Efficient motif discovery in spatial trajectories using discrete Fréchet distance

National Research Foundation (NRF) Singapore under International Research Centres in Singapore Funding Initiativ

Computational Approaches to Simulation and Analysis of Large Conformational Transitions in Proteins

abstract: In a typical living cell, millions to billions of proteins—nanomachines that fluctuate and cycle among many conformational states—convert available free energy into mechanochemical work. A fundamental goal of biophysics is to ascertain how 3D protein structures encode specific functions, such as catalyzing chemical reactions or transporting nutrients into a cell. Protein dynamics span femtosecond timescales (i.e., covalent bond oscillations) to large conformational transition timescales in, and beyond, the millisecond regime (e.g., glucose transport across a phospholipid bilayer). Actual transition events are fast but rare, occurring orders of magnitude faster than typical metastable equilibrium waiting times. Equilibrium molecular dynamics (EqMD) can capture atomistic detail and solute-solvent interactions, but even microseconds of sampling attainable nowadays still falls orders of magnitude short of transition timescales, especially for large systems, rendering observations of such "rare events" difficult or effectively impossible. Advanced path-sampling methods exploit reduced physical models or biasing to produce plausible transitions while balancing accuracy and efficiency, but quantifying their accuracy relative to other numerical and experimental data has been challenging. Indeed, new horizons in elucidating protein function necessitate that present methodologies be revised to more seamlessly and quantitatively integrate a spectrum of methods, both numerical and experimental. In this dissertation, experimental and computational methods are put into perspective using the enzyme adenylate kinase (AdK) as an illustrative example. We introduce Path Similarity Analysis (PSA)—an integrative computational framework developed to quantify transition path similarity. PSA not only reliably distinguished AdK transitions by the originating method, but also traced pathway differences between two methods back to charge-charge interactions (neglected by the stereochemical model, but not the all-atom force field) in several conserved salt bridges. Cryo-electron microscopy maps of the transporter Bor1p are directly incorporated into EqMD simulations using MD flexible fitting to produce viable structural models and infer a plausible transport mechanism. Conforming to the theme of integration, a short compendium of an exploratory project—developing a hybrid atomistic-continuum method—is presented, including initial results and a novel fluctuating hydrodynamics model and corresponding numerical code.Dissertation/ThesisDoctoral Dissertation Physics 201

Hyperbolic Graph Diffusion Model

Diffusion generative models (DMs) have achieved promising results in image and graph generation. However, real-world graphs, such as social networks, molecular graphs, and traffic graphs, generally share non-Euclidean topologies and hidden hierarchies. For example, the degree distributions of graphs are mostly power-law distributions. The current latent diffusion model embeds the hierarchical data in a Euclidean space, which leads to distortions and interferes with modeling the distribution. Instead, hyperbolic space has been found to be more suitable for capturing complex hierarchical structures due to its exponential growth property. In order to simultaneously utilize the data generation capabilities of diffusion models and the ability of hyperbolic embeddings to extract latent hierarchical distributions, we propose a novel graph generation method called, Hyperbolic Graph Diffusion Model (HGDM), which consists of an auto-encoder to encode nodes into successive hyperbolic embeddings, and a DM that operates in the hyperbolic latent space. HGDM captures the crucial graph structure distributions by constructing a hyperbolic potential node space that incorporates edge information. Extensive experiments show that HGDM achieves better performance in generic graph and molecule generation benchmarks, with a $48\%$ improvement in the quality of graph generation with highly hierarchical structures.Comment: accepted by AAAI 202

Graph Neural Networks for Molecules

Graph neural networks (GNNs), which are capable of learning representations from graphical data, are naturally suitable for modeling molecular systems. This review introduces GNNs and their various applications for small organic molecules. GNNs rely on message-passing operations, a generic yet powerful framework, to update node features iteratively. Many researches design GNN architectures to effectively learn topological information of 2D molecule graphs as well as geometric information of 3D molecular systems. GNNs have been implemented in a wide variety of molecular applications, including molecular property prediction, molecular scoring and docking, molecular optimization and de novo generation, molecular dynamics simulation, etc. Besides, the review also summarizes the recent development of self-supervised learning for molecules with GNNs.Comment: A chapter for the book "Machine Learning in Molecular Sciences". 31 pages, 4 figure

Modeling, Predicting and Capturing Human Mobility

Realistic models of human mobility are critical for modern day applications, specifically for recommendation systems, resource planning and process optimization domains. Given the rapid proliferation of mobile devices equipped with Internet connectivity and GPS functionality today, aggregating large sums of individual geolocation data is feasible. The thesis focuses on methodologies to facilitate data-driven mobility modeling by drawing parallels between the inherent nature of mobility trajectories, statistical physics and information theory. On the applied side, the thesis contributions lie in leveraging the formulated mobility models to construct prediction workflows by adopting a privacy-by-design perspective. This enables end users to derive utility from location-based services while preserving their location privacy. Finally, the thesis presents several approaches to generate large-scale synthetic mobility datasets by applying machine learning approaches to facilitate experimental reproducibility

