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Published in final edited form as: Proteomics. 2024 Mar 8;24(12-13):e2300105. doi: 10.1002/pmic.202300105

Challenges in computational discovery of bioactive peptides in ’omics data

Luis Pedro Coelho 1,2, Célio Dias Santos-Júnior 2,3, Cesar de la Fuente-Nunez 4,5,6,7
PMCID: PMC11537280  NIHMSID: NIHMS2008712  PMID: 38458994

Abstract

Peptides have a plethora of activities in biological systems that can potentially be exploited biotechnologically. Several peptides are used clinically, as well as in industry and agriculture. The increase in available ’omics data has recently provided a large opportunity for mining novel enzymes, biosynthetic gene clusters, and molecules. While these data primarily consist of DNA sequences, other types of data provide important complementary information. Due to their size, the approaches proven successful at discovering novel proteins of canonical size cannot be naïvely applied to the discovery of peptides. Peptides can be encoded directly in the genome as short open reading frames (smORFs), or they can be derived from larger proteins by proteolysis. Both of these peptide classes pose challenges as simple methods for their prediction result in large numbers of false positives. Similarly, functional annotation of larger proteins, traditionally based on sequence similarity to infer orthology and then transferring functions between characterized proteins and uncharacterized ones, cannot be applied for short sequences. The use of these techniques is much more limited and alternative approaches based on machine learning are used instead. Here, we review the limitations of traditional methods as well as the alternative methods that have recently been developed for discovering novel bioactive peptides with a focus on prokaryotic genomes and metagenomes.

Keywords: bioinformatics, biomedicine, data mining < bioinformatics, diseases < biomedicine, infectious

1 ∣. INTRODUCTION

Organisms from all domains of life produce bioactive peptides [1]. This includes humans which produce peptides for signaling as well as for defense against pathogens [2, 3]. While organisms produce them for their fitness benefits, once discovered, they can be harnessed and used for biotechnological applications [4]. Peptides with antimicrobial activity constitute a potential source of alternative antibiotic classes with clinical uses [5-7], while other peptides are potentially useful for cancer therapy [8-10]. Outside the clinic, bioactive peptides have broad applications in the food industry, improving the safety of production and preservation of food [11]. Additionally, the use of peptides is being explored as an alternative to chemical pesticides, offering safe and sustainable options to improve crop production [12, 13] and improve the health of poultry and livestock [11] or in aquaculture [14].

Sequencing projects provide a source of novel sequences which can be explored for discovering novel bioactive peptides (Figure 1). In the last few years, particularly microbiome sequencing has provided a large source of novel sequences for discovering natural products from the global microbiome [15, 16] (see Table 1). The available space of genomes to mine for novel sequences has exploded: modern genome databases contain close to 1 million genomes – the proGenomes3 database, released in 2022 [17], represents a >10x growth compared to the previous version, proGenomes2, from 2020 [18] – and metagenomics contributes an even larger number of genomes and genes [19]. Although the most widely available data source is metagenomic deoxyribonucleic acid (DNA) sequencing, complementary ’omics data is increasingly collected as well, which brings its own challenges, but also can enhance our ability to discover novel peptides of interest. Exploration of the human gut microbiome has already yielded antimicrobial and anticancer peptide candidates [9, 20, 21], which we have recently expanded to cover the global microbiome [22].

FIGURE 1.

FIGURE 1

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Different approaches for genomic mining of bioactive peptides. The schematic illustrates the typical computational framework for identifying functional peptides within bacterial genomes specifically, which are different from those applied to canonical size proteins. As can be seen, the ortholog-based approach does not work well with small proteins, which makes the machine learning a viable option to attribute function to peptides. This characteristically multidisciplinary approach usually reduces considerably the number of identifiable small proteins and makes the detection less reliable.

TABLE 1.

General genomic resources which can serve as sources for finding novel bioactive peptides.

