Probing RNA recognition by human ADAR2 using a high-throughput mutagenesis method.

Adenosine deamination is one of the most prevalent post-transcriptional modifications in mRNA. In humans, ADAR1 and ADAR2 catalyze this modification and their malfunction correlates with disease. Recently our laboratory reported crystal structures of the human ADAR2 deaminase domain bound to duplex RNA revealing a protein loop that binds the RNA on the 5' side of the modification site. This 5' binding loop appears to be one contributor to substrate specificity differences between ADAR family members. In this study, we endeavored to reveal detailed structure-activity relationships in this loop to advance our understanding of RNA recognition by ADAR2. To achieve this goal, we established a high-throughput mutagenesis approach which allows rapid screening of ADAR variants in single yeast cells and provides quantitative evaluation for enzymatic activity. Using this approach, we determined the importance of specific amino acids at 19 different positions in the ADAR2 5' binding loop and revealed six residues that provide essential structural elements supporting the fold of the loop and key RNA-binding functional groups. This work provided new insight into RNA recognition by ADAR2 and established a new tool for defining structure-function relationships in ADAR reactions

Knowledge discovery for friction stir welding via data driven approaches: Part 2 – multiobjective modelling using fuzzy rule based systems

In this final part of this extensive study, a new systematic data-driven fuzzy modelling approach has been developed, taking into account both the modelling accuracy and its interpretability (transparency) as attributes. For the first time, a data-driven modelling framework has been proposed designed and implemented in order to model the intricate FSW behaviours relating to AA5083 aluminium alloy, consisting of the grain size, mechanical properties, as well as internal process properties. As a result, ‘Pareto-optimal’ predictive models have been successfully elicited which, through validations on real data for the aluminium alloy AA5083, have been shown to be accurate, transparent and generic despite the conservative number of data points used for model training and testing. Compared with analytically based methods, the proposed data-driven modelling approach provides a more effective way to construct prediction models for FSW when there is an apparent lack of fundamental process knowledge

Practopoiesis: Or how life fosters a mind

The mind is a biological phenomenon. Thus, biological principles of organization should also be the principles underlying mental operations. Practopoiesis states that the key for achieving intelligence through adaptation is an arrangement in which mechanisms laying a lower level of organization, by their operations and interaction with the environment, enable creation of mechanisms lying at a higher level of organization. When such an organizational advance of a system occurs, it is called a traverse. A case of traverse is when plasticity mechanisms (at a lower level of organization), by their operations, create a neural network anatomy (at a higher level of organization). Another case is the actual production of behavior by that network, whereby the mechanisms of neuronal activity operate to create motor actions. Practopoietic theory explains why the adaptability of a system increases with each increase in the number of traverses. With a larger number of traverses, a system can be relatively small and yet, produce a higher degree of adaptive/intelligent behavior than a system with a lower number of traverses. The present analyses indicate that the two well-known traverses-neural plasticity and neural activity-are not sufficient to explain human mental capabilities. At least one additional traverse is needed, which is named anapoiesis for its contribution in reconstructing knowledge e.g., from long-term memory into working memory. The conclusions bear implications for brain theory, the mind-body explanatory gap, and developments of artificial intelligence technologies.Comment: Revised version in response to reviewer comment

Structural basis for cell surface patterning through NetrinG-NGL interactions

Brain wiring depends on cells making highly localized and selective connections through surface protein-protein interactions, including those between NetrinGs and NetrinG ligands (NGLs). The NetrinGs are members of the structurally uncharacterized netrin family. We present a comprehensive crystallographic analysis comprising NetrinG1-NGL1 and NetrinG2-NGL2 complexes, unliganded NetrinG2 and NGL3. Cognate NetrinG-NGL interactions depend on three specificity-conferring NetrinG loops, clasped tightly by matching NGL surfaces. We engineered these NGL surfaces to implant custom-made affinities for NetrinG1 and NetrinG2. In a cellular patterning assay, we demonstrate that NetrinG-binding selectivity can direct the sorting of a mixed population of NGLs into discrete cell surface subdomains. These results provide a molecular model for selectivity-based patterning in a neuronal recognition system, dysregulation of which is associated with severe neuropsychological disorders

Plasma Membrane-Associated Restriction Factors and Their Counteraction by HIV-1 Accessory Proteins.

