PPiClust: EFFICIENT CLUSTERING OF 3D PROTEIN–PROTEIN INTERACTION INTERFACES

2008 ◽  
Vol 06 (03) ◽  
pp. 415-433 ◽  
Author(s):  
ZEYAR AUNG ◽  
SOON-HENG TAN ◽  
SEE-KIONG NG ◽  
KIAN-LEE TAN
Keyword(s):  
Protein Interactions ◽  
Protein Complexes ◽  
Three Dimensional ◽  
Structural Data ◽  
Structural Basis ◽  
Protein Interface ◽  
Clustering Methods ◽  
Protein Protein Interaction ◽  
Protein Interfaces ◽  
Complex Structural

The biological mechanisms through which proteins interact with one another are best revealed by studying the structural interfaces between interacting proteins. Protein–protein interfaces can be extracted from three-dimensional (3D) structural data of protein complexes and then clustered to derive biological insights. However, conventional protein interface clustering methods lack computational scalability and statistical support. In this work, we present a new method named "PPiClust" to systematically encode, cluster, and analyze similar 3D interface patterns in protein complexes efficiently. Experimental results showed that our method is effective in discovering visually consistent and statistically significant clusters of interfaces, and at the same time sufficiently time-efficient to be performed on a single computer. The interface clusters are also useful for uncovering the structural basis of protein interactions. Analysis of the resulting interface clusters revealed groups of structurally diverse proteins having similar interface patterns. We also found, in some of the interface clusters, the presence of well-known linear binding motifs which were noncontiguous in the primary sequences. These results suggest that PPiClust can discover not only statistically significant, but also biologically significant, protein interface clusters from protein complex structural data.

A structural network associated with the kallikrein-kinin and renin-angiotensin systems

2010 ◽  
Vol 391 (4) ◽  
Author(s):  
Veronika Stoka ◽  
Vito Turk
Keyword(s):  
Protein Interactions ◽  
Three Dimensional ◽  
Type I ◽  
Protein Protein Interactions ◽  
Renin Angiotensin ◽  
Protein Protein Interaction ◽  
Protein Interfaces ◽  
Structural Network ◽  
Domain Interactions ◽  
Fibronectin Type

Abstract The kallikrein-kinin and renin-angiotensin (KKS-RAS) systems represent two highly regulated proteolytic systems that are involved in several physiological and pathological processes. Although their protein-protein interactions can be studied using experimental approaches, it is difficult to differentiate between direct physical interactions and functional associations, which do not involve direct atomic contacts between macromolecules. This information can be obtained from an atomic-resolution characterization of the protein interfaces. As a result of this, various three-dimensional-based protein-protein interaction databases have become available. To gain insight into the multilayered interaction of the KKS-RAS systems, we present a protein network that is built up on three-dimensional domain-domain interactions. The essential domains that link these systems are as follows: Cystatin, Peptidase_C1, Thyroglobulin_1, Insulin, CIMR (Cation-independent mannose-6-phosphate receptor repeat), fn2 (Fibronectin type II domain), fn1 (Fibronectin type I domain), EGF, Trypsin, and Serpin. We found that the CIMR domain is located at the core of the network, thus connecting both systems. From the latter, all domain interactors up to level 4 were retrieved, thus displaying a more comprehensive representation of the KKS-RAS structural network.

scholarly journals Classification and prediction of protein–protein interaction interface using machine learning algorithm

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Subhrangshu Das ◽  
Saikat Chakrabarti
Keyword(s):  
Protein Interaction ◽  
Protein Interactions ◽  
Learning Algorithm ◽  
Protein Complexes ◽  
Three Dimensional ◽  
3D Models ◽  
Structural Features ◽  
Support Vector ◽  
Protein Protein Interaction ◽  
Significant Difference

AbstractStructural insight of the protein–protein interaction (PPI) interface can provide knowledge about the kinetics, thermodynamics and molecular functions of the complex while elucidating its role in diseases and further enabling it as a potential therapeutic target. However, owing to experimental lag in solving protein–protein complex structures, three-dimensional (3D) knowledge of the PPI interfaces can be gained via computational approaches like molecular docking and post-docking analyses. Despite development of numerous docking tools and techniques, success in identification of native like interfaces based on docking score functions is limited. Hence, we employed an in-depth investigation of the structural features of the interface that might successfully delineate native complexes from non-native ones. We identify interface properties, which show statistically significant difference between native and non-native interfaces belonging to homo and hetero, protein–protein complexes. Utilizing these properties, a support vector machine (SVM) based classification scheme has been implemented to differentiate native and non-native like complexes generated using docking decoys. Benchmarking and comparative analyses suggest very good performance of our SVM classifiers. Further, protein interactions, which are proven via experimental findings but not resolved structurally, were subjected to this approach where 3D-models of the complexes were generated and most likely interfaces were predicted. A web server called Protein Complex Prediction by Interface Properties (PCPIP) is developed to predict whether interface of a given protein–protein dimer complex resembles known protein interfaces. The server is freely available at http://www.hpppi.iicb.res.in/pcpip/.

