ABSTRACT
High-content screening (HCS) can generate large multidimensional datasets and when aligned with the appropriate data mining tools, it can yield valuable insights into the mechanism of action of bioactive molecules. However, easy-to-use data mining tools are not widely available, with the result that these datasets are frequently underutilized. Here, we present HC StratoMineR, a web-based tool for high-content data analysis. It is a decision-supportive platform that guides even non-expert users through a high-content data analysis workflow. HC StratoMineR is built by using My Structured Query Language for storage and querying, PHP: Hypertext Preprocessor as the main programming language, and jQuery for additional user interface functionality. R is used for statistical calculations, logic and data visualizations. Furthermore, C++ and graphical processor unit power is diffusely embedded in R by using the rcpp and rpud libraries for operations that are computationally highly intensive. We show that we can use HC StratoMineR for the analysis of multivariate data from a high-content siRNA knock-down screen and a small-molecule screen. It can be used to rapidly filter out undesirable data; to select relevant data; and to perform quality control, data reduction, data exploration, morphological hit picking, and data clustering. Our results demonstrate that HC StratoMineR can be used to functionally categorize HCS hits and, thus, provide valuable information for hit prioritization.
Subject(s)
Data Mining/methods , Databases, Factual/statistics & numerical data , Internet , Statistics as Topic/methods , Cluster Analysis , HeLa Cells , Humans , MCF-7 CellsABSTRACT
We present the results for CAPRI Round 30, the first joint CASP-CAPRI experiment, which brought together experts from the protein structure prediction and protein-protein docking communities. The Round comprised 25 targets from amongst those submitted for the CASP11 prediction experiment of 2014. The targets included mostly homodimers, a few homotetramers, and two heterodimers, and comprised protein chains that could readily be modeled using templates from the Protein Data Bank. On average 24 CAPRI groups and 7 CASP groups submitted docking predictions for each target, and 12 CAPRI groups per target participated in the CAPRI scoring experiment. In total more than 9500 models were assessed against the 3D structures of the corresponding target complexes. Results show that the prediction of homodimer assemblies by homology modeling techniques and docking calculations is quite successful for targets featuring large enough subunit interfaces to represent stable associations. Targets with ambiguous or inaccurate oligomeric state assignments, often featuring crystal contact-sized interfaces, represented a confounding factor. For those, a much poorer prediction performance was achieved, while nonetheless often providing helpful clues on the correct oligomeric state of the protein. The prediction performance was very poor for genuine tetrameric targets, where the inaccuracy of the homology-built subunit models and the smaller pair-wise interfaces severely limited the ability to derive the correct assembly mode. Our analysis also shows that docking procedures tend to perform better than standard homology modeling techniques and that highly accurate models of the protein components are not always required to identify their association modes with acceptable accuracy. Proteins 2016; 84(Suppl 1):323-348. © 2016 Wiley Periodicals, Inc.
Subject(s)
Computational Biology/statistics & numerical data , Models, Statistical , Molecular Docking Simulation , Molecular Dynamics Simulation , Proteins/chemistry , Software , Algorithms , Amino Acid Motifs , Bacteria/chemistry , Binding Sites , Computational Biology/methods , Humans , International Cooperation , Internet , Protein Binding , Protein Conformation, alpha-Helical , Protein Conformation, beta-Strand , Protein Folding , Protein Interaction Domains and Motifs , Protein Multimerization , Protein Structure, Tertiary , Sequence Homology, Amino Acid , ThermodynamicsABSTRACT
PocketOptimizer is a computational method to design protein binding pockets that has been recently developed. Starting from a protein structure an existing small molecule binding pocket is optimized for the recognition of a new ligand. The modular program predicts mutations that will improve the affinity of a target small molecule to the protein of interest using a receptor-ligand scoring function to estimate the binding free energy. PocketOptimizer has been tested in a comprehensive benchmark and predicted mutations have also been used in experimental tests. In this chapter, we will provide general recommendations for usage as well as an in-depth description of all individual PocketOptimizer modules.
