New GO-based Measures in Multiple Network Alignment.

MOTIVATION: Protein-protein interaction (PPI) networks provide valuable insights into the function of biological systems. Aligning multiple PPI networks may expose relationships beyond those observable by pairwise comparisons. However, assessing the biological quality of multiple network alignments is a challenging problem. RESULTS: We propose two new measures to evaluate the quality of multiple network alignments using functional information from Gene Ontology (GO) terms. When aligning multiple real PPI networks across species, we observe that both measures are highly correlated with objective quality indicators, such as common orthologs. Additionally, our measures strongly correlate with an alignment's ability to predict novel GO annotations, which is a unique advantage over existing GO-based measures. AVAILABILITY AND IMPLEMENTATION: The scripts and the links to the raw and alignment data can be accessed at https://github.com/kimiayazdani/GO_Measures.git.

Exploring Mucoadhesive and Toxicological Characteristics Following Modification of Linear Polyethylenimine with Various Anhydrides.

Linear polyethylenimine (L-PEI) has numerous applications, such as in pharmaceutical formulations, gene delivery, and water treatment. However, due to the presence of secondary amine groups, L-PEI shows a relatively high toxicity and low biocompatibility. Here, various organic anhydrides were used to modify L-PEI to reduce its toxicity and enhance its functionality. We selected methacrylic anhydride, crotonic anhydride, maleic anhydride, and succinic anhydride to modify L-PEI. The structure of the resulting derivatives was characterized using 1H NMR and FTIR spectroscopies, and their behavior in aqueous solutions was studied using turbidimetric and electrophoretic mobility measurements over a broad range of pHs. A fluorescence flow through method determined the mucoadhesive properties of the polymers to the bovine palpebral conjunctiva. Methacrylated L-PEI and crotonylated L-PEI showed strong mucoadhesive properties at pH 7.4, likely due to covalent bonding with mucin thiol groups. In contrast, maleylated and succinylated L-PEI were poorly mucoadhesive as the pH was above their isoelectric point, resulting in electrostatic repulsion between the polymers and mucin. The toxicity of these polymers was evaluated using in vivo assays with planaria and the 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyl-2H-tetrazolium bromide (MTT) cell viability assay in human alveolar epithelial cells. Moreover, the irritancy of polymers was assessed using a slug mucosa irritation assay. The results demonstrated that anhydride modification mitigated the adverse toxicity effects seen for parent L-PEI.

Exact p-values for global network alignments via combinatorial analysis of shared GO terms : REFANGO: Rigorous Evaluation of Functional Alignments of Networks using Gene Ontology.

Network alignment aims to uncover topologically similar regions in the protein-protein interaction (PPI) networks of two or more species under the assumption that topologically similar regions tend to perform similar functions. Although there exist a plethora of both network alignment algorithms and measures of topological similarity, currently no "gold standard" exists for evaluating how well either is able to uncover functionally similar regions. Here we propose a formal, mathematically and statistically rigorous method for evaluating the statistical significance of shared GO terms in a global, 1-to-1 alignment between two PPI networks. Given an alignment in which k aligned protein pairs share a particular GO term g, we use a combinatorial argument to precisely quantify the p-value of that alignment with respect to g compared to a random alignment. The p-value of the alignment with respect to all GO terms, including their inter-relationships, is approximated using the Empirical Brown's Method. We note that, just as with BLAST's p-values, this method is not designed to guide an alignment algorithm towards a solution; instead, just as with BLAST, an alignment is guided by a scoring matrix or function; the p-values herein are computed after the fact, providing independent feedback to the user on the biological quality of the alignment that was generated by optimizing the scoring function. Importantly, we demonstrate that among all GO-based measures of network alignments, ours is the only one that correlates with the precision of GO annotation predictions, paving the way for network alignment-based protein function prediction.

Self-Immolative System for Disclosure of Reactive Electrophilic Alkylating Agents: Understanding the Role of the Reporter Group.

The development of stable, efficient chemoselective self-immolative systems, for use in applications such as sensors, requires the optimization of the reactivity and degradation characteristics of the self-immolative unit. In this paper, we describe the effect that the structure of the reporter group has upon the self-immolative efficacy of a prototype system designed for the disclosure of electrophilic alkylating agents. The amine of the reporter group (a nitroaniline unit) was a constituent part of a carbamate that functioned as the self-immolative unit. The number and position of substituents on the nitroaniline unit were found to play a key role in the rate of self-immolative degradation and release of the reporter group. The position of the nitro substituent (meta- vs para-) and the methyl groups in the ortho-position relative to the carbamate exhibited an influence on the rate of elimination and stability of the self-immolative system. The ortho-methyl substituents imparted a twist on the N-C (aromatic) bond leading to increased resonance of the amine nitrogen's lone pair into the carbonyl moiety and a decrease of the leaving character of the carbamate group; concomitantly, this may also make it a less electron-withdrawing group and lead to less acidification of the eliminated ß-hydrogen.

