Discussion of "EQUI-energy sampler" by Kou, Zhou and Wong

Novel sampling algorithms can significantly impact open questions in computational biology, most notably the in silico protein folding problem. By using computational methods, protein folding aims to find the three-dimensional structure of a protein chain given the sequence of its amino acid building blocks. The complexity of the problem strongly depends on the protein representation and its energy function. The more detailed the model, the more complex its corresponding energy function and the more challenge it sets for sampling algorithms. Kou, Zhou and Wong [math.ST/0507080] have introduced a novel sampling method, which could contribute significantly to the field of structural prediction.Comment: Published at http://dx.doi.org/10.1214/009053606000000470 in the Annals of Statistics (http://www.imstat.org/aos/) by the Institute of Mathematical Statistics (http://www.imstat.org

A comprehensive analysis of 40 blind protein structure predictions

BACKGROUND: We thoroughly analyse the results of 40 blind predictions for which an experimental answer was made available at the fourth meeting on the critical assessment of protein structure methods (CASP4). Using our comparative modelling and fold recognition methodologies, we made 29 predictions for targets that had sequence identities ranging from 50% to 10% to the nearest related protein with known structure. Using our ab initio methodologies, we made eleven predictions for targets that had no detectable sequence relationships. RESULTS: For 23 of these proteins, we produced models ranging from 1.0 to 6.0 Å root mean square deviation (RMSD) for the C(α) atoms between the model and the corresponding experimental structure for all or large parts of the protein, with model accuracies scaling fairly linearly with respect to sequence identity (i.e., the higher the sequence identity, the better the prediction). We produced nine models with accuracies ranging from 4.0 to 6.0 Å C(α) RMSD for 60–100 residue proteins (or large fragments of a protein), with a prediction accuracy of 4.0 Å C(α) RMSD for residues 1–80 for T110/rbfa. CONCLUSIONS: The areas of protein structure prediction that work well, and areas that need improvement, are discernable by examining how our methods have performed over the past four CASP experiments. These results have implications for modelling the structure of all tractable proteins encoded by the genome of an organism

Direct enhancement of nuclear singlet order by dynamic nuclear polarization

Hyperpolarized singlet order is available immediately after dissolution DNP, avoiding need for additional preparation steps. We demonstrate this procedure on a sample of [1,2–13C2]pyruvic aci

A geometric knowledge-based coarse-grained scoring potential for structure prediction evaluation

International audienceKnowledge-based protein folding potentials have proven successful in the recent years. Based on statistics of observed interatomic distances, they generally encode pairwise contact information. In this study we present a method that derives multi-body contact potentials from measurements of surface areas using coarse-grained protein models. The measurements are made using a newly implemented geometric construction: the arrangement of circles on a sphere. This construction allows the definition of residue covering areas which are used as parameters to build functions able to distinguish native structures from decoys. These functions, encoding up to 5-body contacts are evaluated on a reference set of 66 structures and its 45000 decoys, and also on the often used lattice ssfit set from the decoys'R us database. We show that the most relevant information for discrimination resides in 2- and 3-body contacts. The potentials we have obtained can be used for evaluation of putative structural models; they could also lead to different types of structure refinement techniques that use multi-body interactions

Effect of Rosuvastatin on Acute Kidney Injury in Sepsis-Associated Acute Respiratory Distress Syndrome.

Background:Acute kidney injury (AKI) commonly occurs in patients with sepsis and acute respiratory distress syndrome (ARDS). Objective:To investigate whether statin treatment is protective against AKI in sepsis-associated ARDS. Design:Secondary analysis of data from Statins for Acutely Injured Lungs in Sepsis (SAILS), a randomized controlled trial that tested the impact of rosuvastatin therapy on mortality in patients with sepsis-associated ARDS. Setting:44 hospitals in the National Heart, Lung, and Blood Institute ARDS Clinical Trials Network. Patients:644 of 745 participants in SAILS who had available baseline serum creatinine data and who were not on chronic dialysis. Measurements:Our primary outcome was AKI defined using the Kidney Disease Improving Global Outcomes creatinine criteria. Randomization to rosuvastatin vs placebo was the primary predictor. Additional covariates include demographics, ARDS etiology, and severity of illness. Methods:We used multivariable logistic regression to analyze AKI outcomes in 511 individuals without AKI at randomization, and 93 with stage 1 AKI at randomization. Results:Among individuals without AKI at randomization, rosuvastatin treatment did not change the risk of AKI (adjusted odds ratio: 0.99, 95% confidence interval [CI]: 0.67-1.44). Among those with preexisting stage 1 AKI, rosuvastatin treatment was associated with an increased risk of worsening AKI (adjusted odds ratio: 3.06, 95% CI: 1.14-8.22). When serum creatinine was adjusted for cumulative fluid balance among those with preexisting stage 1 AKI, rosuvastatin was no longer associated worsening AKI (adjusted odds ratio: 1.85, 95% CI: 0.70-4.84). Limitations:Sample size, lack of urine output data, and prehospitalization baseline creatinine. Conclusion:Treatment with rosuvastatin in patients with sepsis-associated ARDS did not protect against de novo AKI or worsening of preexisting AKI

