The emergence of protein complexes: quaternary structure, dynamics and allostery

2012 ◽  
Vol 40 (3) ◽  
pp. 475-491 ◽  
Author(s):  
Tina Perica ◽  
Joseph A. Marsh ◽  
Filipa L. Sousa ◽  
Eviatar Natan ◽  
Lucy J. Colwell ◽  
...  
Keyword(s):  
Protein Interactions ◽  
Biological Networks ◽  
Protein Function ◽  
Quaternary Structure ◽  
Protein Complexes ◽  
Protein Structures ◽  
Allosteric Communication ◽  
Physical Interactions ◽  
Correlated Mutations ◽  
Analytical Approaches

All proteins require physical interactions with other proteins in order to perform their functions. Most of them oligomerize into homomers, and a vast majority of these homomers interact with other proteins, at least part of the time, forming transient or obligate heteromers. In the present paper, we review the structural, biophysical and evolutionary aspects of these protein interactions. We discuss how protein function and stability benefit from oligomerization, as well as evolutionary pathways by which oligomers emerge, mostly from the perspective of homomers. Finally, we emphasize the specificities of heteromeric complexes and their structure and evolution. We also discuss two analytical approaches increasingly being used to study protein structures as well as their interactions. First, we review the use of the biological networks and graph theory for analysis of protein interactions and structure. Secondly, we discuss recent advances in techniques for detecting correlated mutations, with the emphasis on their role in identifying pathways of allosteric communication.

scholarly journals Simplified geometric representations of protein structures identify complementary interaction interfaces

2019 ◽  
Author(s):  
Caitlyn L. McCafferty ◽  
Edward M. Marcotte ◽  
David W. Taylor
Keyword(s):  
Conformational Changes ◽  
Protein Interaction ◽  
Protein Function ◽  
Large Scale ◽  
Predictive Accuracy ◽  
Protein Complexes ◽  
Protein Structures ◽  
Molecular Properties ◽  
3D Structures ◽  
Geometric Representations

ABSTRACTProtein-protein interactions are critical to protein function, but three-dimensional (3D) arrangements of interacting proteins have proven hard to predict, even given the identities and 3D structures of the interacting partners. Specifically, identifying the relevant pairwise interaction surfaces remains difficult, often relying on shape complementarity with molecular docking while accounting for molecular motions to optimize rigid 3D translations and rotations. However, such approaches can be computationally expensive, and faster, less accurate approximations may prove useful for large-scale prediction and assembly of 3D structures of multi-protein complexes. We asked if a reduced representation of protein geometry retains enough information about molecular properties to predict pairwise protein interaction interfaces that are tolerant of limited structural rearrangements. Here, we describe a cuboid transformation of 3D protein accessible surfaces on which molecular properties such as charge, hydrophobicity, and mutation rate can be easily mapped, implemented in the MorphProt package. Pairs of surfaces are compared to rapidly assess partner-specific potential surface complementarity. On two available benchmarks of 85 overall known protein complexes, we observed F1 scores (a weighted combination of precision and recall) of 19-34% at correctly identifying protein interaction surfaces, comparable to more computationally intensive 3D docking methods in the annual Critical Assessment of PRedicted Interactions. Furthermore, we examined the effect of molecular motion through normal mode simulation on a benchmark receptor-ligand pair and observed no marked loss of predictive accuracy for distortions of up to 6 Å RMSD. Thus, a cuboid transformation of protein surfaces retains considerable information about surface complementarity, offers enhanced speed of comparison relative to more complex geometric representations, and exhibits tolerance to conformational changes.

scholarly journals Algorithms for protein interaction networks

2005 ◽  
Vol 33 (3) ◽  
pp. 530-534 ◽  
Author(s):  
M. Lappe ◽  
L. Holm
Keyword(s):  
Protein Interactions ◽  
Biological Networks ◽  
Protein Function ◽  
Large Scale ◽  
Sequence Similarity ◽  
Functional Characterization ◽  
Interaction Networks ◽  
Computational Techniques ◽  
Interaction Patterns ◽  
Main Challenge

