Theoretical Investigation on the Biomolecular Systems using Multiscale Modelling

Die Untersuchung von Protein-Ligand-Wechselwirkungen ist für biomolekulare Systeme von entscheidender Bedeutung und eine Herausforderung. Insbesondere haben traditionelle Laborexperimente oft Schwierigkeiten, die Mechanismen der Reaktionen zu erklären, während klassische theoretische Berechnungsmethoden Defizite im Umgang mit der System- und Zeitskala biomolekularer Systeme aufweisen. In dieser Arbeit werden sogenannte enhanced Sampling-Methoden auf der Grundlage von Molekulardynamiksimulationen (MD) und Algorithmen für künstliche neuronale Netze (ANN), die auf semi-empirischen quantenmechanischen (QM) Ansätzen beruhen, zur Untersuchung verschiedener biomolekularer Systeme eingesetzt. Im ersten Teil wurde die Wirt-Gast-Chemie von [4+4]- und [2+3]-Iminkäfigen untersucht. Bei der Untersuchung von [4+4]-Käfigen wurde der Aufnahmeprozess von unterschiedlich großen Ammoniumionen in Käfigen mit alternativen Volumina durch wohltemperierte Metadynamik (MetaD) simuliert. Es wurden drei mögliche Mechanismen vorgeschlagen, um die Gastaufnahmeprozesse zu erklären. Bei der Untersuchung von [2+3]-Käfigen wurde der Stickstoffmolekültransfer in drei verschiedenen Käfigkristallen mit Funnel-Metadynamik (FM) berechnet. Die erhaltenen freien Energieflächen deuten auf die Existenz von zwei möglichen Wegen hin, auf denen der Stickstofftransfer erfolgen kann. Im zweiten Teil wurde eine neuartige Fluoreszenzsonde auf der Basis eines Glukose bindenden Proteins untersucht. Ein detailliertes molekulares Verständnis der Veränderungen an der Glukosebindestelle aufgrund von Mutationen und deren Auswirkungen auf die Glukosebindung wurde durch MD-Simulationen erreicht. Die Energetik der Dissoziation von Protein und Glukose wurde aufgedeckt und stimmte mit den experimentellen Ergebnissen überein. Schließlich wurde eine Reihe von künstlichen neuronalen Netzen (ANNs) trainiert, um die falsche Darstellung von angeregten Zuständen durch LC-DFTB zu korrigieren, wenn Energieniveaus kreuzen. Die meisten der trainierten Maschinen sind in der Lage, die durch LC-DFTB verursachten Fehler bei der Beschreibung des angeregten Zustands zuverlässig zu korrigieren, während die für Farbstoffgeometrien in Wasser trainierte Maschine weniger genaue Ergebnisse liefert und weiteres Training erfordert

Unravelling the mechanism of glucose binding in a protein-based fluorescence probe: molecular dynamics simulation with a tailor-made charge model

Fluorophores linked to the glucose/galactose-binding protein (GGBP) are a promising class of glucose sensors with potential application in medical devices for diabetes patients. Several different fluorophores at different positions in the protein were tested experimentally so far, but a deeper molecular understanding of their function is still missing. In this work, we use molecular dynamics simulations to investigate the mechanism of glucose binding in the GGBP-Badan triple mutant and make a comparison to the GGBP wild-type protein. The aim is to achieve a detailed molecular understanding of changes in the glucose binding site due to the mutations and their effect on glucose binding. Free simulations give an insight into the changes of the hydrogen-bonding network in the active site and into the mechanisms of glucose binding. Additionally, metadynamics simulations for wild type and mutant unravel the energetics of binding/unbinding in these proteins. Computed free energies for the opening of the binding pocket for the wild-type and the mutant agree well with the experimental data. Further, the simulations also give an insight into the changes of the chromophore conformations upon glucose binding, which can help to understand fluorescence changes. Therefore, the molecular details unravelled in this work may support effective optimisation strategies for the construction of more efficient glucose sensors

Host‐Guest Chemistry of Truncated Tetrahedral Imine Cages with Ammonium Ions

Three shape-persistent [4+4] imine cages with truncated tetrahedral geometry with different window sizes were studied as hosts for the encapsulation of tetra-n-alkylammonium salts of various bulkiness. In various solvents the cages behave differently. For instance, in dichloromethane the cage with smallest window size takes up NEt$_{4}$t$^{+}$ but not NMe$_{4}$t$^{+}$, which is in contrast to the two cages with larger windows hosting both ions. To find out the reason for this, kinetic experiments were carried out to determine the velocity of uptake but also to deduce the activation barriers for these processes. To support the experimental results, calculations for the guest uptakes have been performed by molecular mechanics’ simulations. Finally, the complexation of pharmaceutical interested compounds, such as acetylcholine, muscarine or denatonium have been determined by NMR experiments

