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Traditional and Computational Screening of Non-Toxic Peptides and Approaches to Improving Selectivity. Pharmaceuticals (Basel) 2022; 15:ph15030323. [PMID: 35337121 PMCID: PMC8953747 DOI: 10.3390/ph15030323] [Citation(s) in RCA: 15] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/26/2022] [Revised: 03/01/2022] [Accepted: 03/04/2022] [Indexed: 12/27/2022] Open
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.
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Timmons PB, Hewage CM. ENNAACT is a novel tool which employs neural networks for anticancer activity classification for therapeutic peptides. Biomed Pharmacother 2020; 133:111051. [PMID: 33254015 DOI: 10.1016/j.biopha.2020.111051] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/28/2020] [Revised: 10/08/2020] [Accepted: 11/19/2020] [Indexed: 12/12/2022] Open
Abstract
The prevalence of cancer as a threat to human life, responsible for 9.6 million deaths worldwide in 2018, motivates the search for new anticancer agents. While many options are currently available for treatment, these are often expensive and impact the human body unfavourably. Anticancer peptides represent a promising emerging field of anticancer therapeutics, which are characterized by favourable toxicity profile. The development of accurate in silico methods for anticancer peptide prediction is of paramount importance, as the amount of available sequence data is growing each year. This study leverages advances in machine learning research to produce a novel sequence-based deep neural network classifier for anticancer peptide activity. The classifier achieves performance comparable to the best-in-class, with a cross-validated accuracy of 98.3%, Matthews correlation coefficient of 0.91 and an Area Under the Curve of 0.95. This innovative classifier is available as a web server at https://research.timmons.eu/ennaact, facilitating in silico screening and design of new anticancer peptide chemotherapeutics by the research community.
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Affiliation(s)
- Patrick Brendan Timmons
- UCD School of Biomolecular and Biomedical Science, UCD Centre for Synthesis and Chemical Biology, UCD Conway Institute, University College Dublin, Dublin 4, Ireland
| | - Chandralal M Hewage
- UCD School of Biomolecular and Biomedical Science, UCD Centre for Synthesis and Chemical Biology, UCD Conway Institute, University College Dublin, Dublin 4, Ireland.
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Lin ZH, Long HX, Bo Z, Wang YQ, Wu YZ. New descriptors of amino acids and their application to peptide QSAR study. Peptides 2008; 29:1798-805. [PMID: 18606203 DOI: 10.1016/j.peptides.2008.06.004] [Citation(s) in RCA: 61] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/10/2008] [Revised: 06/09/2008] [Accepted: 06/10/2008] [Indexed: 11/18/2022]
Abstract
A new set of descriptors was derived from a matrix of three structural variables of the natural amino acid, including van der Waal's volume, net charge index and hydrophobic parameter of side residues. They were selected from many properties of amino acid residues, which have been validated being the key factors to influence the interaction between peptides and its protein receptor. They were then applied to structure characterization and QSAR analysis on bitter tasting di-peptide, agiotensin-converting enzyme inhibitor and bactericidal peptides by using multiple linear regression (MLR) method. The leave one out cross validation values (Q(2)) were 0.921, 0.943 and 0.773. The multiple correlation coefficients (R(2)) were 0.948, 0.970 and 0.926, the root mean square (RMS) error for estimated error were 0.165, 0.154 and 0.41, respectively for bitter tasting di-peptide, angiotensin-converting enzyme inhibitor and bactericidal peptides. Test sets of peptides were used to validate the quantitative model, and it was shown that all these QSAR models had good predictability for outside samples. The results showed that, in comparison with the conventional descriptors, the new set of descriptors is a useful structure characterization method for peptide QSAR analysis, which has multiple advantages, such as definite physical and chemical meaning, easy to get, and good structural characterization ability.
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Affiliation(s)
- Zhi-hua Lin
- College of Bioengineering, Chongqing Institute of Technology, Chongqing 400050, People's Republic of China.
