In 2003, it was demonstrated for the first time that bacteria possess protein-tyrosine kinases (BY-kinases), capable of phosphorylating other cellular proteins and regulating their activity. It soon became apparent that these kinases phosphorylate a number of protein substrates, involved in different cellular processes. More recently, we found out that BY-kinases can be activated by several distinct protein interactants, and are capable of engaging in cross-phosphorylation with other kinases. Evolutionary studies based on genome comparison indicate that BY-kinases exist only in bacteria. They are non-essential (present in about 40% bacterial genomes), and their knockouts lead to pleiotropic phenotypes, since they phosphorylate many substrates. Surprisingly, BY-kinase genes accumulate mutations at an increased rate (non-synonymous substitution rate significantly higher than other bacterial genes). One direct consequence of this phenomenon is no detectable co-evolution between kinases and their substrates. Their promiscuity towards substrates thus seems to be “hard-wired”, but why would bacteria maintain such promiscuous regulatory devices? One explanation is the maintenance of BY-kinases as rapidly evolving regulators, which can readily adopt new substrates when environmental changes impose selective pressure for quick evolution of new regulatory modules. Their role is clearly not to act as master regulators, dedicated to triggering a single response, but they might rather be employed to contribute to fine-tuning and improving robustness of various cellular responses. This unique feature makes BY-kinases a potentially useful tool in synthetic biology. While other bacterial kinases are very specific and their signaling pathways insulated, BY-kinase can relatively easily be engineered to adopt new substrates and control new biosynthetic processes. Since they are absent in humans, and regulate some key functions in pathogenic bacteria, they are also very promising targets for new antibacterial drugs.
The classical assumption that one drug cures a single disease by binding to a single drug-target has been shown to be inaccurate. Recent studies estimate that each drug on average binds to at least six known and several unknown targets. Identifying the “off-targets” can help understand the side effects and toxicity of the drug. Moreover, off-targets for a given drug may inspire “drug repositioning”, where a drug already approved for one condition is redirected to treat another condition, thereby overcoming delays and costs associated with clinical trials and drug approval.
In this talk, I will introduce our work along this direction. We have developed a structural alignment method that can precisely identify structural similarities between arbitrary types of interaction interfaces, such as the drug-target interaction. We have further developed a novel computational framework, iDTP that constructs the structural signatures of approved and experimental drugs, based on which we predict new targets for these drugs. Our method combines information from several sources including sequence independent structural alignment, sequence similarity, drug-target tissue expression data, and text mining.
In a cross-validation study, we used iDTP to predict the known targets of 11 drugs, with 63% sensitivity and 81% specificity. We then predicted novel targets for these drugs—two that are of high pharmacological interest, the peroxisome proliferator-activated receptor gamma and the oncogene B-cell lymphoma 2, were successfully validated through in vitro binding experiments.
Protein kinase autophosphorylation is a common regulatory mechanism in cell signaling pathways. Several autophosphorylation complexes have been identified in crystals of protein kinases, with a known serine, threonine, or tyrosine autophosphorylation site of one kinase monomer sitting in the active site of another monomer of the same protein in the crystal.
We utilized a structural bioinformatics method to identify all such autophosphorylation complexes in X-ray crystallographic structures in the Protein Data Bank (PDB) by generating all unique kinase/kinase interfaces within and between asymmetric units of each crystal and measuring the distance between the hydroxyl oxygen of potential autophosphorylation sites and the oxygen atoms of the active site aspartic acid residue side chain.
We have identified 15 unique autophosphorylation complexes in the PDB, of which 5 complexes have not previously been described in the relevant publications on the crystal structures (N-terminal juxtamembrane regions of CSF1R and EPHA2, activation loop tyrosines of LCK and IGF1R, and a serine in a nuclear localization signal region of CLK2. Mutation of residues in the autophosphorylation complex interface of LCK either severely impaired autophosphorylation or increased it.
Taking the autophosphorylation complexes as a whole and comparing them with peptide-substrate/kinase complexes, we observe a number of important features among them. The novel and previously observed autophosphorylation sites are conserved in many kinases, indicating that by homology we can extend the relevance of these complexes to many other clinically relevant drug targets.
The protein structure database was established in 1971. At the time it contained seven structures, today there are more than 100,000. The improvement is not only a matter of quantity, but also of quality. Did we effectively exploit this information to gain knowledge? The answer is certainly affirmative.
