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Translational Bioinformatics and Drug Discovery

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The greatest challenge of the postgenomic era is to understand the function of genes and gene products in multiple organisms –including humans– both from fundamental and applied perspectives. This will ultimately enable the design of new diagnostic tools and pharmacological agents and facilitate efficacious treatment of many pathological processes. These developments are largely guided and enabled by recent advances in translational bioinformatics, computer sciences, quantitative biology and system-based medicine focused on understanding of the biological principles underpinning the heterogeneity of human disease and facilitating predictive and personalized medicine solutions. In the course of next decade, health care and medicine will be transformed from reactively treating illness to proactively maintaining health — from an averaged picture of disease to a systems biology view of individualized care. The complexity of human disease and cancer requires an integrated approach using computational and experimental high-throughput genetic, molecular, and clinical phenotype analysis. We are focused on the development and application of an integrated platform of computational and experimental approaches to(a) advance identification, prediction and functional analysis of genetic and molecular signatures associated with cancer (b) enable the design of personalized and system-based cancer medicine to combat specific genomic profiles; and (c) facilitate biomedical and clinical research on the “bench to bedside” path. We also expanded our recent efforts in supporting a new pathway-based and network-centric paradigm for quantitative analysis of human disease and in silico discovery of anti-cancer therapeutic agents that is based on targeted polypharmacology of signal transduction networks.

A central goal of cancer research involves the discovery and functional characterization of the mutated genes that are implicated in tumorigenesis. Although the application of DNA sequencing and gene expression microarray technologies has accelerated insights into the molecular basis of cancer, the identification and characterization of genetic variants that influence particular human phenotypes and, most emphatically, susceptibility to common diseases of contemporary public health concern such as diabetes, cancers, and neuropsychiatric illness, have been extraordinarily difficult. Recent studies investigating the genetic determinants of cancer suggest that only some of the genetic alterations contributing to tumorigenesis may be inherited, while somatically acquired mutations can contribute decisively during the transition of a normal cell to a cancer cell. A systematic understanding of the genetic and molecular determinants of cancers has already begun to have a transformative effect on the study and treatment of cancer, most notably due to discovered patterns of somatically acquired mutations in the protein genes which are commonly associated with the disease. The recent developments in sequencing and functional studies have facilitateda series of integrated studies, combining genetic and functional approaches to identify underlying molecular signatures of cancer mutations in protein kinase genes. One of the focuses of my research program is the development of novel bioinformaticsand computational biology approaches to systematically and comprehensively study the influence of naturally occurring sequence variation, somatic mutations and drug resistant mutations on protein kinase and molecular chaperone function tounderstand molecular basis of cancer and advance drug discovery of personalized anti-cancer therapeutics.The protein kinase family is an ideal family to achieve this objective because of the growing wealth of structural and functional information about these genes, as well as the prominent role that protein kinases play as therapeutic targets for cancer intervention.

We are involved in cross-disciplinary and collaborative research that combines computational and systems biology approaches with chemical genomics and molecular profiling technologies in predicting molecular signatures of human disease and applications in discovery of personalized anti-cancer therapeutics. For a number of years, We have been developing algorithms and computational methods that will facilitate the characterization of the influence of genes and genetic variants on molecular phenotypes. These include the analysis and mining of single nucleotide polymorphisms data, quantitative characterization of the significance of naturally-occurring amino acid substitutions on protein structure, the evaluation of in silico effect of amino acid substitutions on protein function, protein-protein and protein-ligand interactions, and the development of algorithms and tools for integration of in silico predictions with in vitro/in vivo functional studies. Our research focuses on the biological significance of genetic variations identified in tumors, and is based on integration of genetic, functional, and structural insights into the molecular basis of tumorigenesis. We have been employing multidisciplinary approaches and collaborations with Scripps Genomic Medicine and Bioinformatics Core of Scripps Translational Science Institute to develop a pipeline of bioinformatics analyses and structurally informed functionalannotations and predictions of somatic alterations in the kinase genes based on targeted resequencing of breast cancer tumors.We are developing a systematic platform of computational approaches, models and tools to facilitate genome-wide identification, prediction and functional characterization of molecular signatures of cancer-causing mutations. We are embarked on a comprehensive in silico functional profiling of candidatemutations identified in genome wide screens to determine which mutations contribute to transformation andrepresent true therapeutic targets for the treatment of humanmalignancies. We integrate machine learning and Bayesian feature selection methods with pathway-based genetic association analysis, network reconstruction, and structure-based analysis to identify functionally related gene modules affected by somatic mutations, The bioinformatics pipeline of computational approaches is then integrated with experimental tools to allow a quantitative functional analysis of SNPs and cancer mutations and decipher how variations in protein kinase sequence, evolution and structure can lead to complex disease phenotypes. The ultimate overarching goal is to understand genetic and molecular mechanisms of human disease andintegrate predictive cancer biomarkers into computational and experimental chemical genomics strategies to identify and design chemical probes of clinical significance. In collaboration with Scripps Translational Science Institute, we are developing a prototype of a “bench to bedside” path : From kinome-wide resequencing and functional analysis of mutants to predictive disease biomarkers, drug targets and discovery of personalized medicine targeting genomic variants in cancer.

