Modeling Symmetric Macromolecular Structures in Rosetta3

Symmetric protein assemblies play important roles in many biochemical processes. However, the large size of such systems is challenging for traditional structure modeling methods. This paper describes the implementation of a general framework for modeling arbitrary symmetric systems in Rosetta3. We describe the various types of symmetries relevant to the study of protein structure that may be modeled using Rosetta's symmetric framework. We then describe how this symmetric framework is efficiently implemented within Rosetta, which restricts the conformational search space by sampling only symmetric degrees of freedom, and explicitly simulates only a subset of the interacting monomers. Finally, we describe structure prediction and design applications that utilize the Rosetta3 symmetric modeling capabilities, and provide a guide to running simulations on symmetric systems

An siRNA Screen in Pancreatic Beta Cells Reveals a Role for Gpr27 in Insulin Production

The prevalence of type 2 diabetes in the United States is projected to double or triple by 2050. We reasoned that the genes that modulate insulin production might be new targets for diabetes therapeutics. Therefore, we developed an siRNA screening system to identify genes important for the activity of the insulin promoter in beta cells. We created a subclone of the MIN6 mouse pancreatic beta cell line that expresses destabilized GFP under the control of a 362 base pair fragment of the human insulin promoter and the mCherry red fluorescent protein under the control of the constitutively active rous sarcoma virus promoter. The ratio of the GFP to mCherry fluorescence of a cell indicates its insulin promoter activity. As G protein coupled receptors (GPCRs) have emerged as novel targets for diabetes therapies, we used this cell line to screen an siRNA library targeting all known mouse GPCRs. We identified several known GPCR regulators of insulin secretion as regulators of the insulin promoter. One of the top positive regulators was Gpr27, an orphan GPCR with no known role in beta cell function. We show that knockdown of Gpr27 reduces endogenous mouse insulin promoter activity and glucose stimulated insulin secretion. Furthermore, we show that Pdx1 is important for Gpr27's effect on the insulin promoter and insulin secretion. Finally, the over-expression of Gpr27 in 293T cells increases inositol phosphate levels, while knockdown of Gpr27 in MIN6 cells reduces inositol phosphate levels, suggesting this orphan GPCR might couple to Gq/11. In summary, we demonstrate a MIN6-based siRNA screening system that allows rapid identification of novel positive and negative regulators of the insulin promoter. Using this system, we identify Gpr27 as a positive regulator of insulin production

Rationally Designed Turn Promoting Mutation in the Amyloid-β Peptide Sequence Stabilizes Oligomers in Solution

Enhanced production of a 42-residue beta amyloid peptide (Aβ42) in affected parts of the brain has been suggested to be the main causative factor for the development of Alzheimer's Disease (AD). The severity of the disease depends not only on the amount of the peptide but also its conformational transition leading to the formation of oligomeric amyloid-derived diffusible ligands (ADDLs) in the brain of AD patients. Despite being significant to the understanding of AD mechanism, no atomic-resolution structures are available for these species due to the evanescent nature of ADDLs that hinders most structural biophysical investigations. Based on our molecular modeling and computational studies, we have designed Met35Nle and G37p mutations in the Aβ42 peptide (Aβ42Nle35p37) that appear to organize Aβ42 into stable oligomers. 2D NMR on the Aβ42Nle35p37 peptide revealed the occurrence of two β-turns in the V24-N27 and V36-V39 stretches that could be the possible cause for the oligomer stability. We did not observe corresponding NOEs for the V24-N27 turn in the Aβ21–43Nle35p37 fragment suggesting the need for the longer length amyloid peptide to form the stable oligomer promoting conformation. Because of the presence of two turns in the mutant peptide which were absent in solid state NMR structures for the fibrils, we propose, fibril formation might be hindered. The biophysical information obtained in this work could aid in the development of structural models for toxic oligomer formation that could facilitate the development of therapeutic approaches to AD

In Silico Elucidation of the Recognition Dynamics of Ubiquitin

Elucidation of the mechanism of biomacromolecular recognition events has been a topic of intense interest over the past century. The inherent dynamic nature of both protein and ligand molecules along with the continuous reshaping of the energy landscape during the binding process renders it difficult to characterize this process at atomic detail. Here, we investigate the recognition dynamics of ubiquitin via microsecond all-atom molecular dynamics simulation providing both thermodynamic and kinetic information. The high-level of consistency found with respect to experimental NMR data lends support to the accuracy of the in silico representation of the conformational substates and their interconversions of free ubiquitin. Using an energy-based reweighting approach, the statistical distribution of conformational states of ubiquitin is monitored as a function of the distance between ubiquitin and its binding partner Hrs-UIM. It is found that extensive and dense sampling of conformational space afforded by the µs MD trajectory is essential for the elucidation of the binding mechanism as is Boltzmann sampling, overcoming inherent limitations of sparsely sampled empirical ensembles. The results reveal a population redistribution mechanism that takes effect when the ligand is at intermediate range of 1–2 nm from ubiquitin. This mechanism, which may be depicted as a superposition of the conformational selection and induced fit mechanisms, also applies to other binding partners of ubiquitin, such as the GGA3 GAT domain

