Wladimir Labeikovsky

Bio: Wladimir Labeikovsky is an academic researcher from Howard Hughes Medical Institute. The author has contributed to research in topics: Creatine kinase & Protein structure. The author has an hindex of 8, co-authored 10 publications receiving 1412 citations. Previous affiliations of Wladimir Labeikovsky include National Science Foundation & Rockefeller University.

Intrinsic dynamics of an enzyme underlies catalysis

Abstract: A unique feature of chemical catalysis mediated by enzymes is that the catalytically reactive atoms are embedded within a folded protein. Although current understanding of enzyme function has been focused on the chemical reactions and static three-dimensional structures, the dynamic nature of proteins has been proposed to have a function in catalysis. The concept of conformational substates has been described; however, the challenge is to unravel the intimate linkage between protein flexibility and enzymatic function. Here we show that the intrinsic plasticity of the protein is a key characteristic of catalysis. The dynamics of the prolyl cis-trans isomerase cyclophilin A (CypA) in its substrate-free state and during catalysis were characterized with NMR relaxation experiments. The characteristic enzyme motions detected during catalysis are already present in the free enzyme with frequencies corresponding to the catalytic turnover rates. This correlation suggests that the protein motions necessary for catalysis are an intrinsic property of the enzyme and may even limit the overall turnover rate. Motion is localized not only to the active site but also to a wider dynamic network. Whereas coupled networks in proteins have been proposed previously, we experimentally measured the collective nature of motions with the use of mutant forms of CypA. We propose that the pre-existence of collective dynamics in enzymes before catalysis is a common feature of biocatalysts and that proteins have evolved under synergistic pressure between structure and dynamics.

Changes in the mitochondrial proteome from mouse hearts deficient in creatine kinase

Abstract: Creatine kinase (CK) is an abundant enzyme, important for maintenance of high-energy phosphate homeostasis in many tissues including heart. Double-knockout CK (DbKO-CK) mice missing both the muscle (MM) and sarcomeric mitochondrial (ScMit) isoforms of CK have recently been studied. Despite a large change in skeletal muscle function in DbKO-CK mice, there is little functional change in the heart. To investigate whether there are specific changes in cardiac mitochondrial proteins associated with the loss of MM- and ScMit-CK isoforms, we have used difference gel electrophoresis (DIGE) to compare mitochondrial proteins from wild-type and DbKO-CK mice. Mass spectrometry fingerprinting was used to identify 40 spots as known mitochondrial proteins. We have discovered that the loss of MM- and ScMit-CK isoforms did not cause large scale changes in heart mitochondrial proteins. The loss of ScMit-CK was readily detected in the DbKO-CK samples. We have also detected a large decrease in the precursor form of aconitase. Furthermore, two mitochondrial protein differences have been found in the parent mouse strains of the DbKO-CK mice.

Dynamic personalities of proteins.

Abstract: Because proteins are central to cellular function, researchers have sought to uncover the secrets of how these complex macromolecules execute such a fascinating variety of functions. Although static structures are known for many proteins, the functions of proteins are governed ultimately by their dynamic character (or 'personality'). The dream is to 'watch' proteins in action in real time at atomic resolution. This requires addition of a fourth dimension, time, to structural biology so that the positions in space and time of all atoms in a protein can be described in detail.

The role of dynamic conformational ensembles in biomolecular recognition

Abstract: Molecular recognition is central to all biological processes. For the past 50 years, Koshland's 'induced fit' hypothesis has been the textbook explanation for molecular recognition events. However, recent experimental evidence supports an alternative mechanism. 'Conformational selection' postulates that all protein conformations pre-exist, and the ligand selects the most favored conformation. Following binding the ensemble undergoes a population shift, redistributing the conformational states. Both conformational selection and induced fit appear to play roles. Following binding by a primary conformational selection event, optimization of side chain and backbone interactions is likely to proceed by an induced fit mechanism. Conformational selection has been observed for protein-ligand, protein-protein, protein-DNA, protein-RNA and RNA-ligand interactions. These data support a new molecular recognition paradigm for processes as diverse as signaling, catalysis, gene regulation and protein aggregation in disease, which has the potential to significantly impact our views and strategies in drug design, biomolecular engineering and molecular evolution.

Atomic-Level Characterization of the Structural Dynamics of Proteins

Abstract: Molecular dynamics (MD) simulations are widely used to study protein motions at an atomic level of detail, but they have been limited to time scales shorter than those of many biologically critical conformational changes. We examined two fundamental processes in protein dynamics—protein folding and conformational change within the folded state—by means of extremely long all-atom MD simulations conducted on a special-purpose machine. Equilibrium simulations of a WW protein domain captured multiple folding and unfolding events that consistently follow a well-defined folding pathway; separate simulations of the protein’s constituent substructures shed light on possible determinants of this pathway. A 1-millisecond simulation of the folded protein BPTI reveals a small number of structurally distinct conformational states whose reversible interconversion is slower than local relaxations within those states by a factor of more than 1000.

Markov models of molecular kinetics: Generation and validation

Abstract: Markov state models of molecular kinetics (MSMs), in which the long-time statistical dynamics of a molecule is approximated by a Markov chain on a discrete partition of configuration space, have seen widespread use in recent years. This approach has many appealing characteristics compared to straightforward molecular dynamics simulation and analysis, including the potential to mitigate the sampling problem by extracting long-time kinetic information from short trajectories and the ability to straightforwardly calculate expectation values and statistical uncertainties of various stationary and dynamical molecular observables. In this paper, we summarize the current state of the art in generation and validation of MSMs and give some important new results. We describe an upper bound for the approximation error made by modeling molecular dynamics with a MSM and we show that this error can be made arbitrarily small with surprisingly little effort. In contrast to previous practice, it becomes clear that the best MSM is not obtained by the most metastable discretization, but the MSM can be much improved if non-metastable states are introduced near the transition states. Moreover, we show that it is not necessary to resolve all slow processes by the state space partitioning, but individual dynamical processes of interest can be resolved separately. We also present an efficient estimator for reversible transition matrices and a robust test to validate that a MSM reproduces the kinetics of the molecular dynamics data.