Biology subjects

Sanchez Rodriguez, F.

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

4 recordsLinked to original sources

bioRxiv2024

Using deep learning predictions reveals a large number of register errors in PDB deposits

The accuracy of the information in the Protein Data Bank (PDB) is of great importance for the myriad downstream applications that make use of protein structural information. Despite best efforts, the occasional introduction of errors is inevitable, especially where the experimental data are of limited resolution. We have previously established a novel protein structure validation approach based on spotting inconsistencies between the residue contacts and distances observed in a structural model and those computationally predicted by methods such as AlphaFold 2. It is particularly well-suited to the detection of register errors. Importantly, the new approach is orthogonal to traditional methods based on stereochemistry or map-model agreement, and is resolution-independent. Here we identify thousands of likely register errors by scanning 3-5[A] resolution structures in the PDB. Unlike most methods, application of our approach yields suggested corrections to the register of affected regions which we show, even by limited implementation, lead to improved refinement statistics in the vast majority of cases. A few limitations and confounding factors such as fold-switching proteins are characterised, but we expect our approach to have broad application in spotting potential issues in current accessions and, through its implementation and distribution in CCP4, helping ensure the accuracy of future deposits.

bioinformatics↗

bioRxiv2024

Deep-learning protein structure predictions suggest likely molecular functions for three uncharacterised polytopic membrane proteins from the P. falciparum apicoplast

Malaria is a burdensome disease to humanity caused chiefly by the still poorly understood parasite genus Plasmodium. Much of the pathogenic success of these and other related parasites is due to the presence of the apicoplast, a comparatively poorly characterised biosynthetic organelle containing many proteins of unknown function. Here we present AlphaFold2 protein structure predictions together with further in silico analyses to infer molecular functions for the three uncharacterised transmembrane apicoplast proteins PF3D7_0622700, PF3D7_0908100 and PF3D7_1021300. The targets PF3D7_0622700 and PF3D7_0908100 are shown herein to belong to the polytopic Major Facilitator and Cation-Proton Antiporter and Anion Transporter superfamilies respectively, confirming previous suspicions for PF3D7_0908100 of a transporter function. Importantly, our docking screens further suggest pyridoxal-5-phosphate may be transported by PF3D7_0622700, and PF3D7_0908100 likely transports a larger negatively charged metabolite. These findings will help direct experimental assays to confirm what apicoplast metabolites these proteins may transport. PF3D7_1021300 is proposed to possess a six transmembrane alpha-helix domain of a currently unknown fold which may also possess a transporter molecular function. This work highlights the power of high accuracy protein structure predictions to illuminate proteins of unknown structure and function.

bioinformatics↗

bioRxiv2020

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↗

bioRxiv2020

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↗