Can Magnetic Dipole Transition Moment Be Engineered?

The development of chiral compounds with enhanced chiroptical properties is an important challenge to improve device applications. To that end, an optimization of the electric and magnetic dipole transition moments of the molecule is necessary. Nevertheless, the relationship between chemical structure and such quantum mechanical properties is not always clear. That is the case of magnetic dipole transition moment (m) for which no general trends for its optimization have been suggested. In this work we propose a general rationalization for improving the magnitude of m in different families of chiral compounds. Performing a clustering analysis of hundreds of transitions, we have been able to identify a single group in which |m| value is maximized along the helix axis. More interestingly, we have found an accurate linear relationship (up to R2 =0.994) between the maximum value of this parameter and the area of the inner cavity of the helix, thus resembling classical behavior of solenoids. This research provides a tool for the rationalized synthesis of compounds with improved chiroptical responses.

Fuzzy Models and Resolution Methods for Covering Location Problems: an Annotated Bibliography

In this paper, the literature associated with the covering location problems addressing uncertainty under a fuzzy approach is reviewed. Specifically, the papers related to the most commonly applied models such as set covering location problem, maximal covering location problem, and hub covering location problem are examined. An annotated bibliography is presented in which such papers have been classified according to the following criteria: the fuzzy items considered in the proposed model, the type of problem addressed, the fuzzy approach applied, the method of resolution, and field of application considered. This research provides useful information that helps to identify some opportunities for the application of fuzzy approaches to the covering location problems.

Cluster-based comparison of the peptide mass fingerprint obtained by MALDI-TOF mass spectrometry. A case study: long-term stability of rituximab

We evaluated the use of the peptide mass fingerprint (PMF) obtained by matrix assisted laser desorption and ionization (MALDI) time-of-flight mass spectrometry (TOF-MS) to track changes in the structure of a protein. The first problem we had to overcome was the inherent complexity of the PMF, which makes it difficult to compare. We dealt with this problem by developing a cluster-based comparison algorithm which takes into account the proportional error made by the mass spectrometer. This procedure involves grouping together similar masses in an intelligent manner, so that we can determine which data correspond to the same peptide (any slight differences can be explained as experimental errors), and which of them are too different and thus more likely to represent different peptides. The proposed algorithm was applied to track changes in a commercially available monoclonal antibody (mAb), namely rituximab (RTX), prepared under the usual hospital conditions and stored refrigerated (4 °C) and frozen (-20 °C) for a long term study. PMFs were obtained periodically over three months. For each checked time, five replicates of the PMFs were obtained in order to evaluate the similarities between them by means of the occurrences of the particular peptides (m/z). After applying the algorithm to the PMF, different approaches were used to analyse the results. Surprisingly, all of them suggested that there were no differences between the two storage conditions tested, i.e. the RTX samples were almost equally well preserved when stored refrigerated at 4 °C or frozen at -20 °C. The cluster-based methodology is new in protein mass spectrometry and could be useful as an easy test for major changes in proteins and biopharmaceutics for diverse applications in industry and other fields, and could provide additional stability data in relation to the practical use of anticancer drugs.

Attractor metabolic networks

BACKGROUND

The experimental observations and numerical studies with dissipative metabolic networks have shown that cellular enzymatic activity self-organizes spontaneously leading to the emergence of a Systemic Metabolic Structure in the cell, characterized by a set of different enzymatic reactions always locked into active states (metabolic core) while the rest of the catalytic processes are only intermittently active. This global metabolic structure was verified for Escherichia coli, Helicobacter pylori and Saccharomyces cerevisiae, and it seems to be a common key feature to all cellular organisms. In concordance with these observations, the cell can be considered a complex metabolic network which mainly integrates a large ensemble of self-organized multienzymatic complexes interconnected by substrate fluxes and regulatory signals, where multiple autonomous oscillatory and quasi-stationary catalytic patterns simultaneously emerge. The network adjusts the internal metabolic activities to the external change by means of flux plasticity and structural plasticity.

METHODOLOGY/PRINCIPAL FINDINGS

In order to research the systemic mechanisms involved in the regulation of the cellular enzymatic activity we have studied different catalytic activities of a dissipative metabolic network under different external stimuli. The emergent biochemical data have been analysed using statistical mechanic tools, studying some macroscopic properties such as the global information and the energy of the system. We have also obtained an equivalent Hopfield network using a Boltzmann machine. Our main result shows that the dissipative metabolic network can behave as an attractor metabolic network.

CONCLUSIONS/SIGNIFICANCE

We have found that the systemic enzymatic activities are governed by attractors with capacity to store functional metabolic patterns which can be correctly recovered from specific input stimuli. The network attractors regulate the catalytic patterns, modify the efficiency in the connection between the multienzymatic complexes, and stably retain these modifications. Here for the first time, we have introduced the general concept of attractor metabolic network, in which this dynamic behavior is observed.

A simple and fast heuristic for protein structure comparison

Background

Protein structure comparison is a key problem in bioinformatics. There exist several methods for doing protein comparison, being the solution of the Maximum Contact Map Overlap problem (MAX-CMO) one of the alternatives available. Although this problem may be solved using exact algorithms, researchers require approximate algorithms that obtain good quality solutions using less computational resources than the formers.

Results

We propose a variable neighborhood search metaheuristic for solving MAX-CMO. We analyze this strategy in two aspects: 1) from an optimization point of view the strategy is tested on two different datasets, obtaining an error of 3.5%(over 2702 pairs) and 1.7% (over 161 pairs) with respect to optimal values; thus leading to high accurate solutions in a simpler and less expensive way than exact algorithms; 2) in terms of protein structure classification, we conduct experiments on three datasets and show that is feasible to detect structural similarities at SCOP's family and CATH's architecture levels using normalized overlap values. Some limitations and the role of normalization are outlined for doing classification at SCOP's fold level.

Conclusion

We designed, implemented and tested.a new tool for solving MAX-CMO, based on a well-known metaheuristic technique. The good balance between solution's quality and computational effort makes it a valuable tool. Moreover, to the best of our knowledge, this is the first time the MAX-CMO measure is tested at SCOP's fold and CATH's architecture levels with encouraging results.

Software is available for download at .

Measuring the similarity of protein structures by means of the universal similarity metric

MOTIVATION

As an increasing number of protein structures become available, the need for algorithms that can quantify the similarity between protein structures increases as well. Thus, the comparison of proteins' structures, and their clustering accordingly to a given similarity measure, is at the core of today's biomedical research. In this paper, we show how an algorithmic information theory inspired Universal Similarity Metric (USM) can be used to calculate similarities between protein pairs. The method, besides being theoretically supported, is surprisingly simple to implement and computationally efficient.

RESULTS

Structural similarity between proteins in four different datasets was measured using the USM. The sample employed represented alpha, beta, alpha-beta, tim-barrel, globins and serpine protein types. The use of the proposed metric allows for a correct measurement of similarity and classification of the proteins in the four datasets.

AVAILABILITY

All the scripts and programs used for the preparation of this paper are available at http://www.cs.nott.ac.uk/~nxk/USM/protocol.html. In that web-page the reader will find a brief description on how to use the various scripts and programs.