To explore the role of Arg82 in the catalysis of proton transfer in bacteriorhodopsin, we replaced Arg82 with Lys, which is also positively charged at neutral pH but has an intrinsic pKa of about 1.7 pH units lower th...
To explore the role of Arg82 in the catalysis of proton transfer in bacteriorhodopsin, we replaced Arg82 with Lys, which is also positively charged at neutral pH but has an intrinsic pKa of about 1.7 pH units lower than that of Arg. In the R82K mutant expressed in Halobacterium salinarium, we found the following: (1) The pKa of the purple-to-blue transition at low pH (which reflects the pKa Asp85) is 3.6 ±0.1. At high pH a second inflection in the blue-to-purple transition with pKa = 8.0 is found. The complex titration behavior of Asp85 indicates that the pKa of Asp85 depends on the protonation state of another amino acid residue, X’, which has a pKa = 8.0 in R82K. The fit of the experimental data to a model of two interacting residues shows that deprotonation of X’ at high pH causes a shift in the p of Asp85 from 3.7 to 6.0. In turn, protonation of Asp85 decreases the pKa of X’ by 2.3 pH units. This suggests that X’ can release a proton upon formation of the M intermediate and the concomitant protonation of Asp85 in the photocycle. (2) The rate constant of dark adaptation, kda, is proportional to the fraction of blue membrane between pH 2 and 10, indicating that thermal isomerization proceeds through the transient protonation of Asp85. The pH dependence of kda shows that two groups with pKa1 = 3.9 and pKa2 = 8.0 control the rate of dark adaptation in R82K. The 1.7 pH unit shift in pka2 in R82K compared to the wild type (WT) (pka2 = 9.7) supports the hypothesis that X’ is Arg82 in WT and Lys82 in R82K (or at least that these groups are the principal part of a cluster of residues that constitute X'). (3) Under steady state illumination, the efficiency of proton transport in R82K incorporated in phosphatidylcholine vesicles is at least 40% of that in the WT. A flash-induced transient signal of the pH-sensitive dye pyranine is s
We present a method to identify and extract virus particle images from noisy spot-scan electron cryomicroscopy images. We use a template matching algorithm to identify virus particles. Due to the high spatial variatio...
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We present a method to identify and extract virus particle images from noisy spot-scan electron cryomicroscopy images. We use a template matching algorithm to identify virus particles. Due to the high spatial variation and low contrast of these images we employ a series of preprocessing operations before performing the template matching. These preprocessing operations detect and remove the black areas present in spot-scan images using spatial histogram operations and binary mathematical morphology. Following the detailed description of our method we discuss the consistency and accuracy of particle selection.
Three-dimensional protein structural data at the molecular level are pivotal for successful precision medicine. Such data are crucial not only for discovering drugs that act to block the active site of the target muta...
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Three-dimensional protein structural data at the molecular level are pivotal for successful precision medicine. Such data are crucial not only for discovering drugs that act to block the active site of the target mutant protein but also for clarifying to the patient and the clinician how the mutations harbored by the patient work. The relative paucity of structural data reflects their cost, challenges in their interpretation, and lack of clinical guidelines for their utilization. Rapid technological advancements in experimental high-resolution structural determination increasingly generate structures. computationally, modeling algorithms, including molecular dynamics simulations, are becoming more powerful, as are compute-intensive hardware, particularly graphics processing units, overlapping with the inception of the exascale era. Accessible, freely available, and detailed structural and dynamical data can be merged with big data to powerfully transform personalizedpharmacology. Here we review protein and emerging genome high-resolution data, along with means, applications, and examples underscoring their usefulness in precision medicine.
Biomedical data scientists study many types of networks, ranging from those formed by neurons to those created by molecular interactions. People often criticize these networks as uninterpretable diagrams termed hairba...
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Biomedical data scientists study many types of networks, ranging from those formed by neurons to those created by molecular interactions. People often criticize these networks as uninterpretable diagrams termed hairballs; however, here we show that molecular biological networks can be interpreted in several straightforward ways. First, we can break down a network into smaller components, focusing on individual pathways and modules. Second, we can compute global statistics describing the network as a whole. Third, we can compare networks. These comparisons can be within the same context (e.g., between two gene regulatory networks) or cross-disciplinary (e.g., between regulatory networks and governmental hierarchies). The latter comparisons can transfer a formalism, such as that for Markov chains, from one context to another or relate our intuitions in a familiar setting (e.g., social networks) to the relatively unfamiliar molecular context. Finally, key aspects of molecular networks are dynamics and evolution, i.e., how they evolve over time and how genetic variants affect them. By studying the relationships between variants in networks, we can begin to interpret many common diseases, such as cancer and heart disease.
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