Blind predictions

Both theory (CASP6 blind prediction, Fig. 5.3a) and experiment (carried out within CASP6 as well, Fig. 5.3b) give the target molecule containing five a-helices and two fi-pleated sheets (wide arrows). These secondary structure elements interact and form the unique (native) tertiary structure, which is able to perform its biological function. Both structures in atomic resolution differ by the r.m.s. equal to 2.9 A, which is a rather small deviation. 

Like QSAR models, hierarchical schemes are ordinarily optimized in two steps (1), calibration of the component model parameters to meet accepted criteria for performance (e.g., predictivity, number of false negatives) using a training set of chemicals (2) validation of the scheme by assessing its ability to blind-predict test chemicals of known activity. It is presumed that the chemicals in the training and test sets share the same chemical space, range of activity, mode-of-action, and so on. The entire scheme and each component model are relined during validation. 

Katritch V, Rueda M, Lam PC-H, Yeager M, Abagyan R (2010) GPCR 3D homology models for ligand screening lessons learned from blind predictions of adenosine A2a receptor complex. Proteins 78 197-211... 

Richter, A., Brendler, V., and Nebelung, C., Blind prediction of Cu(II) soiption onto goethite Cunent capabilities of diffuse double layer model, Geochim. Cosmochim. Acta, 69, 2725, 2005. 

Assessment of secondary structure predictions these methods involve statistical analyses of applying the methods to known structures and recomputing their secondary structure as well as blind predictions (see also Section 6.5.1). 

Theoretical predictions are risky. Therefore for almost all such prediction experimental validation is required. Nevertheless, often the models can indicate appropriate ways for validation or further experiments. These experiments can be expected to be time-consuming, and expensive. Furthermore, the protein actually needs to be available for the suggested experiments. All of this limits the applicability of experimental validation. Therefore, it is mandatory to reduce errors as much as possible and to indicate the expected error range via computer-based predictions. This is not a trivial problem for structure prediction, though. An estimation of the performance and accuracy of the respective methods can be obtained from large scale comparative benchmarking, from successful blind predictions and from a community wide assessment experiment (CASP [109, 229]/ CAFASP [283]). These are addressed in turn in the following ... 

A blind prediction is a computer-based construction of 3D models of a protein sequence for which no structure is known at the time. CASP (comparative assessment of structure prediction methods [109, 274-276]) is a worldwide contest of protein structure prediction that takes place every two years. During the CASP experiments, a set of automated numerical evaluation tools have been implemented [110, 111, 277, 278] to cope with the large number of predictions in a way that is as objective as possible. In fact, the experiment is also devoted to the research and development of such unbiased methods. However, there is still quite some controversy on the criteria to judge protein structure predictions and the corresponding models [339]. 

Performing a validated blind prediction is a difficult task and requires the availability of the software, a test scenario, and the experimental facilities for validating the study. For protein-protein docking, a blind prediction of the complex between TEM-1 /Mactamase and the inhibitor BLIP was performed by 6 independent groups. All groups were able to identify the correct complex within 2 A RMSD [153]. 

Details on the modeling approaches used by the different participants in the Benchmark are described in companion papers to this Symposium. The performance of the test and a comparison of calculated and most significant measured variables are described in the next three sections. The Benchmark was conceived as a blind prediction exercise. The graphs presented include the prediction made by different teams. Some of the participants performed later additional analysis once the actual field data was officially released. 

Only a reduced number of modeling teams participated in the blind prediction of Part B. As shown in the comparisons of RH and stress variables only three teams (CNS, SKB and SKI) were able to provide predictions for the full... 

As in Part B, only a reduced number of modelling teams provided blind predictions for the rock behavior, once the expansive bentonite barrier was in place. Coupled THM models are also required for this part of the Benchmark although the temperature increase plays a dominant effect on the rock behavior. As it is frequently the case, temperature changes are well reproduced in general terms. Rock water pressures development integrates two separate phenomena the modification of the... 

Abstract The Canadian Nuclear Safety Commission (CNSC) used the finite element code FRACON to perform blind predictions of the FEBEX heater experiment. The FRACON code numerically solves the extended equations of Biot s poro-elasticity. The rock was assumed to be linearly elastic, however, the poro-elastic coefficients of variably saturated bentonite were expressed as functions of net stress and void ratio using the state surface equation obtained from suction-controlled oedometer tests. In this paper, we will summarize our approach and predictive results for the Thermo-Hydro-Mechanical response of the bentonite. It is shown that the model correctly predicts drying of the bentonite near the heaters and re-saturation near the rock interface. The evolution of temperature and the heater thermal output were reasonably well predicted by the model. The trends in the total stresses developed in the bentonite were also correctly predicted, however the absolute values were underestimated probably due to the neglect of pore pressure build-up in the rock mass. 

These blind predictions of the FEBEX data do not make a strong case that, for this particular geomechanical situation, a coupled analysis is entirely necessary. The granite in this case is sparsely fractured, and most of the inflow occurs at the lamprophyre and other more fractured areas. Also, the rock mass is sufficiently nonporous and saturated that inelastic deformation of the rock matrix is not a significant issue for repository performance. However, the exercise was very valuable for developing rationale for modeling the more complex coupled problems associated with the introduction of the bentonite barrier and the heat of the simulated waste. 

A simplified axisymmetric model of the in situ THM test at the Kamaishi mine was simulated by five different numerical models. Although the model geometry is much simplified compared to the field test conditions, improved simulation of the general THM responses were obtained as compared to previous blind predictions performed within the DECOVALEX II project. The measures taken for improvement were ... 

The number of atoms taken into account in MD nowadays may reach a million. The real problem is not the size of the system, but rather its complexity and the wealth of possible structures, with their too large number to be investigated. Some problems may be simplified by considering a quantum-mechanical part in the details and a classical part described by Newton equations. Another important problem is to predict the 3-D structure of proteins, starting from the available amino acid sequence. Every two years beginning in 1994, CASP (Critical Assessment of techniques for protein Structure Prediction) has been organized in California. CASP is a kind of scientific competition, in which theoretical laboratories (knowing only the amino acid sequence) make blind predictions about 3-D protein structures about to be determined in experimental laboratories. Most of the theoretical methods are based on the similarity of the sequence to a sequence from the Protein Data Bank of the 3-D structures, only some of the methods are related to chemical physics. Fig. 7.17 shows an example of the latter. 

To cross-validate the results, each group of structurally related proteins is left out of the training set in turn and used to test the network. Such a partitioning scheme (in contrast to a jackknife one, for example) minimizes the likelihood of biasing the results in favor of structural descriptors (see Section II). Its use yields true predictions (denoted cv ) in contrast to fits of the data, in which aU the proteins are included during the training (denoted tm )- The latter tend to yield inflated accuracy statistics, but we describe them here as well for comparison with earlier studies [12,13,20,47], which failed to cross-validate their results [however, it should be noted that the relationship in Ref. 12 has been used successfully for blind predictions (K. W. Plaxco and D. Baker, personal communication)]. 

These experiments provided an opportunity to conduct blind predictive modelling to test the... 

The coupled models PRECIP and CHEQMATE were used to provide blind predictive calculations based upon known parameters and... 

A Survey of the Results of 12 Years of Blind Predictions on 45 Targets... 

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