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Why is the biological hydrophobicity scale more accurate than earlier experimental hydrophobicity scales?
Stockholm University, Faculty of Science, Department of Biochemistry and Biophysics.
Stockholm University, Faculty of Science, Department of Biochemistry and Biophysics.ORCID iD: 0000-0002-7115-9751
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.

Place, publisher, year, edition, pages
2014. Vol. 82, no 9, 2190-2198 p.
Keyword [en]
hydrophobicity scale, membrane proteins, traiislocon recognition, protein structure predictions, secondary structopology prediction
National Category
Biochemistry and Molecular Biology
Research subject
Biochemistry
Identifiers
URN: urn:nbn:se:su:diva-107801DOI: 10.1002/prot.24582ISI: 000340940300040OAI: oai:DiVA.org:su-107801DiVA: diva2:752600
Note

AuthorCount:2;

Available from: 2014-10-05 Created: 2014-09-29 Last updated: 2017-12-05Bibliographically approved
In thesis
1. Topology Prediction of α-Helical Transmembrane Proteins
Open this publication in new window or tab >>Topology Prediction of α-Helical Transmembrane Proteins
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:nbn:se:su:diva-129061 (URN)
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

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