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What role does a protein's size have on protein-protein interactions?

What role does a protein's size have on protein-protein interactions?


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Protein-protein interactions are when two or more proteins bind together, possibly for some important biological function. Recently, I'm starting to look more into proteins, and in particular, networks related to proteins, such as protein-protein interaction networks. I found myself thinking about the following question:

Question: Does the relative size of two proteins significantly affect how likely they are to interact? Does it affect how they interact?

It would be plausible for me to download a protein-protein interaction network, and calculate their relative size from the proteins' PDB files. However, I predict that the results from such a computation would not offer much insight into why (or why not) the protein sizes affect binding.


Protein interactions occur mostly (if not all) through residues that are on the "surface" and exposed to the milieu in which they exist, be it cytoplasmic or extracellular. So, a naive thought would be to guess that a greater surface area means a greater swathe of exposed regions and probable interacting parts. One protein can bind many others, even simultaneously, through different such interaction surfaces. However, other factors also determine whether a given set of two proteins interact, most important being where they localize in a given cell. If one protein is nuclear but the other on the plasma membrane, it is highly unlikely that they ever interact even if thermodynamics highly favors complex formation. Interaction between two proteins also depends on their respective conformations, which can further be regulated by events such as post translational modifications (eg., phosphorylation by a kinase), bound nucleotide state (eg., GTP vs GDP in a G protein). Last, scaffolds can also sequester and organize protein clusters together spatially and increase the probability of them interacting. All put together, I think it is highly unlikely that just looking at 3D surface area of a protein will say much about its "interactability" with other proteins.


@gkadam is correct and quite exhaustive. There are a lot of things that can affect protein binding, and many of them have little to do with the protein itself, but instead where it is and what else it is doing.

All other things being equal, smaller proteins will form networks more quickly than larger ones for kinetic reasons.

Size differences don't affect how likely two proteins are to interact by itself. Big proteins and little ones interact all the time.


Inhibition of protein –protein interactions using designed molecules

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In silico disease model: from simple networks to complex diseases

Debmalya Barh , . Vasco Azevedo , in Animal Biotechnology (Second Edition) , 2020

Protein–protein interactions

PPIs predictions are methods used to predict the outcome of pairs or groups of protein interactions. These predictions are done in vivo and various methods can be used to carry out the predictions. Interaction prediction is important as it helps researchers make inferences of the outcomes of PPI. PPI can be studied by phylogenetic profiling, identifying structural patterns and homologous pairs, intracellular localization, and posttranslational modifications among others ( Choi, 2007 ). A survey of available tools and web servers for analysis of PPIs is provided by Tuncbag et al. (2009) .


Inhibitors of Protein–Protein Interactions: Small Molecules, Peptides and Macrocycles Editor: Ali Tavassoli

Protein–protein interactions (PPI) are at the heart of the majority of cellular processes, and are frequently dysregulated or usurped in disease. Given this central role, the inhibition of PPIs has been of significant interest as a means of treating a wide variety of diseases. However, there are inherent challenges in developing molecules capable of disrupting the relatively featureless and large interfacial areas involved. Despite this, there have been a number of successes in this field in recent years using both traditional drug discovery approaches and innovative, interdisciplinary strategies using novel chemical scaffolds. This book comprehensively covers the various aspects of PPI inhibition, encompassing small molecules, peptidomimetics, cyclic peptides, stapled peptides and macrocycles. Illustrated throughout with successful case studies, this book provides a holistic, cutting-edge view of the subject area and is ideal for chemical biologists and medicinal chemists interested in developing PPI inhibitors.


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Protein interactions are fundamentally characterized as stable or transient, and both types of interactions can be either strong or weak. Stable interactions are those associated with proteins that are purified as multi-subunit complexes, and the subunits of these complexes can be identical or different. Hemoglobin and core RNA polymerase are examples of multi-subunit interactions that form stable complexes.

