One word to spell the future out loud

Over the last century, Biology has seen a steady increase in the number of macromolecular structures being resolved by means of either X-ray crystallography, NMR or, more recently, cryo-electron microscopy (cryo-EM). However, even since the early days it appeared clear that structure alone wasn’t the endpoint for investigating molecular behaviour. Antibody research in the 1930’s showed there were limitations to fully explaining molecular function by solely looking at structure as in order to bind a large diversity of molecules, the molecular structure of antibodies needs to contain a certain degree of plasticity. This suggested that looking at an ensemble of molecular conformations interconverting over time, rather than a single structure, was probably the best way to understand how molecules function. In other words, a key factor regulating molecular function is molecular dynamics, which experimental techniques have historically struggled to define at high-resolution. This has on the other hand been successfully tackled by computational approaches that have evolved extremely fast through developments in both algorithms and hardware.

The rapid evolution of molecular hardware has probably been the major developmental factor of the field, with scientists continuously eager to simulate larger molecular complexes, for longer time.

If you were to ask a computational biophysicist like me, to describe the future in a single word, you would probably receive the answer in the acronym GPU. Graphical processing units (GPUs), thanks to their high core density, have revolutionised the computation of molecular dynamics. By looking back just a few years we

Re-design proteins to make them resist high temperatures

Proteins are the workhorses of cells and perform a myriad of different functions: from providing structural integrity to the cell to constantly transforming metabolites in a highly efficient manner (enzymes). They have evolved over millions of years to fulfil their function, which over time has become optimal for their individual micro-environment. Nevertheless, such a micro-environment does not often match the micro-environment in which industry could make good use of a protein function. A multitude of industrial processes are indeed carried out at temperatures well in excess of the thermal stability of proteins, leading to their inactivation.

We are therefore developing and applying a series of algorithms that look at the sequences of a multitude of proteins to redesign and target their resistance to high temperatures. By aligning the sequences of proteins from common ancestors (homologues) and different species, we are able to identify the protein building blocks (amino-acids) that are important to the fitness of a protein structure.

Using pulsed electric fields to increase food safety

One of the largest problems in the New Zealand’s food industry is to keep food safe from the effects of pathogens, in particular a strain of E. coli producing a protein called Shiga toxin. Little is known about inactivating the toxin by using non-conventional food processing methods and most of the attention is indeed devoted to prevent the infection of cattle or meat by conventional antibacterial means, which, in turn, means preventing E. coli’s growth. We are focusing on an alternative strategy to inactivate toxins in food, by triggering the loss of a toxin’s structure by applying pulsed electric fields (PEFs). Applying PEFs consist of exposing food samples to short but powerful electric fields. Nevertheless, nothing is known regarding the effects of PEFs on the stability of proteins. We are performing simulations in which the pentameric Shiga toxin produced by E. coli is exposed to pulsed electric fields featuring different electric field strengths, frequencies and/or exposure times. In our simulations we then monitor the disrupting effects of the applied field on the toxin’s structure, which mediates its toxicity by binding receptors on the surface of human cells (Figure 2). Thus, by disrupting the toxin’s structure, its ability to interact with human cells and trigger a toxic response would be hindered. Overall, we thus plan to provide the industry with guidelines on what is required to inactivate the Shiga toxin, without affecting the organoleptic properties of meat. Interestingly, once this protocol is optimised for the Shiga toxin we will expand our study to other toxins, with the overarching goal of bringing the highly predictive potential of molecular simulations to service industry. Overall, we thus plan to provide the industry with guidelines on what is required to inactivate the Shiga toxin, without affecting the organoleptic properties of meat. Interestingly, once this protocol is optimised for the Shiga toxin we will expand our study to other toxins, with the overarching goal of bringing the highly predictive potential of molecular simulations to service industry.

Rational design of molecular motors to obtain carbohydrates with desired physical properties

Great attention in biology is given to so-called molecular motors. Molecular motors are proteins that can accomplish multiple chemical reactions on other molecules (their substrates) in a highly efficient way. They can perform several reaction cycles without having to dissociate and reassociate from their substrate (for a new reaction cycle). This makes molecular motors very efficient, at the point that they are involved in a large number of fundamental biological processes. In the past, we have discovered a particular class of molecular called Pectin methyl esterase enzymes (PMEs), as they work on pectin. Pectin is a polysaccharide that is extremely important to maintaining the integrity of the plant cell wall and equally crucial to the food industry for its jellifying properties in the presence of calcium. Interestingly, the jellification properties of pectin are strictly related to PMEs, as they control the number of negatively charged sugars that promote jellification by binding positively charged calcium ions.
With an ad-hoc computational workflow we are therefore trying to answer the following question: is it possible to design new PMEs so to have a selected amount and distribution of negatively charged sugars and control the jellification properties of pectin for targeted uses in the food industry? Our intent for this project is to design PME enzymes that are able to provide pectin with defined physico-chemical properties, by modulating the charge distribution of pectic polysaccharides upon the modifications promoted by specifically engineered PMEs (Figure 3). Besides PMEs, their natural inhibitors (called PME inhibitors or PMEIs) are of paramount importance for the food industry as, by stopping the activity of PMEs in fruit, they can control fruit juice clarification, often hindered by overly charged pectin in juice. By increasing the thermal stability of PMEIs we are designing new inhibitors that can be applied in high-temperature food processing processes of fruit juice manufacturing.

Molecular simulations in the cloud: from highly parallelized to highly replicated runs

Molecular simulations aiming to sample the dynamics of molecular systems, either through Monte Carlo methods or integrating equations of motion over time, are heavily stochastic. The conformational space of even small molecules is enormous and computational sampling of the functional space (let alone the whole conformational space of a molecule) is extremely challenging. For the simulation of larger molecular systems, scalability of molecular dynamics codes allows us to split the computation, in other words the molecular system, across a large number of computing nodes that work in parallel. Molecular dynamics simulations are “infamous” for being heavily parallelised. However, besides the need to simulate ever-growing molecular systems (composed of several hundred thousand to millions of atoms), statistical robustness is also required to achieve confidence for the claims of a scientific study. To obtain this, it is a must to perform several molecular dynamics simulation runs (replicates), starting from slightly different initial conditions (i.e. the initial velocities assigned to the particles to simulate) and to monitor the evolution of the simulation, finally obtaining population-averaged via time-averaged results.

Replicates are virtually independent from each other, and for smaller molecular systems do not need a heavy parallelisation scheme or multiple nodes. Moreover, especially considering the power of GPU computing, longer timescales, which are directly linked to the investigation of molecular function, can be accessed. Overall, the need to simulate a large amount of replicated simulations, with small differences in the set of initial conditions, makes this problem highly suitable to be set up in a cloud-based infrastructure: with several replicates running on separate instances that do not communicate or work in parallel. With respect to these needs, Nectar instances are an invaluable resource for the understanding and design of molecular systems through molecular dynamics simulations.