Cryo-ET of Env on intact HIV virions reveals structural variation and positioning on the Gag lattice

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Discovery of potent iminoheterocycle BACE1 inhibitors

The synthesis of a series of iminoheterocycles and their structure–activity relationships (SAR) as inhibitors of the aspartyl protease BACE1 will be detailed. An effort to access the S3 subsite directly from the S1 subsite initially yielded compounds with sub-micromolar potency. A subset of compounds from this effort unexpectedly occupied a different binding site and displayed excellent BACE1 affinities. Select compounds from this subset acutely lowered Aβ₄₀ levels upon subcutaneous and oral administration to rats.     

Methyl-containing pharmaceuticals: Methylation in drug design

The importance of methyl groups in modulating biological activity, selectivity, solubility, metabolism and pharmacokinetic/pharmacodynamic properties of biologically active molecules is highlighted. The information compiled from selected beneficial cases, focuses mostly on marketed drugs and clinical candidates, and indicates that the methylation strategy has been successful in drug design.     

Harnessing biosynthesis and quantitative NMR for late stage functionalization of lead molecules: Application to the M1 positive allosteric modulator (PAM) program

A facile method for late stage diversification of lead molecules for the M1 PAM program using biosynthesis is described. Liver microsomes from several species are screened to identify a high turnover system. Subsequent incubations using less than 1?mg of substrate generate nanomole quantities of drug metabolites that are purified, characterized by microcryoprobe NMR spectroscopy, and quantified to known concentrations to enable rapid biology testing. The late-stage diversification of lead compounds provides rapid SAR feedback to the medicinal chemistry design cycle.     

Linking species richness curves from non-contiguous sampling to contiguous-nested SAR: An empirical study

The species–area relationship (SAR) is one of the few generalizations in ecology. However, many different relationships are denoted as SARs. Here, we empirically evaluated the differences between SARs derived from nested-contiguous and non-contiguous sampling designs, using plants, birds and butterflies datasets from Great Britain, Greece, Massachusetts, New York and San Diego. The shape of SAR depends on the sampling scheme, but there is little empirical documentation on the magnitude of the deviation between different types of SARs and the factors affecting it. We implemented a strictly nested sampling design to construct nested-contiguous SAR (SA_CR), and systematic nested but non-contiguous, and random designs to construct non-contiguous species richness curves (SA_SRs for systematic and SACs for random designs) per dataset. The SA_CR lay below any SA_SR and most of the SACs. The deviation between them was related to the exponent f of the power law relationship between sampled area and extent. The lower the exponent f, the higher was the deviation between the curves. We linked SA_CR to SA_SR and SAC through the concept of “effective” area (A_e), i.e. the nested-contiguous area containing equal number of species with the accumulated sampled area (A_S) of a non-contiguous sampling. The relationship between effective and sampled area was modeled as log(A_e) = klog(A_S). A Generalized Linear Model was used to estimate the values of k from sampling design and dataset properties. The parameter k increased with the average distance between samples and with beta diversity, while k decreased with f. For both systematic and random sampling, the model performed well in predicting effective area in both the training set and in the test set which was totally independent from the training one. Through effective area, we can link different types of species richness curves based on sampling design properties, sampling effort, spatial scale and beta diversity patterns.     

Evolving cell models for systems and synthetic biology

This paper proposes a new methodology for the automated design of cell models for systems and synthetic biology. Our modelling framework is based on P systems, a discrete, stochastic and modular formal modelling language. The automated design of biological models comprising the optimization of the model structure and its stochastic kinetic constants is performed using an evolutionary algorithm. The evolutionary algorithm evolves model structures by combining different modules taken from a predefined module library and then it fine-tunes the associated stochastic kinetic constants. We investigate four alternative objective functions for the fitness calculation within the evolutionary algorithm: (1) equally weighted sum method, (2) normalization method, (3) randomly weighted sum method, and (4) equally weighted product method. The effectiveness of the methodology is tested on four case studies of increasing complexity including negative and positive autoregulation as well as two gene networks implementing a pulse generator and a bandwidth detector. We provide a systematic analysis of the evolutionary algorithm’s results as well as of the resulting evolved cell models.     

Confidence Intervals on the Total Variance in an Unbalanced Two-fold Nested Design

Confidence intervals on the total variance in an unbalanced random two-fold nested design are constructed and compared. Computer simulation indicates the proposed intervals provide confidence coefficients that are generally close to the stated level.     

Redesigning HVEM Interface for Selective Binding to LIGHT,BTLA, and CD160

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