. Finally, we used the prediction pipeline for all PFAM repeat families without resolved structures and found that forty-one of them could be modelled with high accuracy. 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.
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| 10. |
- Bendz, Maria, et al.
(författare)
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Membrane protein shaving with thermolysin can be used to evaluate topology predictors
- 2013
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Ingår i: Proteomics. - : Wiley. - 1615-9853 .- 1615-9861. ; 13:9, s. 1467-1480
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Tidskriftsartikel (refereegranskat)abstract
- Topology analysis of membrane proteins can be obtained by enzymatic shaving in combination with MS identification of peptides. Ideally, such analysis could provide quite detailed information about the membrane spanning regions. Here, we examine the ability of some shaving enzymes to provide large-scale analysis of membrane proteome topologies. To compare different shaving enzymes, we first analyzed the detected peptides from two over-expressed proteins. Second, we analyzed the peptides from non-over-expressed Escherichia coli membrane proteins with known structure to evaluate the shaving methods. Finally, the identified peptides were used to test the accuracy of a number of topology predictors. At the end we suggest that the usage of thermolysin, an enzyme working at the natural pH of the cell for membrane shaving, is superior because: (i) we detect a similar number of peptides and proteins using thermolysin and trypsin; (ii) thermolysin shaving can be run at a natural pH and (iii) the incubation time is quite short. (iv) Fewer detected peptides from thermolysin shaving originate from the transmembrane regions. Using thermolysin shaving we can also provide a clear separation between the best and the less accurate topology predictors, indicating that using data from shaving can provide valuable information when developing new topology predictors.
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