Emerging Computational Approaches for Antimicrobial Peptide Discovery.

In the last two decades many reports have addressed the application of artificial intelligence (AI) in the search and design of antimicrobial peptides (AMPs). AI has been represented by machine learning (ML) algorithms that use sequence-based features for the discovery of new peptidic scaffolds with promising biological activity. From AI perspective, evolutionary algorithms have been also applied to the rational generation of peptide libraries aimed at the optimization/design of AMPs. However, the literature has scarcely dedicated to other emerging non-conventional in silico approaches for the search/design of such bioactive peptides. Thus, the first motivation here is to bring up some non-standard peptide features that have been used to build classical ML predictive models. Secondly, it is valuable to highlight emerging ML algorithms and alternative computational tools to predict/design AMPs as well as to explore their chemical space. Another point worthy of mention is the recent application of evolutionary algorithms that actually simulate sequence evolution to both the generation of diversity-oriented peptide libraries and the optimization of hit peptides. Last but not least, included here some new considerations in proteogenomic analyses currently incorporated into the computational workflow for unravelling AMPs in natural sources.

Acute depletion of telomerase components DKC1 and NOP10 induces oxidative stress and disrupts ribosomal biogenesis via NPM1 and activation of the P53 pathway.

Mutations in DKC1, NOP10, and TINF2 genes, coding for proteins in telomerase and shelterin complexes, are responsible for diverse diseases known as telomeropathies and ribosomopathies, including dyskeratosis congenita (DC, ORPHA 1775). These genes contribute to the DC phenotype through mechanisms that are not completely understood. We previously demonstrated in models of DC that oxidative stress is an early and independent event that occurs prior to telomere shortening. To clarify the mechanisms that induce oxidative stress, we silenced genes DKC1, NOP10, and TINF2 with siRNA technology. With RNA array hybridisation, we found several altered pathways for each siRNA model. Afterwards, we identified common related genes. The silenced cell line with the most deregulated genes and pathways was siNOP10, followed by siDKC1, and then by siTINF2 to a lesser extent. The siDKC1 and siNOP10 models shared altered expression of genes in the p53 pathway, while siNOP10 and siTINF2 had the adherens junction pathway in common. We also observed that depletion of DKC1 and NOP10 H/ACA ribonucleoprotein produced ribosomal biogenesis impairment which, in turn, promoted p53 pathway activation. Finally, we found that those enzymes responsible for GSH synthesis were down-regulated in models of siDKC1 and siNOP10. In contrast, the silenced cells for TINF2 showed no disruption of ribosomal biogenesis or oxidative stress and did not produce p53 pathway activation. These results indicate that depletion of DKC1 and NOP10 promotes oxidative stress and disrupts ribosomal biogenesis which, in turn, activates the p53 pathway.

From genetics to epigenetics to unravel the etiology of adolescent idiopathic scoliosis.

Scoliosis is defined as the three-dimensional (3D) structural deformity of the spine with a radiological lateral Cobb angle (a measure of spinal curvature) of ≥10° that can be caused by congenital, developmental or degenerative problems. However, those cases whose etiology is still unknown, and affect healthy children and adolescents during growth, are the commonest form of spinal deformity, known as adolescent idiopathic scoliosis (AIS). In AIS management, early diagnosis and the accurate prediction of curve progression are most important because they can decrease negative long-term effects of AIS treatment, such as unnecessary bracing, frequent exposure to radiation, as well as saving the high costs of AIS treatment. Despite efforts made to identify a method or technique capable of predicting AIS progression, this challenge still remains unresolved. Genetics and epigenetics, and the application of machine learning and artificial intelligence technologies, open up new avenues to not only clarify AIS etiology, but to also identify potential biomarkers that can substantially improve the clinical management of these patients. This review presents the most relevant biomarkers to help explain the etiopathogenesis of AIS and provide new potential biomarkers to be validated in large clinical trials so they can be finally implemented into clinical settings.

Graph Theory-Based Sequence Descriptors as Remote Homology Predictors.

