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Reassessing a sparse energetic network within a single protein domain.

ABSTRACT: Understanding the molecular principles that govern allosteric communication is an important goal in protein science. One way allostery could be transmitted is via sparse energetic networks of residues, and one such evolutionary conserved network was identified in the PDZ domain family of proteins by multiple sequence alignment [Lockless SW, Ranganathan R (1999) Science 286:295-299]. We have reassessed the energetic coupling of these residues by double mutant cycles together with ligand binding and stability experiments and found that coupling is not a special property of the coevolved network of residues in PDZ domains. The observed coupling for ligand binding is better explained by a distance relationship, where residues close in space are more likely to couple than distal residues. Our study demonstrates that statistical coupling from sequence analysis is not necessarily a reporter of energetic coupling and allostery.

SUBMITTER: Chi CN

PROVIDER: S-EPMC2290805 | biostudies-literature | 2008 Mar

REPOSITORIES: biostudies-literature

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Reassessing a sparse energetic network within a single protein domain.

Chi Celestine N CN Elfström Lisa L Shi Yao Y Snäll Tord T Engström Ake A Jemth Per P

Proceedings of the National Academy of Sciences of the United States of America 20080313 12

Understanding the molecular principles that govern allosteric communication is an important goal in protein science. One way allostery could be transmitted is via sparse energetic networks of residues, and one such evolutionary conserved network was identified in the PDZ domain family of proteins by multiple sequence alignment [Lockless SW, Ranganathan R (1999) Science 286:295-299]. We have reassessed the energetic coupling of these residues by double mutant cycles together with ligand binding a ...[more]

PMID: 18339805

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Project description:Summary Decreasing projection views to a lower X-ray radiation dose usually leads to severe streak artifacts. To improve image quality from sparse-view data, a multi-domain integrative Swin transformer network (MIST-net) was developed and is reported in this article. First, MIST-net incorporated lavish domain features from data, residual data, image, and residual image using flexible network architectures, where a residual data and residual image sub-network was considered as a data consistency module to eliminate interpolation and reconstruction errors. Second, a trainable edge enhancement filter was incorporated to detect and protect image edges. Third, a high-quality reconstruction Swin transformer (i.e., Recformer) was designed to capture image global features. The experimental results on numerical and real cardiac clinical datasets with 48 views demonstrated that our proposed MIST-net provided better image quality with more small features and sharp edges than other competitors. Highlights • A multi-domain integrative Swin transformer was proposed for spare-data imaging• A trainable image edge enhancement filter recovers sharp image edges and features• A modified vision transformer first demonstrates its great potential in tomography• Patient clinical cardiac and numerical data demonstrate our MIST power The bigger picture Decreasing projection views to a lower X-ray radiation dose usually leads to severe streak artifacts. To improve reconstructed image quality from sparse-view data, we develop a multi-domain integrative Swin transformer (MIST) network in this study. The proposed MIST-net incorporates lavish domain features from data, residual data, image, and residual image using flexible network architectures, which help deeply mine the data and image features. To detect image features and protect image edges, the trainable edge enhancement filter is further incorporated to the network for improving encode-decode ability. A high-quality reconstruction transformer was designed to improve the ability of global feature extraction. Our results from both simulation and real cardiac data demonstrated the great potential of MIST. A multi-domain integrative Swin transformer network (MIST-net) demonstrates feasibility, advantages, and great potential in sparse-view tomographic reconstruction. MIST-net mainly integrates data, image, residual data, and residual image domain information into a unified model with a trainable edge enhancement filter and advanced transformer AI technique to realize ultra-sparse-data (48 views) high-fidelity imaging.

Project description:X-ray free-electron lasers (XFELs) have inspired the development of serial femtosecond crystallography (SFX) as a method to solve the structure of proteins. SFX datasets are collected from a sequence of protein microcrystals injected across ultrashort X-ray pulses. The idea behind SFX is that diffraction from the intense, ultrashort X-ray pulses leaves the crystal before the crystal is obliterated by the effects of the X-ray pulse. The success of SFX at XFELs has catalyzed interest in analogous experiments at synchrotron-radiation (SR) sources, where data are collected from many small crystals and the ultrashort pulses are replaced by exposure times that are kept short enough to avoid significant crystal damage. The diffraction signal from each short exposure is so 'sparse' in recorded photons that the process of recording the crystal intensity is itself a reconstruction problem. Using the EMC algorithm, a successful reconstruction is demonstrated here in a sparsity regime where there are no Bragg peaks that conventionally would serve to determine the orientation of the crystal in each exposure. In this proof-of-principle experiment, a hen egg-white lysozyme (HEWL) crystal rotating about a single axis was illuminated by an X-ray beam from an X-ray generator to simulate the diffraction patterns of microcrystals from synchrotron radiation. Millions of these sparse frames, typically containing only ?200 photons per frame, were recorded using a fast-framing detector. It is shown that reconstruction of three-dimensional diffraction intensity is possible using the EMC algorithm, even with these extremely sparse frames and without knowledge of the rotation angle. Further, the reconstructed intensity can be phased and refined to solve the protein structure using traditional crystallographic software. This suggests that synchrotron-based serial crystallography of micrometre-sized crystals can be practical with the aid of the EMC algorithm even in cases where the data are sparse.

Dataset Information

Reassessing a sparse energetic network within a single protein domain.

Publications

Reassessing a sparse energetic network within a single protein domain.

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