Alternating evolutionary pressure in a genetic algorithm facilitates protein model selection

<p>Abstract</p> <p>Background</p> <p>Automatic protein modelling pipelines are becoming ever more accurate; this has come hand in hand with an increasingly complicated interplay between all components involved. Nevertheless, there are still potential improvements to be made in template selection, refinement and protein model selection.</p> <p>Results</p> <p>In the context of an automatic modelling pipeline, we analysed each step separately, revealing several non-intuitive trends and explored a new strategy for protein conformation sampling using Genetic Algorithms (GA). We apply the concept of alternating evolutionary pressure (AEP), i.e. intermediate rounds within the GA runs where unrestrained, linear growth of the model populations is allowed.</p> <p>Conclusion</p> <p>This approach improves the overall performance of the GA by allowing models to overcome local energy barriers. AEP enabled the selection of the best models in 40% of all targets; compared to 25% for a normal GA.</p

No statistical support for correlation between the positions of protein interaction sites and alternatively spliced regions

BACKGROUND: Alternative splicing is an efficient mechanism for increasing the variety of functions fulfilled by proteins in a living cell. It has been previously demonstrated that alternatively spliced regions often comprise functionally important and conserved sequence motifs. The objective of this work was to test the hypothesis that alternative splicing is correlated with contact regions of protein-protein interactions. RESULTS: Protein sequence spans involved in contacts with an interaction partner were delineated from atomic structures of transient interaction complexes and juxtaposed with the location of alternatively spliced regions detected by comparative genome analysis and spliced alignment. The total of 42 alternatively spliced isoforms were identified in 21 amino acid chains involved in biomolecular interactions. Using this limited dataset and a variety of sophisticated counting procedures we were not able to establish a statistically significant correlation between the positions of protein interaction sites and alternatively spliced regions. CONCLUSIONS: This finding contradicts a naïve hypothesis that alternatively spliced regions would correlate with points of contact. One possible explanation for that could be that all alternative splicing events change the spatial structure of the interacting domain to a sufficient degree to preclude interaction. This is indirectly supported by the observed lack of difference in the behaviour of relatively short regions affected by alternative splicing and cases when large portions of proteins are removed. More structural data on complexes of interacting proteins, including structures of alternative isoforms, are needed to test this conjecture

A dyad of lymphoblastic lysosomal cysteine proteases degrades the antileukemic drug L-asparaginase

l-Asparaginase is a key therapeutic agent for treatment of childhood acute lymphoblastic leukemia (ALL). There is wide individual variation in pharmacokinetics, and little is known about its metabolism. The mechanisms of therapeutic failure with l-asparaginase remain speculative. Here, we now report that 2 lysosomal cysteine proteases present in lymphoblasts are able to degrade l-asparaginase. Cathepsin B (CTSB), which is produced constitutively by normal and leukemic cells, degraded asparaginase produced by Escherichia coli (ASNase) and Erwinia chrysanthemi. Asparaginyl endopeptidase (AEP), which is overexpressed predominantly in high-risk subsets of ALL, specifically degraded ASNase. AEP thereby destroys ASNase activity and may also potentiate antigen processing, leading to allergic reactions. Using AEP-mediated cleavage sequences, we modeled the effects of the protease on ASNase and created a number of recombinant ASNase products. The N24 residue on the flexible active loop was identified as the primary AEP cleavage site. Sole modification at this site rendered ASNase resistant to AEP cleavage and suggested a key role for the flexible active loop in determining ASNase activity. We therefore propose what we believe to be a novel mechanism of drug resistance to ASNase. Our results may help to identify alternative therapeutic strategies with the potential of further improving outcome in childhood ALL

Alternating evolutionary pressure in a genetic algorithm facilitates protein model selection-3

Rgy basins. A relatively small change in structural conformation can have a marked effect on a model's energy; SC scores are given along the energy landscape (top and bottom). A curved representation is chosen to highlight the three-dimensional nature of energy landscapes. Dark circles are conformations that are scored; light grey circles are non-scored, intermediate, conformations. In case (a) one non-scored intermediate conformation is needed to climb a small energy barrier. A more difficult case is shown in (b), where a maximum of two consecutive intermediate conformations are required, before energy evaluation. Case (c) shows a scenario where no intermediate conformation is required.<p><b>Copyright information:</b></p><p>Taken from "Alternating evolutionary pressure in a genetic algorithm facilitates protein model selection"</p><p>http://www.biomedcentral.com/1472-6807/8/34</p><p>BMC Structural Biology 2008;8():34-34.</p><p>Published online 1 Aug 2008</p><p>PMCID:PMC2527322.</p><p></p

Alternating evolutionary pressure in a genetic algorithm facilitates protein model selection-5

