Shared structural features of Miro binding control mitochondrial homeostasis.

Miro proteins are universally conserved mitochondrial calcium-binding GTPases that regulate a multitude of mitochondrial processes, including transport, clearance, and lipid trafficking. The exact role of Miro in these functions is unclear but involves binding to a variety of client proteins. How this binding is operated at the molecular level and whether and how it is important for mitochondrial health, however, remains unknown. Here, we show that known Miro interactors-namely, CENPF, Trak, and MYO19-all use a similar short motif to bind the same structural element: a highly conserved hydrophobic pocket in the first calcium-binding domain of Miro. Using these Miro-binding motifs, we identified direct interactors de novo, including MTFR1/2/1L, the lipid transporters Mdm34 and VPS13D, and the ubiquitin E3-ligase Parkin. Given the shared binding mechanism of these functionally diverse clients and its conservation across eukaryotes, we propose that Miro is a universal mitochondrial adaptor coordinating mitochondrial health.

Rewiring phospholipid biosynthesis reveals resilience to membrane perturbations and uncovers regulators of lipid homeostasis.

The organelles of eukaryotic cells differ in their membrane lipid composition. This heterogeneity is achieved by the localization of lipid synthesizing and modifying enzymes to specific compartments, as well as by intracellular lipid transport that utilizes vesicular and non-vesicular routes to ferry lipids from their place of synthesis to their destination. For instance, the major and essential phospholipids, phosphatidylethanolamine (PE) and phosphatidylcholine (PC), can be produced by multiple pathways and, in the case of PE, also at multiple locations. However, the molecular components that underlie lipid homeostasis as well as the routes allowing their distribution remain unclear. Here, we present an approach in which we simplify and rewire yeast phospholipid synthesis by redirecting PE and PC synthesis reactions to distinct subcellular locations using chimeric enzymes fused to specific organelle targeting motifs. In rewired conditions, viability is expected to depend on homeostatic adaptation to the ensuing lipostatic perturbations and on efficient interorganelle lipid transport. We therefore performed genetic screens to identify factors involved in both of these processes. Among the candidates identified, we find genes linked to transcriptional regulation of lipid homeostasis, lipid metabolism, and transport. In particular, we identify a requirement for Csf1-an uncharacterized protein harboring a Chorein-N lipid transport motif-for survival under certain rewired conditions as well as lipidomic adaptation to cold, implicating Csf1 in interorganelle lipid transport and homeostatic adaptation.

Csf1: A Putative Lipid Transport Protein Required for Homeoviscous Adaptation of the Lipidome.

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.

Accurate prediction of protein torsion angles using evolutionary signatures and recurrent neural network.

The amino acid sequence of a protein contains all the necessary information to specify its shape, which dictates its biological activities. However, it is challenging and expensive to experimentally determine the three-dimensional structure of proteins. The backbone torsion angles play a critical role in protein structure prediction, and accurately predicting the angles can considerably advance the tertiary structure prediction by accelerating efficient sampling of the large conformational space for low energy structures. Here we first time propose evolutionary signatures computed from protein sequence profiles, and a novel recurrent architecture, termed ESIDEN, that adopts a straightforward architecture of recurrent neural networks with a small number of learnable parameters. The ESIDEN can capture efficient information from both the classic and new features benefiting from different recurrent architectures in processing information. On the other hand, compared to widely used classic features, the new features, especially the Ramachandran basin potential, provide statistical and evolutionary information to improve prediction accuracy. On four widely used benchmark datasets, the ESIDEN significantly improves the accuracy in predicting the torsion angles by comparison to the best-so-far methods. As demonstrated in the present study, the predicted angles can be used as structural constraints to accurately infer protein tertiary structures. Moreover, the proposed features would pave the way to improve machine learning-based methods in protein folding and structure prediction, as well as function prediction. The source code and data are available at the website https://kornmann.bioch.ox.ac.uk/leri/resources/download.html .

Leri: A web-server for identifying protein functional networks from evolutionary couplings.

