Four small puzzles that Rosetta doesn't solve

A complete macromolecule modeling package must be able to solve the simplest structure prediction problems. Despite recent successes in high resolution structure modeling and design, the Rosetta software suite fares poorly on deceptively small protein and RNA puzzles, some as small as four residues. To illustrate these problems, this manuscript presents extensive Rosetta results for four well-defined test cases: the 20-residue mini-protein Trp cage, an even smaller disulfide-stabilized conotoxin, the reactive loop of a serine protease inhibitor, and a UUCG RNA tetraloop. In contrast to previous Rosetta studies, several lines of evidence indicate that conformational sampling is not the major bottleneck in modeling these small systems. Instead, approximations and omissions in the Rosetta all-atom energy function currently preclude discriminating experimentally observed conformations from de novo models at atomic resolution. These molecular "puzzles" should serve as useful model systems for developers wishing to make foundational improvements to this powerful modeling suite.Comment: Published in PLoS One as a manuscript for the RosettaCon 2010 Special Collectio

Calibur: a tool for clustering large numbers of protein decoys

<p>Abstract</p> <p>Background</p> <p>Ab initio protein structure prediction methods generate numerous structural candidates, which are referred to as decoys. The decoy with the most number of neighbors of up to a threshold distance is typically identified as the most representative decoy. However, the clustering of decoys needed for this criterion involves computations with runtimes that are at best quadratic in the number of decoys. As a result currently there is no tool that is designed to exactly cluster very large numbers of decoys, thus creating a bottleneck in the analysis.</p> <p>Results</p> <p>Using three strategies aimed at enhancing performance (proximate decoys organization, preliminary screening via lower and upper bounds, outliers filtering) we designed and implemented a software tool for clustering decoys called Calibur. We show empirical results indicating the effectiveness of each of the strategies employed. The strategies are further fine-tuned according to their effectiveness.</p> <p>Calibur demonstrated the ability to scale well with respect to increases in the number of decoys. For a sample size of approximately 30 thousand decoys, Calibur completed the analysis in one third of the time required when the strategies are not used.</p> <p>For practical use Calibur is able to automatically discover from the input decoys a suitable threshold distance for clustering. Several methods for this discovery are implemented in Calibur, where by default a very fast one is used. Using the default method Calibur reported relatively good decoys in our tests.</p> <p>Conclusions</p> <p>Calibur's ability to handle very large protein decoy sets makes it a useful tool for clustering decoys in ab initio protein structure prediction. As the number of decoys generated in these methods increases, we believe Calibur will come in important for progress in the field.</p

Potentials of Mean Force for Protein Structure Prediction Vindicated, Formalized and Generalized

Understanding protein structure is of crucial importance in science, medicine and biotechnology. For about two decades, knowledge based potentials based on pairwise distances -- so-called "potentials of mean force" (PMFs) -- have been center stage in the prediction and design of protein structure and the simulation of protein folding. However, the validity, scope and limitations of these potentials are still vigorously debated and disputed, and the optimal choice of the reference state -- a necessary component of these potentials -- is an unsolved problem. PMFs are loosely justified by analogy to the reversible work theorem in statistical physics, or by a statistical argument based on a likelihood function. Both justifications are insightful but leave many questions unanswered. Here, we show for the first time that PMFs can be seen as approximations to quantities that do have a rigorous probabilistic justification: they naturally arise when probability distributions over different features of proteins need to be combined. We call these quantities reference ratio distributions deriving from the application of the reference ratio method. This new view is not only of theoretical relevance, but leads to many insights that are of direct practical use: the reference state is uniquely defined and does not require external physical insights; the approach can be generalized beyond pairwise distances to arbitrary features of protein structure; and it becomes clear for which purposes the use of these quantities is justified. We illustrate these insights with two applications, involving the radius of gyration and hydrogen bonding. In the latter case, we also show how the reference ratio method can be iteratively applied to sculpt an energy funnel. Our results considerably increase the understanding and scope of energy functions derived from known biomolecular structures

