Multiscale computation delivers organophosphorus reactivity and stereoselectivity to immunoglobulin scavengers

Quantum mechanics/molecular mechanics (QM/MM) maturation of an immunoglobulin (Ig) powered by supercomputation delivers novel functionality to this catalytic template and facilitates artificial evolution of biocatalysts. We here employ density functional theory-based (DFT-b) tight binding and funnel metadynamics to advance our earlier QM/MM maturation of A17 Ig-paraoxonase (WTIgP) as a reactibody for organophosphorus toxins. It enables regulation of biocatalytic activity for tyrosine nucleophilic attack on phosphorus. The single amino acid substitution l-Leu47Lys results in 340-fold enhanced reactivity for paraoxon. The computed ground-state complex shows substrate-induced ionization of the nucleophilic l-Tyr37, now H-bonded to l-Lys47, resulting from repositioning of l-Lys47. Multiple antibody structural homologs, selected by phenylphosphonate covalent capture, show contrasting enantioselectivities for a P-chiral phenylphosphonate toxin. That is defined by crystallographic analysis of phenylphosphonylated reaction products for antibodies A5 and WTIgP. DFT-b analysis using QM regions based on these structures identifies transition states for the favored and disfavored reactions with surprising results. This stereoselection analysis is extended by funnel metadynamics to a range of WTIgP variants whose predicted stereoselectivity is endorsed by experimental analysis. The algorithms used here offer prospects for tailored design of highly evolved, genetically encoded organophosphorus scavengers and for broader functionalities of members of the Ig superfamily, including cell surface-exposed receptors.

Multiscale Computation and Dynamic Attention in Biological and Artificial Intelligence

Biological and artificial intelligence (AI) are often defined by their capacity to achieve a hierarchy of short-term and long-term goals that require incorporating information over time and space at both local and global scales. More advanced forms of this capacity involve the adaptive modulation of integration across scales, which resolve computational inefficiency and explore-exploit dilemmas at the same time. Research in neuroscience and AI have both made progress towards understanding architectures that achieve this. Insight into biological computations come from phenomena such as decision inertia, habit formation, information search, risky choices and foraging. Across these domains, the brain is equipped with mechanisms (such as the dorsal anterior cingulate and dorsolateral prefrontal cortex) that can represent and modulate across scales, both with top-down control processes and by local to global consolidation as information progresses from sensory to prefrontal areas. Paralleling these biological architectures, progress in AI is marked by innovations in dynamic multiscale modulation, moving from recurrent and convolutional neural networks—with fixed scalings—to attention, transformers, dynamic convolutions, and consciousness priors—which modulate scale to input and increase scale breadth. The use and development of these multiscale innovations in robotic agents, game AI, and natural language processing (NLP) are pushing the boundaries of AI achievements. By juxtaposing biological and artificial intelligence, the present work underscores the critical importance of multiscale processing to general intelligence, as well as highlighting innovations and differences between the future of biological and artificial intelligence.

Navigating Uncertain Environments: Multiscale Computation in Biological and Artificial Intelligence

After significant expansion and revision of the original preprint content during peer review, the full article has passed peer review and can now be accessed (open access) at the Brain Sciences journal where it was accepted: https://www.mdpi.com/2076-3425/10/6/396 The title formally was "Navigating Uncertain Environments: Multiscale Computation in Biological and Artificial Intelligence", but is now "Multiscale Computation and Dynamic Attention in Biological and Artificial Intelligence " to reflect the changed content. We have uploaded a one page preview of the accepted revised manuscript on psyarxiv to replace the outdated preprint, follow the link above for the full article.