Database Description Link
GMGCv1 [19] A database that provides a comprehensive catalog of microbial genes and genomes from diverse environments, including metagenomes, single-cell genomes, and isolate genomes https://gmgc.embl.de/
Earth Microbiome Catalog [23] A database of genes and genomes collected from several microbiomes across the earth https://portal.nersc.gov/GEM/
Human microbiome blueprint [24] ~100k MAGs catalog of microbes from human microbiota https://ftp.ebi.ac.uk/pub/databases/metagenomics/umgs_analyses/
Genomes OnLine Database (GOLD) [25] An open-access repository of genome and metagenome sequencing projects with their associated metadata. Provides login-free access to a growing catalogue of manually curated public projects from all over the world https://gold.jgi.doe.gov/
NCBI [26] A database that contains metagenomic classification tools to match sequences against a database of microbial genomes to identify the microbial community structure and function https://ncbi.nlm.nih.gov/
ProGenomes [17] A database of high-quality, complete, and draft prokaryotic genomes that can be used for comparative genomics, metagenomics, and other studies https://progenomes.embl.de
GTDB [27] A database that provides a standardized and comprehensive genome-based taxonomy for bacteria and archaea https://gtdb.ecogenomic.org
Animal Metagenomes Database [28] A database of animal metagenomes that allows users to browse, search, and download animal metagenomic data of interest based on different attributes of the metadata such as animal species https://figshare.com/articles/dataset/AnimalMetagenome_DB_a_database_for_animal_metagenomes/19728619
MGnify [29] Database containing metagenomes, genomes, MAGs and other information at the global level with their own annotation pipeline. https://www.ebi.ac.uk/metagenomics
IMG/M [30] A database that provides tools for analyzing microbial communities sequenced by the Joint Genome Institute (JGI) https://img.jgi.doe.gov
PATRIC [31] A database that provides bacterial genomic data and tools for analyzing bacterial pathogens https://www.bv-brc.org/
MG-RAST [32] A database that provides tools for analyzing metagenomic data and a repository for metagenomic datasets https://www.mg-rast.org/
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GMGCv1, Global Microbial Gene Catalog version 1; GTDB, Genome Taxonomy Database; IMG/M, Integrated Microbial Genomes & Microbiomes; MG-RAST, Metagenomics Rapid Annotation using Subsystem Technology; NCBI, National Center for Biotechnology Information; PATRIC, Pathosystems Resource Integration Center.

However, while this wealth of data represents an opportunity to find novel bioactive sequences, the techniques used for finding functional genes of longer size, comprising bioinformatics approaches developed over several decades, are not applicable to smaller gene and protein sequences. Instead, specific techniques and methods are required (Figure 1). Here, we review both the difficulties in adapting existing approaches as well as some of the ongoing efforts to overcome them. This is still an emerging and active field of research, but approaches based on artificial intelligence rather than pure sequence similarity appear to be more successful [33-35].

2 ∣. TRADITIONAL GENE MINING METHODS DO NOT WORK FOR PEPTIDES

At a high-level, the most widely used approach starts with genomic sequencing (including, nowadays, metagenomes, i.e., sequenced microbial communities), and first predicting genes coding for proteins [36] (see Figure 1). Using computational methods, mostly based on homology (or orthology [37]), or resorting to data types beyond sequencing such as transcriptomics [16] or proteomics [38], proteins with functions of interest are identified for further exploration.

When considering small sequences, traditional methods underperform at all these steps (Figure 1). For gene finding, we first need to take into consideration that peptides can be derived from either longer precursor proteins by proteolysis, when they are sometimes referred to as being encrypted, or encoded as their own genes (small open reading frames – small/short Open Reading Frame [smORF] – encoded peptides or smORF-encoded peptides [SEPs]) [39-42]. These two categories pose different challenges.

If we consider first the case of SEPs, then the problem is gene prediction from sequence. For prokaryotes, Prodigal (including its metagenomic mode, sometimes referred to as metaProdigal) is one of the most widely used tools [36]. It uses models learned from model organisms to identify features of protein coding genes which can be used to identify novel genes. However, it only returns predictions above a minimum of 90 bp (base pairs, corresponding to 30 amino acids) and those below 250 bp (corresponding to 83 amino acids) are internally penalized by the model and less likely to be output. These thresholds and penalties are necessary to reduce the number of false positive predictions [36]. Other tools, such as Glimmer3 [43] or MetaGeneMark [44] use a similar combination of hard thresholds and penalties against short genes to reduce false positive predictions. Furthermore, smORFs can often even be found overlapping canonical genes [45], which traditional tools rarely consider. Thus, while predicting canonical length protein-coding genes can be done automatically errors in a full ab initio form (i.e., based only on features of the sequence), the same methods cannot be naively applied to predicting small proteins. The challenge of finding reliable smORFs in genomic sequences has been known for a long time [46], but there are fundamental limitations: for example, while a long coding sequence without a stop codon can be indicative of selection, smORFs can appear purely by chance [47].

