Identification of an epitope in the substance P receptor important for recognition of the common carboxyl-terminal tachykinin sequence.

The substance P receptor and the anti-substance P antibody NC1 share the ability to bind to the COOH terminus of substance P. Sequence analysis identified a direct noninterrupted homology of 5 residues in the two molecules. Replacement of Gly166 and Tyr167 in this epitope of the substance P receptor by the corresponding substance K receptor amino acids (Cys and Phe) increases the affinity toward substance P 2-fold and toward substance K and neurokinin B 11- and 21-fold, respectively. A significantly larger effect of the mutation is observed for the hexapeptides of substance P and substance K which show a mutation-induced increase in binding energy of more than 2 kcal/mol. Hence, the NH2 terminus of substance P and, to a lesser extent, of substance K masks the effect of the mutation. I conclude that the epitope is important for recognition of the common COOH terminus of the tachykinins and for preservation of selectivity. The data furthermore suggest that formation of the peptide-receptor complex occurs through a composite set of interactions which are not adequately described by the two-site/no cooperativity "address-message" model

On Adaptive Stochastic Optimization for Streaming Data: A Newton's Method with O(dN) Operations

Stochastic optimization methods encounter new challenges in the realm of streaming, characterized by a continuous flow of large, high-dimensional data. While first-order methods, like stochastic gradient descent, are the natural choice, they often struggle with ill-conditioned problems. In contrast, second-order methods, such as Newton's methods, offer a potential solution, but their computational demands render them impractical. This paper introduces adaptive stochastic optimization methods that bridge the gap between addressing ill-conditioned problems while functioning in a streaming context. Notably, we present an adaptive inversion-free Newton's method with a computational complexity matching that of first-order methods, $\mathcal{O}(dN)$, where $d$ represents the number of dimensions/features, and $N$ the number of data. Theoretical analysis confirms their asymptotic efficiency, and empirical evidence demonstrates their effectiveness, especially in scenarios involving complex covariance structures and challenging initializations. In particular, our adaptive Newton's methods outperform existing methods, while maintaining favorable computational efficiency

BOF-UCB: A Bayesian-Optimistic Frequentist Algorithm for Non-Stationary Contextual Bandits

We propose a novel Bayesian-Optimistic Frequentist Upper Confidence Bound (BOF-UCB) algorithm for stochastic contextual linear bandits in non-stationary environments. This unique combination of Bayesian and frequentist principles enhances adaptability and performance in dynamic settings. The BOF-UCB algorithm utilizes sequential Bayesian updates to infer the posterior distribution of the unknown regression parameter, and subsequently employs a frequentist approach to compute the Upper Confidence Bound (UCB) by maximizing the expected reward over the posterior distribution. We provide theoretical guarantees of BOF-UCB's performance and demonstrate its effectiveness in balancing exploration and exploitation on synthetic datasets and classical control tasks in a reinforcement learning setting. Our results show that BOF-UCB outperforms existing methods, making it a promising solution for sequential decision-making in non-stationary environments

A Mutation Changes Ligand Selectivity and Transmembrane Signaling Preference of the Neurokinin-1 Receptor

Abstract We studied the biochemical properties of a genetically engineered neurokinin-1 receptor (NK1R) in which two residues lying on the extracellular edge of the fourth transmembrane domain were replaced by equivalently located elements of the neurokinin-2 receptor (G166C, Y167F NK1R mutant). The mutation produced two effects. The first is enhancement of the apparent binding affinity for heterologous tachykinins (substance K and neurokinin B) and for N- or C-terminal modified analogues of substance P, but not for substance P itself, its full-length analogues, and several peptide and nonpeptide antagonists. Only two antagonists, as exceptions, were found to exhibit a diminished affinity for the mutant receptor. The second effect is a shift in NK1R preference for distinct G protein-mediated signaling pathways. NK1R-mediated phosphoinositide hydrolysis was enhanced both in transiently and permanently transfected cells, while stimulation of cAMP accumulation did not change in transient expression experiments and was reduced in permanently expressing cells. The effect of the mutation on ligand affinity was not related to any obvious structural commonality, nor to the selectivity for different neurokinin receptors or the agonistic/antagonistic nature of the ligand. However, all ligands responding to the mutation appear to share the ability to induce phosphoinositide signaling more efficiently than cAMP responses when binding to NK1R. We suggest that the mutation shifts the internal equilibria of different functional forms of NK1R. A theoretical analysis according to a multistate allosteric model suggests that the link between binding and biological changes can result from altered stability constants of substates in the conformational space of the receptor

