Modulation of the Disordered Conformational Ensembles of the p53 Transactivation Domain by Cancer-Associated Mutations

Citation: Ganguly, D., & Chen, J. H. (2015). Modulation of the Disordered Conformational Ensembles of the p53 Transactivation Domain by Cancer-Associated Mutations. Plos Computational Biology, 11(4), 19. doi:10.1371/journal.pcbi.1004247Intrinsically disordered proteins (IDPs) are frequently associated with human diseases such as cancers, and about one-fourth of disease-associated missense mutations have been mapped into predicted disordered regions. Understanding how these mutations affect the structure-function relationship of IDPs is a formidable task that requires detailed characterization of the disordered conformational ensembles. Implicit solvent coupled with enhanced sampling has been proposed to provide a balance between accuracy and efficiency necessary for systematic and comparative assessments of the effects of mutations as well as post-translational modifications on IDP structure and interaction. Here, we utilize a recently developed replica exchange with guided annealing enhanced sampling technique to calculate well-converged atomistic conformational ensembles of the intrinsically disordered transactivation domain (TAD) of tumor suppressor p53 and several cancer-associated mutants in implicit solvent. The simulations are critically assessed by quantitative comparisons with several types of experimental data that provide structural information on both secondary and tertiary levels. The results show that the calculated ensembles reproduce local structural features of wild-type p53-TAD and the effects of K24N mutation quantitatively. On the tertiary level, the simulated ensembles are overly compact, even though they appear to recapitulate the overall features of transient long-range contacts qualitatively. A key finding is that, while p53-TAD and its cancer mutants sample a similar set of conformational states, cancer mutants could introduce both local and long-range structural modulations to potentially perturb the balance of p53 binding to various regulatory proteins and further alter how this balance is regulated by multisite phosphorylation of p53-TAD. The current study clearly demonstrates the promise of atomistic simulations for detailed characterization of IDP conformations, and at the same time reveals important limitations in the current implicit solvent protein force field that must be sufficiently addressed for reliable description of long-range structural features of the disordered ensembles

Simulation of 3D Model, Shape, and Appearance Aging by Physical, Chemical, Biological, Environmental, and Weathering Effects

Physical, chemical, biological, environmental, and weathering effects produce a range of 3D model, shape, and appearance changes. Time introduces an assortment of aging, weathering, and decay processes such as dust, mold, patina, and fractures. These time-varying imperfections provide the viewer with important visual cues for realism and age. Existing approaches that create realistic aging effects still require an excessive amount of time and effort by extremely skilled artists to tediously hand fashion blemishes or simulate simple procedural rules. Most techniques do not scale well to large virtual environments. These limitations have prevented widespread utilization of many aging and weathering algorithms. We introduce a novel method for geometrically and visually simulating these processes in order to create visually realistic scenes. This work proposes the ``mu-ton system, a framework for scattering numerous mu-ton particles throughout an environment to mutate and age the world. We take a point based representation to discretize both the decay effects and the underlying geometry. The mu-ton particles simulate interactions between multiple phenomena. This mutation process changes both the physical properties of the external surface layer and the internal volume substrate. The mutation may add or subtract imperfections into the environment as objects age. First we review related work in aging and weathering, and illustrate the limitations of the current data-driven and physically based approaches. We provide a taxonomy of aging processes. We then describe the structure for our ``mu-ton framework, and we provide the user a short tutorial how to setup different effects. The first application of the ``mu-ton system focuses on inorganic aging and decay. We demonstrate changing material properties on a variety of objects, and simulate their transformation. We show the application of our system aging a simple city alley on different materials. The second application of the ``mu-ton system focuses organic aging. We provide details on simulating a variety of growth processes. We then evaluate and analyze the ``mu-ton framework and compare our results with ``gamma-ton tracing. Finally, we outline the contributions this thesis provides to computer-based aging and weathering simulation

How Reinforcement Learning can improve Video Games Development: Dreamer and P2E Algorithms in the SheepRL Framework

Artificial Intelligence (AI) in video games is along-standing research area. It studies how to use AI technologies to achieve human-level performance when playing games. For years now, ReinforcementLearning (RL) algorithms have outperformed the best human players in most video games. For this reason, it is interesting to investigate whether RL can still be used in the video game industry or whether the relationship between RL and the video game industry should remain purely academic. This work focuses on two primary objectives within the video game industry: (i) Testing and Debugging: how RL can be exploited in order to uncover latent bugs, assess game difficulty, and refine the design of the video game. (ii) Non-Playable Characters (NPC) Creation and Generalization: is RL the best strategy to efficiently create NPCs or the RL algorithms have become too advanced? This thesis explores the feasibility of using the state-of-the-art Dreamer algorithm in automated testing and NPCs creation for video games; in addition, it proposes SheepRL a scalable open source framework for running experiments in a distributed manner

