Improving protein secondary structure prediction using a simple k-mer model

Motivation: Some first order methods for protein sequence analysis inherently treat each position as independent. We develop a general framework for introducing longer range interactions. We then demonstrate the power of our approach by applying it to secondary structure prediction; under the independence assumption, sequences produced by existing methods can produce features that are not protein like, an extreme example being a helix of length 1. Our goal was to make the predictions from state of the art methods more realistic, without loss of performance by other measures

Finding Direction in the Search for Selection.

Tests for positive selection have mostly been developed to look for diversifying selection where change away from the current amino acid is often favorable. However, in many cases we are interested in directional selection where there is a shift toward specific amino acids, resulting in increased fitness in the species. Recently, a few methods have been developed to detect and characterize directional selection on a molecular level. Using the results of evolutionary simulations as well as HIV drug resistance data as models of directional selection, we compare two such methods with each other, as well as against a standard method for detecting diversifying selection. We find that the method to detect diversifying selection also detects directional selection under certain conditions. One method developed for detecting directional selection is powerful and accurate for a wide range of conditions, while the other can generate an excessive number of false positives

Assessing Predictors of Changes in Protein Stability upon Mutation Using Self-Consistency

<div><p>The ability to predict the effect of mutations on protein stability is important for a wide range of tasks, from protein engineering to assessing the impact of SNPs to understanding basic protein biophysics. A number of methods have been developed that make these predictions, but assessing the accuracy of these tools is difficult given the limitations and inconsistencies of the experimental data. We evaluate four different methods based on the ability of these methods to generate consistent results for forward and back mutations, and examine how this ability varies with the nature and location of the mutation. We find that, while one method seems to outperform the others, the ability of these methods to make accurate predictions is limited.</p> </div

A comparison of the value with the RMSD datasets and RSA datasets scaled by RMS of the predictions.

<p>The center bars represent the calculated value for each of the methods. The top and bottom bars represent the 67% confidence intervals and the thin vertical lines extend to the 95% confidence intervals. The order of methods is Rosetta (black), FoldX (red), Eris (green) and iMutant3.0 (blue). The open RMSD bars represent those pairs of proteins with small changes in the two structures (RMSD) and the shaded bars represent the pairs with larger changes. The open RSA bars represent those mutations that are buried within the protein (RSA) and the shaded bars are those mutations that are more exposed. The RMSD split shows that Rosetta and I-Mutant3.0 do slightly better on structures with a lower RMSD value, while Eris performs equally as well on both sets. FoldX shows the most change between these two protein sets. All the methods perform better on exposed mutations than buried mutations, with Rosetta doing the best on buried and FoldX doing the best on exposed.</p

A scatter diagram of against .

<p>Values are in kcal/mol. The blue dots represent the exposed set of the mutations (relative solvent accessibility ) and the red dots represent the buried set. The dotted lines represent the expectation that .</p

A comparison of the methods for bias, , and scaled by RMS of the predictions.

<p>The center bars represent the calculated value for each of the methods. The top and bottom bars represent the 67% confidence intervals and the thin vertical lines extend to the 95% confidence intervals. The order of methods is Rosetta (black), FoldX (red), Eris (green), and iMutant3.0 (blue). For Rosetta, FoldX, and Eris the contributing factor for appears to be the Variance, while I-Mutant3.0 seems to be affected more by the bias.</p

Predict-2nd: a tool for generalized protein local structure prediction

Motivation: Predictions of protein local structure, derived from sequence alignment information alone, provide visualization tools for biologists to evaluate the importance of amino acid residue positions of interest in the absence of X-ray crystal/NMR structures or homology models. They are also useful as inputs to sequence analysis and modeling tools, such as hidden Markov models (HMMs), which can be used to search for homology in databases of known protein structure. In addition, local structure predictions can be used as a component of cost functions in genetic algorithms that predict protein tertiary structure. We have developed a program (predict-2nd) that trains multilayer neural networks and have applied it to numerous local structure alphabets, tuning network parameters such as the number of layers, the number of units in each layer and the window sizes of each layer. We have had the most success with four-layer networks, with gradually increasing window sizes at each layer