Prediction of protein structures and protein-protein interactions: a bioinformatics approach

2021 ◽  
Author(s):  
Huiling Chen
Keyword(s):  
Protein Interactions ◽  
Protein Structures ◽  
Protein Protein Interactions

scholarly journals De novo design of a reversible phosphorylation-dependent switch for membrane targeting

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Leon Harrington ◽  
Jordan M. Fletcher ◽  
Tamara Heermann ◽  
Derek N. Woolfson ◽  
Petra Schwille
Keyword(s):  
Protein Interactions ◽  
Lipid Membrane ◽  
De Novo ◽  
Protein Localization ◽  
Protein Structures ◽  
Spatiotemporal Pattern ◽  
Membrane Targeting ◽  
Protein Protein Interactions ◽  
Reversible Phosphorylation ◽  
Potential Applications

AbstractModules that switch protein-protein interactions on and off are essential to develop synthetic biology; for example, to construct orthogonal signaling pathways, to control artificial protein structures dynamically, and for protein localization in cells or protocells. In nature, the E. coli MinCDE system couples nucleotide-dependent switching of MinD dimerization to membrane targeting to trigger spatiotemporal pattern formation. Here we present a de novo peptide-based molecular switch that toggles reversibly between monomer and dimer in response to phosphorylation and dephosphorylation. In combination with other modules, we construct fusion proteins that couple switching to lipid-membrane targeting by: (i) tethering a ‘cargo’ molecule reversibly to a permanent membrane ‘anchor’; and (ii) creating a ‘membrane-avidity switch’ that mimics the MinD system but operates by reversible phosphorylation. These minimal, de novo molecular switches have potential applications for introducing dynamic processes into designed and engineered proteins to augment functions in living cells and add functionality to protocells.

scholarly journals Molecular dynamics shows complex interplay and long-range effects of post-translational modifications in yeast protein interactions

2021 ◽  
Vol 17 (5) ◽  
pp. e1008988
Author(s):  
Nikolina ŠoŠtarić ◽  
Vera van Noort
Keyword(s):  
Molecular Dynamics ◽  
Conformational Changes ◽  
Protein Interactions ◽  
Protein Structures ◽  
Cellular Protein ◽  
Free Energy Calculations ◽  
Protein Protein Interactions ◽  
Post Translational Modifications ◽  
Protein Properties ◽  
Dynamics Simulations

Post-translational modifications (PTMs) play a vital, yet often overlooked role in the living cells through modulation of protein properties, such as localization and affinity towards their interactors, thereby enabling quick adaptation to changing environmental conditions. We have previously benchmarked a computational framework for the prediction of PTMs’ effects on the stability of protein-protein interactions, which has molecular dynamics simulations followed by free energy calculations at its core. In the present work, we apply this framework to publicly available data on Saccharomyces cerevisiae protein structures and PTM sites, identified in both normal and stress conditions. We predict proteome-wide effects of acetylations and phosphorylations on protein-protein interactions and find that acetylations more frequently have locally stabilizing roles in protein interactions, while the opposite is true for phosphorylations. However, the overall impact of PTMs on protein-protein interactions is more complex than a simple sum of local changes caused by the introduction of PTMs and adds to our understanding of PTM cross-talk. We further use the obtained data to calculate the conformational changes brought about by PTMs. Finally, conservation of the analyzed PTM residues in orthologues shows that some predictions for yeast proteins will be mirrored to other organisms, including human. This work, therefore, contributes to our overall understanding of the modulation of the cellular protein interaction networks in yeast and beyond.

scholarly journals FrustratometeR: an R-package to compute Local frustration in protein structures, point mutants and MD simulations

2020 ◽  
Author(s):  
Atilio O. Rausch ◽  
Maria I. Freiberger ◽  
Cesar O. Leonetti ◽  
Diego M. Luna ◽  
Leandro G. Radusky ◽  
...  
Keyword(s):  
Protein Interactions ◽  
Large Scale ◽  
Protein Structures ◽  
Md Simulations ◽  
R Package ◽  
Protein Protein Interactions ◽  
Large Scale Analysis ◽  
Functional Aspects ◽  
Catalytic Sites ◽  
Polypeptide Chains