Sampling-Based Exploration Strategies for Mobile Robot Autonomy

A novel, sampling-based exploration strategy is introduced for Unmanned Ground Vehicles (UGV) to efficiently map large GPS-deprived underground environments. It is compared to state-of-the-art approaches and performs on a similar level, while it is not designed for a specific robot or sensor configuration like the other approaches. The introduced exploration strategy, which is called Random-Sampling-Based Next-Best View Exploration (RNE), uses a Rapidly-exploring Random Graph (RRG) to find possible view points in an area around the robot. They are compared with a computation-efficient Sparse Ray Polling (SRP) in a voxel grid to find the next-best view for the exploration. Each node in the exploration graph built with RRG is evaluated regarding the ability of the UGV to traverse it, which is derived from an occupancy grid map. It is also used to create a topology-based graph where nodes are placed centrally to reduce the risk of collisions and increase the amount of observable space. Nodes that fall outside the local exploration area are stored in a global graph and are connected with a Traveling Salesman Problem solver to explore them later

Safe Multi-Agent Reinforcement Learning with Quantitatively Verified Constraints

Multi-agent reinforcement learning is a machine learning technique that involves multiple agents attempting to solve sequential decision-making problems. This learn- ing is driven by objectives and failures modelled as positive numerical rewards and negative numerical punishments, respectively. These multi-agent systems explore shared environments in order to find the highest cumulative reward for the sequential decision-making problem. Multi-agent reinforcement learning within autonomous systems has become a prominent research area with many examples of success and potential applications. However, the safety-critical nature of many of these potential applications is currently underexplored—and under-supported. Reinforcement learn- ing, being a stochastic process, is unpredictable, meaning there are no assurances that these systems will not harm themselves, other expensive equipment, or humans. This thesis introduces Assured Multi-Agent Reinforcement Learning (AMARL) to mitigate these issues. This approach constrains the actions of learning systems during and after a learning process. Unlike previous multi-agent reinforcement learning methods, AMARL synthesises constraints through the formal verification of abstracted multi- agent Markov decision processes that model the environment’s functional and safety aspects. Learned policies guided by these constraints are guaranteed to satisfy strict functional and safety requirements and are Pareto-optimal with respect to a set of op- timisation objectives. Two AMARL extensions are also introduced in the thesis. Firstly, the thesis presents a Partial Policy Reuse method that allows the use of previously learned knowledge to reduce AMARL learning time significantly when initial models are inaccurate. Secondly, an Adaptive Constraints method is introduced to enable agents to adapt to environmental changes by constraining their learning through a procedure that follows the styling of monitoring, analysis, planning, and execution during runtime. AMARL and its extensions are evaluated within three case studies from different navigation-based domains and shown to produce policies that meet strict safety and functional requirements

Contributions to Localization, Mapping and Navigation in Mobile Robotics

This thesis focuses on the problem of enabling mobile robots to autonomously build world models of their environments and to employ them as a reference to self–localization and navigation. For mobile robots to become truly autonomous and useful, they must be able of reliably moving towards the locations required by their tasks. This simple requirement gives raise to countless problems that have populated research in the mobile robotics community for the last two decades. Among these issues, two of the most relevant are: (i) secure autonomous navigation, that is, moving to a target avoiding collisions and (ii) the employment of an adequate world model for robot self-referencing within the environment and also for locating places of interest. The present thesis introduces several contributions to both research fields. Among the contributions of this thesis we find a novel approach to extend SLAM to large-scale scenarios by means of a seamless integration of geometric and topological map building in a probabilistic framework that estimates the hybrid metric-topological (HMT) state space of the robot path. The proposed framework unifies the research areas of topological mapping, reasoning on topological maps and metric SLAM, providing also a natural integration of SLAM and the “robot awakening” problem. Other contributions of this thesis cover a wide variety of topics, such as optimal estimation in particle filters, a new probabilistic observation model for laser scanners based on consensus theory, a novel measure of the uncertainty in grid mapping, an efficient method for range-only SLAM, a grounded method for partitioning large maps into submaps, a multi-hypotheses approach to grid map matching, and a mathematical framework for extending simple obstacle avoidance methods to realistic robots