Computational modeling of substrate specificity and catalysis of the β-secretase (BACE1) enzyme

In this combined MD simulation and DFT study, interactions of the wild-type (WT) amyloid precursor protein (APP) and its Swedish variant (SW), Lys670 → Asn and Met671 → Leu, with the beta-secretase (BACE1) enzyme and their cleavage mechanisms have been investigated. BACE1 catalyzes the rate-limiting step in the generation of 40-42 amino acid long Alzheimer amyloid beta (Aβ) peptides. All key structural parameters such as position of the flap, volume of the active site, electrostatic binding energy, structures, and positions of the inserts A, D, and F and 10s loop obtained from the MD simulations show that, in comparison to the WT-substrate, BACE1 exhibits greater affinity for the SW-substrate and orients it in a more reactive conformation. The enzyme-substrate models derived from the MD simulations were further utilized to investigate the general acid/base mechanism used by BACE1 to hydrolytically cleave these substrates. This mechanism proceeds through the following two steps: (1) formation of the gem-diol intermediate and (2) cleavage of the peptide bond. For the WT-substrate, the overall barrier of 22.4 kcal/mol for formation of the gem-diol intermediate is 3.3 kcal/mol higher than for the SW-substrate (19.1 kcal/mol). This process is found to be the rate-limiting in the entire mechanism. The computed barrier is in agreement with the measured barrier of ca. 18.00 kcal/mol for the WT-substrate and supports the experimental observation that the cleavage of the SW-substrate is 60 times more efficient than the WT-substrate.     

Lysosome membrane lipid microdomains: novel regulators of chaperone-mediated autophagy

Chaperone-mediated autophagy (CMA) is a selective mechanism for the degradation of soluble cytosolic proteins in lysosomes. The limiting step of this type of autophagy is the binding of substrates to the lysosome-associated membrane protein type 2A (LAMP-2A). In this work, we identify a dynamic subcompartmentalization of LAMP-2A in the lysosomal membrane, which underlies the molecular basis for the regulation of LAMP-2A function in CMA. A percentage of LAMP-2A localizes in discrete lysosomal membrane regions during resting conditions, but it exits these regions during CMA activation. Disruption of these regions by cholesterol-depleting agents or expression of a mutant LAMP-2A excluded from these regions enhances CMA activity, whereas loading of lysosomes with cholesterol significantly reduces CMA. Organization of LAMP-2A into multimeric complexes, required for translocation of substrates into lysosomes via CMA, only occurs outside the lipid-enriched membrane microdomains, whereas the LAMP-2A located within these regions is susceptible to proteolytic cleavage and degradation. Our results support that changes in the dynamic distribution of LAMP-2A into and out of discrete microdomains of the lysosomal membrane contribute to regulate CMA.     

Compositional reasoning of performance in component-based distributed systems

Rapid prototyping of distributed systems can be achieved by integrating commercial off-the-shelf (COTS) components. With components as the building blocks, it is important to predict the performance of the system based on the performance of individual components. In this paper, performance prediction of a system consisting of a small number of components is investigated under different inter-component communication patterns, and the number of threads provided by components. Based on the experimental results, it can be inferred that the proposed composition rules provide a reasonably accurate prediction of the performance of a system made out of these components.

Impairment of <Emphasis Type="Italic">ntc</Emphasis>A gene revealed its role in regulating iron homeostasis,ROS production and cellular phenotype under iron deficiency in cyanobacterium <Emphasis Type="Italic">Anabaena</Emphasis> sp. PCC 7120

Iron deficiency ends up into several unavoidable consequences including damaging oxidative stress in cyanobacteria. NtcA is a global nitrogen regulator controls wide range of metabolisms in addition to regulation of nitrogen metabolism. In present communication, NtcA based regulation of iron homeostasis, ROS production and cellular phenotype under iron deficiency in Anabaena 7120 has been investigated. NtcA regulates the concentration dependent iron uptake by controlling the expression of furA gene. NtcA also regulated pigment synthesis and phenotypic alterations in Anabaena 7120. A significant increase in ROS production and corresponding reduction in the activities of antioxidative enzymes (SOD, CAT, APX and GR) in CSE2 mutant strain in contrast to wild type Anabaena 7120 also suggested the possible involvement of NtcA in protection against oxidative stress in iron deficiency. NtcA has no impact on the expression of furB and furC in spite of presence of consensus NtcA binding site (NBS) and ?10 boxes in their promoter. NtcA also regulates the thylakoid arrangement as well as related photosynthetic and respiration rates under iron deficiency in Anabaena 7120. Overall results suggested that NtcA regulates iron acquisition and in turn protect Anabaena cells from the damaging effects of oxidative stress induced under iron deficiency.     