The functional characterization of all genes and their gene products is the main challenge of the postgenomic era. Recent experimental and computational techniques have enabled the study of interactions among all proteins on a large scale. In this paper, approaches will be presented to exploit interaction information for the inference of protein structure, function, signalling pathways and ultimately entire interactomes. Interaction networks can be modelled as graphs, showing the operation of gene function in terms of protein interactions. Since the architecture of biological networks differs distinctly from random networks, these functional maps contain a signal that can be used for predictive purposes. Protein function and structure can be predicted by matching interaction patterns, without the requirement of sequence similarity. Moving on to a higher level definition of protein function, the question arises how to decompose complex networks into meaningful subsets. An algorithm will be demonstrated, which extracts whole signal-transduction pathways from noisy graphs derived from text-mining the biological literature. Finally, an algorithmic strategy is formulated that enables the proteomics community to build a reliable scaffold of the interactome in a fraction of the time compared with uncoordinated efforts.

scholarly journals Human mitochondrial protein complexes revealed by large-scale coevolution analysis and deep learning-based structure modeling

2021 ◽  
Author(s):  
Jimin Pei ◽  
Jing Zhang ◽  
Qian Cong
Keyword(s):  
Deep Learning ◽  
Protein Interactions ◽  
Large Scale ◽  
Protein Complexes ◽  
Mitochondrial Protein ◽  
Protein Structures ◽  
Complex Structures ◽  
Protein Protein Interactions ◽  
Learning Methods ◽  
Contact Probability

AbstractRecent development of deep-learning methods has led to a breakthrough in the prediction accuracy of 3-dimensional protein structures. Extending these methods to protein pairs is expected to allow large-scale detection of protein-protein interactions and modeling protein complexes at the proteome level. We applied RoseTTAFold and AlphaFold2, two of the latest deep-learning methods for structure predictions, to analyze coevolution of human proteins residing in mitochondria, an organelle of vital importance in many cellular processes including energy production, metabolism, cell death, and antiviral response. Variations in mitochondrial proteins have been linked to a plethora of human diseases and genetic conditions. RoseTTAFold, with high computational speed, was used to predict the coevolution of about 95% of mitochondrial protein pairs. Top-ranked pairs were further subject to the modeling of the complex structures by AlphaFold2, which also produced contact probability with high precision and in many cases consistent with RoseTTAFold. Most of the top ranked pairs with high contact probability were supported by known protein-protein interactions and/or similarities to experimental structural complexes. For high-scoring pairs without experimental complex structures, our coevolution analyses and structural models shed light on the details of their interfaces, including CHCHD4-AIFM1, MTERF3-TRUB2, FMC1-ATPAF2, ECSIT-NDUFAF1 and COQ7-COQ9, among others. We also identified novel PPIs (PYURF-NDUFAF5, LYRM1-MTRF1L and COA8-COX10) for several proteins without experimentally characterized interaction partners, leading to predictions of their molecular functions and the biological processes they are involved in.

PTGL: extension to graph-based topologies of cryo-EM data for large protein structures

2020 ◽  
Author(s):  
Jan Niclas Wolf ◽  
Marcus Keßler ◽  
Jörg Ackermann ◽  
Ina Koch
Keyword(s):  
Quaternary Structure ◽  
Protein Complexes ◽  
Protein Structures ◽  
Supplementary Information ◽  
Large Protein ◽  
Protein Topology ◽  
Large Structures ◽  
Structure Level ◽  
File Parser ◽  
Topology Graph

Abstract Summary We provide a software to describe the topology of large protein complexes based mainly on cryo-EM data and stored as macromolecular Crystallographic Information Files (mmCIFs) in the PDB. The software extends the Protein Topology Graph Library and implements an efficient file parser to analyze mmCIFs. The extended Protein Topology Graph Library includes a graph-based representation of the topology of protein complexes on the supersecondary and quaternary structure level. The library holds topology graphs of 151 837 PDB files; 921 of them are large structures. The abstraction of protein structure complexes to undirected labeled graphs enables classification and comparison of large protein complexes on quaternary structure level. Availability and implementation Online access at http://ptgl.uni-frankfurt.de. Source code in Java under GNU public license 2.0 at https://github.com/MolBIFFM/vplg. Supplementary information Supplementary data are available at Bioinformatics online.

