id	author	title	date	pages	extension	mime	words	sentences	flesch	summary	cache	txt
cord-352481-iq3wor3w	Postic, Guillaume	An information gain-based approach for evaluating protein structure models	2020-08-18		.txt	text/plain	6518	328	52	Although these statistical potentials are not to be confused with their physics-based counterparts of the same name—i.e. PMFs obtained by molecular dynamics simulations—their particular success in assessing the native-like character of protein structure predictions has lead authors to consider the computed scores as approximations of the free energy. In this article, we present a conceptually new method for ranking protein structure models by quality, which is (i) independent of any physics-based explanation and (ii) relevant to statistics and to a general definition of information gain. As a proof of concept, we have built two scoring functions, respectively based on the new and the PMF equations, and compared their performance at ranking predicted structures of proteins by their quality. Using the reference dataset 3DRobot (n=60,200 structures) [35] , we show that the scoring function built with our new formalism is more accurate than statistical PMFs, based on three types of performance evaluation.	./cache/cord-352481-iq3wor3w.txt	./txt/cord-352481-iq3wor3w.txt