Decoding Protein-protein Interactions: An Overview

2020 ◽  
Vol 20 (10) ◽  
pp. 855-882
Author(s):  
Olivia Slater ◽  
Bethany Miller ◽  
Maria Kontoyianni
Keyword(s):  
Drug Discovery ◽  
Protein Interactions ◽  
Drug Repurposing ◽  
Protein Docking ◽  
Target Space ◽  
Protein Protein Interactions ◽  
X Ray Crystallography ◽  
Protein Protein Interaction ◽  
Interaction Sites ◽  
Long Time

Drug discovery has focused on the paradigm “one drug, one target” for a long time. However, small molecules can act at multiple macromolecular targets, which serves as the basis for drug repurposing. In an effort to expand the target space, and given advances in X-ray crystallography, protein-protein interactions have become an emerging focus area of drug discovery enterprises. Proteins interact with other biomolecules and it is this intricate network of interactions that determines the behavior of the system and its biological processes. In this review, we briefly discuss networks in disease, followed by computational methods for protein-protein complex prediction. Computational methodologies and techniques employed towards objectives such as protein-protein docking, protein-protein interactions, and interface predictions are described extensively. Docking aims at producing a complex between proteins, while interface predictions identify a subset of residues on one protein that could interact with a partner, and protein-protein interaction sites address whether two proteins interact. In addition, approaches to predict hot spots and binding sites are presented along with a representative example of our internal project on the chemokine CXC receptor 3 B-isoform and predictive modeling with IP10 and PF4.

Biophysical and computational fragment-based approaches to targeting protein–protein interactions: applications in structure-guided drug discovery

2012 ◽  
Vol 45 (4) ◽  
pp. 383-426 ◽  
Author(s):  
Anja Winter ◽  
Alicia P. Higueruelo ◽  
May Marsh ◽  
Anna Sigurdardottir ◽  
Will R Pitt ◽  
...  
Keyword(s):  
Drug Discovery ◽  
Protein Interactions ◽  
Active Sites ◽  
Current Knowledge ◽  
Chemical Space ◽  
Isothermal Calorimetry ◽  
Protein Protein Interactions ◽  
Regulatory Pathways ◽  
Drug Candidates ◽  
X Ray Crystallography

AbstractDrug discovery has classically targeted the active sites of enzymes or ligand-binding sites of receptors and ion channels. In an attempt to improve selectivity of drug candidates, modulation of protein–protein interfaces (PPIs) of multiprotein complexes that mediate conformation or colocation of components of cell-regulatory pathways has become a focus of interest. However, PPIs in multiprotein systems continue to pose significant challenges, as they are generally large, flat and poor in distinguishing features, making the design of small molecule antagonists a difficult task. Nevertheless, encouragement has come from the recognition that a few amino acids – so-called hotspots – may contribute the majority of interaction-free energy. The challenges posed by protein–protein interactions have led to a wellspring of creative approaches, including proteomimetics, stapled α-helical peptides and a plethora of antibody inspired molecular designs. Here, we review a more generic approach: fragment-based drug discovery. Fragments allow novel areas of chemical space to be explored more efficiently, but the initial hits have low affinity. This means that they will not normally disrupt PPIs, unless they are tethered, an approach that has been pioneered by Wells and co-workers. An alternative fragment-based approach is to stabilise the uncomplexed components of the multiprotein system in solution and employ conventional fragment-based screening. Here, we describe the current knowledge of the structures and properties of protein–protein interactions and the small molecules that can modulate them. We then describe the use of sensitive biophysical methods – nuclear magnetic resonance, X-ray crystallography, surface plasmon resonance, differential scanning fluorimetry or isothermal calorimetry – to screen and validate fragment binding. Fragment hits can subsequently be evolved into larger molecules with higher affinity and potency. These may provide new leads for drug candidates that target protein–protein interactions and have therapeutic value.

