C9orf72 repeat expansions cause neurodegeneration in Drosophila through arginine-rich proteins

An expanded GGGGCC repeat in C9orf72 is the most common genetic cause of frontotemporal dementia and amyotrophic lateral sclerosis. A fundamental question is whether toxicity is driven by the repeat RNA itself and/or by dipeptide repeat proteins generated by repeat-associated, non-ATG translation. To address this question we developed in vitro and in vivo models to dissect repeat RNA and dipeptide repeat protein toxicity. Expression of pure repeats in Drosophila caused adult-onset neurodegeneration attributable to poly-(glycine-arginine) proteins. Thus, expanded repeats promoted neurodegeneration through neurotoxic proteins. Expression of individual dipeptide repeat proteins with a non-GGGGCC RNA sequence showed both poly-(glycine-arginine) and poly-(proline-arginine) proteins caused neurodegeneration. These findings are consistent with a dual toxicity mechanism, whereby both arginine-rich proteins and repeat RNA contribute to C9orf72-mediated neurodegeneration

Maximum Flux Transition Paths of Conformational Change

Given two metastable states A and B of a biomolecular system, the problem is to calculate the likely paths of the transition from A to B. Such a calculation is more informative and more manageable if done for a reduced set of collective variables chosen so that paths cluster in collective variable space. The computational task becomes that of computing the "center" of such a cluster. A good way to define the center employs the concept of a committor, whose value at a point in collective variable space is the probability that a trajectory at that point will reach B before A. The committor "foliates" the transition region into a set of isocommittors. The maximum flux transition path is defined as a path that crosses each isocommittor at a point which (locally) has the highest crossing rate of distinct reactive trajectories. (This path is different from that of the MaxFlux method of Huo and Straub.) It is argued that such a path is nearer to an ideal path than others that have been proposed with the possible exception of the finite-temperature string method path. To make the calculation tractable, three approximations are introduced, yielding a path that is the solution of a nonsingular two-point boundary-value problem. For such a problem, one can construct a simple and robust algorithm. One such algorithm and its performance is discussed.Comment: 7 figure

Structuring and sampling complex conformation space: Weighted ensemble dynamics simulations

Based on multiple simulation trajectories, which started from dispersively selected initial conformations, the weighted ensemble dynamics method is designed to robustly and systematically explore the hierarchical structure of complex conformational space through the spectral analysis of the variance-covariance matrix of trajectory-mapped vectors. Non-degenerate ground state of the matrix directly predicts the ergodicity of simulation data. The ground state could be adopted as statistical weights of trajectories to correctly reconstruct the equilibrium properties, even though each trajectory only explores part of the conformational space. Otherwise, the degree of degeneracy simply gives the number of meta-stable states of the system under the time scale of individual trajectory. Manipulation on the eigenvectors leads to the classification of trajectories into non-transition ones within the states and transition ones between them. The transition states may also be predicted without a priori knowledge of the system. We demonstrate the application of the general method both to the system with a one-dimensional glassy potential and with the one of alanine dipeptide in explicit solvent.Comment: 13 pages, 7 figures. Phys Rev E 2009 (in press

Enhanced sampling of multidimensional free-energy landscapes using adaptive biasing forces

We propose an adaptive biasing algorithm aimed at enhancing the sampling of multimodal measures by Langevin dynamics. The underlying idea consists in generalizing the standard adaptive biasing force method commonly used in conjunction with molecular dynamics to handle in a more effective fashion multidimensional reaction coordinates. The proposed approach is anticipated to be particularly useful for reaction coordinates, the components of which are weakly coupled, as illuminated in a mathematical analysis of the long-time convergence of the algorithm. The strength as well as the intrinsic limitation of the method are discussed and illustrated in two realistic test cases

Pathogenic determinants and mechanisms of ALS/FTD linked to hexanucleotide repeat expansions in the C9orf72 gene.

