PyProGA-A PyMOL plugin for protein residue network analysis.

Signal flow direction is one of the most important features of the protein-protein interactions in signaling networks. However, almost all the outcomes of current high-throughout techniques for protein-protein interactions mapping are usually supposed to be non-directional. Based on the pairwise interaction domains, here we defined a novel parameter protein interaction directional score and then used it to predict the direction of signal flow between proteins in proteome-wide signaling networks. Using 5-fold cross-validation, our approach obtained a satisfied performance with the accuracy 89.79%, coverage 48.08%, and error ratio 16.91%. As an application, we established an integrated human directional protein interaction network, including 2,237 proteins and 5,530 interactions, and inferred a large amount of novel signaling pathways. Directional protein interaction network was strongly supported by the known signaling pathways literature (with the 87.5% accuracy) and further analyses on the biological annotation, subcellular localization, and network topology property. Thus, this study provided an effective method to define the upstream/downstream relations of interacting protein pairs and a powerful tool to unravel the unknown signaling pathways.

Proteins are vital components of living systems, serving as building blocks, molecular machines, enzymes, receptors, ion channels, sensors, and transporters. Protein-protein interactions (PPIs) are a key part of their function. There are more than 645,000 reported disease-relevant PPIs in the human interactome, but drugs have been developed for only 2% of these targets. The advances in PPI-focused drug discovery are highly dependent on the availability of structural data and accurate computational tools for analysis of this data. Quantum mechanical approaches are often too expensive computationally, but the fragment molecular orbital (FMO) method offers an excellent solution that combines accuracy, speed and the ability to reveal key interactions that would otherwise be hard to detect. FMO provides essential information for PPI drug discovery, namely, identification of key interactions formed between residues of two proteins, including their strength (in kcal/mol) and their chemical nature (electrostatic or hydrophobic). In this chapter, we have demonstrated how three different FMO-based approaches (pair interaction energy analysis (PIE analysis), subsystem analysis (SA) and analysis of protein residue networks (PRNs)) have been applied to study PPI in three protein-protein complexes.

Accuracy of the fragment molecular orbital (FMO) calculations for DNA: Total energy, molecular orbital, and inter-fragment interaction energy

Using a combination of tandem affinity purification tagging and mass spectrometry, we characterized a novel, evolutionarily conserved protein phosphatase 4 (PP4)-containing complex (PP4cs, protein phosphatase 4, cisplatin-sensitive complex) that plays a critical role in the eukaryotic DNA damage response. PP4cs is comprised of the catalytic subunit PP4C; a known regulatory subunit, PP4R2; and a novel protein that we termed PP4R3. The Saccharomyces cerevisiae PP4R3 ortholog Psy2 was identified previously in a screen for sensitivity to the DNA-damaging agent and anticancer drug cisplatin. We demonstrated that deletion of any of the PP4cs complex orthologs in S. cerevisiae elicited cisplatin hypersensitivity. Furthermore human PP4R3 complemented the yeast psy2 deletion, and Drosophila melanogaster lacking functional PP4R3 (flfl) exhibited cisplatin hypersensitivity, suggesting a highly conserved role for PP4cs in DNA damage repair. Finally we found that PP4R3 may target PP4cs to the DNA damage repair machinery at least in part via an interaction with Rad53 (CHK2).

Recently, we have developed a new simulation scheme with dissipative particle dynamics (DPD) based on non-empirical interaction parameters derived from a series of fragment molecular orbital (FMO) calculations. This approach (FMO–DPD) was applied to folding simulations of Chignolin and Superchignolin mini-proteins. Their characteristic hairpin structure was obtained from an elongated form within short computation time. Essential residue-residue interactions such as hydrogen bonding and CH/π were observed at the final form. FMO–DPD should have a potential applicability to nano-bio systems involving proteins.

Single-Chain Lanthanide Luminescence Biosensors for Cell-Based Imaging and Screening of Protein-Protein Interactions.

