One of the major difficulties in paleontology is the acquisition of fossil data from the 10% of Earth's terrestrial surface that is covered by thick glaciers and ice sheets. Here we reveal that DNA and amino acids from buried organisms can be recovered from the basal sections of deep ice cores and allow reconstructions of past flora and fauna. We show that high altitude southern Greenland, currently lying below more than two kilometers of ice, was once inhabited by a diverse array of conifer trees and insects that may date back more than 450 thousand years. The results provide the first direct evidence in support of a forested southern Greenland and suggest that many deep ice cores may contain genetic records of paleoenvironments in their basal sections.The environmental histories of high latitude regions such as Greenland and Antarctica are poorly understood because much of the fossil evidence is hidden below kilometer thick ice sheets (1-3). Here, we test the idea that the basal sections of deep ice cores can act as archives for ancient biomolecules and show that these molecules can be used to reconstruct significant parts of the past plant and animal life in currently ice covered areas.The samples studied come from the basal impurity rich (silty) ice sections of the 2km long Dye 3 core from south-central Greenland (4), the 3km long GRIP core from the summit of the UKPMC Funders Group Author Manuscript UKPMC Funders Group Author ManuscriptGreenland ice sheet (5), and the Late Holocene John Evans Glacier on Ellesmere Island, Nunavut, northern Canada (Fig. 1A,B). The latter sample was included as a control to test for potential exotic DNA because the glacier has recently overridden a land surface with a known vegetation cover (6). As an additional test for long-distance atmospheric dispersal of DNA, we included five control samples of debris-free Holocene and Pleistocene ice taken just above the basal silty samples from the Dye 3 and GRIP ice cores (Fig. 1B). Finally, our analyses included sediment samples from the Kap København Formation from the northernmost part of Greenland, dated to 2.4 million years before present (Ma BP) (1,2).The silty ice yielded only few pollen grains and no macrofossils (7). However, the Dye 3 and John Evans Glacier silty ice samples showed low levels of amino acid racemization (Fig. 1A, insert), indicating good organic matter preservation (8). Therefore, following previous success with permafrost and cave sediments (9-11), we attempted to amplify ancient DNA from the ice. This was done following strict criteria to secure authenticity (12-14), including covering the surface of the frozen cores with plasmid DNA to control for potential contamination that may have entered the interior of the samples through cracks or during the sampling procedure (7). PCR products of the plasmid DNA were obtained only from extracts of the outer ice scrapings but not from the interior, confirming that sample contamination had not penetrated the cores.We could reproducibly PCR amplify short ampli...

A key component of computational biology is to compare the results of computer modelling with experimental measurements. Despite substantial progress in the models and algorithms used in many areas of computational biology, such comparisons sometimes reveal that the computations are not in quantitative agreement with experimental data. The principle of maximum entropy is a general procedure for constructing probability distributions in the light of new data, making it a natural tool in cases when an initial model provides results that are at odds with experiments. The number of maximum entropy applications in our field has grown steadily in recent years, in areas as diverse as sequence analysis, structural modelling, and neurobiology. In this Perspectives article, we give a broad introduction to the method, in an attempt to encourage its further adoption. The general procedure is explained in the context of a simple example, after which we proceed with a real-world application in the field of molecular simulations, where the maximum entropy procedure has recently provided new insight. Given the limited accuracy of force fields, macromolecular simulations sometimes produce results that are at not in complete and quantitative accordance with experiments. A common solution to this problem is to explicitly ensure agreement between the two by perturbing the potential energy function towards the experimental data. So far, a general consensus for how such perturbations should be implemented has been lacking. Three very recent papers have explored this problem using the maximum entropy approach, providing both new theoretical and practical insights to the problem. We highlight each of these contributions in turn and conclude with a discussion on remaining challenges.

Despite significant progress in recent years, protein structure prediction maintains its status as one of the prime unsolved problems in computational biology. One of the key remaining challenges is an efficient probabilistic exploration of the structural space that correctly reflects the relative conformational stabilities. Here, we present a fully probabilistic, continuous model of local protein structure in atomic detail. The generative model makes efficient conformational sampling possible and provides a framework for the rigorous analysis of local sequence-structure correlations in the native state. Our method represents a significant theoretical and practical improvement over the widely used fragment assembly technique by avoiding the drawbacks associated with a discrete and nonprobabilistic approach.conformational sampling ͉ directional statistics ͉ probabilistic model ͉ TorusDBN ͉ Bayesian network P rotein structure prediction remains one of the greatest challenges in computational biology. The problem itself is easily posed: predict the three-dimensional structure of a protein given its amino acid sequence. Significant progress has been made in the last decade, and, especially, knowledge-based methods are becoming increasingly accurate in predicting structures of small globular proteins (1). In such methods, an explicit treatment of local structure has proven to be an important ingredient. The search through conformational space can be greatly simplified through the restriction of the angular degrees of freedom in the protein backbone by allowing only angles that are known to appear in the native structures of real proteins. In practice, the angular preferences are typically enforced by using a technique called fragment assembly. The idea is to select a set of small structural fragments with strong sequence-structure relationships from the database of solved structures and subsequently assemble these building blocks to form complete structures. Although the idea was originally conceived in crystallography (2), it had a great impact on the protein structureprediction field when it was first introduced a decade ago (3). Today, fragment assembly stands as one of the most important single steps forward in tertiary structure prediction, contributing significantly to the progress we have seen in this field in recent years (4, 5).Despite their success, fragment-assembly approaches generally lack a proper statistical foundation, or equivalently, a consistent way to evaluate their contributions to the global free energy. When a fragment-assembly method is used, structure prediction normally proceeds by a Markov Chain Monte Carlo (MCMC) algorithm, where candidate structures are proposed by the fragment assembler and then accepted or rejected based on an energy function. The theoretical basis of MCMC is the existence of a stationary probability distribution dictating the transition probabilities of the Markov chain. In the context of statistical physics, this stationary distribution is given by the conformational ...