Length, Protein-Protein Interactions, and Complexity

The evolutionary reason for the increase in gene length from archaea to prokaryotes to eukaryotes observed in large scale genome sequencing efforts has been unclear. We propose here that the increasing complexity of protein-protein interactions has driven the selection of longer proteins, as longer proteins are more able to distinguish among a larger number of distinct interactions due to their greater average surface area. Annotated protein sequences available from the SWISS-PROT database were analyzed for thirteen eukaryotes, eight bacteria, and two archaea species. The number of subcellular locations to which each protein is associated is used as a measure of the number of interactions to which a protein participates. Two databases of yeast protein-protein interactions were used as another measure of the number of interactions to which each \emph{S. cerevisiae} protein participates. Protein length is shown to correlate with both number of subcellular locations to which a protein is associated and number of interactions as measured by yeast two-hybrid experiments. Protein length is also shown to correlate with the probability that the protein is encoded by an essential gene. Interestingly, average protein length and number of subcellular locations are not significantly different between all human proteins and protein targets of known, marketed drugs. Increased protein length appears to be a significant mechanism by which the increasing complexity of protein-protein interaction networks is accommodated within the natural evolution of species. Consideration of protein length may be a valuable tool in drug design, one that predicts different strategies for inhibiting interactions in aberrant and normal pathways.Comment: 13 pages, 5 figures, 2 tables, to appear in Physica

A simple physical model for scaling in protein-protein interaction networks

It has recently been demonstrated that many biological networks exhibit a scale-free topology where the probability of observing a node with a certain number of edges (k) follows a power law: i.e. p(k) ~ k^-g. This observation has been reproduced by evolutionary models. Here we consider the network of protein-protein interactions and demonstrate that two published independent measurements of these interactions produce graphs that are only weakly correlated with one another despite their strikingly similar topology. We then propose a physical model based on the fundamental principle that (de)solvation is a major physical factor in protein-protein interactions. This model reproduces not only the scale-free nature of such graphs but also a number of higher-order correlations in these networks. A key support of the model is provided by the discovery of a significant correlation between number of interactions made by a protein and the fraction of hydrophobic residues on its surface. The model presented in this paper represents the first physical model for experimentally determined protein-protein interactions that comprehensively reproduces the topological features of interaction networks. These results have profound implications for understanding not only protein-protein interactions but also other types of scale-free networks.Comment: 50 pages, 17 figure

PAX6 protein-protein interactions

The gene PAX6 is located on chromosome 11 (1 lpl3) and encodes a transcription factor (PAX6) that is expressed early in development. The PAX6 protein is expressed in the developing eye, regions of the brain, central nervous system (CNS), nasal epithelium and pancreas. PAX6 is best known for its role in eye development with heterozygous mutations causing congenital ocular malformations. However, it must be remembered that PAX6 has multiple functions in the brain including specification of neuronal subtypes and axon guidance.There is growing understanding of the role of PAX6 as a transcription factor during development, and many of its DNA targets have recently been defined. However, almost nothing is known about the proteins with which PAX6 interacts.In the initial stage of my research I identified a conserved region consisting of the final 32 amino acids of the PST (proline, serine and threonine rich) domain of PAX6. Based on sequence homology and secondary structure predictions I classed this region as a novel domain, the 'C terminal domain'. Next I used the yeast 2-hybrid system to investigate possible PAX6 protein interactions. By screening a mouse brain cDNA library with the C terminal domain and whole PST domain, I identified three novel and interesting interactors, Homer3, Dncll and Triml 1.I re-confirmed these interactions in a pairwise manner using the yeast 2-hybrid system, and I showed that the C terminal domain was vital for the interactions between PAX6 and Homer3 or Dncll. Furthermore, certain C terminal mutations that are known to cause ocular malformations in patients are also sufficient to reduce or abolish these interactions. I attempted to further characterise the interactions by co-immunoprecipitation. However, this was not possible due to technical difficulties.Although speculative at present, the finding that PAX6 may interact with Homer3 (a component of the post synaptic density) and Dncll (a subunit of the motor protein dynein) alludes to an interesting new role for PAX6 in neurogenesis

