The goals of this project are to:
- study the changes in structure and function that occur to protein complexes,
antibodies and pharmaceuticals due to changes in hydration, salt, and pH levels;
- optimize the functionality of a class of novel protein-based biosensors including the effect of the changes above; and,
- build and develop further R&D interactions with industry regarding 1. and 2. including Kerry Group , APC , Fionnanchtain, and other partners.
Structural changes occur to foods ingredients, bio-actives, and pharmaceuticals including antibodies on drying and other methods of processing/purification. Controlling such changes are important to avoid structural damage and to maximize functionality.
The computer-based simulation tools we develop to understand the nano-scale mechanisms include a novel particle insertion method (allowing for instance the hydration levels to be changed), combined with a variety of rare-event based methods, and molecular dynamics engines such as LAMMPS and - GROMACS which are capable of simulating large systems consisting of millions of atoms on massively parallel computing platforms.
We are using such tools to optimize a protein-based patent pending Biosensor developed with applications in Medical Diagnostics, Scientific Visualization, and Therapeutics.
At the heart of the sensor is a novel protein-based molecular switch which allows extremely sensitive real-time measurement of molecular targets to be made, and to turn on or off protein functions and other processes accordingly. Protein functions and processes of interest include fluorescence, resonance energy transfer, enzymatic activity, and toxicity. Depending on the application, the sensor needs to be functional in a variety of environments, ranging from organelles in living cells to bodily fluids such a blood serum and water.
Since April 8, 2020, we have been working on diagnostic sensors for COVID 19, and influenza A/B in the context of an industry-supported research collaboration with University College Dublin. Optimizing or tuning the sensor design to suit different solvent environments and targets can be greatly facilitated using E-CAM simulation modules, as well as other community software. While coarse-grained simulations are possible in some contexts, detailed simulations at a molecular level require HPC resources and state of the art software. Optimized protein sequences are then expressed using biotechnology and molecular biology laboratory-based methods, and tested.
List of Tasks
- Complete, test and fully optimize our particle insertion method in a variety of contexts (i.e. changes in salt, hydration, pH, and guest molecules).
- Develop a systematic approach to optimize molecular biosensors. This work is ongoing.
- Design, optimize, and validate protein-based sensors for COVID 19, influenza A/B for various reporters. This work is ongoing, and is complemented by experimental work of collaborators in molecular biology, and a diagnostic company focusing on productization and marketing.
- Perform a detailed commercial feasibility study for the sensor, identifying target markets and industry partners, and a commercialization strategy leading to a Start-Up. This task is complete.
- Preparation of an Enterprise Ireland Commercialization Proposal for diagnostic sensors with the goal to establish a start-up company in 2021/22
List of Modules
- PIcore - Core module for the calculation of free energies due to particle insertion -complete - author MacKernan.
- PIhydrate - hydration extension of PIcore -complete-author MacKernan
- PI Insertion_utils Python based code that extends PIcore and PIhydration to multiple insertions of more general molecule types - authors Kumar and MacKernan - complete- and being merged
- Comparative-metadynamics - this module facilitates extrapolating free energy surface (FES) feature information from short, non-converged
simulations of mutated systems complete and almost merged - authors Jaafar and MacKernan
- Hybrid Alchemy Module - this module written in Python implements Hybrid Monte Carlo Alchemical Molecular Dynamics to greatly accerate equilibration of dense systems. It extends earlier work by Ron Elber and co-workers to more general systems and uses LAMMPS. The module is basically complete, but some additional cleaning is required before it can be merged -expecteed delivery date October 2020 -Authors Finnegan, Kumar, Jaafar and MacKernan
- Modeller-tools - this module written in Python facilitate the building of fusion proteins and drives modeller writtenj by Andrej Sali. The -expected delivery is 21 September 2020. Note that for IP reasons the license of software will be restricted. Authors Kumar, Jaafar and MacKernan
- fesviewer-vmd - this module written in Python and tcl combines visually free energy surface graphys as produed by PLUMED and Python based viewers with VMD. In particular it allows the user to select points on a free energy surface with their mouse which the automatically selects corresponding molecular configurations and displays with VMD. It facilitates validation anbd understanding of data. The expected delivery is 28 September 2020. Note that for IP reasons the license of software will be restricted. Authors Kumar, Jaafar and MacKernan
- LAMMPS-python-interface - This python interface increases the flexibility of the standard LAMMPS interface and has been accepted by LAMMPS but needs to be updated to most recent version of lammps before it becomes part of the standard lammps distribution - should be able to merge this the week of Sept 28 2020. Authors Kumar, and MacKernan
List of publications
- Inventor, D. MacKernan, International Patent Application PCT/IB2017/055432, September 2017 (Applicant University College Dublin)
- Benchmarking a Fast Proton Titration Scheme in Implicit Solvent for Biomolecular Simulations
F. L. Barroso da Silva and D. MacKernan, J. Chem. Theory Comput.2017, 13, 2915-2929
Open access version
- Unimolecular FRET sensors: Simple linker designs and properties
S. Sanyal, D. F. Coker, D. MacKernan, Nano Communication Networks 2018, 18, 44–50
Open access version
Understanding and Controlling Food Protein Folding and Aggregation and taste: perspectives from experiment and simulation
F. L. Barroso da Silva, P. Carloni, D. Cheung, G. Cottone, S. Donnini, E. A. Foegeding, M. Gulzar, J. C. Jacquier, V. Lobaskin, D. MacKernan, Z. Mohammad Hosseini Naveh, R. Radhakrishnan, and E. E. Santiso, Annual Review of Food Science and Technology, 2020, 11:1, 365-387
- Unfolding the prospects of computational (bio)materials modelling
G.J.A. Sevink , J.A. Liwo, P. Asinari, D. MacKernan, G. Milano and I. Pagonbarraga.
J. Chem. Phys. 153, 100901 (2020); https://doi-org.ucd.idm.oclc.org/10.1063/5.0019773