Application of molecular simulation techniques to the design of nanosystems

Nanotechnology is a multidisciplinary branch of science and technology that involves a widerange of different fields such as chemistry, materials science, physics or chemical engineeringwhose goal is the production of new functional materials and devicesthrough the control of their organization at the atomic and molecular scale.Nanotechnology has jumped from research laboratories to our daily life and today all theprogresses made in this field have been translated into direct applications in different fields being electronics and computer science and biomedicine, where the most striking advances have beendone.What differences nanotechnology from traditional chemistry and physics can be summarized inthree points: (i) Analysis and control of the matterat the atomic and molecular level focusing in individual atoms; (ii) the appearance of novel physical properties because of the nanoscopicdimensions; (iii) the possibility of generating new complex functional systems with novelproperties.Modeling and theory are becoming vital to designing and improving nanodevices. The intrinsicnature of nano and supramolecular scale that involves tens, hundreds and thousands of atomsmakes computational chemistry the perfect ally to design new devices and predict their properties. Computational chemistry provides the perfect tools to describe the electronic structureand the dynamic behavior, as well as the properties derived from them, through quantummechanics and classical mechanics formalisms.The suitability of such techniques in the design and improvement of nanodevices as well as theprediction of their properties is clearly proven throughout the four blocks in which this thesis isdivided:· Nanotubes based on natural peptide sequencesNanotubes have gained extensive interest because of their applicability in different fieldsranging from medicine to electronics. Among nanotubes, those based on natural peptidesequences taken from some natural proteins with a tubular or fibrillar motif are gaining abroad attention because of their high biocompatibility, the possibility of adding functionalitiesby tuning them and their potentiality to self-assemble. The enhancement of the ability to retain the tubular geometry of such structures can be achieved by substituting targeted amino acids located in the more flexible parts of the nanoconstruct by synthetic amino acids withlow conformational flexibility providing a larger rigidity to the overall structure.· Dendronized polymersDendronized polymers are a specific kind of macromolecule structure that consists of a linearpolymeric backbone where dendritic units are attached regularly leading to a highly branchedthree-dimensional architecture. This fact provides dendronized polymers the peculiarity of the coexistence within the same macromolecule of three topological regions: (i) the internalbackbone; (ii) the dendron region around the backbone and (iii) the external surface. Thesemolecules have a wide range of applications in different fields such as biomedical engineering, host-guest chemistry or catalysis.· Theoretical study of ð-conjugated systemsConducting polymers are polymers bearing a characteristic polyconjugated nature which makethem electronic conductors. In particular thiophene-based conducting polymers have been widely studied because of their electric and nonlinear optical properties, excellent environmentalstability and relatively low cost of production. Due to the crucial role played by the electronicstructure of these systems in their relevant properties, a good knowledge of it is a key factor todesign and improve new conducting polymers. To achieve this goal QM calculations suitperfectly to get accurate estimates of such properties.· Molecular actuators and sensors based on conducting polymersBoth experimental and computational research in nanoactuators and nanosensors are widelyreported in the literature. Among them, those based in conducting polymers are flourishingbecause of their great transport properties, electrical conductivity or rate of energy migrationwhich provide amplified sensitivity in nanosensors and a rapid response in nanoactuators. In thissense electron-rich thiophene-based oligomers and polymers combined with versatilecalix[4]arenes units are presented in the present thesis. Calix[4]arenes are synthetic macrocyclic molecules consisting of four phenol or anisole rings connected via methylene bridges that canhost different guest molecules leading to conformational rearrangement of the whole device making it useful to be employed as a sensor or actuator

Special Topics in Information Technology

This open access book presents thirteen outstanding doctoral dissertations in Information Technology from the Department of Electronics, Information and Bioengineering, Politecnico di Milano, Italy. Information Technology has always been highly interdisciplinary, as many aspects have to be considered in IT systems. The doctoral studies program in IT at Politecnico di Milano emphasizes this interdisciplinary nature, which is becoming more and more important in recent technological advances, in collaborative projects, and in the education of young researchers. Accordingly, the focus of advanced research is on pursuing a rigorous approach to specific research topics starting from a broad background in various areas of Information Technology, especially Computer Science and Engineering, Electronics, Systems and Control, and Telecommunications. Each year, more than 50 PhDs graduate from the program. This book gathers the outcomes of the thirteen best theses defended in 2020-21 and selected for the IT PhD Award. Each of the authors provides a chapter summarizing his/her findings, including an introduction, description of methods, main achievements and future work on the topic. Hence, the book provides a cutting-edge overview of the latest research trends in Information Technology at Politecnico di Milano, presented in an easy-to-read format that will also appeal to non-specialists