When it comes to finding encrypted peptides, it is similarly difficult to avoid high rates of false positives. A further complication is that, in the case of encrypted peptides, the function of the precursor protein may be unrelated to the function of the peptide [41, 48].

After having predicted a potential peptide, the next step is functional prediction (Figure 1). For larger sequences, this is most often performed using concepts related to homology, that is, by finding another better studied protein (or a group of proteins) with a shared common ancestor and transferring the knowledge from the better characterized protein to a new one [37, 49-51]. The development of algorithms to perform this reliably and efficiently has been one of the achievements of the field of bioinformatics, including fundamental tools such as BLAST [49], and more modern approaches such as DIAMOND [50] and MMSeqs2 [52] that can scale to the very large databases available now.

These methods cannot be naïvely applied to short sequences, as finding statistically reliable matches is difficult unless one is dealing with very close relatives. In the case of finding a small peptide match in a large database, even a high-identity match can occur by chance and this is not sufficient evidence of shared ancestry. Therefore, when applied to short sequences, these methods only work for finding sequences that are close to already characterized ones [53]. Thus, these methods cannot be extended to searching for matches in large datasets. Finally, short proteins appear to be under lower purifying selection than traditional genes (at least in humans), which additionally complicates the process of finding homologous genes [54].

In summary, the traditional sequence to gene to function pipeline that has been developed by the field of bioinformatics over several decades is not necessarily adequate for discovering bioactive peptides.

3 ∣. MACHINE LEARNING APPROACHES FOR PREDICTION OF PEPTIDES

For predicting smORFs in genomes with high confidence, one approach is to partly abandon the ab initio approach and rely on homology and signals of conservation. sORF finder learns a hexamer-based model which relies on trained models and finding homologs smORFs to find signatures of purifying selection [55]. In their survey of smORFs from the global microbiome, Sberro et al. [56] used a similar concept implemented in the tool RNACode, which similarly relies on multiple homologous sequences as inputs [57]. Later, the same group used this database to build predictive models which form the basis of the tools smORFinder [58]. Alternatively, tools such as Random forest-based tool for the prediction of Small Encoded Proteins (RanSEPs) and ProsmORF-pred employ machine learning models based on features of the sequence [59, 60].

A few approaches have previously been employed for functional prediction of peptides. For example, smORFunction uses co-expression to predict the function of small proteins [61]. This approach uses expression profiling to correlate the expression of a particular smORF with other annotated proteins, which is a classical approach for functional annotation in longer proteins [62, 63]. This can also be performed in communities, using metatranscriptomics in parallel with metagenomics [64].

Given that it is not trivial to obtain transcriptomics data and that some of the functions that peptides are used for may only be triggered in specific conditions, it is still important to attempt to obtain functional predictions from sequence alone. For this task, orthology finding is the major computational approach for canonical-length proteins. As discussed above, these approaches are not directly applicable for shorter sequences and machine-learning based methods are instead the most widely used approaches [65-67]. In this framework, a dataset of annotated peptides is used to train a model which is then applied to other candidate sequences.

For users of prediction tools, it is crucial to understand the quality of the predictions. For the purposes of mining sequences from large-scale data, the crucial metric is the precision: the fraction of peptides predicted as active that are truly active. It is important to note that the classifier will be applied to the outputs of the earlier steps in the pipeline, which – as we noted above – are themselves often false positives. Thus, the classifier will be applied to several times more negative inputs than positive ones. Nonetheless, many methods have been evaluated on test datasets where there is an equal number of positive and negative examples. In this context, a model can exhibit apparent high accuracy, but only achieve mediocre results in practice. For example, if the true fraction of active peptides is 1% and a model has 98% precision and recall with an equal propensity for both false positives and false negatives, then precision will be only 17%.

For use in gene mining applications, appropriate validation datasets need to be selected [68] to avoid pervasive overestimation [69]. Alternatively, methods can be tuned to provide conservative estimates, minimizing the rate of false positives even at the cost of lower recall [20, 70]. When the goal is the discovery of new bioactive peptides, the computational process aims to produce a set of candidates that will then be validated experimentally: the goal is to obtain a small number of very high-quality predictions.

