Tubulin polymers, microtubules, can switch abruptly from the assembly to shortening. These infrequent transitions, termed "catastrophes", affect numerous cellular processes but the underlying mechanisms are elusive. We approached this complex stochastic system using advanced coarse-grained molecular dynamics modeling of tubulin-tubulin interactions. Unlike in previous simplified models of dynamic microtubules, the catastrophes in this model arise owing to fluctuations in the composition and conformation of a growing microtubule tip, most notably in the number of protofilament curls. In our model, dynamic evolution of the stochastic microtubule tip configurations over a long timescale, known as the system's "aging", gives rise to the nonexponential distribution of microtubule lifetimes, consistent with experiment. We show that aging takes place in the absence of visible changes in the microtubule wall or tip, as this complex molecular-mechanical system evolves slowly and asymptotically toward the steady-state level of the catastrophe-promoting configurations. This new, to our knowledge, theoretical basis will assist detailed mechanistic investigations of the mechanisms of action of different microtubule-binding proteins and drugs, thereby enabling accurate control over the microtubule dynamics to treat various pathologies.

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The adequate choice of the docking target function impacts the accuracy of the ligand positioning as well as the accuracy of the protein-ligand binding energy calculation. To evaluate a docking target function we compared positions of its minima with the experimentally known pose of the ligand in the protein active site. We evaluated five docking target functions based on either the MMFF94 force field or the PM7 quantum-chemical method with or without implicit solvent models: PCM, COSMO, and SGB. Each function was tested on the same set of 16 protein-ligand complexes. For exhaustive low-energy minima search the novel MPI parallelized docking program FLM and large supercomputer resources were used. Protein-ligand binding energies calculated using low-energy minima were compared with experimental values. It was demonstrated that the docking target function on the base of the MMFF94 force field in vacuo can be used for discovery of native or near native ligand positions by finding the low-energy local minima spectrum of the target function. The importance of solute-solvent interaction for the correct ligand positioning is demonstrated. It is shown that docking accuracy can be improved by replacement of the MMFF94 force field by the new semiempirical quantum-chemical PM7 method.

The ability of proteins and enzymes to maintain a functionally active conformation under adverse environmental conditions is an important feature of biocatalysts, vaccines, and biopharmaceutical proteins. From an evolutionary perspective, robust stability of proteins improves their biological fitness and allows for further optimization. Viewed from an industrial perspective, enzyme stability is crucial for the practical application of enzymes under the required reaction conditions. In this review, we analyze bioinformatic-driven strategies that are used to predict structural changes that can be applied to wild type proteins in order to produce more stable variants. The most commonly employed techniques can be classified into stochastic approaches, empirical or systematic rational design strategies, and design of chimeric proteins. We conclude that bioinformatic analysis can be efficiently used to study large protein superfamilies systematically as well as to predict particular structural changes which increase enzyme stability. Evolution has created a diversity of protein properties that are encoded in genomic sequences and structural data. Bioinformatics has the power to uncover this evolutionary code and provide a reproducible selection of hotspots - key residues to be mutated in order to produce more stable and functionally diverse proteins and enzymes. Further development of systematic bioinformatic procedures is needed to organize and analyze sequences and structures of proteins within large superfamilies and to link them to function, as well as to provide knowledge-based predictions for experimental evaluation.