Perturbation centrality and turbine: a novel centrality measure obtained using a versatile network dynamics tool

Network-driven cancer cell avatars for combination discovery and biomarker identification for DNA damage response inhibitors

Combination therapy is well established as a key intervention strategy for cancer treatment, with the potential to overcome monotherapy resistance and deliver a more durable efficacy. However, given the scale of unexplored potential target space and the resulting combinatorial explosion, identifying efficacious drug combinations is a critical unmet need that is still evolving. In this paper, we demonstrate a network biology-driven, simulation-based solution, the Simulated Cell™. Integration of omics data with a curated signaling network enables the accurate and interpretable prediction of 66,348 combination-cell line pairs obtained from a large-scale combinatorial drug sensitivity screen of 684 combinations across 97 cancer cell lines (BAC = 0.62, AUC = 0.7). We highlight drug combination pairs that interact with DNA Damage Response pathways and are predicted to be synergistic, and deep network insight to identify biomarkers driving combination synergy. We demonstrate that the cancer cell 'avatars' capture the biological complexity of their in vitro counterparts, enabling the identification of pathway-level mechanisms of combination benefit to guide clinical translatability.

A Two-Phase Feature Selection Method for Identifying Influential Spreaders of Disease Epidemics in Complex Networks

Network epidemiology plays a fundamental role in understanding the relationship between network structure and epidemic dynamics, among which identifying influential spreaders is especially important. Most previous studies aim to propose a centrality measure based on network topology to reflect the influence of spreaders, which manifest limited universality. Machine learning enhances the identification of influential spreaders by combining multiple centralities. However, several centrality measures utilized in machine learning methods, such as closeness centrality, exhibit high computational complexity when confronted with large network sizes. Here, we propose a two-phase feature selection method for identifying influential spreaders with a reduced feature dimension. Depending on the definition of influential spreaders, we obtain the optimal feature combination for different synthetic networks. Our results demonstrate that when the datasets are mildly or moderately imbalanced, for Barabasi-Albert (BA) scale-free networks, the centralities' combination with the two-hop neighborhood is fundamental, and for Erdős-Rényi (ER) random graphs, the centralities' combination with the degree centrality is essential. Meanwhile, for Watts-Strogatz (WS) small world networks, feature selection is unnecessary. We also conduct experiments on real-world networks, and the features selected display a high similarity with synthetic networks. Our method provides a new path for identifying superspreaders for the control of epidemics.

Drug repurposing for Basal breast cancer subpopulations using modular network signatures

Breast cancer is characterized as being a heterogeneous pathology with a broad phenotype variability. Breast cancer subtypes have been developed in order to capture some of this heterogeneity. Each of these breast cancer subtypes, in turns retains varied characteristic features impacting diagnostic, prognostic and therapeutics. Basal breast tumors, in particular have been challenging in these regards. Basal breast cancer is often more aggressive, of rapid evolution and no tailor-made targeted therapies are available yet to treat it. Arguably, epigenetic variability is behind some of these intricacies. It is possible to further classify basal breast tumor in groups based on their non-coding transcriptome and methylome profiles. It is expected that these groups will have differences in survival as well as in sensitivity to certain classes of drugs. With this in mind, we implemented a computational learning approach to infer different subpopulations of basal breast cancer (from TCGA multi-omic data) based on their epigenetic signatures. Such epigenomic signatures were associated with different survival profiles; we then identified their associated gene co-expression network structure, extracted a signature based on modules within these networks, and use these signatures to find and prioritize drugs (in the LINCS dataset) that may be used to target these types of cancer. In this way we are introducing the analytical workflow for an epigenomic signature-based drug repurposing structure.

