The structure of the acto-myosin subfragment 1 complex: Results of searches using data from electron microscopy and x-ray crystallography

Surmises of how myosin subfragment 1 (S1) interacts with actin filaments in muscle contraction rest upon knowing the relative arrangement of the two proteins. Although there exist crystallographic structures for both S1 and actin, as well as electron microscopy data for the acto–S1 complex (AS1), modeling of this arrangement has so far only been done “by eye.” Here we report fitted AS1 structures obtained using a quantitative method that is both more objective and makes more complete use of the data. Using undistorted crystallographic results, the best-fit AS1 structure shows significant differences from that obtained by visual fitting. The best fit is produced using the F-actin model of Holmes et al. [Holmes, K. C., Popp, D., Gebhard, W. & Kabsch, W. (1990) Nature (London) 347, 44–49]. S1 residues at the AS1 interface are now found at a higher radius as well as being translated axially and rotated azimuthally. Fits using S1 plus loops missing from the crystal structure were achieved using a homology search method to predict loop structures. These improved fits favor an arrangement in which the loop at the 50- to 20-kDa domain junction of S1 is located near the N terminus of actin. Rigid-body movements of the lower 50-kDa domain, which further improve the fit, produce closure of the large 50-kDa domain cleft and bring conserved residues in the lower 50-kDa domain into an apparently appropriate orientation for close interaction with actin. This finding supports the idea that binding of ATP to AS1 at the end of the ATPase cycle disrupts the actin binding site by changing the conformation of the 50-kDa cleft of S1.

Mutant conformation of p53 translated in vitro or in vivo requires functional HSP90.

The p53 mutant, 143ala, was translated in vitro in either rabbit reticulocyte lysate (RRL) or wheat germ extract (WGE). In RRL, p53-143ala protein of both mutant and wild-type conformation, as detected immunologically with conformation-specific antibodies, was translated. The chaperone protein HSP90, present in RRL, was found to coprecipitate only with the mutated conformation of p53. Geldanamycin, shown previously to bind to HSP90 and destabilize its association with other proteins, decreased the amount of immunologically detectable mutated p53 and increased the amount of detectable wild-type protein, without affecting the total translation of p53. When translated in WGE, known to contain functionally deficient HSP90, p53-143ala produced p53 protein, which was not recognized by a mutated conformation-specific antibody. In contrast, the synthesis of conformationally detectable wild-type p53 in this system was not compromised. Reconstitution of HSP90 function in WGE permitted synthesis of conformationally detectable mutated p53, and this was abrogated by geldanamycin. Finally, when p53-143ala was stably tansfected into yeast engineered to be defective for HSP90 function, conformational recognition of mutated p53 was impaired. When stable transfectants of p53-143ala were prepared in yeast expressing wild-type HSP90, conformational recognition of mutated p53 was antagonized by macbecin I, a geldanamycin analog also known to bind HSP90. Taken together, these data demonstrate a role for HSP90 in the achievement and/or stabilization of the mutated conformation of p53-143ala. Furthermore, we show that the mutated conformation of p53 can be pharmacologically antagonized by drugs targeting HSP90.

A maximum likelihood algorithm for genome mapping of cytogenetic loci from meiotic configuration data.

Frequencies of meiotic configurations in cytogenetic stocks are dependent on chiasma frequencies in segments defined by centromeres, breakpoints, and telomeres. The expectation maximization algorithm is proposed as a general method to perform maximum likelihood estimations of the chiasma frequencies in the intervals between such locations. The estimates can be translated via mapping functions into genetic maps of cytogenetic landmarks. One set of observational data was analyzed to exemplify application of these methods, results of which were largely concordant with other comparable data. The method was also tested by Monte Carlo simulation of frequencies of meiotic configurations from a monotelodisomic translocation heterozygote, assuming six different sample sizes. The estimate averages were always close to the values given initially to the parameters. The maximum likelihood estimation procedures can be extended readily to other kinds of cytogenetic stocks and allow the pooling of diverse cytogenetic data to collectively estimate lengths of segments, arms, and chromosomes.