A HIDDEN MARKOV MODEL FOR PREDICTING PROTEIN INTERFACES

2007 ◽  
Vol 05 (03) ◽  
pp. 739-753 ◽  
Author(s):  
CAO NGUYEN ◽  
KATHELEEN J. GARDINER ◽  
KRZYSZTOF J. CIOS
Keyword(s):  
Markov Model ◽  
Hidden Markov Model ◽  
Protein Interactions ◽  
Protein Function ◽  
Structural Information ◽  
Protein Complexes ◽  
Hidden Markov ◽  
Transition Probability ◽  
Accessible Surface Area ◽  
Protein Chain

Protein–protein interactions play a defining role in protein function. Identifying the sites of interaction in a protein is a critical problem for understanding its functional mechanisms, as well as for drug design. To predict sites within a protein chain that participate in protein complexes, we have developed a novel method based on the Hidden Markov Model, which combines several biological characteristics of the sequences neighboring a target residue: structural information, accessible surface area, and transition probability among amino acids. We have evaluated the method using 5-fold cross-validation on 139 unique proteins and demonstrated precision of 66% and recall of 61% in identifying interfaces. These results are better than those achieved by other methods used for identification of interfaces.

scholarly journals Augmenting protein network embeddings with sequence information

2019 ◽  
Author(s):  
Hassan Kané ◽  
Mohamed Coulibali ◽  
Ali Abdalla ◽  
Pelkins Ajanoh
Keyword(s):  
Protein Interactions ◽  
Protein Function ◽  
Quaternary Structure ◽  
Protein Function Prediction ◽  
Representation Learning ◽  
Specific Protein ◽  
Sequence Information ◽  
Protein Protein Interactions ◽  
Tissue Specific ◽  
Protein Protein Interaction

ABSTRACTComputational methods that infer the function of proteins are key to understanding life at the molecular level. In recent years, representation learning has emerged as a powerful paradigm to discover new patterns among entities as varied as images, words, speech, molecules. In typical representation learning, there is only one source of data or one level of abstraction at which the learned representation occurs. However, proteins can be described by their primary, secondary, tertiary, and quaternary structure or even as nodes in protein-protein interaction networks. Given that protein function is an emergent property of all these levels of interactions in this work, we learn joint representations from both amino acid sequence and multilayer networks representing tissue-specific protein-protein interactions. Using these hybrid representations, we show that simple machine learning models trained using these hybrid representations outperform existing network-based methods on the task of tissue-specific protein function prediction on 13 out of 13 tissues. Furthermore, these representations outperform existing ones by 14% on average.

scholarly journals PatchMAN docking: Modeling peptide-protein interactions in the context of the receptor surface

2021 ◽  
Author(s):  
Alisa Khramushin ◽  
Tomer Tsaban ◽  
Julia Varga ◽  
Orly Avraham ◽  
Ora Schueler-Furman
Keyword(s):  
Protein Interactions ◽  
Protein Complexes ◽  
Protein Structures ◽  
Binding Pocket ◽  
Protein Docking ◽  
Peptide Sequence ◽  
Sequence Information ◽  
Peptide Docking ◽  
Docking Approach ◽  
Conformer Ensemble

AbstractPeptide docking can be perceived as a subproblem of protein-protein docking. However, due to the short length and flexible nature of peptides, many do not adopt one defined conformation prior to binding. Therefore, to tackle a peptide docking problem, not only the relative orientation between the two partners, but also the bound conformation of the peptide needs to be modeled. Traditional peptide-centered approaches use information about the peptide sequence to generate a representative conformer ensemble, which can then be rigid body docked to the receptor. Alternatively, one may look at this problem from the viewpoint of the receptor, namely that the protein surface defines the peptide bound conformation.We present PatchMAN (Patch-Motif AligNments), a novel peptide docking approach which uses structural motifs to map the receptor surface with backbone scaffolds extracted from protein structures. On a non-redundant set of protein-peptide complexes, starting from free receptor structures, PatchMAN successfully models and identifies near-native peptide-protein complexes in 62% / 81% within 2.5Å / 5Å RMSD, with corresponding sampling in 81% / 100% of the cases, outperforming other approaches. PatchMAN leverages the observation that structural units of peptides with their binding pocket can be found not only within interfaces, but also within monomers. We show that the conformation of the bound peptide is sampled based on the structural context of the receptor only, without taking into account any sequence information. Beyond peptide docking, this approach opens exciting new avenues to study principles of peptide-protein association, and to the design of new peptide binders.

scholarly journals Energy Transfer as A Driving Force in Nucleic Acid–Protein Interactions

