AIMC Topic: Crystallography, X-Ray

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Inhibiting heme piracy by pathogenic Escherichia coli using de novo-designed proteins.

Nature communications
Iron is an essential nutrient for most bacteria and is often growth-limiting during infection, due to the host sequestering free iron as part of the innate immune response. To obtain the iron required for growth, many bacterial pathogens encode trans...

Harnessing protein language model for structure-based discovery of highly efficient and robust PET hydrolases.

Nature communications
Plastic waste, particularly polyethylene terephthalate (PET), presents significant environmental challenges, driving extensive research into enzymatic biodegradation. However, existing PET hydrolases (PETases) are limited by narrow sequence diversity...

MIC: A deep learning tool for assigning ions and waters in cryo-EM and crystal structures.

Nature communications
At sufficiently high resolution, x-ray crystallography and cryogenic electron microscopy are capable of resolving small spherical map features corresponding to either water or ions. Correct classification of these sites provides crucial insight for u...

Robust Lightweight Graph Neural Network Framework for Accelerating Crystal Structure Prediction.

Journal of chemical information and modeling
This work presents a crystal structure prediction framework that employs a structural search using a derivative-free optimization method, with a supervised Graph Neural Network (GNN) model as the energy evaluator. We address the limitations of existi...

Cyclic peptide structure prediction and design using AlphaFold2.

Nature communications
Small cyclic peptides have gained significant traction as a therapeutic modality; however, the development of deep learning methods for accurately designing such peptides has been slow, mostly due to the lack of sufficiently large training sets. Here...

Crystal Structure Prediction Using a Self-Attention Neural Network and Semantic Segmentation.

Journal of chemical information and modeling
The development of new materials is a time-consuming and resource-intensive process. Deep learning has emerged as a promising approach to accelerate this process. However, accurately predicting crystal structures using deep learning remains a signifi...

Atomic context-conditioned protein sequence design using LigandMPNN.

Nature methods
Protein sequence design in the context of small molecules, nucleotides and metals is critical to enzyme and small-molecule binder and sensor design, but current state-of-the-art deep-learning-based sequence design methods are unable to model nonprote...

Deep-Learning-Driven Discovery of SN3-1, a Potent NLRP3 Inhibitor with Therapeutic Potential for Inflammatory Diseases.

Journal of medicinal chemistry
The NLRP3 inflammasome plays a central role in the pathogenesis of various intractable human diseases, making it an urgent target for therapeutic intervention. Here, we report the development of SN3-1, a novel orally potent NLRP3 inhibitor, designed ...

An artificial intelligence accelerated virtual screening platform for drug discovery.

Nature communications
Structure-based virtual screening is a key tool in early drug discovery, with growing interest in the screening of multi-billion chemical compound libraries. However, the success of virtual screening crucially depends on the accuracy of the binding p...

Deep-learning map segmentation for protein X-ray crystallographic structure determination.

Acta crystallographica. Section D, Structural biology
When solving a structure of a protein from single-wavelength anomalous diffraction X-ray data, the initial phases obtained by phasing from an anomalously scattering substructure usually need to be improved by an iterated electron-density modification...