Transient interactions are expected to control the majority of cellular processes. As the name implies, transient interactions are temporary in nature and typically require a set of conditions that promote the interaction, such as phosphorylation, conformational changes or localization to discrete areas of the cell. Transient interactions can be strong or weak, and fast or slow. While in contact with their binding partners, transiently interacting proteins are involved in a wide range of cellular processes, including protein modification, transport, folding, signaling, apoptosis and cell cycling. The following example provides an illustration of protein interactions that regulate apoptotic and anti-apoptotic processes.


Heavy BAD protein–protein interaction. Panel A: Coomassie-stained SDS-PAGE gel of recombinant light and heavy BAD-GST-HA-6xHIS purified from HeLa IVT lysates (L), using glutathione resin (E1) and cobalt resin (E2) tandem affinity. The flow-through (FT) from each column is indicated. Panel B: Schematic of BAD phosphorylation and protein interactions during cell survival and cell death (i.e., apoptosis). Panel C: BAD protein sequence coverage showing identified Akt consensus phosphorylation sites (red box). Panel D: MS spectra of stable isotope-labeled BAD peptide HSSYPAGTEDDEGmGEEPSPFr.

Proteins bind to each other through a combination of hydrophobic bonding, van der Waals forces, and salt bridges at specific binding domains on each protein. These domains can be small binding clefts or large surfaces and can be just a few peptides long or span hundreds of amino acids. The strength of the binding is influenced by the size of the binding domain. One example of a common surface domain that facilitates stable protein–protein interactions is the leucine zipper, which consists of α-helices on each protein that bind to each other in a parallel fashion through the hydrophobic bonding of regularly-spaced leucine residues on each α-helix that project between the adjacent helical peptide chains. Because of the tight molecular packing, leucine zippers provide stable binding for multi-protein complexes, although all leucine zippers do not bind identically due to non-leucine amino acids in the α-helix that can reduce the molecular packing and therefore the strength of the interaction.

Two Src homology (SH) domains, SH2 and SH3, are examples of common transient binding domains that bind short peptide sequences and are commonly found in signaling proteins. The SH2 domain recognizes peptide sequences with phosphorylated tyrosine residues, which are often indicative of protein activation. SH2 domains play a key role in growth factor receptor signaling, during which ligand-mediated receptor phosphorylation at tyrosine residues recruits downstream effectors that recognize these residues via their SH2 domains. The SH3 domain usually recognizes proline-rich peptide sequences and is commonly used by kinases, phospholipases and GTPases to identify target proteins. Although both SH2 and SH3 domains generally bind to these motifs, specificity for distinct protein interactions is dictated by neighboring amino acid residues in the respective motif.

The result of two or more proteins that interact with a specific functional objective can be demonstrated in several different ways. The measurable effects of protein interactions have been outlined as follows:

  • Alter the kinetic properties of enzymes, which may be the result of subtle changes in substrate binding or allosteric effects
  • Allow for substrate channeling by moving a substrate between domains or subunits, resulting ultimately in an intended end product
  • Create a new binding site, typically for small effector molecules
  • Inactivate or destroy a protein
  • Change the specificity of a protein for its substrate through the interaction with different binding partners, e.g., demonstrate a new function that neither protein can exhibit alone
  • Serve a regulatory role in either an upstream or a downstream event

Usually a combination of techniques is necessary to validate, characterize and confirm protein interactions. Previously unknown proteins may be discovered by their association with one or more proteins that are known. Protein interaction analysis may also uncover unique, unforeseen functional roles for well-known proteins. The discovery or verification of an interaction is the first step on the road to understanding where, how and under what conditions these proteins interact in vivo and the functional implications of these interactions.

While the various methods and approaches to studying protein–protein interactions are too numerous to describe here, the table below and the remainder of this section focuses on common methods to analyze protein–protein interactions and the types of interactions that can be studies using each method. In summary, stable protein–protein interactions are easiest to isolate by physical methods like co-immunoprecipitation and pull-down assays because the protein complex does not disassemble over time. Weak or transient interactions can be identified using these methods by first covalently crosslinking the proteins to freeze the interaction during the co-IP or pull-down. Alternatively, crosslinking, along with label transfer and far–western blot analysis, can be performed independent of other methods to identify protein–protein interactions.

