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"computational design"
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Computationally designed peptide macrocycle inhibitors of New Delhi metallo-β-lactamase 1
The rise of antibiotic resistance calls for new therapeutics targeting resistance factors such as the New Delhi metallo-β-lactamase 1 (NDM-1), a bacterial enzyme that degrades β-lactam antibiotics. We present structure-guided computational methods for designing peptide macrocycles built from mixtures of L- and D-amino acids that are able to bind to and inhibit targets of therapeutic interest. Our methods explicitly consider the propensity of a peptide to favor a binding-competent conformation, which we found to predict rank order of experimentally observed IC50 values across seven designed NDM-1- inhibiting peptides. We were able to determine X-ray crystal structures of three of the designed inhibitors in complex with NDM-1, and in all three the conformation of the peptide is very close to the computationally designed model. In two of the three structures, the binding mode with NDM-1 is also very similar to the design model, while in the third, we observed an alternative binding mode likely arising from internal symmetry in the shape of the design combined with flexibility of the target. Although challenges remain in robustly predicting target backbone changes, binding mode, and the effects of mutations on binding affinity, our methods for designing ordered, binding-competent macrocycles should have broad applicability to a wide range of therapeutic targets.
Journal Article
Computational trust models and machine learning
\"This book provides an introduction to computational trust models from a machine learning perspective. After reviewing traditional computational trust models, it discusses a new trend of applying formerly unused machine learning methodologies, such as supervised learning. The application of various learning algorithms, such as linear regression, matrix decomposition, and decision trees, illustrates how to translate the trust modeling problem into a (supervised) learning problem. The book also shows how novel machine learning techniques can improve the accuracy of trust assessment compared to traditional approaches\"-- Provided by publisher.
Book
Computational Design of Alloys for Energy Technologies
Considering both the threats of the energy crisis, namely soaring costs of greenhouse gas emission-producing energy and climate change, it is essential to increase the pace of material discovery and enable rapid paths for material qualification to advance clean energy technologies. In 2017, the US Department of Energy, Office of Fossil Energy and Carbon Management, launched the eXtremeMAT (XMAT) consortium of seven national laboratories to bring together state-of-the-art microstructure-based computational modeling, data science, and cutting-edge experimental tools across the National Laboratory enterprise, in conjunction with industry partnership, to accelerate development and deployment of new heat-resistant alloys. In \"Predictive crystal plasticity modeling of single crystal nickel based on first-principles calculations,\" the multiple scales that govern mechanical behavior of alloys are linked by Qin et al. using a computational approach in which elastic strains imposed during the calculation of ideal shear strength are combined with a model for the evolution of the overall dislocation network to predict hardening at larger strains in single-crystal Ni. [...]in \"Crack formation in chill block melt spinning solidification process: a comparative analysis using OpenFOAM®,\" Pagnola, Barcelo and Useche used CFD with the volume of fluid model to study bubble formation for two non-isothermal, immiscible, and compressible fluids.
Journal Article
Computational‐Design Enabled Wearable and Tunable Metamaterials via Freeform Auxetics for Magnetic Resonance Imaging
Metamaterials hold significant promise for enhancing the imaging capabilities of magnetic resonance imaging (MRI) machines as an additive technology, due to their unique ability to enhance local magnetic fields. However, despite their potential, the metamaterials reported in the context of MRI applications have often been impractical. This impracticality arises from their predominantly flat configurations and their susceptibility to shifts in resonance frequencies, preventing them from realizing their optimal performance. Here, a computational method for designing wearable and tunable metamaterials via freeform auxetics is introduced. The proposed computational‐design tools yield an approach to solving the complex circle packing problems in an interactive and efficient manner, thus facilitating the development of deployable metamaterials configured in freeform shapes. With such tools, the developed metamaterials may readily conform to a patient's knee, ankle, head, or any part of the body in need of imaging, and while ensuring an optimal resonance frequency, thereby paving the way for the widespread adoption of metamaterials in clinical MRI applications. A computational method is reported to design wearable and tunable metamaterials via freeform auxetics for magnetic resonance imaging. The proposed computational design tool offers an approach to solving complex circle packing problems in an interactive and efficient manner, thereby facilitating the design of deployable structures and the creation of mechanically tunable metamaterials configured in freeform shapes.
