Accessory mutations balance the marginal stability of the HIV-1 protease in drug resistance

The HIV-1 protease is a major target of inhibitor drugs in AIDS therapies. The therapies are impaired by mutations of the HIV-1 protease that can lead to resistance to protease inhibitors. These mutations are classified into major mutations, which usually occur first and clearly reduce the susceptibility to protease inhibitors, and minor, accessory mutations that occur later and individually do not substantially affect the susceptibility to inhibitors. Major mutations are predominantly located in the active site of the HIV-1 protease and can directly interfere with inhibitor binding. Minor mutations, in contrast, are typically located distal to the active site. A central question is how these distal mutations contribute to resistance development. In this article, we present a systematic computational investigation of stability changes caused by major and minor mutations of the HIV-1 protease. As most small single-domain proteins, the HIV-1 protease is only marginally stable. Mutations that destabilize the folded, active state of the protease therefore can shift the conformational equilibrium towards the unfolded, inactive state. We find that the most frequent major mutations destabilize the HIV-1 protease, whereas roughly half of the frequent minor mutations are stabilizing. An analysis of protease sequences from patients in treatment indicates that the stabilizing minor mutations are frequently correlated with destabilizing major mutations, and that highly resistant HIV-1 proteases exhibit significant fractions of stabilizing mutations. Our results thus indicate a central role of minor mutations in balancing the marginal stability of the protease against the destabilization induced by the most frequent major mutation

Qualitative prediction of blood–brain barrier permeability on a large and refined dataset

The prediction of blood–brain barrier permeation is vitally important for the optimization of drugs targeting the central nervous system as well as for avoiding side effects of peripheral drugs. Following a previously proposed model on blood–brain barrier penetration, we calculated the cross-sectional area perpendicular to the amphiphilic axis. We obtained a high correlation between calculated and experimental cross-sectional area (r = 0.898, n = 32). Based on these results, we examined a correlation of the calculated cross-sectional area with blood–brain barrier penetration given by logBB values. We combined various literature data sets to form a large-scale logBB dataset with 362 experimental logBB values. Quantitative models were calculated using bootstrap validated multiple linear regression. Qualitative models were built by a bootstrapped random forest algorithm. Both methods found similar descriptors such as polar surface area, pKa, logP, charges and number of positive ionisable groups to be predictive for logBB. In contrast to our initial assumption, we were not able to obtain models with the cross-sectional area chosen as relevant parameter for both approaches. Comparing those two different techniques, qualitative random forest models are better suited for blood-brain barrier permeability prediction, especially when reducing the number of descriptors and using a large dataset. A random forest prediction system (ntrees = 5) based on only four descriptors yields a validated accuracy of 88%

New autocorrelation QTMS-based descriptors for use in QSAM of peptides

The autocorrelation analysis of the quantum topological molecular similarity (QTMS) has been used to discover some new indices for amino acids. QTMS descriptors include both physical and topological property molecules and produce a matrix of variables per molecule. To handle cubic array of QTMS descriptor calculated for a list of 20 naturally occurring amino acids, autocorrelation analysis has been performed. The proposed indices were validated by construction of quantitative sequence-activity model for three sets of peptides with different chain sizes. Modeling procedure used genetic algorithm-partial least square as variable selection and regression tools. The obtained models were of better or similar qualities compared with previously reported models for the same data sets. Also, the proposed indices in this article were able to determine active site region of the peptides under study

A Simple Image Analysis Method for Determination of Glucose by using Glucose Oxidase CdTe/TGA Quantum Dots

Glucose, as the major energy source in cellular metabolism, plays an important role in the natural growth of cells. Herein, a simple, rapid and low-cost method for the glucose determination by utilizing glucose oxidase and CdTe/thioglycolic acid (TGA) quantum dots (QDs) on a thin layer chromatography (TLC) plate has been described. The detection was based on the combination of the glucose enzymatic reaction and the quenching effect of H 2 O 2 on the CdTe/TGA quantum dots photoluminescence. This QDs-based assay exhibits several advantages. Enzyme immobilization and QDs modification process are not required and the high stability of the QDs towards photobleaching is beneficial to this sensing system. The proposed method is linear in concentration range of 1.00 × 10 -1 -3.00 × 10 -5 M of glucose and has a detection limit of 1.25 × 10 -8 M. The results of real sample analysis show that the glucose oxidase CdTe/TGA QDs system would be a promising glucose-biosensing system

A Simple Image Analysis Method for Determination of Glucose by using Glucose Oxidase CdTe/TGA Quantum Dots

Glucose, as the major energy source in cellular metabolism, plays an important role in the natural growth of cells. Herein, a simple, rapid and low-cost method for the glucose determination by utilizing glucose oxidase and CdTe/thioglycolic acid (TGA) quantum dots (QDs) on a thin layer chromatography (TLC) plate has been described. The detection was based on the combination of the glucose enzymatic reaction and the quenching effect of H2O2 on the CdTe/TGA quantum dots photoluminescence. This QDs-based assay exhibits several advantages. Enzyme immobilization and QDs modification process are not required and the high stability of the QDs towards photobleaching is beneficial to this sensing system. The proposed method is linear in concentration range of 1.00 × 10-1-3.00 × 10-5 M of glucose and has a detection limit of 1.25 × 10-8 M. The results of real sample analysis show that the glucose oxidase CdTe/TGA QDs system would be a promising glucose-biosensing system