BLANT-fast graphlet sampling tool.

SUMMARY: BLAST creates local sequence alignments by first building a database of small k-letter sub-sequences called k-mers. Identical k-mers from different regions provide 'seeds' for longer local alignments. This seed-and-extend heuristic makes BLAST extremely fast and has led to its almost exclusive use despite the existence of more accurate, but slower, algorithms. In this paper, we introduce the Basic Local Alignment for Networks Tool (BLANT). BLANT is the analog of BLAST, but for networks: given an input graph, it samples small, induced, k-node sub-graphs called k-graphlets. Graphlets have been used to classify networks, quantify structure, align networks both locally and globally, identify topology-function relationships and build taxonomic trees without the use of sequences. Given an input network, BLANT produces millions of graphlet samples in seconds-orders of magnitude faster than existing methods. BLANT offers sampled graphlets in various forms: distributions of graphlets or their orbits; graphlet degree or graphlet orbit degree vectors, the latter being compatible with ORCA; or an index to be used as the basis for seed-and-extend local alignments. We demonstrate BLANT's usefelness by using its indexing mode to find functional similarity between yeast and human PPI networks. AVAILABILITY AND IMPLEMENTATION: BLANT is written in C and is available at https://github.com/waynebhayes/BLANT/releases. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

SANA NetGO: a combinatorial approach to using Gene Ontology (GO) terms to score network alignments.

Motivation: Gene Ontology (GO) terms are frequently used to score alignments between protein-protein interaction (PPI) networks. Methods exist to measure GO similarity between proteins in isolation, but proteins in a network alignment are not isolated: each pairing is dependent on every other via the alignment itself. Existing measures fail to take into account the frequency of GO terms across networks, instead imposing arbitrary rules on when to allow GO terms. Results: Here we develop NetGO, a new measure that naturally weighs infrequent, informative GO terms more heavily than frequent, less informative GO terms, without arbitrary cutoffs, instead downweighting GO terms according to their frequency in the networks being aligned. This is a global measure applicable only to alignments, independent of pairwise GO measures, in the same sense that the edge-based EC or S3 scores are global measures of topological similarity independent of pairwise topological similarities. We demonstrate the superiority of NetGO in alignments of predetermined quality and show that NetGO correlates with alignment quality better than any existing GO-based alignment measures. We also demonstrate that NetGO provides a measure of taxonomic similarity between species, consistent with existing taxonomic measuresa feature not shared with existing GObased network alignment measures. Finally, we re-score alignments produced by almost a dozen aligners from a previous study and show that NetGO does a better job at separating good alignments from bad ones. Availability and implementation: Available as part of SANA. Contact: whayes@uci.edu. Supplementary information: Supplementary data are available at Bioinformatics online.

SANA: simulated annealing far outperforms many other search algorithms for biological network alignment.

SUMMARY: Every alignment algorithm consists of two orthogonal components: an objective function M measuring the quality of an alignment, and a search algorithm that explores the space of alignments looking for ones scoring well according to M . We introduce a new search algorithm called SANA (Simulated Annealing Network Aligner) and apply it to protein-protein interaction networks using S 3 as the topological measure. Compared against 12 recent algorithms, SANA produces 5-10 times as many correct node pairings as the others when the correct answer is known. We expose an anti-correlation in many existing aligners between their ability to produce good topological vs. functional similarity scores, whereas SANA usually outscores other methods in both measures. If given the perfect objective function encoding the identity mapping, SANA quickly converges to the perfect solution while many other algorithms falter. We observe that when aligning networks with a known mapping and optimizing only S 3 , SANA creates alignments that are not perfect and yet whose S 3 scores match that of the perfect alignment. We call this phenomenon saturation of the topological score . Saturation implies that a measure's correlation with alignment correctness falters before the perfect alignment is reached. This, combined with SANA's ability to produce the perfect alignment if given the perfect objective function, suggests that better objective functions may lead to dramatically better alignments. We conclude that future work should focus on finding better objective functions, and offer SANA as the search algorithm of choice. AVAILABILITY AND IMPLEMENTATION: Software available at http://sana.ics.uci.edu . CONTACT: whayes@uci.edu. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

A BMP-FGF morphogen toggle switch drives the ultrasensitive expression of multiple genes in the developing forebrain.

