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James A. Spudich

Researcher at Stanford University

Publications -  342
Citations -  38644

James A. Spudich is an academic researcher from Stanford University. The author has contributed to research in topics: Myosin & Actin. The author has an hindex of 94, co-authored 329 publications receiving 36707 citations. Previous affiliations of James A. Spudich include University of California, San Diego & National Centre for Biological Sciences.

Papers
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Journal ArticleDOI

The Regulation of Rabbit Skeletal Muscle Contraction I. BIOCHEMICAL STUDIES OF THE INTERACTION OF THE TROPOMYOSIN-TROPONIN COMPLEX WITH ACTIN AND THE PROTEOLYTIC FRAGMENTS OF MYOSIN

TL;DR: Actin purified by a new, simple, and rapid purification procedure activated the ATPase activity of both heavy meromyosin and Subfragment 1 of heavy mercyosin, and this activation was not inhibited by the removal of Ca2+.
Journal ArticleDOI

Single myosin molecule mechanics: piconewton forces and nanometre steps

TL;DR: A new in vitro assay using a feedback enhanced laser trap system allows direct measurement of force and displacement that results from the interaction of a single myosin molecule with a single suspended actin filament.
Book ChapterDOI

Purification of muscle actin.

TL;DR: This chapter presents several techniques for further purification of the actin, finding that extensive dialysis results in eventual dissociation of actin-myosin complexes and appearance of myosin in the final product.
Journal ArticleDOI

Disruption of the Dictyostelium myosin heavy chain gene by homologous recombination

TL;DR: Data are presented demonstrating that integration of a transfected plasmid by homologous recombination occurs in the motile eukaryotic cell Dictyostelium discoideum, providing genetic proof that the intact myosin molecule is required for cytokinesis and not for karyokinesis.
Journal ArticleDOI

Optimized localization analysis for single-molecule tracking and super-resolution microscopy.

TL;DR: Both theory and experimental data showed that unweighted least-squares fitting of a Gaussian squanders one-third of the available information, a popular formula for its precision exaggerates beyond Fisher's information limit, and weighted least-Squares may do worse, whereas maximum-likelihood fitting is practically optimal.