Leveraging Artificial Intelligence to Expedite Antibody Design and Enhance Antibody-Antigen Interactions

This perspective sheds light on the transformative impact of recent computational advancements in the field of protein therapeutics, with a particular focus on the design and development of antibodies. Cutting-edge computational methods have revolutionized our understanding of protein-protein interactions (PPIs), enhancing the efficacy of protein therapeutics in preclinical and clinical settings. Central to these advancements is the application of machine learning and deep learning, which offers unprecedented insights into the intricate mechanisms of PPIs and facilitates precise control over protein functions. Despite these advancements, the complex structural nuances of antibodies pose ongoing challenges in their design and optimization. Our review provides a comprehensive exploration of the latest deep learning approaches, including language models and diffusion techniques, and their role in surmounting these challenges. We also present a critical analysis of these methods, offering insights to drive further progress in this rapidly evolving field. The paper includes practical recommendations for the application of these computational techniques, supplemented with independent benchmark studies. These studies focus on key performance metrics such as accuracy and the ease of program execution, providing a valuable resource for researchers engaged in antibody design and development. Through this detailed perspective, we aim to contribute to the advancement of antibody design, equipping researchers with the tools and knowledge to navigate the complexities of this field.

Build-a-bug workshop: Using microbial-host interactions and synthetic biology tools to create cancer therapies

Many systemically administered cancer therapies exhibit dose-limiting toxicities that reduce their effectiveness. To increase efficacy, bacterial delivery platforms have been developed that improve safety and prolong treatment. Bacteria are a unique class of therapy that selectively colonizes most solid tumors. As delivery vehicles, bacteria have been genetically modified to express a range of therapies that match multiple cancer indications. In this review, we describe a modular "build-a-bug" method that focuses on five design characteristics: bacterial strain (chassis), therapeutic compound, delivery method, immune-modulating features, and genetic control circuits. We emphasize how fundamental research into gut microbe pathogenesis has created safe bacterial therapies, some of which have entered clinical trials. The genomes of gut microbes are fertile grounds for discovery of components to improve delivery and modulate host immune responses. Future work coupling these delivery vehicles with insights from gut microbes could lead to the next generation of microbial cancer therapy.

Clinical and biological consequences of respiratory syncytial virus genetic diversity

Respiratory syncytial virus (RSV) is one of the most common etiological agents of global acute respiratory tract infections with a disproportionate burden among infants, individuals over the age of 65, and immunocompromised populations. The two major subtypes of RSV (A and B) co-circulate with a predominance of either group during different epidemic seasons, with frequently emerging genotypes due to RSV's high genetic variability. Global surveillance systems have improved our understanding of seasonality, disease burden, and genomic evolution of RSV through genotyping by sequencing of attachment (G) glycoprotein. However, the integration of these systems into international infrastructures is in its infancy, resulting in a relatively low number (~2200) of publicly available RSV genomes. These limitations in surveillance hinder our ability to contextualize RSV evolution past current canonical attachment glycoprotein (G)-oriented understanding, thus resulting in gaps in understanding of how genetic diversity can play a role in clinical outcome, therapeutic efficacy, and the host immune response. Furthermore, utilizing emerging RSV genotype information from surveillance and testing the impact of viral evolution using molecular techniques allows us to establish causation between the clinical and biological consequences of arising genotypes, which subsequently aids in informed vaccine design and future vaccination strategy. In this review, we aim to discuss the findings from current molecular surveillance efforts and the gaps in knowledge surrounding the consequence of RSV genetic diversity on disease severity, therapeutic efficacy, and RSV-host interactions.

Space and Well-Being in High Security Environments

Research into the spatial dimensions of deprivation of liberty and psychiatric hospitalization has a long and complex tradition. In this context, the increasing numbers of prisoners and patients in forensic hospitals have impressively shown how difficult it is to ensure security, therapy and rehabilitation when space is scarce or not well-suited. In this narrative review, we present the main findings of recent lines of research on spaces in prisons and forensic psychiatric wards, with particular attention to the links between overcrowding in prisons and secure forensic psychiatric hospitals and violence, the foundations of prison and hospital architecture, and on how the design of spaces in prisons and hospitals can influence well-being. We assess and discuss these findings in the context of the current debate on how well-being in secure spaces can support the achievement of rehabilitation goals even in overcrowded institutions.

