Exploring the adenylation domain repertoire of nonribosomal peptide synthetases using an ensemble of sequence-search methods

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.

Putative Antimicrobial Peptides of the Posterior Salivary Glands from the Cephalopod Octopus vulgaris Revealed by Exploring a Composite Protein Database

Cephalopods, successful predators, can use a mixture of substances to subdue their prey, becoming interesting sources of bioactive compounds. In addition to neurotoxins and enzymes, the presence of antimicrobial compounds has been reported. Recently, the transcriptome and the whole proteome of the Octopus vulgaris salivary apparatus were released, but the role of some compounds—e.g., histones, antimicrobial peptides (AMPs), and toxins—remains unclear. Herein, we profiled the proteome of the posterior salivary glands (PSGs) of O. vulgaris using two sample preparation protocols combined with a shotgun-proteomics approach. Protein identification was performed against a composite database comprising data from the UniProtKB, all transcriptomes available from the cephalopods’ PSGs, and a comprehensive non-redundant AMPs database. Out of the 10,075 proteins clustered in 1868 protein groups, 90 clusters corresponded to venom protein toxin families. Additionally, we detected putative AMPs clustered with histones previously found as abundant proteins in the saliva of O. vulgaris. Some of these histones, such as H2A and H2B, are involved in systemic inflammatory responses and their antimicrobial effects have been demonstrated. These results not only confirm the production of enzymes and toxins by the O. vulgaris PSGs but also suggest their involvement in the first line of defense against microbes.

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.

Genomics-driven discovery of a biosynthetic gene cluster required for the synthesis of BII-Rafflesfungin from the fungus Phoma sp. F3723

Background

Phomafungin is a recently reported broad spectrum antifungal compound but its biosynthetic pathway is unknown. We combed publicly available Phoma genomes but failed to find any putative biosynthetic gene cluster that could account for its biosynthesis.

Results

Therefore, we sequenced the genome of one of our Phoma strains (F3723) previously identified as having antifungal activity in a high-throughput screen. We found a biosynthetic gene cluster that was predicted to synthesize a cyclic lipodepsipeptide that differs in the amino acid composition compared to Phomafungin. Antifungal activity guided isolation yielded a new compound, BII-Rafflesfungin, the structure of which was determined.

Conclusions

We describe the NRPS-t1PKS cluster ‘BIIRfg’ compatible with the synthesis of the cyclic lipodepsipeptide BII-Rafflesfungin [HMHDA-L-Ala-L-Glu-L-Asn-L-Ser-L-Ser-D-Ser-D-allo-Thr-Gly]. We report new Stachelhaus codes for Ala, Glu, Asn, Ser, Thr, and Gly. We propose a mechanism for BII-Rafflesfungin biosynthesis, which involves the formation of the lipid part by BIIRfg_PKS followed by activation and transfer of the lipid chain by a predicted AMP-ligase on to the first PCP domain of the BIIRfg_NRPS gene.

Electronic supplementary material

The online version of this article (10.1186/s12864-019-5762-6) contains supplementary material, which is available to authorized users.

Surveying alignment-free features for Ortholog detection in related yeast proteomes by using supervised big data classifiers

BACKGROUND

The development of new ortholog detection algorithms and the improvement of existing ones are of major importance in functional genomics. We have previously introduced a successful supervised pairwise ortholog classification approach implemented in a big data platform that considered several pairwise protein features and the low ortholog pair ratios found between two annotated proteomes (Galpert, D et al., BioMed Research International, 2015). The supervised models were built and tested using a Saccharomycete yeast benchmark dataset proposed by Salichos and Rokas (2011). Despite several pairwise protein features being combined in a supervised big data approach; they all, to some extent were alignment-based features and the proposed algorithms were evaluated on a unique test set. Here, we aim to evaluate the impact of alignment-free features on the performance of supervised models implemented in the Spark big data platform for pairwise ortholog detection in several related yeast proteomes.

RESULTS

The Spark Random Forest and Decision Trees with oversampling and undersampling techniques, and built with only alignment-based similarity measures or combined with several alignment-free pairwise protein features showed the highest classification performance for ortholog detection in three yeast proteome pairs. Although such supervised approaches outperformed traditional methods, there were no significant differences between the exclusive use of alignment-based similarity measures and their combination with alignment-free features, even within the twilight zone of the studied proteomes. Just when alignment-based and alignment-free features were combined in Spark Decision Trees with imbalance management, a higher success rate (98.71%) within the twilight zone could be achieved for a yeast proteome pair that underwent a whole genome duplication. The feature selection study showed that alignment-based features were top-ranked for the best classifiers while the runners-up were alignment-free features related to amino acid composition.

CONCLUSIONS

The incorporation of alignment-free features in supervised big data models did not significantly improve ortholog detection in yeast proteomes regarding the classification qualities achieved with just alignment-based similarity measures. However, the similarity of their classification performance to that of traditional ortholog detection methods encourages the evaluation of other alignment-free protein pair descriptors in future research.

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).