Emerging landscape of molecular interaction networks: Opportunities, challenges and prospects

Network biology finds application in interpreting molecular interaction networks and providing insightful inferences using graph theoretical analysis of biological systems. The integration of computational bio-modelling approaches with different hybrid network-based techniques provides additional information about the behaviour of complex systems. With increasing advances in high-throughput technologies in biological research, attempts have been made to incorporate this information into network structures, which has led to a continuous update of network biology approaches over time. The newly minted centrality measures accommodate the details of omics data and regulatory network structure information. The unification of graph network properties with classical mathematical and computational modelling approaches and technologically advanced approaches like machine-learning- and artificial intelligence-based algorithms leverages the potential application of these techniques. These computational advances prove beneficial and serve various applications such as essential gene prediction, identification of drug–disease interaction and gene prioritization. Hence, in this review, we have provided a comprehensive overview of the emerging landscape of molecular interaction networks using graph theoretical approaches. With the aim to provide information on the wide range of applications of network biology approaches in understanding the interaction and regulation of genes, proteins, enzymes and metabolites at different molecular levels, we have reviewed the methods that utilize network topological properties, emerging hybrid network-based approaches and applications that integrate machine learning techniques to analyse molecular interaction networks. Further, we have discussed the applications of these approaches in biomedical research with a note on future prospects.

Role of a high centrality residue in protein dynamics and thermal stability

Centralities determined from Residue Interaction Networks (RIN) in proteins have been used to predict aspects of their structure and dynamics. Here, we correlate the Eigenvector Centrality (Ec) with the rate constant for thermal denaturation (kden) of the HisF protein from Thermotoga maritima based on 12 single alanine substitution mutants. The molecular basis for this correlation was further explored by studying a mutant containing a replacement of a high Ec residue, Y182A, which displayed increased kden at 80 °C. The crystallographic structure of this mutant showed few changes, mostly in two flexible loops. The 1H-15N -HSQC showed only subtle changes of cross peak positions for residues located near the mutation site and scattered throughout the structure. However, the comparison of the RIN showed that Y182 is the vertex of a set of high centrality residues that spreads throughout the HisF structure, which is lacking in the mutant. Cross-correlation displacements of Cα calculated from a molecular dynamics simulation at different temperatures showed that the Y182A mutation reduced the correlated movements in the HisF structure above 70 °C. 1H-15N NMR chemical shift covariance using temperature as perturbation were consistent with these results. In conclusion the increase in temperature drives the structure of the mutant HisF-Y182A into a less connected state, richer in non-concerted motions, located predominantly in the C-terminal half of the protein where Y182 is placed. Conversely, wild-type HisF responds to increased temperature as a single unit. Hence the replacement of a high Ec residue alters the distribution of thermal energy through HisF structure.

Exploration of Target Synergy in Cancer Treatment by Cell-Based Screening Assay and Network Propagation Analysis

Computational approaches have previously been introduced to predict compounds with activity against multiple targets or compound combinations with synergistic functional effects. By contrast, there are no computational studies available that explore combinations of targets that might act synergistically upon small molecule treatment. Herein, we introduce an approach designed to identify synergistic target pairs on the basis of cell-based screening data and compounds with known target annotations. The targets involved in forming synergistic pairs were analyzed through a novel network propagation algorithm for rationalizing possible common synergy mechanisms. This algorithm enabled further analysis of each synergistic target pair and the identification of "interactors", i.e., proteins with higher propagation scores than would be expected by adding the individual contributions of each target in the synergistic pair. We detected 137 synergistic target pairs including 51 unique targets. A global network analysis of these 51 targets made it possible to derive a subnetwork of proteins with significant synergy. Furthermore, interactors were identified for 87 synergistic target pairs upon individual analysis of the network propagation of each pair. These interactors were associated with pathways related to cancer and apoptosis, membrane transport, and steroid metabolism and provided possible explanations of synergistic effects.