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Li Z, Wu S, Chen Z, Ye N, Yang S, Liao C, Zhang M, Yang L, Mei H, Yang Y, Zhao N, Zhou Y, Zhou P, Xiong Q, Xu H, Liu S, Ling Z, Chen G, Li G. Structural parameterization and functional prediction of antigenic polypeptome sequences with biological activity through quantitative sequence-activity models (QSAM) by molecular electronegativity edge-distance vector (VMED). SCIENCE IN CHINA. SERIES C, LIFE SCIENCES 2007; 50:706-16. [PMID: 17879071 PMCID: PMC7089106 DOI: 10.1007/s11427-007-0080-7] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Received: 09/30/2006] [Accepted: 06/14/2007] [Indexed: 11/18/2022]
Abstract
Only from the primary structures of peptides, a new set of descriptors called the molecular electronegativity edge-distance vector (VMED) was proposed and applied to describing and characterizing the molecular structures of oligopeptides and polypeptides, based on the electronegativity of each atom or electronic charge index (ECI) of atomic clusters and the bonding distance between atom-pairs. Here, the molecular structures of antigenic polypeptides were well expressed in order to propose the automated technique for the computerized identification of helper T lymphocyte (Th) epitopes. Furthermore, a modified MED vector was proposed from the primary structures of polypeptides, based on the ECI and the relative bonding distance of the fundamental skeleton groups. The side-chains of each amino acid were here treated as a pseudo-atom. The developed VMED was easy to calculate and able to work. Some quantitative model was established for 28 immunogenic or antigenic polypeptides (AGPP) with 14 (1-14) A(d) and 14 other restricted activities assigned as "1"(+) and "0"(-), respectively. The latter comprised 6 A(b)(15-20), 3 A(k)(21-23), 2 E(k)(24-26), 2 H-2(k)(27 and 28) restricted sequences. Good results were obtained with 90% correct classification (only 2 wrong ones for 20 training samples) and 100% correct prediction (none wrong for 8 testing samples); while contrastively 100% correct classification (none wrong for 20 training samples) and 88% correct classification (1 wrong for 8 testing samples). Both stochastic samplings and cross validations were performed to demonstrate good performance. The described method may also be suitable for estimation and prediction of classes I and II for major histocompatibility antigen (MHC) epitope of human. It will be useful in immune identification and recognition of proteins and genes and in the design and development of subunit vaccines. Several quantitative structure activity relationship (QSAR) models were developed for various oligopeptides and polypeptides including 58 dipeptides and 31 pentapeptides with angiotensin converting enzyme (ACE) inhibition by multiple linear regression (MLR) method. In order to explain the ability to characterize molecular structure of polypeptides, a molecular modeling investigation on QSAR was performed for functional prediction of polypeptide sequences with antigenic activity and heptapeptide sequences with tachykinin activity through quantitative sequence-activity models (QSAMs) by the molecular electronegativity edge-distance vector (VMED). The results showed that VMED exhibited both excellent structural selectivity and good activity prediction. Moreover, the results showed that VMED behaved quite well for both QSAR and QSAM of poly-and oligopeptides, which exhibited both good estimation ability and prediction power, equal to or better than those reported in the previous references. Finally, a preliminary conclusion was drawn: both classical and modified MED vectors were very useful structural descriptors. Some suggestions were proposed for further studies on QSAR/QSAM of proteins in various fields.
