Riboswitch RNAs fold into complex tertiary structures upon binding to their cognate ligand. Ligand recognition is accomplished by key residues in the binding pocket. In addition, it often crucially depends on the stability of peripheral structural elements. The ligand-bound complex of the guanine-sensing riboswitch from Bacillus subtilis, for example, is stabilized by extensive interactions bet...

Virtual Screening methodologies have emerged as efficient alternatives for the discovery of new drug candidates. At the same time, ensemble methods are nowadays frequently used to overcome the limitations of employing a single model in ligand-based drug design. However, many applications of ensemble methods to this area do not consider important aspects related to both virtual screening and the...

D the last decade, the classical view of a protein existing in equilibrium between discrete conformational states has undergone a radical transformation. Experimental observations, particularly those obtained from NMRdetected hydrogenydeuterium exchange, indicate that even under native conditions proteins need to be considered as complex statistical ensembles (see for example refs. 1–13). In th...

G protein-coupled receptors are vital membrane proteins that allosterically transduce biomolecular signals across the cell membrane. However, the process by which ligand binding induces protein conformation changes is not well understood biophysically. Rhodopsin, the mammalian dim-light receptor, is a unique test case for understanding these processes because of its switch-like activity; the li...

Ligand docking to flexible protein molecules can be efficiently carried out through ensemble docking to multiple protein conformations, either from experimental X-ray structures or from in silico simulations. The success of ensemble docking often requires the careful selection of complementary protein conformations, through docking and scoring of known co-crystallized ligands. False positives, ...

the article suggests an algorithm for regular classifier ensemble methodology. the proposed methodology is based on possibilistic aggregation to classify samples. the argued method optimizes an objective function that combines environment recognition, multi-criteria aggregation term and a learning term. the optimization aims at learning backgrounds as solid clusters in subspaces of the high-dim...