Towards structure-based protein drug design

2011 ◽  
Vol 39 (5) ◽  
pp. 1382-1386 ◽  
Author(s):  
Changsheng Zhang ◽  
Luhua Lai
Keyword(s):  
Drug Discovery ◽  
Drug Design ◽  
Small Molecule ◽  
Protein Interactions ◽  
De Novo ◽  
Protein Docking ◽  
Erythropoietin Receptor ◽  
Protein Drug ◽  
Protein Protein Interactions ◽  
Structure Based Drug Design

Structure-based drug design for chemical molecules has been widely used in drug discovery in the last 30 years. Many successful applications have been reported, especially in the field of virtual screening based on molecular docking. Recently, there has been much progress in fragment-based as well as de novo drug discovery. As many protein–protein interactions can be used as key targets for drug design, one of the solutions is to design protein drugs based directly on the protein complexes or the target structure. Compared with protein–ligand interactions, protein–protein interactions are more complicated and present more challenges for design. Over the last decade, both sampling efficiency and scoring accuracy of protein–protein docking have increased significantly. We have developed several strategies for structure-based protein drug design. A grafting strategy for key interaction residues has been developed and successfully applied in designing erythropoietin receptor-binding proteins. Similarly to small-molecule design, we also tested de novo protein-binder design and a virtual screen of protein binders using protein–protein docking calculations. In comparison with the development of structure-based small-molecule drug design, we believe that structure-based protein drug design has come of age.

scholarly journals Identification of β-Strand Mediated Protein-Protein Interaction Inhibitors Using Ligand-Directed Fragment Ligation

2020 ◽  
Author(s):  
Zsófia Hegedüs Zsófia Hegedüs ◽  
Fruzsina Hobor ◽  
Deborah K. Shoemark ◽  
sergio Celis ◽  
Lu-Yun Lian ◽  
...  
Keyword(s):  
Protein Interactions ◽  
Electrostatic Interactions ◽  
Significant Proportion ◽  
Selective Inhibition ◽  
Peptide Sequence ◽  
Protein Protein Interactions ◽  
X Ray Crystallography ◽  
Protein Protein Interaction ◽  
Ligand Discovery ◽  
Dynamics Simulations

β-Strand mediated protein-protein interactions (PPIs) represent underexploited targets for chemical probe development despite representing a significant proportion of known and therapeutically relevant PPI targets. β-strand mimicry is challenging given that both amino acid side-chains and backbone hydrogen-bonds are typically required for molecular recognition, yet these are oriented along perpendicular vectors. This paper describes an alternative approach using GKAP/SHANK1 PDZ as a model and dynamic ligation screening to identify small-molecule replacements for tranches of peptide sequence. A peptide truncation of GKAP functionalized at the N- and C-termini with acylhydrazone groups was used as an anchor. Reversible acylhydrazone bond exchange with a library of aldehyde fragments in the presence of the protein as template and <i>in situ</i> screening using a fluorescence anisotropy (FA) assay identified peptide hybrid hits with comparable affinity to the GKAP peptide binding sequence. Identified hits were validated using FA, ITC, NMR and X-ray crystallography to confirm selective inhibition of the target PDZ-mediated PPI and mode of binding. These analyses together with molecular dynamics simulations demonstrated the ligands make transient interactions with an unoccupied basic patch through electrostatic interactions, establishing proof-of-concept that this unbiased approach to ligand discovery represents a powerful addition to the armory of tools that can be used to identify PPI modulators.

New perceptions of transcription factor properties from NMR

1998 ◽  
Vol 76 (2-3) ◽  
pp. 368-378 ◽  
Author(s):  
Stefan Bagby ◽  
Cheryl H Arrowsmith ◽  
Mitsuhiko Ikura
Keyword(s):  
Transcription Factor ◽  
Protein Interactions ◽  
Dna Interaction ◽  
Protein Protein Interactions ◽  
Nmr Studies ◽  
X Ray ◽  
X Ray Crystallography ◽  
Protein Protein Interaction ◽  
Structural Problems ◽  
Induced Folding

The complementarity of NMR and X-ray crystallography for biomacromolecular studies has been particularly evident in analysis of transcription factor structures and interactions. While X-ray crystallography can be used to tackle relatively complicated structural problems including multicomponent (three and higher) complexes, NMR studies have provided new insights into the nature of protein-DNA and protein-protein interactions that would be difficult to obtain by other biophysical methods. We describe herein some of the novel and important information recently derived from NMR studies of transcription factors.Key words: protein-DNA interaction, protein-protein interaction, induced folding, conformational fluctuations, transcriptional regulation.

scholarly journals Characterization of known protein complexes using k-connectivity and other topological measures

