FOLD-RATE: prediction of protein folding rates from amino acid sequence

We have developed a web server, FOLD-RATE, for predicting the folding rates of proteins from their amino acid sequences. The relationship between amino acid properties and protein folding rates has been systematically analyzed and a statistical method based on linear regression technique has been proposed for predicting the folding rate of proteins. We found that the classification of proteins into different structural classes shows an excellent correlation between amino acid properties and folding rates of two and three-state proteins. Consequently, different regression equations have been developed for proteins belonging to all-α, all-β and mixed class. We observed an excellent agreement between predicted and experimentally observed folding rates of proteins; the correlation coefficients are, 0.99, 0.97 and 0.90, respectively, for all-α, all-β and mixed class proteins. The prediction server is freely available at

Update of PROFEAT: a web server for computing structural and physicochemical features of proteins and peptides from amino acid sequence

Sequence-derived structural and physicochemical features have been extensively used for analyzing and predicting structural, functional, expression and interaction profiles of proteins and peptides. PROFEAT has been developed as a web server for computing commonly used features of proteins and peptides from amino acid sequence. To facilitate more extensive studies of protein and peptides, numerous improvements and updates have been made to PROFEAT. We added new functions for computing descriptors of protein–protein and protein–small molecule interactions, segment descriptors for local properties of protein sequences, topological descriptors for peptide sequences and small molecule structures. We also added new feature groups for proteins and peptides (pseudo-amino acid composition, amphiphilic pseudo-amino acid composition, total amino acid properties and atomic-level topological descriptors) as well as for small molecules (atomic-level topological descriptors). Overall, PROFEAT computes 11 feature groups of descriptors for proteins and peptides, and a feature group of more than 400 descriptors for small molecules plus the derived features for protein–protein and protein–small molecule interactions. Our computational algorithms have been extensively tested and used in a number of published works for predicting proteins of specific structural or functional classes, protein–protein interactions, peptides of specific functions and quantitative structure activity relationships of small molecules. PROFEAT is accessible free of charge at http://bidd.cz3.nus.edu.sg/cgi-bin/prof/protein/profnew.cgi

Computational and Experimental Approaches to Reveal the Effects of Single Nucleotide Polymorphisms with Respect to Disease Diagnostics

DNA mutations are the cause of many human diseases and they are the reason for natural differences among individuals by affecting the structure, function, interactions, and other properties of DNA and expressed proteins. The ability to predict whether a given mutation is disease-causing or harmless is of great importance for the early detection of patients with a high risk of developing a particular disease and would pave the way for personalized medicine and diagnostics. Here we review existing methods and techniques to study and predict the effects of DNA mutations from three different perspectives: in silico, in vitro and in vivo. It is emphasized that the problem is complicated and successful detection of a pathogenic mutation frequently requires a combination of several methods and a knowledge of the biological phenomena associated with the corresponding macromolecules

The Structure and Internal Dynamics of Polypeptides Probed with Pyrene Excimer Fluorescence

A combination of pyrene excimer fluorescence, fluorescence blob model (FBM), and molecular mechanics optimizations (MMOs) was applied to characterize a series of pyrene-labeled polypeptides. Analysis of the fluorescence decays with the FBM yielded information on the number (Nblobexp) of amino acids (aa’s) contained within the volume of a blob and the rate constant (kblob) at which two aa’s labeled with pyrene encountered one another inside a blob. The Nblobexp value was used as a structural parameter which was compared to the Nblobtheo value obtained from MMOs conducted on constructs of pyrene-labeled polypeptides to determine the conformation adopted by the rigid polypeptides in solution. The relationship that exists between Nblobexp and macromolecular conformation was established by using a series of pyrene-labeled homopolypeptides, namely poly(L-glutamic acid) (PLGlu), poly(D,L-glutamic acid) (PDLGlu), and poly(L-lysine) (PLL). The coiled conformations of PDLGlu and PLL yielded the smallest blob sizes, and thus the smallest Nblobexp values, due to their elongated conformation on the length-scale of a blob. Nblobexp was found to be significantly larger, when PLGlu and PLL adopted an alpha-helical conformation, indicating that the blob size was directly related to conformational density. This information was then used to identify the unknown conformation of PLGlu in DMSO and determine the interhelical distance in bundles of PLL alpha-helices. To study the effect of ionic interactions, the protonated and deprotonated forms of PDLGlu and PLGlu were compared. Nblobexp was found to remain constant, indicating that the temporal window provided by an excited pyrene was sufficient to study the rigid polypeptides even when their dynamics were slowed by the repulsion of like charges. Next, the effect that the aa sequence had on Nblobexp and the internal dynamics (kblob×Nblobexp) of a coiled polypeptide was investigated. To do this, a series of pyrene-labeled copolypeptides were synthesized through the ring opening polymerization of the N carboxyanhydride monomers obtained from racemic mixtures of glutamic acid and either glycine, alanine, or carbobenzyloxylysine (Lys(Z)) to yield PGlyGlu, PAlaGlu, and PLys(Z)Glu, respectively. Both Nblobexp and kblob×Nblobexp increased with decreasing aa’s side chain size (SCS). This indicated that the reduced steric hindrance generated by the smaller SCSs increased the internal dynamics and conformational freedom of the polypeptides. The effect, that the degree of comonomer incorporation had on the chain dynamics and internal density of polypeptides, was determined with a series of PAlaGlu samples prepared with alanine contents between 24 and 58 mol%. The constant Nblobexp value obtained with these samples suggested that the blob size was independent of the comonomer composition and instead depended only on the presence of a few small aa’s. Lastly, the relationship between Nblobexp and the side chain length (SCL) linking pyrene to the polypeptide was determined using a series of PDLGlu and PLL samples adopting a coiled conformation. This allowed for the quantitative determination of the contribution of the pyrenyl label to Nblobexp. After correcting for its contribution and taking into account the effect of SCS and SCL on Nblobexp, a program was implemented to estimate the blob size of each aa depending on the aa sequence in a heterogeneous polypeptide, such as a protein. This program was employed to determine the folding time of 145 proteins segmented into blobs. Comparison of the calculated and experimental folding times yielded a correlation coefficient of 0.73, demonstrating the ability of blob-based approaches, such as the ones described in this thesis, to predict the folding time of proteins. In summary, this thesis has introduced a novel blob-based approach to predict the folding time of proteins based on parameters that can be determined experimentally from the use of pyrene excimer fluorescence and racemic polypeptides. It opens an alternate path toward developing a better understanding for proteins on how to fold graciously (C. Levinthal)