Cholesterol-Induced Protein Sorting: An Analysis of Energetic Feasibility

AbstractThe mechanism(s) underlying the sorting of integral membrane proteins between the Golgi complex and the plasma membrane remain uncertain because no specific Golgi retention signal has been found. Moreover one can alter a protein's eventual localization simply by altering the length of its transmembrane domain (TMD). M. S. Bretscher and S. Munro (Science. 261:1280–1281, 1993) therefore proposed a physical sorting mechanism based on the hydrophobic match between the proteins’ TMD and the bilayer thickness, in which cholesterol would regulate protein sorting by increasing the lipid bilayer thickness. In this model, Golgi proteins with short TMDs would be excluded from cholesterol-enriched domains (lipid rafts) that are incorporated into transport vesicles destined for the plasma membrane. Although attractive, this model remains unproven. We therefore evaluated the energetic feasibility of a cholesterol-dependent sorting process using the theory of elastic liquid crystal deformations. We show that the distribution of proteins between cholesterol-enriched and cholesterol-poor bilayer domains can be regulated by cholesterol-induced changes in the bilayer physical properties. Changes in bilayer thickness per se, however, have only a modest effect on sorting; the major effect arises because cholesterol changes also the bilayer material properties, which augments the energetic penalty for incorporating short TMDs into cholesterol-enriched domains. We conclude that cholesterol-induced changes in the bilayer physical properties allow for effective and accurate sorting which will be important generally for protein partitioning between different membrane domains

Analysis of heterogeneity and epistasis in physiological mixed populations by combined structural equation modelling and latent class analysis

<p>Abstract</p> <p>Background</p> <p>Biological systems are interacting, molecular networks in which genetic variation contributes to phenotypic heterogeneity. This heterogeneity is traditionally modelled as a dichotomous trait (e.g. affected vs. non-affected). This is far too simplistic considering the complexity and genetic variations of such networks.</p> <p>Methods</p> <p>In this study on type 2 diabetes mellitus, heterogeneity was resolved in a latent class framework combined with structural equation modelling using phenotypic indicators of distinct physiological processes. We modelled the clinical condition "the metabolic syndrome", which is known to be a heterogeneous and polygenic condition with a clinical endpoint (type 2 diabetes mellitus). In the model presented here, genetic factors were not included and no genetic model is assumed except that genes operate in networks. The impact of stratification of the study population on genetic interaction was demonstrated by analysis of several genes previously associated with the metabolic syndrome and type 2 diabetes mellitus.</p> <p>Results</p> <p>The analysis revealed the existence of 19 distinct subpopulations with a different propensity to develop diabetes mellitus within a large healthy study population. The allocation of subjects into subpopulations was highly accurate with an entropy measure of nearly 0.9. Although very few gene variants were directly associated with metabolic syndrome in the total study sample, almost one third of all possible epistatic interactions were highly significant. In particular, the number of interactions increased after stratifying the study population, suggesting that interactions are masked in heterogenous populations. In addition, the genetic variance increased by an average of 35-fold when analysed in the subpopulations.</p> <p>Conclusion</p> <p>The major conclusions from this study are that the likelihood of detecting true association between genetic variants and complex traits increases tremendously when studied in physiological homogenous subpopulations and on inclusion of epistasis in the analysis, whereas epistasis (i.e. genetic networks) is ubiquitous and should be the basis in modelling any biological process.</p