Short Peptide Self-Assembly in the Martini Coarse-Grain Force Field Family.

Pivotal to the success of any computational experiment is the ability to make reliable predictions about the system under study and the time required to yield these results. Biomolecular interactions is one area of research that sits in every camp of resolution vs the time required, from the quantum mechanical level to in vivo studies. At an approximate midpoint, there is coarse-grained molecular dynamics, for which the Martini force fields have become the most widely used, fast enough to simulate the entire membrane of a mitochondrion though lacking atom-specific precision. While many force fields have been parametrized to account for a specific system under study, the Martini force field has aimed at casting a wider net with more generalized bead types that have demonstrated suitability for broad use and reuse in applications from protein-graphene oxide coassembly to polysaccharides interactions.In this Account, the progressive (Martini versions 1 through 3) and peripheral (Sour Martini, constant pH, Martini Straight, Dry Martini, etc.) developmental trajectory of the Martini force field will be analyzed in terms of self-assembling systems with a focus on short (two to three amino acids) peptide self-assembly in aqueous environments. In particular, this will focus on the effects of the Martini solvent model and compare how changes in bead definitions and mapping have effects on different systems. Considerable effort in the development of Martini has been expended to reduce the "stickiness" of amino acids to better simulate proteins in bilayers. We have included in this Account a short study of dipeptide self-assembly in water, using all mainstream Martini force fields, to examine their ability to reproduce this behavior. The three most recently released versions of Martini and variations in their solvents are used to simulate in triplicate all 400 dipeptides of the 20 gene-encoded amino acids. The ability of the force fields to model the self-assembly of the dipeptides in aqueoues environments is determined by the measurement of the aggregation propensity, and additional descriptors are used to gain further insight into the dipeptide aggregates.

Martinoid: the peptoid martini force field.

Many exciting innovations have been made in the development of assembling peptoid materials. Typically, these have utilised large oligomeric sequences, though elsewhere the study of peptide self-assembly has yielded numerous examples of assemblers below 6-8 residues in length, evidencing that minimal peptoid assemblers are not only feasible but expected. A productive means of discovering such materials is through the application of in silico screening methods, which often benefit from the use of coarse-grained molecular dynamics (CG-MD) simulations. At the current level of development, CG models for peptoids are insufficient and we have been motivated to develop a Martini forcefield compatible peptoid model. A dual bottom-up and top-down parameterisation approach has been adopted, in keeping with the Martini parameterisation methodology, targeting the reproduction of atomistic MD dynamics and trends in experimentally obtained log D7.4 partition coefficients, respectively. This work has yielded valuable insights into the practicalities of parameterising peptoid monomers. Additionally, we demonstrate that our model can reproduce the experimental observations of two very different peptoid assembly systems, namely peptoid nanosheets and minimal tripeptoid assembly. Further we can simulate the peptoid helix secondary structure relevant for antimicrobial sequences. To be of maximum usefulness to the peptoid research community, we have developed freely available code to generate all requisite simulation files for the application of this model with Gromacs MD software.

An active machine learning discovery platform for membrane-disrupting and pore-forming peptides.

Membrane-disrupting and pore-forming peptides (PFPs) play a substantial role in bionanotechnology and can determine the life and death of cells. The control of chemical and ion transport through cell membranes is essential to maintaining concentration gradients. Likewise, the delivery of drugs and intracellular proteins aided by pore-forming agents is of interest in treating malfunctioning cells. Known PFPs tend to be up to 50 residues in length, which is commensurate with the thickness of a lipid bilayer. Accordingly, few short PFPs are known. Here we show that the discovery of PFPs can be accelerated via an active machine learning approach. The approach identified 71 potential PFPs from the 25.6 billion octapeptide sequence space; 13 sequences were tested experimentally, and all were found to have the predicted membrane-disrupting ability, with 1 forming highly stable pores. Experimental verification of the predicted pore-forming ability demonstrated that a range of short peptides can form pores in membranes, while the positioning and characteristics of residues that favour pore-forming behaviour were identified. This approach identified more ultrashort (8-residues, unmodified, non-cyclic) PFPs than previously known. We anticipate our findings and methodology will be useful in discovering new pore-forming and membrane-disrupting peptides for a range of applications from nanoreactors to therapeutics.

Integrating Computation, Experiment, and Machine Learning in the Design of Peptide-Based Supramolecular Materials and Systems.

Interest in peptide-based supramolecular materials has grown extensively since the 1980s and the application of computational methods has paralleled this. These methods contribute to the understanding of experimental observations based on interactions and inform the design of new supramolecular systems. They are also used to virtually screen and navigate these very large design spaces. Increasingly, the use of artificial intelligence is employed to screen far more candidates than traditional methods. Based on a brief history of computational and experimentally integrated investigations of peptide structures, we explore recent impactful examples of computationally driven investigation into peptide self-assembly, focusing on recent advances in methodology development. It is clear that the integration between experiment and computation to understand and design new systems is becoming near seamless in this growing field.

Alkali Metal Dihydropyridines in Transfer Hydrogenation Catalysis of Imines: Amide Basicity versus Hydride Surrogacy.

