| 1. |
- Attwood, Misty M.
(författare)
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Membrane-bound proteins : Characterization, evolution, and functional analysis
- 2020
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- Alpha-helical transmembrane proteins are important components of many essential cell processes including signal transduction, transport of molecules across membranes, protein and membrane trafficking, and structural and adhesion activities, amongst others. Their involvement in critical networks makes them the focus of interest in investigating disease pathways, as candidate drug targets, and in evolutionary analyses to identify homologous protein families and possible functional activities. Transmembrane (TM) proteins can be categorized into major groups based the same gross structure, i.e., the number of transmembrane helices, which are often correlated with specific functional activities, for example as receptors or transporters. The focus of this thesis was to analyze the evolution of the membrane proteome from the last holozoan common ancestor (LHCA) through metazoans to garner insight into the fundamental functional clusters that underlie metazoan diversity and innovation. Twenty-four eukaryotic proteomes were analyzed, with results showing more than 70% of metazoan transmembrane protein families have a pre-metazoan origin. In concert with that, we characterized the previously unstudied groups of human proteins with three, four, and five membrane-spanning regions (3TM, 4TM, and 5TM) and analyzed their functional activities, involvement in disease pathways, and unique characteristics. Combined, we manually curated and classified nearly 11% of the human transmembrane proteome with these three studies. The 3TM data set included 152 proteins, with nearly 45% that localize specifically to the endoplasmic reticulum (ER), and are involved in membrane biosynthesis and lipid biogenesis, proteins trafficking, catabolic processes, and signal transduction due to the large ionotropic glutamate receptor family. The 373 proteins identified in the 4TM data set are predominantly involved in transport activities, as well as cell communication and adhesion, and function as structural elements. The compact 5TM data set includes 58 proteins that engage in localization and transport activities, such as protein targeting, membrane trafficking, and vesicle transport. Notably, ~60% are identified as cancer prognostic markers that are associated with clinical outcomes of different tumour types. This thesis investigates the evolutionary origins of the human transmembrane proteome, characterizes formerly dark areas of the membrane proteome, and extends the fundamental knowledge of transmembrane proteins.
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| 2. |
- 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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| 3. |
- Bryant, Patrick, et al.
(författare)
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Decomposing Structural Response Due to Sequence Changes in Protein Domains with Machine Learning
- 2020
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Ingår i: Journal of Molecular Biology. - : Elsevier BV. - 0022-2836 .- 1089-8638. ; 432:16, s. 4435-4446
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Tidskriftsartikel (refereegranskat)abstract
- How protein domain structure changes in response to mutations is not well understood. Some mutations change the structure drastically, while most only result in small changes. To gain an understanding of this, we decompose the relationship between changes in domain sequence and structure using machine learning. We select pairs of evolutionarily related domains with a broad range of evolutionary distances. In contrast to earlier studies, we do not find a strictly linear relationship between sequence and structural changes. We train a random forest regressor that predicts the structural similarity between pairs with an average accuracy of 0.029 IDDT ( local Distance Difference Test) score, and a correlation coefficient of 0.92. Decomposing the feature importance shows that the domain length, or analogously, size is the most important feature. Our model enables assessing deviations in relative structural response, and thus prediction of evolutionary trajectories, in protein domains across evolution.
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| 4. |
- Bryant, Patrick, et al.
(författare)
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Estimating the impact of mobility patterns on COVID-19 infection rates in 11 European countries
- 2020
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Ingår i: PeerJ. - : PeerJ. - 2167-8359. ; 8
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Tidskriftsartikel (refereegranskat)abstract
- Background: As governments across Europe have issued non-pharmaceutical interventions (NPIs) such as social distancing and school closing, the mobility patterns in these countries have changed. Most states have implemented similar NPIs at similar time points. However, it is likely different countries and populations respond differently to the NPIs and that these differences cause mobility patterns and thereby the epidemic development to change.Methods: We build a Bayesian model that estimates the number of deaths on a given day dependent on changes in the basic reproductive number, R-0, due to differences in mobility patterns. We utilise mobility data from Google mobility reports using five different categories: retail and recreation, grocery and pharmacy, transit stations, workplace and residential. The importance of each mobility category for predicting changes in R-0 is estimated through the model.Findings: The changes in mobility have a considerable overlap with the introduction of governmental NPIs, highlighting the importance of government action for population behavioural change. The shift in mobility in all categories shows high correlations with the death rates 1 month later. Reduction of movement within the grocery and pharmacy sector is estimated to account for most of the decrease in R-0.Interpretation: Our model predicts 3-week epidemic forecasts, using real-time observations of changes in mobility patterns, which can provide governments with direct feedback on the effects of their NPIs. The model predicts the changes in a majority of the countries accurately but overestimates the impact of NPIs in Sweden and Denmark and underestimates them in France and Belgium. We also note that the exponential nature of all epidemiological models based on the basic reproductive number, R-0 cause small errors to have extensive effects on the predicted outcome.
