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The de novo design of antimicrobial therapeutics involves the exploration of a vast chemical repertoire to find compounds with broad-spectrum potency and low toxicity. Here, we report an efficient computational method for the generation of antimicrobials with desired attributes. The method leverages guidance from classifiers trained on an informative latent space of molecules modelled using a deep generative autoencoder, and screens the generated molecules using deep-learning classifiers as well as physicochemical features derived from high-throughput molecular dynamics simulations. Within 48 days, we identified, synthesized and experimentally tested 20 candidate antimicrobial peptides, of which two displayed high potency against diverse Gram-positive and Gram-negative pathogens (including multidrug-resistant Klebsiella pneumoniae) and a low propensity to induce drug resistance in Escherichia coli. Both peptides have low toxicity, as validated in vitro and in mice. We also show using live-cell confocal imaging that the bactericidal mode of action of the peptides involves the formation of membrane pores. The combination of deep learning and molecular dynamics may accelerate the discovery of potent and selective broad-spectrum antimicrobials. A computational method leveraging deep learning and molecular dynamics simulations enables the rapid discovery of antimicrobial peptides with low toxicity and with high potency against diverse Gram-positive and Gram-negative pathogens.

Carbon capture and storage is part of the roadmap towards net zero for many countries around the world, since emissions from existing infrastructure are close to estimated carbon budgets. To address this problem, currently 87 carbon capture projects are proposed worldwide in the next 10 years. A major class of commercial carbon capture technology involves capture systems using solvents. Commonly carbon capture solvents feature blends of amines and water. Whilst these blends have proved valuable there is an increasing need to identify new candidate molecules which are more efficient and improve performance. Systematic approaches to improve on the current technology are now needed with increasing urgency to expedite the introduction of cutting edge carbon capture methods. Here, we present a chemical space analysis of amines and carbon capture usage. We proceed to show a framework for computational screening relevant to carbon capture solvents. We demonstrate the use of cloud computing, novel molecular representations and machine learning to screen potential candidates. We show the utility of machine learning in this field for high throughput virtual screening with an exemplar application to absorption capacity classification. Additionally, we highlight the need for improved data awareness and accessibility to enable this field to advance at a pace commensurate to its global importance. Our research brings together multiple methods and domains of expertise to accelerate the discovery of carbon capture solvents.

Cyclic peptides have the potential to bind to challenging targets, which are undruggable with small molecules, but their application is limited by low membrane permeability. Here, using a series of cyclic pentapeptides, we showed that established physicochemical criteria of permeable peptides are heavily violated. We revealed that a dominant core conformation, stabilized by amides' shielding pattern, could guide the design of novel compounds. As a result, counter-intuitive strategies, such as incorporation of polar residues, can be beneficial for permeability. We further find that core globularity is a promising descriptor, which can extend the capability of standard predictive models.

Disruption of cell membranes is a fundamental host defense response found in virtually all forms of life. The molecular mechanisms vary but generally lead to energetically favored circular nanopores. Here, we report an elaborate fractal rupture pattern induced by a single side-chain mutation in ultrashort (8–11-mers) helical peptides, which otherwise form transmembrane pores. In contrast to known mechanisms, this mode of membrane disruption is restricted to the upper leaflet of the bilayer where it exhibits propagating fronts of peptide-lipid interfaces that are strikingly similar to viscous instabilities in fluid flow. The two distinct disruption modes, pores and fractal patterns, are both strongly antimicrobial, but only the fractal rupture is nonhemolytic. The results offer wide implications for elucidating differential membrane targeting phenomena defined at the nanoscale.\ud \ud \ud