Borders are important as they demarcate developing tissue into distinct functional units. A key challenge is the discovery of mechanisms that can convert morphogen gradients into tissue borders. While mechanisms that produce ultrasensitive cellular responses provide a solution, how extracellular morphogens drive such mechanisms remains poorly understood. Here, we show how Bone Morphogenetic Protein (BMP) and Fibroblast Growth Factor (FGF) pathways interact to generate ultrasensitivity and borders in the dorsal telencephalon. BMP and FGF signaling manipulations in explants produced border defects suggestive of cross inhibition within single cells, which was confirmed in dissociated cultures. Using mathematical modeling, we designed experiments that ruled out alternative cross inhibition mechanisms and identified a cross-inhibitory positive feedback (CIPF) mechanism, or "toggle switch", which acts upstream of transcriptional targets in dorsal telencephalic cells. CIPF explained several cellular phenomena important for border formation such as threshold tuning, ultrasensitivity, and hysteresis. CIPF explicitly links graded morphogen signaling in the telencephalon to switch-like cellular responses and has the ability to form multiple borders and scale pattern to size. These benefits may apply to other developmental systems.

Microphase separation induced in the melt of Pluronic copolymers by blending with a hydrogen bonding urea-urethane end-capped supramolecular polymer.

Blending with a hydrogen-bonding supramolecular polymer is shown to be a successful novel strategy to induce microphase-separation in the melt of a Pluronic polyether block copolymer. The supramolecular polymer is a polybutadiene derivative with urea-urethane end caps. Microphase separation is analysed using small-angle X-ray scattering and its influence on the macroscopic rheological properties is analysed. FTIR spectroscopy provides a detailed picture of the inter-molecular interactions between the polymer chains that induces conformational changes leading to microphase separation.

Self-immolative base-mediated conjugate release from triazolylmethylcarbamates.

A range of carbamate functionalized 1,4-disubstituted triazoles featuring a base sensitive trigger residue, plus a model aromatic amine reporter group, were prepared via copper(i) catalysed azide-alkyne cycloaddition and evaluated for their self-immolative characteristics. This study revealed a clear structure-reactivity relationship, via Hammett analysis, between the structure of the 1,4-disubstituted triazole and the rate of self-immolative release of the amine reporter group, thus demonstrating that under basic conditions this type of triazole derivative has the potential to be employed in a range of chemical release systems.

Graphlet-based measures are suitable for biological network comparison.

MOTIVATION: Large amounts of biological network data exist for many species. Analogous to sequence comparison, network comparison aims to provide biological insight. Graphlet-based methods are proving to be useful in this respect. Recently some doubt has arisen concerning the applicability of graphlet-based measures to low edge density networks-in particular that the methods are 'unstable'-and further that no existing network model matches the structure found in real biological networks. RESULTS: We demonstrate that it is the model networks themselves that are 'unstable' at low edge density and that graphlet-based measures correctly reflect this instability. Furthermore, while model network topology is unstable at low edge density, biological network topology is stable. In particular, one must distinguish between average density and local density. While model networks of low average edge densities also have low local edge density, that is not the case with protein-protein interaction (PPI) networks: real PPI networks have low average edge density, but high local edge densities, and hence, they (and thus graphlet-based measures) are stable on these networks. Finally, we use a recently devised non-parametric statistical test to demonstrate that PPI networks of many species are well-fit by several models not previously tested. In addition, we model several viral PPI networks for the first time and demonstrate an exceptionally good fit between the data and theoretical models.

An effective route to the additive manufacturing of a mechanically gradient supramolecular polymer nanocomposite structure.

3D Printing techniques are additive methods of fabricating parts directly from computer-aided designs. Whilst the clearest benefit is the realisation of geometrical freedom, multi-material printing allows the introduction of compositional variation and highly tailored product functionality. The paper reports a proof-of-concept additive manufacturing study to deposit a supramolecular polymer and a complementary organic filler to form composites with gradient composition to enable spatial distribution of mechanical properties and functionality by tuning the number of supramolecular interactions. We use a dual-feed extrusion 3D printing process, with feed stocks based on the supramolecular polymer and its organic composite, delivered at ratios predetermined. This allows for production of a graded specimen with varying filler concentration that dictates the mechanical properties. The printed specimen was inspected under dynamic load in a tensile test using digital image correlation to produce full-field deformation maps, which showed clear differences in deformation in regions with varying compositions, corresponding to the designed-in variations. This approach affords a novel method for printing material with graded mechanical properties which are not currently commercially available or easily accessible, however, the method can potentially be directly translated to the generation of biomaterial-based composites featuring gradients of mechanical properties.

