Prediction of protein assemblies, the next frontier: The CASP14-CAPRI experiment.

We present the results for CAPRI Round 50, the fourth joint CASP-CAPRI protein assembly prediction challenge. The Round comprised a total of twelve targets, including six dimers, three trimers, and three higher-order oligomers. Four of these were easy targets, for which good structural templates were available either for the full assembly, or for the main interfaces (of the higher-order oligomers). Eight were difficult targets for which only distantly related templates were found for the individual subunits. Twenty-five CAPRI groups including eight automatic servers submitted ~1250 models per target. Twenty groups including six servers participated in the CAPRI scoring challenge submitted ~190 models per target. The accuracy of the predicted models was evaluated using the classical CAPRI criteria. The prediction performance was measured by a weighted scoring scheme that takes into account the number of models of acceptable quality or higher submitted by each group as part of their five top-ranking models. Compared to the previous CASP-CAPRI challenge, top performing groups submitted such models for a larger fraction (70-75%) of the targets in this Round, but fewer of these models were of high accuracy. Scorer groups achieved stronger performance with more groups submitting correct models for 70-80% of the targets or achieving high accuracy predictions. Servers performed less well in general, except for the MDOCKPP and LZERD servers, who performed on par with human groups. In addition to these results, major advances in methodology are discussed, providing an informative overview of where the prediction of protein assemblies currently stands.

Docking unbound proteins with MIAX: a novel algorithm for protein-protein soft docking.

We propose a new methodology for "soft'' docking unbound protein molecules (reported at the isolated state). The methodology is characterized by its simplicity and easiness of embedment in any rigid body docking process based on point complementarity. It is oriented to allow limited free but not unrealistic interpenetration of the side chains of protein surface amino acid residues. The central step to the technique is a filtering process similar to those in image processing. The methodology assists in deletion of atomic-scale details on the surface of the interacting monomers, leading to the extraction of the most characteristic flattened shape for the molecule as well as the definition of a soft layer of atoms to allow smooth interpenetration of the interacting molecules during the docking process. Although the methodology does not perform structural or conformational rearrangements in the interacting monomers, results output by the algorithm are in fair agreement with the relative position of the monomer in experimentally reported complexes. The algorithm performs especially well in cases where the complexity of the protein surfaces is high, that is in hetero dimmer complex prediction. The algorithm is oriented to play the role of a fast screening engine for proteins known to interact but for which no information other than that of the structures at the isolated state is available. Consequently the importance of the methodology will increase in structural-function studies of thousand of proteins derived from large scale genome sequencing projects being executed all around the globe.

Folding pattern recognition in proteins using spectral analysis methods.

Divergence in sequence through evolution precludes sequence alignment based homology methodologies for protein folding prediction from detecting structural and folding similarities for distantly related protein. Homolog coverage of actual data bases is also a factor playing a critical role in the performance of those methodologies, the factor being conspicuously apparent in what is called the twilight zone of sequence homology in which proteins of high degree of similarity in both biological function and structure are found but for which the amino acid sequence homology ranges from about 20% to less than 30%. In contrast to these methodologies a strategy is proposed here based on a different concept of sequence homology. This concept is derived from a periodicity analysis of the physicochemical properties of the residues constituting proteins primary structures. The analysis is performed using a front-end processing technique in automatic speech recognition by means of which the cepstrum (measure of the periodic wiggliness of a frequency response) is computed that leads to a spectral envelope that depicts the subtle periodicity in physicochemical characteristics of the sequence. Homology in sequences is then derived by alignment of spectral envelopes. Proteins sharing common folding patterns and biological function but low sequence homology can then be detected by the similarity in spectral dimension. The methodology applied to protein folding recognition underscores in many cases other methodologies in the twilight zone.