IDPM: an online database for ion distribution in protein molecules

Metabolic adaptations determine whether natural killer cells fail or thrive within the tumor microenvironment

Natural Killer (NK) cells are a top contender in the development of adoptive cell therapies for cancer due to their diverse antitumor functions and ability to restrict their activation against nonmalignant cells. Despite their success in hematologic malignancies, NK cell-based therapies have been limited in the context of solid tumors. Tumor cells undergo various metabolic adaptations to sustain the immense energy demands that are needed to support their rapid and uncontrolled proliferation. As a result, the tumor microenvironment (TME) is depleted of nutrients needed to fuel immune cell activity and contains several immunosuppressive metabolites that hinder NK cell antitumor functions. Further, we now know that NK cell metabolic status is a main determining factor of their effector functions. Hence, the ability of NK cells to withstand and adapt to these metabolically hostile conditions is imperative for effective and sustained antitumor activity in the TME. With this in mind, we review the consequences of metabolic hostility in the TME on NK cell metabolism and function. We also discuss tumor-like metabolic programs in NK cell induced by STAT3-mediated expansion that adapt NK cells to thrive in the TME. Finally, we examine how other approaches can be applied to enhance NK cell metabolism in tumors.

A scoring function for the prediction of protein complex interfaces based on the neighborhood preferences of amino acids

Proteins often assemble into functional complexes, the structures of which are more difficult to obtain than those of the individual protein molecules. Given the structures of the subunits, it is possible to predict plausible complex models via computational methods such as molecular docking. Assessing the quality of the predicted models is crucial to obtain correct complex structures. Here, an energy-scoring function was developed based on the interfacial residues of structures in the Protein Data Bank. The statistically derived energy function (Nepre) imitates the neighborhood preferences of amino acids, including the types and relative positions of neighboring residues. Based on the preference statistics, a program iNepre was implemented and its performance was evaluated with several benchmarking decoy data sets. The results show that iNepre scores are powerful in model ranking to select the best protein complex structures.

Characterization of the Hydration Process of Phospholipid-Mimetic Polymers Using Air-Injection-Mediated Liquid Exclusion Methods

2-Methacryloyloxyethyl phosphorylcholine (MPC) polymers including hydrophobic units such as poly(MPC-co-butyl methacrylate) (PMB) and poly(MPC-co-dodecyl methacrylate) (PMD) are used as coating agents for medical devices because of their antifouling effects. In this study, the whole hydration process of MPC polymer-coated surfaces was investigated using air-injection-mediated liquid exclusion (AILE) methods in which the liquid exclusion diameter during air injection was correlated to the water-repelling property. The prejetted and standard AILE methods showed the initial change from a dry to a wet state and the swelling behaviors of the MPC polymers, respectively. The liquid exclusion diameter of the MPC polymer-coated surfaces increased with an increase in the immersion time in various aqueous solutions such as deionized water, phosphate-buffered saline (PBS), and cell culture media. Moreover, the liquid exclusion diameter of the PMD-coated surface was larger than that of the PMB-coated one. Ellipsometry directly indicated the polymer layers swollen in water. Scanning probe microscopy (SPM) revealed that nanosized protuberances were formed in water, especially at the PMD-coated surface. The different swelling behaviors of these MPC polymer-coated surfaces affected the liquid exclusion diameters. Thus, the AILE methods are a powerful tool to elucidate the hydration process in various liquid media.

Neighborhood Preference of Amino Acids in Protein Structures and its Applications in Protein Structure Assessment

Amino acids form protein 3D structures in unique manners such that the folded structure is stable and functional under physiological conditions. Non-specific and non-covalent interactions between amino acids exhibit neighborhood preferences. Based on structural information from the protein data bank, a statistical energy function was derived to quantify amino acid neighborhood preferences. The neighborhood of one amino acid is defined by its contacting residues, and the energy function is determined by the neighboring residue types and relative positions. The neighborhood preference of amino acids was exploited to facilitate structural quality assessment, which was implemented in the neighborhood preference program NEPRE. The source codes are available via https://github.com/LiuLab-CSRC/NePre.