Janus PEG-based dendrimers for use in combination therapy: controlled multi-drug loading and sequential release.

The increasing use of drug combinations to treat disease states, such as cancer, calls for improved delivery systems that are able to deliver multiple agents. Herein, we report a series of novel Janus dendrimers with potential for use in combination therapy. Different generations (first and second) of PEG-based dendrons containing two different "model drugs", benzyl alcohol (BA) and 3-phenylpropionic acid (PPA), were synthesized. BA and PPA were attached via two different linkers (carbonate and ester, respectively) to promote differential drug release. The four dendrons were coupled together via (3 + 2) cycloaddition chemistries to afford four Janus dendrimers, which contained varying amounts and different ratios of BA and PPA, namely, (BA)(2)-G1-G1-(PPA)(2), (BA)(4)-G2-G1-(PPA)(2), (BA)(2)-G1-G2-(PPA)(4), and (BA)(4)-G2-G2-(PPA)(4). Release studies in plasma showed that the dendrimers provided sequential release of the two model drugs, with BA being released faster than PPA from all of the dendrons. The different dendrimers allowed delivery of increasing amounts (0.15-0.30 mM) and in exact molecular ratios (1:2; 2:1; 1:2; 2:2) of the two model drug compounds. The dendrimers were noncytotoxic (100% viability at 1 mg/mL) toward human umbilical vein endothelial cells (HUVEC) and nontoxic toward red blood cells, as confirmed by hemolysis studies. These studies demonstrate that these Janus PEG-based dendrimers offer great potential for the delivery of drugs via combination therapy.

High-strength, healable, supramolecular polymer nanocomposites.

A supramolecular polymer blend, formed via π-π interactions between a π-electron rich pyrenyl end-capped oligomer and a chain-folding oligomer containing pairs of π-electron poor naphthalene-diimide (NDI) units, has been reinforced with cellulose nanocrystals (CNCs) to afford a healable nanocomposite material. Nanocomposites with varying weight percentage of CNCs (from 1.25 to 20.0 wt %) within the healable supramolecular polymeric matrix have been prepared via solvent casting followed by compression molding, and their mechanical properties and healing behavior have been evaluated. It is found that homogeneously dispersed films can be formed with CNCs at less than 10 wt %. Above 10 wt % CNC heterogeneous nanocomposites were obtained. All the nanocomposites formed could be rehealed upon exposure to elevated temperatures although, for the homogeneous films, it was found that the healing rate was reduced with increasing CNC content. The best combination of healing efficiency and mechanical properties was obtained with the 7.5 wt % CNC nanocomposite which exhibited a tensile modulus enhanced by as much as a factor of 20 over the matrix material alone and could be fully rehealed at 85 °C within 30 min. Thus it is demonstrated that supramolecular nanocomposites can afford greatly enhanced mechanical properties relative to the unreinforced polymer, while still allowing efficient thermal healing.

Self-assembly studies of a chiral bisurea-based superhydrogelator.

A chiral bisurea-based superhydrogelator that is capable of forming supramolecular hydrogels at concentrations as low as 0.2 mM is reported. This soft material has been characterized by thermal studies, rheology, X-ray diffraction analysis, transmission electron microscopy (TEM), and by various spectroscopic techniques (electronic and vibrational circular dichroism and by FTIR and Raman spectroscopy). The expression of chirality on the molecular and supramolecular levels has been studied and a clear amplification of its chirality into the achiral analogue has been observed. Furthermore, thermal analysis showed that the hydrogelation of compound 1 has a high response to temperature, which corresponds to an enthalpy-driven self-assembly process. These particular thermal characteristics make these materials easy to handle for soft-application technologies.

pH-Tunable hydrogelators for water purification: structural optimisation and evaluation.

