Normal mode analysis of macromolecular systems with the mobile block Hessian method

Until recently, normal mode analysis (NMA) was limited to small proteins, not only because the required energy minimization is a computationally exhausting task, but also because NMA requires the expensive diagonalization of a 3N(a) x 3N(a) matrix with N-a the number of atoms. A series of simplified models has been proposed, in particular the Rotation-Translation Blocks (RTB) method by Tama et al. for the simulation of proteins. It makes use of the concept that a peptide chain or protein can be seen as a subsequent set of rigid components, i.e. the peptide units. A peptide chain is thus divided into rigid blocks with six degrees of freedom each. Recently we developed the Mobile Block Hessian (MBH) method, which in a sense has similar features as the RTB method. The main difference is that MBH was developed to deal with partially optimized systems. The position/orientation of each block is optimized while the internal geometry is kept fixed at a plausible - but not necessarily optimized - geometry. This reduces the computational cost of the energy minimization. Applying the standard NMA on a partially optimized structure however results in spurious imaginary frequencies and unwanted coordinate dependence. The MBH avoids these unphysical effects by taking into account energy gradient corrections. Moreover the number of variables is reduced, which facilitates the diagonalization of the Hessian. In the original implementation of MBH, atoms could only be part of one rigid block. The MBH is now extended to the case where atoms can be part of two or more blocks. Two basic linkages can be realized: (1) blocks connected by one link atom, or (2) by two link atoms, where the latter is referred to as the hinge type connection. In this work we present the MBH concept and illustrate its performance with the crambin protein as an example

G-CSF Prevents the Progression of Structural Disintegration of White Matter Tracts in Amyotrophic Lateral Sclerosis: A Pilot Trial

Background: The hematopoietic protein Granulocyte-colony stimulating factor (G-CSF) has neuroprotective and regenerative properties. The G-CSF receptor is expressed by motoneurons, and G-CSF protects cultured motoneuronal cells from apoptosis. It therefore appears as an attractive and feasible drug candidate for the treatment of amyotrophic lateral sclerosis (ALS). The current pilot study was performed to determine whether treatment with G-CSF in ALS patients is feasible.Methods: Ten patients with definite ALS were entered into a double-blind, placebo-controlled, randomized trial. Patients received either 10 mu g/kg BW G-CSF or placebo subcutaneously for the first 10 days and from day 20 to 25 of the study. Clinical outcome was assessed by changes in the ALS functional rating scale (ALSFRS), a comprehensive neuropsychological test battery, and by examining hand activities of daily living over the course of the study (100 days). The total number of adverse events (AE) and treatment-related AEs, discontinuation due to treatment-related AEs, laboratory parameters including leukocyte, erythrocyte, and platelet count, as well as vital signs were examined as safety endpoints. Furthermore, we explored potential effects of G-CSF on structural cerebral abnormalities on the basis of voxel-wise statistics of Diffusion Tensor Imaging (DTI), brain volumetry, and voxel-based morphometry.Results: Treatment was well-tolerated. No significant differences were found between groups in clinical tests and brain volumetry from baseline to day 100. However, DTI analysis revealed significant reductions of fractional anisotropy (FA) encompassing diffuse areas of the brain when patients were compared to controls. On longitudinal analysis, the placebo group showed significant greater and more widespread decline in FA than the ALS patients treated with G-CSF.Conclusions: Subcutaneous G-CSF treatment in ALS patients appears as feasible approach. Although exploratory analysis of clinical data showed no significant effect, DTI measurements suggest that the widespread and progressive microstructural neural damage in ALS can be modulated by G-CSF treatment. These findings may carry significant implications for further clinical trials on ALS using growth factors