Pairwise covariance adds little to secondary structure prediction but improves the prediction of non-canonical local structure

<p>Abstract</p> <p>Background</p> <p>Amino acid sequence probability distributions, or profiles, have been used successfully to predict secondary structure and local structure in proteins. Profile models assume the statistical independence of each position in the sequence, but the energetics of protein folding is better captured in a scoring function that is based on pairwise interactions, like a force field.</p> <p>Results</p> <p>I-sites motifs are short sequence/structure motifs that populate the protein structure database due to energy-driven convergent evolution. Here we show that a pairwise covariant sequence model does not predict alpha helix or beta strand significantly better overall than a profile-based model, but it does improve the prediction of certain loop motifs. The finding is best explained by considering secondary structure profiles as multivariant, all-or-none models, which subsume covariant models. Pairwise covariance is nonetheless present and energetically rational. Examples of negative design are present, where the covariances disfavor non-native structures.</p> <p>Conclusion</p> <p>Measured pairwise covariances are shown to be statistically robust in cross-validation tests, as long as the amino acid alphabet is reduced to nine classes. An updated I-sites local structure motif library that provides sequence covariance information for all types of local structure in globular proteins and a web server for local structure prediction are available at <url>http://www.bioinfo.rpi.edu/bystrc/hmmstr/server.php</url>.</p

Calcium-fortified beverage supplementation on body composition in postmenopausal women

BACKGROUND: We investigated the effects of a calcium-fortified beverage supplemented over 12 months on body composition in postmenopausal women (n = 37, age = 48–75 y). METHODS: Body composition (total-body percent fat, %Fat(TB); abdominal percent fat, %Fat(AB)) was measured with dual energy x-ray absorptiometry. After baseline assessments, subjects were randomly assigned to a free-living control group (CTL) or the supplement group (1,125 mg Ca(++)/d, CAL). Dietary intake was assessed with 3-day diet records taken at baseline and 12 months (POST). Physical activity was measured using the Yale Physical Activity Survey. RESULTS: At 12 months, the dietary calcium to protein ratio in the CAL group (32.3 ± 15.6 mg/g) was greater than the CTL group (15.2 ± 7.5 mg/g). There were no differences from baseline to POST between groups for changes in body weight (CAL = 0.1 ± 3.0 kg; CTL = 0.0 ± 2.9 kg), %Fat(TB )(CAL = 0.0 ± 2.4%; CTL = 0.5 ± 5.4%), %Fat(AB )(CAL = -0.4 ± 8.7%; CTL = 0.6 ± 8.7%), or fat mass (CAL = 1.3 ± 2.6 kg; CTL = 1.3 ± 2.7 kg). CONCLUSION: These results indicate that increasing the calcium to protein ratio over two-fold by consuming a calcium-fortified beverage for 12 months did not decrease body weight, body fat, or abdominal fat composition in postmenopausal women

A Novel Side-Chain Orientation Dependent Potential Derived from Random-Walk Reference State for Protein Fold Selection and Structure Prediction

An accurate potential function is essential to attack protein folding and structure prediction problems. The key to developing efficient knowledge-based potential functions is to design reference states that can appropriately counteract generic interactions. The reference states of many knowledge-based distance-dependent atomic potential functions were derived from non-interacting particles such as ideal gas, however, which ignored the inherent sequence connectivity and entropic elasticity of proteins.We developed a new pair-wise distance-dependent, atomic statistical potential function (RW), using an ideal random-walk chain as reference state, which was optimized on CASP models and then benchmarked on nine structural decoy sets. Second, we incorporated a new side-chain orientation-dependent energy term into RW (RWplus) and found that the side-chain packing orientation specificity can further improve the decoy recognition ability of the statistical potential.RW and RWplus demonstrate a significantly better ability than the best performing pair-wise distance-dependent atomic potential functions in both native and near-native model selections. It has higher energy-RMSD and energy-TM-score correlations compared with other potentials of the same type in real-life structure assembly decoys. When benchmarked with a comprehensive list of publicly available potentials, RW and RWplus shows comparable performance to the state-of-the-art scoring functions, including those combining terms from multiple resources. These data demonstrate the usefulness of random-walk chain as reference states which correctly account for sequence connectivity and entropic elasticity of proteins. It shows potential usefulness in structure recognition and protein folding simulations. The RW and RWplus potentials, as well as the newly generated I-TASSER decoys, are freely available in http://zhanglab.ccmb.med.umich.edu/RW

