Predicting solvated peptide conformations via global minimization of energetic atom-to-atom interactions

The Crucial Role of PPARγ-Egr-1-Pro-Inflammatory Mediators Axis in IgG Immune Complex-Induced Acute Lung Injury

Background

The ligand-activated transcription factor peroxisome proliferator-activated receptor (PPAR) γ plays crucial roles in diverse biological processes including cellular metabolism, differentiation, development, and immune response. However, during IgG immune complex (IgG-IC)-induced acute lung inflammation, its expression and function in the pulmonary tissue remains unknown.

Objectives

The study is designed to determine the effect of PPARγ on IgG-IC-triggered acute lung inflammation, and the underlying mechanisms, which might provide theoretical basis for therapy of acute lung inflammation.

Setting

Department of Pathogenic Biology and Immunology, Medical School of Southeast University

Subjects

Mice with down-regulated/up-regulated PPARγ activity or down-regulation of Early growth response protein 1 (Egr-1) expression, and the corresponding controls.

Interventions

Acute lung inflammation is induced in the mice by airway deposition of IgG-IC. Activation of PPARγ is achieved by using its agonist Rosiglitazone or adenoviral vectors that could mediate overexpression of PPARγ. PPARγ activity is suppressed by application of its antagonist GW9662 or shRNA. Egr-1 expression is down-regulated by using the gene specific shRNA.

Measures and Main Results

We find that during IgG-IC-induced acute lung inflammation, PPARγ expression at both RNA and protein levels is repressed, which is consistent with the results obtained from macrophages treated with IgG-IC. Furthermore, both in vivo and in vitro data show that PPARγ activation reduces IgG-IC-mediated pro-inflammatory mediators’ production, thereby alleviating lung injury. In terms of mechanism, we observe that the generation of Egr-1 elicited by IgG-IC is inhibited by PPARγ. As an important transcription factor, Egr-1 transcription is substantially increased by IgG-IC in both in vivo and in vitro studies, leading to augmented protein expression, thus amplifying IgG-IC-triggered expressions of inflammatory factors via association with their promoters.

Conclusion

During IgG-IC-stimulated acute lung inflammation, PPARγ activation can relieve the inflammatory response by suppressing the expression of its downstream target Egr-1 that directly binds to the promoter regions of several inflammation-associated genes. Therefore, regulation of PPARγ-Egr-1-pro-inflammatory mediators axis by PPARγ agonist Rosiglitazone may represent a novel strategy for blockade of acute lung injury.

Deterministic global optimization of molecular structures using interval analysis

The search for the global minimum of a molecular potential energy surface is a challenging problem. The molecular structure corresponding to the global minimum is of particular importance because it usually dictates both the physical and chemical properties of the molecule. The existence of an extremely large number of local minima, the number of which may increase exponentially with the size of the molecule, makes this global minimization problem extremely difficult. A new strategy is described here for solving such global minimization problems deterministically. The methodology is based on interval analysis, and provides a mathematical and computational guarantee that the molecular structure with the global minimum potential energy will be found. The technique is demonstrated using two sets of example problems. The first set involves a relatively simple potential model, and problems with up to 40 atoms. The second set involves a more realistic potential energy function, representative of those in current use, and problems with up to 11 atoms.

ASTRO-FOLD: a combinatorial and global optimization framework for Ab initio prediction of three-dimensional structures of proteins from the amino acid sequence

The field of computational biology has been revolutionized by recent advances in genomics. The completion of a number of genome projects, including that of the human genome, has paved the way toward a variety of challenges and opportunities in bioinformatics and biological systems engineering. One of the first challenges has been the determination of the structures of proteins encoded by the individual genes. This problem, which represents the progression from sequence to structure (genomics to structural genomics), has been widely known as the structure-prediction-in-protein-folding problem. We present the development and application of ASTRO-FOLD, a novel and complete approach for the ab initio prediction of protein structures given only the amino acid sequences of the proteins. The approach exhibits many novel components and the merits of its application are examined for a suite of protein systems, including a number of targets from several critical-assessment-of-structure-prediction experiments.

Hybrid global optimization algorithms for protein structure prediction: alternating hybrids

Hybrid global optimization methods attempt to combine the beneficial features of two or more algorithms, and can be powerful methods for solving challenging nonconvex optimization problems. In this paper, novel classes of hybrid global optimization methods, termed alternating hybrids, are introduced for application as a tool in treating the peptide and protein structure prediction problems. In particular, these new optimization methods take the form of hybrids between a deterministic global optimization algorithm, the alphaBB, and a stochastically based method, conformational space annealing (CSA). The alphaBB method, as a theoretically proven global optimization approach, exhibits consistency, as it guarantees convergence to the global minimum for twice-continuously differentiable constrained nonlinear programming problems, but can benefit from computationally related enhancements. On the other hand, the independent CSA algorithm is highly efficient, though the method lacks theoretical guarantees of convergence. Furthermore, both the alphaBB method and the CSA method are found to identify ensembles of low-energy conformers, an important feature for determining the true free energy minimum of the system. The proposed hybrid methods combine the desirable features of efficiency and consistency, thus enabling the accurate prediction of the structures of larger peptides. Computational studies for met-enkephalin and melittin, employing sequential and parallel computing frameworks, demonstrate the promise for these proposed hybrid methods.