Chemical kinomics: a powerful strategy for target deconvolution

Kinomics is an emerging and promising approach for deciphering kinomes. Chemical kinomics is a discipline of chemical genomics that is also referred to as "chemogenomics", which is derived from chemistry and biology. Chemical kinomics has become a powerful approach to decipher complicated phosphorylation-based cellular signaling networks with the aid of small molecules that modulate kinase functions. Moreover, chemical kinomics has played a pivotal role in the field of kinase drug discovery as it enables identification of new molecular targets of small molecule kinase modulators and/or exploitation of novel functions of known kinases and has also provided novel chemical entities as hit/lead compounds. In this short review, contemporary chemical kinomics technologies such as activity-based protein profiling, T7 kinasetagged phages, kinobeads, three-hybrid systems, fluorescenttagged kinase binding assays, and chemical genomic profiling are discussed along with a novel allosteric Bcr-Abl kinase inhibitor (GNF-2/GNF-5) as a successful application of chemical kinomics approaches.     

Chemical genomics and medicinal systems biology: chemical control of genomic networks in human systems biology for innovative medicine

With advances in determining the entire DNA sequence of the human genome, it is now critical to systematically identify the function of a number of genes in the human genome. These biological challenges, especially those in human diseases, should be addressed in human cells in which conventional (e.g. genetic) approaches have been extremely difficult to implement. To overcome this, several approaches have been initiated. This review will focus on the development of a novel "chemical genetic/genomic approach" that uses small molecules to "probe and identify" the function of genes in specific biological processes or pathways in human cells. Due to the close relationship of small molecules with drugs, these systematic and integrative studies will lead to the "medicinal systems biology approach" which is critical to "formulate and modulate" complex biological (disease) networks by small molecules (drugs) in human bio-systems.     

Identifying Novel Drug Indications through Automated Reasoning

Background

With the large amount of pharmacological and biological knowledge available in literature, finding novel drug indications for existing drugs using in silico approaches has become increasingly feasible. Typical literature-based approaches generate new hypotheses in the form of protein-protein interactions networks by means of linking concepts based on their cooccurrences within abstracts. However, this kind of approaches tends to generate too many hypotheses, and identifying new drug indications from large networks can be a time-consuming process.

Methodology

In this work, we developed a method that acquires the necessary facts from literature and knowledge bases, and identifies new drug indications through automated reasoning. This is achieved by encoding the molecular effects caused by drug-target interactions and links to various diseases and drug mechanism as domain knowledge in AnsProlog, a declarative language that is useful for automated reasoning, including reasoning with incomplete information. Unlike other literature-based approaches, our approach is more fine-grained, especially in identifying indirect relationships for drug indications.

Conclusion/Significance

To evaluate the capability of our approach in inferring novel drug indications, we applied our method to 943 drugs from DrugBank and asked if any of these drugs have potential anti-cancer activities based on information on their targets and molecular interaction types alone. A total of 507 drugs were found to have the potential to be used for cancer treatments. Among the potential anti-cancer drugs, 67 out of 81 drugs (a recall of 82.7%) are indeed known cancer drugs. In addition, 144 out of 289 drugs (a recall of 49.8%) are non-cancer drugs that are currently tested in clinical trials for cancer treatments. These results suggest that our method is able to infer drug indications (original or alternative) based on their molecular targets and interactions alone and has the potential to discover novel drug indications for existing drugs.     

A global perspective of radiation-induced signal transduction pathways in cancer therapeutics

Radiation is a well established therapeutic modality for the treatment of solid tumors. By merging molecular biological approaches with radiation biology, a significant number of signaling events elicited by ionizing radiation have been delineated. These signaling pathways include events leading to cell cycle arrest, apoptosis or cell survival. There are two major signaling events that affect radiation response. One is the intrinsic/constitutive pro-survival signaling event that is present in proliferating tumor cells while the other is "induced pro-survival event" in response to radiation, both of these events confer resistance to the killing effects of radiation. In this review, signaling pathways that lead to either apoptosis or survival of cells following ionizing radiation are discussed in detail. In addition, mechanisms of action for gene/drug based inhibitors that modulate the expression and function of various genes and gene products involved in pro-survival signaling pathways are described. Further, novel strategies to abrogate the "induced radiation resistance" leading to enhanced therapeutic efficacy of ionizing radiation have been proposed. These novel strategies include the use of radio-gene therapy, low dose fractionated radiation therapy as a chemopotentiator and therapeutic utility of high radiation dose induced bystander effect. The complete understanding of the molecular pathways leading to apoptosis/survival of cells following ionizing radiation will help in tailoring more effective novel strategies and treatment modalities for complete eradication of cancer.     

