Dataset Information

Structural Modeling of Cell Wall Peptidase CwpFM (EntFM) Reveals Distinct Intrinsically Disordered Extensions Specific to Pathogenic Bacillus cereus Strains.

ABSTRACT: The emergence of B. cereus as an opportunistic food-borne pathogen has intensified the need to distinguish strains of public health concern. The heterogeneity of the diseases associated with B. cereus infections emphasizes the versatility of these bacteria strains to colonize their host. Nevertheless, the molecular basis of these differences remains unclear. Several toxins are involved in virulence, particularly in gastrointestinal disorders, but there are currently no biological markers able to differentiate pathogenic from harmless strains. We have previously shown that CwpFM is a cell wall peptidase involved in B. cereus virulence. Here, we report a sequence/structure/function characterization of 39 CwpFM sequences, chosen from a collection of B. cereus with diverse virulence phenotypes, from harmless to highly pathogenic strains. CwpFM is homology-modeled in silico as an exported papain-like endopeptidase, with an N-terminal end composed of three successive bacterial Src Homology 3 domains (SH3b1-3) likely to control protein-protein interactions in signaling pathways, and a C-terminal end that contains a catalytic NLPC_P60 domain primed to form a competent active site. We confirmed in vitro that CwpFM is an endopeptidase with a moderate peptidoglycan hydrolase activity. Remarkably, CwpFMs from pathogenic strains harbor a specific stretch of twenty residues intrinsically disordered, inserted between the SH3b3 and the catalytic NLPC_P60 domain. This strongly suggests this linker as a marker of differentiation between B. cereus strains. We believe that our findings improve our understanding of the pathogenicity of B. cereus while advancing both clinical diagnosis and food safety.

SUBMITTER: Tran SL

PROVIDER: S-EPMC7551459 | biostudies-literature | 2020 Sep

REPOSITORIES: biostudies-literature

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Publications

Structural Modeling of Cell Wall Peptidase CwpFM (EntFM) Reveals Distinct Intrinsically Disordered Extensions Specific to Pathogenic Bacillus cereus Strains.

Tran Seav-Ly SL Cormontagne Delphine D Vidic Jasmina J André-Leroux Gwenaëlle G Ramarao Nalini N

Toxins 20200914 9

The emergence of B. cereus as an opportunistic food-borne pathogen has intensified the need to distinguish strains of public health concern. The heterogeneity of the diseases associated with B. cereus infections emphasizes the versatility of these bacteria strains to colonize their host. Nevertheless, the molecular basis of these differences remains unclear. Several toxins are involved in virulence, particularly in gastrointestinal disorders, but there are currently no biological m ...[more]

PMID: 32937845

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Project description:Intrinsically disordered proteins have dynamic structures through which they play key biological roles. The elucidation of their conformational ensembles is a challenging problem requiring an integrated use of computational and experimental methods. Molecular simulations are a valuable computational strategy for constructing structural ensembles of disordered proteins but are highly resource-intensive. Recently, machine learning approaches based on deep generative models that learn from simulation data have emerged as an efficient alternative for generating structural ensembles. However, such methods currently suffer from limited transferability when modeling sequences and conformations absent in the training data. Here, we develop a novel generative model that achieves high levels of transferability for intrinsically disordered protein ensembles. The approach, named idpSAM, is a latent diffusion model based on transformer neural networks. It combines an autoencoder to learn a representation of protein geometry and a diffusion model to sample novel conformations in the encoded space. IdpSAM was trained on a large dataset of simulations of disordered protein regions performed with the ABSINTH implicit solvent model. Thanks to the expressiveness of its neural networks and its training stability, idpSAM faithfully captures 3D structural ensembles of test sequences with no similarity in the training set. Our study also demonstrates the potential for generating full conformational ensembles from datasets with limited sampling and underscores the importance of training set size for generalization. We believe that idpSAM represents a significant progress in transferable protein ensemble modeling through machine learning.Author summaryProteins are essential molecules in living organisms and some of them have highly dynamical structures, which makes understanding their biological roles challenging. Disordered proteins can be studied through a combination of computer simulations and experiments. Computer simulations are often resource-intensive. Recently, machine learning has been used to make this process more efficient. The strategy is to learn from previous simulations to model the heterogenous conformations of proteins. However, such methods still suffer from poor transferability, meaning that they tend to make incorrect predictions on proteins not seen in training data. In this study, we present idpSAM, a method based on generative artificial intelligence for modeling the structures of disordered proteins. The model was trained using a vast dataset and, thanks to its architecture and training procedure, it performs well on not just proteins in the training set but achieves high levels transferability to proteins unseen in training. This advancement is a step forward in modeling biologically relevant disordered proteins. It shows how the combination of generative modeling and large training sets and can aid us understand how dynamical proteins behave.

Dataset Information

Structural Modeling of Cell Wall Peptidase CwpFM (EntFM) Reveals Distinct Intrinsically Disordered Extensions Specific to Pathogenic Bacillus cereus Strains.

Publications

Structural Modeling of Cell Wall Peptidase CwpFM (EntFM) Reveals Distinct Intrinsically Disordered Extensions Specific to Pathogenic <i>Bacillus cereus</i> Strains.

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