The plasma membrane is a site of conflict between host defenses and many viruses. One aspect of this conflict is the host's attempt to eliminate infected cells using innate and adaptive cell-mediated immune mechanisms that recognize features of the plasma membrane characteristic of viral infection. Another is the expression of plasma membrane-associated proteins, so-called restriction factors, which inhibit enveloped virions directly. HIV-1 encodes two countermeasures to these host defenses: The membrane-associated accessory proteins Vpu and Nef. In addition to inhibiting cell-mediated immune-surveillance, Vpu and Nef counteract membrane-associated restriction factors. These include BST-2, which traps newly formed virions at the plasma membrane unless counteracted by Vpu, and SERINC5, which decreases the infectivity of virions unless counteracted by Nef. Here we review key features of these two antiviral proteins, and we review Vpu and Nef, which deplete them from the plasma membrane by co-opting specific cellular proteins and pathways of membrane trafficking and protein-degradation. We also discuss other plasma membrane proteins modulated by HIV-1, particularly CD4, which, if not opposed in infected cells by Vpu and Nef, inhibits viral infectivity and increases the sensitivity of the viral envelope glycoprotein to host immunity

A Three Monoclonal Antibody Combination Potently Neutralizes Multiple Botulinum Neurotoxin Serotype E Subtypes.

Human botulism is most commonly caused by botulinum neurotoxin (BoNT) serotypes A, B, and E. For this work, we sought to develop a human monoclonal antibody (mAb)-based antitoxin capable of binding and neutralizing multiple subtypes of BoNT/E. Libraries of yeast-displayed single chain Fv (scFv) antibodies were created from the heavy and light chain variable region genes of humans immunized with pentavalent-toxoid- and BoNT/E-binding scFv isolated by Fluorescence-Activated Cell Sorting (FACS). A total of 10 scFv were isolated that bound one or more BoNT/E subtypes with nanomolar-level equilibrium dissociation constants (KD). By diversifying the V-regions of the lead mAbs and selecting for cross-reactivity, we generated three scFv that bound all four BoNT/E subtypes tested at three non-overlapping epitopes. The scFvs were converted to IgG that had KD values for the different BoNT/E subtypes ranging from 9.7 nM to 2.28 pM. An equimolar combination of the three mAbs was able to potently neutralize BoNT/E1, BoNT/E3, and BoNT/E4 in a mouse neutralization assay. The mAbs have potential utility as therapeutics and as diagnostics capable of recognizing multiple BoNT/E subtypes. A derivative of the three-antibody combination (NTM-1633) is in pre-clinical development with an investigational new drug (IND) application filing expected in 2018

Evaluating Visual Conversational Agents via Cooperative Human-AI Games

As AI continues to advance, human-AI teams are inevitable. However, progress in AI is routinely measured in isolation, without a human in the loop. It is crucial to benchmark progress in AI, not just in isolation, but also in terms of how it translates to helping humans perform certain tasks, i.e., the performance of human-AI teams. In this work, we design a cooperative game - GuessWhich - to measure human-AI team performance in the specific context of the AI being a visual conversational agent. GuessWhich involves live interaction between the human and the AI. The AI, which we call ALICE, is provided an image which is unseen by the human. Following a brief description of the image, the human questions ALICE about this secret image to identify it from a fixed pool of images. We measure performance of the human-ALICE team by the number of guesses it takes the human to correctly identify the secret image after a fixed number of dialog rounds with ALICE. We compare performance of the human-ALICE teams for two versions of ALICE. Our human studies suggest a counterintuitive trend - that while AI literature shows that one version outperforms the other when paired with an AI questioner bot, we find that this improvement in AI-AI performance does not translate to improved human-AI performance. This suggests a mismatch between benchmarking of AI in isolation and in the context of human-AI teams.Comment: HCOMP 201