F1000Research ◽  
2015 ◽  
Vol 2 ◽  
pp. 172
Author(s):  
Suzanne R Gallagher ◽  
Debra S Goldberg
Keyword(s):  
Protein Interactions ◽  
Protein Complexes ◽  
Clustering Coefficient ◽  
Edge Density ◽  
Protein Protein Interactions ◽  
X Ray Crystallography ◽  
Protein Protein Interaction ◽  
Unknown Protein ◽  
Topological Measures

Many protein complexes are densely packed, so proteins within complexes often interact with several other proteins in the complex. Steric constraints prevent most proteins from simultaneously binding more than a handful of other proteins, regardless of the number of proteins in the complex. Because of this, as complex size increases, several measures of the complex decrease within protein-protein interaction networks. However, k-connectivity, the number of vertices or edges that need to be removed in order to disconnect a graph, may be consistently high for protein complexes. The property of k-connectivity has been little used previously in the investigation of protein-protein interactions. To understand the discriminative power of k-connectivity and other topological measures for identifying unknown protein complexes, we characterized these properties in known Saccharomyces cerevisiae protein complexes in networks generated both from highly accurate X-ray crystallography experiments which give an accurate model of each complex, and also as the complexes appear in high-throughput yeast 2-hybrid studies in which new complexes may be discovered. We also computed these properties for appropriate random subgraphs.We found that clustering coefficient, mutual clustering coefficient, and k-connectivity are better indicators of known protein complexes than edge density, degree, or betweenness. This suggests new directions for future protein complex-finding algorithms.

scholarly journals Identification of molecular glues of the SLP76/14-3-3 protein–protein interaction

2021 ◽  
Author(s):  
Lorenzo Soini ◽  
Martin Redhead ◽  
Marta Westwood ◽  
Seppe Leysen ◽  
Jeremy Davis ◽  
...  
Keyword(s):  
Drug Discovery ◽  
Protein Interaction ◽  
Protein Interactions ◽  
Protein Protein Interactions ◽  
Protein Protein Interaction

The stabilisation of protein–protein interactions (PPIs) through molecular glues is a novel and promising approach in drug discovery.

scholarly journals Benchmarking AlphaFold for protein complex modeling reveals accuracy determinants

2021 ◽  
Author(s):  
Rui Yin ◽  
Brandon Y Feng ◽  
Amitabh Varshney ◽  
Brian G Pierce
Keyword(s):  
Deep Learning ◽  
Protein Interactions ◽  
Protein Complexes ◽  
Protein Docking ◽  
Protein Protein Interactions ◽  
Protein Protein Interaction ◽  
Future Developments ◽  
Massive Number ◽  
Improved Performance

High resolution experimental structural determination of protein-protein interactions has led to valuable mechanistic insights, yet due to the massive number of interactions and experimental limitations there is a need for computational methods that can accurately model their structures. Here we explore the use of the recently developed deep learning method, AlphaFold, to predict structures of protein complexes from sequence. With a benchmark of 152 diverse heterodimeric protein complexes, multiple implementations and parameters of AlphaFold were tested for accuracy. Remarkably, many cases had highly accurate models generated as top-ranked predictions, greatly surpassing the performance of unbound protein-protein docking, whereas antibody-antigen docking was largely unsuccessful. While AlphaFold-generated accuracy predictions were able to discriminate near-native models, previously developed scoring protocols improved performance. Our study demonstrates that end-to-end deep learning can accurately model transient protein complexes, and identifies areas for improvement to guide future developments to reliably model any protein-protein interaction of interest.

scholarly journals Identification of β-Strand Mediated Protein-Protein Interaction Inhibitors Using Ligand-Directed Fragment Ligation

2020 ◽  
Author(s):  
Zsófia Hegedüs Zsófia Hegedüs ◽  
Fruzsina Hobor ◽  
Deborah K. Shoemark ◽  
sergio Celis ◽  
Lu-Yun Lian ◽  
...  
Keyword(s):  
Protein Interactions ◽  
Electrostatic Interactions ◽  
Significant Proportion ◽  
Selective Inhibition ◽  
Peptide Sequence ◽  
Protein Protein Interactions ◽  
X Ray Crystallography ◽  
Protein Protein Interaction ◽  
Ligand Discovery ◽  
Dynamics Simulations

