| 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. |
- Dimou, Niki L., et al.
(författare)
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GWAR : robust analysis and meta-analysis of genome-wide association studies
- 2017
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Ingår i: Bioinformatics. - : Oxford University Press (OUP). - 1367-4803 .- 1367-4811. ; 33:10, s. 1521-1527
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Tidskriftsartikel (refereegranskat)abstract
- Motivation: In the context of genome-wide association studies (GWAS), there is a variety of statistical techniques in order to conduct the analysis, but, in most cases, the underlying genetic model is usually unknown. Under these circumstances, the classical Cochran-Armitage trend test (CATT) is suboptimal. Robust procedures that maximize the power and preserve the nominal type I error rate are preferable. Moreover, performing a meta-analysis using robust procedures is of great interest and has never been addressed in the past. The primary goal of this work is to implement several robust methods for analysis and meta-analysis in the statistical package Stata and subsequently to make the software available to the scientific community. Results: The CATT under a recessive, additive and dominant model of inheritance as well as robust methods based on the Maximum Efficiency Robust Test statistic, the MAX statistic and the MIN2 were implemented in Stata. Concerning MAX and MIN2, we calculated their asymptotic null distributions relying on numerical integration resulting in a great gain in computational time without losing accuracy. All the aforementioned approaches were employed in a fixed or a random effects meta-analysis setting using summary data with weights equal to the reciprocal of the combined cases and controls. Overall, this is the first complete effort to implement procedures for analysis and meta-analysis in GWAS using Stata.
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| 3. |
- 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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| 4. |
- Contreras, F.-Xabier, et al.
(författare)
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Molecular recognition of a single sphingolipid species by a protein's transmembrane domain
- 2012
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Ingår i: Nature. - : Springer Science and Business Media LLC. - 0028-0836 .- 1476-4687. ; 481:7382, s. 525-529
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Tidskriftsartikel (refereegranskat)abstract
- Functioning and processing of membrane proteins critically depend on the way their transmembrane segments are embedded in the membrane. Sphingolipids are structural components of membranes and can also act as intracellular second messengers. Not much is known of sphingolipids binding to transmembrane domains (TMDs) of proteins within the hydrophobic bilayer, and how this could affect protein function. Here we show a direct and highly specific interaction of exclusively one sphingomyelin species, SM 18, with the TMD of the COPI machinery protein p24 (ref. 2). Strikingly, the interaction depends on both the headgroup and the backbone of the sphingolipid, and on a signature sequence (VXXTLXXIY) within the TMD. Molecular dynamics simulations show a close interaction of SM 18 with the TMD. We suggest a role of SM 18 in regulating the equilibrium between an inactive monomeric and an active oligomeric state of the p24 protein, which in turn regulates COPI-dependent transport. Bioinformatic analyses predict that the signature sequence represents a conserved sphingolipid-binding cavity in a variety of mammalian membrane proteins. Thus, in addition to a function as second messengers, sphingolipids can act as cofactors to regulate the function of transmembrane proteins. Our discovery of an unprecedented specificity of interaction of a TMD with an individual sphingolipid species adds to our understanding of why biological membranes are assembled from such a large variety of different lipids.
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| 5. |
- 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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| 6. |
- Hu, Hailong, et al.
(författare)
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A Bi-LSTM Based Ensemble Algorithm for Prediction of Protein Secondary Structure
- 2019
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Ingår i: Applied Sciences. - : MDPI AG. - 2076-3417. ; 9:17
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Tidskriftsartikel (refereegranskat)abstract
- The prediction of protein secondary structure continues to be an active area of research in bioinformatics. In this paper, a Bi-LSTM based ensemble model is developed for the prediction of protein secondary structure. The ensemble model with dual loss function consists of five sub-models, which are finally joined by a Bi-LSTM layer. In contrast to existing ensemble methods, which generally train each sub-model and then join them as a whole, this ensemble model and sub-models can be trained simultaneously and the performance of each model can be observed and compared during the training process. Three independent test sets (e.g., data1199, 513 protein Cuff & Barton set (CB513) and 203 proteins from Critical Appraisals Skills Programme (CASP203)) are employed to test the method. On average, the ensemble model achieved 84.3% in Q(3) accuracy and 81.9% in segment overlap measure (SOV) score by using 10-fold cross validation. There is an improvement of up to 1% over some state-of-the-art prediction methods of protein secondary structure.
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| 7. |
- Pozzati, Gabriele, et al.
(författare)
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Limits and potential of combined folding and docking
- 2022
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Ingår i: Bioinformatics. - : Oxford University Press (OUP). - 1367-4803 .- 1367-4811. ; 38:4, s. 954-961
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Tidskriftsartikel (refereegranskat)abstract
- 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.
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| 8. |
- Larsson, Per, 1978-
(författare)
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Prediction, modeling, and refinement of protein structure
- 2010
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- Accurate predictions of protein structure are important for understanding many processes in cells. The interactions that govern protein folding and structure are complex, and still far from completely understood. However, progress is being made in many areas. Here, efforts to improve the overall quality of protein structure models are described. From a pure evolutionary perspective, in which proteins are viewed in the light of gradually accumulated mutations on the sequence level, it is shown how information from multiple sources helps to create more accurate models. A very simple but surprisingly accurate method for assigning confidence measures for protein structures is also tested. In contrast to models based on evolution, physics based methods view protein structures as the result of physical interactions between atoms. Newly implemented methods are described that both increase the time-scales accessible for molecular dynamics simulations almost 10-fold, and that to some extent might be able to refine protein structures. Finally, I compare the efficiency and properties of different techniques for protein structure refinement.
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| 9. |
- 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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| 10. |
- Elofsson, Arne, et al.
(författare)
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Methods for estimation of model accuracy in CASP12
- 2018
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Ingår i: Proteins. - : Wiley. - 0887-3585 .- 1097-0134. ; 86:S1, s. 361-373
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Tidskriftsartikel (refereegranskat)abstract
- Methods to reliably estimate the quality of 3D models of proteins are essential drivers for the wide adoption and serious acceptance of protein structure predictions by life scientists. In this article, the most successful groups in CASP12 describe their latest methods for estimates of model accuracy (EMA). We show that pure single model accuracy estimation methods have shown clear progress since CASP11; the 3 top methods (MESHI, ProQ3, SVMQA) all perform better than the top method of CASP11 (ProQ2). Although the pure single model accuracy estimation methods outperform quasi-single (ModFOLD6 variations) and consensus methods (Pcons, ModFOLDclust2, Pcomb-domain, and Wallner) in model selection, they are still not as good as those methods in absolute model quality estimation and predictions of local quality. Finally, we show that when using contact-based model quality measures (CAD, lDDT) the single model quality methods perform relatively better.
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