Molecules ◽  
2019 ◽  
Vol 24 (7) ◽  
pp. 1443
Author(s):  
Zavyalova ◽  
Kopylov
Keyword(s):  
Energy Transfer ◽  
Nucleic Acid ◽  
Protein Interactions ◽  
Protein Complexes ◽  
Protein Structures ◽  
Quantitative Structure Activity Relationship ◽  
Efficient Energy Transfer ◽  
Acid Protein ◽  
G Quadruplex ◽  
Interface Organization

Many nucleic acid–protein structures have been resolved, though quantitative structure-activity relationship remains unclear in many cases. Thrombin complexes with G-quadruplex aptamers are striking examples of a lack of any correlation between affinity, interface organization, and other common parameters. Here, we tested the hypothesis that affinity of the aptamer–protein complex is determined with the capacity of the interface to dissipate energy of binding. Description and detailed analysis of 63 nucleic acid–protein structures discriminated peculiarities of high-affinity nucleic acid–protein complexes. The size of the amino acid sidechain in the interface was demonstrated to be the most significant parameter that correlates with affinity of aptamers. This observation could be explained in terms of need of efficient energy transfer from interacting residues. Application of energy dissipation theory provided an illustrative tool for estimation of efficiency of aptamer–protein complexes. These results are of great importance for a design of efficient aptamers.

Udock, the interactive docking entertainment system

2014 ◽  
Vol 169 ◽  
pp. 425-441 ◽  
Author(s):  
Guillaume Levieux ◽  
Guillaume Tiger ◽  
Stéphanie Mader ◽  
Jean-François Zagury ◽  
Stéphane Natkin ◽  
...  
Keyword(s):  
Protein Interactions ◽  
Protein Complexes ◽  
Protein Structures ◽  
Protein Docking ◽  
Conformational Space ◽  
Protein Protein Interactions ◽  
Binding Interface ◽  
Protein Interfaces ◽  
Almost All ◽  
Experimental Partner

Protein–protein interactions play a crucial role in biological processes. Protein docking calculations' goal is to predict, given two proteins of known structures, the associate conformation of the corresponding complex. Here, we present a new interactive protein docking system, Udock, that makes use of users' cognitive capabilities added up. In Udock, the users tackle simplified representations of protein structures and explore protein–protein interfaces’ conformational space using a gamified interactive docking system with on the fly scoring. We assumed that if given appropriate tools, a naïve user's cognitive capabilities could provide relevant data for (1) the prediction of correct interfaces in binary protein complexes and (2) the identification of the experimental partner in interaction among a set of decoys. To explore this approach experimentally, we conducted a preliminary two week long playtest where the registered users could perform a cross-docking on a dataset comprising 4 binary protein complexes. The users explored almost all the surface of the proteins that were available in the dataset but favored certain regions that seemed more attractive as potential docking spots. These favored regions were located inside or nearby the experimental binding interface for 5 out of the 8 proteins in the dataset. For most of them, the best scores were obtained with the experimental partner. The alpha version of Udock is freely accessible at http://udock.fr.

scholarly journals Probing DNA-protein interactions using single-molecule diffusivity contrast

2021 ◽  
Author(s):  
Hugh Wilson ◽  
Miles Lee ◽  
Quan Wang
Keyword(s):  
Nucleic Acid ◽  
Single Molecule ◽  
Protein Interactions ◽  
Protein Complexes ◽  
Protein Structures ◽  
Single Particle Tracking ◽  
Correlation Spectroscopy ◽  
Modeling Framework ◽  
Single Dna Molecules ◽  
Protein Nucleic Acid

Single-molecule fluorescence investigations of protein-nucleic acid interactions require robust means to identify the binding state of individual substrate molecules in real time. Here we show that diffusivity contrast, widely used in fluorescence correlation spectroscopy at the ensemble level and in single-particle tracking on individual (but slowly diffusing) species, can be used as a general readout to determine the binding state of single DNA molecules with unlabeled proteins in solution. We first describe the technical basis of drift-free single-molecule diffusivity measurements in an Anti-Brownian ELetrokinetic (ABEL) trap. We then cross-validate our method with protein-induced fluorescence enhancement (PIFE), a popular technique to detect protein binding on nucleic acid substrates with single-molecule sensitivity. We extend an existing hydrodynamic modeling framework to link measured diffusivity to particular DNA-protein structures and obtain good agreement between the measured and predicted diffusivity values. Finally, we show that combining diffusivity contrast with PIFE allows simultaneous mapping of binding stoichiometry and location on individual DNA-protein complexes, potentially enhancing single-molecule views of relevant biophysical processes.

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