Diverse and Faithful Knowledge-Grounded Dialogue Generation via Sequential Posterior Inference

The capability to generate responses with diversity and faithfulness using factual knowledge is paramount for creating a human-like, trustworthy dialogue system. Common strategies either adopt a two-step paradigm, which optimizes knowledge selection and response generation separately, and may overlook the inherent correlation between these two tasks, or leverage conditional variational method to jointly optimize knowledge selection and response generation by employing an inference network. In this paper, we present an end-to-end learning framework, termed Sequential Posterior Inference (SPI), capable of selecting knowledge and generating dialogues by approximately sampling from the posterior distribution. Unlike other methods, SPI does not require the inference network or assume a simple geometry of the posterior distribution. This straightforward and intuitive inference procedure of SPI directly queries the response generation model, allowing for accurate knowledge selection and generation of faithful responses. In addition to modeling contributions, our experimental results on two common dialogue datasets (Wizard of Wikipedia and Holl-E) demonstrate that SPI outperforms previous strong baselines according to both automatic and human evaluation metrics

Transformer-Based Visual Segmentation: A Survey

Visual segmentation seeks to partition images, video frames, or point clouds into multiple segments or groups. This technique has numerous real-world applications, such as autonomous driving, image editing, robot sensing, and medical analysis. Over the past decade, deep learning-based methods have made remarkable strides in this area. Recently, transformers, a type of neural network based on self-attention originally designed for natural language processing, have considerably surpassed previous convolutional or recurrent approaches in various vision processing tasks. Specifically, vision transformers offer robust, unified, and even simpler solutions for various segmentation tasks. This survey provides a thorough overview of transformer-based visual segmentation, summarizing recent advancements. We first review the background, encompassing problem definitions, datasets, and prior convolutional methods. Next, we summarize a meta-architecture that unifies all recent transformer-based approaches. Based on this meta-architecture, we examine various method designs, including modifications to the meta-architecture and associated applications. We also present several closely related settings, including 3D point cloud segmentation, foundation model tuning, domain-aware segmentation, efficient segmentation, and medical segmentation. Additionally, we compile and re-evaluate the reviewed methods on several well-established datasets. Finally, we identify open challenges in this field and propose directions for future research. The project page can be found at https://github.com/lxtGH/Awesome-Segmenation-With-Transformer. We will also continually monitor developments in this rapidly evolving field.Comment: Work in progress. Github: https://github.com/lxtGH/Awesome-Segmenation-With-Transforme

SMPLer-X: Scaling Up Expressive Human Pose and Shape Estimation

Expressive human pose and shape estimation (EHPS) unifies body, hands, and face motion capture with numerous applications. Despite encouraging progress, current state-of-the-art methods still depend largely on a confined set of training datasets. In this work, we investigate scaling up EHPS towards the first generalist foundation model (dubbed SMPLer-X), with up to ViT-Huge as the backbone and training with up to 4.5M instances from diverse data sources. With big data and the large model, SMPLer-X exhibits strong performance across diverse test benchmarks and excellent transferability to even unseen environments. 1) For the data scaling, we perform a systematic investigation on 32 EHPS datasets, including a wide range of scenarios that a model trained on any single dataset cannot handle. More importantly, capitalizing on insights obtained from the extensive benchmarking process, we optimize our training scheme and select datasets that lead to a significant leap in EHPS capabilities. 2) For the model scaling, we take advantage of vision transformers to study the scaling law of model sizes in EHPS. Moreover, our finetuning strategy turn SMPLer-X into specialist models, allowing them to achieve further performance boosts. Notably, our foundation model SMPLer-X consistently delivers state-of-the-art results on seven benchmarks such as AGORA (107.2 mm NMVE), UBody (57.4 mm PVE), EgoBody (63.6 mm PVE), and EHF (62.3 mm PVE without finetuning). Homepage: https://caizhongang.github.io/projects/SMPLer-X/Comment: Homepage: https://caizhongang.github.io/projects/SMPLer-X