Once folded natural protein molecules have few energetic conflicts within their polypeptide chains. Many protein structures do however contain regions where energetic conflicts remain after folding, i.e. they have highly frustrated regions. These regions, kept in place over evolutionary and physiological timescales, are related to several functional aspects of natural proteins such as protein-protein interactions, small ligand recognition, catalytic sites and allostery. Here we present FrustratometeR, an R package that easily computes local energetic frustration on a personal computer or a cluster. This package facilitates large scale analysis of local frustration, point mutants and MD trajectories, allowing straightforward integration of local frustration analysis in to pipelines for protein structural analysis.Availability and implementation: https://github.com/proteinphysiologylab/frustratometeR

IN SILICO SCREENING OF PROTEIN–PROTEIN INTERACTIONS WITH ALL-TO-ALL RIGID DOCKING AND CLUSTERING: AN APPLICATION TO PATHWAY ANALYSIS

2009 ◽  
Vol 07 (06) ◽  
pp. 991-1012 ◽  
Author(s):  
YURI MATSUZAKI ◽  
YUSUKE MATSUZAKI ◽  
TOSHIYUKI SATO ◽  
YUTAKA AKIYAMA
Keyword(s):  
Pathway Analysis ◽  
Structure Data ◽  
Protein Interactions ◽  
Tertiary Structure ◽  
Protein Structures ◽  
Protein Docking ◽  
Protein Protein Interactions ◽  
Benchmark Data ◽  
Computational Screening ◽  
F Measure

We propose a computational screening system of protein–protein interactions using tertiary structure data. Our system combines all-to-all protein docking and clustering to find interacting protein pairs. We tuned our prediction system by applying various parameters and clustering algorithms and succeeded in outperforming previous methods. This method was also applied to a biological pathway estimation problem to show its use in network level analysis. The structural data were collected from the Protein Data Bank, PDB. Then all-to-all docking among target protein structures was conducted using a conventional protein–protein docking software package, ZDOCK. The highest-ranked 2000 decoys were clustered based on structural similarity among the predicted docking forms. The features of generated clusters were analyzed to estimate the biological relevance of protein–protein interactions. Our system achieves a best F-measure value of 0.43 when applied to a subset of general protein–protein docking benchmark data. The same system was applied to protein data in a bacterial chemotaxis pathway, utilizing essentially the same parameter set as the benchmark data. We obtained 0.45 for the F-measure value. The proposed approach to computational PPI detection is a promising methodology for mediating between structural studies and systems biology by utilizing cumulative protein structure data for pathway analysis.

scholarly journals Sequential digestion with Trypsin and Elastase in cross-linking/mass spectrometry

2018 ◽  
Author(s):  
Therese Dau ◽  
Kapil Gupta ◽  
Imre Berger ◽  
Juri Rappsilber
Keyword(s):  
Mass Spectrometry ◽  
Protein Interactions ◽  
Protein Structures ◽  
Cross Linking ◽  
Cleavage Sites ◽  
Protein Modelling ◽  
Protein Protein Interactions ◽  
Important Approach ◽  
Cross Links ◽  
Sequential Digestion

ABSTRACTCross-linking/mass spectrometry has become an important approach for studying protein structures and protein-protein interactions. The amino acid composition of some protein regions impedes the detection of cross-linked residues, although it would yield invaluable information for protein modelling. Here, we report on a sequential digestion strategy with trypsin and elastase to penetrate regions with a low density of trypsin cleavage sites. We exploited intrinsic substrate recognition properties of elastase to specifically target larger tryptic peptides. Our application of this protocol to the TAF4-12 complex allowed us to identify cross-links in previously inaccessible regions.

scholarly journals Evolutionary couplings detect side-chain interactions

2018 ◽  
Author(s):  
Adam J. Hockenberry ◽  
Claus O. Wilke
Keyword(s):  
Protein Interactions ◽  
De Novo ◽  
Protein Structures ◽  
Side Chain ◽  
Protein Protein Interactions ◽  
Sequence Alignments ◽  
Practical Applications ◽  
Residue Contacts ◽  
Coupling Algorithms ◽  
Evolutionary Coupling

