Protein folding rates correlate with heterogeneity of folding mechanism

By observing trends in the folding kinetics of experimental 2-state proteins at their transition midpoints, and by observing trends in the barrier heights of numerous simulations of coarse grained, C-alpha model, Go proteins, we show that folding rates correlate with the degree of heterogeneity in the formation of native contacts. Statistically significant correlations are observed between folding rates and measures of heterogeneity inherent in the native topology, as well as between rates and the variance in the distribution of either experimentally measured or simulated phi-values.Comment: 11 pages, 3 figures, 1 tabl

Nucleation phenomena in protein folding: The modulating role of protein sequence

For the vast majority of naturally occurring, small, single domain proteins folding is often described as a two-state process that lacks detectable intermediates. This observation has often been rationalized on the basis of a nucleation mechanism for protein folding whose basic premise is the idea that after completion of a specific set of contacts forming the so-called folding nucleus the native state is achieved promptly. Here we propose a methodology to identify folding nuclei in small lattice polymers and apply it to the study of protein molecules with chain length N=48. To investigate the extent to which protein topology is a robust determinant of the nucleation mechanism we compare the nucleation scenario of a native-centric model with that of a sequence specific model sharing the same native fold. To evaluate the impact of the sequence's finner details in the nucleation mechanism we consider the folding of two non- homologous sequences. We conclude that in a sequence-specific model the folding nucleus is, to some extent, formed by the most stable contacts in the protein and that the less stable linkages in the folding nucleus are solely determined by the fold's topology. We have also found that independently of protein sequence the folding nucleus performs the same `topological' function. This unifying feature of the nucleation mechanism results from the residues forming the folding nucleus being distributed along the protein chain in a similar and well-defined manner that is determined by the fold's topological features.Comment: 10 Figures. J. Physics: Condensed Matter (to appear

Improved general regression network for protein domain boundary prediction

Background: Protein domains present some of the most useful information that can be used to understand protein structure and functions. Recent research on protein domain boundary prediction has been mainly based on widely known machine learning techniques, such as Artificial Neural Networks and Support Vector Machines. In this study, we propose a new machine learning model (IGRN) that can achieve accurate and reliable classification, with significantly reduced computations. The IGRN was trained using a PSSM (Position Specific Scoring Matrix), secondary structure, solvent accessibility information and inter-domain linker index to detect possible domain boundaries for a target sequence. Results: The proposed model achieved average prediction accuracy of 67% on the Benchmark_2 dataset for domain boundary identification in multi-domains proteins and showed superior predictive performance and generalisation ability among the most widely used neural network models. With the CASP7 benchmark dataset, it also demonstrated comparable performance to existing domain boundary predictors such as DOMpro, DomPred, DomSSEA, DomCut and DomainDiscovery with 70.10% prediction accuracy. Conclusion: The performance of proposed model has been compared favourably to the performance of other existing machine learning based methods as well as widely known domain boundary predictors on two benchmark datasets and excels in the identification of domain boundaries in terms of model bias, generalisation and computational requirements. © 2008 Yoo et al; licensee BioMed Central Ltd

Altering APP Proteolysis: Increasing sAPPalpha Production by Targeting Dimerization of the APP Ectodomain

One of the events associated with Alzheimer's disease is the dysregulation of α- versus β-cleavage of the amyloid precursor protein (APP). The product of α-cleavage (sAPPα) has neuroprotective properties, while Aβ1-42 peptide, a product of β-cleavage, is neurotoxic. Dimerization of APP has been shown to influence the relative rate of α- and β- cleavage of APP. Thus finding compounds that interfere with dimerization of the APP ectodomain and increase the α-cleavage of APP could lead to the development of new therapies for Alzheimer's disease. Examining the intrinsic fluorescence of a fragment of the ectodomain of APP, which dimerizes through the E2 and Aβ-cognate domains, revealed significant changes in the fluorescence of the fragment upon binding of Aβ oligomers—which bind to dimers of the ectodomain— and Aβ fragments—which destabilize dimers of the ectodomain. This technique was extended to show that RERMS-containing peptides (APP695 328–332), disulfiram, and sulfiram also inhibit dimerization of the ectodomain fragment. This activity was confirmed with small angle x-ray scattering. Analysis of the activity of disulfiram and sulfiram in an AlphaLISA assay indicated that both compounds significantly enhance the production of sAPPα by 7W-CHO and B103 neuroblastoma cells. These observations demonstrate that there is a class of compounds that modulates the conformation of the APP ectodomain and influences the ratio of α- to β-cleavage of APP. These compounds provide a rationale for the development of a new class of therapeutics for Alzheimer's disease