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The current capacity of computers makes it possible to perform simulations ofsmall systems with portable, explicit-solvent potentials achieving high degreeof accuracy. However, simplified models must be employed to exploit thebehaviour of large systems or to perform systematic scans of smaller systems.While powerful algorithms are available to facilitate the sampling of theconformational space, successful applications of such models are hindered bythe availability of simple enough potentials able to satisfactorily reproduceknown properties of the system. We develop an interatomic potential to accountfor a number of properties of proteins in a computationally economic way. Thepotential is defined within an all-atom, implicit solvent model by contactfunctions between the different atom types. The associated numerical values canbe optimised by an iterative Monte Carlo scheme on any available experimentaldata, provided that they are expressible as thermal averages of someconformational properties. We test this model on three different proteins, forwhich we also perform a scan of all possible point mutations with explicitconformational sampling. The resulting models, optimised solely on a subset ofnative distances, not only reproduce the native conformations within a fewAngstroms from the experimental ones, but show the cooperative transitionbetween native and denatured state and correctly predict the measuredfree--energy changes associated with point mutations. Moreover, differentlyfrom other structure-based models, our method leaves a residual degree offrustration, which is known to be present in protein molecules.

Iterative derivation of effective potentials to sample the conformational space of proteins at atomistic scale