Improved protein structure selection using decoy-dependent discriminatory functions

BACKGROUND: A key component in protein structure prediction is a scoring or discriminatory function that can distinguish near-native conformations from misfolded ones. Various types of scoring functions have been developed to accomplish this goal, but their performance is not adequate to solve the structure selection problem. In addition, there is poor correlation between the scores and the accuracy of the generated conformations. RESULTS: We present a simple and nonparametric formula to estimate the accuracy of predicted conformations (or decoys). This scoring function, called the density score function, evaluates decoy conformations by performing an all-against-all C(α )RMSD (Root Mean Square Deviation) calculation in a given decoy set. We tested the density score function on 83 decoy sets grouped by their generation methods (4state_reduced, fisa, fisa_casp3, lmds, lattice_ssfit, semfold and Rosetta). The density scores have correlations as high as 0.9 with the C(α )RMSDs of the decoy conformations, measured relative to the experimental conformation for each decoy. We previously developed a residue-specific all-atom probability discriminatory function (RAPDF), which compiles statistics from a database of experimentally determined conformations, to aid in structure selection. Here, we present a decoy-dependent discriminatory function called self-RAPDF, where we compiled the atom-atom contact probabilities from all the conformations in a decoy set instead of using an ensemble of native conformations, with a weighting scheme based on the density scores. The self-RAPDF has a higher correlation with C(α )RMSD than RAPDF for 76/83 decoy sets, and selects better near-native conformations for 62/83 decoy sets. Self-RAPDF may be useful not only for selecting near-native conformations from decoy sets, but also for fold simulations and protein structure refinement. CONCLUSIONS: Both the density score and the self-RAPDF functions are decoy-dependent scoring functions for improved protein structure selection. Their success indicates that information from the ensemble of decoy conformations can be used to derive statistical probabilities and facilitate the identification of near-native structures

Concomitant medication use and clinical outcome of repetitive Transcranial Magnetic Stimulation (rTMS) treatment of Major Depressive Disorder.

BackgroundRepetitive Transcranial Magnetic Stimulation (rTMS) is commonly administered to Major Depressive Disorder (MDD) patients taking psychotropic medications, yet the effects on treatment outcomes remain unknown. We explored how concomitant medication use relates to clinical response to a standard course of rTMS.MethodsMedications were tabulated for 181 MDD patients who underwent a six-week rTMS treatment course. All patients received 10 Hz rTMS administered to left dorsolateral prefrontal cortex (DLPFC), with 1 Hz administered to right DLPFC in patients with inadequate response to and/or intolerance of left-sided stimulation. Primary outcomes were change in Inventory of Depressive Symptomatology Self Report (IDS-SR30) total score after 2, 4, and 6 weeks.ResultsUse of benzodiazepines was associated with less improvement at week 2, whereas use of psychostimulants was associated with greater improvement at week 2 and across 6 weeks. These effects were significant controlling for baseline variables including age, overall symptom severity, and severity of anxiety symptoms. Response rates at week 6 were lower in benzodiazepine users versus non-users (16.4% vs. 35.5%, p = 0.008), and higher in psychostimulant users versus non-users (39.2% vs. 22.0%, p = 0.02).ConclusionsConcomitant medication use may impact rTMS treatment outcome. While the differences reported here could be considered clinically significant, results were not corrected for multiple comparisons and findings should be replicated before clinicians incorporate the evidence into clinical practice. Prospective, hypothesis-based treatment studies will aid in determining causal relationships between medication treatments and outcome