PrinCCes: Continuity-based geometric decomposition and systematic visualization of the void repertoire of proteins

Negative Influence by the Force: Mechanically Induced Hyperpolarization via K2P Background Potassium Channels

The two-pore domain K_2P subunits form background (leak) potassium channels, which are characterized by constitutive, although not necessarily constant activity, at all membrane potential values. Among the fifteen pore-forming K_2P subunits encoded by the KCNK genes, the three members of the TREK subfamily, TREK-1, TREK-2, and TRAAK are mechanosensitive ion channels. Mechanically induced opening of these channels generally results in outward K⁺ current under physiological conditions, with consequent hyperpolarization and inhibition of membrane potential-dependent cellular functions. In the past decade, great advances have been made in the investigation of the molecular determinants of mechanosensation, and members of the TREK subfamily have emerged among the best-understood examples of mammalian ion channels directly influenced by the tension of the phospholipid bilayer. In parallel, the crucial contribution of mechano-gated TREK channels to the regulation of membrane potential in several cell types has been reported. In this review, we summarize the general principles underlying the mechanical activation of K_2P channels, and focus on the physiological roles of mechanically induced hyperpolarization.

CavBench: A benchmark for protein cavity detection methods

Extensive research has been applied to discover new techniques and methods to model protein-ligand interactions. In particular, considerable efforts focused on identifying candidate binding sites, which quite often are active sites that correspond to protein pockets or cavities. Thus, these cavities play an important role in molecular docking. However, there is no established benchmark to assess the accuracy of new cavity detection methods. In practice, each new technique is evaluated using a small set of proteins with known binding sites as ground-truth. However, studies supported by large datasets of known cavities and/or binding sites and statistical classification (i.e., false positives, false negatives, true positives, and true negatives) would yield much stronger and reliable assessments. To this end, we propose CavBench, a generic and extensible benchmark to compare different cavity detection methods relative to diverse ground truth datasets (e.g., PDBsum) using statistical classification methods.

Geometric Detection Algorithms for Cavities on Protein Surfaces in Molecular Graphics: A Survey

Detecting and analyzing protein cavities provides significant information about active sites for biological processes (e.g., protein-protein or protein-ligand binding) in molecular graphics and modeling. Using the three-dimensional structure of a given protein (i.e., atom types and their locations in 3D) as retrieved from a PDB (Protein Data Bank) file, it is now computationally viable to determine a description of these cavities. Such cavities correspond to pockets, clefts, invaginations, voids, tunnels, channels, and grooves on the surface of a given protein. In this work, we survey the literature on protein cavity computation and classify algorithmic approaches into three categories: evolution-based, energy-based, and geometry-based. Our survey focuses on geometric algorithms, whose taxonomy is extended to include not only sphere-, grid-, and tessellation-based methods, but also surface-based, hybrid geometric, consensus, and time-varying methods. Finally, we detail those techniques that have been customized for GPU (Graphics Processing Unit) computing.