Artificial Intelligence for Autonomous Molecular Design: A Perspective

Domain-aware artificial intelligence has been increasingly adopted in recent years to expedite molecular design in various applications, including drug design and discovery. Recent advances in areas such as physics-informed machine learning and reasoning, software engineering, high-end hardware development, and computing infrastructures are providing opportunities to build scalable and explainable AI molecular discovery systems. This could improve a design hypothesis through feedback analysis, data integration that can provide a basis for the introduction of end-to-end automation for compound discovery and optimization, and enable more intelligent searches of chemical space. Several state-of-the-art ML architectures are predominantly and independently used for predicting the properties of small molecules, their high throughput synthesis, and screening, iteratively identifying and optimizing lead therapeutic candidates. However, such deep learning and ML approaches also raise considerable conceptual, technical, scalability, and end-to-end error quantification challenges, as well as skepticism about the current AI hype to build automated tools. To this end, synergistically and intelligently using these individual components along with robust quantum physics-based molecular representation and data generation tools in a closed-loop holds enormous promise for accelerated therapeutic design to critically analyze the opportunities and challenges for their more widespread application. This article aims to identify the most recent technology and breakthrough achieved by each of the components and discusses how such autonomous AI and ML workflows can be integrated to radically accelerate the protein target or disease model-based probe design that can be iteratively validated experimentally. Taken together, this could significantly reduce the timeline for end-to-end therapeutic discovery and optimization upon the arrival of any novel zoonotic transmission event. Our article serves as a guide for medicinal, computational chemistry and biology, analytical chemistry, and the ML community to practice autonomous molecular design in precision medicine and drug discovery.

The Relationship between Sustainable Built Environment, Art Therapy and Therapeutic Design in Promoting Health and Well-Being

At present, a smart city from the perspective of the United Nations Sustainable Development Goals (SDGs) emphasizes the importance of providing citizens with promising health and well-being. However, with the continuous impact of coronavirus disease 2019 (COVID-19) and the increase of city population, the health of citizens is facing new challenges. Therefore, this paper aims to assess the relationship between building, environment, landscape design, art therapy (AT), and therapeutic design (TD) in promoting health within the context of sustainable development. It also summarizes the existing applied research areas and potential value of TD that informs future research. This paper adopts the macro-quantitative and micro-qualitative research methods of bibliometric analysis. The results show that: the built environment and AT are related to sustainable development, and closely associated with health and well-being; the application of TD in the environment, architecture, space, and landscape fields promotes the realization of SDGs and lays the foundation for integrating digital technologies such as Building Information Modeling (BIM) into the design process to potentially solve the challenges of TD; and the principle of TD can consider design elements and characteristics from based on people's health needs to better promote human health and well-being.

An Achilles' heel in an amyloidogenic protein and its repair: insulin fibrillation and therapeutic design

Insulin fibrillation provides a model for a broad class of amyloidogenic diseases. Conformational distortion of the native monomer leads to aggregation-coupled misfolding. Whereas beta-cells are protected from proteotoxicity by hexamer assembly, fibrillation limits the storage and use of insulin at elevated temperatures. Here, we have investigated conformational distortions of an engineered insulin monomer in relation to the structure of an insulin fibril. Anomalous (13)C NMR chemical shifts and rapid (15)N-detected (1)H-(2)H amide-proton exchange were observed in one of the three classical alpha-helices (residues A1-A8) of the hormone, suggesting a conformational equilibrium between locally folded and unfolded A-chain segments. Whereas hexamer assembly resolves these anomalies in accordance with its protective role, solid-state (13)C NMR studies suggest that the A-chain segment participates in a fibril-specific beta-sheet. Accordingly, we investigated whether helicogenic substitutions in the A1-A8 segment might delay fibrillation. Simultaneous substitution of three beta-branched residues (Ile(A2) --> Leu, Val(A3) --> Leu, and Thr(A8) --> His) yielded an analog with reduced thermodynamic stability but marked resistance to fibrillation. Whereas amide-proton exchange in the A1-A8 segment remained rapid, (13)Calpha chemical shifts exhibited a more helical pattern. This analog is essentially without activity, however, as Ile(A2) and Val(A3) define conserved receptor contacts. To obtain active analogs, substitutions were restricted to A8. These analogs exhibit high receptor-binding affinity; representative potency in a rodent model of diabetes mellitus was similar to wild-type insulin. Although (13)Calpha chemical shifts remain anomalous, significant protection from fibrillation is retained. Together, our studies define an "Achilles' heel" in a globular protein whose repair may enhance the stability of pharmaceutical formulations and broaden their therapeutic deployment in the developing world.