Dataset Information

Computational validation of protein nanotubes.

ABSTRACT: We present a novel generic computational method to assess protein nanotubes with variable diameter sizes at the atomic level given their low resolution protomeric structures. The method is based on the symmetrical assembly of a repeating protein subunit into a nanotube. Given the protein unit-cell, and the tube diameter and helicity, we carry out all-atom molecular dynamics simulations, combined with a unique mathematical transformation. This allows us to mimic nanotubes of even huge sizes without end or edge effects. All our simulation setups attempt to explicitly adhere to the conditions under which the experiments were conducted. Thus, we are able to obtain high resolution atomic-scale structures at reasonable computational costs. We expect that our approach would prove useful in assessing protein nanotubes, as well as in silico constructions of novel nanobiomaterials.

SUBMITTER: Buch I

PROVIDER: S-EPMC3536542 | biostudies-literature | 2009 Mar

REPOSITORIES: biostudies-literature

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Computational validation of protein nanotubes.

Buch Idit I Brooks Bernard R BR Wolfson Haim J HJ Nussinov Ruth R

Nano letters 20090301 3

We present a novel generic computational method to assess protein nanotubes with variable diameter sizes at the atomic level given their low resolution protomeric structures. The method is based on the symmetrical assembly of a repeating protein subunit into a nanotube. Given the protein unit-cell, and the tube diameter and helicity, we carry out all-atom molecular dynamics simulations, combined with a unique mathematical transformation. This allows us to mimic nanotubes of even huge sizes witho ...[more]

PMID: 19199488

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Project description:People tend to update beliefs about their future outcomes in a valence-dependent way: they are likely to incorporate good news and to neglect bad news. However, belief formation is a complex process which depends not only on motivational factors such as the desire for favorable conclusions, but also on multiple cognitive variables such as prior beliefs, knowledge about personal vulnerabilities and resources, and the size of the probabilities and estimation errors. Thus, we applied computational modeling in order to test for valence-induced biases in updating while formally controlling for relevant cognitive factors. We compared biased and unbiased Bayesian models of belief updating, and specified alternative models based on reinforcement learning. The experiment consisted of 80 trials with 80 different adverse future life events. In each trial, participants estimated the base rate of one of these events and estimated their own risk of experiencing the event before and after being confronted with the actual base rate. Belief updates corresponded to the difference between the two self-risk estimates. Valence-dependent updating was assessed by comparing trials with good news (better-than-expected base rates) with trials with bad news (worse-than-expected base rates). After receiving bad relative to good news, participants' updates were smaller and deviated more strongly from rational Bayesian predictions, indicating a valence-induced bias. Model comparison revealed that the biased (i.e., optimistic) Bayesian model of belief updating better accounted for data than the unbiased (i.e., rational) Bayesian model, confirming that the valence of the new information influenced the amount of updating. Moreover, alternative computational modeling based on reinforcement learning demonstrated higher learning rates for good than for bad news, as well as a moderating role of personal knowledge. Finally, in this specific experimental context, the approach based on reinforcement learning was superior to the Bayesian approach. The computational validation of valence-dependent belief updating represents a novel support for a genuine optimism bias in human belief formation. Moreover, the precise control of relevant cognitive variables justifies the conclusion that the motivation to adopt the most favorable self-referential conclusions biases human judgments.

Dataset Information

Computational validation of protein nanotubes.

Publications

Computational validation of protein nanotubes.

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