Small- and intermediate-conductance Ca2+-activated potassium channels, activated by Ca2+-bound calmodulin, play an important role in regulating membrane excitability. These channels are also linked to clinical abnormalities. A tremendous amount of effort has been devoted to developing small molecule compounds targeting these channels. However, these compounds often suffer from low potency and lack of selectivity, hindering their potentials for clinical use. A key contributing factor is the lack of knowledge of the binding site(s) for these compounds. Here we demonstrate by X-ray crystallography that the binding pocket for the compounds of the 1-EBIO class is located at the calmodulin-channel interface. We show that, based on structure data and molecular docking, mutations of the channel can effectively change the potency of these compounds. Our results provide insight into the molecular nature of the binding pocket and its contribution to the potency and selectivity of the compounds of the 1-EBIO class.
BackgroundThe Biochemical Algorithms Library (BALL) is a comprehensive rapid application development framework for structural bioinformatics. It provides an extensive C++ class library of data structures and algorithms for molecular modeling and structural bioinformatics. Using BALL as a programming toolbox does not only allow to greatly reduce application development times but also helps in ensuring stability and correctness by avoiding the error-prone reimplementation of complex algorithms and replacing them with calls into the library that has been well-tested by a large number of developers. In the ten years since its original publication, BALL has seen a substantial increase in functionality and numerous other improvements.ResultsHere, we discuss BALL's current functionality and highlight the key additions and improvements: support for additional file formats, molecular edit-functionality, new molecular mechanics force fields, novel energy minimization techniques, docking algorithms, and support for cheminformatics.ConclusionsBALL is available for all major operating systems, including Linux, Windows, and MacOS X. It is available free of charge under the Lesser GNU Public License (LPGL). Parts of the code are distributed under the GNU Public License (GPL). BALL is available as source code and binary packages from the project web site at http://www.ball-project.org. Recently, it has been accepted into the debian project; integration into further distributions is currently pursued.
Engineering specific interactions between proteins and small molecules is extremely useful for biological studies, as these interactions are essential for molecular recognition. Furthermore, many biotechnological applications are made possible by such an engineering approach, ranging from biosensors to the design of custom enzyme catalysts. Here, we present a novel method for the computational design of protein-small ligand binding named PocketOptimizer. The program can be used to modify protein binding pocket residues to improve or establish binding of a small molecule. It is a modular pipeline based on a number of customizable molecular modeling tools to predict mutations that alter the affinity of a target protein to its ligand. At its heart it uses a receptor-ligand scoring function to estimate the binding free energy between protein and ligand. We compiled a benchmark set that we used to systematically assess the performance of our method. It consists of proteins for which mutational variants with different binding affinities for their ligands and experimentally determined structures exist. Within this test set PocketOptimizer correctly predicts the mutant with the higher affinity in about 69% of the cases. A detailed analysis of the results reveals that the strengths of PocketOptimizer lie in the correct introduction of stabilizing hydrogen bonds to the ligand, as well as in the improved geometric complemetarity between ligand and binding pocket. Apart from the novel method for binding pocket design we also introduce a much needed benchmark data set for the comparison of affinities of mutant binding pockets, and that we use to asses programs for in silico design of ligand binding.
Molecular docking of small-molecules is an important procedure for computer-aided drug design. Modeling receptor side chain flexibility is often important or even crucial, as it allows the receptor to adopt new conformations as induced by ligand binding. However, the accurate and efficient incorporation of receptor side chain flexibility has proven to be a challenge due to the huge computational complexity required to adequately address this problem. Here we describe a new docking approach with a very fast, graph-based optimization algorithm for assignment of the near-optimal set of residue rotamers. We extensively validate our approach using the 40 DUD target benchmarks commonly used to assess virtual screening performance and demonstrate a large improvement using the developed side chain optimization over rigid receptor docking (average ROC AUC of 0.693 vs. 0.623). Compared to numerous benchmarks, the overall performance is better than nearly all other commonly used procedures. Furthermore, we provide a detailed analysis of the level of receptor flexibility observed in docking results for different classes of residues and elucidate potential avenues for further improvement.
Correctly predicting off-targets for a given molecular structure, which would have the ability to bind a large range of ligands, is both particularly difficult and important if they share no significant sequence or fold similarity with the respective molecular target (“distant off-targets”). A novel approach for identification of off-targets by direct superposition of protein binding pocket surfaces is presented and applied to a set of well-studied and highly relevant drug targets, including representative kinases and nuclear hormone receptors. The entire Protein Data Bank is searched for similar binding pockets and convincing distant off-target candidates were identified that share no significant sequence or fold similarity with the respective target structure. These putative target off-target pairs are further supported by the existence of compounds that bind strongly to both with high topological similarity, and in some cases, literature examples of individual compounds that bind to both. Also, our results clearly show that it is possible for binding pockets to exhibit a striking surface similarity, while the respective off-target shares neither significant sequence nor significant fold similarity with the respective molecular target (“distant off-target”).
Plasma membrane damage commonly occurs during cellular growth and development. To counteract these potentially lethal injuries, membrane repair mechanisms have evolved, which promote the integrity of the lipid bilayer. Although the membrane of fungi is the target of important clinical drugs and agricultural fungicides, the molecular mechanisms which mediate membrane repair in these organisms remain elusive. Here we identify the penta-EF-hand protein PEF1 of the genetic model fungus Neurospora crassa as part of a cellular response mechanism against different types of membrane injury. Deletion of the pef1 gene in the wild type and different lysis-prone gene knockout mutants revealed a function of the protein in maintaining cell integrity during cell-cell fusion and in the presence of pore-forming drugs, such as the plant defense compound tomatine. By fluorescence and live-cell imaging we show that green fluorescent protein (GFP)-tagged PEF1 accumulates at the sites of membrane injury in a Ca 2+-dependent manner. Sitedirected mutagenesis identified Ca 2+-binding domains essential for the spatial dynamics and function of the protein. In addition, the subcellular localization of PEF1 revealed that the syncytial fungal colony undergoes compartmentation in response to antifungal treatment. We propose that plasma membrane repair in fungi constitutes an additional line of defense against membrane-disturbing drugs, thereby expanding the current model of fungal drug resistance mechanisms. KEYWORDS membrane repair; penta-EF-hand protein; cell fusion; Neurospora crassa; antifungal drug
ballaxy consists of three parts: some minor modifications to the Galaxy system, a collection of tools and an integration into the BALL framework and the BALLView application for molecular modelling. Modifications to Galaxy will be submitted to the Galaxy project, and the BALL and BALLView integrations will be integrated in the next major BALL release. After acceptance of the modifications into the Galaxy project, we will publish all ballaxy tools via the Galaxy toolshed. In the meantime, all three components are available from http://www.ball-project.org/ballaxy. Also, docker images for ballaxy are available at https://registry.hub.docker.com/u/anhi/ballaxy/dockerfile/. ballaxy is licensed under the terms of the GPL.
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