Journal Article
Convolutional neural network for assisting accuracy of personalized clavicle bone implant designs
The clavicle is a long bone that tends to be frequently fractured in the midshaft region. The plate and screw fixing method is mainly applied to address this issue. This study aims to construct a clavicle bone implant design with a consideration to achieve a high accuracy and high-quality surface between the plate and the clavicle surface. The computational tomography scanning (CT-scan) image series data were processed using a convolutional neural network (CNN) to classify the clavicle image. The CNN outcomes were gathered as three-dimensional (3D) volume data of clavicle bone. This 3D model was then proposed for the plate design. The CNN testing results of 97.4% for the image clavicle bones classification, whereas the prints of the 3D model from clavicle bone and its plate and screw design reveal compatibility between the bone surface and the plate surface. Overall, the CNN application to the series of CT images could ease the classification of clavicle bone images that would precisely construct the 3D model of clavicle bone and its suitable clavicle bone plate design. This study could contribute as a guideline for other bone plate areas that need to fit the patient’s bone geometry.
Journal Article
Macromolecular modeling and design in Rosetta: recent methods and frameworks
The Rosetta software for macromolecular modeling, docking and design is extensively used in laboratories worldwide. During two decades of development by a community of laboratories at more than 60 institutions, Rosetta has been continuously refactored and extended. Its advantages are its performance and interoperability between broad modeling capabilities. Here we review tools developed in the last 5 years, including over 80 methods. We discuss improvements to the score function, user interfaces and usability. Rosetta is available at
http://www.rosettacommons.org
.
This Perspective reviews tools developed over the past five years in the macromolecular modeling, docking and design software Rosetta.
Journal Article
Engineered ACE2 receptor traps potently neutralize SARS-CoV-2
An essential mechanism for severe acute respiratory syndrome coronavirus 1 (SARS-CoV-1) and severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection begins with the viral spike protein binding to the human receptor protein angiotensin-converting enzyme II (ACE2). Here, we describe a stepwise engineering approach to generate a set of affinity optimized, enzymatically inactivated ACE2 variants that potently block SARS-CoV-2 infection of cells. These optimized receptor traps tightly bind the receptor binding domain (RBD) of the viral spike protein and prevent entry into host cells. We first computationally designed the ACE2–RBD interface using a two-stage flexible protein backbone design process that improved affinity for the RBD by up to 12-fold. These designed receptor variants were affinity matured an additional 14-fold by random mutagenesis and selection using yeast surface display. The highest-affinity variant contained seven amino acid changes and bound to the RBD 170-fold more tightly than wild-type ACE2. With the addition of the natural ACE2 collectrin domain and fusion to a human immunoglobulin crystallizable fragment (Fc) domain for increased stabilization and avidity, the most optimal ACE2 receptor traps neutralized SARS-CoV-2–pseudotyped lentivirus and authentic SARS-CoV-2 virus with half-maximal inhibitory concentrations (IC50s) in the 10- to 100-ng/mL range. Engineered ACE2 receptor traps offer a promising route to fighting infections by SARS-CoV-2 and other ACE2-using coronaviruses, with the key advantage that viral resistance would also likely impair viral entry. Moreover, such traps can be predesigned for viruses with known entry receptors for faster therapeutic response without the need for neutralizing antibodies isolated from convalescent patients.
Journal Article
Optimization-based design exploration of building massing typologies—EvoMass and a typology-oriented computational design optimization method for early-stage performance-based building massing design
In the past decade, there has been an increasing recognition of the role of computational design optimization in early-stage performance-based architectural design exploration. However, it remains challenging for designers to apply such optimization-based design explorations in practice. To address this issue, this paper introduces a design tool, called EvoMass, and an associated design method that facilitates design exploration for building massing typologies in performance-based design tasks. EvoMass is capable of offering architects design options reflecting performance-related building massing typologies for the design task, without necessitating advanced computational design skills. More importantly, it can provide architects with insights into the underlying performance implications, thereby enhancing early-stage performance-based design exploration. EvoMass and its associated design method overcome the limitation in the conventional typology-first-optimization-second design procedure adopted by most existing tools, and it promotes a typology-oriented design exploration method of using computational optimization in performance-based architectural design. To demonstrate the efficacy of EvoMass, case studies derived from architectural design studio tasks, incorporating daylighting, solar exposure, and subjective design intents, and the result of a user survey are presented, which highlights how EvoMass and the performance-based design optimization and exploration can enable architects to achieve a more performance-aware design.
Journal Article
A teaching strategies model experiment for computational design thinking
This study aims to share an educational model experiment for teaching computational thinking with hands-on activities. There is a gap between today’s architectural education system and computational thinking. The exercises aim to fill this gap. In this study, conventional and computational design processes are not considered as two opposing poles, but as integrated processes and as a bridge between these processes. Starting from Gagné’s model, the learning process classification is reinterpreted, and the exercise processes are discussed in the titles of reception, expectancy, computation and semantic encoding, responding and creating alternatives. The outcome of this study will be a discussion on the first results, observations, and feedback from the students about the educational model attempted to be created.
Journal Article