We present here the parmbsc0 force field, a refinement of the AMBER parm99 force field, where emphasis has been made on the correct representation of the alpha/gamma concerted rotation in nucleic acids (NAs). The modified force field corrects overpopulations of the alpha/gamma = (g+,t) backbone that were seen in long (more than 10 ns) simulations with previous AMBER parameter sets (parm94-99). The force field has been derived by fitting to high-level quantum mechanical data and verified by comparison with very high-level quantum mechanical calculations and by a very extensive comparison between simulations and experimental data. The set of validation simulations includes two of the longest trajectories published to date for the DNA duplex (200 ns each) and the largest variety of NA structures studied to date (15 different NA families and 97 individual structures). The total simulation time used to validate the force field includes near 1 mus of state-of-the-art molecular dynamics simulations in aqueous solution.

We present parmbsc1, a new force-field for DNA atomistic simulation, which has been parameterized from high-level quantum mechanical data and tested for nearly 100 systems (~140 μs) covering most of the DNA structural space. Parmbsc1 provides high quality results in diverse systems, solving problems of previous force-fields. Parmbsc1 aims to be a reference force-field for the study of DNA in the next decade. Parameters and trajectories are available at http://mmb.irbbarcelona.org/ParmBSC1/.

It is well recognized that base sequence exerts a significant influence on the properties of DNA and plays a significant role in protein–DNA interactions vital for cellular processes. Understanding and predicting base sequence effects requires an extensive structural and dynamic dataset which is currently unavailable from experiment. A consortium of laboratories was consequently formed to obtain this information using molecular simulations. This article describes results providing information not only on all 10 unique base pair steps, but also on all possible nearest-neighbor effects on these steps. These results are derived from simulations of 50–100 ns on 39 different DNA oligomers in explicit solvent and using a physiological salt concentration. We demonstrate that the simulations are converged in terms of helical and backbone parameters. The results show that nearest-neighbor effects on base pair steps are very significant, implying that dinucleotide models are insufficient for predicting sequence-dependent behavior. Flanking base sequences can notably lead to base pair step parameters in dynamic equilibrium between two conformational sub-states. Although this study only provides limited data on next-nearest-neighbor effects, we suggest that such effects should be analyzed before attempting to predict the sequence-dependent behavior of DNA.

We present the results of microsecond molecular dynamics simulations carried out by the ABC group of laboratories on a set of B-DNA oligomers containing the 136 distinct tetranucleotide base sequences. We demonstrate that the resulting trajectories have extensively sampled the conformational space accessible to B-DNA at room temperature. We confirm that base sequence effects depend strongly not only on the specific base pair step, but also on the specific base pairs that flank each step. Beyond sequence effects on average helical parameters and conformational fluctuations, we also identify tetranucleotide sequences that oscillate between several distinct conformational substates. By analyzing the conformation of the phosphodiester backbones, it is possible to understand for which sequences these substates will arise, and what impact they will have on specific helical parameters.

The dynamics of proteins in aqueous solution has been investigated through a massive approach based on ''state of the art'' molecular dynamics simulations performed for all protein metafolds using the four most popular force fields (OPLS, CHARMM, AMBER, and GROMOS). A detailed analysis of the massive database of trajectories (>1.5 terabytes of data obtained using Ϸ50 years of CPU) allowed us to obtain a robust-consensus picture of protein dynamics in aqueous solution.force field ͉ molecular dynamics ͉ molecular modeling ͉ protein structure

Abstract:We present the first microsecond MD simulation of B-DNA. Trajectory shows good agreement with available data and clarifies the µs dynamics of DNA. The duplex is sampling the B-conformation, but many relevant local transitions are found, including S f N repuckers (up to 7 N-sugars are found simultaneously), local BII transitions (15% of the dinucleotides are in BII-form; some of these forms are stable for up to 7 ns), and sequence-dependent R/γ transitions (happening in the 7-50 ns time scale, and being stable for up to 80 ns). Partial and total openings are often detected, but no base flipping is found. A‚T openings happen after amplification of propeller twist movements, while G‚C pairs (which can be opened for up to 1 ns) are opened by a complex mechanism which is often catalyzed by cations. A high affinity Na + binding site is found in the center of the minor groove. Access to this site by cations is difficult (average entry time 400 ns), but once inside, the ion remains for long periods of time (10-15 ns), producing a sizable narrowing of the minor groove. The essential dynamics of DNA fits well with the pattern of deformation needed to (i) sample uncommon right-handed forms and (ii) sample conformations adopted by DNA when bound to proteins. Clearly, DNA has evolved to be not only a stable structure able to maintain and transmit the genetic information but also a flexible entity whose intrinsic pattern of deformability matches its functional needs.

