Literature DB >> 33520197

In silico prediction of structure and function for a large family of transmembrane proteins that includes human Tmem41b.

Shahram Mesdaghi1, David L Murphy1, Filomeno Sánchez Rodríguez1, J Javier Burgos-Mármol1, Daniel J Rigden1.   

Abstract

Background: Recent strides in computational structural biology have opened up an opportunity to understand previously uncharacterised proteins.  The under-representation of transmembrane proteins in the Protein Data Bank highlights the need to apply new and advanced bioinformatics methods to shed light on their structure and function.  This study focuses on a family of transmembrane proteins containing the Pfam domain PF09335 ('SNARE_ASSOC'/ 'VTT '/'Tvp38'/'DedA'). One prominent member, Tmem41b, has been shown to be involved in early stages of autophagosome formation and is vital in mouse embryonic development as well as being identified as a viral host factor of SARS-CoV-2.
Methods: We used evolutionary covariance-derived information to construct and validate ab initio models, make domain boundary predictions and infer local structural features. 
Results: The results from the structural bioinformatics analysis of Tmem41b and its homologues showed that they contain a tandem repeat that is clearly visible in evolutionary covariance data but much less so by sequence analysis.  Furthermore, cross-referencing of other prediction data with covariance analysis showed that the internal repeat features two-fold rotational symmetry.  Ab initio modelling of Tmem41b and homologues reinforces these structural predictions.  Local structural features predicted to be present in Tmem41b were also present in Cl -/H + antiporters.  Conclusions: The results of this study strongly point to Tmem41b and its homologues being transporters for an as-yet uncharacterised substrate and possibly using H + antiporter activity as its mechanism for transport. Copyright:
© 2021 Mesdaghi S et al.

Entities:  

Keywords:  DedA; SARS-CoV-2; Tmem41b; VTT domain; ab initio modelling; autophagy; bioinformatics; contact predictions; evolutionary covariance

Mesh:

Substances:

Year:  2020        PMID: 33520197      PMCID: PMC7818093.2          DOI: 10.12688/f1000research.27676.2

Source DB:  PubMed          Journal:  F1000Res        ISSN: 2046-1402


  59 in total

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