scholarly journals Sequence co-evolution gives 3D contacts and structures of protein complexes

2014 ◽  
Author(s):  
Thomas A. Hopf ◽  
Charlotta P.I. Schärfe ◽  
João P.G.L.M. Rodrigues ◽  
Anna G. Green ◽  
Chris Sander ◽  
...  
Keyword(s):  
Protein Interactions ◽  
Protein Complexes ◽  
3D Structure ◽  
Three Dimensional ◽  
Dimensional Structure ◽  
Evolutionary Sequence ◽  
Protein Protein Interactions ◽  
Interacting Protein ◽  
Protein Protein Interaction ◽  
Unknown Structure

Protein-protein interactions are fundamental to many biological processes. Experimental screens have identified tens of thousands of interactions and structural biology has provided detailed functional insight for select 3D protein complexes. An alternative rich source of information about protein interactions is the evolutionary sequence record. Building on earlier work, we show that analysis of correlated evolutionary sequence changes across proteins identifies residues that are close in space with sufficient accuracy to determine the three-dimensional structure of the protein complexes. We evaluate prediction performance in blinded tests on 76 complexes of known 3D structure, predict protein-protein contacts in 32 complexes of unknown structure, and demonstrate how evolutionary couplings can be used to distinguish between interacting and non-interacting protein pairs in a large complex. With the current growth of sequence databases, we expect that the method can be generalized to genome-wide elucidation of protein-protein interaction networks and used for interaction predictions at residue resolution.

scholarly journals Sequence co-evolution gives 3D contacts and structures of protein complexes

eLife ◽  
2014 ◽  
Vol 3 ◽  
Author(s):  
Thomas A Hopf ◽  
Charlotta P I Schärfe ◽  
João P G L M Rodrigues ◽  
Anna G Green ◽  
Oliver Kohlbacher ◽  
...  
Keyword(s):  
Protein Interactions ◽  
Protein Complexes ◽  
3D Structure ◽  
Three Dimensional ◽  
Dimensional Structure ◽  
Evolutionary Sequence ◽  
Protein Protein Interactions ◽  
Interacting Protein ◽  
Protein Protein Interaction ◽  
Unknown Structure

Protein–protein interactions are fundamental to many biological processes. Experimental screens have identified tens of thousands of interactions, and structural biology has provided detailed functional insight for select 3D protein complexes. An alternative rich source of information about protein interactions is the evolutionary sequence record. Building on earlier work, we show that analysis of correlated evolutionary sequence changes across proteins identifies residues that are close in space with sufficient accuracy to determine the three-dimensional structure of the protein complexes. We evaluate prediction performance in blinded tests on 76 complexes of known 3D structure, predict protein–protein contacts in 32 complexes of unknown structure, and demonstrate how evolutionary couplings can be used to distinguish between interacting and non-interacting protein pairs in a large complex. With the current growth of sequences, we expect that the method can be generalized to genome-wide elucidation of protein–protein interaction networks and used for interaction predictions at residue resolution.

scholarly journals Protein-protein docking using learned three-dimensional representations

2019 ◽  
Author(s):  
Georgy Derevyanko ◽  
Guillaume Lamoureux
Keyword(s):  
Protein Interactions ◽  
Network Architecture ◽  
Protein Complexes ◽  
Three Dimensional ◽  
Spatial Arrangement ◽  
Protein Docking ◽  
Protein Protein Interactions ◽  
Translational Invariance ◽  
Shape Complementarity ◽  
Spatial Features