For prediction of antimicrobial peptides (AMPs), many combinations of features, and machine learning algorithms have been tried [71-74]. Rather than sequence similarity, these methods exploit properties characteristic to AMPs, such as amino acid characteristics and distribution. Anticancer or antiviral peptide predictors have also been proposed based on similar principles [75] and some tools aim to classify multiple classes simultaneously [76-78].

Structure prediction has undergone a revolution with the advent of deep learning-based methods, in particular AlphaFold [79] and AlphaFold2 (AF2) [80]. The best performances are obtained with multiple-sequence alignments requiring homology finding, which, as discussed earlier, is not trivial with small proteins.

Results on small peptides have shown that AF2 performs well in certain simple folds such as membrane-associated α-helices and β-hairpins, with mixed results in other classes [81]. Overall, though, it appears that although folding methods may require some adaptation to work consistently well on small peptides, unlike some of the other problems listed before, there is no fundamental reason why they should not perform well once trained on peptide sequences, except that compared to longer sequences, small peptides may have a less well-defined structure.

4 ∣. ’OMICS DATA BEYOND DNA SEQUENCES

Other data sources can be used to provide evidence for genomic predictions. In this context, transcriptomics (high-throughput RNA sequencing [RNA-seq]), proteomics, and ribosome profiling (Ribo-seq, where molecules being actively transcribed are identified) are the most widely used methods. They can be used individually or combined together [82, 83].

Transcriptomics can be particularly valuable when studying complex organisms where production of active secreted peptides can be localized to a particular tissue or cell type. In that case, targeted transcriptomics of those tissues can provide a source enriched in active peptides [84]. In the case of communities, it is possible to obtain meta-transcriptomes, but these are not as widely available as metagenomes given the difficulty in obtaining them in the lab.

Proteomics can, in principle, provide direct detection of peptides, particularly if protocols are adapted to this task [85, 86]. It is important to note, however, that processing proteomics data is often done by matching to databases. As most extant databases do not contain short peptides, novel peptides will not be reported. Furthermore, in the case of encrypted peptides, the detection of the peptide may be reported as a detection of the original full-length protein. Even for independent smORFs, short sequences may not produce enough unique spectra so that a confident detection can be reported. Using machine learning, data processing pipelines can be adapted to the task of finding small peptides in proteomics data [87] (Figure 1).

Ribo-seq, or ribosome profiling, is the sequencing of messenger RNA (ribonucleic acid) (mRNA) transcripts that are attached to ribosomes. The result is that transcripts that are being actively translated get captured and can then be mapped back to the genome to identify actively translated sequences. With this approach, transcription of noncanonical proteins can be observed, including smORFs. For this class of peptides, Ribo-seq (and meta-Ribo-seq) can indeed provide strong evidence of translation. From the point of view of mining and peptide discovery, the paucity of datasets is currently the main limitation in this field.

5 ∣. DATABASES OF ACTIVE PEPTIDES

Databases of active peptides are useful for cataloguing known peptides for human exploration. Furthermore, they are crucial for training machine learning methods as described above. Many databases of AMPs have been published [88-96] and this field has been reviewed elsewhere [1].

Several groups have performed smORF screens in select species [97], while others have collated smORFs from the published literature to build aggregate databases (Table 2). The web resource sORF.org uses Ribo-seq data [98, 99], Small Proteins Identified from Ribosome Profiling Database (smPROT) incorporated mass spectrometry data and literature mining [100], ARA-PEPs collated putative SEPs in Arabidopsis thaliana [101], and Plant small ORFs database (PsORF) applied automated detection methods to 36 different plant species [102]. The MetamORF database reworked and standardized data from several projects on human and mouse smORFs [103]. Crucially, these databases are focused on a relatively small number of eukaryotic species. The database DBsmORF is one exception that was built from prokaryotic genomes and metagenomes [58], namely Reference Sequence Database (NCBI) (RefSeq) and Human Microbiome Project (HMP), based on the tool SmORFinder [58], while the recently made available Global Microbial smORF Catalogue (GMSC) explores the global microbiome [104, 105].

TABLE 2.

Databases dedicated to smORFs.