More than 100,000 protein structures are now known at atomic detail. However, far more are not yet known, particularly among large or complex proteins. Often, experimental information is only semireliable because it is uncertain, limited, or confusing in important ways. Some experiments give sparse information, some give ambiguous or nonspecific information, and others give uncertain information-where some is right, some is wrong, but we don't know which. We describe a method called Modeling Employing Limited Data (MELD) that can harness such problematic information in a physics-based, Bayesian framework for improved structure determination. We apply MELD to eight proteins of known structure for which such problematic structural data are available, including a sparse NMR dataset, two ambiguous EPR datasets, and four uncertain datasets taken from sequence evolution data. MELD gives excellent structures, indicating its promise for experimental biomolecule structure determination where only semireliable data are available.protein structure | molecular modeling | integrative structural biology | Bayesian inference I ncreasingly, structures are determined using integrative structural biology approaches, where direct experimental data are combined with computer-based models (1). Important successes in integrative structural biology have come from pioneering methods such as Modeler (2, 3), methods based on Rosetta (4-7), and others (8). Atomistic molecular dynamics (MD) simulations can be a powerful tool in integrative structural biology, because they capture physical principles and thermodynamic forcesinformation that is otherwise orthogonal to purely structural observations. However, there remain many situations in which it is not yet possible to properly integrate external knowledge with atomistic MD to infer biomolecular structures. Often, the external knowledge is challenging in one or more of the following ways. (i) Sparse data provide too little information to fully constrain the structure. (ii) Ambiguous data are not very specific, allowing alternative structural interpretations. (iii) Uncertain data cannot be interpreted at face value, because they contain false-positive signals that can be misdirective. Determining new challenging protein structures requires ways to handle semireliable data.Here, we describe a physics-based, Bayesian computational method called MELD (Modeling Employing Limited Data). It is a procedure for making rigorous inferences from limited or uncertain data. We build upon previous Bayesian approaches (9-14), which share the key feature of combining prior belief with the available data to produce statistically consistent samples from a posterior distribution, rather than searching for a single well-scoring model. The key properties of MELD are the rigorous treatment of statistical mechanics, a novel likelihood function that can handle uncertain data, and a graphics processing unit (GPU)-accelerated sampling strategy that makes the calculations tractable.MELD uses free energy as the princi...

It has been known for decades that DNA is extremely flexible and polymorphic, but our knowledge of its accessible conformational space remains limited. Structural data, primarily from X-ray diffraction studies, is sparse in comparison to the manifold configurations possible, and direct experimental examinations of DNA's flexibility still suffer from many limitations. In the face of these shortcomings, molecular dynamics (MD) is now an essential tool in the study of DNA. It affords detailed structural and dynamical insights, which explains its recent transition from a small number of highly specialized laboratories to a large variety of groups dealing with challenging biological problems. MD is now making an irreversible journey to the mainstream of research in biology, with the attendant opportunities and challenges. But given the speed with which MD studies of DNA have spread, the roots remain somewhat shallow: in many cases, there is a lack of deep knowledge about the foundations, strengths, and limits of the technique. In this Account, we discuss how MD has become the most important source of structural and flexibility data on DNA, focusing on advances since 2007 of atomistic MD in the description of DNA under near-physiological conditions and highlighting the possibilities and shortcomings of the technique. The evolution in the field over the past four years is a prelude to the ongoing revolution. The technique has gained in robustness and predictive power, which when coupled with the spectacular improvements in software and hardware has enabled the tackling of systems of increasing complexity. Simulation times of microseconds have now been achieved, with even longer times when specialized hardware is used. As a result, we have seen the first real-time simulation of large conformational transitions, including folding and unfolding of short DNA duplexes. Noteworthy advances have also been made in the study of DNA-ligand interactions, and we predict that a global thermodynamic and kinetic picture of the binding landscape of DNA will become available in a few years. MD will become a crucial tool in areas such as biomolecular engineering and synthetic biology. MD has also been shown to be an excellent source of parameters for mesoscopic models of DNA flexibility. Such models can be refined through atomistic MD simulations on small duplexes and then applied to the study of entire chromosomes. Recent evidence suggests that MD-derived elastic models can successfully predict the position of regulatory regions in DNA and can help advance our understanding of nucleosome positioning and chromatin plasticity. If these results are confirmed, MD simulations can become the ultimate tool to decipher a physical code that can contribute to gene regulation. We are entering the golden age of MD simulations of DNA. Undoubtedly, the expectations are high, but the challenges are also enormous. These include the need for more accurate potential energy functionals and for longer and more complex simulations in more realistic syst...