Complexity of avian evolution revealed by family-level genomes.

Despite tremendous efforts in the past decades, relationships among main avian lineages remain heavily debated without a clear resolution. Discrepancies have been attributed to diversity of species sampled, phylogenetic method, and the choice of genomic regions 1-3. Here, we address these issues by analyzing genomes of 363 bird species 4 (218 taxonomic families, 92% of total). Using intergenic regions and coalescent methods, we present a well-supported tree but also a remarkable degree of discordance. The tree confirms that Neoaves experienced rapid radiation at or near the Cretaceous-Paleogene (K-Pg) boundary. Sufficient loci rather than extensive taxon sampling were more effective in resolving difficult nodes. Remaining recalcitrant nodes involve species that challenge modeling due to extreme GC content, variable substitution rates, incomplete lineage sorting, or complex evolutionary events such as ancient hybridization. Assessment of the impacts of different genomic partitions showed high heterogeneity across the genome. We discovered sharp increases in effective population size, substitution rates, and relative brain size following the K-Pg extinction event, supporting the hypothesis that emerging ecological opportunities catalyzed the diversification of modern birds. The resulting phylogenetic estimate offers novel insights into the rapid radiation of modern birds and provides a taxon-rich backbone tree for future comparative studies.

A region of suppressed recombination misleads neoavian phylogenomics.

Genomes are typically mosaics of regions with different evolutionary histories. When speciation events are closely spaced in time, recombination makes the regions sharing the same history small, and the evolutionary history changes rapidly as we move along the genome. When examining rapid radiations such as the early diversification of Neoaves 66 Mya, typically no consistent history is observed across segments exceeding kilobases of the genome. Here, we report an exception. We found that a 21-Mb region in avian genomes, mapped to chicken chromosome 4, shows an extremely strong and discordance-free signal for a history different from that of the inferred species tree. Such a strong discordance-free signal, indicative of suppressed recombination across many millions of base pairs, is not observed elsewhere in the genome for any deep avian relationships. Although long regions with suppressed recombination have been documented in recently diverged species, our results pertain to relationships dating circa 65 Mya. We provide evidence that this strong signal may be due to an ancient rearrangement that blocked recombination and remained polymorphic for several million years prior to fixation. We show that the presence of this region has misled previous phylogenomic efforts with lower taxon sampling, showing the interplay between taxon and locus sampling. We predict that similar ancient rearrangements may confound phylogenetic analyses in other clades, pointing to a need for new analytical models that incorporate the possibility of such events.

Evolutionary Analysis of Cnidaria Small Cysteine-Rich Proteins (SCRiPs), an Enigmatic Neurotoxin Family from Stony Corals and Sea Anemones (Anthozoa: Hexacorallia).

Cnidarians (corals, sea anemones, and jellyfish) produce toxins that play central roles in key ecological processes, including predation, defense, and competition, being the oldest extant venomous animal lineage. Cnidaria small cysteine-rich proteins (SCRiPs) were the first family of neurotoxins detected in stony corals, one of the ocean's most crucial foundation species. Yet, their molecular evolution remains poorly understood. Moreover, the lack of a clear classification system has hindered the establishment of an accurate and phylogenetically informed nomenclature. In this study, we extensively surveyed 117 genomes and 103 transcriptomes of cnidarians to identify orthologous SCRiP gene sequences. We annotated a total of 168 novel putative SCRiPs from over 36 species of stony corals and 12 species of sea anemones. Phylogenetic reconstruction identified four distinct SCRiP subfamilies, according to strict discrimination criteria based on well-supported monophyly with a high percentage of nucleotide and amino acids' identity. Although there is a high prevalence of purifying selection for most SCRiP subfamilies, with few positively selected sites detected, a subset of Acroporidae sequences is influenced by diversifying positive selection, suggesting potential neofunctionalizations related to the fine-tuning of toxin potency. We propose a new nomenclature classification system relying on the phylogenetic distribution and evolution of SCRiPs across Anthozoa, which will further assist future proteomic and functional research efforts.

Disclosing the hidden nucleotide sequences: a journey into DNA barcoding of raptor species in public repositories.