Alignment-free (AF) methodologies have increased in popularity in the last decades as alternative tools to alignment-based (AB) algorithms for performing comparative sequence analyses. They have been especially useful to detect remote homologs within the twilight zone of highly diverse gene/protein families and superfamilies. The most popular alignment-free methodologies, as well as their applications to classification problems, have been described in previous reviews. Despite a new set of graph theory-derived sequence/structural descriptors that have been gaining relevance in the detection of remote homology, they have been omitted as AF predictors when the topic is addressed. Here, we first go over the most popular AF approaches used for detecting homology signals within the twilight zone and then bring out the state-of-the-art tools encoding graph theory-derived sequence/structure descriptors and their success for identifying remote homologs. We also highlight the tendency of integrating AF features/measures with the AB ones, either into the same prediction model or by assembling the predictions from different algorithms using voting/weighting strategies, for improving the detection of remote signals. Lastly, we briefly discuss the efforts made to scale up AB and AF features/measures for the comparison of multiple genomes and proteomes. Alongside the achieved experiences in remote homology detection by both the most popular AF tools and other less known ones, we provide our own using the graphical-numerical methodologies, MARCH-INSIDE, TI2BioP, and ProtDCal. We also present a new Python-based tool (SeqDivA) with a friendly graphical user interface (GUI) for delimiting the twilight zone by using several similar criteria.

Role of glutathione in the regulation of epigenetic mechanisms in disease.

Epigenetics is a rapidly growing field that studies gene expression modifications not involving changes in the DNA sequence. Histone H3, one of the basic proteins in the nucleosomes that make up chromatin, is S-glutathionylated in mammalian cells and tissues, making Gamma-L-glutamyl-L-cysteinylglycine, glutathione (GSH), a physiological antioxidant and second messenger in cells, a new post-translational modifier of the histone code that alters the structure of the nucleosome. However, the role of GSH in the epigenetic mechanisms likely goes beyond a mere structural function. Evidence supports the hypothesis that there is a link between GSH metabolism and the control of epigenetic mechanisms at different levels (i.e., substrate availability, enzymatic activity for DNA methylation, changes in the expression of microRNAs, and participation in the histone code). However, little is known about the molecular pathways by which GSH can control epigenetic events. Studying mutations in enzymes involved in GSH metabolism and the alterations of the levels of cofactors affecting epigenetic mechanisms appears challenging. However, the number of diseases induced by aberrant epigenetic regulation is growing, so elucidating the intricate network between GSH metabolism, oxidative stress and epigenetics could shed light on how their deregulation contributes to the development of neurodegeneration, cancer, metabolic pathologies and many other types of diseases.

How the Protein Architecture of RNases III Influences their Substrate Specificity?

BACKGROUND: The Ribonuclease III (RNase III) enzymatic class is involved in many important biological processes from bacteria to higher eukaryotes. Consequently, they have been useful as drug-target candidates for drug development. Despite their high molecular diversity, RNases III share common structural and catalytic features and some degree of enzymatic activity. However, the role of accessory domains as key determinants of substrate selectivity and over the biological function of each RNase III type is still under study. RESULTS: The in vitro enzymatic activity of three RNase III members from class I (Escherichia coli RNase III, Schizosaccharomyces pombe Pac1 and Saccharomyces cerevisiae Rntp1) and the human Drosha placed in class II was revisited against four different substrates. These two RNase III classes comprise members showing different domain organization. Enzymatic activity differences were found among members of the class I, which were even higher when the human Drosha (class II) was tested. The substrate promiscuity of the E. coli RNase III was corroborated in vivo through its expression in S. cerevisiae, as reported previously, but was extended here to Pichia pastoris. The putative molecular mechanisms contributing for the lethal effect of the heterologous RNase III on the yeast lives were deeply discussed. CONCLUSION: The new generated biochemical data integrated with previous available information affirmed that RNases III substrate specificity as well as their cellular biological role is highly influenced by its protein structure architecture. This fact also allowed drawing evolutionary links between RNase III members from their structural and substrate specificity differences.

Alignment-Free Methods for the Detection and Specificity Prediction of Adenylation Domains.

