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Cordoves-Delgado G, García-Jacas CR. Predicting Antimicrobial Peptides Using ESMFold-Predicted Structures and ESM-2-Based Amino Acid Features with Graph Deep Learning. J Chem Inf Model 2024; 64:4310-4321. [PMID: 38739853 DOI: 10.1021/acs.jcim.3c02061] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/16/2024]
Abstract
Currently, antimicrobial resistance constitutes a serious threat to human health. Drugs based on antimicrobial peptides (AMPs) constitute one of the alternatives to address it. Shallow and deep learning (DL)-based models have mainly been built from amino acid sequences to predict AMPs. Recent advances in tertiary (3D) structure prediction have opened new opportunities in this field. In this sense, models based on graphs derived from predicted peptide structures have recently been proposed. However, these models are not in correspondence with state-of-the-art approaches to codify evolutionary information, and, in addition, they are memory- and time-consuming because depend on multiple sequence alignment. Herein, we presented a framework to create alignment-free models based on graph representations generated from ESMFold-predicted peptide structures, whose nodes are characterized with amino acid-level evolutionary information derived from the Evolutionary Scale Modeling (ESM-2) models. A graph attention network (GAT) was implemented to assess the usefulness of the framework in the AMP classification. To this end, a set comprised of 67,058 peptides was used. It was demonstrated that the proposed methodology allowed to build GAT models with generalization abilities consistently better than 20 state-of-the-art non-DL-based and DL-based models. The best GAT models were developed using evolutionary information derived from the 36- and 33-layer ESM-2 models. Similarity studies showed that the best-built GAT models codified different chemical spaces, and thus they were fused to significantly improve the classification. In general, the results suggest that esm-AxP-GDL is a promissory tool to develop good, structure-dependent, and alignment-free models that can be successfully applied in the screening of large data sets. This framework should not only be useful to classify AMPs but also for modeling other peptide and protein activities.
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Affiliation(s)
- Greneter Cordoves-Delgado
- Departamento de Ciencias de la Computación, Centro de Investigación Científica y de Educación Superior de Ensenada (CICESE), 22860 Ensenada, Baja California, México
| | - César R García-Jacas
- Cátedras CONAHCYT - Departamento de Ciencias de la Computación, Centro de Investigación Científica y de Educación Superior de Ensenada (CICESE), 22860 Ensenada, Baja California, México
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Obradović D, Stavrianidi A, Fedorova E, Bogojević A, Shpigun O, Buryak A, Lazović S. A comparative study of the predictive performance of different descriptor calculation tools: Molecular-based elution order modeling and interpretation of retention mechanism for isomeric compounds from METLIN database. J Chromatogr A 2024; 1719:464731. [PMID: 38377661 DOI: 10.1016/j.chroma.2024.464731] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/28/2023] [Revised: 02/08/2024] [Accepted: 02/09/2024] [Indexed: 02/22/2024]
Abstract
In the pharmaceutical industry, the need for analytical standards is a bottleneck for comprehensive evaluation and quality control of intermediate and end products. These are complex mixtures containing structurally related molecules. In this regard, chromatographic peak annotation, especially for critical pairs of isomers and closest structural analogs, can be supported by using a Quantitative Structure Retention Relationship (QSRR) approach. In our study, we investigated the fundamental basis of the reversed-phase (RP) retention mechanism for 1141 isomeric compounds from the METLIN SMRT dataset. Nine different descriptor calculation tools combined with different feature selection methods (genetic algorithm (GA), stepwise, Boruta) and machine learning (ML) approaches (support vector machine (SVM), multiple linear regression (MLR), random forest (RF), XGBoost) were applied to provide a reliable molecular structure-based interpretation of RP retention behaviour of the isomeric compounds. Strict internal and external validation metrics were used to select models with the best predictive capabilities (rtest > 0.73, order of elution > 60 %). For the developed models, mean absolute errors were in the range of 60 to 110 s. Stepwise and GA showed the most suitable performance as descriptor selection methods, while SVM and XGBoost modeling gave satisfactory predictive characteristics in most cases. Validation performed on the published experimental data for structurally related pharmaceutical compounds confirmed the best accuracy of MLR modeling in combination with GA feature selection of general physico-chemical properties. The resulting models will be useful for the prediction of separation and identification of structurally related compounds in pharmaceutical analysis, providing a simultaneous understanding of the interaction mechanisms leading to their retention under RP conditions.