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Affiliation(s)
- ZhiLiang Li
- College of Chemistry and Chemical Engineering/Key Laboratory for Chemobiomedical Science and Engineering under Chongqing Municipality, College of Life Science and Biological Engineering/Key Laboratory for Biomechanics and Tissue Engineering under Ministry of Education, Chongqing University, Chongqing, 400044 China
- State Key Laboratory for Chemobiosensors and Chemobiometrics under MOST at Hunan University, Changsha, 410012 China
| | - ShiRong Wu
- College of Chemistry and Chemical Engineering/Key Laboratory for Chemobiomedical Science and Engineering under Chongqing Municipality, College of Life Science and Biological Engineering/Key Laboratory for Biomechanics and Tissue Engineering under Ministry of Education, Chongqing University, Chongqing, 400044 China
- State Key Laboratory for Chemobiosensors and Chemobiometrics under MOST at Hunan University, Changsha, 410012 China
| | - ZeCong Chen
- College of Chemistry and Chemical Engineering/Key Laboratory for Chemobiomedical Science and Engineering under Chongqing Municipality, College of Life Science and Biological Engineering/Key Laboratory for Biomechanics and Tissue Engineering under Ministry of Education, Chongqing University, Chongqing, 400044 China
- State Key Laboratory for Chemobiosensors and Chemobiometrics under MOST at Hunan University, Changsha, 410012 China
| | - Nancy Ye
- College of Chemistry and Chemical Engineering/Key Laboratory for Chemobiomedical Science and Engineering under Chongqing Municipality, College of Life Science and Biological Engineering/Key Laboratory for Biomechanics and Tissue Engineering under Ministry of Education, Chongqing University, Chongqing, 400044 China
- State Key Laboratory for Chemobiosensors and Chemobiometrics under MOST at Hunan University, Changsha, 410012 China
| | - ShengXi Yang
- College of Chemistry and Chemical Engineering/Key Laboratory for Chemobiomedical Science and Engineering under Chongqing Municipality, College of Life Science and Biological Engineering/Key Laboratory for Biomechanics and Tissue Engineering under Ministry of Education, Chongqing University, Chongqing, 400044 China
- State Key Laboratory for Chemobiosensors and Chemobiometrics under MOST at Hunan University, Changsha, 410012 China
| | - ChunYang Liao
- College of Chemistry and Chemical Engineering/Key Laboratory for Chemobiomedical Science and Engineering under Chongqing Municipality, College of Life Science and Biological Engineering/Key Laboratory for Biomechanics and Tissue Engineering under Ministry of Education, Chongqing University, Chongqing, 400044 China
- State Key Laboratory for Chemobiosensors and Chemobiometrics under MOST at Hunan University, Changsha, 410012 China
| | - MengJun Zhang
- College of Chemistry and Chemical Engineering/Key Laboratory for Chemobiomedical Science and Engineering under Chongqing Municipality, College of Life Science and Biological Engineering/Key Laboratory for Biomechanics and Tissue Engineering under Ministry of Education, Chongqing University, Chongqing, 400044 China
- State Key Laboratory for Chemobiosensors and Chemobiometrics under MOST at Hunan University, Changsha, 410012 China
- Department of Medical Analysis/PLA Center of Bioinformatics Immunology, Surgeon Third University, Chongqing, 400031 China
| | - Li Yang
- College of Chemistry and Chemical Engineering/Key Laboratory for Chemobiomedical Science and Engineering under Chongqing Municipality, College of Life Science and Biological Engineering/Key Laboratory for Biomechanics and Tissue Engineering under Ministry of Education, Chongqing University, Chongqing, 400044 China
- State Key Laboratory for Chemobiosensors and Chemobiometrics under MOST at Hunan University, Changsha, 410012 China
| | - Hu Mei
- College of Chemistry and Chemical Engineering/Key Laboratory for Chemobiomedical Science and Engineering under Chongqing Municipality, College of Life Science and Biological Engineering/Key Laboratory for Biomechanics and Tissue Engineering under Ministry of Education, Chongqing University, Chongqing, 400044 China
- State Key Laboratory for Chemobiosensors and Chemobiometrics under MOST at Hunan University, Changsha, 410012 China
- Technology Centre for Life Sciences, Singapore Polytechnic, 500 Dover Road, Singapore, 139651 Singapore
| | - Yan Yang
