Protein secondary structure prediction began in 1951 when Pauling and Corey predicted helical and sheet conformations for protein polypeptide backbone even before the first protein structure was determined. Sixty-five years later, powerful new methods breathe new life into this field. The highest three-state accuracy without relying on structure templates is now at 82-84%, a number unthinkable just a few years ago. These improvements came from increasingly larger databases of protein sequences and structures for training, the use of template secondary structure information and more powerful deep learning techniques. As we are approaching to the theoretical limit of three-state prediction (88-90%), alternative to secondary structure prediction (prediction of backbone torsion angles and Ca-atom-based angles and torsion angles) not only has more room for further improvement but also allows direct prediction of three-dimensional fragment structures with constantly improved accuracy. About 20% of all 40-residue fragments in a database of 1199 non-redundant proteins have < 6 A° root-mean-squared distance from the native conformations by SPIDER2. More powerful deep learning methods with improved capability of capturing long-range interactions begin to emerge as the next generation of techniques for secondary structure prediction. The time has come to finish off the final stretch of the long march towards protein secondary structure prediction.

Sixty-five years of the long march in protein secondary structure prediction: the final stretch?

In the last decade, de novo protein structure prediction accuracy for individual proteins has improved significantly by utilizing deep learning (DL) methods for harvesting the co-evolution information from large multiple sequence alignments (MSA). In CASP14, the best method could predict the structure of most proteins with impressive accuracy. The same approach can, in principle, also be used to extract information about evolutionary-based contacts across protein-protein interfaces. However, most of the earlier studies have not used the latest DL methods for inter-chain contact distance predictions. In this paper, we showed for the first time that using one of the best DL-based residue-residue contact prediction methods (trRosetta), it is possible to simultaneously predict both the tertiary and quaternary structures of some protein pairs, even when the structures of the monomers are not known. Straightforward application of this method to a standard dataset for protein-protein docking yielded limited success, however, using alternative methods for MSA generating allowed us to dock accurately significantly more proteins. We also introduced a novel scoring function, PconsDock, that accurately separates 98% of correctly and incorrectly folded and docked proteins and thus this function can be used to evaluate the quality of the resulting docking models. The average performance of the method is comparable to the use of traditional, template-based or ab initio shape-complementarity-only docking methods, however, no a priori structural information for the individual proteins is needed. Moreover, the results of traditional and fold-and-dock approaches are complementary and thus a combined docking pipeline should increase overall docking success significantly. The dock-and-fold pipeline helped us to generate the best model for one of the CASP14 oligomeric targets, H1065.

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Andrea Pagnini

Papers

Estimation of Residue-Residue Coevolution using Direct Coupling Analysis Identifies Many Native Contacts Across a Large Number of Domain Families