Alexander van Teijlingen

Tooth enamel is characterised by an intricate hierarchical organization of apatite nanocrystals that bestows high stiffness, hardness, and fracture toughness. However, enamel does not possess the ability to regenerate, and achieving the artificial restoration of its microstructure and mechanical properties in clinical settings has proven challenging. To tackle this issue, we engineer a tuneable and resilient supramolecular matrix based on elastin-like recombinamers (ELRs) that imitates the structure and function of the enamel-developing matrix. When applied as a coating on the surface of teeth exhibiting different levels of erosion, the matrix is stable and can trigger epitaxial growth of apatite nanocrystals, recreating the microarchitecture of the different anatomical regions of enamel and restoring the mechanical properties. The study demonstrates the translational potential of our mineralising technology for treating loss of enamel in clinical settings such as the treatment of enamel erosion and dental hypersensitivity.

Automated descriptors to relate peptide self-assembly with macroscopic properties, enabling machine learning for targeted supramolecular gel discovery.

Prediction of small molecule pK_a values via evaluation of thermodynamic cycles by ANI-like neural network potentials.

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 are 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 range of applications from nanoreactors to therapeutics.

At the start of the PhD journey everything is new for the postgraduate student. There will be a whole array of regulations from the university to become familiar with, at the same time as getting familiar with your supervisors and learning to work with fellow postgraduate students and starting to do the actual PhD work. Finding out how and when the university assesses progress is often far down the list of priorities for a new PhD student. We argue that it is important to know the rules around progress and transfer assessment, since these regulations, and the expectations of what the student should produce, may vary between universities and even between faculties. The paper concludes with a set of recommendations for students, supervisors and universities to consider.

The past year saw an exponential growth in the use of machine learning using AI (artificial intelligence) and particularly Generative AI (GenAI) such as ChatGPT. The latter has seen a spectacular rise in the public debate and in the mass media. Those not involved in the development of AI were amazed by the capabilities of ChatGPT to produce text equal to the average human produced texts. There is no doubt that the adoption of AI is advancing rapidly. To test the ability of ChatGPT in its free version, we posed simple questions about the topic we had previously published. After reading the short essay produced by ChatGPT we repeated the question whilst asking for references to be included. We were surprised by the quality of this very general piece of work. In many UK universities there is a debate starting about students’ use of ChatGPT, and how difficult it is to distinguish between work produced by the average student and that produced by AI. There is a similar problem for editors and reviewers of academic journals. It really boils down to the question: 'How can you be certain the submitted manuscript came from a human source?’ However, we feel the progress of AI is not all doom and gloom. We outline some of the key problems around AI and academic publishing, but also opportunities arising from the use of AI in this area.

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 D_7.4 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.

Emulsions are commonly used for drug delivery, yet they are usually limited to exclusively delivering either lipophilic compounds or hydrophilic compounds. This separation negates possible synergetic therapeutic roles between such compounds. Here, we introduce a novel design for a short peptide that can stabilize emulsions. Upon binding certain metal ions, the peptide acts as a molecular switch, changes conformation, and becomes amphiphilic. Spectroscopic methods, NMR, and molecular dynamics provide information on the mechanism of this complexation-triggered amphiphilicity. The stability of these unique emulsions is based on histidine-metal bonds, which break at low pH values, selectively releasing their contents at the extracellular pH of tumors. Paclitaxel-encapsulated emulsion demonstrated strong activity against HeLa cells with an IC₅₀ of 70 nM, possibly enhanced by the simultaneous release of Zn²⁺ ions. Importantly, the emulsion was easily functionalized with various hexahistidine-tagged motifs that can supply the emulsions with many functions beyond drug delivery.

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.