- College of Chemistry and Chemical Engineering/Key Laboratory for Chemobiomedical Science and Engineering under Chongqing Municipality, College of Life Science and Biological Engineering/Key Laboratory for Biomechanics and Tissue Engineering under Ministry of Education, Chongqing University, Chongqing, 400044 China
- State Key Laboratory for Chemobiosensors and Chemobiometrics under MOST at Hunan University, Changsha, 410012 China
| | - Na Zhao
- College of Chemistry and Chemical Engineering/Key Laboratory for Chemobiomedical Science and Engineering under Chongqing Municipality, College of Life Science and Biological Engineering/Key Laboratory for Biomechanics and Tissue Engineering under Ministry of Education, Chongqing University, Chongqing, 400044 China
- State Key Laboratory for Chemobiosensors and Chemobiometrics under MOST at Hunan University, Changsha, 410012 China
| | - Yuan Zhou
- College of Chemistry and Chemical Engineering/Key Laboratory for Chemobiomedical Science and Engineering under Chongqing Municipality, College of Life Science and Biological Engineering/Key Laboratory for Biomechanics and Tissue Engineering under Ministry of Education, Chongqing University, Chongqing, 400044 China
- State Key Laboratory for Chemobiosensors and Chemobiometrics under MOST at Hunan University, Changsha, 410012 China
| | - Ping Zhou
- College of Chemistry and Chemical Engineering/Key Laboratory for Chemobiomedical Science and Engineering under Chongqing Municipality, College of Life Science and Biological Engineering/Key Laboratory for Biomechanics and Tissue Engineering under Ministry of Education, Chongqing University, Chongqing, 400044 China
- State Key Laboratory for Chemobiosensors and Chemobiometrics under MOST at Hunan University, Changsha, 410012 China
| | - Qing Xiong
- College of Chemistry and Chemical Engineering/Key Laboratory for Chemobiomedical Science and Engineering under Chongqing Municipality, College of Life Science and Biological Engineering/Key Laboratory for Biomechanics and Tissue Engineering under Ministry of Education, Chongqing University, Chongqing, 400044 China
- State Key Laboratory for Chemobiosensors and Chemobiometrics under MOST at Hunan University, Changsha, 410012 China
| | - Hong Xu
- College of Chemistry and Chemical Engineering/Key Laboratory for Chemobiomedical Science and Engineering under Chongqing Municipality, College of Life Science and Biological Engineering/Key Laboratory for Biomechanics and Tissue Engineering under Ministry of Education, Chongqing University, Chongqing, 400044 China
- State Key Laboratory for Chemobiosensors and Chemobiometrics under MOST at Hunan University, Changsha, 410012 China
| | - ShuShen Liu
- College of Chemistry and Chemical Engineering/Key Laboratory for Chemobiomedical Science and Engineering under Chongqing Municipality, College of Life Science and Biological Engineering/Key Laboratory for Biomechanics and Tissue Engineering under Ministry of Education, Chongqing University, Chongqing, 400044 China
- State Key Laboratory for Chemobiosensors and Chemobiometrics under MOST at Hunan University, Changsha, 410012 China
| | - ZiHua Ling
- College of Chemistry and Chemical Engineering/Key Laboratory for Chemobiomedical Science and Engineering under Chongqing Municipality, College of Life Science and Biological Engineering/Key Laboratory for Biomechanics and Tissue Engineering under Ministry of Education, Chongqing University, Chongqing, 400044 China
- State Key Laboratory for Chemobiosensors and Chemobiometrics under MOST at Hunan University, Changsha, 410012 China
| | - Gang Chen
- College of Chemistry and Chemical Engineering/Key Laboratory for Chemobiomedical Science and Engineering under Chongqing Municipality, College of Life Science and Biological Engineering/Key Laboratory for Biomechanics and Tissue Engineering under Ministry of Education, Chongqing University, Chongqing, 400044 China
- State Key Laboratory for Chemobiosensors and Chemobiometrics under MOST at Hunan University, Changsha, 410012 China
- Technology Centre for Life Sciences, Singapore Polytechnic, 500 Dover Road, Singapore, 139651 Singapore
| | - GenRong Li
- College of Chemistry and Chemical Engineering/Key Laboratory for Chemobiomedical Science and Engineering under Chongqing Municipality, College of Life Science and Biological Engineering/Key Laboratory for Biomechanics and Tissue Engineering under Ministry of Education, Chongqing University, Chongqing, 400044 China
- State Key Laboratory for Chemobiosensors and Chemobiometrics under MOST at Hunan University, Changsha, 410012 China
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