Evaluation of structural features in fungal cytochromes P450 predicted to rule catalytic diversification

Fungi belong to the large kingdom of lower eukaryotic organisms encompassing yeasts along with filamentous and dimorphic members. Microbial P450 enzymes have contributed to exploration of and adaptation to diverse ecological niches such as conversion of lipophilic compounds to more hydrophilic derivatives or degradation of a vast array of environmental toxicants. To better understand diversification of the catalytic behavior of fungal P450s, detailed insight into the molecular machinery steering oxidative attack on the distinctly structured endogenous and xenobiotic substrates is of preeminent interest. Based on a general, CYP102A1-related template the bulk of predicted substrate/inhibitor-binding determinants were shown to cluster near the distal heme face within the six known substrate recognition sites (SRSs) made up by the α-helical B′/F/G/I tetrad, the B′–C interhelical loop and strands of the β6-sheet, population density being highest in the structurally flexible SRS-1 and SRS-4 domains, showing a low degree of conservation. Reactivity toward ligands favorably coincides with the lipophilicity/hydrophilicity profile and bulkiness of critical amino acids acting as selective filters. Some decisive elements may also serve in maintenance of catalytic competence via their action as gatekeepers directing substrate access/positioning or stabilizers of the heme environment enabling dioxygen activation. Non-SRS residues seem to control spin state equilibria and attract redox partners by electrostatic forces. Of note, the inhibitory potency of azole-type fungicides is likely to arise from perturbation of the complex interplay of the mechanistic principles addressed above. Knowledge-supported exploitation of the topological data will be helpful in the manufacture of commodity/specialty chemicals as well as therapeutic agents. Also, engineered fungal P450s may be used to improve pollutant-specific bioremediation of contaminated soils.     

From monomer to fibril: Abeta-amyloid binding to Aducanumab antibody studied by molecular dynamics simulation

Alzheimer's disease is one of the most common causes of dementia. It is believed that the aggregation of short Aβ -peptides to form oligomeric and protofibrillar amyloid assemblies plays a central role for disease-relevant neurotoxicity. In recent years, passive immunotherapy has been introduced as a potential treatment strategy with anti-amyloid antibodies binding to Aβ -amyloids and inducing their subsequent degradation by the immune system. Although so far mostly unsuccessful in clinical studies, the high-dosed application of the monoclonal antibody Aducanumab has shown therapeutic potential that might be attributed to its much greater affinity to Aβ -aggregates vs monomeric Aβ -peptides. In order to better understand how Aducanumab interacts with aggregated Aβ -forms compared to monomers, we have generated structural model complexes based on the known structure of Aducanumab in complex with an Aβ_{2 − 7} -eptitope. Structural models of Aducanumab bound to full-sequence Aβ_{1 − 40} -monomers, oligomers, protofilaments and mature fibrils were generated and investigated using extensive molecular dynamics simulations to characterize the flexibility and possible additional interactions. Indeed, an aggregate-specific N-terminal binding motif was found in case of Aducanumab binding to oligomers, protofilaments and fibrils that is located next to but not overlapping with the epitope binding site found in the crystal structure with Aβ_{2 − 7} . Analysis of binding energetics indicates that this motif binds weaker than the epitope but likely contributes to Aducanumab's preference for aggregated Aβ -species. The predicted aggregate-specific binding motif could potentially serve as a basis to reengineer Aducanumab for further enhanced preference to bind Aβ -aggregates vs monomers.     

S-adenosylmethionine and methylthioadenosine boost cellular productivities of antibody forming Chinese hamster ovary cells

The improvement of cell specific productivities for the formation of therapeutic proteins is an important step towards intensified production processes. Among others, the induction of the desired production phenotype via proper media additives is a feasible solution provided that said compounds adequately trigger metabolic and regulatory programs inside the cells. In this study, S-(5′-adenosyl)- l -methionine (SAM) and 5′-deoxy-5′-(methylthio)adenosine (MTA) were found to stimulate cell specific productivities up to approx. 50% while keeping viable cell densities transiently high and partially arresting the cell cycle in an anti-IL-8-producing CHO-DP12 cell line. Noteworthy, MTA turned out to be the chemical degradation product of the methyl group donor SAM and is consumed by the cells.     

