The Genetic Algorithm Applied as a Modelling Tool to Predict the Fold of Small Proteins with Different Topologies

The genetic algorithm exploits the principles of natural evolution. Solution trials are evolved by mutation, recombination and selection until they achieve near optimal solutions [1].Our own approach has now been developed [2] after a general overview on the application potential for protein structure analysis [3] to a tool to delineate the three-dimensional topology for the mainchain of small proteins [4], no matter whether they are largely helical, are mixed or -strand rich [5].Results on several protein examples for these different modelling tasks are presented and compared with the experimentally observed structures (RMSDs are around 4.5-5.5 Å). To start a modelling trial only the protein sequence and knowledge of its secondary structure is required. The fittest folds obtained after the evolution at the end of the simulations yield the three dimensional models of the fold. Current limitations are protein size (generally less than 100 aminoacids), number of secondary structure elements [7-8] and irregular topologies (e.g. ferridoxins).Further, preliminary results from current simulations are illustrated. We now want to apply simple experimental or other information, which is available long before the three-dimensional structure of the protein becomes known, to refine the modelling of the protein fold and tackle also more difficult modelling examples by our tool.Supplementary material to this paper is available in electronic form at http://dx.doi.org/10.1007/s0089460020304     

Significant factors selection in the chemical and enzymatic hydrolysis of lignocellulosic residues by a genetic algorithm analysis and comparison with the standard Plackett-Burman methodology

A comparison between the classic Plackett-Burman design (PB) ANOVA analysis and a genetic algorithm (GA) approach to identify significant factors have been carried out. This comparison was made by applying both analyses to data obtained from the experimental results when optimizing both chemical and enzymatic hydrolysis of three lignocellulosic feedstocks (corn and wheat bran, and pine sawdust) by a PB experimental design. Depending on the kind of biomass and the hydrolysis being considered, different results were obtained. Interestingly, some interactions were found to be significant by the GA approach and allowed to identify significant factors, that otherwise, based only in the classic PB analysis, would have not been taken into account in a further optimization step. Improvements in the fitting of c.a. 80% were obtained when comparing the coefficient of determination (R2) computed for both methods.     

Direct prediction of profiles of sequences compatible with a protein structure by neural networks with fragment‐based local and energy‐based nonlocal profiles

Locating sequences compatible with a protein structural fold is the well‐known inverse protein‐folding problem. While significant progress has been made, the success rate of protein design remains low. As a result, a library of designed sequences or profile of sequences is currently employed for guiding experimental screening or directed evolution. Sequence profiles can be computationally predicted by iterative mutations of a random sequence to produce energy‐optimized sequences, or by combining sequences of structurally similar fragments in a template library. The latter approach is computationally more efficient but yields less accurate profiles than the former because of lacking tertiary structural information. Here we present a method called SPIN that predicts Sequence Profiles by Integrated Neural network based on fragment‐derived sequence profiles and structure‐derived energy profiles. SPIN improves over the fragment‐derived profile by 6.7% (from 23.6 to 30.3%) in sequence identity between predicted and wild‐type sequences. The method also reduces the number of residues in low complex regions by 15.7% and has a significantly better balance of hydrophilic and hydrophobic residues at protein surface. The accuracy of sequence profiles obtained is comparable to those generated from the protein design program RosettaDesign 3.5. This highly efficient method for predicting sequence profiles from structures will be useful as a single‐body scoring term for improving scoring functions used in protein design and fold recognition. It also complements protein design programs in guiding experimental design of the sequence library for screening and directed evolution of designed sequences. The SPIN server is available at http://sparks‐lab.org . Proteins 2014; 82:2565–2573. © 2014 Wiley Periodicals, Inc.     

Partial venom gland transcriptome of a Drosophila parasitoid wasp, Leptopilina heterotoma, reveals novel and shared bioactive profiles with stinging Hymenoptera

Analysis of natural host-parasite relationships reveals the evolutionary forces that shape the delicate and unique specificity characteristic of such interactions. The accessory long gland-reservoir complex of the wasp Leptopilina heterotoma (Figitidae) produces venom with virus-like particles. Upon delivery, venom components delay host larval development and completely block host immune responses. The host range of this Drosophila endoparasitoid notably includes the highly-studied model organism, Drosophila melanogaster. Categorization of 827 unigenes, using similarity as an indicator of putative homology, reveals that approximately 25% are novel or classified as hypothetical proteins. Most of the remaining unigenes are related to processes involved in signaling, cell cycle, and cell physiology including detoxification, protein biogenesis, and hormone production. Analysis of L. heterotoma's predicted venom gland proteins demonstrates conservation among endo- and ectoparasitoids within the Apocrita (e.g., this wasp and the jewel wasp Nasonia vitripennis) and stinging aculeates (e.g., the honey bee and ants). Enzyme and KEGG pathway profiling predicts that kinases, esterases, and hydrolases may contribute to venom activity in this unique wasp. To our knowledge, this investigation is among the first functional genomic studies for a natural parasitic wasp of Drosophila. Our findings will help explain how L. heterotoma shuts down its hosts' immunity and shed light on the molecular basis of a natural arms race between these insects.