Identifying adenylation domains (A-domains) and their substrate specificity can aid the detection of nonribosomal peptide synthetases (NRPS) at genome/proteome level and allow inferring the structure of oligopeptides with relevant biological activities. However, that is challenging task due to the high sequence diversity of A-domains (~10-40 % of amino acid identity) and their selectivity for 50 different natural/unnatural amino acids. Altogether these characteristics make their detection and the prediction of their substrate specificity a real challenge when using traditional sequence alignment methods, e.g., BLAST searches. In this chapter we describe two workflows based on alignment-free methods intended for the identification and substrate specificity prediction of A-domains. To identify A-domains we introduce a graphical-numerical method, implemented in TI2BioP version 2.0 (topological indices to biopolymers), which in a first step uses protein four-color maps to represent A-domains. In a second step, simple topological indices (TIs), called spectral moments, are derived from the graphical representations of known A-domains (positive dataset) and of unrelated but well-characterized sequences (negative set). Spectral moments are then used as input predictors for statistical classification techniques to build alignment-free models. Finally, the resulting alignment-free models can be used to explore entire proteomes for unannotated A-domains. In addition, this graphical-numerical methodology works as a sequence-search method that can be ensemble with homology-based tools to deeply explore the A-domain signature and cope with the diversity of this class (Aguero-Chapin et al., PLoS One 8(7):e65926, 2013). The second workflow for the prediction of A-domain's substrate specificity is based on alignment-free models constructed by transductive support vector machines (TSVMs) that incorporate information of uncharacterized A-domains. The construction of the models was implemented in the NRPSpredictor and in a first step uses the physicochemical fingerprint of the 34 residues lining the active site of the phenylalanine-adenylation domain of gramicidin synthetase A [PDB ID 1 amu] to derive a feature vector. Homologous positions were extracted for A-domains with known and unknown substrate specificities and turned into feature vectors. At the same time, A-domains with known specificities towards similar substrates were clustered by physicochemical properties of amino acids (AA). In a second step, support vector machines (SVMs) were optimized from feature vectors of characterized A-domains in each of the resulting clusters. Later, SVMs were used in the variant of TSVMs that integrate a fraction of uncharacterized A-domains during training to predict unknown specificities. Finally, uncharacterized A-domains were scored by each of the constructed alignment-free models (TSVM) representing each substrate specificity resulting from the clustering. The model producing the largest score for the uncharacterized A-domain assigns the substrate specificity to it (Rausch et al., Nucleic Acids Res 33:5799-5808, 2005).

Harmonization of QSAR Best Practices and Molecular Docking Provides an Efficient Virtual Screening Tool for Discovering New G-Quadruplex Ligands.

Telomeres and telomerase are key players in tumorogenesis. Among the various strategies proposed for telomerase inhibition or telomere uncapping, the stabilization of telomeric G-quadruplex (G4) structures is a very promising one. Additionally, G4 stabilizing ligands also act over tumors mediated by the alternative elongation of telomeres. Accordingly, the discovery of novel compounds able to act on telomeres and/or inhibit the telomerase enzyme by stabilizing DNA telomeric G4 structures as well as the development of approaches efficiently prioritizing such compounds constitute active areas of research in computational medicinal chemistry and anticancer drug discovery. In this direction, we applied a virtual screening strategy based on the rigorous application of QSAR best practices and its harmonized integration with structure-based methods. More than 600,000 compounds from commercial databases were screened, the first 99 compounds were prioritized, and 21 commercially available and structurally diverse candidates were purchased and submitted to experimental assays. Such strategy proved to be highly efficient in the prioritization of G4 stabilizer hits, with a hit rate of 23.5%. The best G4 stabilizer hit found exhibited a shift in melting temperature from FRET assay of +7.3 °C at 5 µM, while three other candidates also exhibited a promising stabilizing profile. The two most promising candidates also exhibited a good telomerase inhibitory ability and a mild inhibition of HeLa cells growth. None of these candidates showed antiproliferative effects in normal fibroblasts. Finally, the proposed virtual screening strategy proved to be a practical and reliable tool for the discovery of novel G4 ligands which can be used as starting points of further optimization campaigns.

FDA-approved drugs selected using virtual screening bind specifically to G-quadruplex DNA.

Guanine-rich sequences found in telomeres and oncogene promoters have the ability to form G-quadruplex structures. In this paper we describe the use of a virtual screening assay to search a database of FDA-approved compounds for compounds with the potential to bind G-quadruplex DNA. More than 750 telomerase inhibitors were identified in a literature search as acting through G-quadruplex stabilization, and from evaluation of these compounds, theoretical models capable of discriminating new compounds that bind G-quadruplex DNA were developed. Six compounds predicted to bind to the G-quadruplex structure were tested for their ability to bind to the human telomeric DNA sequence. Prochloroperazine, promazine, and chlorpromazine stabilized the G-quadruplex structure as determined by fluorescence resonance energy transfer techniques. These compounds also bound to promoter sequences of oncogenes such as c-myc and K-ras. Amitriptyline, imipramine, and loxapine were less stabilizing but did bind to the G-quadruplex. The ability of prochloroperazine, promazine, and chlorpromazine to recognize G-quadruplex structures was confirmed using a fluorescent intercalator displacement assay, in which displacement of thiazole orange from G-quadruplex structures was demonstrated. Interestingly, these compounds exhibited selectivity for the G-quadruplex structure as all had poor affinity for the duplex sequence.

