Browsing by Author "Phillips, George N. Jr."

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    A Global Optimization Method for the Molecular Replacement Problem in X-ray Crystallography
    (2002-06) Jamrog, Diane C.; Phillips, George N. Jr.; Tapia, Richard A.; Zhang, Yin
    The primary technique for determining the three-dimensional structure of a protein molecule is X-ray crystallography, from which the molecular replacement (MR) problem often arises as a critical step. The MR problem is a global optimization problem to locate an optimal position of a model protein, whose structure is similar to the unknown protein structure that is to be determined, so that at this position the model protein will produce calculated intensities closest to those observed from an X-ray crystallography experiment. Improving the applicability and robustness of MR methods is an important research topic because commonly used traditional MR methods, though often successful, have their limitations in solving difficult problems. We introduce a new global optimization strategy that combines a coarse-grid search, using a surrogate function, with extensive multi-start local optimization. A new MR code, called SOMoRe, based on this strategy is developed and tested on four realistic problems, including two difficult problems that traditional MR codes failed to solve directly. SOMoRe was able to solve each test problem without any complication, and SOMoRe solved a MR problem using a less complete model than the models required by three other programs. These results indicate that the new method is promising and should enhance the applicability and robustness of the MR methodology.
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    Structure of RNA 3'-phosphate cyclase bound to substrate RNA
    (Cold Spring Harbor Laboratory Press, 2014) Desai, Kevin K.; Bingman, Craig A.; Cheng, Chin L.; Phillips, George N. Jr.; Raines, Ronald T.
    RNA 3′-phosphate cyclase (RtcA) catalyzes the ATP-dependent cyclization of a 3′-phosphate to form a 2′,3′-cyclic phosphate at RNA termini. Cyclization proceeds through RtcA–AMP and RNA(3′)pp(5′)A covalent intermediates, which are analogous to intermediates formed during catalysis by the tRNA ligase RtcB. Here we present a crystal structure of Pyrococcus horikoshii RtcA in complex with a 3′-phosphate terminated RNA and adenosine in the AMP-binding pocket. Our data reveal that RtcA recognizes substrate RNA by ensuring that the terminal 3′-phosphate makes a large contribution to RNA binding. Furthermore, the RNA 3′-phosphate is poised for in-line attack on the P–N bond that links the phosphorous atom of AMP to Nε of His307. Thus, we provide the first insights into RNA 3′-phosphate termini recognition and the mechanism of 3′-phosphate activation by an Rtc enzyme.
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    The Effect of the Separation of Variables on the Molecular Replacement Method
    (2000-06) Jamrog, Diane C.; Phillips, George N. Jr.; Tapia, Richard A.; Zhang, Yin
    Traditional approaches for solving the molecular replacement problem separate a six-dimensional optimization problem into two three-dimensional ones in order to reduce the computational cost. There are, however, serious drawbacks in such a separation of the rotational and translational degrees of freedom. In this paper, we present computational experiments indicating that even under ideal conditions the separation can fail to preserve the correspondence between the global minima of a target function and the correct rotations when low resolution data are used. This phenomenon is a reason why only high resolution data are used in traditional approaches for solving the molecular replacement problem. In this paper, we provide a theoretical explanation for this phenomenon. In order to solve difficult molecular replacement problems, we believe that low resolution terms should be utilized because they generate smooth, shape-defining components in a target function, making it more amenable to global optimization. This study indicates that in order to utilize low resolution data in the molecular replacement method, we need to consider all degrees of freedom simultaneously. The full-dimensional optimization formulation, once a prohibitive procedure due to its high computational cost, should now be feasible given the current state of computational resources and algorithms.