Computational redesign of endonuclease DNA binding and cleavage specificity

The reprogramming of DNA-binding specificity is an important challenge for computational protein design that tests current understanding of protein-DNA recognition, and has considerable practical relevance for biotechnology and medicine. Here we describe the computational redesign of the cleavage specificity of the intron-encoded homing endonuclease I-MsoI using a physically realistic atomic-level forcefield. Using an in silico screen, we identified single base-pair substitutions predicted to disrupt binding by the wild-type enzyme, and then optimized the identities and conformations of clusters of amino acids around each of these unfavourable substitutions using Monte Carlo sampling. A redesigned enzyme that was predicted to display altered target site specificity, while maintaining wild-type binding affinity, was experimentally characterized. The redesigned enzyme binds and cleaves the redesigned recognition site approximately 10,000 times more effectively than does the wild-type enzyme, with a level of target discrimination comparable to the original endonuclease. Determination of the structure of the redesigned nuclease-recognition site complex by X-ray crystallography confirms the accuracy of the computationally predicted interface. These results suggest that computational protein design methods can have an important role in the creation of novel highly specific endonucleases for gene therapy and other applications.     

Cortical substrates for exploratory decisions in humans

Decision making in an uncertain environment poses a conflict between the opposing demands of gathering and exploiting information. In a classic illustration of this 'exploration-exploitation' dilemma, a gambler choosing between multiple slot machines balances the desire to select what seems, on the basis of accumulated experience, the richest option, against the desire to choose a less familiar option that might turn out more advantageous (and thereby provide information for improving future decisions). Far from representing idle curiosity, such exploration is often critical for organisms to discover how best to harvest resources such as food and water. In appetitive choice, substantial experimental evidence, underpinned by computational reinforcement learning (RL) theory, indicates that a dopaminergic, striatal and medial prefrontal network mediates learning to exploit. In contrast, although exploration has been well studied from both theoretical and ethological perspectives, its neural substrates are much less clear. Here we show, in a gambling task, that human subjects' choices can be characterized by a computationally well-regarded strategy for addressing the explore/exploit dilemma. Furthermore, using this characterization to classify decisions as exploratory or exploitative, we employ functional magnetic resonance imaging to show that the frontopolar cortex and intraparietal sulcus are preferentially active during exploratory decisions. In contrast, regions of striatum and ventromedial prefrontal cortex exhibit activity characteristic of an involvement in value-based exploitative decision making. The results suggest a model of action selection under uncertainty that involves switching between exploratory and exploitative behavioural modes, and provide a computationally precise characterization of the contribution of key decision-related brain systems to each of these functions.     

Language understanding is grounded in experiential simulations: a response to Weiskopf

Several disciplines within the cognitive sciences have advanced the idea that people comprehend the actions of others, including the linguistic meanings they communicate, through embodied simulations where they imaginatively recreate the actions they observe or hear about. This claim has important consequences for theories of mind and meaning, such as that people’s use and interpretation of language emerges as a kind of bodily activity that is an essential part of ordinary cognition. Daniel Weiskopf presents several arguments against the idea that experiential simulations play a major role in immediate language use and meaning. We offer several rebuttals to Weiskopf, in which we critique his interpretation of simulation theory, present additional psycholinguistic evidence supportive of the simulation perspective, and suggest that a more traditional theory of linguistic meaning and processing has little psychological and empirical validity.     

A family study of endophenotypes for psychosis within an early intervention programme in Hong Kong: Rationale and preliminary findings

The study of endophenotypes may be a viable strategy to tackle the genetic complexity and phenotypic heterogeneity of psychosis, but this research direction is relatively under-developed in China as compared to Western countries. We have recently initiated one of the first family studies of endophenotypes for psychosis in China. Patients entering an established early psychosis intervention service are recruited into this research project for phenotyping, endophenotyping and genotyping. At the endophenotypic...     

