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Topology Prediction of α-Helical Transmembrane Proteins
Stockholm University, Faculty of Science, Department of Biochemistry and Biophysics.
2016 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

Membrane proteins fulfil a number of tasks in cells, including signalling, cell-cell interaction, and the transportation of molecules. The prominence of these tasks makes membrane proteins an important target for clinical drugs. Because of the decreasing price of sequencing, the number of sequences known is increasing at such a rate that manual annotations cannot compete. Here, topology prediction is a way to provide additional information. It predicts the location and number of transmembrane helices in the protein and the orientation inside the membrane. An important factor to detect transmembrane helices is their hydrophobicity, which can be calculated using dedicated scales. In the first paper, we studied the difference between several hydrophobicity scales and evaluated their performance. We showed that while they appear to be similar, their performance for topology prediction differs significantly. The better performing scales appear to measure the probability of amino acids to be within a transmembrane helix, instead of just being located in a hydrophobic environment.

Around 20% of the transmembrane helices are too hydrophilic to explain their insertion with hydrophobicity alone. These are referred to as marginally hydrophobic helices. In the second paper, we studied three of these helices experimentally and performed an analysis on membrane proteins. The experiments show that for all three helices positive charges on the N-terminal side of the subsequent helix are important to insert, but only two need the subsequent helix. Additionally, the analysis shows that not only the N-terminal helices are more hydrophobic, but also the C-terminal transmembrane helices.

In Paper III, the finding from the second paper was used to improve the topology prediction. By extending our hidden Markov model with N- and C-terminal helix states, we were able to set stricter cut-offs. This improved the general topology prediction and in particular miss-prediction in large N- and C-terminal domains, as well the separation between transmembrane and non-transmembrane proteins.

Lastly, we contribute several new features to our consensus topology predictor, TOPCONS. We added states for the detection of signal peptides to its hidden Markov model and thus reduce the over-prediction of transmembrane helices. With a new method for the generation of profile files, it is possible to increase the size of the database used to find homologous proteins and decrease the running time by 75%.

Place, publisher, year, edition, pages
Stockholm: Department of Biochemistry and Biophysics, Stockholm University , 2016. , 46 p.
National Category
Bioinformatics (Computational Biology)
Research subject
Biochemistry
Identifiers
URN: urn:nbn:se:su:diva-129061OAI: oai:DiVA.org:su-129061DiVA: diva2:919418
Public defence
2016-06-03, Magnélisalen, Kemiska övningslaboratoriet, Svante Arrhenius väg 16 B, Stockholm, 10:00 (English)
Opponent
Supervisors
Available from: 2016-05-11 Created: 2016-04-13 Last updated: 2017-02-24Bibliographically approved
List of papers
1. Why is the biological hydrophobicity scale more accurate than earlier experimental hydrophobicity scales?
Open this publication in new window or tab >>Why is the biological hydrophobicity scale more accurate than earlier experimental hydrophobicity scales?
2014 (English)In: Proteins: Structure, Function, and Genetics, ISSN 0887-3585, E-ISSN 1097-0134, Vol. 82, no 9, 2190-2198 p.Article in journal (Refereed) Published
Abstract [en]

The recognition of transmembrane helices by the translocon is primarily guided by the average hydrophobicity of the protential transmembrane helix, However, he exact hydrophobicity of each amino acid can he identified in several diferent ways. The free energy of transfer for amino acid analogues between a hydrophobic media, for example, octanol and water can be measured or obtained from simulations, the hydrophobicity can also be estimated by statistical properties from known transmembrane segments and finally the contribution of each amino acid type for the probability of traitslocon recognition has recently been measured directly. Although these scales correlate quite well, there are dear differences between them and it is not well understood which scale represents neither the biology best nor what the differences are. Here, we try to provide some answers to this by studying the ability of different scales to recognize transmembrane helices and predict the topology of transmembrane proteins. From this analysis it is clear that the biological hydrophobicity scale as well scales created from statistical analysis of membrane helices perform better than earlier experimental scales that are mainly based on measurements of amino acid analogs and not directly on transmeiribrane helix recognition. Using these results we identified the properties of the scales that perform better than other scales. We find, for instance, that the better performing scales consider proline more hydrophilic. This shows that tratismembrarie recognition is not only governed by pure hydrophobicity but also by the helix preferences for amino acids, as praline is a strong helix breaker.