BACKGROUND: In nucleotide public repositories, studies discovered data errors which resulted in incorrect species identification of several accipitrid raptors considered for conservation. Mislabeling, particularly in cases of cryptic species complexes and closely related species, which were identified based on morphological characteristics, was discovered. Prioritizing accurate species labeling, morphological taxonomy, and voucher documentation is crucial to rectify spurious data. OBJECTIVE: Our study aimed to identify an effective DNA barcoding tool that accurately reflects the efficiency status of barcodes in raptor species (Accipitridae). METHODS: Barcode sequences, including 889 sequences from the mitochondrial cytochrome c oxidase I (COI) gene and 1052 sequences from cytochrome b (Cytb), from 150 raptor species within the Accipitridae family were analyzed. RESULTS: The highest percentage of intraspecific nearest neighbors from the nearest neighbor test was 88.05% for COI and 95.00% for Cytb, suggesting that the Cytb gene is a more suitable marker for accurately identifying raptor species and can serve as a standard region for DNA barcoding. In both datasets, a positive barcoding gap representing the difference between inter-and intra-specific sequence divergences was observed. For COI and Cytb, the cut-off score sequence divergences for species identification were 4.00% and 3.00%, respectively. CONCLUSION: Greater accuracy was demonstrated for the Cytb gene, making it the preferred primary DNA barcoding marker for raptors.

Optimizing Microsatellite Marker Panels for Genetic Diversity and Population Genetic Studies: An Ant Colony Algorithm Approach with Polymorphic Information Content.

Microsatellites are polymorphic and cost-effective. Optimizing reduced microsatellite panels using heuristic algorithms eases budget constraints in genetic diversity and population genetic assessments. Microsatellite marker efficiency is strongly associated with its polymorphism and is quantified as the polymorphic information content (PIC). Nevertheless, marker selection cannot rely solely on PIC. In this study, the ant colony optimization (ACO) algorithm, a widely recognized optimization method, was adopted to create an enhanced selection scheme for refining microsatellite marker panels, called the PIC-ACO selection scheme. The algorithm was fine-tuned and validated using extensive datasets of chicken (Gallus gallus) and Chinese gorals (Naemorhedus griseus) from our previous studies. In contrast to basic optimization algorithms that stochastically initialize potential outputs, our selection algorithm utilizes the PIC values of markers to prime the ACO process. This increases the global solution discovery speed while reducing the likelihood of becoming trapped in local solutions. This process facilitated the acquisition of a cost-efficient and optimized microsatellite marker panel for studying genetic diversity and population genetic datasets. The established microsatellite efficiency metrics such as PIC, allele richness, and heterozygosity were correlated with the actual effectiveness of the microsatellite marker panel. This approach could substantially reduce budgetary barriers to population genetic assessments, breeding, and conservation programs.

Overcoming taxonomic challenges in DNA barcoding for improvement of identification and preservation of clariid catfish species.

DNA barcoding without assessing reliability and validity causes taxonomic errors of species identification, which is responsible for disruptions of their conservation and aquaculture industry. Although DNA barcoding facilitates molecular identification and phylogenetic analysis of species, its availability in clariid catfish lineage remains uncertain. In this study, DNA barcoding was developed and validated for clariid catfish. 2,970 barcode sequences from mitochondrial cytochrome c oxidase I (COI) and cytochrome b (Cytb) genes and D-loop sequences were analyzed for 37 clariid catfish species. The highest intraspecific nearest neighbor distances were 85.47%, 98.03%, and 89.10% for COI, Cytb, and D-loop sequences, respectively. This suggests that the Cytb gene is the most appropriate for identifying clariid catfish and can serve as a standard region for DNA barcoding. A positive barcoding gap between interspecific and intraspecific sequence divergence was observed in the Cytb dataset but not in the COI and D-loop datasets. Intraspecific variation was typically less than 4.4%, whereas interspecific variation was generally more than 66.9%. However, a species complex was detected in walking catfish and significant intraspecific sequence divergence was observed in North African catfish. These findings suggest the need to focus on developing a DNA barcoding system for classifying clariid catfish properly and to validate its efficacy for a wider range of clariid catfish. With an enriched database of multiple sequences from a target species and its genus, species identification can be more accurate and biodiversity assessment of the species can be facilitated.

MicrosatNavigator: exploring nonrandom distribution and lineage-specificity of microsatellite repeat motifs on vertebrate sex chromosomes across 186 whole genomes.