BackgroundMany biological processes are carried out by proteins interacting with each other in the form of protein complexes. However, large-scale detection of protein complexes has remained constrained by experimental limitations. As such, computational detection of protein complexes by applying clustering algorithms on the abundantly available protein-protein interaction (PPI) networks is an important alternative. However, many current algorithms have overlooked the importance of selecting seeds for expansion into clusters without excluding important proteins and including many noisy ones, while ensuring a high degree of functional homogeneity amongst the proteins detected for the complexes.ResultsWe designed a novel method called Probabilistic Local Walks (PLW) which clusters regions in a PPI network with high functional similarity to find protein complex cores with high precision and efficiency in (|V| log |V| + |E|) time. A seed selection strategy, which prioritises seeds with dense neighbourhoods, was devised. We defined a topological measure, called common neighbour similarity, to estimate the functional similarity of two proteins given the number of their common neighbours.ConclusionsOur proposed PLW algorithm achieved the highest F-measure (recall and precision) when compared to 11 state-of-the-art methods on yeast protein interaction data, with an improvement of 16.7% over the next highest score. Our experiments also demonstrated that our seed selection strategy is able to increase algorithm precision when applied to three previous protein complex mining techniques.AvailabilityThe software, datasets and predicted complexes are available at http://wonglkd.github.io/PLW

Seeing red: a modified RUBY reporter assay for visualizing inplanta protein-protein interactions.

Crosslinking strategies have emerged as an attractive technology for deciphering protein complexes and protein-protein interactions (PPIs). However, commonly used crosslinking strategies present significant challenges for the precise analysis of protein complexes and dynamic PPIs in native biological environments. Here, we report the development of the first visible-light-inducible lysine-specific homobifunctional photo-crosslinkers and introduce Visible-light-controlled Lysine-selective crosslinking (VL-XL) strategy for in-depth analysis of protein complexes and profiling dynamic interactomes in live cells. By synergistically integrating the advantages of temporal control, high biocompatibility, and lysine selectivity, the VL-XL strategy not only provides an effective solution for protein complexes studies-achieving residue-specific crosslinked peptides, delivering high-confidence data and streamlined mass spectrometry (MS) data analysis-but also reveals dynamic interactomes in various scenarios. The VL-XL strategy successfully profiles the time-resolved, epidermal growth factor (EGF)-stimulated epidermal growth factor receptor (EGFR) interactome, providing valuable insights into regulatory mechanisms of EGFR signaling. More importantly, the VL-XL strategy effectively unveils molecular glue degrader-induced E3 ligase interactome, leading to discovery of neo-substrates such as Sestrin-2 (SESN2) and opening an innovative avenue for identifying novel targets for degradation. Overall, the VL-XL strategy providesa robust chemical tool, inspiring innovative solutions to address unresolved questions in multiple fields.

Cystic fibrosis (CF) is a frequent autosomal recessive disorder caused by mutation of a gene encoding a multifunctional transmembrane protein, the cystic fibrosis transmembrane conductance regulator (CFTR), located in the apical membrane of epithelial cells lining exocrine glands. In an attempt to get a more complete picture of the pleiotropic effects of the CFTR defect on epithelial cells and particularly on the membrane compartment, a bidimensional blue native (BN)/SDS-PAGE-based proteomic approach was used on colonic crypt samples from control and CFTR knock-out mice (cftr-/-). This approach overcomes the difficulties of membrane protein analysis by conventional two-dimensional PAGE and is able to resolve multiprotein complexes. Used here for the first time on crude membrane proteins that were extracted from murine colonic crypts, BN/SDS-PAGE allows effective separation of protein species and complexes of various origins, including mitochondria, plasma membrane, and intracellular compartments. The major statistically significant difference in protein maps obtained with samples from control and cftr-/- mice was unambiguously identified as mClCA3, a member of a family of calcium-activated chloride channels considered to be key molecules in mucus secretion by goblet cells. On the basis of this finding, we evaluated the overall expression and localization of mClCA3 in the colonic epithelium and in the lung of mice by immunoblot analysis and immunohistochemistry. We found that mClCA3 expression was significantly decreased in the colon and lung of the cftr-/- mice. In an ex vivo assay, we found that the Ca2+-dependent (carbachol-stimulated) glycoprotein secretion strongly inhibited by the calcium-activated chloride channel blocker niflumic acid (100 microm) was impaired in the distal colon of cftr-/- mice. These results support the conclusion that a ClCA-related function in the CF colon depends on CFTR expression and may be correlated with the impaired expression of mClCA3.