Inhibition of cholesterol recycling impairs cellular PrPSc propagation

The infectious agent in prion diseases consists of an aberrantly folded isoform of the cellular prion protein (PrPc), termed PrPSc, which accumulates in brains of affected individuals. Studies on prion-infected cultured cells indicate that cellular cholesterol homeostasis influences PrPSc propagation. Here, we demonstrate that the cellular PrPSc content decreases upon accumulation of cholesterol in late endosomes, as induced by NPC-1 knock-down or treatment with U18666A. PrPc trafficking, lipid raft association, and membrane turnover are not significantly altered by such treatments. Cellular PrPSc formation is not impaired, suggesting that PrPSc degradation is increased by intracellular cholesterol accumulation. Interestingly, PrPSc propagation in U18666A-treated cells was partially restored by overexpression of rab 9, which causes redistribution of cholesterol and possibly of PrPSc to the trans-Golgi network. Surprisingly, rab 9 overexpression itself reduced cellular PrPSc content, indicating that PrPSc production is highly sensitive to alterations in dynamics of vesicle trafficking

Using neural networks and evolutionary information in decoy discrimination for protein tertiary structure prediction

Background: We present a novel method of protein fold decoy discrimination using machine learning, more specifically using neural networks. Here, decoy discrimination is represented as a machine learning problem, where neural networks are used to learn the native-like features of protein structures using a set of positive and negative training examples. A set of native protein structures provides the positive training examples, while negative training examples are simulated decoy structures obtained by reversing the sequences of native structures. Various features are extracted from the training dataset of positive and negative examples and used as inputs to the neural networks.Results: Results have shown that the best performing neural network is the one that uses input information comprising of PSI-BLAST [1] profiles of residue pairs, pairwise distance and the relative solvent accessibilities of the residues. This neural network is the best among all methods tested in discriminating the native structure from a set of decoys for all decoy datasets tested. Conclusion: This method is demonstrated to be viable, and furthermore evolutionary information is successfully used in the neural networks to improve decoy discrimination

The incidence of experimental smoking in school children: an 8-year follow-up of the child and adolescent behaviors in long-term evolution (CABLE) study

<p>Abstract</p> <p>Background</p> <p>Studies have established that most regular adult smokers become addicted in their adolescent years. We investigated the incidence of and risk factors associated with initial experimental smoking among a group of school children who were followed for 8 years.</p> <p>Methods</p> <p>We used cohort data collected as part of the Child and Adolescent Behaviors in Long-term Evolution (CABLE) study, which selected nine elementary schools each from an urban area (Taipei City) and a rural area (Hsingchu county) in northern Taiwan. From 2002 to 2008, children were asked annually whether they had smoked in the previous year. An accelerated lifetime model with Weibull distribution was used to examine the factors associated with experimental smoking.</p> <p>Results</p> <p>In 2001, 2686 4^th-graders participated in the study. For each year from 2002 to 2008, their incidences of trial smoking were 3.1%, 4.0%, 2.8%, 6.0%, 5.3%, 5.0% and 6.0%, respectively. There was an increase from 7^thto 8^thgrade (6.0%). Children who were males, lived in rural areas, came from single-parent families, had parents who smoked, and had peers who smoked were more likely to try smoking earlier. The influence of parents and peers on experimental smoking demonstrated gradient effects.</p> <p>Conclusions</p> <p>This study used a cohort to examine incidence and multiple influences, including individual factors, familial factors, and community factors, on experimental smoking in adolescents. The findings fit the social ecological model, highlighting the influences of family and friends. School and community attachment were associated with experimental smoking in teenagers.</p