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.

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.

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.

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.

Common Neighbors Extension of the Sticky Model for PPI Networks Evaluated by Global and Local Graphlet Similarity.

The structure of protein-protein interaction (PPI) networks has been studied for over a decade. Many theoretical models have been proposed to model PPI network structure, but continuing noise and incompleteness in these networks make conclusions about their structure difficult. Using newer, larger networks from Sept. 2018 BioGRID and Jan. 2019 IID, we show the joint distribution of degree products and common neighbors has a greater impact on PPI edge connectivity than their individual distributions, and introduce two new models (CN and STICKY-CN) for PPI networks employing these features. Since graphlet-based measures are believed to be among the most discerning and sensitive network comparison tools available, we assess their overall global and local fits to PPI networks using Graphlet Kernel (GK). We fit 10 theoretical models to nine BioGRID networks and twelve Integrated Interactive Database (IID) networks and find: (1) STICKY and STICKY-CN are the overall globally best fitting models according to GK, (2) Hyperbolic Geometric Graph model is a better fit than any STICKY-based model on 4 species, (3) though STICKY-CN provides a better local fit than the STICKY model, the CN model provides the greatest local fit over most species. We conclude that the inclusion of CN into STICKY-CN makes it the best overall fit for PPI networks as it is a good fit locally and globally.

An Introductory Guide to Aligning Networks Using SANA, the Simulated Annealing Network Aligner.

Sequence alignment has had an enormous impact on our understanding of biology, evolution, and disease. The alignment of biological networks holds similar promise. Biological networks generally model interactions between biomolecules such as proteins, genes, metabolites, or mRNAs. There is strong evidence that the network topology-the "structure" of the network-is correlated with the functions performed, so that network topology can be used to help predict or understand function. However, unlike sequence comparison and alignment-which is an essentially solved problem-network comparison and alignment is an NP-complete problem for which heuristic algorithms must be used.Here we introduce SANA, the Simulated Annealing Network Aligner. SANA is one of many algorithms proposed for the arena of biological network alignment. In the context of global network alignment, SANA stands out for its speed, memory efficiency, ease-of-use, and flexibility in the arena of producing alignments between two or more networks. SANA produces better alignments in minutes on a laptop than most other algorithms can produce in hours or days of CPU time on large server-class machines. We walk the user through how to use SANA for several types of biomolecular networks.

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.

Self-immolative systems for the disclosure of reactive electrophilic alkylating agents.

In this paper we report the design, synthesis and assessment of the first examples of self-immolative systems triggered by non-acidic electrophilic agents such as methyl, allyl or benzylic halides. These systems provide a visual colorimetric disclosure response upon exposure to these electrophilic reagents under mild, basic conditions without the need for the use of analytical instrumentation.

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.

Inducing hardening and healability in poly(ethylene-co-acrylic acid) via blending with complementary low molecular weight additives.

The design and synthesis of low molecular weight additives based on self-assembling nitroarylurea units, and their compatibility with poly(ethylene-co-acrylic acid) copolymers are reported. The self-assembly properties of the low molecular weight additives have been demonstrated in a series of gelation studies. Upon blending at low percentage weights (≤5%) with poly(ethylene-co-acrylic acid) the additives were capable of increasing the stress and strain to failure when compared to the parent copolymer. By varying the percentage weight of the additive as well as the type of additive the mechanical properties of poly(ethylene-co-acrylic acid) could be tailored. Finally, the healability characteristics of the blends were improved when compared to the original polymer via the introduction of a supramolecular 'network within a network'.

Graphettes: Constant-time determination of graphlet and orbit identity including (possibly disconnected) graphlets up to size 8.

Graphlets are small connected induced subgraphs of a larger graph G. Graphlets are now commonly used to quantify local and global topology of networks in the field. Methods exist to exhaustively enumerate all graphlets (and their orbits) in large networks as efficiently as possible using orbit counting equations. However, the number of graphlets in G is exponential in both the number of nodes and edges in G. Enumerating them all is already unacceptably expensive on existing large networks, and the problem will only get worse as networks continue to grow in size and density. Here we introduce an efficient method designed to aid statistical sampling of graphlets up to size k = 8 from a large network. We define graphettes as the generalization of graphlets allowing for disconnected graphlets. Given a particular (undirected) graphette g, we introduce the idea of the canonical graphette [Formula: see text] as a representative member of the isomorphism group Iso(g) of g. We compute the mapping [Formula: see text], in the form of a lookup table, from all 2k(k - 1)/2 undirected graphettes g of size k ≤ 8 to their canonical representatives [Formula: see text], as well as the permutation that transforms g to [Formula: see text]. We also compute all automorphism orbits for each canonical graphette. Thus, given any k ≤ 8 nodes in a graph G, we can in constant time infer which graphette it is, as well as which orbit each of the k nodes belongs to. Sampling a large number N of such k-sets of nodes provides an approximation of both the distribution of graphlets and orbits across G, and the orbit degree vector at each node.

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.

3D Printing of Biocompatible Supramolecular Polymers and their Composites.

A series of polymers capable of self-assembling into infinite networks via supramolecular interactions have been designed, synthesized, and characterized for use in 3D printing applications. The biocompatible polymers and their composites with silica nanoparticles were successfully utilized to deposit both simple cubic structures, as well as a more complex twisted pyramidal feature. The polymers were found to be not toxic to a chondrogenic cell line, according to ISO 10993-5 and 10993-12 standard tests and the cells attached to the supramolecular polymers as demonstrated by confocal microscopy. Silica nanoparticles were then dispersed within the polymer matrix, yielding a composite material which was optimized for inkjet printing. The hybrid material showed promise in preliminary tests to facilitate the 3D deposition of a more complex structure.

An adhesive elastomeric supramolecular polyurethane healable at body temperature.

In this paper, we report the synthesis and healing ability of a non-cytotoxic supramolecular polyurethane network whose mechanical properties can be recovered efficiently (>99%) at the temperature of the human body (37 °C). Rheological analysis revealed an acceleration in the drop of the storage modulus above 37 °C, on account of the dissociation of the supramolecular polyurethane network, and this decrease in viscosity enables the efficient recovery of the mechanical properties. Microscopic and mechanical characterisation has shown that this material is able to recover mechanical properties across a damage site with minimal contact required between the interfaces and also demonstrated that the mechanical properties improved when compared to other low temperature healing elastomers or gel-like materials. The supramolecular polyurethane was found to be non-toxic in a cytotoxicity assay carried out in human skin fibroblasts (cell viability > 94% and non-significantly different compared to the untreated control). This supramolecular network material also exhibited excellent adhesion to pig skin and could be healed completely in situ post damage indicating that biomedical applications could be targeted, such as artificial skin or wound dressings with supramolecular materials of this type.

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.