Common methods to analyze the various types of protein interactions

MethodProtein–protein interactions
Co-immunoprecipitation (co-IP)Stable or strong
Pull-down assayStable or strong
Crosslinking protein interaction analysisTransient or weak
Label transfer protein interaction analysisTransient or weak
Far–western blot analysisModerately stable

Co-immunoprecipitation (co-IP) is a popular technique for protein interaction discovery. Co-IP is conducted in essentially the same manner as an immunoprecipitation (IP) of a single protein, except that the target protein precipitated by the antibody, also called the "bait", is used to co-precipitate a binding partner/protein complex, or "prey", from a lysate. Essentially, the interacting protein is bound to the target antigen, which is bound by the antibody that is immobilized to the support. Immunoprecipitated proteins and their binding partners are commonly detected by sodium dodecyl sulfate–polyacrylamide gel electrophoresis (SDS-PAGE) and western blot analysis. The assumption that is usually made when associated proteins are co-precipitated is that these proteins are related to the function of the target antigen at the cellular level. This is only an assumption, however, that is subject to further verification.

Co-immunoprecipitation of cyclin B and Cdk1. The Thermo Scientific Pierce Protein A/G Magnetic Beads bind to Cdk1 antibody complexed with Cdk1. Cyclin B is bound to the Cdk1, and is captured along with its binding partner.


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Hydrogen Bonds in Proteins: Role and Strength

Hydrogen Bonds in Proteins: Role and Strength

University of York, York, UK

University of York, York, UK

University of York, York, UK

University of York, York, UK

Abstract

Hydrogen bonds provide most of the directional interactions that underpin protein folding, protein structure and molecular recognition. The core of most protein structures is composed of secondary structures such as α helix and β sheet. This satisfies the hydrogen-bonding potential between main chain carbonyl oxygen and amide nitrogen buried in the hydrophobic core of the protein. Hydrogen bonding between a protein and its ligands (protein, nucleic acid, substrate, effector or inhibitor) provides a directionality and specificity of interaction that is a fundamental aspect of molecular recognition. The energetics and kinetics of hydrogen bonding therefore need to be optimal to allow the rapid sampling and kinetics of folding, conferring stability to the protein structure and providing the specificity required for selective macromolecular interactions.

Key concepts:

A hydrogen bond is formed by the interaction of a hydrogen atom that is covalently bonded to an electronegative atom (donor) with another electronegative atom (acceptor).

Hydrogen bonding confers rigidity to the protein structure and specificity to intermolecular interactions.

The accepted (and most frequently observed) geometry for a hydrogen bond is a distance of less than 2.5 Å (1.9 Å) between hydrogen and the acceptor and a donor-hydrogen-acceptor angle of between 90° and 180° (160°).

During protein folding, the burial of hydrophobic side-chains requires intramolecular hydrogen bonds to be formed between the main chain polar groups.

The most stable conformations of polypeptide chains that maximize intrachain hydrogen-bonding potential are α helices and β sheets.

Specificity in molecular recognition is driven by the interaction of complementary hydrogen-bonding groups on interacting surfaces.


Detecting protein–protein interactions by far western blotting

Far western blotting (WB) was derived from the standard WB method to detect protein–protein interactions in vitro. In Far WB, proteins in a cell lysate containing prey proteins are firstly separated by SDS or native PAGE, and transferred to a membrane, as in a standard WB. The proteins in the membrane are then denatured and renatured. The membrane is then blocked and probed, usually with purified bait protein(s). The bait proteins are detected on spots in the membrane where a prey protein is located if the bait proteins and the prey protein together form a complex. Compared with other biochemical binding assays, Far WB allows prey proteins to be endogenously expressed without purification. Unlike most methods using cell lysates (e.g., co-immunoprecipitation (co-IP)) or living cells (e.g., fluorescent resonance energy transfer (FRET)), Far WB determines whether two proteins bind to each other directly. Furthermore, in cases where they bind to each other indirectly, Far WB allows the examination of candidate protein(s) that form a complex between them. Typically, 2–3 d are required to carry out the experiment.


