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Correction published on 18 March 2024, see Int. J. Mol. Sci. 2024, 25(6), 3422.
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Communication

The First Quarter Century of the Dense Alignment Surface Transmembrane Prediction Method

by
Miklós Cserző
1,2,
Birgit Eisenhaber
3,4,5,
Frank Eisenhaber
3,4,5,6,
Csaba Magyar
1 and
István Simon
1,*
1
Institute of Enzymology, Research Centre for Natural Sciences, 1117 Budapest, Hungary
2
Department of Physiology, Faculty of Medicine, Semmelweis University, 1094 Budapest, Hungary
3
Bioinformatics Institute, Agency for Science, Technology and Research (A*STAR), Singapore 138671, Singapore
4
Genome Institute of Singapore, Agency for Science, Technology and Research (A*STAR), Singapore 138671, Singapore
5
LASA—Lausitz Advanced Scientific Applications gGmbH, 02943 Weißwasser, Germany
6
School of Biological Sciences, Nanyang Technological University (NTU), Singapore 637551, Singapore
*
Author to whom correspondence should be addressed.
Int. J. Mol. Sci. 2023, 24(18), 14016; https://doi.org/10.3390/ijms241814016
Submission received: 18 August 2023 / Revised: 5 September 2023 / Accepted: 9 September 2023 / Published: 13 September 2023 / Corrected: 18 March 2024
(This article belongs to the Special Issue Protein Structure Research)
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Abstract

The dense alignment surface (DAS) transmembrane (TM) prediction method was first published more than 25 years ago. DAS was the one of the earliest tools to discriminate TM proteins from globular ones and to predict the sequence positions of TM helices in proteins with high accuracy from their amino acid sequence alone. The algorithmic improvements that followed in 2002 (DAS-TMfilter) made it one of the best performing tools among those relying on local sequence information for TM prediction. Since then, many more experimental data about membrane proteins (including thousands of 3D structures of membrane proteins) have accumulated but there has been no significant improvement concerning performance in the area of TM helix prediction tools. Here, we report a new implementation of the DAS-TMfilter prediction web server. We reevaluated the performance of the method using a five-times-larger, updated test dataset. We found that the method performs at essentially the same accuracy as the original even without any change to the parametrization of the program despite the much larger dataset. Thus, the approach captures the physico-chemistry of TM helices well, essentially solving this scientific problem.
Keywords: transmembrane proteins; transmembrane prediction; multiple sequence alignment; dot-plots transmembrane proteins; transmembrane prediction; multiple sequence alignment; dot-plots

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MDPI and ACS Style

Cserző, M.; Eisenhaber, B.; Eisenhaber, F.; Magyar, C.; Simon, I. The First Quarter Century of the Dense Alignment Surface Transmembrane Prediction Method. Int. J. Mol. Sci. 2023, 24, 14016. https://doi.org/10.3390/ijms241814016

AMA Style

Cserző M, Eisenhaber B, Eisenhaber F, Magyar C, Simon I. The First Quarter Century of the Dense Alignment Surface Transmembrane Prediction Method. International Journal of Molecular Sciences. 2023; 24(18):14016. https://doi.org/10.3390/ijms241814016

Chicago/Turabian Style

Cserző, Miklós, Birgit Eisenhaber, Frank Eisenhaber, Csaba Magyar, and István Simon. 2023. "The First Quarter Century of the Dense Alignment Surface Transmembrane Prediction Method" International Journal of Molecular Sciences 24, no. 18: 14016. https://doi.org/10.3390/ijms241814016

APA Style

Cserző, M., Eisenhaber, B., Eisenhaber, F., Magyar, C., & Simon, I. (2023). The First Quarter Century of the Dense Alignment Surface Transmembrane Prediction Method. International Journal of Molecular Sciences, 24(18), 14016. https://doi.org/10.3390/ijms241814016

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