Microsatellites are short tandem DNA repeats, ubiquitous in genomes. They are believed to be under selection pressure, considering their high distribution and abundance beyond chance or random accumulation. However, limited analysis of microsatellites in single taxonomic groups makes it challenging to understand their evolutionary significance across taxonomic boundaries. Despite abundant genomic information, microsatellites have been studied in limited contexts and within a few species, warranting an unbiased examination of their genome-wide distribution in distinct versus closely related-clades. Large-scale comparisons have revealed relevant trends, especially in vertebrates. Here, "MicrosatNavigator", a new tool that allows quick and reliable investigation of perfect microsatellites in DNA sequences, was developed. This tool can identify microsatellites across the entire genome sequences. Using this tool, microsatellite repeat motifs were identified in the genome sequences of 186 vertebrates. A significant positive correlation was noted between the abundance, density, length, and GC bias of microsatellites and specific lineages. The (AC)n motif is the most prevalent in vertebrate genomes, showing distinct patterns in closely related species. Longer microsatellites were observed on sex chromosomes in birds and mammals but not on autosomes. Microsatellites on sex chromosomes of non-fish vertebrates have the lowest GC content, whereas high-GC microsatellites (≥ 50 M% GC) are preferred in bony and cartilaginous fishes. Thus, similar selective forces and mutational processes may constrain GC-rich microsatellites to different clades. These findings should facilitate investigations into the roles of microsatellites in sex chromosome differentiation and provide candidate microsatellites for functional analysis across the vertebrate evolutionary spectrum.

Testing phylogenetic signal with categorical traits and tree uncertainty.

SUMMARY: The phylogenetic signal, frequently used to identify signatures of adaptive evolution or important associations between genes and phenotypes, measures the tendency for recently diverged species to resemble each other more than distantly related species. An example of such a measure is the δ statistic, which uses Shannon entropy to measure the degree of phylogenetic signal between a categorical trait and a phylogeny. In this study, we refined this statistic to account for tree uncertainty, resulting in more accurate assessments of phylogenetic associations. In addition, we provided a more accessible and computationally efficient implementation of the δ statistic that will facilitate its use by the evolutionary community. AVAILABILITY AND IMPLEMENTATION: github.com/diogo-s-ribeiro/delta-statistic.

A 2022 Update on Computational Approaches to the Discovery and Design of Antimicrobial Peptides.

The antimicrobial resistance process has been accelerated by the over-prescription and misuse of antibiotics [...].

Complex Networks Analyses of Antibiofilm Peptides: An Emerging Tool for Next-Generation Antimicrobials' Discovery.

Microbial biofilms cause several environmental and industrial issues, even affecting human health. Although they have long represented a threat due to their resistance to antibiotics, there are currently no approved antibiofilm agents for clinical treatments. The multi-functionality of antimicrobial peptides (AMPs), including their antibiofilm activity and their potential to target multiple microbes, has motivated the synthesis of AMPs and their relatives for developing antibiofilm agents for clinical purposes. Antibiofilm peptides (ABFPs) have been organized in databases that have allowed the building of prediction tools which have assisted in the discovery/design of new antibiofilm agents. However, the complex network approach has not yet been explored as an assistant tool for this aim. Herein, a kind of similarity network called the half-space proximal network (HSPN) is applied to represent/analyze the chemical space of ABFPs, aiming to identify privileged scaffolds for the development of next-generation antimicrobials that are able to target both planktonic and biofilm microbial forms. Such analyses also considered the metadata associated with the ABFPs, such as origin, other activities, targets, etc., in which the relationships were projected by multilayer networks called metadata networks (METNs). From the complex networks' mining, a reduced but informative set of 66 ABFPs was extracted, representing the original antibiofilm space. This subset contained the most central to atypical ABFPs, some of them having the desired properties for developing next-generation antimicrobials. Therefore, this subset is advisable for assisting the search for/design of both new antibiofilms and antimicrobial agents. The provided ABFP motifs list, discovered within the HSPN communities, is also useful for the same purpose.

Parallel evolution of fish bi-modal breathing and expansion of olfactory receptor (OR) genes: toward a universal ORs nomenclature.