Mutations close to a hub residue affect the distant active site of a GH1 β-glucosidase

The tertiary structure of proteins has been represented as a network, in which residues are nodes and their contacts are edges. Protein structure networks contain residues, called hubs or central, which are essential to form short connection pathways between any pair of nodes. Hence hub residues may effectively spread structural perturbations through the protein. To test whether modifications nearby to hub residues could affect the enzyme active site, mutations were introduced in the β-glycosidase Sfβgly (PDB-ID: 5CG0) directed to residues that form an α-helix (260–265) and a β-strand (335–337) close to one of its main hub residues, F251, which is approximately 14 Å from the Sfβgly active site. Replacement of residues A263 and A264, which side-chains project from the α-helix towards F251, decreased the rate of substrate hydrolysis. Mutation A263F was shown to weaken noncovalent interactions involved in transition state stabilization within the Sfβgly active site. Mutations placed on the opposite side of the same α-helix did not show these effects. Consistently, replacement of V336, which side-chain protrudes from a β-strand face towards F251, inactivated Sfβgly. Next to V336, mutation S337F also caused a decrease in noncovalent interactions involved in transition state stabilization. Therefore, we suggest that mutations A263F, A264F, V336F and S337F may directly perturb the position of the hub F251, which could propagate these perturbations into the Sfβgly active site through short connection pathways along the protein network.

A network model predicts the intensity of residue-protein thermal coupling

Motivation

The flow of vibrational energy in proteins has been shown not to obey expectations for isotropic media. The existence of preferential pathways for energy transport, with probable connections to allostery mechanisms, has been repeatedly demonstrated. Here, we investigate whether, by representing a set of protein structures as networks of interacting amino acid residues, we are able to model heat diffusion and predict residue-protein vibrational couplings, as measured by the Anisotropic Thermal Diffusion (ATD) computational protocol of modified molecular dynamics simulations.

Results

We revisit the structural rationales for the precise definition of a contact between amino acid residues. Using this definition to describe a set of proteins as contact networks where each node corresponds to a residue, we show that node centrality, particularly closeness centrality and eigenvector centrality , correlates to the strength of the vibrational coupling of each residue to the rest of the structure. We then construct an analytically solvable model of heat diffusion on a network, whose solution incorporates an explicit dependence on the connectivity of the heated node, as described by a perturbed graph Laplacian Matrix.

Availability and Implementation

An implementation of the described model is available at http://leandro.iqm.unicamp.br/atd-scripts .

Contact

leandro@iqm.unicamp.br.

Intracellular and intercellular signaling networks in cancer initiation, development and precision anti-cancer therapy: RAS acts as contextual signaling hub

Cancer initiation and development are increasingly perceived as systems-level phenomena, where intra- and inter-cellular signaling networks of the ecosystem of cancer and stromal cells offer efficient methodologies for outcome prediction and intervention design. Within this framework, RAS emerges as a 'contextual signaling hub', i.e. the final result of RAS activation or inhibition is determined by the signaling network context. Current therapies often 'train' cancer cells shifting them to a novel attractor, which has increased metastatic potential and drug resistance. The few therapy-surviving cancer cells are surrounded by massive cell death triggering a primordial adaptive and reparative general wound healing response. Overall, dynamic analysis of patient- and disease-stage specific intracellular and intercellular signaling networks may open new areas of anticancer therapy using multitarget drugs, drugs combinations, edgetic drugs, as well as help design 'gentler', differentiation and maintenance therapies.

CentiServer: A Comprehensive Resource, Web-Based Application and R Package for Centrality Analysis

Various disciplines are trying to solve one of the most noteworthy queries and broadly used concepts in biology, essentiality. Centrality is a primary index and a promising method for identifying essential nodes, particularly in biological networks. The newly created CentiServer is a comprehensive online resource that provides over 110 definitions of different centrality indices, their computational methods, and algorithms in the form of an encyclopedia. In addition, CentiServer allows users to calculate 55 centralities with the help of an interactive web-based application tool and provides a numerical result as a comma separated value (csv) file format or a mapped graphical format as a graph modeling language (GML) file. The standalone version of this application has been developed in the form of an R package. The web-based application (CentiServer) and R package (centiserve) are freely available at http://www.centiserver.org/

Targets of drugs are generally, and targets of drugs having side effects are specifically good spreaders of human interactome perturbations

Network-based methods are playing an increasingly important role in drug design. Our main question in this paper was whether the efficiency of drug target proteins to spread perturbations in the human interactome is larger if the binding drugs have side effects, as compared to those which have no reported side effects. Our results showed that in general, drug targets were better spreaders of perturbations than non-target proteins, and in particular, targets of drugs with side effects were also better spreaders of perturbations than targets of drugs having no reported side effects in human protein-protein interaction networks. Colorectal cancer-related proteins were good spreaders and had a high centrality, while type 2 diabetes-related proteins showed an average spreading efficiency and had an average centrality in the human interactome. Moreover, the interactome-distance between drug targets and disease-related proteins was higher in diabetes than in colorectal cancer. Our results may help a better understanding of the network position and dynamics of drug targets and disease-related proteins, and may contribute to develop additional, network-based tests to increase the potential safety of drug candidates.

