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isom lab @ the university of miami

 

pH regulation of cell signaling systems

 

systems/precision pharmacology

 

high content screening

 

synthetic biology

 

cell stress

 

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OUR RESEARCH FOCUS

PH REGULATION OF CELL SURFACE RECEPTORS

PH REGULATION OF G PROTEIN-COUPLED RECEPTORS

G protein-coupled receptors (GPCRs) are both the largest class of cell surface receptors in humans, and the receptor family most targeted by therapeutic drugs. Major efforts in the lab are focused on understanding  how  acidic pH changes regulate GPCR-drug interactions and signaling. Our objective is to identify GPCR-drug interactions (in)capable of endomembrane signaling, and (in)sensitive to disease-related acidosis.

PH REGULATION OF CELL SURFACE RECEPTORS

Our lab studies  proteins and cell-surface receptors that detect and transduce cellular signals. In certain biological scenarios—cancer, hypoxia, nutrient stress, inflammation, endocytosis—these signaling proteins encounter acidic conditions. Our lab uses experimental and computational approaches to understand how signaling proteins and their constituent networks are regulated by such  pH changes.

STRUCTURE-BASED PROTEIN INFORMATICS & PRECISION MEDICINE

PROTEIN ELECTROINFORMATICS

The need for pH regulation of molecular, cellular, and physiological processes is wide-ranging, as it exists in all domains of life and viruses.  At the molecular level, pH changes act to regulate the structure, function, and interaction of proteins, RNA, DNA, lipids, metabolites, and drugs.      pH effects in  proteins, which are mediated by ionizable side chains and electrostatic forces, are difficult to predict from sequence and structure.  In the lab we develop informatics algorithms to address this challenge, and validate our findings at the lab bench using a variety of approaches.

CANCER INFORMATICS & PRECISION MEDICINE

Genome sequencing efforts are continually expanding our knowledge of inherited and somatic mutations associated with cancer and disease. However, our understanding of how this 1-dimensional sequence data relates to 3-dimensional protein structure-function remains unexplored.  In parallel with our electroinformatics research , we are developing code to map millions of disease mutations to hundreds of protein structures. Our objective is to predictively distinguish between mutations that are benign or deleterious to protein structure-function. In the wet lab we validate these predictions using our high throughput  infrastructure for protein production and characterization.

CELL STRESS RESPONSES

PH REGULATION OF STRESS-RESPONSE SIGNALING NETWORKS

A surprising variety of conditions cause a significant drop in cellular pH, such as nutrient limitation, oxidative stress, and ischemia. Recent studies indicate that these acidic pH signals serve a cytoprotective function by mediating unexpected changes in cellular biochemistry and signaling. Examples include changes in the material properties of cytoplasm, and assembly of proteins into phase-separated macromolecular structures. We aim to identify the signaling cascades that regulate these processes.

PHASE-SEPARATED MACROMOLECULAR STRUCTURES

We are also interested in identifying proteins that are predisposed to assemble into phase-separated structures in response to pH changes.  This interest is an extension of our studies on protein electroinformatics. Our objective is to identify topological patterns of ionizable side chains that are characteristic of higher-order structures stimulated by pH stress. In the longer term, our goal is to use these insights to develop predictive approaches that assess the scope of assembly formation in proteomes.

Our major computational efforts in the lab are focused on mining protein structural information for actionable intelligence. We use computational geometry as our primary conceptual framework for identifying residue topologies that are predictive of protein function. We develop our own algorithms, primarily in Python, and use the incredible high performance computing infrastructure in place at the University of Miami to routinely undertake millions of calculations on thousands of protein structures.

Our major experimental efforts are organized to broadly support our computational research. We have a variety of protein- and cell-based assays in place for rapidly quantifying biochemical parameters such as protein stability, ligand and drug affinity, receptor-drug interactions, and changes in cellular pH. Our core experimental infrastructure comprises technologies that include a liquid handling robotic screening platform, highly sensitive microplate readers, and novel cell-based model systems.

COMPUTATIONAL OVERVIEW

EXPERIMENTAL OVERVIEW

OUR APPROACH COMBINES COMPUTATION & EXPERIMENT

OUR RESEARCH TEAM

DAN ISOM, PH.D.

PRINCIPAL INVESTIGATOR

GRADUATE STUDENT

NICK KAPOLKA

DISOM at MIAMI.EDU

GRADUATE STUDENT

GEOFF TAGHON

RESEARCH ASSOCIATE

WILL MORGAN

NEWS

March 2017

Nick Kapolka and Geoff Taghon join the lab as PIBS students.

February 2017

Geoff Taghon begins a rotation in the lab.

December 2016

PIBs graduate students Nick Kapolka, Mike Watson, and Nadia Peyravian complete their rotations in the lab and present their work to the community.

NOVEMBER 2016

PIBs graduate students Nick Kapolka, Mike Watson, and Nadia Peyravian begin rotations in the lab.

AUGUST 2016

Dan obtains a MIRA Award from the National Institutes of Health.

RESOURCES

pHinder is a structure-based analytics  program that identifies and predicts functionally important electrostatic interactions in proteins. pHinder differs from other structure-based protein electrostatics calculations by considering only the spatial organization (i.e., topology)

of charged amino acid side chains (Asp, Glu, His, Cys, Lys, Arg) in proteins. These ionizable side chains can be organized into networks that regulate protein structure, stability, and function. The pHinder algorithm identifies these structural networks.

You should use pHinder if you are interested in a protein that may be regulated by physiological pH changes. Within cells, pH is dynamic and can vary dramatically. For example, the steady-state pH of cytoplasm is more than two units higher (7.2) than the pH within late endosomes (5.0). Furthermore, the pH outside of cells (7.4) is acidified in diseases such as cancer, asthma, and cistic fibrosis. The pHinder program is designed to help identify proteins that may be affected by these pH changes.

PHINDER

pHinder output consists of scripts and data that are visualized in Pymol. Most researchers have access to Pymol through their institution. If this is not your case, an educational version of Pymol is available for download.

OVERVIEW

Our resource offering are currently a work-in-progress. Efforts are now  underway to post our source code to a public GitHub repository, and provide downloads to bundled executables for many of the programs that comprise the pHinder software platform.

We are no longer accepting online submissions for pHinder calculations as we work to provide executables for downlad that will provide greater flexibility for the end user, and distributed computing power in support of our efforts to identify new pH sensors via crowd sourcing.

VISUALIZING PHINDER RESULTS

SUBMITTING A PHINDER CALCULATION

ISOM LAB

ACTIONABLE INTELLIGENCE THRU PROTEIN INFORMATICS