Olfactory receptors (ORs) play a key role in the prime sensorial perception, being highly relevant for intra/interspecific interactions. ORs are a subgroup of G-protein coupled receptors that exhibit highly complex subgenomes in vertebrates. However, OR repertoires remain poorly studied in fish lineages, precluding finely retracing their origin, evolution, and diversification, especially in the most basal groups. Here, we conduct an exhaustive gene screening upon 43 high-quality fish genomes exhibiting varied gene repertoires (2-583 genes). While the early vertebrates performed gas exchange through gills, we hypothesize that the emergence of new breathing structures (swim bladder and paired lungs) in early osteichthyans may be associated with expansions in the ORs gene families sensitive to airborne molecules. Additionally, we verify that the OR repertoire of moderns actinopterygians has not increased as expected following a whole genome duplication, likely due to regulatory mechanisms compensating the gene load excess. Finally, we identify 25 distinct OR families, allowing us to propose an updated universal nomenclature for the fish ORs.

Omics and Remote Homology Integration to Decipher Protein Functionality.

In the recent years, several "omics" technologies based on specific biomolecules (from DNA, RNA, proteins, or metabolites) have won growing importance in the scientific field. Despite each omics possess their own laboratorial protocols, they share a background of bioinformatic tools for data integration and analysis. A recent subset of bioinformatic tools, based on available templates or remote homology protocols, allow computational fast and high-accuracy prediction of protein structures. The quickly predict of actually unsolved protein structures, together with late omics findings allow a boost of scientific advances in multiple fields such as cancer, longevity, immunity, mitochondrial function, toxicology, drug design, biosensors, and recombinant protein engineering. In this chapter, we assessed methodological approaches for the integration of omics and remote homology inferences to decipher protein functionality, opening the door to the next era of biological knowledge.

Divergent sensory and immune gene evolution in sea turtles with contrasting demographic and life histories.

Sea turtles represent an ancient lineage of marine vertebrates that evolved from terrestrial ancestors over 100 Mya. The genomic basis of the unique physiological and ecological traits enabling these species to thrive in diverse marine habitats remains largely unknown. Additionally, many populations have drastically declined due to anthropogenic activities over the past two centuries, and their recovery is a high global conservation priority. We generated and analyzed high-quality reference genomes for the leatherback (Dermochelys coriacea) and green (Chelonia mydas) turtles, representing the two extant sea turtle families. These genomes are highly syntenic and homologous, but localized regions of noncollinearity were associated with higher copy numbers of immune, zinc-finger, and olfactory receptor (OR) genes in green turtles, with ORs related to waterborne odorants greatly expanded in green turtles. Our findings suggest that divergent evolution of these key gene families may underlie immunological and sensory adaptations assisting navigation, occupancy of neritic versus pelagic environments, and diet specialization. Reduced collinearity was especially prevalent in microchromosomes, with greater gene content, heterozygosity, and genetic distances between species, supporting their critical role in vertebrate evolutionary adaptation. Finally, diversity and demographic histories starkly contrasted between species, indicating that leatherback turtles have had a low yet stable effective population size, exhibit extremely low diversity compared with other reptiles, and harbor a higher genetic load compared with green turtles, reinforcing concern over their persistence under future climate scenarios. These genomes provide invaluable resources for advancing our understanding of evolution and conservation best practices in an imperiled vertebrate lineage.

Risk assessment of cyanobacteria toxic metabolites on freshwater ecosystems applying molecular methods.

Cyanobacteria, ancient prokaryotes, interfere with ecosystem water quality through the production of cyanotoxins and bloom formation. Therefore, for water safety and public health reasons, the application of faster, sensitive, and specific tools on its risk assessment is demanded. Polymerase chain reaction (PCR) coupled with DNA sequencing can be a helpful tool for the presence and potential to cyanotoxicity. To achieve these, seven waterbodies located on the North and Center regions of Portugal were sampled for two monitoring periods (2017 and 2018). Thus, given the five risk levels proposed (none up to four cyanotoxins - mcyA, cyrC, anaC, sxtI - being detected per risk level), results showed that the great majority of the ecosystems analyzed on the presence of blooms and under climate change phenomenon (heat waves) had an elevated risk (up to four cyanotoxins being detected) corresponding to a situation of high potential of cyanotoxicity. In the opposite conditions (i.e., absence of blooms and heat waves), the risk was lowered to none or only one cyanotoxin being detected. Two ecosystems escaped this trend and demonstrated little to no alterations among risk levels from 1 year to another corresponding to a high potential of cyanotoxicity and cyanotoxins persistence in comparison to other studied ecosystems. Overall, the risk assessment undertaken suggests that other ecosystems ecological variables (physical, hydrological, or chemical) are interfering on the occurrence and persistence of cyanotoxins biosynthesis genes. Given the observed conditions (eutrophic status, bloom occurrence, and heat waves) of the analyzed ecosystems, cyanobacterial potential for toxicity seems to have increased, suggesting a need of the incorporation of other cyanotoxins apart of the regulated microcystins-LR on cyanotoxins surveillance programs of Portugal.