Structural genomics provides an important approach for characterizing and understanding systems biology. As a step toward better integrating protein three-dimensional (3D) structural information in cancer systems biology, we have constructed a Human Cancer Pathway Protein Interaction Network (HCPIN) by analysis of several classical cancer-associated signaling pathways and their physical protein-protein interactions. Many well known cancer-associated proteins play central roles as "hubs" or "bottlenecks" in the HCPIN. At least half of HCPIN proteins are either directly associated with or interact with multiple signaling pathways. Although some 45% of residues in these proteins are in sequence segments that meet criteria sufficient for approximate homology modeling (Basic Local Alignment Search Tool (BLAST) E-value <10(-6)), only approximately 20% of residues in these proteins are structurally covered using high accuracy homology modeling criteria (i.e. BLAST E-value <10(-6) and at least 80% sequence identity) or by actual experimental structures. The HCPIN Website provides a comprehensive description of this biomedically important multipathway network together with experimental and homology models of HCPIN proteins useful for cancer biology research. To complement and enrich cancer systems biology, the Northeast Structural Genomics Consortium is targeting >1000 human proteins and protein domains from the HCPIN for sample production and 3D structure determination. The long range goal of this effort is to provide a comprehensive 3D structure-function database for human cancer-associated proteins and protein complexes in the context of their interaction networks. The network-based target selection (BioNet) approach described here is an example of a general strategy for targeting co-functioning proteins by structural genomics projects.

We developed the world's first web-based public database for the storage, management, and sharing of fragment molecular orbital (FMO) calculation data sets describing the complex interactions between biomacromolecules, named FMO Database (https://drugdesign.riken.jp/FMODB/). Each entry in the database contains relevant background information on how the data was compiled as well as the total energy of each molecular system and interfragment interaction energy (IFIE) and pair interaction energy decomposition analysis (PIEDA) values. Currently, the database contains more than 13 600 FMO calculation data sets, and a comprehensive search function implemented at the front-end. The procedure for selecting target proteins, preprocessing the experimental structures, construction of the database, and details of the database front-end were described. Then, we demonstrated a use of the FMODB by comparing IFIE value distributions of hydrogen bond, ion-pair, and XH/π interactions obtained by FMO method to those by molecular mechanics approach. From the comparison, the statistical analysis of the data provided standard reference values for the three types of interactions that will be useful for determining whether each interaction in a given system is relatively strong or weak compared to the interactions contained within the data in the FMODB. In the final part, we demonstrate the use of the database to examine the contribution of halogen atoms to the binding affinity between human cathepsin L and its inhibitors. We found that the electrostatic term derived by PIEDA greatly correlated with the binding affinities of the halogen containing cathepsin L inhibitors, indicating the importance of QM calculation for quantitative analysis of halogen interactions. Thus, the FMO calculation data in FMODB will be useful for conducting statistical analyses to drug discovery, for conducting molecular recognition studies in structural biology, and for other studies involving quantum mechanics-based interactions.