Attractor Structures of Signaling Networks: Consequences of Different Conformational Barcode Dynamics and Their Relations to Network-Based Drug Design

Conformational barcodes tag functional sites of proteins and are decoded by interacting molecules transmitting the incoming signal. Conformational barcodes are modified by all co-occurring allosteric events induced by post-translational modifications, pathogen, drug binding, etc. We argue that fuzziness (plasticity) of conformational barcodes may be increased by disordered protein structures, by integrative plasticity of multi-phosphorylation events, by increased intracellular water content (decreased molecular crowding) and by increased action of molecular chaperones. This leads to increased plasticity of signaling and cellular networks. Increased plasticity is both substantiated by and inducing an increased noise level. Using the versatile network dynamics tool, Turbine (www.turbine.linkgroup.hu), here we show that the 10 % noise level expected in cellular systems shifts a cancer-related signaling network of human cells from its proliferative attractors to its largest, apoptotic attractor representing their health-preserving response in the carcinogen containing and tumor suppressor deficient environment modeled in our study. Thus, fuzzy conformational barcodes may not only make the cellular system more plastic, and therefore more adaptable, but may also stabilize the complex system allowing better access to its largest attractor.

The construction of an amino acid network for understanding protein structure and function

Amino acid networks (AANs) are undirected networks consisting of amino acid residues and their interactions in three-dimensional protein structures. The analysis of AANs provides novel insight into protein science, and several common amino acid network properties have revealed diverse classes of proteins. In this review, we first summarize methods for the construction and characterization of AANs. We then compare software tools for the construction and analysis of AANs. Finally, we review the application of AANs for understanding protein structure and function, including the identification of functional residues, the prediction of protein folding, analyzing protein stability and protein-protein interactions, and for understanding communication within and between proteins.

Cancer stem cells display extremely large evolvability: alternating plastic and rigid networks as a potential Mechanism: network models, novel therapeutic target strategies, and the contributions of hypoxia, inflammation and cellular senescence

Cancer is increasingly perceived as a systems-level, network phenomenon. The major trend of malignant transformation can be described as a two-phase process, where an initial increase of network plasticity is followed by a decrease of plasticity at late stages of tumor development. The fluctuating intensity of stress factors, like hypoxia, inflammation and the either cooperative or hostile interactions of tumor inter-cellular networks, all increase the adaptation potential of cancer cells. This may lead to the bypass of cellular senescence, and to the development of cancer stem cells. We propose that the central tenet of cancer stem cell definition lies exactly in the indefinability of cancer stem cells. Actual properties of cancer stem cells depend on the individual "stress-history" of the given tumor. Cancer stem cells are characterized by an extremely large evolvability (i.e. a capacity to generate heritable phenotypic variation), which corresponds well with the defining hallmarks of cancer stem cells: the possession of the capacity to self-renew and to repeatedly re-build the heterogeneous lineages of cancer cells that comprise a tumor in new environments. Cancer stem cells represent a cell population, which is adapted to adapt. We argue that the high evolvability of cancer stem cells is helped by their repeated transitions between plastic (proliferative, symmetrically dividing) and rigid (quiescent, asymmetrically dividing, often more invasive) phenotypes having plastic and rigid networks. Thus, cancer stem cells reverse and replay cancer development multiple times. We describe network models potentially explaining cancer stem cell-like behavior. Finally, we propose novel strategies including combination therapies and multi-target drugs to overcome the Nietzschean dilemma of cancer stem cell targeting: "what does not kill me makes me stronger".