Network Science and Group Fusion Similarity-Based Searching to Explore the Chemical Space of Antiparasitic Peptides.

Antimicrobial peptides (AMPs) have appeared as promising compounds to treat a wide range of diseases. Their clinical potentialities reside in the wide range of mechanisms they can use for both killing microbes and modulating immune responses. However, the hugeness of the AMPs' chemical space (AMPCS), represented by more than 1065 unique sequences, has represented a big challenge for the discovery of new promising therapeutic peptides and for the identification of common structural motifs. Here, we introduce network science and a similarity searching approach to discover new promising AMPs, specifically antiparasitic peptides (APPs). We exploited the network-based representation of APPs' chemical space (APPCS) to retrieve valuable information by using three network types: chemical space (CSN), half-space proximal (HSPN), and metadata (METN). Some centrality measures were applied to identify in each network the most important and nonredundant peptides. Then, these central peptides were considered as queries (Qs) in group fusion similarity-based searches against a comprehensive collection of known AMPs, stored in the graph database StarPepDB, to propose new potential APPs. The performance of the resulting multiquery similarity-based search models (mQSSMs) was evaluated in five benchmarking data sets of APP/non-APPs. The predictions performed by the best mQSSM showed a strong-to-very-strong performance since their external Matthews correlation coefficient (MCC) values ranged from 0.834 to 0.965. Outstanding MCC values (>0.85) were attained by the mQSSM with 219 Qs from both networks CSN and HSPN with 0.5 as similarity threshold in external data sets. Then, the performance of our best mQSSM was compared with the APPs prediction servers AMPDiscover and AMPFun. The proposed model showed its relevance by outperforming state-of-the-art machine learning models to predict APPs. After applying the best mQSSM and additional filters on the non-APP space from StarPepDB, 95 AMPs were repurposed as potential APP hits. Due to the high sequence diversity of these peptides, different computational approaches were applied to identify relevant motifs for searching and designing new APPs. Lastly, we identified 11 promising APP lead candidates by using our best mQSSMs together with diversity-based network analyses, and 24 web servers for activity/toxicity and drug-like properties. These results support that network-based similarity searches can be an effective and reliable strategy to identify APPs. The proposed models and pipeline are freely available through the StarPep toolbox software at http://mobiosd-hub.com/starpep.

ABP-Finder: A Tool to Identify Antibacterial Peptides and the Gram-Staining Type of Targeted Bacteria.

Multi-drug resistance in bacteria is a major health problem worldwide. To overcome this issue, new approaches allowing for the identification and development of antibacterial agents are urgently needed. Peptides, due to their binding specificity and low expected side effects, are promising candidates for a new generation of antibiotics. For over two decades, a large diversity of antimicrobial peptides (AMPs) has been discovered and annotated in public databases. The AMP family encompasses nearly 20 biological functions, thus representing a potentially valuable resource for data mining analyses. Nonetheless, despite the availability of machine learning-based approaches focused on AMPs, these tools lack evidence of successful application for AMPs' discovery, and many are not designed to predict a specific function for putative AMPs, such as antibacterial activity. Consequently, among the apparent variety of data mining methods to screen peptide sequences for antibacterial activity, only few tools can deal with such task consistently, although with limited precision and generally no information about the possible targets. Here, we addressed this gap by introducing a tool specifically designed to identify antibacterial peptides (ABPs) with an estimation of which type of bacteria is susceptible to the action of these peptides, according to their response to the Gram-staining assay. Our tool is freely available via a web server named ABP-Finder. This new method ranks within the top state-of-the-art ABP predictors, particularly in terms of precision. Importantly, we showed the successful application of ABP-Finder for the screening of a large peptide library from the human urine peptidome and the identification of an antibacterial peptide.

