A Continuum Poisson-Boltzmann Model for Membrane Channel Proteins

Membrane proteins constitute a large portion of the human proteome and perform a variety of important functions as membrane receptors, transport proteins, enzymes, signaling proteins, and more. The computational studies of membrane proteins are usually much more complicated than those of globular proteins. Here we propose a new continuum model for Poisson-Boltzmann calculations of membrane channel proteins. Major improvements over the existing continuum slab model are as follows: 1) The location and thickness of the slab model are fine-tuned based on explicit-solvent MD simulations. 2) The highly different accessibility in the membrane and water regions are addressed with a two-step, two-probe grid labeling procedure, and 3) The water pores/channels are automatically identified. The new continuum membrane model is optimized (by adjusting the membrane probe, as well as the slab thickness and center) to best reproduce the distributions of buried water molecules in the membrane region as sampled in explicit water simulations. Our optimization also shows that the widely adopted water probe of 1.4 {\AA} for globular proteins is a very reasonable default value for membrane protein simulations. It gives an overall minimum number of inconsistencies between the continuum and explicit representations of water distributions in membrane channel proteins, at least in the water accessible pore/channel regions that we focus on. Finally, we validate the new membrane model by carrying out binding affinity calculations for a potassium channel, and we observe a good agreement with experiment results.Comment: 40 pages, 6 figures, 5 table

Spin-Mediated Consciousness Theory: An Approach Based On Pan-Protopsychism

As an alternative to our original dualistic approach, we present here our spin-mediated consciousness theory based on pan-protopsychism. We postulate that consciousness is intrinsically connected to quantum mechanical spin since said spin is embedded in the microscopic structure of spacetime and may be more fundamental than spacetime itself. Thus, we theorize that consciousness emerges quantum mechanically from the collective dynamics of "protopsychic" spins under the influence of spacetime dynamics. That is, spin is the "pixel" of mind. The unity of mind is achieved by quantum entanglement of the mind-pixels. Applying these ideas to the particular structures and dynamics of the brain, we postulate that the human mind works as follows: The nuclear spin ensembles ("NSE") in both neural membranes and proteins quantum mechanically process consciousness-related information such that conscious experience emerges from the collapses of entangled quantum states of NSE under the influence of the underlying spacetime dynamics. Said information is communicated to NSE through strong spin-spin couplings by biologically available unpaired electronic spins such as those carried by rapidly diffusing oxygen molecules and neural transmitter nitric oxides that extract information from their diffusing pathways in the brain. In turn, the dynamics of NSE has effects through spin chemistry on the classical neural activities such as action potentials and receptor functions thus influencing the classical neural networks of said brain. We also present supporting evidence and make important predictions. We stress that our theory is experimentally verifiable with present technologies

Inferring Microscopic Kinetic Rates from Stationary State Distributions.

We present a principled approach for estimating the matrix of microscopic transition probabilities among states of a Markov process, given only its stationary state population distribution and a single average global kinetic observable. We adapt Maximum Caliber, a variational principle in which the path entropy is maximized over the distribution of all possible trajectories, subject to basic kinetic constraints and some average dynamical observables. We illustrate the method by computing the solvation dynamics of water molecules from molecular dynamics trajectories