Supporting Information

S1 Fig

The top five GO-terms were included that were found significantly enriched for each PTM-type. Each element in the heat map (Euclidean distance hierarchical clustering, average linkage) represents the grey-scale-encoded p-value, in which a particular combination of PTM-type and GO-term was found significantly enriched. To he combined whole UniProtKB-GOA for all the selected species was used as the background set, Fisher’s exact test with FDR correction was used for the enrichment analysis, and the p-value (FDR) threshold indicating significance was set to 0.01.

S2 Fig

The red (blue) asterisks on the top of violin plot represents the corresponding non-PTM group has a significantly higher (lower) median value compared to the non-PTM group (*: p-value 0.05, **: p-value 0.01) according to a Mann-Whitney test.

S3 Fig

The species are ordered according to their phylogenetic relationships as shown on the left. For every PTM-type, the log-2 of fold difference value for the degree/clustering coefficient/closeness centrality value relative to the respective value associated with proteins not carrying this particular PTM-type are given for PINs based on STRING and IntAct, respectively. Color scale indicates increased (red) or decreased (blue) values in the PTM-set relative to the non-PTM-set with symmetric color intervals (i.e. full color saturation based on the maximal absolute increase or decrease fold difference observed across all values in the table.) Bold-font (underlined) fold-changes indicate significant fold-changes at pπ.05 (pπ.01) by Mann-Whitney test with FDR correction, the values in red or blue text represent significantly higher or lower network properties which are inconsistent with the background color.

S4 Fig

Protein sets were selected to contain one PTM-type only (one-PTM-type-only dataset). The species are ordered according to their phylogenetic relationships as shown on the left. For every PTM-type, the log-2 of fold difference value for the degree/clustering coefficient/closeness centrality value relative to the respective value associated with proteins not carrying this particular PTM-type are given for PINs based on STRING and IntAct, respectively. Color scale indicates increased (red) or decreased (blue) values in the PTM-set relative to the non-PTM-set with symmetric color intervals (i.e. full color saturation based on the maximal absolute increase or decrease fold difference observed across all values in the table.) Bold-font (underlined) fold-changes indicate significant fold-changes at pπ.05 (pπ.01) by Mann-Whitney test with FDR correction, the values in red or blue text represent significantly higher or lower network properties, which are inconsistent with the background color based on mean (not median) values.

S5 Fig

Increased frequencies of protein-protein interactions (designated as protein A and B, respectively) carrying the respective PTM-types relative to expectation. Line width is proportional to the number of species, which exhibit significant interactions of PTM-types carried by interacting proteins. The contingency table for the Fisher exact test contained the respective counts for number of proteins associated with a particular PTM-pair versus all alternative pairings and whether they have been reported to interact or not with applied FDR-corrected p-value threshold of π.01.

S6 Fig

The red line connects the mean network property values of proteins associated with different numbers of PTMs. Associated Pearson linear correlation coefficients, r, (and p-values) were: degree r = 0.056 (1.46E-06), clustering coefficient: r = -0.068 (6.27E-08), closeness centrality: r = 0.164 (0.00).


