We have simulated an odor ligand's dynamic behavior in the binding region of an olfactory receptor (OR). Our short timescale computational studies (up to 200 ps) have helped identify unprecedented postdocking ligand behavior of ligands. From in vacuo molecular dynamics simulations of interactions between models of rat OR I7 and 10 aldehyde ligands, we have identified a dissociative pathway along which the ligand exits and enters the OR-binding pocket--a transit event. The ligand's transit through the receptor's binding region may mark the beginning of a signal transduction cascade leading to odor recognition. We have graphically traced the rotameric changes in key OR amino acid side chains during the transit. Our results have helped substantiate or refute previously held notions of amino acid contribution to ligand stability in the binding pocket. Our observations of ligand activity when compared to those of experimental (electroolfactogram response) OR-activation studies provide a view to predicting the stability of ligands in the binding pocket as a precursor to OR activation by the ligand.

Language for number is an important case study of the relationship between language and cognition because the mechanisms of non-verbal numerical cognition are well-understood. When the Pirahã (an Amazonian hunter-gatherer tribe who have no exact number words) are tested in non-verbal numerical tasks, they are able to perform one-to-one matching tasks but make errors in more difficult tasks. Their pattern of errors suggests that they are using analog magnitude estimation, an evolutionarily-and developmentally-conserved mechanism for estimating quantities. Here we show that English-speaking participants rely on the same mechanisms when verbal number representations are unavailable due to verbal interference. Followup experiments demonstrate that the effects of verbal interference are primarily manifest during encoding of quantity information, and-using a new procedure for matching difficulty of interference tasks for individual participants-that the effects are restricted to verbal interference. These results are consistent with the hypothesis that number words are used online to encode, store, and manipulate numerical information. This linguistic strategy complements, rather than altering or replacing, non-verbal representations.

Providing a rationale that associates a chemical structure of an odorant to its induced perception has been sought for a long time. To achieve this, a detailed atomic structure of both the odorant and the olfactory receptor must be known. State-of-the-art techniques to model the 3D structure of an olfactory receptor in complex with various odorants are presented here. These range from sequence alignment with known structures to molecular dynamics simulations in a realistic environment.

Olfactory receptors (ORs) are a type of GTP-binding protein-coupled receptor (GPCR). These receptors are responsible for mediating the sense of smell through their interaction with odor ligands. OR-odorant interactions marks the first step in the process that leads to olfaction. Computational studies on model OR structures can generate focused and novel hypotheses for further bench investigation by providing a view of these interactions at the molecular level beyond inferences that are drawn merely from static docking. Here we have shown the specific advantages of simulating the dynamic environment associated with OR-odorant interactions. We present a rigorous protocol which ranges from the creation of a computationally derived model of an olfactory receptor to simulating the interactions between an OR and an odorant molecule. Given the ubiquitous occurrence of GPCRs in the membranes of cells, we anticipate that our OR-developed methodology will serve as a model for the computational structural biology of all GPCRs.

Human olfactory receptor, hOR17-210, is identified as a pseudogene in the human genome. Experimental data has shown however, that the gene product of frame-shifted, cloned hOR17-210 cDNA was able to bind an odorant-binding protein and is narrowly tuned for excitation by cyclic ketones. Supported by experimental results, we used the bioinformatics methods of sequence analysis (genome-wide and pair-wise), computational protein modeling and docking, to show that functionality in this receptor is retained due to sequence-structure features not previously observed in mammalian ORs. This receptor does not possess the first two transmembrane helical domains (of seven typically seen in GPCRs). It however, possesses an additional TM that has not been observed in other human olfactory receptors. By incorporating these novel structural features, we created two putative models for this receptor. We also docked odor ligands that were experimentally showntobind hOR17-210. We show how and why structural modifications of OR17-210 do not hinder this receptor's functionality. Our studies reveal that novel gene rearrangements that result in sequence and structural diversity may have a bearing on OR and GPCR function and evolution.

Using computational methods, which allow mechanistic insights at a molecular level, we explored the olfactory receptor (OR)-odor interactions for 2 mouse ORs, S79 and S86. Both ORs have been previously experimentally, functionally characterized. The odors used were mostly carboxylic acids, which differed in chain length, substituents on the primary carbon atom-chain and degree of unsaturation. These odors elicited varied activation responses from both ORs. Our studies revealed that both receptors have 2 distinct binding sites. Preferential binding in 1 of the 2 sites is correlated with OR activation. The activating odorants: nonanedioic acid, heptanoic acid, and octanoic acid for OR S79 and nonanoic acid for OR S86 preferentially bind in the region bound by transmembranes (TMs [helical domains]) III, IV, V, and VI. The non excitatory odorants heptanol for S79 and heptanoic acid for S86 showed a greater likelihood of binding in the region bound by TMs I, II, III, and VII. Nanosecond-scale molecular dynamics simulations of the physiologically relevant conditions of docked OR-odorant complexes enabled us to quantitatively assess the roles of individual OR amino acids in odor binding. Amino acid-odorant contact maps and distance determinations over the course of the simulations lend support to our conclusions.

This article presents the latest developments in neuroscience information dissemination through the SenseLab suite of databases: NeuronDB, CellPropDB, ORDB, OdorDB, OdorMapDB, ModelDB and BrainPharm. These databases include information related to: (i) neuronal membrane properties and neuronal models, and (ii) genetics, genomics, proteomics and imaging studies of the olfactory system. We describe here: the new features for each database, the evolution of SenseLab's unifying database architecture and instances of SenseLab database interoperation with other neuroscience online resources.

This paper presents a semi-analytical method of suppressing acoustic scattering using reinforcement learning (RL) algorithms. We give a RL agent control over design parameters of a planar configuration of cylindrical scatterers in water. These design parameters control the position and radius of the scatterers. As these cylinders encounter an incident acoustic wave, the scattering pattern is described by a function called total scattering cross section (TSCS). Through evaluating the gradients of TSCS and other information about the state of the configuration, the RL agent perturbatively adjusts design parameters, considering multiple scattering between the scatterers. As each adjustment is made, the RL agent receives a reward negatively proportional to the root mean square of the TSCS across a range of wavenumbers. Through maximizing its reward per episode, the agent discovers designs with low scattering. Specifically, the double deep Q-learning network and the deep deterministic policy gradient algorithms are employed in our models. Designs discovered by the RL algorithms performed well when compared to a state-of-the-art optimization algorithm using fmincon.