Progress and challenges in predicting protein interfaces

*These authors contributed equally to this work. The majority of biological processes are mediated via protein–protein interactions. Determination of residues participating in such interactions improves our understanding of molecular mechanisms and facilitates the development of therapeutics. Experimental approaches to identifying interacting residues, such as mutagenesis, are costly and time-consuming and thus, computational methods for this purpose could streamline conventional pipelines. Here we review the field of computational protein interface prediction. We make a distinction between methods which address proteins in general and those targeted at antibodies, owing to the radically different binding mechanism of antibodies. We organize the multitude of currently available methods hierarchically based on required input and prediction principles to provide an overview of the field. Key words: protein–protein interaction; protein interface prediction; antibody antigen interaction Protein interfaces Proteins interact with other proteins, DNA, RNA and small mol-ecules to perform their cellular tasks. Knowledge of protein interfaces and the residues involved is vital to fully understand molecular mechanisms and to identify potential drug target

Scoring docking conformations using predicted protein interfaces

BACKGROUND: Since proteins function by interacting with other molecules, analysis of protein-protein interactions is essential for comprehending biological processes. Whereas understanding of atomic interactions within a complex is especially useful for drug design, limitations of experimental techniques have restricted their practical use. Despite progress in docking predictions, there is still room for improvement. In this study, we contribute to this topic by proposing T-PioDock, a framework for detection of a native-like docked complex 3D structure. T-PioDock supports the identification of near-native conformations from 3D models that docking software produced by scoring those models using binding interfaces predicted by the interface predictor, Template based Protein Interface Prediction (T-PIP). RESULTS: First, exhaustive evaluation of interface predictors demonstrates that T-PIP, whose predictions are customised to target complexity, is a state-of-the-art method. Second, comparative study between T-PioDock and other state-of-the-art scoring methods establishes T-PioDock as the best performing approach. Moreover, there is good correlation between T-PioDock performance and quality of docking models, which suggests that progress in docking will lead to even better results at recognising near-native conformations. CONCLUSION: Accurate identification of near-native conformations remains a challenging task. Although availability of 3D complexes will benefit from template-based methods such as T-PioDock, we have identified specific limitations which need to be addressed. First, docking software are still not able to produce native like models for every target. Second, current interface predictors do not explicitly consider pairwise residue interactions between proteins and their interacting partners which leaves ambiguity when assessing quality of complex conformations

Structural complex prediction based on protein interface recognition

This dissertation contributes to the state of the art in protein interface prediction and detection of native-like docked poses by re-ranking them using protein interface knowledge. We started by investigating binding site patterns among homologues of a target protein in order to create a 3D motif. This structural binding site descriptor enables the re-ranking of docked complexes of the target protein. Although 3D motifs provide biological insight of protein interactions and have usage in real applications, they are not suitable for high through-put analysis. Therefore, we introduced a novel protein interface prediction framework which uses a weighted scoring schema to detect interface residues of the target protein using its homologues. The weights quantify both homology closeness between the target protein and its homologues and the diversity between the interacting partners of these homologues. The main novelty of this predictor is that it takes into account the nature of homologues interacting partners. It was further exploited for the development of a method for re-ranking docked conformations using predicted interface residues. We have evaluated both our interface predictor and re-ranking of docked poses using standard benchmarks. Comparisons to current state-of-the-art methods reveal that the proposed approaches outperform all their competitors. However, similarly to current interface predictors, our framework does not explicitly refer to pairwise residue interactions which leaves ambiguity when assessing quality of complex conformations. In addition, the performance of our interface predictor generally does not outperform the best available homologue interfaces if it was used as prediction. Therefore, we investigated the detection of the best homologue using the 'binding site transitivity' concept: given two query protein chains, interfaces of the first query protein are structurally compared against binding sites of the homologues' partners of the second query chain. This method not only allows detection of the best homologue for a reasonable number of proteins but also produces a docked structure of the two query chains. Finally, experiment suggests a meta interface predictor combining the prediction of our former interface predictor with the latter predictor based on binding site transitivity could further improve interface prediction

Structure prediction of LDLR-HNP1 complex based on docking enhanced by LDLR binding 3D motif

Human antimicrobial peptides (AMPs), including defensins, have come under intense scrutiny owing to their key multiple roles as antimicrobial agents. Not only do they display direct action on microbes, but also recently they have shown to interact with the immune system to increase antimicrobial activity. Unfortunately, since mechanisms involved in the binding of AMPs to mammalian cells are largely unknown, their potential as novel anti-infective agents cannot be exploited yet. Following the reported interaction of Human Neutrophil Peptide 1 dimer (HNP1) with a low density lipoprotein receptor (LDLR), a computational study was conducted to discover their putative mode of interaction. State-of-the-art docking software produced a set of LDLR-HNP1 complex 3D models. Creation of a 3D motif capturing atomic interactions of LDLR binding interface allowed selection of the most plausible configurations. Eventually, only two models were in agreement with the literature. Binding energy estimations revealed that not only one of them is particularly stable, but also interaction with LDLR weakens significantly bonds within the HNP1 dimer. This may be significant since it suggests a mechanism for internalisation of HNP1 in mammalian cells. In addition to a novel approach for complex structure prediction, this study proposes a 3D model of the LDLR-HNP1 complex which highlights the key residues which are involved in the interactions. The putative identification of the receptor binding mechanism should inform the future design of synthetic HNPs to afford maximum internalisation, which could lead to novel anti-infective drugs