Navigating cancer network attractors for tumor-specific therapy

Cells employ highly dynamic signaling networks to drive biological decision processes. Perturbations to these signaling networks may attract cells to new malignant signaling and phenotypic states, termed cancer network attractors, that result in cancer development. As different cancer cells reach these malignant states by accumulating different molecular alterations, uncovering these mechanisms represents a grand challenge in cancer biology. Addressing this challenge will require new systems-based strategies that capture the intrinsic properties of cancer signaling networks and provide deeper understanding of the processes by which genetic lesions perturb these networks and lead to disease phenotypes. Network biology will help circumvent fundamental obstacles in cancer treatment, such as drug resistance and metastasis, empowering personalized and tumor-specific cancer therapies.     

Targeting the Bcl-2-regulated apoptosis pathway by BH3 mimetics: a breakthrough in anticancer therapy?

Induction of apoptosis in tumor cells by direct activation of the Bcl-2-regulated apoptosis pathway by small molecule drugs carries high hopes to overcome the shortcomings of current anticancer therapies. This novel therapy concept builds on emerging insights into how Bcl-2-like molecules maintain mitochondrial integrity and how pro-apoptotic BH3-only proteins lead to its disruption. Means to unleash the pro-apoptotic potential of BH3-only proteins in tumor cells, or to bypass the need for BH3-only proteins by directly blocking possible interactions of Bcl-2-like pro-survival molecules with Bax and/or Bak, constitute interesting options for the design of novel anticancer therapies. For the optimization and clinical implementation of these novel anticancer strategies, a detailed understanding of the role of individual BH3-only proteins in cell death signaling in healthy cells and during tumor suppression is required. In this review, we will touch on the latest findings on BH3-only protein function and attempts to define the molecular properties of the so-called 'BH3 mimetics,' a novel class of anticancer agents, able to prompt apoptosis in tumor cells, regardless of their p53 or Bcl-2 status.     

Cell surface biomarkers of embryonic stem cells

Embryonic stem cells (ESCs) can give rise to any adult cell type and thus offer enormous potential for regenerative medicine and drug discovery. Molecular biomarkers serve as valuable tools to classify and isolate ESCs and to monitor their differentiation state by antibody-based techniques. A number of biomarkers, such as certain cell surface antigens, are used to assign pluripotent ESCs; however, accumulating evidence suggests that ESCs are heterogeneous in morphology, phenotype and function, and are thereby classified into subpopulations characterized by multiple sets of molecular biomarkers. Biomarker discovery is also important for ESC biology to elucidate the molecular mechanisms that regulate pluripotency and differentiation. This review summarizes studies of ESC biomarker discovery. "Genome-wide" expression profiling of ESC mRNAs and proteins and direct analyses of the cell surface subproteome have demonstrated that ESCs express a diverse range of biomarkers, cell surface antigens, and signaling molecules found in different cell lineages, as well as a number of key molecules that assure "stemness". Clearly, future quantitative proteomics approaches will enhance our knowledge of the stage- and lineage-specific expression of the proteome and its temporal changes upon differentiation, and provide a more detailed view of nascent and clonally amplified ESCs.     

Cancer proteomics: from signaling networks to tumor markers

Along with the great strides that have been made towards understanding cancer, has come a realization of the complexity of molecular events that lead to malignancy. Proteomics-based approaches, which enable the quantitative investigation of both cellular protein expression levels and protein–protein interactions involved in signaling networks, promise to define the molecules controlling the processes involved in cancer.     

