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Shehu A Mao J Gibori GB Halperin J Le J Devi YS Merrill B Kiyokawa H Gibori G 《Molecular endocrinology (Baltimore, Md.)》2008,22(10):2268-2277
Our laboratory has previously cloned and purified a protein named PRAP (prolactin receptor-associated protein) that was shown to be a novel 17beta-hydroxysteroid dehydrogenase (HSD) enzyme with dual activity. This enzyme, renamed HSD17B7 or PRAP/17beta-HSD7, converts estrone to estradiol and is also involved in cholesterol biosynthesis. The major site of its expression is the corpus luteum of a great number of species including rodents and humans. To examine the functional significance of HSD17B7 in pregnancy, we generated a knockout mouse model with targeted deletions of exons 1-4 of this gene. We anticipated a mouse with a severe fertility defect due to its inability to regulate estrogen levels during pregnancy. The heterozygous mutant mice are normal in their development and gross anatomy. The females cycle normally, and both male and female are fertile with normal litter size. To our surprise, the breeding of heterozygous mice yielded no viable HSD17B7 null mice. However, we found HSD17B7 null embryo alive in utero on d 8.5 and d 9.5. By d 10.5, the fetuses grow and suffer from severe brain malformation and heart defect. Because the brain depends on in situ cholesterol biosynthesis for its development beginning at d 10, the major cause of fetal death appears to be due to the cholesterol synthetic activity of this enzyme. By ablating HSD17B7 function, we have uncovered, in vivo, an important requirement for this enzyme during fetal development. 相似文献
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Characterizing protein flexibility is an important goal for understanding the physical-chemical principles governing biological function. This paper presents a Fragment Ensemble Method to capture the mobility of a protein fragment such as a missing loop and its extension into a Protein Ensemble Method to characterize the mobility of an entire protein at equilibrium. The underlying approach in both methods is to combine a geometric exploration of conformational space with a statistical mechanics formulation to generate an ensemble of physical conformations on which thermodynamic quantities can be measured as ensemble averages. The Fragment Ensemble Method is validated by applying it to characterize loop mobility in both instances of strongly stable and disordered loop fragments. In each instance, fluctuations measured over generated ensembles are consistent with data from experiment and simulation. The Protein Ensemble Method captures the mobility of an entire protein by generating and combining ensembles of conformations for consecutive overlapping fragments defined over the protein sequence. This method is validated by applying it to characterize flexibility in ubiquitin and protein G. Thermodynamic quantities measured over the ensembles generated for both proteins are fully consistent with available experimental data. On these proteins, the method recovers nontrivial data such as order parameters, residual dipolar couplings, and scalar couplings. Results presented in this work suggest that the proposed methods can provide insight into the interplay between protein flexibility and function. 相似文献
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Numerical solutions of the chemical master equation (CME) are important for understanding the stochasticity of biochemical systems. However, solving CMEs is a formidable task. This task is complicated due to the nonlinear nature of the reactions and the size of the networks which result in different realizations. Most importantly, the exponential growth of the size of the state-space, with respect to the number of different species in the system makes this a challenging assignment. When the biochemical system has a large number of variables, the CME solution becomes intractable. We introduce the intelligent state projection (ISP) method to use in the stochastic analysis of these systems. For any biochemical reaction network, it is important to capture more than one moment: this allows one to describe the system’s dynamic behaviour. ISP is based on a state-space search and the data structure standards of artificial intelligence (AI). It can be used to explore and update the states of a biochemical system. To support the expansion in ISP, we also develop a Bayesian likelihood node projection (BLNP) function to predict the likelihood of the states. To demonstrate the acceptability and effectiveness of our method, we apply the ISP method to several biological models discussed in prior literature. The results of our computational experiments reveal that the ISP method is effective both in terms of the speed and accuracy of the expansion, and the accuracy of the solution. This method also provides a better understanding of the state-space of the system in terms of blueprint patterns. The ISP is the de-novo method which addresses both accuracy and performance problems for CME solutions. It systematically expands the projection space based on predefined inputs. This ensures accuracy in the approximation and an exact analytical solution for the time of interest. The ISP was more effective both in predicting the behavior of the state-space of the system and in performance management, which is a vital step towards modeling large biochemical systems. 相似文献
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The three-dimensional structure of a protein is a key determinant of its biological function. Given the cost and time required to acquire this structure through experimental means, computational models are necessary to complement wet-lab efforts. Many computational techniques exist for navigating the high-dimensional protein conformational search space, which is explored for low-energy conformations that comprise a protein's native states. This work proposes two strategies to enhance the sampling of conformations near the native state. An enhanced fragment library with greater structural diversity is used to expand the search space in the context of fragment-based assembly. To manage the increased complexity of the search space, only a representative subset of the sampled conformations is retained to further guide the search towards the native state. Our results make the case that these two strategies greatly enhance the sampling of the conformational space near the native state. A detailed comparative analysis shows that our approach performs as well as state-of-the-art ab initio structure prediction protocols. 相似文献
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Background
Despite computational challenges, elucidating conformations that a protein system assumes under physiologic conditions for the purpose of biological activity is a central problem in computational structural biology. While these conformations are associated with low energies in the energy surface that underlies the protein conformational space, few existing conformational search algorithms focus on explicitly sampling low-energy local minima in the protein energy surface.Methods
This work proposes a novel probabilistic search framework, PLOW, that explicitly samples low-energy local minima in the protein energy surface. The framework combines algorithmic ingredients from evolutionary computation and computational structural biology to effectively explore the subspace of local minima. A greedy local search maps a conformation sampled in conformational space to a nearby local minimum. A perturbation move jumps out of a local minimum to obtain a new starting conformation for the greedy local search. The process repeats in an iterative fashion, resulting in a trajectory-based exploration of the subspace of local minima.Results and conclusions
The analysis of PLOW's performance shows that, by navigating only the subspace of local minima, PLOW is able to sample conformations near a protein's native structure, either more effectively or as well as state-of-the-art methods that focus on reproducing the native structure for a protein system. Analysis of the actual subspace of local minima shows that PLOW samples this subspace more effectively that a naive sampling approach. Additional theoretical analysis reveals that the perturbation function employed by PLOW is key to its ability to sample a diverse set of low-energy conformations. This analysis also suggests directions for further research and novel applications for the proposed framework.29.
Y Sangeeta Devi Aurora Shehu Julia Halperin Carlos Stocco Jamie Le Anita M Seibold Geula Gibori 《Reproductive biology and endocrinology : RB&E》2009,7(1):1-12
Background
Externalization of phosphatidylserine (EPS) occurs in apoptotic-like spermatozoa and could be used to remove them from sperm preparations to enhance sperm quality for assisted medical procreation. We first characterized EPS in sperms from infertile patients in terms of frequency of EPS spermatozoa as well as localization of phosphatidylserine (PS) on spermatozoa. Subsequently, we determined the impact of depleting EPS spermatozoa on sperm quality. 相似文献30.