Integrin αIIbß3 intermediates: From molecular dynamics to adhesion assembly.

The platelet integrin αIIbß3 undergoes long-range conformational transitions associated with its functional conversion from inactive (low-affinity) to active (high-affinity) during hemostasis. Although new conformations that are intermediate between the well-characterized bent and extended states have been identified, their molecular dynamic properties and functions in the assembly of adhesions remain largely unexplored. In this study, we evaluated the properties of intermediate conformations of integrin αIIbß3 and characterized their effects on the assembly of adhesions by combining all-atom simulations, principal component analysis, and mesoscale modeling. Our results show that in the low-affinity, bent conformation, the integrin ectodomain tends to pivot around the legs; in intermediate conformations, the headpiece becomes partially extended, away from the lower legs. In the fully open, active state, αIIbß3 is flexible, and the motions between headpiece and lower legs are accompanied by fluctuations of the transmembrane helices. At the mesoscale, bent integrins form only unstable adhesions, but intermediate or open conformations stabilize the adhesions. These studies reveal a mechanism by which small variations in ligand binding affinity and enhancement of the ligand-bound lifetime in the presence of actin retrograde flow stabilize αIIbß3 integrin adhesions.

Microtubule Simulations Provide Insight into the Molecular Mechanism Underlying Dynamic Instability.

The dynamic instability of microtubules (MTs), which refers to their ability to switch between polymerization and depolymerization states, is crucial for their function. It has been proposed that the growing MT ends are protected by a "GTP cap" that consists of GTP-bound tubulin dimers. When the speed of GTP hydrolysis is faster than dimer recruitment, the loss of this GTP cap will lead the MT to undergo rapid disassembly. However, the underlying atomistic mechanistic details of the dynamic instability remains unclear. In this study, we have performed long-time atomistic molecular dynamics simulations (1 µs for each system) for MT patches as well as a short segment of a closed MT in both GTP- and GDP-bound states. Our results confirmed that MTs in the GDP state generally have weaker lateral interactions between neighboring protofilaments (PFs) and less cooperative outward bending conformational change, where the difference between bending angles of neighboring PFs tends to be larger compared with GTP ones. As a result, when the GDP state tubulin dimer is exposed at the growing MT end, these factors will be more likely to cause the MT to undergo rapid disassembly. We also compared simulation results between the special MT seam region and the remaining material and found that the lateral interactions between MT PFs at the seam region were comparatively much weaker. This finding is consistent with the experimental suggestion that the seam region tends to separate during the disassembly process of an MT.

Investigating energy-based pool structure selection in the structure ensemble modeling with experimental distance constraints: The example from a multidomain protein Pub1.

The structural variations of multidomain proteins with flexible parts mediate many biological processes, and a structure ensemble can be determined by selecting a weighted combination of representative structures from a simulated structure pool, producing the best fit to experimental constraints such as interatomic distance. In this study, a hybrid structure-based and physics-based atomistic force field with an efficient sampling strategy is adopted to simulate a model di-domain protein against experimental paramagnetic relaxation enhancement (PRE) data that correspond to distance constraints. The molecular dynamics simulations produce a wide range of conformations depicted on a protein energy landscape. Subsequently, a conformational ensemble recovered with low-energy structures and the minimum-size restraint is identified in good agreement with experimental PRE rates, and the result is also supported by chemical shift perturbations and small-angle X-ray scattering data. It is illustrated that the regularizations of energy and ensemble-size prevent an arbitrary interpretation of protein conformations. Moreover, energy is found to serve as a critical control to refine the structure pool and prevent data overfitting, because the absence of energy regularization exposes ensemble construction to the noise from high-energy structures and causes a more ambiguous representation of protein conformations. Finally, we perform structure-ensemble optimizations with a topology-based structure pool, to enhance the understanding on the ensemble results from different sources of pool candidates.

Identifying essential pairwise interactions in elastic network model using the alpha shape theory.

