{"title":"SMILES校准:用于代谢物和其他小有机分子校准的动态规划方法。","authors":"Alexis L Tang, David A Liberles","doi":"10.1186/s12859-025-06278-y","DOIUrl":null,"url":null,"abstract":"<p><strong>Background: </strong>There is a need for computational approaches to compare small organic molecules based on chemical similarity or for evaluating biochemical transformations. No tool currently exists to generate global molecular alignments for small organic molecules. The study introduces a new approach to molecular alignment in the Simplified Molecular Input Line Entry System (SMILES) format. This method leverages programming and scoring alignments to minimize differences in electronegativity, here using a measure of atomic partial charges to address the challenge of understanding structural transformations in reaction pathways. This can be applied to study transitions from linear to cyclical pathways.</p><p><strong>Results: </strong>The proposed method is based on the Needleman-Wunsch algorithm for sequence alignment, but it uses a modified scoring function for different input data. Validation against a benchmarked dataset from the Krebs cycle, based on the known chemical transformations in the pathway, confirmed the efficacy of the approach in aligning atoms that are known to be the same across the transformation. The algorithm also quantified each transformation of metabolites in the Pentose Phosphate Pathway and in Glycolysis. The method was used to study the difference in chemical similarity over transformations between linear and cyclical pathways. The study found a midpoint dissimilarity peak in cyclical pathways (particularly the Krebs Cycle) and a progressive decrease in molecular similarity in linear pathways, consistent with expectations.</p><p><strong>Conclusions: </strong>The study introduces an algorithm that quantifies molecular transformations in metabolic pathways. The algorithm effectively highlights structural changes and was applied to a hypothesis about the transition from linear to cyclical structures. The software, which provides valuable insights into molecular transformations, is available at: https://github.com/24atang/SMILES-Alignment.git.</p>","PeriodicalId":8958,"journal":{"name":"BMC Bioinformatics","volume":"26 1","pages":"251"},"PeriodicalIF":3.3000,"publicationDate":"2025-10-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12534939/pdf/","citationCount":"0","resultStr":"{\"title\":\"SMILES alignment: a dynamic programming approach for the alignment of metabolites and other small organic molecules.\",\"authors\":\"Alexis L Tang, David A Liberles\",\"doi\":\"10.1186/s12859-025-06278-y\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><strong>Background: </strong>There is a need for computational approaches to compare small organic molecules based on chemical similarity or for evaluating biochemical transformations. No tool currently exists to generate global molecular alignments for small organic molecules. The study introduces a new approach to molecular alignment in the Simplified Molecular Input Line Entry System (SMILES) format. This method leverages programming and scoring alignments to minimize differences in electronegativity, here using a measure of atomic partial charges to address the challenge of understanding structural transformations in reaction pathways. This can be applied to study transitions from linear to cyclical pathways.</p><p><strong>Results: </strong>The proposed method is based on the Needleman-Wunsch algorithm for sequence alignment, but it uses a modified scoring function for different input data. Validation against a benchmarked dataset from the Krebs cycle, based on the known chemical transformations in the pathway, confirmed the efficacy of the approach in aligning atoms that are known to be the same across the transformation. The algorithm also quantified each transformation of metabolites in the Pentose Phosphate Pathway and in Glycolysis. The method was used to study the difference in chemical similarity over transformations between linear and cyclical pathways. The study found a midpoint dissimilarity peak in cyclical pathways (particularly the Krebs Cycle) and a progressive decrease in molecular similarity in linear pathways, consistent with expectations.</p><p><strong>Conclusions: </strong>The study introduces an algorithm that quantifies molecular transformations in metabolic pathways. The algorithm effectively highlights structural changes and was applied to a hypothesis about the transition from linear to cyclical structures. The software, which provides valuable insights into molecular transformations, is available at: https://github.com/24atang/SMILES-Alignment.git.</p>\",\"PeriodicalId\":8958,\"journal\":{\"name\":\"BMC Bioinformatics\",\"volume\":\"26 1\",\"pages\":\"251\"},\"PeriodicalIF\":3.3000,\"publicationDate\":\"2025-10-17\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12534939/pdf/\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"BMC Bioinformatics\",\"FirstCategoryId\":\"99\",\"ListUrlMain\":\"https://doi.org/10.1186/s12859-025-06278-y\",\"RegionNum\":3,\"RegionCategory\":\"生物学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"BIOCHEMICAL RESEARCH METHODS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"BMC Bioinformatics","FirstCategoryId":"99","ListUrlMain":"https://doi.org/10.1186/s12859-025-06278-y","RegionNum":3,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"BIOCHEMICAL RESEARCH METHODS","Score":null,"Total":0}
SMILES alignment: a dynamic programming approach for the alignment of metabolites and other small organic molecules.
Background: There is a need for computational approaches to compare small organic molecules based on chemical similarity or for evaluating biochemical transformations. No tool currently exists to generate global molecular alignments for small organic molecules. The study introduces a new approach to molecular alignment in the Simplified Molecular Input Line Entry System (SMILES) format. This method leverages programming and scoring alignments to minimize differences in electronegativity, here using a measure of atomic partial charges to address the challenge of understanding structural transformations in reaction pathways. This can be applied to study transitions from linear to cyclical pathways.
Results: The proposed method is based on the Needleman-Wunsch algorithm for sequence alignment, but it uses a modified scoring function for different input data. Validation against a benchmarked dataset from the Krebs cycle, based on the known chemical transformations in the pathway, confirmed the efficacy of the approach in aligning atoms that are known to be the same across the transformation. The algorithm also quantified each transformation of metabolites in the Pentose Phosphate Pathway and in Glycolysis. The method was used to study the difference in chemical similarity over transformations between linear and cyclical pathways. The study found a midpoint dissimilarity peak in cyclical pathways (particularly the Krebs Cycle) and a progressive decrease in molecular similarity in linear pathways, consistent with expectations.
Conclusions: The study introduces an algorithm that quantifies molecular transformations in metabolic pathways. The algorithm effectively highlights structural changes and was applied to a hypothesis about the transition from linear to cyclical structures. The software, which provides valuable insights into molecular transformations, is available at: https://github.com/24atang/SMILES-Alignment.git.
期刊介绍:
BMC Bioinformatics is an open access, peer-reviewed journal that considers articles on all aspects of the development, testing and novel application of computational and statistical methods for the modeling and analysis of all kinds of biological data, as well as other areas of computational biology.
BMC Bioinformatics is part of the BMC series which publishes subject-specific journals focused on the needs of individual research communities across all areas of biology and medicine. We offer an efficient, fair and friendly peer review service, and are committed to publishing all sound science, provided that there is some advance in knowledge presented by the work.