An anti-DNA antibody prefers damaged dsDNA over native.

DNA-protein interactions, including DNA-antibody complexes, have both fundamental and practical significance. In particular, antibodies against double-stranded DNA play an important role in the pathogenesis of autoimmune diseases. Elucidation of structural mechanisms of an antigen recognition and interaction of anti-DNA antibodies provides a basis for understanding the role of DNA-containing immune complexes in human pathologies and for new treatments. Here we used Molecular Dynamic simulations of bimolecular complexes of a segment of dsDNA with a monoclonal anti-DNA antibody's Fab-fragment to obtain detailed structural and physical characteristics of the dynamic intermolecular interactions. Using a computationally modified crystal structure of a Fab-DNA complex (PDB: 3VW3), we studied in silico equilibrium Molecular Dynamics of the Fab-fragment associated with two homologous dsDNA fragments, containing or not containing dimerized thymine, a product of DNA photodamage. The Fab-fragment interactions with the thymine dimer-containing DNA was thermodynamically more stable than with the native DNA. The amino acid residues constituting a paratope and the complementary nucleotide epitopes for both Fab-DNA constructs were identified. Stacking and electrostatic interactions were shown to play the main role in the antibody-dsDNA contacts, while hydrogen bonds were less significant. The aggregate of data show that the chemically modified dsDNA (containing a covalent thymine dimer) has a higher affinity toward the antibody and forms a stronger immune complex. These findings provide a mechanistic insight into formation and properties of the pathogenic anti-DNA antibodies in autoimmune diseases, such as systemic lupus erythematosus, associated with skin photosensibilization and DNA photodamage.

[Molecular dynamics of immune complex of photoadduct-containing DNA with Fab-Anti-DNA antibody fragment].

Antibodies to DNA play an important role in the pathogenesis of autoimmune diseases. The elucidation of structural mechanisms of both the antigen recognition and the interaction of anti-DNA antibodies with DNA will help to understand the role of DNA-containing immune complexes in various pathologies and can provide a basis for new treatment modalities. Moreover, the DNA-antibody complex is an analog of specific intracellular DNA-protein interactions. In this work, we used in silico molecular dynamic simulations of bimolecular complexes of the dsDNA segment containing the Fab fragment of an anti-DNA antibody to obtain the detailed thermodynamic and structural characteristics of dynamic intermolecular interactions. Using computationally modified crystal structure of the Fab-DNA complex (PDB ID: 3VW3), we studied the equilibrium molecular dynamics of the 64M-5 antibody Fab fragment associated with the dsDNA fragment containing the thymine dimer, the product of DNA photodamage. Amino acid residues that constitute paratopes and the complementary nucleotide epitopes for the Fab-DNA construct were identified. Stacking and electrostatic interactions were found to play the main role in mediating the most specific antibody-dsDNA contacts, while hydrogen bonds were less significant. These findings may shed light on the formation and properties of pathogenic anti-DNA antibodies in autoimmune diseases, such as systemic lupus erythematosus associated with skin photosensitivity and DNA photodamage.

Generation of random numbers on graphics processors: forced indentation in silico of the bacteriophage HK97.

The use of graphics processing units (GPUs) in simulation applications offers a significant speed gain as compared to computations on central processing units (CPUs). Many simulation methods require a large number of independent random variables generated at each step. We present two approaches for implementation of random number generators (RNGs) on a GPU. In the one-RNG-per-thread approach, one RNG produces a stream of random numbers in each thread of execution, whereas the one-RNG-for-all-threads method builds on the ability of different threads to communicate, thus, sharing random seeds across an entire GPU device. We used these approaches to implement Ran2, Hybrid Taus, and Lagged Fibonacci algorithms on a GPU. We profiled the performance of these generators in terms of the computational time, memory usage, and the speedup factor (CPU time/GPU time). These generators have been incorporated into the program for Langevin simulations of biomolecules fully implemented on the GPU. The ∼250-fold computational speedup on the GPU allowed us to carry out single-molecule dynamic force measurements in silico to explore the mechanical properties of the bacteriophage HK97 in the experimental subsecond time scale. We found that the nanomechanical response of HK97 depends on the conditions of force application, including the rate of change and geometry of the mechanical perturbation. Hence, using the GPU-based implementation of RNGs, presented here, in conjunction with Langevin simulations, makes it possible to directly compare the results of dynamic force measurements in vitro and in silico.

Order statistics theory of unfolding of multimeric proteins.

Dynamic force spectroscopy has become indispensable for the exploration of the mechanical properties of proteins. In force-ramp experiments, performed by utilizing a time-dependent pulling force, the peak forces for unfolding transitions in a multimeric protein (D)(N) are used to map the free energy landscape for unfolding for a protein domain D. We show that theoretical modeling of unfolding transitions based on combining the observed first (f(1)), second (f(2)), …, N(th) (f(N)) unfolding forces for a protein tandem of fixed length N, and pooling the force data for tandems of different length, n(1)

Sop-GPU: accelerating biomolecular simulations in the centisecond timescale using graphics processors.

Theoretical exploration of fundamental biological processes involving the forced unraveling of multimeric proteins, the sliding motion in protein fibers and the mechanical deformation of biomolecular assemblies under physiological force loads is challenging even for distributed computing systems. Using a C(α)-based coarse-grained self organized polymer (SOP) model, we implemented the Langevin simulations of proteins on graphics processing units (SOP-GPU program). We assessed the computational performance of an end-to-end application of the program, where all the steps of the algorithm are running on a GPU, by profiling the simulation time and memory usage for a number of test systems. The ∼90-fold computational speedup on a GPU, compared with an optimized central processing unit program, enabled us to follow the dynamics in the centisecond timescale, and to obtain the force-extension profiles using experimental pulling speeds (v(f) = 1-10 µm/s) employed in atomic force microscopy and in optical tweezers-based dynamic force spectroscopy. We found that the mechanical molecular response critically depends on the conditions of force application and that the kinetics and pathways for unfolding change drastically even upon a modest 10-fold increase in v(f). This implies that, to resolve accurately the free energy landscape and to relate the results of single-molecule experiments in vitro and in silico, molecular simulations should be carried out under the experimentally relevant force loads. This can be accomplished in reasonable wall-clock time for biomolecules of size as large as 10(5) residues using the SOP-GPU package.

Nonparametric density estimation and optimal bandwidth selection for protein unfolding and unbinding data.

Dynamic force spectroscopy and steered molecular simulations have become powerful tools for analyzing the mechanical properties of proteins, and the strength of protein-protein complexes and aggregates. Probability density functions of the unfolding forces and unfolding times for proteins, and rupture forces and bond lifetimes for protein-protein complexes allow quantification of the forced unfolding and unbinding transitions, and mapping the biomolecular free energy landscape. The inference of the unknown probability distribution functions from the experimental and simulated forced unfolding and unbinding data, as well as the assessment of analytically tractable models of the protein unfolding and unbinding requires the use of a bandwidth. The choice of this quantity is typically subjective as it draws heavily on the investigator's intuition and past experience. We describe several approaches for selecting the "optimal bandwidth" for nonparametric density estimators, such as the traditionally used histogram and the more advanced kernel density estimators. The performance of these methods is tested on unimodal and multimodal skewed, long-tailed distributed data, as typically observed in force spectroscopy experiments and in molecular pulling simulations. The results of these studies can serve as a guideline for selecting the optimal bandwidth to resolve the underlying distributions from the forced unfolding and unbinding data for proteins.