Database Description Link
MetamORF [103] A repository of unique short open reading frames identified by both experimental and computational approaches https://metamorf.hb.univ-amu.fr/
SmProt [100, 106] A repository with comprehensive annotation of small proteins identified from ribosome profiling http://bigdata.ibp.ac.cn/SmProt/
sORFs.org [98, 99] A repository of small ORFs identified by ribosome profiling https://www.sorfs.org
DBsmORF [58] Database and web portal designed to help you identify small open reading frames (smORFs) in your microbial sequencing datasets http://104.154.134.205:3838/DBsmORF/
GMSC [104, 105] Database collating smSORFs from the global microbiome https://gmsc.big-data-biology.org/
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GMSC, Global Microbial smORF Catalogue; ORF, Open Reading Frame; smORFs, small/short Open Reading Frames; smPROT, Small Proteins Identified from Ribosome Profiling Database.

6 ∣. CONCLUSIONS

Mining high-throughput ’omics data (primarily genomic sequences) represents an exciting opportunity to find novel bioactive peptides, which are much needed in several biotechnological and medical applications. However, to do so requires a set of computational tools and protocols very different (or at least adapted) from the ones that have been so successful for longer sequences. In particular, machine learning and artificial intelligence play a bigger role in this area compared to canonical proteins where sequence similarity (to infer homology and orthology) is the main tool for analyzing sequences.

Genomic (DNA) information is the most abundant, which includes metagenomics. Combining multiple sources of information (multi’omics) is one approach to reduce the issue of false positive predictions. Ribo-seq, transcriptomics, and proteomics can all be used to increase confidence in the activity of a specific sequence. In the case of analyzing complex communities, these methods are still not widely used as they are much more challenging to execute. Computational methods to analyze these data sources also need to be adapted to small sequences and their results can be less reliable than is the case of canonical-length sequences.

Despite the challenges, there is immense potential in exploiting the vast genomic information and other resources that have become available in the last several years, which will continue to grow. Groundtruth experimental validation will always be needed, but computational methods can help us accelerate the discovery of novel, functional peptides.

ACKNOWLEDGMENTS

Cesar de la Fuente-Nunez holds a Presidential Professorship at the University of Pennsylvania, is a recipient of the Langer Prize by the AIChE Foundation, and acknowledges funding from the IADR Innovation in Oral Care Award, the Procter & Gamble Company, United Therapeutics, a BBRF Young Investigator Grant, the Nemirovsky Prize, Penn Health-Tech Accelerator Award, the Dean’s Innovation Fund from the Perelman School of Medicine at the University of Pennsylvania, the National Institute of General Medical Sciences of the National Institutes of Health under award number R35GM138201, and the Defense Threat Reduction Agency (DTRA; HDTRA11810041, HDTRA1-21-1-0014, and HDTRA1-23-1-0001). Luis Pedro Coelho is supported by the Australian Research Council (grant number FT230100724). We thank the Coelho and de la Fuente Lab members for insightful discussions.

Abbreviations:

AF2

AlphaFold2

AMP

antimicrobial peptide

bp

base pairs

DNA

deoxyribonucleic acid

GMGCv1

Global Microbial Gene Catalog version 1

GMSC

Global Microbial smORF Catalogue

GOLD

Genomes Online Database

GTDB

Genome Taxonomy Database

HMP

Human Microbiome Project

IMG/M

Integrated Microbial Genomes & Microbiomes

MAGs

metagenome assembled genomes

MG-RAST

Metagenomics Rapid Annotation using Subsystem Technology

mRNA

messenger RNA (ribonucleic acid)

NCBI

National Center for Biotechnology Information

ORF

Open Reading Frame

PATRIC

Pathosystems Resource Integration Center

RanSEPs

Random forest-based tool for the prediction of Small Encoded Proteins in bacterial genomes

Ribo-Seq

ribosome profiling

RNA-Seq

high-throughput RNA sequencing

SEP

smORF-encoded peptide

smORF

small/short Open Reading Frame

smPROT

Small Proteins Identified from Ribosome Profiling Database

RefSeq

Reference Sequence Database (NCBI)

PsORF

Plant small ORFs database

Footnotes

CONFLICT OF INTEREST STATEMENT

The authors declare no conflicts of interest.

DATA AVAILABILITY STATEMENT

The data that support the findings of this study are available from the upon reasonable request.

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Data Availability Statement

The data that support the findings of this study are available from the upon reasonable request.

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