Comparison of Driver Brake Reaction Times to Multimodal Rear-end Collision Warnings

This study examined the effectiveness of rear-end collision warnings presented in different sensory modalities as a function of warning timing in a driving simulator. Drivers experienced four warning conditions: no warning, visual, auditory, and tactile. The warnings activated when the time-to-collision (TTC) reached a critical value of 3.0 or 5.0 s TTC. Driver reaction time (RT) was captured from the time the driver crossed the warning activation threshold to brake initiation. Mean driver RT data showed that the tactile warning significantly outperformed the visual warning, providing support for tactile displays as effective rear-end collision warnings

Allosteric Communication Occurs via Networks of Tertiary and Quaternary Motions in Proteins

Allosteric proteins bind an effector molecule at one site resulting in a functional change at a second site. We hypothesize that allosteric communication in proteins relies upon networks of quaternary (collective, rigid-body) and tertiary (residue–residue contact) motions. We argue that cyclic topology of these networks is necessary for allosteric communication. An automated algorithm identifies rigid bodies from the displacement between the inactive and the active structures and constructs “quaternary networks” from these rigid bodies and the substrate and effector ligands. We then integrate quaternary networks with a coarse-grained representation of contact rearrangements to form “global communication networks” (GCNs). The GCN reveals allosteric communication among all substrate and effector sites in 15 of 18 multidomain and multimeric proteins, while tertiary and quaternary networks exhibit such communication in only 4 and 3 of these proteins, respectively. Furthermore, in 7 of the 15 proteins connected by the GCN, 50% or more of the substrate-effector paths via the GCN are “interdependent” paths that do not exist via either the tertiary or the quaternary network. Substrate-effector “pathways” typically are not linear but rather consist of polycyclic networks of rigid bodies and clusters of rearranging residue contacts. These results argue for broad applicability of allosteric communication based on structural changes and demonstrate the utility of the GCN. Global communication networks may inform a variety of experiments on allosteric proteins as well as the design of allostery into non-allosteric proteins

ECONOMICS OF VARIABLE RATE NEMATICIDE FOR SUGAR BEETS

The benefit of applying fumigant for control of the sugar beet nematode on a variable versus uniform rate basis is examined. Compared to fumigating an entire filed at a constant full-label rate, varialbe rate application provides a savings ranging from $31/ac (heavily infested field) to$69/ac (lightly infested field).Crop Production/Industries,

Hopping maps for photosynthetic reaction centers

Photosynthetic reaction centers (PRCs) employ multiple-step tunneling (hopping) to separate electrons and holes that ultimately drive the chemistry required for metabolism. We recently developed hopping maps that can be used to interpret the rates and energetics of electron/hole hopping in three-site (donor–intermediate–acceptor) tunneling reactions, including those in PRCs. Here we analyze several key ET reactions in PRCs, including forward ET in the L-branch, and hopping that could involve thermodynamically uphill intermediates in the M-branch, which is ET-inactive in vivo. We also explore charge recombination reactions, which could involve hopping. Our hopping maps support the view that electron flow in PRCs involves strong electronic coupling between cofactors and reorganization energies that are among the lowest in biology (≤0.4 eV)

Structure-based neural network protein–carbohydrate interaction predictions at the residue level

Carbohydrates dynamically and transiently interact with proteins for cell–cell recognition, cellular differentiation, immune response, and many other cellular processes. Despite the molecular importance of these interactions, there are currently few reliable computational tools to predict potential carbohydrate-binding sites on any given protein. Here, we present two deep learning (DL) models named CArbohydrate–Protein interaction Site IdentiFier (CAPSIF) that predicts non-covalent carbohydrate-binding sites on proteins: (1) a 3D-UNet voxel-based neural network model (CAPSIF:V) and (2) an equivariant graph neural network model (CAPSIF:G). While both models outperform previous surrogate methods used for carbohydrate-binding site prediction, CAPSIF:V performs better than CAPSIF:G, achieving test Dice scores of 0.597 and 0.543 and test set Matthews correlation coefficients (MCCs) of 0.599 and 0.538, respectively. We further tested CAPSIF:V on AlphaFold2-predicted protein structures. CAPSIF:V performed equivalently on both experimentally determined structures and AlphaFold2-predicted structures. Finally, we demonstrate how CAPSIF models can be used in conjunction with local glycan-docking protocols, such as GlycanDock, to predict bound protein–carbohydrate structures

Redox properties of tyrosine and related molecules

Redox reactions of tyrosine play key roles in many biological processes, including water oxidation and DNA synthesis. We first review the redox properties of tyrosine (and other phenols) in small molecules and related polypeptides, then report work on (H20)/(Y48)-modified Pseudomonas aeruginosa azurin. The crystal structure of this protein (1.18 Å resolution) shows that H20 is strongly hydrogen bonded to Y48 (2.7–2.8 Å tyrosine-O to histidine-N distance). A firm conclusion is that proper tuning of the tyrosine potential by a proton-accepting base is critical for biological redox functions