I will illustrate how this wealth of experimental data has allowed us to explore the landscape of macromolecular structures on one side, and to uncover the properties of specific protein families on the other. The latter plays an essential role in pursuing exciting new avenues in biomedical and biotechnological sciences.
Experimental data are also part of a virtuous cycle whereby they reinforce and guide our ability to infer unknown macromolecular structures, which, while providing relevant information to scientists, permits to gauge the level of our understanding of the complex problem of protein folding. A paradigmatic example of the latter is represented by the “Critical Assessment of Techniques for Protein Structure Prediction” (CASP) initiative that I will briefly discuss.
While bioinformatics has witnessed enormous technological advances since the turn of the millennium, progress in the EHR field has been stymied by outdated approaches entrenched through ill-conceived government mandates. In the US, especially, the dominant EHR systems are expensive, difficult to use, fail to ensure even a minimal level of interoperability, and detract from patient care.
I will outline the reasons for some of these failures, and sketch an evolutionary path towards the sort of EHR landscape that will be needed in the future, in which consistency with biomedical ontologies will play a central role.
The genome of metazoans is organized according to a genomic code which comprises three laws:
1) Compositional correlations hold between contiguous coding and non-coding sequences, as well as among the three codon positions of protein-coding genes; these correlations are the consequence of the fact that the genomes under consideration consist of fairly homogeneous, long (≥200Kb) sequences, the isochores;
2) Although isochores are defined on the basis of purely compositional properties, GC levels of isochores are correlated with all tested structural and functional properties of the genome;
3) GC levels of isochores are correlated with chromosome architecture from interphase to metaphase; in the case of interphase the correlation concerns isochores and the three-dimensional “topological associated domains” (TADs); in the case of mitotic chromosomes, the correlation concerns isochores and chromosomal bands.
Finally, the genomic code is the fourth and last pillar of molecular biology, the first three pillars being 1) the double helix structure of DNA; 2) the regulation of gene expression in prokaryotes; and 3) the genetic code.
The yeast Saccharomyces cerevisiae is widely used for production of fuels, chemicals, pharmaceuticals and materials. Through metabolic engineering of this yeast a number of novel new industrial processes have been developed over the last 10 years. Besides its wide industrial use, S. cerevisiae serves as an eukaryal model organism, and many systems biology tools have therefore been developed for this organism. Among these genome-scale metabolic models have shown to be most successful as they easy integrate with omics data and at the same time have been shown to have excellent predictive power.
Despite our extensive knowledge of yeast metabolism and its regulation we are still facing challenges when we want to engineer complex traits, such as improved tolerance to toxic metabolites like butanol and elevated temperatures or when we want to engineer the highly complex protein secretory pathway. In this presentation it will be demonstrated how we can combine directed evolution with systems biology analysis to identify novel targets for rational design-build-test of yeast strains that have improved phenotypic properties.
In this lecture an overview of systems biology of yeast will be presented together with examples of how genome-scale metabolic modeling can be used for prediction of cellular growth at different conditions. Examples will also be given on how adaptive laboratory evolution can be used for identifying targets for improving tolerance towards butanol, increased temperature and low pH and for improving secretion of heterologous proteins.
Mass spectrometry (MS)-based proteomics is a widely used and powerful tool for profiling systems-wide protein expression changes. It can be applied for various purposes, e.g. biomarker discovery in diseases and study of drug responses. Although RNA-based high-throughput methods have been useful in providing glimpses into the underlying molecular processes, the evidences they provide are indirect. Furthermore, RNA and corresponding protein levels have been known to have poor correlation. On the other hand, MS-based proteomics tend to have consistency issues (poor reproducibility and inter-sample agreement) and coverage issues (inability to detect the entire proteome) that need to be urgently addressed.
In this talk, I will discuss how these issues can be addressed by proteomic profile analysis techniques that use biological networks (especially protein complexes) as the biological context. In particular, I will describe several techniques that we have been developing for network-based analysis of proteomics profile. And I will present evidence that these techniques are useful in identifying proteomics-profile analysis results that are more consistent, more reproducible, and more biologically coherent, and that these techniques allow expansion of the detected proteome to uncover and/or discover novel proteins.
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