Helical sulfonyl-γ-AApeptides modulating Aß oligomerization and cytotoxicity by recognizing Aß helix.

In contrast to prevalent strategies which make use of ß-sheet mimetics to block Aß fibrillar growth, in this study, we designed a series of sulfonyl-γ-AApeptide helices that targeted the crucial α-helix domain of Aß13-26 and stabilized Aß conformation to avoid forming the neurotoxic Aß oligomeric ß-sheets. Biophysical assays such as amyloid kinetics and TEM demonstrated that the Aß oligomerization and fibrillation could be greatly prevented and even reversed in the presence of sulfonyl-γ-AApeptides in a sequence-specific and dose-dependent manner. The studies based on circular dichroism, Two-dimensional nuclear magnetic resonance spectroscopy (2D-NMR) spectra unambiguously suggested that the sulfonyl-γ-AApeptide Ab-6 could bind to the central region of Aß42 and induce α-helix conformation in Aß. Additionally, Electrospray ionisation-ion mobility spectrometry-mass spectrometry (ESI-IMS-MS) was employed to rule out a colloidal mechanism of inhibitor and clearly supported the capability of Ab-6 for inhibiting the formation of Aß aggregated forms. Furthermore, Ab-6 could rescue neuroblastoma cells by eradicating Aß-mediated cytotoxicity even in the presence of pre-formed Aß aggregates. The confocal microscopy demonstrated that Ab-6 could still specifically bind Aß42 and colocalize into mitochondria in the cellular environment, suggesting the rescue of cell viability might be due to the protection of mitochondrial function otherwise impaired by Aß42 aggregation. Taken together, our studies indicated that sulfonyl-γ-AApeptides as helical peptidomimetics could direct Aß into the off-pathway helical secondary structure, thereby preventing the formation of Aß oligomerization, fibrillation and rescuing Aß induced cell cytotoxicity.

A Quantum-like Model of Interdependence for Embodied Human-Machine Teams: Reviewing the Path to Autonomy Facing Complexity and Uncertainty.

In this review, our goal is to design and test quantum-like algorithms for Artificial Intelligence (AI) in open systems to structure a human-machine team to be able to reach its maximum performance. Unlike the laboratory, in open systems, teams face complexity, uncertainty and conflict. All task domains have complexity levels-some low, and others high. Complexity in this new domain is affected by the environment and the task, which are both affected by uncertainty and conflict. We contrast individual and interdependence approaches to teams. The traditional and individual approach focuses on building teams and systems by aggregating the best available information for individuals, their thoughts, behaviors and skills. Its concepts are characterized chiefly by one-to-one relations between mind and body, a summation of disembodied individual mental and physical attributes, and degrees of freedom corresponding to the number of members in a team; however, this approach is characterized by the many researchers who have invested in it for almost a century with few results that can be generalized to human-machine interactions; by the replication crisis of today (e.g., the invalid scale for self-esteem); and by its many disembodied concepts. In contrast, our approach is based on the quantum-like nature of interdependence. It allows us theorization about the bistability of mind and body, but it poses a measurement problem and a non-factorable nature. Bistability addresses team structure and performance; the measurement problem solves the replication crisis; and the non-factorable aspect of teams reduces the degrees of freedom and the information derivable from teammates to match findings by the National Academies of Science. We review the science of teams and human-machine team research in the laboratory versus in the open field; justifications for rejecting traditional social science while supporting our approach; a fuller understanding of the complexity of teams and tasks; the mathematics involved; a review of results from our quantum-like model in the open field (e.g., tradeoffs between team structure and performance); and the path forward to advance the science of interdependence and autonomy.

Interdependent Autonomous Human-Machine Systems: The Complementarity of Fitness, Vulnerability and Evolution.

For the science of autonomous human-machine systems, traditional causal-time interpretations of reality in known contexts are sufficient for rational decisions and actions to be taken, but not for uncertain or dynamic contexts, nor for building the best teams. First, unlike game theory where the contexts are constructed for players, or machine learning where contexts must be stable, when facing uncertainty or conflict, a rational process is insufficient for decisions or actions to be taken; second, as supported by the literature, rational explanations cannot disaggregate human-machine teams. In the first case, interdependent humans facing uncertainty spontaneously engage in debate over complementary tradeoffs in a search for the best path forward, characterized by maximum entropy production (MEP); however, in the second case, signified by a reduction in structural entropy production (SEP), interdependent team structures make it rationally impossible to discern what creates better teams. In our review of evidence for SEP-MEP complementarity for teams, we found that structural redundancy for top global oil producers, replicated for top global militaries, impedes interdependence and promotes corruption. Next, using UN data for Middle Eastern North African nations plus Israel, we found that a nation's structure of education is significantly associated with MEP by the number of patents it produces; this conflicts with our earlier finding that a U.S. Air Force education in air combat maneuvering was not associated with the best performance in air combat, but air combat flight training was. These last two results exemplify that SEP-MEP interactions by the team's best members are made by orthogonal contributions. We extend our theory to find that competition between teams hinges on vulnerability, a complementary excess of SEP and reduced MEP, which generalizes to autonomous human-machine systems.

