Dystrophin is the largest protein isoform (427 kDa) expressed from the gene defective in Duchenne muscular dystrophy, a lethal muscle-wasting and genetically inherited disease. Dystrophin, localized within a cytoplasmic lattice termed costameres, connects the intracellular cytoskeleton of a myofiber through the cell membrane (sarcolemma) to the surrounding extracellular matrix. In spite of its mechanical regulation roles in stabilizing the sarcolemma during muscle contraction, the underlying molecular mechanism is still elusive. Here, we systematically investigated the mechanical stability and kinetics of the force-bearing central domain of human dystrophin that contains 24 spectrin repeats using magnetic tweezers. We show that the stochastic unfolding and refolding of central domain of dystrophin is able to keep the forces below 25 pN over a significant length change up to ~ 800 nm in physiological level of pulling speeds. These results suggest that dystrophin may serve as a molecular shock absorber that defines the physiological level of force in the dystrophin mediated force-transmission pathway during muscle contraction/stretch, thereby stabilizing the sarcolemma.

We here report on the assessment of the model refinement predictions submitted to the 12th Experiment on the Critical Assessment of Protein Structure Prediction (CASP12). This is the fifth refinement experiment since CASP8 (2008) and, as with the previous experiments, the predictors were invited to refine selected server models received in the regular (nonrefinement) stage of the CASP experiment. We assessed the submitted models using a combination of standard CASP measures. The coefficients for the linear combination of Z-scores (the CASP12 score) have been obtained by a machine learning algorithm trained on the results of visual inspection. We identified eight groups that improve both the backbone conformation and the side chain positioning for the majority of targets. Albeit the top methods adopted distinctively different approaches, their overall performance was almost indistinguishable, with each of them excelling in different scores or target subsets. What is more, there were a few novel approaches that, while doing worse than average in most cases, provided the best refinements for a few targets, showing significant latitude for further innovation in the field.

Path Collective Variables (PCVs) are a set of path-like variables that have been successfully used to investigate complex chemical and biological processes and compute their associated free energy surfaces and kinetics. Their current implementation relies on general, but at times inefficient, metrics (such as RMSD or DRMSD) to evaluate the distance between the instantaneous conformational state during the simulation and the reference coordinates defining the path. In this work, we present a new algorithm to construct optimal PCVs metrics as linear combinations of different CVs weighted through a spectral gap optimization procedure. The method was tested first on a simple model, trialanine peptide in vacuo and then on a more complex path of an anticancer inhibitor binding to its pharmacological target. We also compared the results to those obtained with other path-based algorithms. We find that not only our proposed approach is able to automatically select relevant CVs for the PCVs metric, but also that the resulting PCVs allow to reconstruct the associated free energy very

Calculating absolute binding free energies is challenging and important. In this paper, we test some recently developed metadynamics-based methods and develop a new combination with a Hamiltonian replica-exchange approach. The methods were tested on 18 chemically diverse ligands with a wide range of different binding affinities to a complex target; namely, human soluble epoxide hydrolase. The results suggest that metadynamics with a funnel-shaped restraint can be used to calculate, in a computationally affordable and relatively accurate way, the absolute binding free energy for small fragments. When used in combination with an optimal pathlike variable obtained using machine learning or with the Hamiltonian replica-exchange algorithm SWISH, this method can achieve reasonably accurate results for increasingly complex ligands, with a good balance of computational cost and speed. An additional benefit of using the combination of metadynamics and SWISH is that it also provides useful information about the role of water in the binding mechanism.

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