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| 5. |
- Delucchi, Matteo, et al.
(författare)
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A New Census of Protein Tandem Repeats and Their Relationship with Intrinsic Disorder
- 2020
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Ingår i: Genes. - : MDPI AG. - 2073-4425. ; 11:4
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Tidskriftsartikel (refereegranskat)abstract
- Protein tandem repeats (TRs) are often associated with immunity-related functions and diseases. Since that last census of protein TRs in 1999, the number of curated proteins increased more than seven-fold and new TR prediction methods were published. TRs appear to be enriched with intrinsic disorder and vice versa. The significance and the biological reasons for this association are unknown. Here, we characterize protein TRs across all kingdoms of life and their overlap with intrinsic disorder in unprecedented detail. Using state-of-the-art prediction methods, we estimate that 50.9% of proteins contain at least one TR, often located at the sequence flanks. Positive linear correlation between the proportion of TRs and the protein length was observed universally, with Eukaryotes in general having more TRs, but when the difference in length is taken into account the difference is quite small. TRs were enriched with disorder-promoting amino acids and were inside intrinsically disordered regions. Many such TRs were homorepeats. Our results support that TRs mostly originate by duplication and are involved in essential functions such as transcription processes, structural organization, electron transport and iron-binding. In viruses, TRs are found in proteins essential for virulence.
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| 6. |
- Hatos, Andras, et al.
(författare)
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DisProt : intrinsic protein disorder annotation in 2020
- 2020
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Ingår i: Nucleic Acids Research. - : Oxford University Press (OUP). - 0305-1048 .- 1362-4962. ; 48:D1, s. D269-D276
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Tidskriftsartikel (refereegranskat)abstract
- 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.
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| 7. |
- Li, Zhong, et al.
(författare)
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Protein Contact Map Prediction Based on ResNet and DenseNet
- 2020
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Ingår i: BioMed Research International. - : Hindawi Limited. - 2314-6133 .- 2314-6141. ; 2020
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Tidskriftsartikel (refereegranskat)abstract
- Residue-residue contact prediction has become an increasingly important tool for modeling the three-dimensional structure of a protein when no homologous structure is available. Ultradeep residual neural network (ResNet) has become the most popular method for making contact predictions because it captures the contextual information between residues. In this paper, we propose a novel deep neural network framework for contact prediction which combines ResNet and DenseNet. This framework uses 1D ResNet to process sequential features, and besides PSSM, SS3, and solvent accessibility, we have introduced a new feature, position-specific frequency matrix (PSFM), as an input. Using ResNet's residual module and identity mapping, it can effectively process sequential features after which the outer concatenation function is used for sequential and pairwise features. Prediction accuracy is improved following a final processing step using the dense connection of DenseNet. The prediction accuracy of the protein contact map shows that our method is more effective than other popular methods due to the new network architecture and the added feature input.
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| 8. |
- Naderi, Reyhaneh, et al.
(författare)
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Using Micro- and Macro-Level Network Metrics Unveils Top Communicative Gene Modules in Psoriasis
- 2020
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Ingår i: Genes. - : MDPI AG. - 2073-4425. ; 11:8
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Tidskriftsartikel (refereegranskat)abstract
- (1) Background: Psoriasis is a multifactorial chronic inflammatory disorder of the skin, with significant morbidity, characterized by hyperproliferation of the epidermis. Even though psoriasis' etiology is not fully understood, it is believed to be multifactorial, with numerous key components. (2) Methods: In order to cast light on the complex molecular interactions in psoriasis vulgaris at both protein-protein interactions and transcriptomics levels, we studied a set of microarray gene expression analyses consisting of 170 paired lesional and non-lesional samples. Afterwards, a network analysis was conducted on the protein-protein interaction network of differentially expressed genes based on micro- and macro-level network metrics at a systemic level standpoint. (3) Results: We found 17 top communicative genes, all of which were experimentally proven to be pivotal in psoriasis, which were identified in two modules, namely the cell cycle and immune system. Intra- and inter-gene interaction subnetworks from the top communicative genes might provide further insight into the corresponding characteristic interactions. (4) Conclusions: Potential gene combinations for therapeutic/diagnostics purposes were identified. Moreover, our proposed workflow could be of interest to a broader range of future biological network analysis studies.
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