Skip to main page content
U.S. flag

An official website of the United States government

Dot gov

The .gov means it’s official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

Https

The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
Review
. 2022 Mar 8;15(3):323.
doi: 10.3390/ph15030323.

Traditional and Computational Screening of Non-Toxic Peptides and Approaches to Improving Selectivity

Alberto A Robles-Loaiza  1 , Edgar A Pinos-Tamayo  2 , Bruno Mendes  1 , Josselyn A Ortega-Pila  1 , Carolina Proaño-Bolaños  1 , Fabien Plisson  3 , Cátia Teixeira  4 , Paula Gomes  4 , José R Almeida  1
Affiliations
Review

Traditional and Computational Screening of Non-Toxic Peptides and Approaches to Improving Selectivity

Alberto A Robles-Loaiza et al. Pharmaceuticals (Basel). .

Abstract

Peptides have positively impacted the pharmaceutical industry as drugs, biomarkers, or diagnostic tools of high therapeutic value. However, only a handful have progressed to the market. Toxicity is one of the main obstacles to translating peptides into clinics. Hemolysis or hemotoxicity, the principal source of toxicity, is a natural or disease-induced event leading to the death of vital red blood cells. Initial screenings for toxicity have been widely evaluated using erythrocytes as the gold standard. More recently, many online databases filled with peptide sequences and their biological meta-data have paved the way toward hemolysis prediction using user-friendly, fast-access machine learning-driven programs. This review details the growing contributions of in silico approaches developed in the last decade for the large-scale prediction of erythrocyte lysis induced by peptides. After an overview of the pharmaceutical landscape of peptide therapeutics, we highlighted the relevance of early hemolysis studies in drug development. We emphasized the computational models and algorithms used to this end in light of historical and recent findings in this promising field. We benchmarked seven predictors using peptides from different data sets, having 7-35 amino acids in length. According to our predictions, the models have scored an accuracy over 50.42% and a minimal Matthew's correlation coefficient over 0.11. The maximum values for these statistical parameters achieved 100.0% and 1.00, respectively. Finally, strategies for optimizing peptide selectivity were described, as well as prospects for future investigations. The development of in silico predictive approaches to peptide toxicity has just started, but their important contributions clearly demonstrate their potential for peptide science and computer-aided drug design. Methodology refinement and increasing use will motivate the timely and accurate in silico identification of selective, non-toxic peptide therapeutics.

Keywords: hemolysis; in silico; machine learning; peptides; toxicity.

PubMed Disclaimer

Conflict of interest statement

The authors declare no conflict of interest.

Figures

Figure 1
Figure 1
Main approaches employed to evaluate the toxicity of peptides. Traditional screening of non-toxic peptides is performed using different in vitro techniques, including MTT, LDH, erythrocyte lysis, and ATP-based assays. These methods are based on the measurement of intracellular markers released during cell death or lysis, such as hemoglobin (red blood assay), enzymes (LDH assay), or on the analysis of cell viability determined by enzymatic activity, measured, for example, by the MTT assay or by the amount of cell energy (ATP-based assay). Recently, computational models were reported to assist in peptide toxicity prediction.
Figure 2
Figure 2
Use of erythrocytes in the investigation of selective and potentially translational therapeutic peptides. The hemolysis assay is a standard technique widely used in toxicity screening of drug candidates, especially peptides. The abundance and easy obtainment of RBCs, together with the simplicity of the experiment, contribute to its prioritization in toxicity studies. The RBCs lysis protocol involves a colorimetric assay, which determines the amount of hemoglobin released after peptide-induced cell damage. Serial dilutions of the peptides are first prepared in parallel with the RBCs suspension, which is obtained by centrifugation and dilution. Then, the peptides, positive, negative, and, eventually, other controls are incubated with the RBCs solution to deliver the raw data that is next analyzed and translated into an HC50.
Figure 3
Figure 3
Historical overview of the development of freely available tools and models for prediction of peptide toxicity. Big biomedical peptide data have been explored to design new predictive methods that facilitate adequate access to the full potential of peptides. Despite the many years of peptide science, our literature review demonstrates that these in silico approaches are relatively new. From the pioneering and innovative ClanTox [112] and ToxinPred [40] launched in 2009 and 2013, respectively, ten high-throughput computer toxicity prediction tools were developed that mainly predict peptides’ hemolytic effects. Most of them have been released in the last 5 years. Capecchi et al. [38] and HemoNet [113] are the latest hemolytic classifiers. Some predictors such as HAPPENN [36] and HemoPI [26] have more than one version. HemoPI has 5 SVM methods, while HAPPENN is composed of 3 methods. However, due to the difference in performance reported by the authors, for this chronology, we considered only the best-in-class performance methods. The three Plisson models [37] were considered due to high similarity in performance metrics. Peptide toxicity predictors are highlighted in blue, and the classifiers for predicting peptides’ hemolytic activity are colored in red.
Figure 4
Figure 4
Peptide selectivity optimization strategies. Selectivity is a favorable characteristic of considerable significance for the success rate of drug candidates. However, selectivity optimization is a complex task that must balance the properties that govern toxic and therapeutic effects. Different design and synthesis strategies have contributed to this objective. In the design step, the evaluation of physicochemical properties and SAR relationships integrated with computational techniques play a decisive role in peptide selection. In the same context, cyclization, use of D amino acids, and peptidomimetics have been key for the development of stable and selective peptides.
See this image and copyright information in PMC

Similar articles

See all similar articles

Cited by

See all "Cited by" articles

References

    1. Muttenthaler M., King G.F., Adams D.J., Alewood P.F. Trends in peptide drug discovery. Nat. Rev. Drug Discov. 2021;20:309–325. doi: 10.1038/s41573-020-00135-8. - DOI - PubMed
    1. Apostolopoulos V., Bojarska J., Chai T.-T., Elnagdy S., Kaczmarek K., Matsoukas J., New R., Parang K., Lopez O.P., Parhiz H., et al. A global review on short peptides: Frontiers and perspectives. Molecules. 2021;26:430. doi: 10.3390/molecules26020430. - DOI - PMC - PubMed
    1. De la Torre B.G., Albericio F. Peptide therapeutics 2. Molecules. 2020;25:2293. doi: 10.3390/molecules25102293. - DOI - PMC - PubMed
    1. Ilangala A.B., Lechanteur A., Fillet M., Piel G. Therapeutic peptides for chemotherapy: Trends and challenges for advanced delivery systems. Eur. J. Pharm. Biopharm. 2021;167:140–158. doi: 10.1016/j.ejpb.2021.07.010. - DOI - PubMed
    1. Rastogi S., Shukla S., Kalaivani M., Singh G.N. Peptide-based therapeutics: Quality specifications, regulatory considerations, and prospects. Drug Discov. Today. 2019;24:148–162. doi: 10.1016/j.drudis.2018.10.002. - DOI - PubMed

LinkOut - more resources

-