Background - Vaccine development in the post-genomic era often begins with the in silico screening of genome information, with the most probable protective antigens being predicted rather than requiring causative microorganisms to be grown. Despite the obvious advantages of this approach – such as speed and cost efficiency – its success remains dependent on the accuracy of antigen prediction. Most approaches use sequence alignment to identify antigens. This is problematic for several reasons. Some proteins lack obvious sequence similarity, although they may share similar structures and biological properties. The antigenicity of a sequence may be encoded in a subtle and recondite manner not amendable to direct identification by sequence alignment. The discovery of truly novel antigens will be frustrated by their lack of similarity to antigens of known provenance. To overcome the limitations of alignment-dependent methods, we propose a new alignment-free approach for antigen prediction, which is based on auto cross covariance (ACC) transformation of protein sequences into uniform vectors of principal amino acid properties. Results - Bacterial, viral and tumour protein datasets were used to derive models for prediction of whole protein antigenicity. Every set consisted of 100 known antigens and 100 non-antigens. The derived models were tested by internal leave-one-out cross-validation and external validation using test sets. An additional five training sets for each class of antigens were used to test the stability of the discrimination between antigens and non-antigens. The models performed well in both validations showing prediction accuracy of 70% to 89%. The models were implemented in a server, which we call VaxiJen. Conclusion - VaxiJen is the first server for alignment-independent prediction of protective antigens. It was developed to allow antigen classification solely based on the physicochemical properties of proteins without recourse to sequence alignment. The server can be used on its own or in combination with alignment-based prediction methods.

/pdf/vaxijen-a-server-for-prediction-of-protective-antigens-2ynjuvmdxi.pdf

VaxiJen: a server for prediction of protective antigens, tumour antigens and subunit vaccines

Motivation: Genetic recombination and, in particular, genetic shuffling are at odds with sequence comparison by alignment, which assumes conservation of contiguity between homologous segments. A variety of theoretical foundations are being used to derive alignment-free methods that overcome this limitation. The formulation of alternative metrics for dissimilarity between sequences and their algorithmic implementations are reviewed. Results: The overwhelming majority of work on alignmentfree sequence has taken place in the past two decades, with most reports published in the past 5 years. Two main categories of methods have been proposed—methods based on word (oligomer) frequency, and methods that do not require resolving the sequence with fixed word length segments. The first category is based on the statistics of word frequency, on the distances defined in a Cartesian space defined by the frequency vectors, and on the information content of frequency distribution. The second category includes the use of Kolmogorov complexity and Chaos Theory. Despite their low visibility, alignment-free metrics are in fact already widely used as pre-selection filters for alignment-based querying of large applications. Recent work is furthering their usage as a scale-independent methodology that is capable of recognizing homology when loss of contiguity is beyond the possibility of alignment. Availability: Most of the alignment-free algorithms reviewed were implemented in MATLAB code and are available at http://bioinformatics.musc.edu/resources.html. Contact: almeidaj@musc.edu; svinga@itqb.unl.pt

/pdf/alignment-free-sequence-comparison-a-review-13eerno6qs.pdf

Alignment-free sequence comparison-a review.

Multi-target strategies for the improved treatment of depressive states: Conceptual foundations and neuronal substrates, drug discovery and therapeutic application.

Adrenocorticotropic hormone and alpha-, beta-, and gamma-melanocyte-stimulating hormones, collectively called melanocortin peptides, exert multiple effects upon the host. These effects range from modulation of fever and inflammation to control of food intake, autonomic functions, and exocrine secretions. Recognition and cloning of five melanocortin receptors (MCRs) has greatly improved understanding of peptide-target cell interactions. Preclinical investigations indicate that activation of certain MCR subtypes, primarily MC1R and MC3R, could be a novel strategy to control inflammatory disorders. As a consequence of reduced translocation of the nuclear factor kappaB to the nucleus, MCR activation causes a collective reduction of the major molecules involved in the inflammatory process. Therefore, anti-inflammatory influences are broad and are not restricted to a specific mediator. Short half-life and lack of selectivity could be an obstacle to the use of the natural melanocortins. However, design and synthesis of new MCR ligands with selective chemical properties are already in progress. This review examines how marshaling MCR could control inflammation.

