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

Pharmaceuticals

José R. Almeida¹  Josselyn A. Ortega-Pila¹  Carolina Proaño-Bolaños¹  Alberto A. Robles-Loaiza¹  Bruno Mendes¹  Fabien Plisson²  Edgar A. Pinos-Tamayo³  Cátia Teixeira⁴  Paula Gomes⁴ 

[1] Biomolecules Discovery Group, Universidad Regional Amazónica Ikiam, Tena 150150, Ecuador;Consejo Nacional de Ciencia y Tecnología, Unidad de Genómica Avanzada, Laboratorio Nacional de Genómica para la Biodiversidad (Langebio), Centro de Investigación Y de Estudios Avanzados del IPN, Irapuato 36824, Mexico;Escuela Superior Politécnica del Litoral, ESPOL, Centro Nacional de Acuicultura e Investigaciones Marinas (CENAIM), Campus Gustavo Galindo Km. 30, 5 Vía Perimetral, Guayaquil 09-01-5863, Ecuador;Laboratório Associado para a Química Verde-REQUIMTE, Departamento de Química e Bioquímica, Faculdade de Ciências, Universidade do Porto, 4169-007 Porto, Portugal;

关键词: in silico;    machine learning;    peptides;    hemolysis;    toxicity;   

DOI  :  10.3390/ph15030323

来源: DOAJ

【 摘 要 】

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.

【 授权许可】

Unknown