| 1. |
- Bendz, Maria, et al.
(författare)
-
Membrane protein shaving with thermolysin can be used to evaluate topology predictors
- 2013
-
Ingår i: Proteomics. - : Wiley. - 1615-9853 .- 1615-9861. ; 13:9, s. 1467-1480
-
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.
|
|
| 2. |
- Moruz, Luminita, 1982-
(författare)
-
Chromatographic retention time prediction and its applications in mass spectrometry-based proteomics
- 2013
-
Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- Mass spectrometry-based methods are among the most commonly used techniques to characterize proteins in biological samples. With rapid technological developments allowing increasing throughput, thousands of proteins can now be monitored in a matter of hours. However, these advances brought a whole new set of analytical challenges. At the moment, it is no longer possible to rely on human experts to process the data. Instead, accurate computational tools are required.In line with these observations, my research work has involved development of computational methods to facilitate the analysis of mass spectrometry-based experiments. In particular, the projects included in this thesis revolve around the chromatography step of such experiments, where peptides are separated according to their hydrophobicity.The first part of the thesis describes an algorithm to predict retention time from peptide sequences. The method provides more accurate predictions compared to previous approaches, while being easily transferable to other chromatography setups. In addition, it gives equally good predictions for peptides carrying arbitrary posttranslational modifications as for unmodified peptides.The second part of the thesis includes two applications of retention time predictions in the context of mass spectrometry-based proteomics experiments. First, we show how theoretical calculations of masses and retention times can be used to infer proteins in shotgun proteomics experiments. Secondly, we illustrate the use of retention time predictions to calculate optimized gradient functions for reversed-phase liquid chromatography.
|
|
| 3. |
- Tsirigos, Konstantinos D., et al.
(författare)
-
A guideline to proteome-wide alpha-helical membrane protein topology predictions
- 2013
-
Ingår i: Proteomics. - : Wiley. - 1615-9853 .- 1615-9861. ; 12:14, s. 2282-2294
-
Tidskriftsartikel (refereegranskat)abstract
- For current state-of-the-art methods, the prediction of correct topology of membrane proteins has been reported to be above 80%. However, this performance has only been observed in small and possibly biased data sets obtained from protein structures or biochemical assays. Here, we test a number of topology predictors on an unseen set of proteins of known structure and also on four genome-scale data sets, including one recent large set of experimentally validated human membrane proteins with glycosylated sites. The set of glycosylated proteins is also used to examine the ability of prediction methods to separate membrane from nonmembrane proteins. The results show that methods utilizing multiple sequence alignments are overall superior to methods that do not. The best performance is obtained by TOPCONS, a consensus method that combines several of the other prediction methods. The best methods to distinguish membrane from nonmembrane proteins belong to the Phobius group of predictors. We further observe that the reported high accuracies in the smaller benchmark sets are not quite maintained in larger scale benchmarks. Instead, we estimate the performance of the best prediction methods for eukaryotic membrane proteins to be between 60% and 70%. The low agreement between predictions from different methods questions earlier estimates about the global properties of the membrane proteome. Finally, we suggest a pipeline to estimate these properties using a combination of the best predictors that could be applied in large-scale proteomics studies of membrane proteins.
|
|
| 4. |
- Tsirigos, Konstantinos D., et al.
(författare)
-
The TOPCONS web server for consensus prediction of membrane protein topology and signal peptides
- 2015
-
Ingår i: Nucleic Acids Research. - : Oxford University Press (OUP). - 0305-1048 .- 1362-4962. ; 43:W1, s. W401-W407
-
Tidskriftsartikel (refereegranskat)abstract
- TOPCONS (http://topcons.net/) is a widely used web server for consensus prediction of membrane protein topology. We hereby present a major update to the server, with some substantial improvements, including the following: (i) TOPCONS can now efficiently separate signal peptides from transmembrane regions. (ii) The server can now differentiate more successfully between globular and membrane proteins. (iii) The server now is even slightly faster, although a much larger database is used to generate the multiple sequence alignments. For most proteins, the final prediction is produced in a matter of seconds. (iv) The user-friendly interface is retained, with the additional feature of submitting batch files and accessing the server programmatically using standard interfaces, making it thus ideal for proteome-wide analyses. Indicatively, the user can now scan the entire human proteome in a few days. (v) For proteins with homology to a known 3D structure, the homology-inferred topology is also displayed. (vi) Finally, the combination of methods currently implemented achieves an overall increase in performance by 4% as compared to the currently available best-scoring methods and TOPCONS is the only method that can identify signal peptides and still maintain a state-of-the-art performance in topology predictions.
|
|