Applying a multiobjective metaheuristic inspired by honey bees to phylogenetic inference

The development of increasingly popular multiobjective metaheuristics has allowed bioinformaticians to deal with optimization problems in computational biology where multiple objective functions must be taken into account. One of the most relevant research topics that can benefit from these techniques is phylogenetic inference. Throughout the years, different researchers have proposed their own view about the reconstruction of ancestral evolutionary relationships among species. As a result, biologists often report different phylogenetic trees from a same dataset when considering distinct optimality principles. In this work, we detail a multiobjective swarm intelligence approach based on the novel Artificial Bee Colony algorithm for inferring phylogenies. The aim of this paper is to propose a complementary view of phylogenetics according to the maximum parsimony and maximum likelihood criteria, in order to generate a set of phylogenetic trees that represent a compromise between these principles. Experimental results on a variety of nucleotide data sets and statistical studies highlight the relevance of the proposal with regard to other multiobjective algorithms and state-of-the-art biological methods.     

Biosynthesis and function of chondroitin sulfate

Background

Chondroitin sulfate proteoglycans (CSPGs) are principal pericellular and extracellular components that form regulatory milieu involving numerous biological and pathophysiological phenomena. Diverse functions of CSPGs can be mainly attributed to structural variability of their polysaccharide moieties, chondroitin sulfate glycosaminoglycans (CS-GAG). Comprehensive understanding of the regulatory mechanisms for CS biosynthesis and its catabolic processes is required in order to understand those functions.

Scope of review

Here, we focus on recent advances in the study of enzymatic regulatory pathways for CS biosynthesis including successive modification/degradation, distinct CS functions, and disease phenotypes that have been revealed by perturbation of the respective enzymes in vitro and in vivo.

Major conclusions

Fine-tuned machineries for CS production/degradation are crucial for the functional expression of CS chains in developmental and pathophysiological processes.

General significance

Control of enzymes responsible for CS biosynthesis/catabolism is a potential target for therapeutic intervention for the CS-associated disorders.     

A Modified Valley Restrained Monte Carlo Method to Efficiently Search the Low Energy Structures of Peptides

Abstract

A new Monte Carlo sampling scheme, namely the Modified Valley Restrained Monte Carlo procedure, is used to obtain the global energy minimum conformations for polypeptides, such as Met-enkephalin and Melittin. For each peptide, we found close agreement with previous results from both theoretical and experimental studies. The simple idea for controlling the step size according to the Valley Function, provides useful suggestions in searching the global energy minimum structures, and furthermore helps solve the multiple minima problem.     

A combined steepest descent and genetic algorithm (SD/GA) approach for the optimization of solvation parameters

An accurate solvation model is essential for computer modeling of protein folding and other biomolecular self-assembly processes. Compared to explicit solvent models, implicit solvent models, such as the Poisson-Boltzmann (PB) with solvent accessible surface area model (PB/SA), offer a much faster speed—the most compelling reason for the popularity of these implicit solvent models. Since these implicit solvent models typically use empirical parameters, such as atomic radii and the surface tensions, an optimal fit of these parameters is crucial for the final accuracy of properties such as solvation free energy and folding free energy. In this paper, we proposed a combined approach, namely SD/GA, which takes the advantage of both local optimization with the steepest descent (SD), and global optimization with the genetic algorithm (GA), for parameters optimization in multi-dimensional space. The SD/GA method is then applied to the optimization of solvation parameters in the non-polar cavity term of the PB/SA model. The results show that the newly optimized parameters from SD/GA not only increase the accuracy in the solvation free energies for ～200 organic molecules, but also significantly improve the free energy landscape of a β-hairpin folding. The current SD/GA method can be readily applied to other multi-dimensional parameter space optimization as well.