Researchers at the Rice University have identified parts of human brain that are responsible for choosing appropriate words during speech. The findings could help scientists in understanding the speech problems, faced by stroke patients..

A significant obstacle to the efficacy of drugs directed against viral targets is the presence of amino acid polymorphisms in the targeted molecules. Amino acid polymorphisms may occur naturally due to the existence of variations within and between viral strains or as the result of mutations associated with drug resistance. An ideal drug will be one that is extremely effective against a primary target and maintains its effectiveness against the most important variations of the target molecule. A drug that simultaneously inhibits different variants of the target will lead to a faster suppression of the virus, retard the appearance of drug-resistant mutants and provide more efficacious and, in the long range, more affordable therapies. Drug molecules with the ability to inhibit several variants of a target with high affinity have been termed adaptive drugs (Nat. Biotechnol. 20 (2002) 15; Biochemistry 42 (2003) 8459; J. Cell. Biochem. S37 (2001) 82). Current drug design paradigms are predicated upon the lock-and-key hypothesis, which emphasizes shape complementarity as a way to attain specificity and improved binding affinity. Shape complementarity is accomplished by the introduction of conformational constraints in the drug molecule. While highly constrained molecules do well against a unique target, they lack the ability to adapt to target variations like those originating from naturally occurring polymorphisms or drug-resistant mutations. Targeting an array of closely related targets rather than a single one while still maintaining selectivity, requires a different approach. A plausible strategy for designing high affinity adaptive inhibitors is to engineer their most critical interactions (for affinity and specificity) with conserved regions of the target while allowing for adaptability through the introduction of flexible asymmetric functionalities in places facing variable regions of the target. The fundamental thermodynamics and structural principles associated with this approach are discussed in this chapter.

Two-body inter-residue contact potentials for proteins have often been extracted and extensively used for threading. Here, we have developed a new scheme to derive four-body contact potentials as a way to consider protein interactions in a more cooperative model. We use several datasets of protein native structures to demonstrate that around 500 chains are sufficient to provide a good estimate of these four-body contact potentials by obtaining convergent threading results. We also have deliberately chosen two sets of protein native structures differing in resolution, one with all chains? resolution better than 1.5 \rA and the other with 94.2% of the structures having a resolution worse than 1.5 \rA to investigate whether potentials from well-refined protein datasets perform better in threading. However, potentials from well-refined proteins did not generate statistically significant better threading results. Our four-body contact potentials can discriminate well between native structures and partially unfolded or deliberately misfolded structures. Compared with another set of four-body contact potentials derived by using a Delaunay tessellation algorithm, our four-body contact potentials appear to offer a better characterization of the interactions between backbones and side chains and provide better threading results, somewhat complementary to those found using other potentials. Proteins 2007. \copyright 2007 Wiley-Liss, Inc.

A systematic study of two coarse-grained techniques for the description of protein dynamics is presented. The two techniques exploit either Brownian or discretemolecular dynamics algorithms applied in the context of simple Calpha-Calpha potentials, like those used in coarse-grained normal mode analysis. Coarse-grained simulations of the flexibility of protein metafolds are compared to those computed with fully atomistic molecular dynamics simulations using state-of-the-art physical potentials and explicit solvent. Both coarse-grained models efficiently capture critical features of the protein dynamics. 10.1529/biophysj.107.119115

One defining goal of synthetic biology is the development of engineering-based approaches that enable the construction of gene-regulatory networks according to ?design specifications? generated from computational modelling. This approach provides a systematic framework for exploring how a given regulatory network generates a particular phenotypic behaviour. Several fundamental gene circuits have been developed using this approach, including toggle switches and oscillators, and these have been applied in new contexts such as triggered biofilm development and cellular population control. Here we describe an engineered genetic oscillator in Escherichia coli that is fast, robust and persistent, with tunable oscillatory periods as fast as 13 min. The oscillator was designed using a previously modelled network architecture comprising linked positive and negative feedback loops. Using a microfluidic platform tailored for single-cell microscopy, we precisely control environmental conditions and monitor oscillations in individual cells through multiple cycles. Experiments reveal remarkable robustness and persistence of oscillations in the designed circuit; almost every cell exhibited large-amplitude fluorescence oscillations throughout observation runs. The oscillatory period can be tuned by altering inducer levels, temperature and the media source. Computational modelling demonstrates that the key design principle for constructing a robust oscillator is a time delay in the negative feedback loop, which can mechanistically arise from the cascade of cellular processes involved in forming a functional transcription factor. The positive feedback loop increases the robustness of the oscillations and allows for greater tunability. Examination of our refined model suggested the existence of a simplified oscillator design without positive feedback, and we construct an oscillator strain confirming this computational prediction.

The morphogenesis checkpoint in budding yeast delays progression through the cell cycle in response to stimuli that prevent bud formation. Central to the checkpoint mechanism is Swe1 kinase: normally inactive, its activation halts cell cycle progression in G2. We propose a molecular network for Swe1 control, based on published observations of budding yeast and analogous control signals in fission yeast. The proposed Swe1 network is merged with a model of cyclin-dependent kinase regulation, converted into a set of differential equations and studied by numerical simulation. The simulations accurately reproduce the phenotypes of a dozen checkpoint mutants. Among other predictions, the model attributes a new role to Hsl1, a kinase known to play a role in Swe1 degradation: Hsl1 must also be indirectly responsible for potent inhibition of Swe1 activity. The model supports the idea that the morphogenesis checkpoint, like other checkpoints, raises the cell size threshold for progression from one phase of the cell cycle to the next.