ProDGe: investigating protein-protein interactions at the domain level

An important goal of systems biology is the identification and investigation of known and predicted protein-protein interactions to obtain more information about new cellular pathways and processes. Proteins interact via domains, thus it is important to know which domains a protein contains and which domains interact with each other. Here we present the Java^TM^ program ProDGe (Protein Domain Gene), which visualizes existing and suggests novel domain-domain interactions and protein-protein interactions at the domain level. The comprehensive dataset behind ProDGe consists of protein, domain and interaction information for both layers, collected and combined appropriately from UniProt, Pfam, DOMINE and IntAct. Based on known domain interactions, ProDGe suggests novel protein interactions and assigns them to four confidence classes, depending on the reliability of the underlying domain interaction. Furthermore, ProDGe is able to identify potential homologous interaction partners in other species, which is particularly helpful when investigating poorly annotated species. We further evaluated and compared experimentally identified protein interactions from IntAct with domain interactions from DOMINE for six species and noticed that 31.13% of all IntAct protein interactions in all six species can be mapped to the actual interacting domains. ProDGe and a comprehensive documentation are freely available at http://www.cogsys.cs.uni-tuebingen.de/software/ProDGe

Measurements of Protein-Protein Interactions by Size Exclusion Chromatography

A method is presented for determining second virial coefficients B_2 of protein solutions from retention time measurements in size exclusion chromatography (SEC). We determine B_2 by analyzing the concentration dependance of the chromatographic partition coefficient. We show the ability of this method to track the evolution of B_2 from positive to negative values in lysozyme and bovine serum albumin solutions. Our SEC results agree quantitatively with data obtained by light scattering.Comment: 18 pages including 1 table and 5 figure

Spectral Analysis of Protein-Protein Interactions in Drosophila melanogaster

Within a case study on the protein-protein interaction network (PIN) of Drosophila melanogaster we investigate the relation between the network's spectral properties and its structural features such as the prevalence of specific subgraphs or duplicate nodes as a result of its evolutionary history. The discrete part of the spectral density shows fingerprints of the PIN's topological features including a preference for loop structures. Duplicate nodes are another prominent feature of PINs and we discuss their representation in the PIN's spectrum as well as their biological implications.Comment: 9 pages RevTeX including 8 figure

Hot-spot analysis for drug discovery targeting protein-protein interactions

Introduction: Protein-protein interactions are important for biological processes and pathological situations, and are attractive targets for drug discovery. However, rational drug design targeting protein-protein interactions is still highly challenging. Hot-spot residues are seen as the best option to target such interactions, but their identification requires detailed structural and energetic characterization, which is only available for a tiny fraction of protein interactions. Areas covered: In this review, the authors cover a variety of computational methods that have been reported for the energetic analysis of protein-protein interfaces in search of hot-spots, and the structural modeling of protein-protein complexes by docking. This can help to rationalize the discovery of small-molecule inhibitors of protein-protein interfaces of therapeutic interest. Computational analysis and docking can help to locate the interface, molecular dynamics can be used to find suitable cavities, and hot-spot predictions can focus the search for inhibitors of protein-protein interactions. Expert opinion: A major difficulty for applying rational drug design methods to protein-protein interactions is that in the majority of cases the complex structure is not available. Fortunately, computational docking can complement experimental data. An interesting aspect to explore in the future is the integration of these strategies for targeting PPIs with large-scale mutational analysis.This work has been funded by grants BIO2016-79930-R and SEV-2015-0493 from the Spanish Ministry of Economy, Industry and Competitiveness, and grant EFA086/15 from EU Interreg V POCTEFA. M Rosell is supported by an FPI fellowship from the Severo Ochoa program. The authors are grateful for the support of the the Joint BSC-CRG-IRB Programme in Computational Biology.Peer ReviewedPostprint (author's final draft

Embedding machine-readable proteins interactions data in scientific articles for easy access and retrieval

Extraction of protein-protein interactions data from scientific literature remains a hard, time- and resource-consuming task. This task would be greatly simplified by embedding in the source, i.e. research articles, a standardized, synthetic, machine-readable codification for protein-protein interactions data description, to make the identification and the retrieval of such very valuable information easier, faster, and more reliable than now.
We shortly discuss how this information can be easily encoded and embedded in research papers with the collaboration of authors and scientific publishers, and propose an online demonstrative tool that shows how to help and allow authors for the easy and fast conversion of such valuable biological data into an embeddable, accessible, computer-readable codification