Keyword
hydrophobicity scale, membrane proteins, traiislocon recognition, protein structure predictions, secondary structopology prediction
National Category
Biochemistry and Molecular Biology
Research subject
Biochemistry
Identifiers
urn:nbn:se:su:diva-107801 (URN)10.1002/prot.24582 (DOI)000340940300040 ()
Note

AuthorCount:2;

Available from: 2014-10-05 Created: 2014-09-29 Last updated: 2017-12-05Bibliographically approved
2. The Positive Inside Rule Is Stronger When Followed by a Transmembrane Helix
Open this publication in new window or tab >>The Positive Inside Rule Is Stronger When Followed by a Transmembrane Helix
Show others...
2014 (English)In: Journal of Molecular Biology, ISSN 0022-2836, E-ISSN 1089-8638, Vol. 426, no 16, 2982-2991 p.Article in journal (Refereed) Published
Abstract [en]

The translocon recognizes transmembrane helices with sufficient level of hydrophobicity and inserts them into the membrane. However, sometimes less hydrophobic helices are also recognized. Positive inside rule, orientational preferences of and specific interactions with neighboring helices have been shown to aid in the recognition of these helices, at least in artificial systems. To better understand how the translocon inserts marginally hydrophobic helices, we studied three naturally occurring marginally hydrophobic helices, which were previously shown to require the subsequent helix for efficient translocon recognition. We find no evidence for specific interactions when we scan all residues in the subsequent helices. Instead, we identify arginines located at the N-terminal part of the subsequent helices that are crucial for the recognition of the marginally hydrophobic transmembrane helices, indicating that the positive inside rule is important. However, in two of the constructs, these arginines do not aid in the recognition without the rest of the subsequent helix; that is, the positive inside rule alone is not sufficient. Instead, the improved recognition of marginally hydrophobic helices can here be explained as follows: the positive inside rule provides an orientational preference of the subsequent helix, which in turn allows the marginally hydrophobic helix to be inserted; that is, the effect of the positive inside rule is stronger if positively charged residues are followed by a transmembrane helix. Such a mechanism obviously cannot aid C-terminal helices, and consequently, we find that the terminal helices in multi-spanning membrane proteins are more hydrophobic than internal helices.

Keyword
marginally hydrophobic helices, translocon recognition, membrane proteins, positive inside rule, orientational preference
National Category
Biochemistry and Molecular Biology
Research subject
Biochemistry
Identifiers
urn:nbn:se:su:diva-107079 (URN)10.1016/j.jmb.2014.06.002 (DOI)000340327500007 ()
Note

AuthorCount:7;

Available from: 2014-09-03 Created: 2014-09-03 Last updated: 2017-12-05Bibliographically approved
3. Improved topology prediction using the terminal hydrophobic helices rule
Open this publication in new window or tab >>Improved topology prediction using the terminal hydrophobic helices rule
2016 (English)In: Bioinformatics, ISSN 1367-4803, E-ISSN 1367-4811, Vol. 32, no 8, 1158-1162 p.Article in journal (Refereed) Published
Abstract [en]

Motivation: The translocon recognizes sufficiently hydrophobic regions of a protein and inserts them into the membrane. Computational methods try to determine what hydrophobic regions are recognized by the translocon. Although these predictions are quite accurate, many methods still fail to distinguish marginally hydrophobic transmembrane (TM) helices and equally hydrophobic regions in soluble protein domains. In vivo, this problem is most likely avoided by targeting of the TM-proteins, so that non-TM proteins never see the translocon. Proteins are targeted to the translocon by an N-terminal signal peptide. The targeting is also aided by the fact that the N-terminal helix is more hydrophobic than other TM-helices. In addition, we also recently found that the C-terminal helix is more hydrophobic than central helices. This information has not been used in earlier topology predictors.

Results: Here, we use the fact that the N- and C-terminal helices are more hydrophobic to develop a new version of the first-principle-based topology predictor, SCAMPI. The new predictor has two main advantages; first, it can be used to efficiently separate membrane and non-membrane proteins directly without the use of an extra prefilter, and second it shows improved performance for predicting the topology of membrane proteins that contain large non-membrane domains.

Availability and implementation: The predictor, a web server and all datasets are available at http://scampi.bioinfo.se/.

National Category
Biological Sciences Bioinformatics (Computational Biology)
Research subject
Biochemistry; Biochemistry towards Bioinformatics
Identifiers
urn:nbn:se:su:diva-129060 (URN)10.1093/bioinformatics/btv709 (DOI)000374476800006 ()
Available from: 2016-04-13 Created: 2016-04-13 Last updated: 2017-11-30Bibliographically approved
4. The TOPCONS web server for consensus prediction of membrane protein topology and signal peptides
Open this publication in new window or tab >>The TOPCONS web server for consensus prediction of membrane protein topology and signal peptides
Show others...
2015 (English)In: Nucleic Acids Research, ISSN 0305-1048, E-ISSN 1362-4962, Vol. 43, no W1, W401-W407 p.Article in journal (Refereed) Published
Abstract [en]

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.

National Category
Biological Sciences
Research subject
Biochemistry; Biochemistry towards Bioinformatics
Identifiers
urn:nbn:se:su:diva-120710 (URN)10.1093/nar/gkv485 (DOI)000359772700063 ()
Funder
Swedish Research Council
Available from: 2015-09-16 Created: 2015-09-15 Last updated: 2017-12-04Bibliographically approved

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