Detection of Design Fault in Dart Placement - intersecting Dart

It is easy to apply lines in design drawings to create different styles, however, there is no guarantee that all the style lines drawn are able to be manufactured. In this paper, we focus on one undeliverable style to enhance our understanding of the relationship between the design sketch and the pattern design process. In order to evidence that such style is unworkable, a systematic display of pattern development, and to be reinforced by mathematical evaluation, will be introduced and addressed. When one can easily detect design fault, waste of product development time can be minimized.     

Dopamine-dependent prediction errors underpin reward-seeking behaviour in humans

Theories of instrumental learning are centred on understanding how success and failure are used to improve future decisions. These theories highlight a central role for reward prediction errors in updating the values associated with available actions. In animals, substantial evidence indicates that the neurotransmitter dopamine might have a key function in this type of learning, through its ability to modulate cortico-striatal synaptic efficacy. However, no direct evidence links dopamine, striatal activity and behavioural choice in humans. Here we show that, during instrumental learning, the magnitude of reward prediction error expressed in the striatum is modulated by the administration of drugs enhancing (3,4-dihydroxy-L-phenylalanine; L-DOPA) or reducing (haloperidol) dopaminergic function. Accordingly, subjects treated with L-DOPA have a greater propensity to choose the most rewarding action relative to subjects treated with haloperidol. Furthermore, incorporating the magnitude of the prediction errors into a standard action-value learning algorithm accurately reproduced subjects' behavioural choices under the different drug conditions. We conclude that dopamine-dependent modulation of striatal activity can account for how the human brain uses reward prediction errors to improve future decisions.     

The type Ia supernova SNLS-03D3bb from a super-Chandrasekhar-mass white dwarf star

The accelerating expansion of the Universe, and the need for dark energy, were inferred from observations of type Ia supernovae. There is a consensus that type Ia supernovae are thermonuclear explosions that destroy carbon-oxygen white dwarf stars that have accreted matter from a companion star, although the nature of this companion remains uncertain. These supernovae are thought to be reliable distance indicators because they have a standard amount of fuel and a uniform trigger: they are predicted to explode when the mass of the white dwarf nears the Chandrasekhar mass of 1.4 solar masses (M(o)). Here we show that the high-redshift supernova SNLS-03D3bb has an exceptionally high luminosity and low kinetic energy that both imply a super-Chandrasekhar-mass progenitor. Super-Chandrasekhar-mass supernovae should occur preferentially in a young stellar population, so this may provide an explanation for the observed trend that overluminous type Ia supernovae occur only in 'young' environments. As this supernova does not obey the relations that allow type Ia supernovae to be calibrated as standard candles, and as no counterparts have been found at low redshift, future cosmology studies will have to consider possible contamination from such events.     

Structural basis of cell surface receptor recognition by botulinum neurotoxin B

Botulinum neurotoxins (BoNTs) are potent bacterial toxins that cause paralysis at femtomolar concentrations by blocking neurotransmitter release. A 'double receptor' model has been proposed in which BoNTs recognize nerve terminals via interactions with both gangliosides and protein receptors that mediate their entry. Of seven BoNTs (subtypes A-G), the putative receptors for BoNT/A, BoNT/B and BoNT/G have been identified, but the molecular details that govern recognition remain undefined. Here we report the crystal structure of full-length BoNT/B in complex with the synaptotagmin II (Syt-II) recognition domain at 2.6 A resolution. The structure of the complex reveals that Syt-II forms a short helix that binds to a hydrophobic groove within the binding domain of BoNT/B. In addition, mutagenesis of amino acid residues within this interface on Syt-II affects binding of BoNT/B. Structural and sequence analysis reveals that this hydrophobic groove is conserved in the BoNT/G and BoNT/B subtypes, but varies in other clostridial neurotoxins. Furthermore, molecular docking studies using the ganglioside G(T1b) indicate that its binding site is more extensive than previously proposed and might form contacts with both BoNT/B and synaptotagmin. The results provide structural insights into how BoNTs recognize protein receptors and reveal a promising target for blocking toxin-receptor recognition.