Project acronym LIQUIDMASS
Project High throughput mass spectrometry of single proteins in liquid environment
Researcher (PI) Montserrat Calleja Gomez
Host Institution (HI) AGENCIA ESTATAL CONSEJO SUPERIOR DEINVESTIGACIONES CIENTIFICAS
Call Details Consolidator Grant (CoG), PE4, ERC-2015-CoG
Summary Although mass spectrometry has brought about major advancements in proteomics in the last decade, protein mass spectrometers still have important limitations. One fundamental limitation is that they require sample ionization, desorption into the gas phase and fragmentation, clearly leading to protein denaturation. Since relevant protein complexes are unstable or transient, their characterization in its native state and physiological environment remains an unexplored route towards the full understanding of protein function and protein interactions. This problem has only been targeted to date through theoretical approaches or low throughput experimental techniques, such as atomic force spectroscopy, optical tweezers or FRET. A high throughput characterization technology capable of addressing single proteins in its native state would have a large impact in proteomics. The goal of LIQUIDMASS is to develop a high throughput spectrometric technique addressing single proteins from complex samples while in physiological conditions. LIQUIDMASS also proposes a new concept for protein spectrometry, by characterizing not only the mass, but also the hydrodynamic radius, geometry and stiffness of single proteins. This multiparameter approach will serve to open up new routes to understand protein structure-function relations by providing insight into the fast conformational changes that occur in liquids. In order to attain these goals, I propose to integrate nanomechanical resonators, nano-optics and nanofluidics. The disruptive approach proposed will bring about new knowledge about protein interactions and protein conformation that is elusive today. The enabling technologies aimed at the LIQUIDMASS will increase our understanding of protein misfolding related diseases, such as Alzheimer’s or diabetes, as well as bring closer a full understanding of the human interactome, contributing to the advancement of the proteomics field.
Summary
Although mass spectrometry has brought about major advancements in proteomics in the last decade, protein mass spectrometers still have important limitations. One fundamental limitation is that they require sample ionization, desorption into the gas phase and fragmentation, clearly leading to protein denaturation. Since relevant protein complexes are unstable or transient, their characterization in its native state and physiological environment remains an unexplored route towards the full understanding of protein function and protein interactions. This problem has only been targeted to date through theoretical approaches or low throughput experimental techniques, such as atomic force spectroscopy, optical tweezers or FRET. A high throughput characterization technology capable of addressing single proteins in its native state would have a large impact in proteomics. The goal of LIQUIDMASS is to develop a high throughput spectrometric technique addressing single proteins from complex samples while in physiological conditions. LIQUIDMASS also proposes a new concept for protein spectrometry, by characterizing not only the mass, but also the hydrodynamic radius, geometry and stiffness of single proteins. This multiparameter approach will serve to open up new routes to understand protein structure-function relations by providing insight into the fast conformational changes that occur in liquids. In order to attain these goals, I propose to integrate nanomechanical resonators, nano-optics and nanofluidics. The disruptive approach proposed will bring about new knowledge about protein interactions and protein conformation that is elusive today. The enabling technologies aimed at the LIQUIDMASS will increase our understanding of protein misfolding related diseases, such as Alzheimer’s or diabetes, as well as bring closer a full understanding of the human interactome, contributing to the advancement of the proteomics field.
Max ERC Funding
2 470 283 €
Duration
Start date: 2016-11-01, End date: 2021-10-31
Project acronym NetMoDEzyme
Project Network models for the computational design of proficient enzymes
Researcher (PI) Silvia Osuna Oliveras
Host Institution (HI) UNIVERSITAT DE GIRONA
Call Details Starting Grant (StG), PE4, ERC-2015-STG
Summary Billions of years of evolution have made enzymes superb catalysts capable of accelerating reactions by several orders of magnitude. The underlying physical principles of their extraordinary catalytic power still remains highly debated, which makes the alteration of natural enzyme activities towards synthetically useful targets a tremendous challenge for modern chemical biology. The routine design of enzymes will, however, have large socio-economic benefits, as because of the enzymatic advantages the production costs of many drugs will be reduced and will allow industries to use environmentally friendly alternatives. The goal of this project is to make the routine design of proficient enzymes possible. Current computational and experimental approaches are able to confer natural enzymes new functionalities but are economically unviable and the catalytic efficiencies lag far behind their natural counterparts. The groundbreaking nature of NetMoDEzyme relies on the application of network models to reduce the complexity of the enzyme design paradigm and completely reformulate previous computational design approaches. The new protocol proposed accurately characterizes the enzyme conformational dynamics and customizes the included mutations by exploiting the correlated movement of the enzyme active site residues with distal regions. The guidelines for mutation are withdrawn from the costly directed evolution experimental technique, and the most proficient enzymes are easily identified via chemoinformatic models. The new strategy will be applied to develop proficient enzymes for the synthesis of enantiomerically pure β-blocker drugs for treating cardiovascular problems at a reduced cost. The experimental assays of our computational predictions will finally elucidate the potential of this genuinely new approach for mimicking Nature’s rules of evolution.