β-Strand mediated protein-protein interactions (PPIs) represent underexploited targets for chemical probe development despite representing a significant proportion of known and therapeutically relevant PPI targets. β-strand mimicry is challenging given that both amino acid side-chains and backbone hydrogen-bonds are typically required for molecular recognition, yet these are oriented along perpendicular vectors. This paper describes an alternative approach using GKAP/SHANK1 PDZ as a model and dynamic ligation screening to identify small-molecule replacements for tranches of peptide sequence. A peptide truncation of GKAP functionalized at the N- and C-termini with acylhydrazone groups was used as an anchor. Reversible acylhydrazone bond exchange with a library of aldehyde fragments in the presence of the protein as template and <i>in situ</i> screening using a fluorescence anisotropy (FA) assay identified peptide hybrid hits with comparable affinity to the GKAP peptide binding sequence. Identified hits were validated using FA, ITC, NMR and X-ray crystallography to confirm selective inhibition of the target PDZ-mediated PPI and mode of binding. These analyses together with molecular dynamics simulations demonstrated the ligands make transient interactions with an unoccupied basic patch through electrostatic interactions, establishing proof-of-concept that this unbiased approach to ligand discovery represents a powerful addition to the armory of tools that can be used to identify PPI modulators.

scholarly journals A Deep Learning and XGBoost-Based Method for Predicting Protein-Protein Interaction Sites

2021 ◽  
Vol 12 ◽  
Author(s):  
Pan Wang ◽  
Guiyang Zhang ◽  
Zu-Guo Yu ◽  
Guohua Huang
Keyword(s):  
Deep Learning ◽  
Protein Interaction ◽  
Protein Interactions ◽  
Gradient Boosting ◽  
Protein Protein Interactions ◽  
Global Features ◽  
Protein Protein Interaction ◽  
Interaction Sites ◽  
Extreme Gradient Boosting ◽  
Protein Interaction Sites

Knowledge about protein-protein interactions is beneficial in understanding cellular mechanisms. Protein-protein interactions are usually determined according to their protein-protein interaction sites. Due to the limitations of current techniques, it is still a challenging task to detect protein-protein interaction sites. In this article, we presented a method based on deep learning and XGBoost (called DeepPPISP-XGB) for predicting protein-protein interaction sites. The deep learning model served as a feature extractor to remove redundant information from protein sequences. The Extreme Gradient Boosting algorithm was used to construct a classifier for predicting protein-protein interaction sites. The DeepPPISP-XGB achieved the following results: area under the receiver operating characteristic curve of 0.681, a recall of 0.624, and area under the precision-recall curve of 0.339, being competitive with the state-of-the-art methods. We also validated the positive role of global features in predicting protein-protein interaction sites.

scholarly journals InterPred: A pipeline to identify and model protein-protein interactions

2016 ◽  
Author(s):  
Claudio Mirabello ◽  
Björn Wallner
Keyword(s):  
Protein Interaction ◽  
Protein Interactions ◽  
Protein Function ◽  
Structural Features ◽  
Protein Docking ◽  
Protein Protein Interactions ◽  
Protein Protein Interaction ◽  
Interaction Detection ◽  
Molecular Detail ◽  
Better Than

AbstractProtein-protein interactions (PPI) are crucial for protein function. There exist many techniques to identify PPIs experimentally, but to determine the interactions in molecular detail is still difficult and very time-consuming. The fact that the number of PPIs is vastly larger than the number of individual proteins makes it practically impossible to characterize all interactions experimentally. Computational approaches that can bridge this gap and predict PPIs and model the interactions in molecular detail are greatly needed. Here we present InterPred, a fully automated pipeline that predicts and model PPIs from sequence using structural modelling combined with massive structural comparisons and molecular docking. A key component of the method is the use of a novel random forest classifier that integrate several structural features to distinguish correct from incorrect protein-protein interaction models. We show that InterPred represents a major improvement in protein-protein interaction detection with a performance comparable or better than experimental high-throughput techniques. We also show that our full-atom protein-protein complex modelling pipeline performs better than state of the art protein docking methods on a standard benchmark set. In addition, InterPred was also one of the top predictors in the latest CAPRI37 experiment.InterPred source code can be downloaded from http://wallnerlab.org/InterPred

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