Fuzzy spherical truncation-based multi-linear protein descriptors: From their definition to application in structural-related predictions.

This study introduces a set of fuzzy spherically truncated three-dimensional (3D) multi-linear descriptors for proteins. These indices codify geometric structural information from kth spherically truncated spatial-(dis)similarity two-tuple and three-tuple tensors. The coefficients of these truncated tensors are calculated by applying a smoothing value to the 3D structural encoding based on the relationships between two and three amino acids of a protein embedded into a sphere. At considering, the geometrical center of the protein matches with center of the sphere, the distance between each amino acid involved in any specific interaction and the geometrical center of the protein can be computed. Then, the fuzzy membership degree of each amino acid from an spherical region of interest is computed by fuzzy membership functions (FMFs). The truncation value is finally a combination of the membership degrees from interacting amino acids, by applying the arithmetic mean as fusion rule. Several fuzzy membership functions with diverse biases on the calculation of amino acids memberships (e.g., Z-shaped (close to the center), PI-shaped (middle region), and A-Gaussian (far from the center)) were considered as well as traditional truncation functions (e.g., Switching). Such truncation functions were comparatively evaluated by exploring: 1) the frequency of membership degrees, 2) the variability and orthogonality analyses among them based on the Shannon Entropy's and Principal Component's methods, respectively, and 3) the prediction performance of alignment-free prediction of protein folding rates and structural classes. These analyses unraveled the singularity of the proposed fuzzy spherically truncated MDs with respect to the classical (non-truncated) ones and respect to the MDs truncated with traditional functions. They also showed an improved prediction power by attaining an external correlation coefficient of 95.82% in the folding rate modelling and an accuracy of 100% in distinguishing structural protein classes. These outcomes are better than the ones attained by existing approaches, justifying the theoretical contribution of this report. Thus, the fuzzy spherically truncated-based protein descriptors from MuLiMs-MCoMPAs (http://tomocomd.com/mulims-mcompas) are promising alignment-free predictors for modeling protein functions and properties.

Statistical Inferences Applying Non-Parametric Data on Cyanobacterial Investigations: Contributions to Water Quality and New Trends under Global Changes on Portuguese Freshwater Ecosystems.

Cyanobacteria are a bloom-forming ancient group of photosynthetic prokaryotes. A rise in temperature is a major contributor to its massive proliferation, namely on freshwater ecosystems, with social and economic impacts. Thus, reliable and cost-effective tools can permit the fast surveillance and assessment of temperature effects on potentially toxic cyanobacteria distribution and impacts. The occurrence of three potentially toxic cyanobacteria species was assessed on seven sampling points across three sampling years. Moreover, the association between the occurrence of those cyanobacteria species with climate change events was addressed. Here, we combined molecular and statistical methods to study the impacts of temperature on the occurrence of three globally occurring cyanotoxin-producing cyanobacteria species-Microcystis aeruginosa (microcystins), Raphidiopsis raciborskii (cylindrospermopsins and saxitoxins) and Planktothrix agardhii (microcystins and saxitoxins). Samples were collected on seven European temperate freshwater systems located on the North and Centre regions of Portugal, across three distinct sampling years with distinct ranges of air temperature. Data support that M. aeruginosa is still a common inhabitant of Portuguese freshwater ecosystems and a new trend was found on R. raciborskii recent invasion and establishment on the colder north ecosystems of Portugal. Additionally, the highest frequency of detection of both cyanobacteria was associated with warmer years. P. agardhii also revealed a new trend, being reported for the first time on North and Centre Regions of Portugal, however with no statistical relation with air temperature, demonstrating a higher ecological fitness. Distinct profiles of the statistical analysis on the three tested cyanobacteria species contribute to deepen the studies on other species as well as of our analyzed species on a global level. This assessment may help to anticipate possible repercussions on water quality and public health due to most probable alterations on cyanotoxins profile given the ecological fitness established among air temperature and PCR detection of potentially toxic cyanobacteria.

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.