Computational tools in the discovery of new G-quadruplex ligands with potential anticancer activity.

Guanine-rich sequences found at telomeres and oncogenes have the capacity to form G-quadruplex (G4) structures. It has been found a relationship between the ability to stabilizing G4 structures and anticancer activity. Guanine quadruplexes stabilization and its implication in cancer phenomena is a therapeutic target relatively recent. Computer-aided drug design has been a very useful tool for the search of new candidates. In last years, methodologies have improved with the development of the computational sciences. The hardware is also enhanced, new techniques are explored. NMR and X-ray information about different targets are discovered continually. The continuous augmentation of new powerful and comprehensive software's with this purpose is other significant factor that contributes to the discovering of new compounds. Nevertheless computer-aided drug design has not been vastly employed in the design of new compound with G4 stabilization activity. All things considered, this review will be focused on the influence of computational techniques on speeding up the discovery of new G4 ligands.

Non-linear models based on simple topological indices to identify RNase III protein members.

Alignment-free classifiers are especially useful in the functional classification of protein classes with variable homology and different domain structures. Thus, the Topological Indices to BioPolymers (TI2BioP) methodology (Agüero-Chapin et al., 2010) inspired in both the TOPS-MODE and the MARCH-INSIDE methodologies allows the calculation of simple topological indices (TIs) as alignment-free classifiers. These indices were derived from the clustering of the amino acids into four classes of hydrophobicity and polarity revealing higher sequence-order information beyond the amino acid composition level. The predictability power of such TIs was evaluated for the first time on the RNase III family, due to the high diversity of its members (primary sequence and domain organization). Three non-linear models were developed for RNase III class prediction: Decision Tree Model (DTM), Artificial Neural Networks (ANN)-model and Hidden Markov Model (HMM). The first two are alignment-free approaches, using TIs as input predictors. Their performances were compared with a non-classical HMM, modified according to our amino acid clustering strategy. The alignment-free models showed similar performances on the training and the test sets reaching values above 90% in the overall classification. The non-classical HMM showed the highest rate in the classification with values above 95% in training and 100% in test. Although the higher accuracy of the HMM, the DTM showed simplicity for the RNase III classification with low computational cost. Such simplicity was evaluated in respect to HMM and ANN models for the functional annotation of a new bacterial RNase III class member, isolated and annotated by our group.

TI2BioP: Topological Indices to BioPolymers. Its practical use to unravel cryptic bacteriocin-like domains.

Bacteriocins are proteinaceous toxins produced and exported by both gram-negative and gram-positive bacteria as a defense mechanism. The bacteriocin protein family is highly diverse, which complicates the identification of bacteriocin-like sequences using alignment approaches. The use of topological indices (TIs) irrespective of sequence similarity can be a promising alternative to predict proteinaceous bacteriocins. Thus, we present Topological Indices to BioPolymers (TI2BioP) as an alignment-free approach inspired in both the Topological Substructural Molecular Design (TOPS-MODE) and Markov Chain Invariants for Network Selection and Design (MARCH-INSIDE) methodology. TI2BioP allows the calculation of the spectral moments as simple TIs to seek quantitative sequence-function relationships (QSFR) models. Since hydrophobicity and basicity are major criteria for the bactericide activity of bacteriocins, the spectral moments ((HP)µ(k)) were derived for the first time from protein artificial secondary structures based on amino acid clustering into a Cartesian system of hydrophobicity and polarity. Several orders of (HP)µ(k) characterized numerically 196 bacteriocin-like sequences and a control group made up of 200 representative CATH domains. Subsequently, they were used to develop an alignment-free QSFR model allowing a 76.92% discrimination of bacteriocin proteins from other domains, a relevant result considering the high sequence diversity among the members of both groups. The model showed a prediction overall performance of 72.16%, detecting specifically 66.7% of proteinaceous bacteriocins whereas the InterProScan retrieved just 60.2%. As a practical validation, the model also predicted successfully the cryptic bactericide function of the Cry 1Ab C-terminal domain from Bacillus thuringiensis's endotoxin, which has not been detected by classical alignment methods.