Peptide interactions with lipid bilayers play a key role in a range of biological processes and depend on electrostatic interactions between charged amino acids and lipid headgroups. Antimicrobial peptides (AMPs) initiate the killing of bacteria by binding to and destabilizing their membranes. The multiple peptide resistance factor (MprF) provides a defense mechanism for bacteria against a broad range of AMPs. MprF reduces the negative charge of bacterial membranes through enzymatic conversion of the anionic lipid phosphatidyl glycerol (PG) to either zwitterionic alanyl-phosphatidyl glycerol (Ala-PG) or cationic lysyl-phosphatidyl glycerol (Lys-PG). The resulting change in the membrane charge is suggested to reduce the binding of AMPs to membranes, thus impeding downstream AMP activity. Using coarse-grained molecular dynamics to investigate the effects of these modified lipids on AMP binding to model membranes, we show that AMPs have substantially reduced affinity for model membranes containing Ala-PG or Lys-PG. More than 5000 simulations in total are used to define the relationship between lipid bilayer composition, peptide sequence (using five different membrane-active peptides), and peptide binding to membranes. The degree of interaction of a peptide with a membrane correlates with the membrane surface charge density. Free energy profile (potential of mean force) calculations reveal that the lipid modifications due to MprF alter the energy barrier to peptide helix penetration of the bilayer. These results will offer a guide to the design of novel peptides, which addresses the issue of resistance via MprF-mediated membrane modification.

Antimicrobial peptides (AMPs) initiate killing of bacteria by binding to and destabilizing their membranes. The multiple peptide resistance factor (MprF) provides a defence mechanism for bacteria against a broad range of AMPs. MprF reduces the negative charge of both Gram-positive and Gram--negative bacterial membranes through enzymatic conversion of the anionic lipid phosphatidyl glycerol (PG) to either zwitterionic alanyl-phosphatidyl glycerol (Ala-PG) or cationic lysylphosphatidyl glycerol (Lys-PG). The resulting change in membrane charge is suggested to reduce AMP-membrane binding and hinder downstream AMP activity. Using molecular dynamics to investigate the effects of these modified lipids on AMP-binding to model membranes, we show that AMPs have substantially reduced affinity for model membranes containing Ala-PG or Lys-PG. A total of ~7000 simulations are used to define the relationship between bilayer composition and binding for 5 different membrane active peptides. The reduction of degree of interaction of a peptide with the membrane is shown to correlate with the change in membrane surface charge density. Free energy profile (potential of mean force) calculations reveal that these lipid modifications alter the energy barrier to peptide helix penetration of the bilayer. These results will enable us to guide design of novel peptides which address the issue of resistance via MprF-mediated membrane modification.

The potentials and forces that govern the interaction of atoms and molecules with each other, and with their environment, are crucial for understanding and simulating complex condensed matter systems. At the same time, they are complicated and hard to describe at a microscopic level. This review gives a pedagogical introduction to a method for treating these interactions in an effective manner that can be used for simulations of noble gas fluids and solids, as well as water, and has potential for applications to more complicated systems.

Simulation and data analysis have evolved into powerful methods for discovering and understanding molecular modes of action and designing new compounds to exploit these modes. The combination provides a strong impetus to create and exploit new tools and techniques at the interfaces between physics, biology, and data science as a pathway to new scientific insight and accelerated discovery. In this context, we explore the rational design of novel antimicrobial peptides (short protein sequences exhibiting broad activity against multiple species of bacteria). We show how datasets can be harvested to reveal features which inform new design concepts. We introduce new analysis and visualization tools: a graphical representation of the k-mer spectrum as a fundamental property encoded in antimicrobial peptide databases and a data-driven representation to illustrate membrane binding and permeation of helical peptides.

Standard molecular dynamics (MD) simulations generally make use of a basic description of intermolecular forces which consists of fixed, pairwise, atom-centred Coulomb, van der Waals and short-range repulsive terms. Important interactions such as many-body polarisation and many-body dispersion which are sensitive to changes in the environment are usually neglected, and their effects treated effectively within mean-field approximations to reproduce a single thermodynamic state point or physical environment. This leads to difficulties in modelling the complex interfaces of interest today where the behaviour may be quite different from the regime of parameterisation. Here, we describe the construction and properties of a Gaussian coarse-grained electronic structure, which naturally generates many-body polarisation and dispersion interactions. The electronic structure arises from a fully quantum mechanical treatment of a set of distributed quantum Drude oscillators (QDOs), harmonic atoms which interact with e...