Protein-protein interaction networks

Protein-protein interactions (PPIs) are essential to almost every process in a cell, so understanding PPIs is crucial for understanding cell physiology in normal and disease states. It is also essential in drug development, since drugs can affect PPIs. Protein-protein interaction networks (PPIN) are mathematical representations of the physical contacts between proteins in the cell. These contacts:

  • are specific
  • occur between defined binding regions in the proteins
  • have a particular biological meaning (i.e., they serve a specific function)

PPI information can represent both transient and stable interactions:

  • Stable interactions are formed in protein complexes (e.g. ribosome, haemoglobin)
  • Transient interactions are brief interactions that modify or carry a protein, leading to further change (e.g. protein kinases, nuclear pore importins). They constitute the most dynamic part of the interactome

Knowledge of PPIs can be used to:

  • assign putative roles to uncharacterised proteins
  • add fine-grained detail about the steps within a signalling pathway
  • characterise the relationships between proteins that form multi-molecular complexes such as the proteasome

The interactome

The interactome is the totality of PPIs that happen in a cell, an organism or a specific biological context. The development of large-scale PPI screening techniques, especially high-throughput affinity purification combined with mass-spectrometry and the yeast two-hybrid assay, has caused an explosion in the amount of PPI data and the construction of ever more complex and complete interactomes (Figure 16). This experimental evidence is complemented by the availability of PPI prediction algorithms. A lot of this information is available through molecular interaction databases such as IntAct.

Figure 16 Yeast (left) and human (right) interactomes obtained using the yeast-two hybrid method. Images reprinted by permission from Macmillan Publishers Ltd: Jeong et al. Nature 2001. 411 (3) and Rual et al. Nature 2005: 437 (4).

It is important to emphasise once more the limitations of available PPI data. Our current knowledge of the interactome is both incomplete and noisy. PPI detection methods have limitations as to how many truly physiological interactions they can detect and they all find false positives and negatives.

On the next few pages we will take a look at some of the properties of protein-protein interaction networks and the implications of these properties for biology.


Structure of Peripheral Proteins

In the image below, several peripheral proteins are labeled. A peripheral protein does not have a definite structure, but it has several key aspects which make it a peripheral protein.

First, all peripheral proteins are associated with the cell membrane. The amino acid sequences of these proteins are unique in that they draw the proteins to the membrane, and they tend to congregate on the surface of the membrane. This allows them to be in the right place to carry out their designated action. In the image, the orange peripheral proteins are seen attached to either the phosphoglyceride lipid molecules which make up the lipid bilayer, or to integral proteins. A protein without these areas of amino acids would not be attracted to the membrane. It would be distributed evenly throughout the cytoplasm, and would not be a peripheral protein.

Second, peripheral proteins do not have a hydrophobic region of amino acids. This, and the polarity of other amino acid groups, keeps the peripheral proteins on the surface of the cell membrane. This is due to the amphipathic nature of phosphoglycerides. This means that the blue “head” region is polar and hydrophilic. The yellow “tails”, which constitute the middle of the membrane, are hydrophobic. To avoid being sucked into the membrane, peripheral proteins often have lots of hydrophilic amino acids exposed on their surface. Integral proteins expose hydrophobic amino acids in the middle, and hydrophilic amino acids on the parts exposed to water. This effectively locks them within the membrane.


Electronic supplementary material

12900_2007_186_MOESM1_ESM.txt

Additional file 1: PDB:1LFD – output of PSAIA Structure analyser in table form. This file includes information about ASA, DPX, CX and hydrophobicity values per residue of PDB:1LFD obtained by PSAIA. In this case, the chains were calculated in bound form. (TXT 239 KB)

12900_2007_186_MOESM2_ESM.xml

Additional file 2: PDB:1LFD – output of PSAIA Structure analyser in XML form. This file includes information about ASA, DPX, CX and hydrophobicity values per residue of PDB:1LFD obtained by PSAIA. In this case, the chains were calculated in bound form. (XML 667 KB)

12900_2007_186_MOESM3_ESM.txt

Additional file 3: PDB:1LFD binding residues – output of PSAIA Interaction Analyser in table form. This file includes information about residues that are included in interaction in PDB:1LFD obtained by maximum distance algorithm from PSAIA application. (TXT 3 KB)

12900_2007_186_MOESM4_ESM.xml

Additional file 4: PDB:1LFD binding residues – output of PSAIA Interaction Analyser in XML form. This file includes information about residues that are included in interaction in PDB:1LFD obtained by maximum distance algorithm from PSAIA application. (XML 23 KB)