Ned by symbols, ordered from bottom to top. The following symbols are given: R – repair, M – minimise, FSF – selection with FSF, FINE – selection with the fine energy function, COM – model selection with the combined energy score, DFIRE – selection using the DFIRE energy function, AVG – selecting the centre of the clustered population, BEST – selecting the best model, GA – application of the GA, GA AEP – application of the GA using alternating evolutionary pressure and GA OLD – application of the previously described GA protocol. The best pipeline uses the AEP2 GA for sampling and the final models are selected using DFIRE.<p><b>Copyright information:</b></p><p>Taken from "Alternating evolutionary pressure in a genetic algorithm facilitates protein model selection"</p><p>http://www.biomedcentral.com/1472-6807/8/34</p><p>BMC Structural Biology 2008;8():34-34.</p><p>Published online 1 Aug 2008</p><p>PMCID:PMC2527322.</p><p></p

Alternating evolutionary pressure in a genetic algorithm facilitates protein model selection-0

decreased SC score after backbone repair. Improved models have a positive value. The distribution is shifted towards model improvement. In the insets, it can be seen that more than 80% of improvement lies within helical regions. Most of the improvement is situated in the protein core, the region between the two terminal elements.<p><b>Copyright information:</b></p><p>Taken from "Alternating evolutionary pressure in a genetic algorithm facilitates protein model selection"</p><p>http://www.biomedcentral.com/1472-6807/8/34</p><p>BMC Structural Biology 2008;8():34-34.</p><p>Published online 1 Aug 2008</p><p>PMCID:PMC2527322.</p><p></p

Alternating evolutionary pressure in a genetic algorithm facilitates protein model selection-7

decreased SC score after backbone repair. Improved models have a positive value. The distribution is shifted towards model improvement. In the insets, it can be seen that more than 80% of improvement lies within helical regions. Most of the improvement is situated in the protein core, the region between the two terminal elements.<p><b>Copyright information:</b></p><p>Taken from "Alternating evolutionary pressure in a genetic algorithm facilitates protein model selection"</p><p>http://www.biomedcentral.com/1472-6807/8/34</p><p>BMC Structural Biology 2008;8():34-34.</p><p>Published online 1 Aug 2008</p><p>PMCID:PMC2527322.</p><p></p

Alternating evolutionary pressure in a genetic algorithm facilitates protein model selection-4

Ored rounds, of four representative targets: T0300 (coarse), T0313 (fine), T0329 (fine) and T0353 (coarse). This is done to show the distribution of the models' coarse and fine energy scores; however, for all four cases both energy scores are used for ranking, as described in the methods. For the two coarse energy plots the corresponding energy funnel for the fine scoring scheme is also not particularly well defined, however, certain trends are easier to observe by plotting the coarse scoring scheme for these cases (see text). AEP0 (standard GA) is coloured black, AEP1 red, AEP2 green and AEP3 blue. For all four cases the energy of the native structure is shown as a red dash on the right y-axis of each graph. In the case of T0300 the energy of the native structure is much higher than a large proportion of the models. This can explained due to a particularly poor agreement between predicted and native secondary structure.<p><b>Copyright information:</b></p><p>Taken from "Alternating evolutionary pressure in a genetic algorithm facilitates protein model selection"</p><p>http://www.biomedcentral.com/1472-6807/8/34</p><p>BMC Structural Biology 2008;8():34-34.</p><p>Published online 1 Aug 2008</p><p>PMCID:PMC2527322.</p><p></p

Alternating evolutionary pressure in a genetic algorithm facilitates protein model selection-6

Res are plotted against the protein size. Models are shown as black stars. As a control, the distribution of X-ray (light) and NMR (dark) structures from the RCSB Protein Data Bank are shown. This plot has been adapted from the results of the ProSA web server [].<p><b>Copyright information:</b></p><p>Taken from "Alternating evolutionary pressure in a genetic algorithm facilitates protein model selection"</p><p>http://www.biomedcentral.com/1472-6807/8/34</p><p>BMC Structural Biology 2008;8():34-34.</p><p>Published online 1 Aug 2008</p><p>PMCID:PMC2527322.</p><p></p

Alternating evolutionary pressure in a genetic algorithm facilitates protein model selection-2

Average of all model distributions are given for repaired (R) and non-repaired (NR) models. The population for the initial, pre-GA models is broad and lies well below the distribution for the post-GA populations. Repaired models show only small improvement for the pre-GA and post-GA energy based model ensembles. However, a clear advantage can be seen once the GA which is directly driven towards the native structure is applied. The populations for the energy driven GA runs can be seen in more detailed in the graph inset. Here, it is also shown how well good models can be selected using different energy functions. These scores are for the averages of the SC scores for the lowest-energy model of each target. The energy functions used are the combined (red dot), the coarse (green diamond), the fine (purple filled square) and DFIRE (blue empty square) for the standard GA and AEP1-3. As the combined energy and DFIRE score has been found to produce the best results, the other scores are not shown for the AEP. The best model selection is seen for AEP2 which has a narrower population distribution with some good individual outliers. The distribution for AEP3 shows a drop in good models; furthermore, a decreased ability to select good models is shown.<p><b>Copyright information:</b></p><p>Taken from "Alternating evolutionary pressure in a genetic algorithm facilitates protein model selection"</p><p>http://www.biomedcentral.com/1472-6807/8/34</p><p>BMC Structural Biology 2008;8():34-34.</p><p>Published online 1 Aug 2008</p><p>PMCID:PMC2527322.</p><p></p