Robust determinants of thermostability highlighted by a codon frequency index capable of discriminating thermophilic from mesophilic genomes

A new hybrid fractal algorithm for predicting thermophilic nucleotide sequences

Knowledge of thermophilic mechanisms about some organisms whose optimum growth temperature (OGT) ranges from 50 to 80 degree plays a major role in helping design stable proteins. How to predict a DNA sequence to be thermophilic is a long but not fairly resolved problem. Chaos game representation (CGR) can investigate the patterns hiding in DNA sequences, and can visually reveal previously unknown structure. Fractal dimensions are good tools to measure sizes of complex, highly irregular geometric objects. In this paper, we convert every DNA sequence into a high dimensional vector by CGR algorithm and fractal dimension, and then predict the DNA sequence thermostability by these fractal features and support vector machine (SVM). We have conducted experiments on three groups: 17-dimensional vector, 65-dimensional vector, and 257-dimensional vector. Each group is evaluated by the 10-fold cross-validation test. For the results, the group of 257-dimensional vector gets the best results: the average accuracy is 0.9456 and average MCC is 0.8878. The results are also compared with the previous work with single CGR features. The comparison shows the high effectiveness of the new hybrid fractal algorithm.

Predicting protein thermostability changes from sequence upon multiple mutations

Motivation: A basic question in protein science is to which extent mutations affect protein thermostability. This knowledge would be particularly relevant for engineering thermostable enzymes. In several experimental approaches, this issue has been serendipitously addressed. It would be therefore convenient providing a computational method that predicts when a given protein mutant is more thermostable than its corresponding wild-type.

Results: We present a new method based on support vector machines that is able to predict whether a set of mutations (including insertion and deletions) can enhance the thermostability of a given protein sequence. When trained and tested on a redundancy-reduced dataset, our predictor achieves 88% accuracy and a correlation coefficient equal to 0.75. Our predictor also correctly classifies 12 out of 14 experimentally characterized protein mutants with enhanced thermostability. Finally, it correctly detects all the 11 mutated proteins whose increase in stability temperature is >10°C.

Availability: The dataset and the list of protein clusters adopted for the SVM cross-validation are available at the web site http://lipid.biocomp.unibo.it/~ludovica/thermo-meso-MUT.

Contact:casadio@alma.unibo.it

Heterologous expression of glycosyl hydrolases in planta: a new departure for biofuels

The concept of expressing non-plant glycosyl hydrolase genes in plant tissue is nearly two decades old, yet relatively little work in this field has been reported. However, resurgent interest in technologies aimed at enabling processes that convert biomass to sugars and fuels has turned attention toward this intuitive solution. There are several challenges facing researchers in this field, including the development of better and more specifically targeted delivery systems for hydrolytic genes, the successful folding and post-translational modification of heterologous proteins and the development of cost-effective process strategies utilizing these transformed plants. The integration of these concepts, from the improvement of biomass production and conversion characteristics to the heterologous production of glycosyl hydrolases in a high yielding bioenergy crop, holds considerable promise for improving the lignocellulosic conversion of biomass to ethanol and subsequently to fuels.