AbstractProtein-protein interactions are determined by a number of hard-to-capture features related to shape complementarity, electrostatics, and hydrophobicity. These features may be intrinsic to the protein or induced by the presence of a partner. A conventional approach to protein-protein docking consists in engineering a small number of spatial features for each protein, and in minimizing the sum of their correlations with respect to the spatial arrangement of the two proteins. To generalize this approach, we introduce a deep neural network architecture that transforms the raw atomic densities of each protein into complex three-dimensional representations. Each point in the volume containing the protein is described by 48 learned features, which are correlated and combined with the features of a second protein to produce a score dependent on the relative position and orientation of the two proteins. The architecture is based on multiple layers of SE(3)-equivariant convolutional neural networks, which provide built-in rotational and translational invariance of the score with respect to the structure of the complex. The model is trained end-to-end on a set of decoy conformations generated from 851 nonredundant protein-protein complexes and is tested on data from the Protein-Protein Docking Benchmark Version 4.0.

scholarly journals Dissecting the conformation of glycans and their interactions with proteins

2020 ◽  
Vol 27 (1) ◽  
Author(s):  
Sheng-Hung Wang ◽  
Tsai-Jung Wu ◽  
Chien-Wei Lee ◽  
John Yu
Keyword(s):  
Conformational Changes ◽  
Molecular Interactions ◽  
Protein Interactions ◽  
Protein Complexes ◽  
Unmet Need ◽  
Protein Molecule ◽  
Structural Basis ◽  
Glycan Structure ◽  
Chronic Obstructive ◽  
Technology Platforms

Abstract The use of in silico strategies to develop the structural basis for a rational optimization of glycan-protein interactions remains a great challenge. This problem derives, in part, from the lack of technologies to quantitatively and qualitatively assess the complex assembling between a glycan and the targeted protein molecule. Since there is an unmet need for developing new sugar-targeted therapeutics, many investigators are searching for technology platforms to elucidate various types of molecular interactions within glycan-protein complexes and aid in the development of glycan-targeted therapies. Here we discuss three important technology platforms commonly used in the assessment of the complex assembly of glycosylated biomolecules, such as glycoproteins or glycosphingolipids: Biacore analysis, molecular docking, and molecular dynamics simulations. We will also discuss the structural investigation of glycosylated biomolecules, including conformational changes of glycans and their impact on molecular interactions within the glycan-protein complex. For glycoproteins, secreted protein acidic and rich in cysteine (SPARC), which is associated with various lung disorders, such as chronic obstructive pulmonary disease (COPD) and lung cancer, will be taken as an example showing that the core fucosylation of N-glycan in SPARC regulates protein-binding affinity with extracellular matrix collagen. For glycosphingolipids (GSLs), Globo H ceramide, an important tumor-associated GSL which is being actively investigated as a target for new cancer immunotherapies, will be used to demonstrate how glycan structure plays a significant role in enhancing angiogenesis in tumor microenvironments.

scholarly journals Engineering selective competitors for the discrimination of highly conserved protein-protein interaction modules

2019 ◽  
Vol 10 (1) ◽  
Author(s):  
Charlotte Rimbault ◽  
Kashyap Maruthi ◽  
Christelle Breillat ◽  
Camille Genuer ◽  
Sara Crespillo ◽  
...  
Keyword(s):  
Protein Interactions ◽  
Pdz Domain ◽  
Specific Inhibitor ◽  
Rational Approach ◽  
Protein Protein Interactions ◽  
Protein Protein Interaction ◽  
Domain Specific ◽  
Postsynaptic Protein ◽  
Protein Interfaces ◽  
Direct Generation

Abstract Designing highly specific modulators of protein-protein interactions (PPIs) is especially challenging in the context of multiple paralogs and conserved interaction surfaces. In this case, direct generation of selective and competitive inhibitors is hindered by high similarity within the evolutionary-related protein interfaces. We report here a strategy that uses a semi-rational approach to separate the modulator design into two functional parts. We first achieve specificity toward a region outside of the interface by using phage display selection coupled with molecular and cellular validation. Highly selective competition is then generated by appending the more degenerate interaction peptide to contact the target interface. We apply this approach to specifically bind a single PDZ domain within the postsynaptic protein PSD-95 over highly similar PDZ domains in PSD-93, SAP-97 and SAP-102. Our work provides a paralog-selective and domain specific inhibitor of PSD-95, and describes a method to efficiently target other conserved PPI modules.

scholarly journals Mass spectrometry-based methods for analysing the mitochondrial interactome in mammalian cells

2019 ◽  
Vol 167 (3) ◽  
pp. 225-231 ◽  
Author(s):  
Takumi Koshiba ◽  
Hidetaka Kosako
Keyword(s):  
Mass Spectrometry ◽  
Protein Interactions ◽  
Mammalian Cells ◽  
Protein Complexes ◽  
Living Cells ◽  
Protein Protein Interactions ◽  
Cellular Functions ◽  
Protein Protein Interaction ◽  
Membrane Protein Complexes ◽  
Protein Protein Interaction Networks

