Extensive amounts of information about protein sequences are becoming available, as demonstrated by the over 79 million entries in the UniProt database. Yet, it is still challenging to obtain proteome-wide experimental information on the structural properties associated with these sequences. Fast computational predictors of secondary structure and of intrinsic disorder of proteins have been developed in order to bridge this gap. These two types of predictions, however, have remained largely separated, often preventing a clear characterization of the structure and dynamics of proteins. Here, we introduce a computational method to predict secondary-structure populations from amino acid sequences, which simultaneously characterizes structure and disorder in a unified statistical mechanics framework. To develop this method, called s2D, we exploited recent advances made in the analysis of NMR chemical shifts that provide quantitative information about the probability distributions of secondary-structure elements in disordered states. The results that we discuss show that the s2D method predicts secondary-structure populations with an average error of about 14%. A validation on three datasets of mostly disordered, mostly structured and partly structured proteins, respectively, shows that its performance is comparable to or better than that of existing predictors of intrinsic disorder and of secondary structure. These results indicate that it is possible to perform rapid and quantitative sequence-based characterizations of the structure and dynamics of proteins through the predictions of the statistical distributions of their ordered and disordered regions.
s2D-2 is a method that provides a unified sequence-based prediction of protein structure and dynamics by estimating the populations of secondary structure elements, thus bridging the gap between methods of predicting intrinsic disorder and secondary structure. Differently from other commonly used three-state secondary structure predictors or intrinsic disorder predictors, the s2D method is trained on solution-based NMR data, which allows to characterise structure and disorder in one unified framework. With the s2D method disordered states that have significant populations of either α-helix or β-strand can be distinguished from fully random coil states without transient secondary structure elements, thus allowing for a characterisation of the conformational properties of disordered states. Similarly, when applied to globular proteins, the s2D method directly provides information about the stability of the predicted secondary structure elements.