Computational modeling of protein assemblies

Soni, Neelesh; Madhusudhan, M.S.

doi:10.1016/j.sbi.2017.04.006

Cited by 50 publications

(42 citation statements)

References 132 publications

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“…Besides of experimentally determined complexes, one can distinguish two main computational techniques to provide structures of putative protein–protein complexes. First, computational protein–protein docking methods can be considered as “ab initio” approaches for generating complex geometries although in practice experimental (or other bioinformatics) data can be included to restrict the search for possible solutions . In addition, the de novo design of protein–protein interactions requires docking or modeling of new protein–protein interfaces .…”

Section: Predicting the Structure Of Protein–protein Complexesmentioning

confidence: 99%

Computational prediction of protein–protein binding affinities

Siebenmorgen

Zacharias

2019

WIREs Comput Mol Sci

130

128

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Protein–protein interactions form central elements of almost all cellular processes. Knowledge of the structure of protein–protein complexes but also of the binding affinity is of major importance to understand the biological function of protein–protein interactions. Even weak transient protein–protein interactions can be of functional relevance for the cell during signal transduction or regulation of metabolism. The structure of a growing number of protein–protein complexes has been solved in recent years. Combined with docking approaches or template‐based methods, it is possible to generate structural models of many putative protein–protein complexes or to design new protein–protein interactions. In order to evaluate the functional relevance of putative or predicted protein–protein complexes, realistic binding affinity prediction is of increasing importance. Several computational tools ranging from simple force‐field or knowledge‐based scoring of single protein–protein complexes to ensemble‐based approaches and rigorous binding free energy simulations are available to predict relative and absolute binding affinities of complexes. With a focus on molecular mechanics force‐field approaches the present review aims at presenting an overview on available methods and discussing advantages, approximations, and limitations of the various methods. This article is categorized under: Molecular and Statistical Mechanics > Molecular Interactions Molecular and Statistical Mechanics > Free Energy Methods Software > Molecular Modeling

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Section: Predicting the Structure Of Protein–protein Complexesmentioning

confidence: 99%

Computational prediction of protein–protein binding affinities

Siebenmorgen

Zacharias

2019

WIREs Comput Mol Sci

130

128

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“…-A second type of approach is based on the analysis of the three-dimensional structure (as obtained by experimental investigation or by homology modeling) of the protein under scrutiny to identify the possible surfaces of interaction with other proteins. Of particular interest in this field are recent developments in protein-protein docking software that lead to the prediction of the possible binding sites between two molecules, allowing the formation of a stable complex (Simpson et al, 2010;Kaczor et al, 2015;Soni and Madhusudhan, 2017). Examples of this approach include the study of the lutropin receptor dimerization (Fanelli, 2007), studies aimed at characterizing the interaction interface in serotonin (5-HT) 4 (Bestel, 2005;Soulier et al, 2007) and rhodopsin complexes (Han et al, 2009), and the generation of models for the heterodimeric mGluR 2 -5-HT 2A complex (Bruno et al, 2009) and for the dopamine D 1 -D 2 receptor dimer ).…”

Section: Interaction Interfacesmentioning

confidence: 99%

G protein-coupled receptor-receptor interactions give integrative dynamics to intercellular communication

Guidolin

Marcoli

Tortorella

et al. 2018

Reviews in the Neurosciences

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Abstract:The proposal of receptor-receptor interactions (RRIs) in the early 1980s broadened the view on the role of G protein-coupled receptors (GPCR) in the dynamics of the intercellular communication. RRIs, indeed, allow GPCR to operate not only as monomers but also as receptor complexes, in which the integration of the incoming signals depends on the number, spatial arrangement, and order of activation of the protomers forming the complex. The main biochemical mechanisms controlling the functional interplay of GPCR in the receptor complexes are direct allosteric interactions between protomer domains. The formation of these macromolecular assemblies has several physiologic implications in terms of the modulation of the signaling pathways and interaction with other membrane proteins. It also impacts on the emerging field of connectomics, as it contributes to set and tune the synaptic strength. Furthermore, recent evidence suggests that the transfer of GPCR and GPCR complexes between cells via the exosome pathway could enable the target cells to recognize/decode transmitters and/or modulators for which they did not express the pertinent receptors. Thus, this process may also open the possibility of a new type of redeployment of neural circuits. The fundamental aspects of GPCR complex formation and function are the focus of the present review article.

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“…Computational techniques for modeling protein complex structures are traditionally divided into templatebased interface modeling and template-free docking methods. 89,90 Template-based modeling makes use of homologous complex structures and is driven by the evolutionary hypothesis that proteins similar in sequence or structure, especially in the interface region, bind in a similar way (Figure 3, label 1). However, experimental structural information is much more abundant for individual proteins than for protein complexes, therefore in many cases a homologous complex structure cannot be identified, but the two individual protein structures are known or can be modeled from structures of individual protein homologs (Figure 3, label 2).…”

Section: Modeling the Structure Of Protein Assembliesmentioning

confidence: 99%

Structural prediction of protein interactions and docking using conservation and coevolution

Andréani

Quignot

Guérois

2020

WIREs Comput Mol Sci

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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.

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Computational modeling of protein assemblies

Cited by 50 publications

References 132 publications

Computational prediction of protein–protein binding affinities

Computational prediction of protein–protein binding affinities

G protein-coupled receptor-receptor interactions give integrative dynamics to intercellular communication

Structural prediction of protein interactions and docking using conservation and coevolution

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