Insight into the Stability of Cross-β Amyloid Fibril from VEALYL Short Peptide with Molecular Dynamics Simulation

Amyloid fibrils are found in many fatal neurodegenerative diseases such as Alzheimer's disease, Parkinson's disease, type II diabetes, and prion disease. The VEALYL short peptide from insulin has been confirmed to aggregate amyloid-like fibrils. However, the aggregation mechanism of amyloid fibril is poorly understood. Here, we utilized molecular dynamics simulation to analyse the stability of VEALYL hexamer. The statistical results indicate that hydrophobic residues play key roles in stabilizing VEALYL hexamer. Single point and two linkage mutants confirmed that Val1, Leu4, and Tyr5 of VEALYL are key residues. The consistency of the results for the VEALYL oligomer suggests that the intermediate states might be trimer (3-0) and pentamer(3-2). These results can help us to obtain an insight into the aggregation mechanism of amyloid fibril. These methods can be used to study the stability of amyloid fibril from other short peptides

Defining Reference Sequences for Nocardia Species by Similarity and Clustering Analyses of 16S rRNA Gene Sequence Data

International audienceBACKGROUND: The intra- and inter-species genetic diversity of bacteria and the absence of 'reference', or the most representative, sequences of individual species present a significant challenge for sequence-based identification. The aims of this study were to determine the utility, and compare the performance of several clustering and classification algorithms to identify the species of 364 sequences of 16S rRNA gene with a defined species in GenBank, and 110 sequences of 16S rRNA gene with no defined species, all within the genus Nocardia. METHODS: A total of 364 16S rRNA gene sequences of Nocardia species were studied. In addition, 110 16S rRNA gene sequences assigned only to the Nocardia genus level at the time of submission to GenBank were used for machine learning classification experiments. Different clustering algorithms were compared with a novel algorithm or the linear mapping (LM) of the distance matrix. Principal Components Analysis was used for the dimensionality reduction and visualization. RESULTS: The LM algorithm achieved the highest performance and classified the set of 364 16S rRNA sequences into 80 clusters, the majority of which (83.52%) corresponded with the original species. The most representative 16S rRNA sequences for individual Nocardia species have been identified as 'centroids' in respective clusters from which the distances to all other sequences were minimized; 110 16S rRNA gene sequences with identifications recorded only at the genus level were classified using machine learning methods. Simple kNN machine learning demonstrated the highest performance and classified Nocardia species sequences with an accuracy of 92.7% and a mean frequency of 0.578. CONCLUSION: The identification of centroids of 16S rRNA gene sequence clusters using novel distance matrix clustering enables the identification of the most representative sequences for each individual species of Nocardia and allows the quantitation of inter- and intra-species variability

The inverted free energy landscape of an intrinsically disordered peptide by simulations and experiments

The free energy landscape theory has been very successful in rationalizing the folding behaviour of globular proteins, as this representation provides intuitive information on the number of states involved in the folding process, their populations and pathways of interconversion. We extend here this formalism to the case of the A\u3b240 peptide, a 40-residue intrinsically disordered protein fragment associated with Alzheimer's disease. By using an advanced sampling technique that enables free energy calculations to reach convergence also in the case of highly disordered states of proteins, we provide a precise structural characterization of the free energy landscape of this peptide. We find that such landscape has inverted features with respect to those typical of folded proteins. While the global free energy minimum consists of highly disordered structures, higher free energy regions correspond to a large variety of transiently structured conformations with secondary structure elements arranged in several different manners, and are not separated from each other by sizeable free energy barriers. From this peculiar structure of the free energy landscape we predict that this peptide should become more structured and not only more compact, with increasing temperatures, and we show that this is the case through a series of biophysical measurements

An in vivo platform for identifying inhibitors of protein aggregation

Protein aggregation underlies an array of human diseases, yet only one small molecule therapeutic has been successfully developed to date. Here, we introduce an in vivo system, based on a β-lactamase tripartite fusion construct, capable of identifying aggregation-prone sequences in the periplasm of Escherichia coli and inhibitors that prevent their aberrant self-assembly. We demonstrate the power of the system using a range of proteins, from small unstructured peptides (islet amyloid polypeptide and amyloid β) to larger, folded immunoglobulin domains. Configured in a 48-well format, the split β-lactamase sensor readily differentiates between aggregation-prone and soluble sequences. Performing the assay in the presence of 109 compounds enabled a rank ordering of inhibition and revealed a new inhibitor of IAPP aggregation. This platform can be applied to both amyloidogenic and other aggregation-prone systems, independent of sequence or size, and can identify small molecules or other factors able to ameliorate or inhibit protein aggregation