Information on the co-evolution of amino acid pairs in a protein can be used for endeavors such as protein engineering, mutation design, and structure prediction. Here we report a method that captures significant determinants of proteins using estimated co-evolution information to identify networks of residues, termed "residue communities", relevant to protein function. On the benchmark dataset (67 proteins with both catalytic and allosteric residues), the Pearson's correlation between the identified residues in the communities at functional sites is 0.53, and it is higher than 0.8 by taking account of conserved residues derived from the method. On the endoplasmic reticulum-mitochondria encounter structure complex, the results indicate three distinguishable residue communities that are relevant to functional roles in the protein family, suggesting that the residue communities could be general evolutionary signatures in proteins. Based on the method, we provide a webserver for the scientific community to explore the signatures in protein families, which establishes a powerful tool to analyze residue-level profiling for the discovery of functional sites and biological pathway identification. This web-server is freely available for non-commercial users at https://kornmann.bioch.ox.ac.uk/leri/services/ecs.html, neither login nor e-mail required.

Sibe: a computation tool to apply protein sequence statistics to predict folding and design in silico.

BACKGROUND: Evolutionary information contained in the amino acid sequences of proteins specifies the biological function and fold, but exactly what information contained in the protein sequence drives both of these processes? Considerable progress has been made to answer this fundamental question, but it remains challenging to explore the potential space of cooperative interactions between amino acids. Statistical analysis plays a significant role in studying such interactions and its use has expanded in recent years to studies ranging from coevolution-guided rational protein design to protein folding in silico. RESULTS: Here we describe a computational tool named Sibe for use in studies of protein sequence, folding, and design using evolutionary coupling between amino acids as a driving factor. In this study, Sibe is used to identify positionally conserved couplings between pairwise amino acids and aid rational protein design. In this process, pairwise couplings are filtered according to the relative entropy computed from the positional conservations and grouped into several 'blocks', which could contribute to driving protein folding and design. A human ß2-adrenergic receptor (ß2AR) was used to demonstrate that those 'blocks' contribute the rational design for specifying functional residues. Sibe also provides folding modules based on both the positionally conserved couplings and well-established statistical potentials for simulating protein folding in silico and predicting tertiary structure. Our results show that statistically inferences of basic evolutionary principles, such as conservations and coupled-mutations, can be used to rapidly design a diverse set of proteins and study protein folding. CONCLUSIONS: The developed software Sibe provides a computational tool for systematical analysis from protein primary to its tertiary structure using the evolutionary couplings as a driving factor. Sibe, written in C++, accounts for compatibility with the 'big data' era in biological science, and it primarily focuses on protein sequence analysis, but it is also applicable to extend to other modeling and predictions of experimental measurements.

De novo protein structure prediction using ultra-fast molecular dynamics simulation.

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.

A Nonhomogeneous Cuckoo Search Algorithm Based on Quantum Mechanism for Real Parameter Optimization.

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.

Protein folds recognized by an intelligent predictor based-on evolutionary and structural information.

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.

Hierarchical particle swarm optimizer for minimizing the non-convex potential energy of molecular structure.

The stable conformation of a molecule is greatly important to uncover the secret of its properties and functions. Generally, the conformation of a molecule will be the most stable when it is of the minimum potential energy. Accordingly, the determination of the conformation can be solved in the optimization framework. It is, however, not an easy task to achieve the only conformation with the lowest energy among all the potential ones because of the high complexity of the energy landscape and the exponential computation increasing with molecular size. In this paper, we develop a hierarchical and heterogeneous particle swarm optimizer (HHPSO) to deal with the problem in the minimization of the potential energy. The proposed method is evaluated over a scalable simplified molecular potential energy function with up to 200 degrees of freedom and a realistic energy function of pseudo-ethane molecule. The experimental results are compared with other six PSO variants and four genetic algorithms. The results show HHPSO is significantly better than the compared PSOs with p-value less than 0.01277 over molecular potential energy function.

Adaptive firefly algorithm: parameter analysis and its application.

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.