Cancer proteomics: from signaling networks to tumor markers

Along with the great strides that have been made towards understanding cancer, has come a realization of the complexity of molecular events that lead to malignancy. Proteomics-based approaches, which enable the quantitative investigation of both cellular protein expression levels and protein–protein interactions involved in signaling networks, promise to define the molecules controlling the processes involved in cancer.     

Protein interaction-targeted drug discovery: evaluating critical issues

Employment of the decision strategies outlined in this general discussion should help to pinpoint mode of activity in drug development and validation. Overall, as a paradigm for drug development, a search for small molecules that can interfere with PPIs would seem to have significant long term potential. At present, the level of structural knowledge in databases is not sufficient to predict in toto the protein binding properties of a modeled drug, but as databases improve, this may become generally feasible. A major point that remains to be determined is how much specificity of protein binding can be incorporated into molecules of generally less than 500 Da. Finally, integration of PPI-targeting strategies with other approaches towards drug design will enhance the number of signaling pathways that can effectively be targeted. These points will be particularly pertinent as technologies permit a systematic identification of encoded protein interactions that govern the proteornic complement of cells.     

Spatial mutation of the T cell immunological synapse

The hardware for intracellular signaling networks consists of cascades of chemical reactions. It is becoming increasingly apparent that the large-scale spatial organization of molecules in these networks can lead to differential outcomes from otherwise chemically equivalent systems. This has amplified interest in controlled spatial organization as a regulator of cellular signal transduction. In response, a new category of experimentation is developing, in which the spatial positions of signaling molecules in living cells are directly manipulated through mechanical means. These methodologies complement conventional genetic and pharmacological approaches, both of which are chemical in nature, by perturbing the system through exclusively physical mechanisms.     

Characteristics of lymphatic endothelial cells in physiological and pathological conditions

Impairment of lymphatic structure and function, e.g., inadequate endothelial permeability and intercellular openings, abnormal lymphangiogenesis and overexpression for immunoreactive agents, will result in tumor metastasis, autoimmune response alteration and accumulation of interstitial fluid and proteins. Recently, several novel molecules have been identified that allow a more precise distinction between lymphatic and blood vascular endothelium. The differences in expression of endothelial markers on the lymphatic vessel strongly suggest the possibility that there will be important divergence in the differentiating and regenerating responses in lymphatic behavior to various pathological processes. Undoubtfully, molecular techniques would also lead to the definition of unique markers found on lymphatic endothelial cells (LECs) in lymphatic-associated diseases which are mostly involved in lymphangiogenesis. This review is mainly concentrated on the characteristics of LECs in diabetes, wound healing, lymphedema and tumor, especially in the experimental models that have offered insight into the LEC role in these diseases affecting the lymphatic system. Increased knowledge of the molecular signaling pathways driving lymphatic development and lymphangiogenesis should boost the impact of therapeutics on the diseases. Although the field about the mechanisms that control the formation and lineage-specific differentiation and function of lymphatic vessels has experienced rapid progress in the past few years, an understanding of the basis of the differences and their implications in the pathological conditions will require much more investigation.     

Natural products: A continuing source of novel drug leads

Background

Nature has been a source of medicinal products for millennia, with many useful drugs developed from plant sources. Following discovery of the penicillins, drug discovery from microbial sources occurred and diving techniques in the 1970s opened the seas. Combinatorial chemistry (late 1980s), shifted the focus of drug discovery efforts from Nature to the laboratory bench.

Scope of Review

This review traces natural products drug discovery, outlining important drugs from natural sources that revolutionized treatment of serious diseases. It is clear Nature will continue to be a major source of new structural leads, and effective drug development depends on multidisciplinary collaborations.

Major Conclusions

The explosion of genetic information led not only to novel screens, but the genetic techniques permitted the implementation of combinatorial biosynthetic technology and genome mining. The knowledge gained has allowed unknown molecules to be identified. These novel bioactive structures can be optimized by using combinatorial chemistry generating new drug candidates for many diseases.