Structure-Based Ligand Design Targeting Pseudomonas aeruginosa LpxA in Lipid A Biosynthesis.

Enzymes involved in lipid A biosynthesis are promising antibacterial drug targets in Gram-negative bacteria. In this study, we use a structure-based design approach to develop a series of novel tetrazole ligands with low µM affinity for LpxA, the first enzyme in the lipid A pathway. Aided by previous structural data, X-ray crystallography, and surface plasmon resonance bioanalysis, we identify 17 hit compounds. Two of these hits were subsequently modified to optimize interactions with three regions of the LpxA active site. This strategy ultimately led to the discovery of ligand L13, which had a KD of 3.0 µM. The results reveal new chemical scaffolds as potential LpxA inhibitors, important binding features for ligand optimization, and protein conformational changes in response to ligand binding. Specifically, they show that a tetrazole ring is well-accommodated in a small cleft formed between Met169, the "hydrophobic-ruler" and His156, both of which demonstrate significant conformational flexibility. Furthermore, we find that the acyl-chain binding pocket is the most tractable region of the active site for realizing affinity gains and, along with a neighboring patch of hydrophobic residues, preferentially binds aliphatic and aromatic groups. The results presented herein provide valuable chemical and structural information for future inhibitor discovery against this important antibacterial drug target.

Use of Microscale Thermophoresis to Measure Protein-Lipid Interactions.

The ability to determine the binding affinity of lipids to proteins is an essential part of understanding protein-lipid interactions in membrane trafficking, signal transduction and cytoskeletal remodeling. Classic tools for measuring such interactions include surface plasmon resonance (SPR) and isothermal titration calorimetry (ITC). While powerful tools, these approaches have setbacks. ITC requires large amounts of purified protein as well as lipids, which can be costly and difficult to produce. Furthermore, ITC as well as SPR are very time consuming, which could add significantly to the cost of performing these experiments. One way to bypass these restrictions is to use the relatively new technique of microscale thermophoresis (MST). MST is fast and cost effective using small amounts of sample to obtain a saturation curve for a given binding event. There currently are two types of MST systems available. One type of MST requires labeling with a fluorophore in the blue or red spectrum. The second system relies on the intrinsic fluorescence of aromatic amino acids in the UV range. Both systems detect the movement of molecules in response to localized induction of heat from an infrared laser. Each approach has its advantages and disadvantages. Label-free MST can use untagged native proteins; however, many analytes, including pharmaceuticals, fluoresce in the UV range, which can interfere with determination of accurate KD values. In comparison, labeled MST allows for a greater diversity of measurable pairwise interactions utilizing fluorescently labeled probes attached to ligands with measurable absorbances in the visible range as opposed to UV, limiting the potential for interfering signals from analytes.

Quantum-Like Interdependence Theory Advances Autonomous Human-Machine Teams (A-HMTs).

As humanity grapples with the concept of autonomy for human-machine teams (A-HMTs), unresolved is the necessity for the control of autonomy that instills trust. For non-autonomous systems in states with a high degree of certainty, rational approaches exist to solve, model or control stable interactions; e.g., game theory, scale-free network theory, multi-agent systems, drone swarms. As an example, guided by artificial intelligence (AI, including machine learning, ML) or by human operators, swarms of drones have made spectacular gains in applications too numerous to list (e.g., crop management; mapping, surveillance and fire-fighting systems; weapon systems). But under states of uncertainty or where conflict exists, rational models fail, exactly where interdependence theory thrives. Large, coupled physical or information systems can also experience synergism or dysergism from interdependence. Synergistically, the best human teams are not only highly interdependent, but they also exploit interdependence to reduce uncertainty, the focus of this work-in-progress and roadmap. We have long argued that interdependence is fundamental to human autonomy in teams. But for A-HMTs, no mathematics exists to build from rational theory or social science for their design nor safe or effective operation, a severe weakness. Compared to the rational and traditional social theory, we hope to advance interdependence theory first by mapping similarities between quantum theory and our prior findings; e.g., to maintain interdependence, we previously established that boundaries reduce dysergic effects to allow teams to function (akin to blocking interference to prevent quantum decoherence). Second, we extend our prior findings with case studies to predict with interdependence theory that as uncertainty increases in non-factorable situations for humans, the duality in two-sided beliefs serves debaters who explore alternatives with tradeoffs in the search for the best path going forward. Third, applied to autonomous teams, we conclude that a machine in an A-HMT must be able to express itself to its human teammates in causal language however imperfectly.