One key factor that limits the predictive power of molecular dynamics simulations is the accuracy and transferability of the input force field. Force fields are challenged by heterogeneous environments, where electronic responses give rise to biologically important forces such as many-body polarisation and dispersion. The importance of polarisation was recognised early-on and described by Cochran in 1959 [Philosophical Magazine 4 (1959) 1082-1086]. However, dispersion forces are still treated at the two-body level and in the dipole limit, although the importance of three-body terms in the condensed phase was demonstrated by Barker in the 1980s [Phys. Rev. Lett. 57 (1986) 230-233]. A way of treating both polarisation and dispersion on an equal basis is to coarse grain the electrons a molecular moiety to a single quantum harmonic oscillator, as suggested as early as the 1960s by Hirschfelder, Curtiss and Bird [The Molecular Theory of Gases and Liquids (1954)]. This treatment, when solved in the strong coupling limit, gives all orders of long-range forces. In the last decade, the tools necessary to exploit this strong coupling limit have been developed, culminating in a transferable model of water with excellent predictive power across the phase diagram. This transferability arises since the environment identifies the form of long range interactions, rather than the expressions selected by the modeller. Here, we discuss the role of electronic coarse-graining in predictive multiscale materials modelling and describe the first implementation of the method in a general purpose molecular dynamics software, QDO_MD., Comment: Accepted preprint in the Journal of Computational Physics

An electronically coarse-grained model for water reveals a persistent vestige of the liquid-gas transition deep into the supercritical region. A crossover in the density dependence of the molecular dipole arises from the onset of non-percolating hydrogen bonds. The crossover points coincide with the Widom line in the scaling region but extend further, tracking the heat capacity maxima, offering evidence for liquid- and gas-like state points in a "one-phase" fluid. The effect is present even in dipole-limit models suggesting that it is common for all molecular liquids exhibiting dipole enhancement in the liquid phase., A Letter with Supp. Material

Water challenges our fundamental understanding of emergent materials properties from a molecular perspective. It exhibits a uniquely rich phenomenology including dramatic variations in behavior over the wide temperature range of the liquid into water's crystalline phases and amorphous states. We show that many-body responses arising from water's electronic structure are essential mechanisms harnessed by the molecule to encode for the distinguishing features of its condensed states. We treat the complete set of these many-body responses nonperturbatively within a coarse-grained electronic structure derived exclusively from single-molecule properties. Such a "strong coupling" approach generates interaction terms of all symmetries to all orders, thereby enabling unique transferability to diverse local environments such as those encountered along the coexistence curve. The symmetries of local motifs that can potentially emerge are not known a priori. Consequently, electronic responses unfiltered by artificial truncation are then required to embody the terms that tip the balance to the correct set of structures. Therefore, our fully responsive molecular model produces, a simple, accurate, and intuitive picture of water's complexity and its molecular origin, predicting water's signature physical properties from ice, through liquid-vapor coexistence, to the critical point.

Interconversion of mechanical and electrical energy via the piezoelectric effect is fundamental to a wide range of technologies. The discovery in the 1990s of giant piezoelectric responses in certain materials has therefore opened new application spaces, but the origin of these properties remains a challenge to our understanding. A key role is played by the presence of a structural instability in these materials at compositions near the “morphotropic phase boundary” (MPB) where the crystal structure changes abruptly and the electromechanical responses are maximal. Here we formulate a simple, unified theoretical description which accounts for extreme piezoelectric response, its observation at compositions near the MPB, accompanied by ultrahigh dielectric constant and mechanical compliances with rather large anisotropies. The resulting model, based upon a Landau free energy expression, is capable of treating the important domain engineered materials and is found to be predictive while maintaining simplicity...

We introduce an electronically coarse-grained description of water representing all long range, many-body electronic responses via an embedded quantum oscillator. Leading-order response coefficients and gas phase electrostatic moments are exactly reproduced. Molecular dynamics, using electronic path integral sampling, shows that this framework is sufficient for a realistic liquid to emerge naturally with transferability extending further to nonambient state points and to the free water surface. The model allows the strength of many-body dispersion and polarization to be adjusted independently and these are found to have significant effects on the condensed phase.