Abstract Protein–protein interactions are essential biologic processes that occur at inter- and intracellular levels. To gain insight into the various complex cellular functions of these interactions, it is necessary to assess them under physiologic conditions. Recent advances in various proteomic technologies allow to investigate protein–protein interaction networks in living cells. The combination of proximity-dependent labelling and chemical cross-linking will greatly enhance our understanding of multi-protein complexes that are difficult to prepare, such as organelle-bound membrane proteins. In this review, we describe our current understanding of mass spectrometry-based proteomics mapping methods for elucidating organelle-bound membrane protein complexes in living cells, with a focus on protein–protein interactions in mitochondrial subcellular compartments.

scholarly journals Evolutionary diversification of protein–protein interactions by interface add-ons

2017 ◽  
Vol 114 (40) ◽  
pp. E8333-E8342 ◽  
Author(s):  
Maximilian G. Plach ◽  
Florian Semmelmann ◽  
Florian Busch ◽  
Markus Busch ◽  
Leonhard Heizinger ◽  
...  
Keyword(s):  
Protein Interaction ◽  
Protein Interactions ◽  
Protein Complexes ◽  
Evolutionary Strategy ◽  
Structural Level ◽  
Protein Protein Interactions ◽  
Protein Protein Interaction ◽  
Evolutionary Diversification

Cells contain a multitude of protein complexes whose subunits interact with high specificity. However, the number of different protein folds and interface geometries found in nature is limited. This raises the question of how protein–protein interaction specificity is achieved on the structural level and how the formation of nonphysiological complexes is avoided. Here, we describe structural elements called interface add-ons that fulfill this function and elucidate their role for the diversification of protein–protein interactions during evolution. We identified interface add-ons in 10% of a representative set of bacterial, heteromeric protein complexes. The importance of interface add-ons for protein–protein interaction specificity is demonstrated by an exemplary experimental characterization of over 30 cognate and hybrid glutamine amidotransferase complexes in combination with comprehensive genetic profiling and protein design. Moreover, growth experiments showed that the lack of interface add-ons can lead to physiologically harmful cross-talk between essential biosynthetic pathways. In sum, our complementary in silico, in vitro, and in vivo analysis argues that interface add-ons are a practical and widespread evolutionary strategy to prevent the formation of nonphysiological complexes by specializing protein–protein interactions.

scholarly journals Conservation of coevolving protein interfaces bridges prokaryote–eukaryote homologies in the twilight zone

2016 ◽  
Vol 113 (52) ◽  
pp. 15018-15023 ◽  
Author(s):  
Juan Rodriguez-Rivas ◽  
Simone Marsili ◽  
David Juan ◽  
Alfonso Valencia
Keyword(s):  
Protein Interactions ◽  
Protein Complexes ◽  
Accurate Information ◽  
Twilight Zone ◽  
Sequence Information ◽  
Protein Protein Interactions ◽  
Sequence Alignments ◽  
Multiple Sequence ◽  
Protein Interfaces ◽  
Recent Developments

Protein–protein interactions are fundamental for the proper functioning of the cell. As a result, protein interaction surfaces are subject to strong evolutionary constraints. Recent developments have shown that residue coevolution provides accurate predictions of heterodimeric protein interfaces from sequence information. So far these approaches have been limited to the analysis of families of prokaryotic complexes for which large multiple sequence alignments of homologous sequences can be compiled. We explore the hypothesis that coevolution points to structurally conserved contacts at protein–protein interfaces, which can be reliably projected to homologous complexes with distantly related sequences. We introduce a domain-centered protocol to study the interplay between residue coevolution and structural conservation of protein–protein interfaces. We show that sequence-based coevolutionary analysis systematically identifies residue contacts at prokaryotic interfaces that are structurally conserved at the interface of their eukaryotic counterparts. In turn, this allows the prediction of conserved contacts at eukaryotic protein–protein interfaces with high confidence using solely mutational patterns extracted from prokaryotic genomes. Even in the context of high divergence in sequence (the twilight zone), where standard homology modeling of protein complexes is unreliable, our approach provides sequence-based accurate information about specific details of protein interactions at the residue level. Selected examples of the application of prokaryotic coevolutionary analysis to the prediction of eukaryotic interfaces further illustrate the potential of this approach.

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