Modelling and Characterization of Glial Fibrillary Acidic Protein

Glial Fibrillary Acidic Protein (GFAP) is an intermediate-filament (IF) protein that maintains the astrocytes of the Central Nervous System in Human. This is differentially expressed during serological studies in inflamed condition such as Rheumatoid Arthritis (RA). Therefore, it is of interest to glean molecular insight using a model of GFAP (49.88 kDa) due to its crystallographic nonavailability. The present study has been taken into consideration to construct computational protein model using Modeller 9.11. The structural relevance of the protein was verified using Gromacs 4.5 followed by validation through PROCHECK, Verify 3D, WHAT-IF, ERRAT and PROVE for reliability. The constructed three dimensional (3D) model of GFAP protein had been scrutinized to reveal the associated functions by identifying ligand binding sites and active sites. Molecular level interaction study revealed five possible surface cavities as active sites. The model finds application in further computational analysis towards drug discovery in order to minimize the effect of inflammation.     

Discovery of genomic regions and candidate genes controlling shelling percentage using QTL‐seq approach in cultivated peanut (Arachis hypogaea L.)

Cultivated peanut (Arachis hypogaea L.) is an important grain legume providing high‐quality cooking oil, rich proteins and other nutrients. Shelling percentage (SP) is the 2nd most important agronomic trait after pod yield and this trait significantly affects the economic value of peanut in the market. Deployment of diagnostic markers through genomics‐assisted breeding (GAB) can accelerate the process of developing improved varieties with enhanced SP. In this context, we deployed the QTL‐seq approach to identify genomic regions and candidate genes controlling SP in a recombinant inbred line population (Yuanza 9102 × Xuzhou 68‐4). Four libraries (two parents and two extreme bulks) were constructed and sequenced, generating 456.89–790.32 million reads and achieving 91.85%–93.18% genome coverage and 14.04–21.37 mean read depth. Comprehensive analysis of two sets of data (Yuanza 9102/two bulks and Xuzhou 68‐4/two bulks) using the QTL‐seq pipeline resulted in discovery of two overlapped genomic regions (2.75 Mb on A09 and 1.1 Mb on B02). Nine candidate genes affected by 10 SNPs with non‐synonymous effects or in UTRs were identified in these regions for SP. Cost‐effective KASP (Kompetitive Allele‐Specific PCR) markers were developed for one SNP from A09 and three SNPs from B02 chromosome. Genotyping of the mapping population with these newly developed KASP markers confirmed the major control and stable expressions of these genomic regions across five environments. The identified candidate genomic regions and genes for SP further provide opportunity for gene cloning and deployment of diagnostic markers in molecular breeding for achieving high SP in improved varieties.     

Exploring protein-protein interactions using the site-identification by ligand competitive saturation methodology

Protein docking methods are powerful computational tools to study protein-protein interactions (PPI). While a significant number of docking algorithms have been developed, they are usually based on rigid protein models or with limited considerations of protein flexibility and the desolvation effect is rarely considered in docking energy functions, which may lower the accuracy of the predictions. To address these issues, we introduce a PPI energy function based on the site-identification by ligand competitive saturation (SILCS) framework and utilize the fast Fourier transform (FFT) correlation approach. The free energy content of the SILCS FragMaps represent an alternative to traditional energy grids and they can be efficiently utilized to guide FFT-based protein docking. Application of the approach to eight diverse test cases, including seven from Protein Docking Benchmark 5.0, showed the PPI prediction using SILCS approach (SILCS-PPI) to be competitive with several commonly used protein docking methods indicating that the method has the ability to both qualitatively and quantitatively inform the prediction of PPI. Results show the utility of the SILCS-PPI docking approach for determination of probability distributions of PPI interactions over the surface of both partner proteins, allowing for identification of alternate binding poses. Such binding poses are confirmed by experimental crystal contacts in our test cases. While more computationally demanding than available PPI docking technologies, we anticipate that the SILCS-PPI docking approach will offer an alternative methodology for improved evaluation of PPIs that could be used in a variety of fields from systems biology to excipient design for biologics-based drugs.