Catalytic reduction of a representative set of imines, both aldimines and ketimines, to amines has been studied using transfer hydrogenation from 1,4-dicyclohexadiene. Unusually, this has been achieved using s-block pre-catalysts, namely 1-metallo-2-tert-butyl-1,2-dihydropyridines, 2-tBuC5 H5 NM, M(tBuDHP), where M=Li-Cs. Reactions have been monitored in C6 D6 and tetrahydrofuran-d8 (THF-d8 ). A definite trend is observed in catalyst efficiency with the heavier alkali metal tBuDHPs outperforming the lighter congeners. In general, Cs(tBuDHP) is the optimal pre-catalyst with, in the best cases, reactions producing quantitative yields of amines in minutes at room temperature using 5 mol % catalyst. Supporting the experimental study, Density Functional Theory (DFT) calculations have also been carried out which reveal that Cs has a pathway with a significantly lower rate determining step than the Li congener. In the postulated initiation pathways DHP can act as either a base or as a surrogate hydride.

Hydrocarbon Soluble Alkali-Metal-Aluminium Hydride Surrog[ATES].

A series of group 1 hydrocarbon-soluble donor free aluminates [AM(t BuDHP)(TMP)Al(i Bu)2 ] (AM=Li, Na, K, Rb) have been synthesised by combining an alkali metal dihydropyridyl unit [(2-t BuC5 H5 N)AM)] containing a surrogate hydride (sp3 C-H) with [(i Bu)2 Al(TMP)]. These aluminates have been characterised by X-ray crystallography and NMR spectroscopy. While the lithium aluminate forms a monomer, the heavier alkali metal aluminates exist as polymeric chains propagated by non-covalent interactions between the alkali metal cations and the alkyldihydropyridyl units. Solvates [(THF)Li(t BuDHP)(TMP)Al(i Bu)2 ] and [(TMEDA)Na(t BuDHP)(TMP)Al(i Bu)2 ] have also been crystallographically characterised. Theoretical calculations show how the dispersion forces tend to increase on moving from Li to Rb, as opposed to the electrostatic forces of stabilization, which are orders of magnitude more significant. Having unique structural features, these bimetallic compounds can be considered as starting points for exploring unique reactivity trends as alkali-metal-aluminium hydride surrog[ATES].

Synthesis of (+)-(R)-Tiruchanduramine.

The absolute stereochemistry of the marine alkaloid (+)-(R)-tiruchanduramine was established via a convergent total synthesis in six steps and 15.5% overall yield from Fmoc-D-Dab(Boc)-OH.

Constant pH Coarse-Grained Molecular Dynamics with Stochastic Charge Neutralization.

pH dependence abounds in biochemical systems; however, many simulation methods used to investigate these systems do not consider this property. Using a modified version of the hybrid non-equilibrium molecular dynamics (MD)/Monte Carlo algorithm, we include a stochastic charge neutralization method, which is particularly suited to the MARTINI force field and enables artifact-free Ewald summation methods in electrostatic calculations. We demonstrate the efficacy of this method by reproducing pH-dependent self-assembly and self-organization behavior previously reported in experimental literature. In addition, we have carried out experimental oleic acid titrations where we report the results in a more relevant way for the comparison with computational methods than has previously been done.

Beyond Tripeptides Two-Step Active Machine Learning for Very Large Data sets.

Self-assembling peptide nanostructures have been shown to be of great importance in nature and have presented many promising applications, for example, in medicine as drug-delivery vehicles, biosensors, and antivirals. Being very promising candidates for the growing field of bottom-up manufacture of functional nanomaterials, previous work (Frederix, et al. 2011 and 2015) has screened all possible amino acid combinations for di- and tripeptides in search of such materials. However, the enormous complexity and variety of linear combinations of the 20 amino acids make exhaustive simulation of all combinations of tetrapeptides and above infeasible. Therefore, we have developed an active machine-learning method (also known as "iterative learning" and "evolutionary search method") which leverages a lower-resolution data set encompassing the whole search space and a just-in-time high-resolution data set which further analyzes those target peptides selected by the lower-resolution model. This model uses newly generated data upon each iteration to improve both lower- and higher-resolution models in the search for ideal candidates. Curation of the lower-resolution data set is explored as a method to control the selected candidates, based on criteria such as log P. A major aim of this method is to produce the best results in the least computationally demanding way. This model has been developed to be broadly applicable to other search spaces with minor changes to the algorithm, allowing its use in other areas of research.

Size-dependent melting point depression of nickel nanoparticles.

We investigate the phase-transition behaviour of nickel nanoparticles (3-6 nm) via dynamic TEM. The nanoparticles were synthesized within a reverse microemulsion and then monitored via dynamic TEM simultaneously while undergoing controlled heating. The size-dependent melting point depression experimentally observed is compared with, and is in good agreement with existing thermodynamic and molecular dynamic predictions.

Artificial Intelligence and Health in Nepal.

The growth in information technology and computer capacity has opened up opportunities to deal with much and much larger data sets than even a decade ago. There has been a technological revolution of big data and Artificial Intelligence (AI). Perhaps many readers would immediately think about robotic surgery or self-driving cars, but there is much more to AI. This Short Communication starts with an overview of the key terms, including AI, machine learning, deep learning and Big Data. This Short Communication highlights so developments of AI in health that could benefit a low-income country like Nepal and stresses the need for Nepal's health and education systems to track such developments and apply them locally. Moreover, Nepal needs to start growing its own AI expertise to help develop national or South Asian solutions. This would require investing in local resources such as access to computer power/capacity as well as training young Nepali to work in AI.