Structure-function analysis and characterization of metalloproteins.

Metalloproteins are proteins that can bind at least one metal ion as a cofactor. They utilize metal ions for a variety of biological purposes, and are essential for all domains of life. Due to the ubiquity of metalloprotein’s involvement across these processes across all domains of life, how proteins coordinate metal ions for different biochemical functions is of great relevance to understanding the implementation of these biological processes. One of the most important aspects of metal binding is its coordination geometry (CG), which often implies functional activities. Most of the current studies are based on the assumption of previously reported CG models founded mainly in a non-biological chemical context. While this general procedure provides us with great measures on the closest CG model a metal site adopts, it also biases and limits the binding ligand selection and coordination results to the canonical CG models examined. Thus, if a CG model exists that has never be reported previously or is not accounted for in a study, instances from the CG would either be misclassified into an expected model and cause a high in-class variation or considered as outliers. To solve this problem, we have developed our analysis, where the less-biased low-variation measure, bond-length, was used determine the binding ligands and the higher-variation measure, angle, was used to cluster the metal shells into canonical or novel CGs with functional associations. This methodology is model-free, and allows us to derive the CG models from the data itself. Thus, we can handle unknown CGs that may cause problems to the classification methods. This new methodology has enabled the discovery of several previously uncharacterized CGs for zinc and other top abundant metalloproteins. By recognizing these novel/aberrant CGs in our clustering analyses, high correlations were achieved between structural and functional descriptions of metal ion coordination

Distributionally Robust Semi-Supervised Learning for People-Centric Sensing

Semi-supervised learning is crucial for alleviating labelling burdens in people-centric sensing. However, human-generated data inherently suffer from distribution shift in semi-supervised learning due to the diverse biological conditions and behavior patterns of humans. To address this problem, we propose a generic distributionally robust model for semi-supervised learning on distributionally shifted data. Considering both the discrepancy and the consistency between the labeled data and the unlabeled data, we learn the latent features that reduce person-specific discrepancy and preserve task-specific consistency. We evaluate our model in a variety of people-centric recognition tasks on real-world datasets, including intention recognition, activity recognition, muscular movement recognition and gesture recognition. The experiment results demonstrate that the proposed model outperforms the state-of-the-art methods.Comment: 8 pages, accepted by AAAI201

A Chemical Interpretation of Protein Electron Density Maps in the Worldwide Protein Data Bank

High-quality three-dimensional structural data is of great value for the functional interpretation of biomacromolecules, especially proteins; however, structural quality varies greatly across the entries in the worldwide Protein Data Bank (wwPDB). Since 2008, the wwPDB has required the inclusion of structure factors with the deposition of x-ray crystallographic structures to support the independent evaluation of structures with respect to the underlying experimental data used to derive those structures. However, interpreting the discrepancies between the structural model and its underlying electron density data is difficult, since derived sigma-scaled electron density maps use arbitrary electron density units which are inconsistent between maps from different wwPDB entries. Therefore, we have developed a method that converts electron density values from sigma-scaled electron density maps into units of electrons. With this conversion, we have developed new methods that can evaluate specific regions of an x-ray crystallographic structure with respect to a physicochemical interpretation of its corresponding electron density map. We have systematically compared all deposited x-ray crystallographic protein models in the wwPDB with their underlying electron density maps, if available, and characterized the electron density in terms of expected numbers of electrons based on the structural model. The methods generated coherent evaluation metrics throughout all PDB entries with associated electron density data, which are consistent with visualization software that would normally be used for manual quality assessment. To our knowledge, this is the first attempt to derive units of electrons directly from electron density maps without the aid of the underlying structure factors. These new metrics are biochemically-informative and can be extremely useful for filtering out low-quality structural regions from inclusion into systematic analyses that span large numbers of PDB entries. Furthermore, these new metrics will improve the ability of non-crystallographers to evaluate regions of interest within PDB entries, since only the PDB structure and the associated electron density maps are needed. These new methods are available as a well-documented Python package on GitHub and the Python Package Index under a modified Clear BSD open source license