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              <text>Bacteriophage are a very diverse set of bacterially targeting viral organisms with a potential to be harnessed as a replacement for antibiotics in the future. The phage Matilda is the focus of my project, and I sought to continue already concluded work on Matilda's structure, as well as produce a purer and more concentrated sample to be used under TEM. This phage has the potential to be used in the future of cystic fibrotic disease treatment, due to its affinity for the bacteria Burkholderia Cenocepacia</text>
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              <text>Bacteriophage, antibiotic resistance, structural biology, phage, protein mapping</text>
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                <text>Oliver Edwards</text>
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                <text>Dr Jeremy Keown, Dr Jessica Lewis, Dr Antonia Sagona</text>
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                <text>Structural identification and purification of a novel bacteriophage for treatment of cystic fibrotic disease</text>
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              <text> Within a biological system, proteins are essential; they are the building blocks of life. Their functions range from performing as antibodies to catalysing reactions as enzymes; so, proteins have an extremely varied and extensive use through a variety of industries. With mutations, their stability and functionality can be modified, with even single-point mutations having a strong effect. Therefore, being able to predict the effect in stability changes of these mutations is essential, but often computationally costly and weak when encountering unseen data. With the rise in popularity of protein language models (PLMs), including ESM, there have been improvements, but these models still encounter the same difficulties. This study explores the effect of ensemble learning on improving protein stability predictions. This is done by combining two base models: a convolutional neural network (CNN) using wild-type and mutant sequences, and a neural network trained on frozen ESM embeddings. They each form two base-learners, where each is trained on half the base training data. To combine their predictions, intermediate meta-learners for each model type and a final meta-learner are used. Results show that ensemble learning improves predictions over base models, showing improvements in all performance metrics used. Notably, even the weaker CNN-based learners contributed to improved ensemble performance, reinforcing the benefit of this type of architecture. Although it is necessary to further optimise hyperparameters and include stronger base models in the future, these results indicate that ensemble learning can lead to more accurate protein stability prediction with minimal additional computational cost.</text>
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              <text>protein stability predictions, mutation effect prediction, ensemble learning</text>
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                <text>Predicting Protein Stability of Single-Point  Mutations using Ensemble Learning and ESM  Embeddings</text>
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                <text>Victoria Krushovska</text>
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              <text>The poster is about investigating  the convergence of neural network in two-timescale regime.</text>
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                <text>Yifan Wang</text>
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                <text>Dr. Ranko Lazic</text>
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                <text>Demonstrating the convergence of neural networks in Two-Timescale Regime</text>
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              <text>Aesthetics in video games, defined as “the desirable emotional responses evoked in the player” in the Mechanics Dynamics and Aesthetics (MDA) framework, can be key to a video game’s success. According to the MDA framework, aesthetics and user engagement are completely dependent on the mechanics of the game and cannot otherwise be influenced. However, if games are seen as an aesthetic artefact, a video game’s form and style should be entertaining on their own, and therefore its art style should influence aesthetics and user engagement. A small metroidvania-style game, Sandcat, was developed in three art styles – digital, pixel and pencil – to test this, and 15 participants played the game in all styles and completed a survey on their experiences. The results showed that while game mechanics had the most influence over aesthetics, art styles still had a significant effect on aesthetics and contributed to a game feeling more ‘usable’ for user engagement. Despite limitations with stretching and blurry assets with the pencil and pixel styles, pencil was the most preferred style (with descriptions of “whimsical” and even more “alive” than other art styles), closely followed by pixel (often described as “nostalgic”), and only one participant preferred digital. Beyond preferences, 80% of participants claimed they would spend more on a game which had their chosen art style, which may imply further commercial implications for developers. Overall, although not as strong as game mechanics, art styles still have a noteworthy influence on video game aesthetics.</text>
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                <text>Irene Peleteiro Paniagua</text>
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                <text>Alex Dixon</text>
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                <text>2025</text>
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              <text>Dementia, tau aggregates, electrophysiology, voltage gated calcium channels</text>
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                <text>Dr Emily Lane-Hill</text>
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                <text>How Tau Alters Synaptic Plasticity</text>
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                <text>2025</text>
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              <text>Protein dynamics, Computer simulations, Enzyme research, Drug discovery, Antibiotics, Protein flexibility, Molecular modelling, Catalysis, Protein design, Structural modelling, Bioinformatics, Computational chemistry, Protein function, Enzyme flexibility, Biotechnology, Synthetic biology, Protein structures, Protein–ligand interactions, Enzyme engineering, Molecular biology, Molecular Dynamics, AMBER, Protein Simulation, Computational Biochemistry, Structural Biology, NRPS, Nonribosomal Peptide Synthetase, Adenylation, Thiolation, ATP, Magnesium coordination, PPant arm, Enzyme Mechanism, Conformational Dynamics, High Performance Computing, ChimeraX, cpptraj, Parameterisation, Force Fields, Protein Engineering</text>
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                <text>Molecular Dynamics-Driven Engineering in Non-ribosomal Peptide Synthetase 5N9X</text>
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                <text>This project involved applying Molecular Dynamics (MD) simulations to study the Streptomyces adenylation–thiolation domain (PDB 5N9X) in non-ribosomal peptide synthetase (NRPS) pathways. Developed and implemented full MD workflows using the AMBER software suite on high-performance computing clusters, including parameterisation of ligands, system minimisation, equilibration, and production runs. Investigated substrate adenylation and thiolation steps, analysing conformational dynamics and catalytic geometries using Chimera and cpptraj.</text>
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                <text>Naim Ahmed</text>
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