Fragment molecular orbital calculations for biomolecules

&lt;p dir="ltr"&gt;Niemann-Pick type C (NPC) disease is a rare lysosomal-storage disease caused by mutations in NPC1 (95% cases) or NPC2 (5% cases). It is widely accepted that NPC1 and NPC2 function together in cholesterol egress from the lysosome, whereby upon mutation, cholesterol and other lipids accumulate and cause major pathologies. Interestingly, it is not fully understood (even in healthy individuals) how cholesterol is transported from NPC1 residing at the lysosomal membrane to the endoplasmic reticulum and plasma membrane. Niemann-Pick type C related protein-1 (Ncr1), the yeast orthologue of NPC1, functions similarly to NPC1; when transfected into a mammalian cell lacking NPC1, Ncr1 rescues cholesterol and sphingolipid accumulation typically seen in diseased cells. My thesis aims to identify and characterise protein-protein interactions (PPIs) with the yeast Ncr1 protein.&lt;/p&gt;&lt;p dir="ltr"&gt;In Chapter 2, I present results from a split-ubiquitin membrane yeast two-hybrid (MYTH) screen that identified six proteins interacting with Ncr1 (Cyb5, Lip1, Pga3, Phs1, Sss1 and Ysy6).Interestingly, several proteins that function in protein complexes also interact with Ncr1; Lip1 and Lag1 (components of the ceramide synthase complex) and Sss1, Sec61, Sbh1, Ssh1 and Sbh2 (components of the Sec61/Ssh1 protein translocation complex). Protein interactions with Cyb5, Lip1 and Sss1 were confirmed using co-immunoprecipitation (Co-IP) analysis, while in silico analyses supported interactions with Cyb5 and Pga3. Lastly, network analysis was conducted to highlight biological processes (protein translocation, folding/maturation of membrane proteins, and lipid metabolism) enriched in the network and thus important to the PPIs with Ncr1.&lt;/p&gt;&lt;p dir="ltr"&gt;In Chapter 3, I aimed to characterise the PPIs occurring with Ncr1. To map the site of interaction on the Ncr1 protein, fragments of Ncr1 were tagged with a yellow-fluorescent protein and protein localisation assessed. Unfortunately, no fragment localised to the vacuolar membrane and subsequent MYTH analysis was not conducted on the mislocalised constructs. To map the site of interaction on the prey proteins, two complementary experiments were conducted. MYTH analyses on prey protein fragments with full-length Ncr1 identified possible regions required for the PPI (Cyb5, 68-120 aa; Lip1, 11-20 aa; Pga3, 1-175 aa; and Ysy6, 15-29 aa). Alanine mutagenesis of cytosolic protruding sequences identified regions required for the PPIs (Cyb5, 2-78 aa; Pga3, 105-215 aa; Phs1, 119-142 aa; and Sss1, 30-44 aa). To further characterise the PPIs, drugs/conditions involved in different aspects of lipid metabolism were incorporated into the MYTH analysis; Ncr1-Cyb5, Ncr1-Lip1 and Ncr1-Pga3 were observed to be important in ergosterol, fatty acid and sphingolipid metabolism.&lt;/p&gt;&lt;p dir="ltr"&gt;In Chapter 4, I investigated the biological significance of the PPIs with Ncr1 via functional analyses on single and double mutant strains so to support the proposed physical interactions identified in Chapter 2 and 3. Cells lacking NCR1 and CYB5 (ncr1Δcyb5Δ) displayed stunted growth; sensitivity to the sphingolipid biosynthesis inhibitor (myriocin) and fatty acid biosynthesis inhibitor (cerulenin); increased number of lipid droplets; and dysregulated ergosterol intermediates. In addition to lipid analysis, intracellular transport via GFP-Atg8 (autophagy marker) and Mup1-GFP (endocytosis marker) analysis was investigated. Cells lacking NCR1 displayed reduced fluorophore expression in the vacuole, therefore suggesting a component of uptake at the vacuolar membrane is dysregulated. Together, this chapter highlighted several intriguing lipid metabolism phenotypes for the NCR1-CYB5 interaction.&lt;/p&gt;&lt;p dir="ltr"&gt;To conclude, in this thesis I set out to identify proteins that physically interact with Ncr1. I identified 11 proteins physically interacting with Ncr1, some of which may provide further molecular insight into previously described functions of Ncr1 in ergosterol metabolism. Additionally, I predict new functions of Ncr1 (and possibly NPC1) in their function in the Cyb5/Cbr1 electron transfer system (via the Cyb5 and Pga3 interactions), the ceramide synthase complex (via the Lip1 and Lag1 interactions); fatty acid elongation (via the Phs1 interaction), and their interaction with the Sec61/Sbh1 protein translocation complex (via the Sss1, Sec61, Sbh1, Ssh1 and Sbh2 interactions). Further work is necessary to gain a more complete understanding of these proposed interactions.&lt;/p&gt;

Identification of correlated inter-residue interactions in protein complex based on the fragment molecular orbital method