Amyotrophic lateral sclerosis (ALS) and frontotemporal dementia (FTD) are two apparently distinct neurodegenerative diseases, the former characterized by selective loss of motor neurons in the brain and spinal cord and the latter characterized by selective atrophy of frontal and temporal lobes. Over the years, however, growing evidence from clinical, pathological and genetic findings has suggested that ALS and FTD belong to the same clinic-pathological spectrum disorder. This concept has been further supported by the identification of the most common genetic cause for both diseases, an aberrantly expanded hexanucleotide repeat GGGGCC/ CCCCGG sequence located in a non-coding region of the gene C9orf72. Three hypotheses have been proposed to explain how this repeats expansion causes diseases: 1) C9orf72 haploinsufficiency-expanded repeats interfere with transcription or translation of the gene, leading to decreased expression of the C9orf72 protein; 2) RNA gain of function-RNA foci formed by sense and antisense transcripts of expanded repeats interact and sequester essential RNA binding proteins, causing neurotoxicity; 3) Repeat associated non-ATG initiated (RAN) translation of expanded sense GGGGCC and antisense CCCCGG repeats produces potential toxic dipeptide repeat protein (DPR). In this review, we assess current evidence supporting or arguing against each proposed mechanism in C9 ALS/FTD disease pathogenesis. Additionally, controversial findings are also discussed. Lastly, we discuss the possibility that the three pathogenic mechanisms are not mutually exclusive and all three might be involved in disease

REinforcement learning based Adaptive samPling: REAPing Rewards by Exploring Protein Conformational Landscapes

One of the key limitations of Molecular Dynamics simulations is the computational intractability of sampling protein conformational landscapes associated with either large system size or long timescales. To overcome this bottleneck, we present the REinforcement learning based Adaptive samPling (REAP) algorithm that aims to efficiently sample conformational space by learning the relative importance of each reaction coordinate as it samples the landscape. To achieve this, the algorithm uses concepts from the field of reinforcement learning, a subset of machine learning, which rewards sampling along important degrees of freedom and disregards others that do not facilitate exploration or exploitation. We demonstrate the effectiveness of REAP by comparing the sampling to long continuous MD simulations and least-counts adaptive sampling on two model landscapes (L-shaped and circular), and realistic systems such as alanine dipeptide and Src kinase. In all four systems, the REAP algorithm consistently demonstrates its ability to explore conformational space faster than the other two methods when comparing the expected values of the landscape discovered for a given amount of time. The key advantage of REAP is on-the-fly estimation of the importance of collective variables, which makes it particularly useful for systems with limited structural information

Efficient model chemistries for peptides. I. Split-valence Gaussian basis sets and the heterolevel approximation in RHF and MP2

We present an exhaustive study of more than 250 ab initio potential energy surfaces (PESs) of the model dipeptide HCO-L-Ala-NH2. The model chemistries (MCs) used are constructed as homo- and heterolevels involving possibly different RHF and MP2 calculations for the geometry and the energy. The basis sets used belong to a sample of 39 selected representants from Pople's split-valence families, ranging from the small 3-21G to the large 6-311++G(2df,2pd). The reference PES to which the rest are compared is the MP2/6-311++G(2df,2pd) homolevel, which, as far as we are aware, is the more accurate PES of a dipeptide in the literature. The aim of the study presented is twofold: On the one hand, the evaluation of the influence of polarization and diffuse functions in the basis set, distinguishing between those placed at 1st-row atoms and those placed at hydrogens, as well as the effect of different contraction and valence splitting schemes. On the other hand, the investigation of the heterolevel assumption, which is defined here to be that which states that heterolevel MCs are more efficient than homolevel MCs. The heterolevel approximation is very commonly used in the literature, but it is seldom checked. As far as we know, the only tests for peptides or related systems, have been performed using a small number of conformers, and this is the first time that this potentially very economical approximation is tested in full PESs. In order to achieve these goals, all data sets have been compared and analyzed in a way which captures the nearness concept in the space of MCs.Comment: 54 pages, 16 figures, LaTeX, AMSTeX, Submitted to J. Comp. Che