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Abstract Peptide‐protein docking is challenging due to the considerable conformational freedom of the peptide. CAPRI rounds 38‐45 included two peptide‐protein interactions, both characterized by a peptide forming an additional beta strand of a beta sheet in the receptor. Using theRosetta FlexPepDockpeptide docking protocol we generated top‐performing, high‐accuracy models for targets 134 and 135, involving an interaction between a peptide derived from L‐MAG with DLC8. In addition, we were able to generate the only medium‐accuracy models for a particularly challenging target, T121. In contrast to the classical peptide‐mediated interaction, in which receptor side chains contact both peptide backbone and side chains, beta‐sheet complementation involves a major contribution to binding by hydrogen bonds between main chain atoms. To establish how binding affinity and specificity are established in this special class of peptide‐protein interactions, we extractedPeptiDBeta, a benchmark of solved structures of different protein domains that are bound by peptides via beta‐sheet complementation, and tested our protocol for global peptide‐dockingPIPER‐FlexPepDockon this dataset. We find that the beta‐strand part of the peptide is sufficient to generate approximate and even high resolution models of many interactions, but inclusion of adjacent motif residues often provides additional information necessary to achieve high resolution model quality. 

Abstract Targets in the protein docking experiment CAPRI (Critical Assessment of Predicted Interactions) generally present new challenges and contribute to new developments in methodology. In rounds 38 to 45 of CAPRI, most targets could be effectively predicted using template‐based methods. However, the server ClusPro required structures rather than sequences as input, and hence we had to generate and dock homology models. The available templates also provided distance restraints that were directly used as input to the server. We show here that such an approach has some advantages. Free docking with template‐based restraints using ClusPro reproduced some interfaces suggested by weak or ambiguous templates while not reproducing others, resulting in correct server predicted models. More recently we developed the fully automated ClusPro TBM server that performs template‐based modeling and thus can use sequences rather than structures of component proteins as input. The performance of the server, freely available for noncommercial use athttps://tbm.cluspro.org, is demonstrated by predicting the protein‐protein targets of rounds 38 to 45 of CAPRI. 

Starting with a crystal structure of a macromolecule, computational structural modeling can help to understand the associated biological processes, structure and function, as well as to reduce the number of further experiments required to characterize a given molecular entity. In the past decade, two classes of powerful automated tools for investigating the binding properties of proteins have been developed: the protein–protein docking program ClusPro and the FTMap and FTSite programs for protein hotspot identification. These methods have been widely used by the research community by means of publicly available online servers, and models built using these automated tools have been reported in a large number of publications. Importantly, additional experimental information can be leveraged to further improve the predictive power of these approaches. Here, an overview of the methods and their biological applications is provided together with a brief interpretation of the results. 

One often observes small but measurable differences in the diffraction data measured from different crystals of a single protein. These differences might reflect structural differences in the protein and may reveal the natural dynamism of the molecule in solution. Partitioning these mixed-state data into single-state clusters is a critical step that could extract information about the dynamic behavior of proteins from hundreds or thousands of single-crystal data sets. Mixed-state data can be obtained deliberately (through intentional perturbation) or inadvertently (while attempting to measure highly redundant single-crystal data). To the extent that different states adopt different molecular structures, one expects to observe differences in the crystals; each of the polystates will create a polymorph of the crystals. After mixed-state diffraction data have been measured, deliberately or inadvertently, the challenge is to sort the data into clusters that may represent relevant biological polystates. Here, this problem is addressed using a simple multi-factor clustering approach that classifies each data set using independent observables, thereby assigning each data set to the correct location in conformational space. This procedure is illustrated using two independent observables, unit-cell parameters and intensities, to cluster mixed-state data from chymotrypsinogen (ChTg) crystals. It is observed that the data populate an arc of the reaction trajectory as ChTg is converted into chymotrypsin.