Assessing behavioural changes in ALS: cross-validation of ALS-specific measures

Objective: The Beaumont Behavioural Inventory (BBI) is a behavioural proxy report for the assessment of behavioural changes in ALS. This tool has been validated against the FrSBe, a non-ALS specific behavioural assessment, and further comparison of the BBI against a disease-specific tool was considered. This study cross-validates the BBI against the ALS-FTD-Q. Methods: 60 ALS patients, 8% also meeting criteria for FTD, were recruited. All patients were evaluated using the BBI and the ALS-FTD-Q, completed by a carer. Correlational analysis was performed to assess construct validity. Precision, sensitivity, specificity and overall accuracy of the BBI, when compared to the ALS-FTD-Q, were obtained. Results: The mean score of the whole sample on the BBI was 11.45±13.06. ALS-FTD patients scored significantly higher than non-demented ALS patients (31.6±14.64, 9.62±11.38; p<.0001). A significant large positive correlation between the BBI and the ALS-FTD-Q was observed (r=.807, p<.0001), and no significant correlations between the BBI and other clinical/demographic characteristics, indicating good convergent and discriminant validity, respectively. 72% of overall concordance was observed. Precision, sensitivity and specificity for the classification of severely impaired patients were adequate. However, lower concordance in the classification of mild behavioural changes was observed, with higher sensitivity using the BBI, most likely secondary to BBI items which endorsed behavioural aspects not measured by the ALS-FTD-Q. Discussion: Good construct validity has been further confirmed when the BBI is compared to an ALS-specific tool. Furthermore, the BBI is a more comprehensive behavioural assessment for ALS, as it measures the whole behavioural spectrum in this condition

"Open Innovation" and "Triple Helix" Models of Innovation: Can Synergy in Innovation Systems Be Measured?

The model of "Open Innovations" (OI) can be compared with the "Triple Helix of University-Industry-Government Relations" (TH) as attempts to find surplus value in bringing industrial innovation closer to public R&D. Whereas the firm is central in the model of OI, the TH adds multi-centeredness: in addition to firms, universities and (e.g., regional) governments can take leading roles in innovation eco-systems. In addition to the (transversal) technology transfer at each moment of time, one can focus on the dynamics in the feedback loops. Under specifiable conditions, feedback loops can be turned into feedforward ones that drive innovation eco-systems towards self-organization and the auto-catalytic generation of new options. The generation of options can be more important than historical realizations ("best practices") for the longer-term viability of knowledge-based innovation systems. A system without sufficient options, for example, is locked-in. The generation of redundancy -- the Triple Helix indicator -- can be used as a measure of unrealized but technologically feasible options given a historical configuration. Different coordination mechanisms (markets, policies, knowledge) provide different perspectives on the same information and thus generate redundancy. Increased redundancy not only stimulates innovation in an eco-system by reducing the prevailing uncertainty; it also enhances the synergy in and innovativeness of an innovation system.Comment: Journal of Open Innovations: Technology, Market and Complexity, 2(1) (2016) 1-12; doi:10.1186/s40852-016-0039-

Truncated and Helix-Constrained Peptides with High Affinity and Specificity for the cFos Coiled-Coil of AP-1

Protein-based therapeutics feature large interacting surfaces. Protein folding endows structural stability to localised surface epitopes, imparting high affinity and target specificity upon interactions with binding partners. However, short synthetic peptides with sequences corresponding to such protein epitopes are unstructured in water and promiscuously bind to proteins with low affinity and specificity. Here we combine structural stability and target specificity of proteins, with low cost and rapid synthesis of small molecules, towards meeting the significant challenge of binding coiled coil proteins in transcriptional regulation. By iteratively truncating a Jun-based peptide from 37 to 22 residues, strategically incorporating i-->i+4 helix-inducing constraints, and positioning unnatural amino acids, we have produced short, water-stable, alpha-helical peptides that bind cFos. A three-dimensional NMR-derived structure for one peptide (24) confirmed a highly stable alpha-helix which was resistant to proteolytic degradation in serum. These short structured peptides are entropically pre-organized for binding with high affinity and specificity to cFos, a key component of the oncogenic transcriptional regulator Activator Protein-1 (AP-1). They competitively antagonized the cJun–cFos coiled-coil interaction. Truncating a Jun-based peptide from 37 to 22 residues decreased the binding enthalpy for cJun by ~9 kcal/mol, but this was compensated by increased conformational entropy (TDS ≤ 7.5 kcal/mol). This study demonstrates that rational design of short peptides constrained by alpha-helical cyclic pentapeptide modules is able to retain parental high helicity, as well as high affinity and specificity for cFos. These are important steps towards small antagonists of the cJun-cFos interaction that mediates gene transcription in cancer and inflammatory diseases

