X-ray crystallography and cryo-electron microscopy are two popular methods for the structure determination of biological molecules. Atomic structures are derived through the fitting and refinement of an initial model into electron density maps constructed by both experiments. Two computational approaches, MDFF and xMDFF, have been developed to facilitate this process by integrating the experime...

Two structure determination methods, based on the molecular dynamics flexible fitting (MDFF) paradigm, are presented that resolve sub-5 Å cryo-electron microscopy (EM) maps with either single structures or ensembles of such structures. The methods, denoted cascade MDFF and resolution exchange MDFF, sequentially re-refine a search model against a series of maps of progressively higher resolution...

The task of classifying small objects is still challenging for current deep learning classification models [such as convolutional neural networks (CNNs) and vision transformers (ViTs)]. We believe that these algorithms are not designed specifically targets, so their feature extraction abilities targets insufficient. To improve the capabilities CNN-based ViT-based objects, two multidomain fusion...

A novel method to flexibly fit atomic structures into electron microscopy (EM) maps using molecular dynamics simulations is presented. The simulations incorporate the EM data as an external potential added to the molecular dynamics force field, allowing all internal features present in the EM map to be used in the fitting process, while the model remains fully flexible and stereochemically corr...

In the last decades, blossoming of experimental breakthroughs in domain electron energy loss spectroscopy (EELS) has triggered a variety theoretical developments. Those have to deal with completely different situations, from atomically resolved phonon mapping circular dichroism passing by surface plasmon mapping. All them rely on very physical approximations and not yet been reconciled, despite...