ArticleBioinformatics (Oxford, England)2018
TMCrys: predict propensity of success for transmembrane protein crystallization.
Article in Bioinformatics (Oxford, England), 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- A Structural Bioinformatics-Guided Study of Adenosine Triphosphate-Binding Cassette (ABC) Transporters and Their Substrates.Membranes · 2025Article
- Sequence-Based Prediction of Transmembrane Protein Crystallization Propensity.Interdisciplinary sciences, computational life sciences · 2021Article
- BCrystal: an interpretable sequence-based protein crystallization predictor.Bioinformatics (Oxford, England) · 2020Article
- The TMCrys server for supporting crystallization of transmembrane proteins.Bioinformatics (Oxford, England) · 2019Article
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Authors and funding
2 authors.
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Abstract
Motivation: Transmembrane proteins (TMPs) are crucial in the life of the cells. As they have special properties, their structure is hard to determine--the PDB database consists of 2% TMPs, despite the fact that they are predicted to make up to 25% of the human proteome. Crystallization prediction methods were developed to aid the target selection for structure determination, however, there is a need for a TMP specific service. Results: Here, we present TMCrys, a crystallization prediction method that surpasses existing prediction methods in performance thanks to its specialization for TMPs. We expect TMCrys to improve target selection of TMPs. Availability and implementation: https://github.com/brgenzim/tmcrys. Supplementary information: Supplementary data are available at Bioinformatics online.
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