 

The developed prototype of a “bench to bedside” path for kinome-wide analysis of cancer mutations and design of targeted therapies.

 

MOLECULAR CHAPERONES AND DRUG DISCOVERY

The molecular chaperone Hsp90 (90 kDa heat-shock protein) is a remarkably versatile protein that mediates several fundamental cellular pathwaysinvolved in the cell proliferation, cell survival, and cellular stress response. Hsp90 is a fundamental hub in protein interaction networks and is critically involved in the hallmark traits of malignancy. Our research is focused on the development and integration of computational and experimental approaches with the goal to advance understanding of the molecular chaperone mechanisms at atomic resolution and facilitate discovery of novel anti-cancer therapies. These approaches allowed to develop and validate mathematical and structural models of Hsp90 allosteric regulation and signal communication pathways. Client proteins of Hsp90 include protein kinases, transcription factors, and other proteins that serve as nodal points in integrating cellular responses to multiple signals. Deregulation of pathways involving these proteins is commonly associated with cancer pathologies. By disabling multiple signaling circuitries, Hsp90 inhibition provides a novel and powerful therapeutic strategy in cancer research, selective for specific cancer mechanisms, yet broadly applicable to disparate tumors with different genetic signatures. Hsp90 Inhibition can suppresses signaling of kinase cancer mutant clients and overcomes drug resistance. Computational profiling and systems-based approaches are integrated with experimental strategies into a systematic platform for selective targeting of Hsp90-kinase protein networks. The developed allosteric Hsp90 modulators can function as specific and personalized therapeutics for inhibiting protein kinase clients and cancer mutants. We have established a close partnership and are involved in collaborative efforts with a number of prominent Hsp90 research groups including Dr. Matts (Oklahoma State University), Dr. Neckers (National Cancer Institute), Dr. Altieri (University of Massachusetts Medical School), Dr. Agard (UCSF), Dr. Pearl (The Institute of Cancer Research, UK) and others. The expertise of our research team and cross-disciplinary collaborations with the Hsp90 experts in molecular and cell biology, biochemistry, NMR, and structural biology will help to develop and validate computational approaches, refine and enhance experimental tools to advance our understanding of the molecular chaperone mechanisms.The insights about molecular mechanisms and function of molecular chaperone are employed in chemical genomics-based profiling, design and biological validation of novel therapeutics of signal transduction networks.

Integrative pipeline of computational and experimental approaches for discovery of allosteric Hsp90 modulators as personalized anti-cancer therapeutics

Molecular Chaperones and Cancer Research

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Our research is focused on the development and integration of computational and experimental approaches for high-throughput ligand screening and design of kinase inhibitors, understanding of the molecular chaperone mechanisms at atomic resolution in order to facilitate discovery of novel anti-cancer therapies.

Computational Studies of the Hsp90 Chaperone and Cancer Research

Our Research is focused on the development and integration of computational and experimental approaches with the goal to advance understanding of the molecular chaperone mechanisms at atomic resolution and facilitate discovery of novel anti-cancer therapies.

Our Projects are :

Development of novel computationalapproaches for molecular modeling of Hsp90dynamics, ligand-based allosteric modulation and inter-domain communication.
Integration of computational and experimental approaches into a system biology platform for structure-based characterization of Hsp90 binding mechanisms.
Computer-based ligand screening, design and biological validation of novel Hsp90 inhibitors

These approaches allowed to develop and validate mathematical and structural models of Hsp90 allosteric regulation and signal communication pathwayS. Client proteins of Hsp90 include protein kinases, transcription factors, and other proteins that serve as nodal points in integrating cellular responses to multiple signals. Deregulation of pathways involving these proteins is commonly associated with cancer pathologies. By disabling multiple signaling circuitries, Hsp90 inhibition provides a novel and powerful therapeutic strategy in cancer research, selective for specific cancer mechanisms, yet broadly applicable to disparate tumors with different genetic signatures. Hsp90 Inhibition can suppresses signaling of kinase cancer mutant clients and overcomes drug resistance. Computational profiling and systems-based approaches are integrated with experimental strategies into a systematic platform for selective targeting of Hsp90-kinase protein networks. The developed allosteric Hsp90 modulators can function as specific and personalized therapeutics for inhibiting protein kinase clients and cancer mutants. We have established a close partnership and are involved in collaborative efforts with a number of prominent Hsp90 research groups including Dr. Matts (Oklahoma State University), Dr. Neckers (National Cancer Institute), Dr. Altieri (University of Massachusetts Medical School), Dr. Agard (UCSF), Dr. Pearl (The Institute of Cancer Research, UK) and others. The expertise of our research team and cross-disciplinary collaborations with the Hsp90 experts in molecular and cell biology, biochemistry, NMR, and structural biology will help to develop and validate computational approaches, refine and enhance experimental tools to advance our understanding of the molecular chaperone mechanisms.The insights about molecular mechanisms and function of molecular chaperone are employed in chemical genomics-based profiling, design and biological validation of novel therapeutics of signal transduction networks.

