Protein-ligand docking is an essential step in modern drug discovery process. The challenge here is to accurately predict and e±ciently optimize the position and orientation of ligands in the binding pocket of a target protein. In this paper, we present a new method called PSOVina which combined the particle swarm optimization (PSO) algorithm with the e±cient BroydenFletcher-Goldfarb-Shannon (BFGS) local search method adopted in AutoDock Vina to tackle the conformational search problem in docking. Using a diverse data set of 201 protein-ligand complexes from the PDBbind database and a full set of ligands and decoys for four representative targets from the directory of useful decoys (DUD) virtual screening data set, we assessed the docking performance of PSOVina in comparison to the original Vina program. Our results showed that PSOVina achieves a remarkable execution time reduction of 51-60% without compromising the prediction accuracies in the docking and virtual screening experiments. This improvement in time e±ciency makes PSOVina a better choice of a docking tool in large-scale protein-ligand docking applications. Our work lays the foundation for the future development of swarm-based algorithms in molecular docking programs. PSOVina is freely available to noncommercial users at http://cbbio.cis.umac.mo.

[Formula: see text]-Helical transmembrane proteins are the most important drug targets in rational drug development. However, solving the experimental structures of these proteins remains difficult, therefore computational methods to accurately and efficiently predict the structures are in great demand. We present an improved structure prediction method TMDIM based on Park et al. (Proteins 57:577-585, 2004) for predicting bitopic transmembrane protein dimers. Three major algorithmic improvements are introduction of the packing type classification, the multiple-condition decoy filtering, and the cluster-based candidate selection. In a test of predicting nine known bitopic dimers, approximately 78% of our predictions achieved a successful fit (RMSD <2.0 Å) and 78% of the cases are better predicted than the two other methods compared. Our method provides an alternative for modeling TM bitopic dimers of unknown structures for further computational studies. TMDIM is freely available on the web at https://cbbio.cis.umac.mo/TMDIM . Website is implemented in PHP, MySQL and Apache, with all major browsers supported.

Background and Purpose: The relearning of functional skills following a stroke is an essential part of the rehabilitation process. Rehabilitation post stroke may facilitate an individual to return to independent living. However, the skills learnt during this process do not necessarily transfer to the skills required for daily functioning. This review addresses the issue of generalisation of skills learnt by discussing the connectionist model. Summary of Review: Connectionism models how the human brain functions and stipulates that the units in an input layer feed their activations forward to the units in the hidden layer for interpretation and then to the output layer for execution. These units are connected and distributed in a connectionist network. The activation of clusters of units in retrained tasks will provide signals to other different but connected tasks that have not been retrained. Adopting the concept of the connectionist model, the relearning of tasks after brain damage enhances the relearnt skills by transferring them to other tasks that share similar units within the same connectionist network, resulting in generalisation. Research evidence has shown that the strategies of self-regulation and mental imagery further enhance the relearning and generalisation of skills among people with brain damage. By identifying a list of daily tasks that fall within a connectionist network and the appropriate use of training strategies, the skills developed during the rehabilitation process could lead to effective task generalisation to suit the needs of independent living and community reintegration of the individual. Conclusions: The Connectionist Model provides a theoretical base for developing evidence-based interventions throughout the acute, rehabilitation and community phases. The Connectionist Model is the theory by which the cognitive skills learned to perform one particular behaviour, or skill, are transferable to executing other similar skills or beahviours without specifically relearning those skills or behaviours.