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 http://modo.ugr.es/jrgonzalez/msvns4maxcmo webcite.This work is supported by Projects HeuriCosc TIN2005-08404-C04-01, HeuriCode TIN2005-08404-C04-03, both from the Spanish Ministry of Education and Science. JRG acknowledges financial support from Project TIC2002-04242-C03-02. Authors thank N. Krasnogor and ProCKSi project (BB/C511764/1) for their support

Drive counts as a method of estimating ungulate density in forests: mission impossible?

Although drive counts are frequently used to estimate the size of deer populations in forests, little is known about how counting methods or the density and social organization of the deer species concerned influence the accuracy of the estimates obtained, and hence their suitability for informing management decisions. As these issues cannot readily be examined for real populations, we conducted a series of ‘virtual experiments’ in a computer simulation model to evaluate the effects of block size, proportion of forest counted, deer density, social aggregation and spatial auto-correlation on the accuracy of drive counts. Simulated populations of red and roe deer were generated on the basis of drive count data obtained from Polish commercial forests. For both deer species, count accuracy increased with increasing density, and decreased as the degree of aggregation, either demographic or spatial, within the population increased. However, the effect of density on accuracy was substantially greater than the effect of aggregation. Although improvements in accuracy could be made by reducing the size of counting blocks for low-density, aggregated populations, these were limited. Increasing the proportion of the forest counted led to greater improvements in accuracy, but the gains were limited compared with the increase in effort required. If it is necessary to estimate the deer population with a high degree of accuracy (e.g. within 10% of the true value), drive counts are likely to be inadequate whatever the deer density. However, if a lower level of accuracy (within 20% or more) is acceptable, our study suggests that at higher deer densities (more than ca. five to seven deer/100 ha) drive counts can provide reliable information on population size

Maximizing conservation evaluation utilization

Evaluation utilization in conservation management emphasizes the use of ‘appropriate’ information from the perspective of an expert provider. An alternative is to emphasize the information needs of recipients. Doing so ensures evaluation information is relevant to expected users and uses. The authors worked with an Australian conservation management agency to address barriers associated with engagement and ensuring relevance to the recipient’s sphere of decision-making. Workshop feedback demonstrated that the process increased the perceived value of the evaluation information. Managers’ reflections on the workshops emphasized factors relating to the decision-making context and the importance of being able to interact with the information in a constructive and non-threatening environment. Identified uses for evaluation information expanded as a result of the workshops to include newfound applications in strategy development and planning, park-specific responses and resource allocation