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The non-vesicular transport of lipids between organelles mediated by lipid transport proteins (LTPs) is a key determinant of organelle biogenesis and function. Despite performing a vital function in organelle homeostasis, none of the LTP-encoding genes identified so far are truly essential, even in the simple genome of yeast, suggesting widespread redundancy. In line with this fact, it has been found that a number of LTPs have overlapping functions, making it challenging to assign unique roles for an individual LTP in lipid distribution. In our genetic screens under stringent conditions in which the distinct function of an LTP might become essential, we stumbled upon Csf1, a highly conserved protein with a Chorein-N motif found in other lipid transporters and unraveled a new function for Csf1 in lipid remodeling and homeoviscous adaptation of the lipidome. Here, we further speculate on the potential mechanisms of how the putative function of Csf1 in lipid transport could be intimately connected to its role in lipid remodeling across organelles. [ABSTRACT FROM AUTHOR]

Modern genomics sequencing techniques have provided a massive amount of protein sequences, but experimental endeavor in determining protein structures is largely lagging far behind the vast and unexplored sequences. Apparently, computational biology is playing a more important role in protein structure prediction than ever. Here, we present a system of de novo predictor, termed NiDelta, building on a deep convolutional neural network and statistical potential enabling molecular dynamics simulation for modeling protein tertiary structure. Combining with evolutionary-based residue-contacts, the presented predictor can predict the tertiary structures of a number of target proteins with remarkable accuracy. The proposed approach is demonstrated by calculations on a set of eighteen large proteins from different fold classes. The results show that the ultra-fast molecular dynamics simulation could dramatically reduce the gap between the sequence and its structure at atom level, and it could also present high efficiency in protein structure determination if sparse experimental data is available. [ABSTRACT FROM AUTHOR]

Cuckoo search (CS) algorithm is a nature-inspired search algorithm, in which all the individuals have identical search behaviors. However, this simple homogeneous search behavior is not always optimal to find the potential solution to a special problem, and it may trap the individuals into local regions leading to premature convergence. To overcome the drawback, this paper presents a new variant of CS algorithm with nonhomogeneous search strategies based on quantum mechanism to enhance search ability of the classical CS algorithm. Featured contributions in this paper include: 1) quantum-based strategy is developed for nonhomogeneous update laws and 2) we, for the first time, present a set of theoretical analyses on CS algorithm as well as the proposed algorithm, respectively, and conclude a set of parameter boundaries guaranteeing the convergence of the CS algorithm and the proposed algorithm. On 24 benchmark functions, we compare our method with five existing CS-based methods and other ten state-of-the-art algorithms. The numerical results demonstrate that the proposed algorithm is significantly better than the original CS algorithm and the rest of compared methods according to two nonparametric tests. [ABSTRACT FROM PUBLISHER]

Protein fold recognition is an important and essential step in determining tertiary structure of a protein in biological science. In this study, a model termed NiRecor is developed for recognizing protein folds based on artificial neural networks incorporated in an adaptive heterogeneous particle swarm optimizer. The main contribution of NiRecor is that it is a data-driven and highly-performing predictor without manually tuning control parameters for different data sets. In biological science, since evolutionary- and structure-based information of amino acid sequences is greatly important in determination of tertiary structure of a protein, accordingly, in NiRecor we employ two different feature sets, which involve position specific scoring matrix and secondary structure prediction matrix, to predict the structural classes of protein folds. The experimental results demonstrate the proposed method is powerful in predicting protein folds with higher precisions by improvements of 1.1 ∼7.8 percentages on three benchmark datasets by comparing with several existing predictors. © 2015 Wiley Periodicals, Inc. [ABSTRACT FROM AUTHOR]

As a nature-inspired search algorithm, firefly algorithm (FA) has several control parameters, which may have great effects on its performance. In this study, we investigate the parameter selection and adaptation strategies in a modified firefly algorithm — adaptive firefly algorithm (AdaFa). There are three strategies in AdaFa including (1) a distance-based light absorption coefficient; (2) a gray coefficient enhancing fireflies to share difference information from attractive ones efficiently; and (3) five different dynamic strategies for the randomization parameter. Promising selections of parameters in the strategies are analyzed to guarantee the efficient performance of AdaFa. AdaFa is validated over widely used benchmark functions, and the numerical experiments and statistical tests yield useful conclusions on the strategies and the parameter selections affecting the performance of AdaFa. When applied to the real-world problem — protein tertiary structure prediction, the results demonstrated improved variants can rebuild the tertiary structure with the average root mean square deviation less than 0.4Å and 1.5Å from the native constrains with noise free and 10% Gaussian white noise. [ABSTRACT FROM AUTHOR]

Data-driven design of accurate and reliable Takagi–Sugeno (T–S) fuzzy systems has attracted a lot of attention, where the model structures and parameters are important and often solved in an optimization framework. The particle swarm optimization (PSO) algorithm is widely applied in the field. However, the classical PSO suffers from premature convergence, and it is trapped easily into local optima, which will significantly affect the model accuracy. To overcome these drawbacks, we have developed a new T–S fuzzy system parameters searching strategy called OptiFel with a heterogeneous multiswarm PSO (MsPSO) to enhance the searching performance. MsPSO groups the whole population into multiple cooperative subswarms, which perform different search behaviors for the potential solutions. We have found that the multiple subswarms strategy proposed in this paper is greatly helpful for finding the optimal parameters suitable for the subspaces of the T–S fuzzy model. Our theoretical proof has also demonstrated that the cooperation among the subswarms can maintain a balance between exploration and exploitation to ensure the particles converge to stable points. Experimental results show that MsPSO performs significantly better than traditional PSO algorithms on six benchmark functions. With the improved MsPSO, OptiFel can generate a good fuzzy system model with high accuracy and strong generalization ability. [ABSTRACT FROM AUTHOR]