Protein Kinases and Cancer Research

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Protein kinase genes are signaling switches with a conserved catalytic domain that phosphorylate protein substrates and play a critical role in cell signaling pathways. A landmark for understanding the molecular basis of kinase function was the elucidation of the crystal structures of protein kinase A. Since this discovery, more than 1000 crystal structures of 119 unique human protein kinases have been solved, resulting in the growing wealth of structural knowledge about the kinase catalytic domain. The crystal structures have revealed considerable structural differences between closely related active and highly specific inactive kinase forms. Drug discovery against protein kinases has concentrated mainly on small molecules that target the ATP binding site of the conserved catalytic domain. However, with over 500 protein kinase genes identified in the human genome and the highly conserved ATP-binding site, a considerable effort is needed to design drugs that select for individual kinase members. A growing number of kinase inhibitors selectively target the inactive conformation whereas other compounds bind to both conformations with similar affinity. Inhibitors that bind to the inactive conformation face weaker competition from cellular ATP and may act primarily by shifting equilibrium between conformational states in a way that prevents kinase activation, rather than by inhibiting kinase activity directly. The complete sequencing of the human genome and high-throughput generationof genomic data have opened avenues for a systematic approachto understanding the complex biology of cancer and clinical targetingof activated oncogenes in cancer.

Our Research is focused on the development of integrated platforms of computational and experimental approaches for high-throughput ligand screening and design of kinase inhibitors.The intellectual merit of our research stems from a significant therapeutic position of the protein kinase family, which is ideally suited for this objective because of the growing wealth of structural and functional information about these genes. Many protein kinases have emerged as important therapeutic targets for combating diseases caused by abnormalities in signal transduction pathways. Protein kinases are the most common protein domains that are implicated in cancer and there are more than 500 encoded in the human genome. Rooted in statistical-mechanical description of biological systems and exploiting fundamental similarities between protein folding and molecular recognition, our approach seeks to establish a novel computational strategy for ligand screening and design of kinase inhibitors based on the energy landscape models of ligand-protein binding. In silico approaches are integrated with a carefully orchestrated funnel of experimental validation and design studies that include high throughput screening and biological analysis, chemical synthesis and library design, kinome-wide inhibitor profiling and biophysical characterization.

Our objective is to develop an integrated platform of validated computational approaches, models and tools to facilitate identification, prediction and functional characterization of molecular signatures of cancer-causing mutations that will enable the design of personalized cancer medicine to combat specific genomic profiles. The protein kinase family is an ideal family to achieve this objective because of the growing wealth of structural and functional information about these genes, as well as the prominent role that protein kinases play as therapeutic targets for cancer intervention.

Our goals are (a) integration of computational predictions and experimental validations of cancer mutations effects in therapeutically important protein kinase targets to provide a platform for structurally informed functional analysis of somatic mutations in the protein kinase genes; (b) structural and biophysical characterization of protein kinase dynamics, stability and binding in the normal and oncogenic states to enable targeted design of specific kinase inhibitors as well as reengineering and optimizing the clinical effects of existing drugs.

OUR PROJECTS

Development machine learning, structural bioinformatics and protein modeling approaches for prediction and characterization of molecular signatures of cancer mutations in protein kinases.
Integration of computational and experimental approaches to characterize structural and functional signatures of cancer mutations in the protein kinase genes.
Application the developed and validated computational models and approaches for structurally informed functional annotation and predictions of somatic mutation in the protein kinases genes.
In silico structure-based design of personalized kinase drugs to combat specific genomic profiles in kinase genes.
Development of a bioinformatics resource for integrative cancer biology studies and personalized drug design.