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Affiliation(s)
- Darija Obradović
- Institute of Physics Belgrade, National Institute of the Republic of Serbia, Pregrevica 118, Belgrade 11080, Serbia
| | - Andrey Stavrianidi
- Chemistry Department, Lomonosov Moscow State University, 1/3 Leninskie Gory, GSP-1, Moscow 119991, Russia; A.N. Frumkin Institute of Physical Chemistry and Electrochemistry, Russian Academy of Sciences, 31 Leninsky Prospect, GSP-1, Moscow 119071, Russia.
| | - Elizaveta Fedorova
- A.N. Frumkin Institute of Physical Chemistry and Electrochemistry, Russian Academy of Sciences, 31 Leninsky Prospect, GSP-1, Moscow 119071, Russia
| | - Aleksandar Bogojević
- Institute of Physics Belgrade, National Institute of the Republic of Serbia, Pregrevica 118, Belgrade 11080, Serbia
| | - Oleg Shpigun
- Chemistry Department, Lomonosov Moscow State University, 1/3 Leninskie Gory, GSP-1, Moscow 119991, Russia
| | - Aleksey Buryak
- A.N. Frumkin Institute of Physical Chemistry and Electrochemistry, Russian Academy of Sciences, 31 Leninsky Prospect, GSP-1, Moscow 119071, Russia
| | - Saša Lazović
- Institute of Physics Belgrade, National Institute of the Republic of Serbia, Pregrevica 118, Belgrade 11080, Serbia
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Fu C, Wang Z, Zhou X, Hu B, Li C, Yang P. Protein-based bioactive coatings: from nanoarchitectonics to applications. Chem Soc Rev 2024; 53:1514-1551. [PMID: 38167899 DOI: 10.1039/d3cs00786c] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/05/2024]
Abstract
Protein-based bioactive coatings have emerged as a versatile and promising strategy for enhancing the performance and biocompatibility of diverse biomedical materials and devices. Through surface modification, these coatings confer novel biofunctional attributes, rendering the material highly bioactive. Their widespread adoption across various domains in recent years underscores their importance. This review systematically elucidates the behavior of protein-based bioactive coatings in organisms and expounds on their underlying mechanisms. Furthermore, it highlights notable advancements in artificial synthesis methodologies and their functional applications in vitro. A focal point is the delineation of assembly strategies employed in crafting protein-based bioactive coatings, which provides a guide for their expansion and sustained implementation. Finally, the current trends, challenges, and future directions of protein-based bioactive coatings are discussed.
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Affiliation(s)
- Chengyu Fu
- Key Laboratory of Applied Surface and Colloid Chemistry, Ministry of Education, School of Chemistry and Chemical Engineering, Shaanxi Normal University, Xi'an 710119, China.
- Xi'an Key Laboratory of Polymeric Soft Matter, School of Chemistry and Chemical Engineering, Shaanxi Normal University, Xi'an 710119, China
- International Joint Research Center on Functional Fiber and Soft Smart Textile, School of Chemistry and Chemical Engineering, Shaanxi Normal University, Xi'an 710119, China
| | - Zhengge Wang
- Key Laboratory of Applied Surface and Colloid Chemistry, Ministry of Education, School of Chemistry and Chemical Engineering, Shaanxi Normal University, Xi'an 710119, China.
- Xi'an Key Laboratory of Polymeric Soft Matter, School of Chemistry and Chemical Engineering, Shaanxi Normal University, Xi'an 710119, China
- International Joint Research Center on Functional Fiber and Soft Smart Textile, School of Chemistry and Chemical Engineering, Shaanxi Normal University, Xi'an 710119, China
| | - Xingyu Zhou
- Key Laboratory of Applied Surface and Colloid Chemistry, Ministry of Education, School of Chemistry and Chemical Engineering, Shaanxi Normal University, Xi'an 710119, China.
- Xi'an Key Laboratory of Polymeric Soft Matter, School of Chemistry and Chemical Engineering, Shaanxi Normal University, Xi'an 710119, China
- International Joint Research Center on Functional Fiber and Soft Smart Textile, School of Chemistry and Chemical Engineering, Shaanxi Normal University, Xi'an 710119, China
| | - Bowen Hu
- Key Laboratory of Applied Surface and Colloid Chemistry, Ministry of Education, School of Chemistry and Chemical Engineering, Shaanxi Normal University, Xi'an 710119, China.
