Structural prediction of protein interactions and docking using conservation and coevolution

Abstract

Knowledge of the detailed structure of macromolecular interactions is key to a better understanding and modulation of essential cellular functions and pathological situations. Great efforts are invested in the development of improved computational prediction methods, including binding site prediction and protein-protein docking. These tools should benefit from the inclusion of evolutionary information, since the pressure to maintain functional interactions leads to conservation signals on protein surfaces at interacting sites and coevolution between contacting positions. However, unveiling such constraints and finding the best way to integrate them into computational pipelines remains a challenging area of research. Here, we first introduce evolutionary properties of interface structures, focusing on recent work exploring evolutionary mechanisms at play in protein interfaces and characterizing the complexity of evolutionary signals, such as interface deep mutational scans. Then, we review binding site predictors and interface structure modeling methods that integrate conservation and coevolution as important ingredients to improve predictive capacity, ending with a section dedicated to the prediction of binding modes between a globular protein domain and a short motif located within an intrinsically disordered or flexible region. Throughout the review, we discuss practical applications and recent promising developments, in particular regarding the use of coevolutionary information for interface structural prediction and the integration of these coevolution signals with machine learning and deep learning algorithms.

QSalignWeb: A Server to Predict and Analyze Protein Quaternary Structure

“…At the same time, predicting the QS of a protein remains challenging even when the tertiary structure is known. The use of evolution and coevolution information together with deep learning has also seen recent developments for scoring and predicting protein-protein interactions ( Andreani et al, 2020 ; Quadir et al, 2021 ; Quignot et al, 2021 ; Yan and Huang, 2021 ). Complementary to such residue-level information, the use of subunit interaction geometry conservation as evidence of a QS being physiological is a powerful approach, which yields accurate predictions ( Dey et al, 2018 ; Dey and Levy, 2021 ).…”

Improved protein docking by predicted interface residues

“…Another approach is to use constraints derived from, e.g. prediction of protein interfaces 14 . The goal of interface prediction is to understand which residues from the surface of one protein are more likely to form contacts with the residues of an interacting partner (interaction patch).…”

Atomic-level evolutionary information improves protein-protein interface scoring

“…Thus, protein interfaces tend to be more conserved than other regions on their surface (Mintseris and Weng, 2005;Teichmann, 2002) and signs of coevolution can be detected at protein interfaces, where potentially disrupting mutations are compensated for with mutations in contacting positions on the protein partner. These phenomena of conservation and coevolution can provide useful information in the analysis and prediction of their 3D interface structures (Andreani, et al, 2020). For example, evolutionary information is at the heart of increasingly popular covariation-based approaches, such as statistical coupling analysis (SCA) (Socolich, et al, 2005) or direct coupling analysis (DCA) (Morcos, et al, 2011), for structural proximity prediction of residues based on multiple sequence alignments (MSAs).…”

“…These approaches can be used to guide protein folding or to supplement predictions of macromolecular interactions (Cocco, et al, 2018;Cong, et al, 2019;Simkovic, et al, 2017). The vast majority of protein interaction site predictors successfully use evolutionary information, be it by sequence conservation, sequence co-evolution, or through homologous structures (Andreani, et al, 2020).…”