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Baylon JL, Ursu O, Muzdalo A, Wassermann AM, Adams GL, Spale M, Mejzlik P, Gromek A, Pisarenko V, Hancharyk D, Jenkins E, Bednar D, Chang C, Clarova K, Glick M, Bitton DA. PepSeA: Peptide Sequence Alignment and Visualization Tools to Enable Lead Optimization. J Chem Inf Model 2022; 62:1259-1267. [PMID: 35192366 DOI: 10.1021/acs.jcim.1c01360] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Abstract
Therapeutic peptides offer potential advantages over small molecules in terms of selectivity, affinity, and their ability to target "undruggable" proteins that are associated with a wide range of pathologies. Despite their importance, current molecular design capabilities that inform medicinal chemistry decisions on peptide programs are limited. More specifically, there are unmet needs for structure-activity relationship (SAR) analysis and visualization of linear, cyclic, and cross-linked peptides containing non-natural motifs, which are widely used in drug discovery. To bridge this gap, we developed PepSeA (Peptide Sequence Alignment and Visualization), an open-source, freely available package of sequence-based tools (https://github.com/Merck/PepSeA). PepSeA enables multiple sequence alignment of non-natural amino acids and enhanced visualization with the hierarchical editing language for macromolecules (HELM). Via stepwise SAR analysis of a ChEMBL peptide data set, we demonstrate the utility of PepSeA to accelerate decision making in lead optimization campaigns in pharmaceutical setting. PepSeA represents an initial attempt to expand cheminformatics capabilities for therapeutic peptides and to enable rapid and more efficient design-make-test cycles.
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Affiliation(s)
- Javier L Baylon
- Computational and Structural Chemistry, Merck & Co., Inc., Boston, Massachusetts 02115, United States
| | - Oleg Ursu
- Computational and Structural Chemistry, Merck & Co., Inc., Boston, Massachusetts 02115, United States
| | - Anja Muzdalo
- R&D Informatics Solutions, MSD Czech Republic s.r.o., Prague 150 00, Czech Republic
| | - Anne Mai Wassermann
- Computational and Structural Chemistry, Merck & Co., Inc., Boston, Massachusetts 02115, United States
| | - Gregory L Adams
- Computational and Structural Chemistry, Merck & Co., Inc., Boston, Massachusetts 02115, United States
| | - Martin Spale
- R&D Informatics Solutions, MSD Czech Republic s.r.o., Prague 150 00, Czech Republic
| | - Petr Mejzlik
- AI & Big Data Analytics, MSD Czech Republic s.r.o., Prague 150 00, Czech Republic
| | - Anna Gromek
- R&D Informatics Solutions, MSD Czech Republic s.r.o., Prague 150 00, Czech Republic
| | - Viktor Pisarenko
- R&D Informatics Solutions, MSD Czech Republic s.r.o., Prague 150 00, Czech Republic
| | - Dzianis Hancharyk
- R&D Informatics Solutions, MSD Czech Republic s.r.o., Prague 150 00, Czech Republic
| | - Esteban Jenkins
- Foundational Data and Analytics, MSD Czech Republic s.r.o., Prague 150 00, Czech Republic
| | - David Bednar
- Foundational Data and Analytics, MSD Czech Republic s.r.o., Prague 150 00, Czech Republic
| | - Charlie Chang
- Discovery Research IT, Merck & Co., Inc., Boston, Massachusetts 02115, United States
| | - Kamila Clarova
- R&D Informatics Solutions, MSD Czech Republic s.r.o., Prague 150 00, Czech Republic.,Department of Informatics and Chemistry, Faculty of Chemical Technology, University of Chemistry and Technology, Prague 166 28, Czech Republic
| | - Meir Glick
- Computational and Structural Chemistry, Merck & Co., Inc., Boston, Massachusetts 02115, United States
| | - Danny A Bitton
- R&D Informatics Solutions, MSD Czech Republic s.r.o., Prague 150 00, Czech Republic
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Ge R, Feng G, Jing X, Zhang R, Wang P, Wu Q. EnACP: An Ensemble Learning Model for Identification of Anticancer Peptides. Front Genet 2020; 11:760. [PMID: 32903636 PMCID: PMC7438906 DOI: 10.3389/fgene.2020.00760] [Citation(s) in RCA: 20] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/20/2020] [Accepted: 06/26/2020] [Indexed: 12/13/2022] Open
Abstract
As cancer remains one of the main threats of human life, developing efficient cancer treatments is urgent. Anticancer peptides, which could overcome the significant side effects and poor results of traditional cancer treatments, have become a new potential alternative these years. However, identifying anticancer peptides by experimental methods is time consuming and resource consuming, it is of great significance to develop effective computational tools to quickly and accurately identify potential anticancer peptides from amino acid sequences. For most current computational methods, feature representation plays a key role in their final successes. This study proposes a novel fast and accurate approach to identify anticancer peptides using diversified feature representations and ensemble learning method. For the feature representations, the information is encoded from multidimensional feature spaces, including sequence composition, sequence-order, physicochemical properties, etc. In order to better model the potential relationships of peptides, multiple ensemble classifiers, LightGBMs, are applied to detect the different feature sets at first. Then the obtained multiple outputs are used as inputs of the support vector machine classifier, which effectively identifies anticancer peptides. Experimental results on cross validation and independent test sets demonstrate that our method can achieve better or comparable performances compared with other state-of-the-art methods.
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Affiliation(s)
- Ruiquan Ge
- Key Laboratory of Complex Systems Modeling and Simulation, School of Computer Science and Technology, Hangzhou Dianzi University, Hangzhou, China
| | - Guanwen Feng
- Xi'an Key Laboratory of Big Data and Intelligent Vision, School of Computer Science and Technology, Xidian University, Xi'an, China
| | - Xiaoyang Jing
- Toyota Technological Institute at Chicago, Chicago, IL, United States
| | - Renfeng Zhang
- Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, China
| | - Pu Wang
- Computer School, Hubei University of Arts and Science, Xiangyang, China
| | - Qing Wu
- Key Laboratory of Complex Systems Modeling and Simulation, School of Computer Science and Technology, Hangzhou Dianzi University, Hangzhou, China
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