Summary
Billions of years of evolution have made enzymes superb catalysts capable of accelerating reactions by several orders of magnitude. The underlying physical principles of their extraordinary catalytic power still remains highly debated, which makes the alteration of natural enzyme activities towards synthetically useful targets a tremendous challenge for modern chemical biology. The routine design of enzymes will, however, have large socio-economic benefits, as because of the enzymatic advantages the production costs of many drugs will be reduced and will allow industries to use environmentally friendly alternatives. The goal of this project is to make the routine design of proficient enzymes possible. Current computational and experimental approaches are able to confer natural enzymes new functionalities but are economically unviable and the catalytic efficiencies lag far behind their natural counterparts. The groundbreaking nature of NetMoDEzyme relies on the application of network models to reduce the complexity of the enzyme design paradigm and completely reformulate previous computational design approaches. The new protocol proposed accurately characterizes the enzyme conformational dynamics and customizes the included mutations by exploiting the correlated movement of the enzyme active site residues with distal regions. The guidelines for mutation are withdrawn from the costly directed evolution experimental technique, and the most proficient enzymes are easily identified via chemoinformatic models. The new strategy will be applied to develop proficient enzymes for the synthesis of enantiomerically pure β-blocker drugs for treating cardiovascular problems at a reduced cost. The experimental assays of our computational predictions will finally elucidate the potential of this genuinely new approach for mimicking Nature’s rules of evolution.
Max ERC Funding
1 445 588 €
Duration
Start date: 2016-05-01, End date: 2021-04-30
Project acronym PRIORS
Project Neural circuit dynamics underlying expectation and their impact on the variability of perceptual choices
Researcher (PI) Jaime de la Rocha Vazquez
Host Institution (HI) CONSORCI INSTITUT D'INVESTIGACIONS BIOMEDIQUES AUGUST PI I SUNYER
Call Details Consolidator Grant (CoG), LS5, ERC-2015-CoG
Summary Just as our experience has its origin in our perceptions, our perceptions are fundamentally shaped by our experience. How does the brain build expectations from experience and how do expectations impact perception? In a Bayesian framework, expectations determine the environment’s prior probability, which combined with stimulus information, can yield optimal decisions. While the accumulation-to-bound model describes temporal integration of sensory inputs and their combination with the prior, we still lack electrophysiological evidence showing neural circuits that integrate previous events adaptively to generate advantageous expectations.
I aim to understand (1) how circuits in the cerebral cortex integrate the recent history of stimuli and rewards to generate expectations, (2) how expectations are combined with sensory input across the processing hierarchy to bias decisions and (3) whether the dynamics of the expectation can dominate neuronal and choice variability. I will train rats in a new auditory discrimination task using predictable stimulus sequences that, once learned, are used to compute adaptive priors that improve discrimination. I will perform population recordings and optogenetic manipulations to identify the brain areas involved in the computation of priors in the task. To reveal the circuit mechanisms underlying the observed dynamics I will train a computational network model to classify fluctuating inputs and, by adapting its dynamics to the statistics of the stimulus sequence, accumulate evidence across trials to maximize performance. The model will generalize the accumulation-to-bound model by integrating information across various time scales and will partition choice variability into that caused by the dynamics of the prior or by fluctuations in the stimulus response. My proposal points at a paradigm shift from viewing neuronal variability as a corrupting source of noise to the result of our brain’s inevitable tendency to predict the future.
Summary
Just as our experience has its origin in our perceptions, our perceptions are fundamentally shaped by our experience. How does the brain build expectations from experience and how do expectations impact perception? In a Bayesian framework, expectations determine the environment’s prior probability, which combined with stimulus information, can yield optimal decisions. While the accumulation-to-bound model describes temporal integration of sensory inputs and their combination with the prior, we still lack electrophysiological evidence showing neural circuits that integrate previous events adaptively to generate advantageous expectations.
I aim to understand (1) how circuits in the cerebral cortex integrate the recent history of stimuli and rewards to generate expectations, (2) how expectations are combined with sensory input across the processing hierarchy to bias decisions and (3) whether the dynamics of the expectation can dominate neuronal and choice variability. I will train rats in a new auditory discrimination task using predictable stimulus sequences that, once learned, are used to compute adaptive priors that improve discrimination. I will perform population recordings and optogenetic manipulations to identify the brain areas involved in the computation of priors in the task. To reveal the circuit mechanisms underlying the observed dynamics I will train a computational network model to classify fluctuating inputs and, by adapting its dynamics to the statistics of the stimulus sequence, accumulate evidence across trials to maximize performance. The model will generalize the accumulation-to-bound model by integrating information across various time scales and will partition choice variability into that caused by the dynamics of the prior or by fluctuations in the stimulus response. My proposal points at a paradigm shift from viewing neuronal variability as a corrupting source of noise to the result of our brain’s inevitable tendency to predict the future.
Max ERC Funding
2 000 000 €
Duration
Start date: 2016-09-01, End date: 2021-08-31