- Xi'an Key Laboratory of Polymeric Soft Matter, School of Chemistry and Chemical Engineering, Shaanxi Normal University, Xi'an 710119, China
- International Joint Research Center on Functional Fiber and Soft Smart Textile, School of Chemistry and Chemical Engineering, Shaanxi Normal University, Xi'an 710119, China
| | - Chen Li
- School of Chemistry and Chemical Engineering, Henan Institute of Science and Technology, Eastern HuaLan Avenue, Xinxiang, Henan 453003, China
| | - Peng Yang
- Key Laboratory of Applied Surface and Colloid Chemistry, Ministry of Education, School of Chemistry and Chemical Engineering, Shaanxi Normal University, Xi'an 710119, China.
- Xi'an Key Laboratory of Polymeric Soft Matter, School of Chemistry and Chemical Engineering, Shaanxi Normal University, Xi'an 710119, China
- International Joint Research Center on Functional Fiber and Soft Smart Textile, School of Chemistry and Chemical Engineering, Shaanxi Normal University, Xi'an 710119, China
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Basu S, Zhao B, Biró B, Faraggi E, Gsponer J, Hu G, Kloczkowski A, Malhis N, Mirdita M, Söding J, Steinegger M, Wang D, Wang K, Xu D, Zhang J, Kurgan L. DescribePROT in 2023: more, higher-quality and experimental annotations and improved data download options. Nucleic Acids Res 2024; 52:D426-D433. [PMID: 37933852 PMCID: PMC10767971 DOI: 10.1093/nar/gkad985] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/14/2023] [Revised: 10/12/2023] [Accepted: 10/16/2023] [Indexed: 11/08/2023] Open
Abstract
The DescribePROT database of amino acid-level descriptors of protein structures and functions was substantially expanded since its release in 2020. This expansion includes substantial increase in the size, scope, and quality of the underlying data, the addition of experimental structural information, the inclusion of new data download options, and an upgraded graphical interface. DescribePROT currently covers 19 structural and functional descriptors for proteins in 273 reference proteomes generated by 11 accurate and complementary predictive tools. Users can search our resource in multiple ways, interact with the data using the graphical interface, and download data at various scales including individual proteins, entire proteomes, and whole database. The annotations in DescribePROT are useful for a broad spectrum of studies that include investigations of protein structure and function, development and validation of predictive tools, and to support efforts in understanding molecular underpinnings of diseases and development of therapeutics. DescribePROT can be freely accessed at http://biomine.cs.vcu.edu/servers/DESCRIBEPROT/.
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Affiliation(s)
- Sushmita Basu
- Department of Computer Science, Virginia Commonwealth University, Richmond, VA, USA
| | - Bi Zhao
- Genomics Program, College of Public Health, University of South Florida, Tampa, FL, USA
| | - Bálint Biró
- Department of Computer Science, Virginia Commonwealth University, Richmond, VA, USA
- Department of Animal Biotechnology, Hungarian University of Agriculture and Life Sciences, Gödöllő, Hungary
| | - Eshel Faraggi
- Physics Department, Indiana University, Indianapolis, IN, USA
| | - Jörg Gsponer
- Michael Smith Laboratories, University of British Columbia, Vancouver, British Columbia, Canada
| | - Gang Hu
- School of Statistics and Data Science, LPMC and KLMDASR, Nankai University, Tianjin, P.R. China
| | - Andrzej Kloczkowski
- The Steve and Cindy Rasmussen Institute for Genomic Medicine, Nationwide Children's Hospital, Columbus, USA
| | - Nawar Malhis
- Michael Smith Laboratories, University of British Columbia, Vancouver, British Columbia, Canada
| | - Milot Mirdita
- School of Biological Sciences, Seoul National University, Seoul, Republic of Korea
| | - Johannes Söding
- Quantitative and Computational Biology, Max Planck Institute for Multidisciplinary Sciences, Göttingen, Germany
| | - Martin Steinegger
- School of Biological Sciences, Seoul National University, Seoul, Republic of Korea
- Institute of Molecular Biology & Genetics, Seoul National University, Seoul, Republic of Korea
- Artificial Intelligence Institute, Seoul National University, Seoul, South Korea
| | - Duolin Wang
- Department of Electrical Engineer and Computer Science, Christopher S. Bond Life Sciences Center, University of Missouri, Columbia, USA
| | - Kui Wang
- School of Statistics and Data Science, LPMC and KLMDASR, Nankai University, Tianjin, P.R. China
| | - Dong Xu
- Department of Electrical Engineer and Computer Science, Christopher S. Bond Life Sciences Center, University of Missouri, Columbia, USA
| | - Jian Zhang
- School of Computer and Information Technology, Xinyang Normal University, Xinyang, P.R. China
| | - Lukasz Kurgan
- Department of Computer Science, Virginia Commonwealth University, Richmond, VA, USA
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