End-of-life modelling of buildings to support more informed decisions towards achieving circular economy targets

The International Journal of Life Cycle Assessment - Life cycle assessment (LCA) is an internationally accepted method to assess the environmental impacts of buildings. A major methodological...     

Evolutionarily conserved motifs and modules in mitochondrial protein–protein interaction networks

Advances in organelle interactomics have led to new insights into organelle functions. In this study, we considered the common mitochondrial PIN of four evolutionarily distant eukaryotic species, namely Homo sapiens, Mus musculus, Drosophila melanogaster and Caenorhabditis elegans. By comparative interactomics analysis of mitochondrial PINs in these organisms, five conserved modules were identified. Modules comprise the main mitochondrial tasks, including proteins involved in translation process, mitochondrial import inner membrane proteins, TCA cycle enzymes, mitochondrial electron transport chain, and metabolic enzymes. Furthermore, we reemphasize that subgraphs of network, i.e., motifs and themes, may represent evolutionarily conserved topological units which are biologically significant.     

Nanosized paclitaxel-loaded niosomes: formulation,in vitro cytotoxicity,and apoptosis gene expression in breast cancer cell lines

Molecular Biology Reports - In this study, the optimized niosomal formulation containing paclitaxel using non-ionic surfactants and cholesterol was designed and its cytotoxic effects against...     

Delaunay‐based nonlocal interactions are sufficient and accurate in protein fold recognition

This study is aimed at showing that considering only nonlocal interactions (interactions of two atoms with a sequence separation larger than five amino acids) extracted using Delaunay tessellation is sufficient and accurate for protein fold recognition. An atomic knowledge‐based potential was extracted based on a Delaunay tessellation with 167 atom types from a sample of the native structures and the normalized energy was calculated for only nonlocal interactions in each structure. The performance of this method was tested on several decoy sets and compared to a method considering all interactions extracted by Delaunay tessellation and three other popular scoring functions. Features such as the contents of different types of interactions and atoms with the highest number of interactions were also studied. The results suggest that considering only nonlocal interactions in a Delaunay tessellation of protein structure is a discrete structure catching deep properties of the three‐dimensional protein data. Proteins 2014; 82:415–423. © 2013 Wiley Periodicals, Inc.     

Discrimination power of knowledge-based potential dictated by the dominant energies in native protein structures

Amino Acids - Extracting a well-designed energy function is important for protein structure evaluation. Knowledge-based potential functions are one type of the energy functions which can be...     

A distance‐dependent atomic knowledge‐based potential and force for discrimination of native structures from decoys

The purpose of this article is to introduce a novel model for discriminating correctly folded proteins from well designed decoy structures using mechanical interatomic forces. In our model, we consider a protein as a collection of springs and the force imposed to each atom is calculated. A potential function is obtained from statistical contact preferences within known protein structures. Combining this function with the spring equation, the interatomic forces are calculated. Finally, we consider a structure and define a score function on the 3D structure of a protein. We compare the force imposed to each atom of a protein with the corresponding atom in the other structures. We then assign larger scores to those atoms with lower forces. The total score is the sum of partial scores of atoms. The optimal structure is assumed to be the one with the highest score in the data set. To evaluate the performance of our model, we apply it on several decoy sets. Proteins 2009. © 2009 Wiley‐Liss, Inc.     

A systematic survey of centrality measures for protein-protein interaction networks

Background

Numerous centrality measures have been introduced to identify “central” nodes in large networks. The availability of a wide range of measures for ranking influential nodes leaves the user to decide which measure may best suit the analysis of a given network. The choice of a suitable measure is furthermore complicated by the impact of the network topology on ranking influential nodes by centrality measures. To approach this problem systematically, we examined the centrality profile of nodes of yeast protein-protein interaction networks (PPINs) in order to detect which centrality measure is succeeding in predicting influential proteins. We studied how different topological network features are reflected in a large set of commonly used centrality measures.

Results

We used yeast PPINs to compare 27 common of centrality measures. The measures characterize and assort influential nodes of the networks. We applied principal component analysis (PCA) and hierarchical clustering and found that the most informative measures depend on the network’s topology. Interestingly, some measures had a high level of contribution in comparison to others in all PPINs, namely Latora closeness, Decay, Lin, Freeman closeness, Diffusion, Residual closeness and Average distance centralities.

Conclusions

The choice of a suitable set of centrality measures is crucial for inferring important functional properties of a network. We concluded that undertaking data reduction using unsupervised machine learning methods helps to choose appropriate variables (centrality measures). Hence, we proposed identifying the contribution proportions of the centrality measures with PCA as a prerequisite step of network analysis before inferring functional consequences, e.g., essentiality of a node.

     

Hydrophobic residues can identify native protein structures

Evaluation of protein structures needs a trustworthy potential function. Although several knowledge‐based potential functions exist, the impact of different types of amino acids in the scoring functions has not been studied yet. Previously, we have reported the importance of nonlocal interactions in scoring function (based on Delaunay tessellation) in discrimination of native structures. Then, we have questioned the structural impact of hydrophobic amino acids in protein fold recognition. Therefore, a Hydrophobic Reduced Model (HRM) was designed to reduce protein structure of FS (Full Structure) into RS (Reduced Structure). RS is considered as a reduced structure of only seven hydrophobic amino acids (L, V, F, I, A, W, Y) and all their interactions. The presented model was evaluated via four different performance metrics including the number of correctly identified natives, the Z‐score of the native energy, the RMSD of the minimum score, and the Pearson correlation coefficient between the energy and the model quality. Results indicated that only nonlocal interactions between hydrophobic amino acids could be sufficient and accurate enough for protein fold recognition. Interestingly, the results of HRM is significantly close to the model that considers all amino acids (20‐amino acid model) to discriminate the native structure of the proteins on eleven decoy sets. This indicates that the power of knowledge‐based potential functions in protein fold recognition is mostly due to hydrophobic interactions. Hence, we suggest combining a different well‐designed scoring function for non‐hydrophobic interactions with HRM to achieve better performance in fold recognition.