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Sanchez Rodriguez, F.

Publications and source records attributed to Sanchez Rodriguez, F..

2 recordsLinked to original sources

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

Recent strides in computational structural biology have opened up an opportunity to understand previously mysterious 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 such a family; transmembrane proteins containing the Pfam domain PF09335 ( SNARE_ASSOC/ VTT / Tvp38). One prominent member, Tmem41b, has been shown to be involved in early stages of autophagosome formation and is vital in mouse embryonic development. Here we use evolutionary covariance-derived information not only to construct and validate ab initio models but also to make domain boundary predictions and infer local structural features. The results from the structural bioinformatics analysis of Tmem41b and its homologues show 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 the covariance analysis shows that the internal repeat features 2-fold rotational symmetry. Ab initio modelling of Tmem41b reinforces these structural predictions. Local structural features predicted to be present in Tmem41b are also present in Cl-/H+ antiporters. These results together strongly point to Tmem41b and its homologues as being transporters for an as-yet uncharacterised substrate and possibly using H+ antiporter activity as its mechanism for transport.

bioinformatics

Helical ensembles out-perform ideal helices in Molecular Replacement

The conventional approach in molecular replacement (MR) is the use of a related structure as a search model. However, this is not always possible as the availability of such structures can be scarce for poorly characterised families of proteins. In these cases, alternative approaches can be explored, such as the use of small ideal fragments that share high albeit local structural similarity with the unknown protein. Earlier versions of AMPLE enabled the trialling of a library of ideal helices, which worked well for largely helical proteins at suitable resolution. Here we explore the performance of libraries of helical ensembles created by clustering helical segments. The impacts of different B-factor treatments and different degrees of structural heterogeneity are explored. We observed a 30% increase in the number of solutions obtained by AMPLE when using this new set of ensembles compared to performance with ideal helices. The boost of performance was notable across three different folds: transmembrane, globular and coiled-coil structures. Furthermore, the increased effectiveness of these ensembles was coupled to a reduction of the time required by AMPLE to reach a solution. AMPLE users can now take full advantage of this new library of search models by activating the "helical ensembles" mode.

bioinformatics