General Significance

The advent of genetic techniques that permitted the isolation / expression of biosynthetic cassettes from microbes may well be the new frontier for natural products lead discovery. It is now apparent that biodiversity may be much greater in those organisms. The numbers of potential species involved in the microbial world are many orders of magnitude greater than those of plants and multi-celled animals. Coupling these numbers to the number of currently unexpressed biosynthetic clusters now identified (> 10 per species) the potential of microbial diversity remains essentially untapped.     

High-throughput ligand screening via preclustering and evolved neural networks

The pathway for novel lead drug discovery has many major deficiencies, the most significant of which is the immense size of small molecule diversity space. Methods that increase the search efficiency and/or reduce the size of the search space, increase the rate at which useful lead compounds are identified. Artificial neural networks optimized via evolutionary computation provide a cost and time-effective solution to this problem. Here, we present results that suggest preclustering of small molecules prior to neural network optimization is useful for generating models of quantitative structure-activity relationships for a set of HIV inhibitors. Using these methods, it is possible to prescreen compounds to separate active from inactive compounds or even actives and mildly active compounds from inactive compounds with high predictive accuracy while simultaneously reducing the feature space. It is also possible to identify "human interpretable" features from the best models that can be used for proposal and synthesis of new compounds in order to optimize potency and specificity.     

Chapter 4: Protein Interactions and Disease

Proteins do not function in isolation; it is their interactions with one another and also with other molecules (e.g. DNA, RNA) that mediate metabolic and signaling pathways, cellular processes, and organismal systems. Due to their central role in biological function, protein interactions also control the mechanisms leading to healthy and diseased states in organisms. Diseases are often caused by mutations affecting the binding interface or leading to biochemically dysfunctional allosteric changes in proteins. Therefore, protein interaction networks can elucidate the molecular basis of disease, which in turn can inform methods for prevention, diagnosis, and treatment. In this chapter, we will describe the computational approaches to predict and map networks of protein interactions and briefly review the experimental methods to detect protein interactions. We will describe the application of protein interaction networks as a translational approach to the study of human disease and evaluate the challenges faced by these approaches.

What to Learn in This Chapter

• Experimental and computational methods to detect protein interactions
• Protein networks and disease
• Studying the genetic and molecular basis of disease
• Using protein interactions to understand disease

This article is part of the “Translational Bioinformatics” collection for PLOS Computational Biology.

     

From protein-protein interactions to protein co-expression networks: a new perspective to evaluate large-scale proteomic data

The reductionist approach of dissecting biological systems into their constituents has been successful in the first stage of the molecular biology to elucidate the chemical basis of several biological processes. This knowledge helped biologists to understand the complexity of the biological systems evidencing that most biological functions do not arise from individual molecules; thus, realizing that the emergent properties of the biological systems cannot be explained or be predicted by investigating individual molecules without taking into consideration their relations. Thanks to the improvement of the current -omics technologies and the increasing understanding of the molecular relationships, even more studies are evaluating the biological systems through approaches based on graph theory. Genomic and proteomic data are often combined with protein-protein interaction (PPI) networks whose structure is routinely analyzed by algorithms and tools to characterize hubs/bottlenecks and topological, functional, and disease modules. On the other hand, co-expression networks represent a complementary procedure that give the opportunity to evaluate at system level including organisms that lack information on PPIs. Based on these premises, we introduce the reader to the PPI and to the co-expression networks, including aspects of reconstruction and analysis. In particular, the new idea to evaluate large-scale proteomic data by means of co-expression networks will be discussed presenting some examples of application. Their use to infer biological knowledge will be shown, and a special attention will be devoted to the topological and module analysis.     

Fragment-based lead discovery: a chemical update

Fragment-based lead discovery constructs drug leads from small molecular fragments. In theory, this is a highly efficient method for drug discovery, and the technique has become enormously popular in the past few years. In this review, I describe how a variety of approaches in fragment-based lead discovery--including NMR, X-ray crystallography, mass spectrometry, functional screening, and in silico screening--have produced drug leads. Although the examples show that the technique can reliably generate potent molecules, there is still much work to be done to maintain the efficiency of molecules' binding affinities as fragments are linked, expanded, and otherwise improved.