Tracing the effect of sequence variations responsible for phenotypic variations and providing insight into the molecular pathologic lesions associated with disease susceptibility can usher in an era of individualized medicine. These integrative cancer biology studies are undertaken to provide: (a) insights into molecular effects of cancer mutations in protein kinases underlying the biology of cancer; (b) practical benefits to cancer research. The results from experimental structural, biophysical and functional studies will be brought back to computational approaches for validation and refinement of the computational models. The insights provided by computational predictions will inform, guide and facilitate experimental studies of our collaborators exploring the molecular pathology of tumorigenesis and the design of personalized kinase cancer agents. To achieve the goals of this project we have assembled an integrated team of accomplished investigators and collaborators with complementary expertise and proven track records in the areas ranging from computational and structural biology to statistical genetics, functional genomics and chemical biology.

Computational Cancer Biology

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The challenge of understanding biological systems at the molecular and systems level as well as the integration of computational and experimental approaches for bridging basic and clinical cancer research is what motivates our vibrant research group. Our scientific interests and research efforts are in the areas of Computational Cancer Biology and Pharmacogenetics, Computational Genomics and Pharmacology, Translational Bioinformatics and Computational Medicine with the focus on the development and integration of computational and experimental approaches for (a) system-based analysis of evolutionary, genetic, molecular and clinical signatures associated with human disease; (b) modeling of complex phenotypes and prediction of cancer biomarkers; (c) design and discovery of targeted and personalized cancer therapeutics and development of expert systems for personalized medicine; (d) integration of computational biology and translational informatics with chemical biology and chemical genomics in translational cancer research; (e) enabling information-driven biomedical research on the “bench to bedside” path.

Main scientific themes of the research program:

Computational Cancer Biology and Pharmacogenetics

  • Integrative analysis of genetic and molecular signatures of human disease at sequence, structure, functional and clinical levels for understanding the molecular basis of cancer and developing new tools for translational research.
  • Computational chemical genomics and pharmacogenetics : development computational approaches and tools for the identification, prediction and functional analysis of cancer variants to enable design of personalized cancer medicine targeting specific genomic profiles.
  • Pathway-based and network-based approaches for analysis of human disease to identify functionally related gene modules targeted by somatic mutations in cancer.

 

Translational Bioinformatics and Computational Medicine

  • Translational bioinformatics approaches in the genome-wide functional analysis of cancer variants and prediction of cancer biomarkers.
  • Computational genomics, proteomics and systems biology approaches for molecular profiling and drug discovery of protein kinases and molecular chaperone inhibitors.
  • Targeted polypharmacology of signal transduction networks and pathway-targeted discovery of anti-cancer therapeutics.
  • Integration of computational biology and translational informatics within the discovery of personalized anti-cancer cancer agents targeting specific genomic profiles
  • Development of knowledge-based personalized medicine decision systems for clinical and translational research.

Dancing through Life: Molecular Dynamics Simulations and Network-Centric Modeling of Allosteric Mechanisms in Hsp70 and Hsp110 Chaperone Proteins.

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Stetz G1, Verkhivker GM1,2.

Author information

1
Graduate Program in Computational and Data Sciences, Schmid College of Science and Technology, Chapman University, Orange, California, United States of America.
2
Chapman University School of Pharmacy, Irvine, California, United States of America.

Abstract

Hsp70 and Hsp110 chaperones play an important role in regulating cellular processes that involve protein folding and stabilization, which are essential for the integrity of signaling networks. Although many aspects of allosteric regulatory mechanisms in Hsp70 and Hsp110 chaperones have been extensively studied and significantly advanced in recent experimental studies, the atomistic picture of signal propagation and energetics of dynamics-based communication still remain unresolved. In this work, we have combined molecular dynamics simulations and protein stability analysis of the chaperone structures with the network modeling of residue interaction networks to characterize molecular determinants of allosteric mechanisms. We have shown that allosteric mechanisms of Hsp70 and Hsp110 chaperones may be primarily determined by nucleotide-induced redistribution of local conformational ensembles in the inter-domain regions and the substrate binding domain. Conformational dynamics and energetics of the peptide substrate binding with the Hsp70 structures has been analyzed using free energy calculations, revealing allosteric hotspots that control negative cooperativity between regulatory sites. The results have indicated that cooperative interactions may promote a population-shift mechanism in Hsp70, in which functional residues are organized in a broad and robust allosteric network that can link the nucleotide-binding site and the substrate-binding regions. A smaller allosteric network in Hsp110 structures may elicit an entropy-driven allostery that occurs in the absence of global structural changes. We have found that global mediating residues with high network centrality may be organized in stable local communities that are indispensable for structural stability and efficient allosteric communications. The network-centric analysis of allosteric interactions has also established that centrality of functional residues could correlate with their sensitivity to mutations across diverse chaperone functions. This study reconciles a wide spectrum of structural and functional experiments by demonstrating how integration of molecular simulations and network-centric modeling may explain thermodynamic and mechanistic aspects of allosteric regulation in chaperones.