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

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.

SUBMITTER: Mesdaghi S

PROVIDER: S-EPMC7818093 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:BACKGROUND: Transmembrane ?-barrel proteins are a special class of transmembrane proteins which play several key roles in human body and diseases. Due to experimental difficulties, the number of transmembrane ?-barrel proteins with known structures is very small. Over the years, a number of learning-based methods have been introduced for recognition and structure prediction of transmembrane ?-barrel proteins. Most of these methods emphasize on homology search rather than any biological or chemical basis. RESULTS: We present a novel graph-theoretic model for classification and structure prediction of transmembrane ?-barrel proteins. This model folds proteins based on energy minimization rather than a homology search, avoiding any assumption on availability of training dataset. The ab initio model presented in this paper is the first method to allow for permutations in the structure of transmembrane proteins and provides more structural information than any known algorithm. The model is also able to recognize ?-barrels by assessing the pseudo free energy. We assess the structure prediction on 41 proteins gathered from existing databases on experimentally validated transmembrane ?-barrel proteins. We show that our approach is quite accurate with over 90% F-score on strands and over 74% F-score on residues. The results are comparable to other algorithms suggesting that our pseudo-energy model is close to the actual physical model. We test our classification approach and show that it is able to reject ?-helical bundles with 100% accuracy and ?-barrel lipocalins with 97% accuracy. CONCLUSIONS: We show that it is possible to design models for classification and structure prediction for transmembrane ?-barrel proteins which do not depend essentially on training sets but on combinatorial properties of the structures to be proved. These models are fairly accurate, robust and can be run very efficiently on PC-like computers. Such models are useful for the genome screening.

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

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