A focused library of potential hydrogelators each containing two substituted aromatic residues separated by a urea or thiourea linkage have been synthesised and characterized. Six of these novel compounds are highly efficient hydrogelators, forming gels in aqueous solution at low concentrations (0.03-0.60 wt%). Gels were formed through a pH switching methodology, by acidification of a basic solution (pH 14 to ≈ 4) either by addition of HCl or via the slow hydrolysis of glucono-δ-lactone. Frequently, gelation was accompanied by a dramatic switch in the absorption spectra of the gelators, resulting in a significant change in colour, typically from a vibrant orange to pale yellow. Each of the gels was capable of sequestering significant quantities of the aromatic cationic dye, methylene blue, from aqueous solution (up to 1.02 g of dye per gram of dry gelator). Cryo-transmission electron microscopy of two of the gels revealed an extensive network of high aspect ratio fibers. The structure of the fibers altered dramatically upon addition of 20 wt% of the dye, resulting in aggregation and significant shortening of the fibrils. This study demonstrates the feasibility for these novel gels finding application as inexpensive and effective water purification platforms.

Novel polyvinylpyrrolidones to improve delivery of poorly water-soluble drugs: from design to synthesis and evaluation.

Polyvinylpyrrolidone is widely used in tablet formulations with the linear form acting as a wetting agent and disintegrant, whereas the cross-linked form is a superdisintegrant. We have previously reported that simply mixing the commercial cross-linked polymer with ibuprofen disrupted drug crystallinity with consequent improvements in drug dissolution behavior. In this study, we have designed and synthesized novel cross-linking agents containing a range of oligoether moieties that have then been polymerized with vinylpyrrolidone to generate a suite of novel excipients with enhanced hydrogen-bonding capabilities. The polymers have a porous surface and swell in the most common solvents and in water, properties that suggest their value as disintegrants. The polymers were evaluated in simple physical mixtures with ibuprofen as a model poorly water-soluble drug. The results show that the novel PVPs induce the drug to become "X-ray amorphous", which increased dissolution to a greater extent than that seen with commercial cross-linked PVP. The polymers stabilize the amorphous drug with no evidence for recrystallization seen after 20 weeks of storage.

SANA: cross-species prediction of Gene Ontology GO annotations via topological network alignment.

Topological network alignment aims to align two networks node-wise in order to maximize the observed common connection (edge) topology between them. The topological alignment of two protein-protein interaction (PPI) networks should thus expose protein pairs with similar interaction partners allowing, for example, the prediction of common Gene Ontology (GO) terms. Unfortunately, no network alignment algorithm based on topology alone has been able to achieve this aim, though those that include sequence similarity have seen some success. We argue that this failure of topology alone is due to the sparsity and incompleteness of the PPI network data of almost all species, which provides the network topology with a small signal-to-noise ratio that is effectively swamped when sequence information is added to the mix. Here we show that the weak signal can be detected using multiple stochastic samples of "good" topological network alignments, which allows us to observe regions of the two networks that are robustly aligned across multiple samples. The resulting network alignment frequency (NAF) strongly correlates with GO-based Resnik semantic similarity and enables the first successful cross-species predictions of GO terms based on topology-only network alignments. Our best predictions have an AUPR of about 0.4, which is competitive with state-of-the-art algorithms, even when there is no observable sequence similarity and no known homology relationship. While our results provide only a "proof of concept" on existing network data, we hypothesize that predicting GO terms from topology-only network alignments will become increasingly practical as the volume and quality of PPI network data increase.

On the current failure-but bright future-of topology-driven biological network alignment.

Since the function of a protein is defined by its interaction partners, and since we expect similar interaction patterns across species, the alignment of protein-protein interaction (PPI) networks between species, based on network topology alone, should uncover functionally related proteins across species. Surprisingly, despite the publication of more than fifty algorithms aimed at performing PPI network alignment, few have demonstrated a statistically significant link between network topology and functional similarity, and none have demonstrated that orthologs can be recovered using network topology alone. We find that the major contributing factors to this surprising failure are: (i) edge densities in most currently available experimental PPI networks are demonstrably too low to expect topological network alignment to succeed; (ii) in the few cases where the edge densities are high enough, some measures of topological similarity easily uncover functionally similar proteins while others do not; and (iii) most network alignment algorithms to date perform poorly at optimizing even their own topological objective functions, hampering their ability to use topology effectively. We demonstrate that SANA-the Simulated Annealing Network Aligner-significantly outperforms existing aligners at optimizing their own objective functions, even achieving near-optimal solutions when the optimal solution is known. We offer the first demonstration of global network alignments based on topology alone that align functionally similar proteins with p-values in some cases below 10-300. We predict that topological network alignment has a bright future as edge densities increase toward the value where good alignments become possible. We demonstrate that when enough common topology is present at high enough edge densities-for example in the recent, partly synthetic networks of the Integrated Interaction Database-topological network alignment easily recovers most orthologs, paving the way toward high-throughput functional prediction based on topology-driven network alignment.