Perspective Combining Experiments and Simulations Using the Maximum Entropy Principle

Abstract: A key component of computational biology is to com-pare the results of computer mod-elling with experimental measure-ments. Despite substantial progress in the models and algorithms used in many areas of computational biology, such comparisons some-times reveal that the computations are not in quantitative agreement with experimental data. The princi-ple 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 mod-elling, and neurobiology. In this Perspectives article, we give a broad introduction to the method, in an attempt to encourage its further adoption. The general pro-cedure 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 some-times 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 imple-mented has been lacking. Three very recent papers have explored this problem using the maximum entropy approach, providing both new theoretical and practical in-sights to the problem. We highlight each of these contributions in turn and conclude with a discussion on remaining challenges