Virtual Reality Applications in Rehabilitation

The final publication is available at Springer via http://dx.doi.org/10.1007/978-3-319-39510-4_1One of the most valuable applications of virtual reality (VR) is in the domain of rehabilitation. After brain injuries or diseases, many patients suffer from impaired physical and/or cognitive capabilities, such as difficulties in moving arms or remembering names. Over the past two decades, VR has been tested and examined as a technology to assist patients’ recovery and rehabilitation, both physical and cognitive. The increasing prevalence of low-cost VR devices brings new opportunities, allowing VR to be used in practice. Using VR devices such as head-mounted displays (HMDs), special virtual scenes can be designed to assist patients in the process of re-training their brain and reorganizing their functions and abilities. However, such VR interfaces and applications must be comprehensively tested and examined for their effectiveness and potential side effects. This paper presents a review of related literature and discusses the new opportunities and challenges. Most of existing studies examined VR as an assessment method rather than a training/exercise method. Nevertheless, promising cases and positive preliminary results have been shown. Considering the increasing need for self-administered, home-based, and personalized rehabilitation, VR rehabilitation is potentially an important approach. This area requires more studies and research effort

Hybridization in parasites: consequences for adaptive evolution, pathogenesis and public health in a changing world

[No abstract available

An exact expression to calculate the derivatives of position-dependent observables in molecular simulations with flexible constraints

In this work, we introduce an algorithm to compute the derivatives of physical observables along the constrained subspace when flexible constraints are imposed on the system (i.e., constraints in which the hard coordinates are fixed to configuration-dependent values). The presented scheme is exact, it does not contain any tunable parameter, and it only requires the calculation and inversion of a sub-block of the Hessian matrix of second derivatives of the function through which the constraints are defined. We also present a practical application to the case in which the sought observables are the Euclidean coordinates of complex molecular systems, and the function whose minimization defines the constraints is the potential energy. Finally, and in order to validate the method, which, as far as we are aware, is the first of its kind in the literature, we compare it to the natural and straightforward finite-differences approach in three molecules of biological relevance: methanol, N-methyl-acetamide and a tri-glycine peptideComment: 13 pages, 8 figures, published versio

Variational Methods for Biomolecular Modeling

Structure, function and dynamics of many biomolecular systems can be characterized by the energetic variational principle and the corresponding systems of partial differential equations (PDEs). This principle allows us to focus on the identification of essential energetic components, the optimal parametrization of energies, and the efficient computational implementation of energy variation or minimization. Given the fact that complex biomolecular systems are structurally non-uniform and their interactions occur through contact interfaces, their free energies are associated with various interfaces as well, such as solute-solvent interface, molecular binding interface, lipid domain interface, and membrane surfaces. This fact motivates the inclusion of interface geometry, particular its curvatures, to the parametrization of free energies. Applications of such interface geometry based energetic variational principles are illustrated through three concrete topics: the multiscale modeling of biomolecular electrostatics and solvation that includes the curvature energy of the molecular surface, the formation of microdomains on lipid membrane due to the geometric and molecular mechanics at the lipid interface, and the mean curvature driven protein localization on membrane surfaces. By further implicitly representing the interface using a phase field function over the entire domain, one can simulate the dynamics of the interface and the corresponding energy variation by evolving the phase field function, achieving significant reduction of the number of degrees of freedom and computational complexity. Strategies for improving the efficiency of computational implementations and for extending applications to coarse-graining or multiscale molecular simulations are outlined.Comment: 36 page