Dataset Information

Average conformations determined from PRE data provide high-resolution maps of transient tertiary interactions in disordered proteins.

ABSTRACT: In the last decade it has become evident that disordered states of proteins play important physiological and pathological roles and that the transient tertiary interactions often present in these systems can play a role in their biological activity. The structural characterization of such states has so far largely relied on ensemble representations, which in principle account for both their local and global structural features. However, these approaches are inherently of low resolution due to the large number of degrees of freedom of conformational ensembles and to the sparse nature of the experimental data used to determine them. Here, we overcome these limitations by showing that tertiary interactions in disordered states can be mapped at high resolution by fitting paramagnetic relaxation enhancement data to a small number of conformations, which can be as low as one. This result opens up the possibility of determining the topology of cooperatively collapsed and hidden folded states when these are present in the vast conformational landscape accessible to disordered states of proteins. As a first application, we study the long-range tertiary interactions of acid-unfolded apomyoglobin from experimentally measured paramagnetic relaxation enhancement data.

SUBMITTER: Silvestre-Ryan J

PROVIDER: S-EPMC3628557 | biostudies-literature | 2013 Apr

REPOSITORIES: biostudies-literature

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Average conformations determined from PRE data provide high-resolution maps of transient tertiary interactions in disordered proteins.

Silvestre-Ryan Jordi J Bertoncini Carlos W CW Fenwick Robert Bryn RB Esteban-Martin Santiago S Salvatella Xavier X

Biophysical journal 20130401 8

In the last decade it has become evident that disordered states of proteins play important physiological and pathological roles and that the transient tertiary interactions often present in these systems can play a role in their biological activity. The structural characterization of such states has so far largely relied on ensemble representations, which in principle account for both their local and global structural features. However, these approaches are inherently of low resolution due to th ...[more]

PMID: 23601321

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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

Average conformations determined from PRE data provide high-resolution maps of transient tertiary interactions in disordered proteins.

Publications

Average conformations determined from PRE data provide high-resolution maps of transient tertiary interactions in disordered proteins.

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