Targeting Melanocortin Receptors as a Novel Strategy to Control Inflammation

Motivation: Allergenicity, like antigenicity and immunogenicity, is a property encoded linearly and non-linearly, and therefore the alignment-based approaches are not able to identify this property unambiguously. A novel alignment-free descriptor-based fingerprint approach is presented here and applied to identify allergens and non-allergens. The approach was implemented into a four step algorithm. Initially, the protein sequences are described by amino acid principal properties as hydrophobicity, size, relative abundance, helix and -strand forming propensities. Then, the generated strings of different length are converted into vectors with equal length by autoand cross-covariance (ACC). The vectors were transformed into binary fingerprints and compared in terms of Tanimoto coefficient. Results: The approach was applied to a set of 2427 known allergens and 2427 non-allergens and identified correctly 88% of them with Matthews correlation coefficient of 0.759. The descriptor fingerprint approach presented here is universal. It could be applied for any classification problem in computational biology. The set of E-descriptors is able to capture the main structural and physicochemical properties of amino acids building the proteins. The ACC transformation overcomes the main problem in the alignment-based comparative studies arising from the different length of the aligned protein sequences. The conversion of protein ACC values into binary descriptor fingerprints allows similarity search and classification. Availability and implementation: The algorithm described in the present study was implemented in a specially designed Web site, named AllergenFP (FP stands for FingerPrint). AllergenFP is written in Python, with GIU in HTML. It is freely accessible at http://ddgpharmfac.net/Allergen FP. Contact: idoytchinova@pharmfac.net or ivanbangov@shu-bg.net

/pdf/allergenfp-allergenicity-prediction-by-descriptor-54or4qjncl.pdf

AllergenFP: allergenicity prediction by descriptor fingerprints

Development of proteo-chemometrics: a novel technology for the analysis of drug-receptor interactions

We have developed an alignment-independent method for classification of G-protein coupled receptors (GPCRs) according to the principal chemical properties of their amino acid sequences. The method relies on a multivariate approach where the primary amino acid sequences are translated into vectors based on the principal physicochemical properties of the amino acids and transformation of the data into a uniform matrix by applying a modified autocross-covariance transform. The application of principal component analysis to a data set of 929 class A GPCRs showed a clear separation of the major classes of GPCRs. The application of partial least squares projection to latent structures created a highly valid model (cross-validated correlation coefficient, Q2 = 0.895) that gave unambiguous classification of the GPCRs in the training set according to their ligand binding class. The model was further validated by external prediction of 535 novel GPCRs not included in the training set. Of the latter, only 14 sequences, confined in rapidly expanding GPCR classes, were mispredicted. Moreover, 90 orphan GPCRs out of 165 were tentatively identified to GPCR ligand binding class. The alignment-independent method could be used to assess the importance of the principal chemical properties of every single amino acid in the protein sequences for their contributions in explaining GPCR family membership. It was then revealed that all amino acids in the unaligned sequences contributed to the classifications, albeit to varying extent; the most important amino acids being those that could also be determined to be conserved by using traditional alignment-based methods.

https://onlinelibrary.wiley.com/doi/pdf/10.1110/ps.2500102

Classification of G-protein coupled receptors by alignment-independent extraction of principal chemical properties of primary amino acid sequences.

We have evaluated the proteochemometrics approach in the analysis of the interactions of a diverse set or organic ligands with subtypes of serotonin, dopamine, histamine, and adrenergic receptors. As used herein, proteochemometrics exploits affinity data for series of organic amines binding to wild-type amine G protein-coupled receptors, correlating it to descriptions and cross-description derived from the primary amino acid sequences of the receptors and the computed structures of the organic compounds. We show that after appropriate data preprocessing, statistically valid models that have good external predictive ability can be created. Evaluation of the models gave important quantitative insight into the mode of interactions of the amine G protein-coupled receptors with their ligands.

Proteochemometrics modeling of the interaction of amine G-protein coupled receptors with a diverse set of ligands.

Motivation: Proteochemometrics is a novel technology for the analysis of interactions of series of proteins with series of ligands. We have here customized it for analysis of large datasets and evaluated it for the modeling of the interaction of psychoactive organic amines with all the five known families of amine G protein-coupled receptors (GPCRs).

Results: The model exploited data for the binding of 22 compounds to 31 amine GPCRs, correlating chemical descriptions and cross-descriptions of compounds and receptors to binding affinity using a novel strategy. A highly valid model (q2 = 0.76) was obtained which was further validated by external predictions using data for 10 other entirely independent compounds, yielding the high q2ext = 0.67. Interpretation of the model reveals molecular interactions that govern psychoactive organic amines overall affinity for amine GPCRs, as well as their selectivity for particular amine GPCRs. The new modeling procedure allows us to obtain fully interpretable proteochemometrics models using essentially unlimited number of ligand and protein descriptors.

Contact: jarl.wikberg@farmbio.uu.se

Supplementary information: Supplementary data are available at Bioinformatics online.

/pdf/improved-approach-for-proteochemometrics-modeling-3ht4muxo4q.pdf

Maris Lapinsh

Papers

Development of proteo-chemometrics: a novel technology for the analysis of drug-receptor interactions

Classification of G-protein coupled receptors by alignment-independent extraction of principal chemical properties of primary amino acid sequences.

Proteochemometrics modeling of the interaction of amine G-protein coupled receptors with a diverse set of ligands.

Improved approach for proteochemometrics modeling: application to organic compound---amine G protein-coupled receptor interactions

Proteochemometrics: A Tool for Modeling the Molecular Interaction Space