Background
Epidemics caused by highly pathogenic avian influenza virus (HPAIV) are a continuing threat to human health and to the world's economy. The development of approaches, which help to understand the significance of structural changes resulting from the alarming mutational propensity for human-to-human transmission of HPAIV, is of particularly interest. Here we compare informational and structural properties of the hemagglutinin (HA) of H5N1 virus and human influenza virus subtypes, which are important for the receptor/virus interaction.

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Characterization of conserved properties of hemagglutinin of H5N1 and human influenza viruses: possible consequences for therapy and infection control

Directed evolution is an important research activity in synthetic biology and biotechnology. Numerous reports describe the application of tedious mutation/screening cycles for the improvement of proteins. Recently, knowledge-based approaches have facilitated the prediction of protein properties and the identification of improved mutants. However, epistatic phenomena constitute an obstacle which can impair the predictions in protein engineering. We present an innovative sequence-activity relationship (innov’SAR) methodology based on digital signal processing combining wet-lab experimentation and computational protein design. In our machine learning approach, a predictive model is developed to find the resulting property of the protein when the n single point mutations are permuted (2n combinations). The originality of our approach is that only sequence information and the fitness of mutants measured in the wet-lab are needed to build models. We illustrate the application of the approach in the case of improving the enantioselectivity of an epoxide hydrolase from Aspergillus niger. n = 9 single point mutants of the enzyme were experimentally assessed for their enantioselectivity and used as a learning dataset to build a model. Based on combinations of the 9 single point mutations (29), the enantioselectivity of these 512 variants were predicted, and candidates were experimentally checked: better mutants with higher enantioselectivity were indeed found.

/pdf/a-machine-learning-approach-for-reliable-prediction-of-amino-1rfzih10jq.pdf

A machine learning approach for reliable prediction of amino acid interactions and its application in the directed evolution of enantioselective enzymes.

The R package SFAPS has been developed for structure/function analysis of protein sequences based on information spectrum method. The informational spectrum method employs the electron-Ion interaction potential parameter as the numerical representation for the protein sequence, and obtains the characteristic frequency of a particular protein interaction after computing the Discrete Fourier Transform (DFT) for protein sequences. The informational spectrum method is often used to analyze protein sequences, so we developed this software, which is implemented as an add-on package to the freely available and widely used statistical language R. Our package is distributed as open source code for Linux, Unix and Microsoft Windows. It is released under the GNU General Public License.

SFAPS: An R package for structure/function analysis of protein sequences based on informational spectrum method

https://hal.archives-ouvertes.fr/hal-00501691/document

Criteria for confirming sequence periodicity identified by Fourier transform analysis: application to GCR2, a candidate plant GPCR?

Complex informational spectrum analysis for protein sequences (CISAPS) and its web-based server are developed and presented. As recent studies show, only the use of the absolute spectrum in the analysis of protein sequences using the informational spectrum analysis is proven to be insufficient. Therefore, CISAPS is developed to consider and provide results in three forms including absolute, real, and imaginary spectrum. Biologically related features to the analysis of influenza A subtypes as presented as a case study in this study can also appear individually either in the real or imaginary spectrum. As the results presented, protein classes can present similarities or differences according to the features extracted from CISAPS web server. These associations are probable to be related with the protein feature that the specific amino acid index represents. In addition, various technical issues such as zero-padding and windowing that may affect the analysis are also addressed. CISAPS uses an expanded list of 611 unique amino acid indices where each one represents a different property to perform the analysis. This web-based server enables researchers with little knowledge of signal processing methods to apply and include complex informational spectrum analysis to their work.

/pdf/cisaps-complex-informational-spectrum-for-the-analysis-of-s2pgevm05z.pdf

CISAPS: Complex Informational Spectrum for the Analysis of Protein Sequences

Informational content of linear macromolecules (DNA, RNA, and proteins) is analyzed by the method of digital signal processing. Each element (amino acid or nucleotide) of macromolecules is represented by the corresponding value of electron-ion interaction potential. This numerical representation of the primary structure of macromolecules is subjected to digital signal processing in order to extract the information corresponding to biological function. It is shown that the multiple cross spectrum of functionally related sequences exhibits significant peak frequencies. In the case of functionally unrelated sequences such peaks were not found. Peak frequencies are different for different biological functions. Based on this, we conjecture that the peak frequencies in the multiple cross spectrum of sequences with the same boilogical function are related to this biological function.

Dimitrijevic

Papers

Is it Possible to Analyze DNA and Protein Sequences by the Methods of Digital Signal Processing