Analysis of a microscopic stochastic model of microtubule dynamic instability

A novel theoretical model of dynamic instability of a system of linear (1D) microtubules (MTs) in a bounded domain is introduced for studying the role of a cell edge in vivo and analyzing the effect of competition for a limited amount of tubulin. The model differs from earlier models in that the evolution of MTs is based on the rates of single unit (e.g., a heterodimer per protofilament) transformations, in contrast to postulating effective rates/frequencies of larger-scale changes, extracted, e.g., from the length history plots of MTs. Spontaneous GTP hydrolysis with finite rate after polymerization is assumed, and theoretical estimates of an effective catastrophe frequency as well as other parameters characterizing MT length distributions and cap size are derived. We implement a simple cap model which does not include vectorial hydrolysis. We demonstrate that our theoretical predictions, such as steady state concentration of free tubulin, and parameters of MT length distributions, are in agreement with the numerical simulations. The present model establishes a quantitative link between microscopic parameters governing the dynamics of MTs and macroscopic characteristics of MTs in a closed system. Lastly, we use a computational Monte Carlo model to provide an explanation for non-exponential MT length distributions observed in experiments. In particular, we show that appearance of such non-exponential distributions in the experiments can occur because the true steady state has not been reached, and/or due to the presence of a cell edge.Comment: 14 pages, 7 figure

Conformational changes in CLIP-170 regulate its binding to microtubules and dynactin localization

Cytoplasmic linker protein (CLIP)-170, CLIP-115, and the dynactin subunit p150Glued are structurally related proteins, which associate specifically with the ends of growing microtubules (MTs). Here, we show that down-regulation of CLIP-170 by RNA interference results in a strongly reduced accumulation of dynactin at the MT tips. The NH2 terminus of p150Glued binds directly to the COOH terminus of CLIP-170 through its second metal-binding motif. p150Glued and LIS1, a dynein-associating protein, compete for the interaction with the CLIP-170 COOH terminus, suggesting that LIS1 can act to release dynactin from the MT tips. We also show that the NH2-terminal part of CLIP-170 itself associates with the CLIP-170 COOH terminus through its first metal-binding motif. By using scanning force microscopy and fluorescence resonance energy transfer-based experiments we provide evidence for an intramolecular interaction between the NH2 and COOH termini of CLIP-170. This interaction interferes with the binding of the CLIP-170 to MTs. We propose that conformational changes in CLIP-170 are important for binding to dynactin, LIS1, and the MT tips

A standardized kinesin nomenclature

In recent years the kinesin superfamily has become so large that several different naming schemes have emerged, leading to confusion and miscommunication. Here, we set forth a standardized kinesin nomenclature based on 14 family designations. The scheme unifies all previous phylogenies and nomenclature proposals, while allowing individual sequence names to remain the same, and for expansion to occur as new sequences are discovered

MTBindingSim: simulate protein binding to microtubules

Summary: Many protein–protein interactions are more complex than can be accounted for by 1:1 binding models. However, biochemists have few tools available to help them recognize and predict the behaviors of these more complicated systems, making it difficult to design experiments that distinguish between possible binding models. MTBindingSim provides researchers with an environment in which they can rapidly compare different models of binding for a given scenario. It is written specifically with microtubule polymers in mind, but many of its models apply equally well to any polymer or any protein–protein interaction. MTBindingSim can thus both help in training intuition about binding models and with experimental design