A neural network has been used to predict both the location and the type of b-turns in a set of 300 nonhomologous protein domains. A substantial improvement in prediction accuracy compared with previous methods has been achieved by incorporating secondary structure information in the input data. The total percentage of residues correctly classified as b-turn or not-b-turn is around 75% with predicted secondary structure information. More significantly, the method gives a Matthews correlation coefficient~MCC! of around 0.35, compared with a typical MCC of around 0.20 using other b-turn prediction methods. Our method also distinguishes the two most numerous and well-defined types of b-turn, types I and II, with a significant level of accuracy~MCCs 0.22 and 0.26, respectively!.

Features of multimeric proteins are reviewed to shed light on the formation of protein assemblies from a structural perspective. The features comprise biochemical and geometric properties. They are compiled on new low-redundancy sets of crystal structures of homomeric proteins with different symmetry and subunit multiplicity, as well as on a set of heteromeric proteins. Crystal structures of likely monomers provide a control group.

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Financial price bubbles have previously been linked with the epidemic-like spread of an investment idea; such bubbles are commonly seen in cryptocurrency prices. This paper aims to predict such bubbles for a number of cryptocurrencies using a hidden Markov model previously utilised to detect influenza epidemic outbreaks, based in this case on the behaviour of novel online social media indicators. To validate the methodology further, a trading strategy is built and tested on historical data. The resulting trading strategy outperforms a buy and hold strategy. The work demonstrates both the broader utility of epidemic-detecting hidden Markov models in the identification of bubble-like behaviour in time series, and that social media can provide valuable predictive information pertaining to cryptocurrency price movements.

Cryptocurrencies have experienced recent surges in interest and price. It has been discovered that there are time intervals where cryptocurrency prices and certain online and social media factors appear related. In addition it has been noted that cryptocurrencies are prone to experience intervals of bubble-like price growth. The hypothesis investigated here is that relationships between online factors and price are dependent on market regime. In this paper, wavelet coherence is used to study co-movement between a cryptocurrency price and its related factors, for a number of examples. This is used alongside a well-known test for financial asset bubbles to explore whether relationships change dependent on regime. The primary finding of this work is that medium-term positive correlations between online factors and price strengthen significantly during bubble-like regimes of the price series; this explains why these relationships have previously been seen to appear and disappear over time. A secondary finding is that short-term relationships between the chosen factors and price appear to be caused by particular market events (such as hacks / security breaches), and are not consistent from one time interval to another in the effect of the factor upon the price. In addition, for the first time, wavelet coherence is used to explore the relationships between different cryptocurrencies.

Cryptocurrencies have recently experienced a new wave of price volatility and interest; activity within social media communities relating to cryptocurrencies has increased significantly.There is currently limited documented knowledge of factors which could indicate future price movements. This paper aims to decipher relationships between cryptocurrency price changes and topic discussion on social media to provide, among other things, an understanding of which topics are indicative of future price movements. To achieve this a well-known dynamic topic modelling approach is applied to social media communication to retrieve information about the temporal occurrence of various topics. A Hawkes model is then applied to find interactions between topics and cryptocurrency prices. The results show particular topics tend to precede certain types of price movements, for example the discussion of 'risk and investment vs trading' being indicative of price falls, the discussion of 'substantial price movements' being indicative of volatility, and the discussion of 'fundamental cryptocurrency value' by technical communities being indicative of price rises. The knowledge of topic relationships gained here could be built into a real-time system, providing trading or alerting signals.

A novel method is presented for the prediction of protein architecture from sequence using neural networks. The method involves the preprocessing of protein sequence data by numerically encoding it and then applying a Fourier transform. The encoded and transformed data are then used to train a neural network to recognize a number of different protein architectures. The method proved significantly better than comparable alternative strategies such as percentage dipeptide frequency, but is still limited by the size of the data set and the input demands of a neural network. Its main potential is as a complement to existing fold recognition techniques, with its ability to identify global symmetries within protein structures its greatest strength.

A global energy minimization method based on what is known about the mechanisms of the GroEL/GroES chaperonin system is applied to two 22‐mers of an off‐lattice protein model whose native states are β‐hairpins and which have structural similarity to short peptides known to interact strongly with the GroEL substrate binding domain. These model substrates have been used by other workers to test the effectiveness of a number of global minimization techniques, and are regarded as providing a significant challenge. The minimization method developed here is progressively elaborated from an initial simple form that targets exposed hydrophobic regions for unfolding to include a refolding phase that encourages the later recompactification of partly unfolded substrate; this refolding phase is seen to be crucial in the successful application of the method. The optimal handling of hydrophilic monomers within the model is also systematically explored, and it is seen that the best interpretation of their role is one that allows the chaperonin model to operate in “proofreading” mode whereby misfolded substrates are recognized by their surface exposure of a large proportion of hydrophobic monomers. The final version of the model allows native‐like structures to be found quickly, on average for the two 22‐mer substrates after 6 or 7 chaperone contacts. These results compare very favorably with those that have been obtained elsewhere using generic global minimization methods such as those based on thermal annealing. The paper concludes with a discussion of the place of the technique within the general category of hypersurface deformation methods for global minimization, and with suggestions as to how the chaperone‐based method developed here could be elaborated so as to be effective on longer substrate chains that give rise to more complex tertiary structures in their native states. © 2001 John Wiley & Sons, Inc. Biopolymers 59: 411–426, 2001