Elastic network models (ENM) are based on the idea that the geometry of a protein structure provides enough information for computing its fluctuations around its equilibrium conformation. This geometry is represented as an elastic network (EN) that is, a network of links between residues. A spring is associated with each of these links. The normal modes of the protein are then identified with the normal modes of the corresponding network of springs. Standard approaches for generating ENs rely on a cutoff distance. There is no consensus on how to choose this cutoff. In this work, we propose instead to filter the set of all residue pairs in a protein using the concept of alpha shapes. The main alpha shape we considered is based on the Delaunay triangulation of the Cα positions; we referred to the corresponding EN as EN(∞). We have shown that heterogeneous anisotropic network models, called αHANMs, that are based on EN(∞) reproduce experimental B-factors very well, with correlation coefficients above 0.99 and root-mean-square deviations below 0.1 Å(2) for a large set of high resolution protein structures. The construction of EN(∞) is simple to implement and may be used automatically for generating ENs for all types of ENMs.

Fluctuation matching approach for elastic network model and structure-based model of biomacromolecules.

Elastic network models (ENMs) based on simple harmonic potential energy function have been proven over the last decade to be reliable computational models for understanding the intrinsic dynamics of biomacromolecules. In the original ENMs, the spring constants for different contact pairs are assumed to be identical, while there are a number of recent developments to determine non-uniform spring constants from atomistic force fields or experimental information. In particular, the fluctuation matching approaches in coarse-grained modeling can be applied to build more realistic heterogeneous ENMs, using information from an atomistic force field or experimental B-factors. The same type of approaches is further implemented to parameterize heterogeneous structure-based models, which can be considered as a natural extension of ENMs in terms of the potential energy function. In this review, we give an overview of different fluctuation matching methods adopted for ENMs and structure-based models, including an improved formulation and algorithm based on the relative entropy scheme.

Accurate optimization of amino acid form factors for computing small-angle X-ray scattering intensity of atomistic protein structures.

Structure modelling via small-angle X-ray scattering (SAXS) data generally requires intensive computations of scattering intensity from any given biomolecular structure, where the accurate evaluation of SAXS profiles using coarse-grained (CG) methods is vital to improve computational efficiency. To date, most CG SAXS computing methods have been based on a single-bead-per-residue approximation but have neglected structural correlations between amino acids. To improve the accuracy of scattering calculations, accurate CG form factors of amino acids are now derived using a rigorous optimization strategy, termed electron-density matching (EDM), to best fit electron-density distributions of protein structures. This EDM method is compared with and tested against other CG SAXS computing methods, and the resulting CG SAXS profiles from EDM agree better with all-atom theoretical SAXS data. By including the protein hydration shell represented by explicit CG water molecules and the correction of protein excluded volume, the developed CG form factors also reproduce the selected experimental SAXS profiles with very small deviations. Taken together, these EDM-derived CG form factors present an accurate and efficient computational approach for SAXS computing, especially when higher molecular details (represented by the q range of the SAXS data) become necessary for effective structure modelling.

Robust Heterogeneous Anisotropic Elastic Network Model Precisely Reproduces the Experimental B-factors of Biomolecules.

A computational method called the progressive fluctuation matching (PFM) is developed for constructing robust heterogeneous anisotropic network models (HANMs) for biomolecular systems. An HANM derived through the PFM approach consists of harmonic springs with realistic positive force constants, and yields the calculated B-factors that are basically identical to the experimental ones. For the four tested protein systems including crambin, trypsin inhibitor, HIV-1 protease, and lysozyme, the root-mean-square deviations between the experimental and the computed B-factors are only 0.060, 0.095, 0.247, and 0.049 Å(2), respectively, and the correlation coefficients are 0.99 for all. By comparing the HANM/ANM normal modes to their counterparts derived from both an atomistic force field and an NMR structure ensemble, it is found that HANM may provide more accurate results on protein dynamics.