12900_2007_186_MOESM5_ESM.xsd

Additional file 5: XML Schema for binding residues. This is the XSD schema definition for XML output file (Additional file 6) (XSD 1 KB)

12900_2007_186_MOESM6_ESM.txt

Additional file 6: PDB:1LFD binding status – output of PSAIA Interaction Analyser in table form. This file includes information on interaction status (in interaction or not in interaction) of a particular residue in PDB:1LFD obtained by maximum distance algorithm from PSAIA application. (TXT 17 KB)

12900_2007_186_MOESM7_ESM.xml

Additional file 7: PDB:1LFD binding status – output of PSAIA Interaction Analyser in XML form. This file includes information on interaction status (in interaction or not in interaction) of a particular residue in PDB:1LFD obtained by maximum distance algorithm from PSAIA application. (XML 124 KB)

12900_2007_186_MOESM8_ESM.xsd

Additional file 8: XML Schema for binding status. This is the XSD schema definition for XML output file (Additional file 9) (XSD 1 KB)

12900_2007_186_MOESM9_ESM.txt

Additional file 9: PDB:1LFD contacts – output of PSAIA Interaction Analyser in table form. This file includes information on interaction partners in PDB:1LFD obtained by maximum distance algorithm from PSAIA application. (TXT 16 KB)

12900_2007_186_MOESM10_ESM.xml

Additional file 10: PDB:1LFD contacts – output of PSAIA Interaction Analyser in XML form. This file includes information on interaction partners in PDB:1LFD obtained by maximum distance algorithm from PSAIA application. (XML 110 KB)

Additional file 11: XML Schema for residue contacts. This is the XSD schema definition for XML output file. (TXT 2 KB)

12900_2007_186_MOESM12_ESM.txt

Additional file 12: XML Schema for peptide structure in bound form. This is the XSD schema definition for XML output file. (TXT 6 KB)

12900_2007_186_MOESM13_ESM.txt

Additional file 13: XML Schema for peptide structure in unbound form. This is the XSD schema definition for XML output file. (TXT 6 KB)

12900_2007_186_MOESM14_ESM.txt

Additional file 14: PDB:1LFD – output of DSSP application. This file includes information about secondary structure and total ASA per residue of PDB:1LFD obtained from DSSP application. (TXT 72 KB)

12900_2007_186_MOESM15_ESM.txt

Additional file 15: PDB:1LFD – output of NACCESS application. This file includes information about total, relative, backbone, side-chain, polar and non-polar ASA per residue of PDB:1LFD obtained from NACCESS application. (TXT 41 KB)

12900_2007_186_MOESM16_ESM.txt

Additional file 16: PDB:1LFD – CX output of PSAIA Structure Analyser. This file includes information about CX per residue of PDB:1LFD obtained by PSAIA. In this case, the chains were calculated in unbound form. (TXT 72 KB)

12900_2007_186_MOESM17_ESM.txt

Additional file 17: PDB:1LFD – output of CX server http://hydra.icgeb.trieste.it/cx/. This file includes information about maximum, minimum and average CX values per residue of PDB:1LFD obtained from CX server. (TXT 25 KB)

12900_2007_186_MOESM18_ESM.txt

Additional file 18: PDB:1LFD – output of dpx server http://hydra.icgeb.trieste.it/dpx/. This file includes information about maximum, minimum and average CX values per residue of PDB:1LFD obtained from CX server. (TXT 25 KB)

12900_2007_186_MOESM19_ESM.exe

Additional file 19: PSAIA – MS Windows setup file. Setup file for installation PSAIA, PSA and PIA. Newer versions of the application can be downloaded from the project web site. (EXE 8 MB)

12900_2007_186_MOESM20_ESM.bin

Additional file 20: PSAIA – Linux installer. PSAIA installer for a x86 Linux platform. Linux installers for other architectures as well as the source code can be downloaded from the project web site. (BIN 7 MB)