Do topological parameters of amino acids within protein contact networks depend on their physico-chemical properties?

The three dimensional structure of a protein is determined by the interactions of its constituent amino acids. Considering the amino acids as nodes and the non-bonded interactions among them in 3D space as edges, researchers have constructed protein contact networks and analyzed the values of several topological parameters to uncover different important aspects of proteins. Here, we have analyzed some of the topological parameters such as degree, strength, clustering coefficients, betweenness and closeness centrality of each of the twenty amino acids in a set of non-redundant proteins covering all classes and folds. The results show that the values of these topological parameters vary widely with different amino acids. Also, these values differ significantly with different length scales of proteins. Most of the hydrophobic residues along with Cys, Arg and His have larger contributions to the long range connectivities than short range. We have also studied whether the values of topological parameters have any significant dependency on the physico-chemical properties of the amino acids. While the clustering coefficients show a strong negative correlation with residual volumes, surface areas and number of atoms in the side chains of amino acids; the degrees, strengths and betweenness show positive correlations with the mentioned properties. All the topological parameters show high dependency on bulkiness and average area buried of the amino acid residues in all-range residue networks. The average degree shows higher dependency on hydrophobicity, while the average strength is more able to capture the essences of surface area, residual volume and number of atoms of amino acids. The hydrophobicities of the amino acids and their corresponding degrees show a higher positive correlation in long range networks (LRNs) than short range networks (SRNs). The closeness centrality shows high correlation with two hydrophobic scales and no correlation with surface area, residual volume or number of atoms in LRNs. We have further explored the relationship in hydrophobic, hydrophilic and charged residues separately. Interestingly, charged residues show a higher dependency on the number of atoms than their residual volumes and surface areas. Finally, we present a linear regression model relating the network parameters with physico-chemical properties of amino acids.     

What Do These Correlations Know about Reality? Nonlocality and the Absurd

In honor of Daniel Greenberger's 65th birthday, I record for posterity two superb examples of his wit, offer a proof of an important theorem on quantum correlations that even those of us over 60 can understand, and suggest, by trying to make it look silly, that invoking quantum nonlocality as an explanation for such correlations may be too cheap a way out of the dilemma they pose.     

Do we know the mass of a black hole? Mass of some cosmological black hole models

Using a cosmological black hole model proposed recently, we have calculated the quasi-local mass of a collapsing structure within a cosmological setting due to different definitions put forward in the last decades to see how similar or different they are. It has been shown that the mass within the horizon follows the familiar Brown–York behavior. It increases, however, outside the horizon again after a short decrease, in contrast to the Schwarzschild case. Further away, near the void, outside the collapsed region, and where the density reaches the background minimum, all the mass definitions roughly coincide. They differ, however, substantially far from it. Generically, we are faced with three different Brown–York mass maxima: near the horizon, around the void between the overdensity region and the background, and another at cosmological distances corresponding to the cosmological horizon. While the latter two maxima are always present, the horizon mass maxima is absent before the onset of the central singularity.     

Toward the computer-aided discovery of FabH inhibitors. Do predictive QSAR models ensure high quality virtual screening performance?

Antibiotic resistance has increased over the past two decades. New approaches for the discovery of novel antibacterials are required and innovative strategies will be necessary to identify novel and effective candidates. Related to this problem, the exploration of bacterial targets that remain unexploited by the current antibiotics in clinical use is required. One of such targets is the \(\beta \) -ketoacyl-acyl carrier protein synthase III (FabH). Here, we report a ligand-based modeling methodology for the virtual-screening of large collections of chemical compounds in the search of potential FabH inhibitors. QSAR models are developed for a diverse dataset of 296 FabH inhibitors using an in-house modeling framework. All models showed high fitting, robustness, and generalization capabilities. We further investigated the performance of the developed models in a virtual screening scenario. To carry out this investigation, we implemented a desirability-based algorithm for decoys selection that was shown effective in the selection of high quality decoys sets. Once the QSAR models were validated in the context of a virtual screening experiment their limitations arise. For this reason, we explored the potential of ensemble modeling to overcome the limitations associated to the use of single classifiers. Through a detailed evaluation of the virtual screening performance of ensemble models it was evidenced, for the first time to our knowledge, the benefits of this approach in a virtual screening scenario. From all the obtained results, we could arrive to a significant main conclusion: at least for FabH inhibitors, virtual screening performance is not guaranteed by predictive QSAR models.