The native conformation of a protein, in a given environment, is determined entirely by the various interatomic interactions dictated by the amino acid sequence (1-3). We describe here knowledge-based approach for protein structure assessment and prediction. Using a well-defined set of highresolution protein structures, we have derived statistical potentials, in the form of atom-pairwise distance probability density functions. These provide a description of pairwise interatomic interactions of native proteins. When applied to highly randomized and noisy structures of proteins distinct from the basis set, native-like structures were obtained to very high precision. The examples tested include proteins of all sizes (from 38 up to 461 amino acids long) and diverse topological structures (alpha, beta and alpha-beta classes). The potentials appear to be sensitive enough to recognize subtle distortions from a native packing structure and in optimization of structures drive them consistently to a higher probability. Therefore they provide a powerful tool for refinement of X-ray and NMR derived structures at arbitrary degrees of initial precision.