AIMC Topic: Crystallography, X-Ray

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Integration of virtual screening and susceptibility test to discover active-site subpocket-specific biogenic inhibitors of Helicobacter pylori shikimate dehydrogenase.

International microbiology : the official journal of the Spanish Society for Microbiology
Shikimate dehydrogenase (HpSDH) (EC 1.1.1.25) is a key enzyme in the shikimate pathway of Helicobacter pylori (H. pylori), which catalyzes the NADPH-dependent reversible reduction of 3-dehydroshikimate to shikimate. Targeting HpSDH has been recognize...

Mathematical deep learning for pose and binding affinity prediction and ranking in D3R Grand Challenges.

Journal of computer-aided molecular design
Advanced mathematics, such as multiscale weighted colored subgraph and element specific persistent homology, and machine learning including deep neural networks were integrated to construct mathematical deep learning models for pose and binding affin...

General Method for the Identification of Crystal Faces Using Raman Spectroscopy Combined with Machine Learning and Application to the Epitaxial Growth of Acetaminophen.

Langmuir : the ACS journal of surfaces and colloids
Crystal morphology is one of the key crystallographic characteristics that governs the macroscopic properties of crystalline materials. The identification of crystal faces, or face indexing, is an important technique that is used to get information r...

Insight Analysis of Promiscuous Estrogen Receptor α-Ligand Binding by a Novel Machine Learning Scheme.

Chemical research in toxicology
Estrogen receptor α (ERα) plays a significant role in occurrence of breast cancer and may cause various adverse side-effects when ERα is an off-target protein. A theoretical model was derived to predict the binding affinity of ERα using the pharmacop...

Classification of crystallization outcomes using deep convolutional neural networks.

PloS one
The Machine Recognition of Crystallization Outcomes (MARCO) initiative has assembled roughly half a million annotated images of macromolecular crystallization experiments from various sources and setups. Here, state-of-the-art machine learning algori...

Protein structure modeling and refinement by global optimization in CASP12.

Proteins
For protein structure modeling in the CASP12 experiment, we have developed a new protocol based on our previous CASP11 approach. The global optimization method of conformational space annealing (CSA) was applied to 3 stages of modeling: multiple sequ...

Assessment of the model refinement category in CASP12.

Proteins
We here report on the assessment of the model refinement predictions submitted to the 12th Experiment on the Critical Assessment of Protein Structure Prediction (CASP12). This is the fifth refinement experiment since CASP8 (2008) and, as with the pre...

Template-based and free modeling of I-TASSER and QUARK pipelines using predicted contact maps in CASP12.

Proteins
We develop two complementary pipelines, "Zhang-Server" and "QUARK", based on I-TASSER and QUARK pipelines for template-based modeling (TBM) and free modeling (FM), and test them in the CASP12 experiment. The combination of I-TASSER and QUARK successf...

Protein contact prediction by integrating deep multiple sequence alignments, coevolution and machine learning.

Proteins
In this study, we report the evaluation of the residue-residue contacts predicted by our three different methods in the CASP12 experiment, focusing on studying the impact of multiple sequence alignment, residue coevolution, and machine learning on co...