Patterns of amino acid covariation in large protein sequence alignments can inform the prediction of de novo protein structures, binding interfaces, and mutational effects. While algorithms that detect these so-called evolutionary couplings between residues have proven useful for practical applications, less is known about how and why these methods perform so well, and what insights into biological processes can be gained from their application. Evolutionary coupling algorithms are commonly benchmarked by comparison to true structural contacts derived from solved protein structures. However, the methods used to determine true structural contacts are not standardized and different definitions of structural contacts may have important consequences for interpreting the results from evolutionary coupling analyses and understanding their overall utility. Here, we show that evolutionary coupling analyses are significantly more likely to identify structural contacts between side-chain atoms than between backbone atoms. We use both simulations and empirical analyses to highlight that purely backbone-based definitions of true residue–residue contacts (i.e., based on the distance between Cα atoms) may underestimate the accuracy of evolutionary coupling algorithms by as much as 40% and that a commonly used reference point (Cβ atoms) underestimates the accuracy by 10–15%. These findings show that co-evolutionary outcomes differ according to which atoms participate in residue–residue interactions and suggest that accounting for different interaction types may lead to further improvements to contact-prediction methods.Significance StatementEvolutionary couplings between residues within a protein can provide valuable information about protein structures, protein-protein interactions, and the mutability of individual residues. However, the mechanistic factors that determine whether two residues will co-evolve remains unknown. We show that structural proximity by itself is not sufficient for co-evolution to occur between residues. Rather, evolutionary couplings between residues are specifically governed by interactions between side-chain atoms. By contrast, intramolecular contacts between atoms in the protein backbone display only a weak signature of evolutionary coupling. These findings highlight that different types of stabilizing contacts exist within protein structures and that these types have a differential impact on the evolution of protein structures that should be considered in co-evolutionary applications.

scholarly journals Obtaining Precise Molecular Information via DNA Nanotechnology

Membranes ◽  
2021 ◽  
Vol 11 (9) ◽  
pp. 683
Author(s):  
Qian Tang ◽  
Da Han
Keyword(s):  
Protein Interactions ◽  
Measurement Techniques ◽  
Protein Structures ◽  
Dna Nanotechnology ◽  
Molecular Structures ◽  
Protein Protein Interactions ◽  
Biochemical Processes ◽  
Molecular Force ◽  
Molecular Information ◽  
Resolution Of Imaging

Precise characterization of biomolecular information such as molecular structures or intermolecular interactions provides essential mechanistic insights into the understanding of biochemical processes. As the resolution of imaging-based measurement techniques improves, so does the quantity of molecular information obtained using these methodologies. DNA (deoxyribonucleic acid) molecule have been used to build a variety of structures and dynamic devices on the nanoscale over the past 20 years, which has provided an accessible platform to manipulate molecules and resolve molecular information with unprecedented precision. In this review, we summarize recent progress related to obtaining precise molecular information using DNA nanotechnology. After a brief introduction to the development and features of structural and dynamic DNA nanotechnology, we outline some of the promising applications of DNA nanotechnology in structural biochemistry and in molecular biophysics. In particular, we highlight the use of DNA nanotechnology in determination of protein structures, protein–protein interactions, and molecular force.

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.

scholarly journals Socket2: A Program for Locating, Visualising, and Analysing Coiled-coil Interfaces in Protein Structures

2021 ◽  
Author(s):  
Prasun Kumar ◽  
Derek N Woolfson
Keyword(s):  
Protein Interactions ◽  
De Novo ◽  
Protein Structures ◽  
Coiled Coil ◽  
Biological Research ◽  
Biological Processes ◽  
Protein Protein Interactions ◽  
Wide Range ◽  
Core Packing ◽  
Natural Amino Acids

Abstract Motivation Protein-protein interactions are central to all biological processes. One frequently observed mode of such interactions is the α-helical coiled coil (CC). Thus, an ability to extract, visualise, and analyse CC interfaces quickly and without expert guidance would facilitate a wide range of biological research. In 2001, we reported Socket, which locates and characterises CCs in protein structures based on the knobs-into-holes (KIH) packing between helices in CCs. Since then, studies of natural and de novo designed CCs have boomed, and the number of CCs in the RCSB PDB has increased rapidly. Therefore, we have updated Socket and made it accessible to expert and non-expert users alike. Results The original Socket only classified CCs with up to 6 helices. Here, we report Socket2, which rectifies this oversight to identify CCs with any number of helices, and KIH interfaces with any of the 20 proteinogenic residues or incorporating non-natural amino acids. In addition, we have developed a new and easy-to-use web server with additional features. These include the use of NGL Viewer for instantly visualising CCs, and tabs for viewing the sequence repeats, helix-packing angles, and core-packing geometries of CCs identified and calculated by Socket2. Availability and implementation Socket2 has been tested on all modern browsers. It can be accessed freely at http://coiledcoils.chm.bris.ac.uk/socket2/home.html. The source code is distributed using an MIT license and available to download under the Downloads tab of the Socket2 home page.

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