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Bogdanova EA, Novoseletsky VN. ProBAN: Neural network algorithm for predicting binding affinity in protein-protein complexes. Proteins 2024; 92:1127-1136. [PMID: 38722047 DOI: 10.1002/prot.26700] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/13/2023] [Revised: 03/22/2024] [Accepted: 04/26/2024] [Indexed: 08/07/2024]
Abstract
Determining binding affinities in protein-protein and protein-peptide complexes is a challenging task that directly impacts the development of peptide and protein pharmaceuticals. Although several models have been proposed to predict the value of the dissociation constant and the Gibbs free energy, they are currently not capable of making stable predictions with high accuracy, in particular for complexes consisting of more than two molecules. In this work, we present ProBAN, a new method for predicting binding affinity in protein-protein complexes based on a deep convolutional neural network. Prediction is carried out for the spatial structures of complexes, presented in the format of a 4D tensor, which includes information about the location of atoms and their abilities to participate in various types of interactions realized in protein-protein and protein-peptide complexes. The effectiveness of the model was assessed both on an internal test data set containing complexes consisting of three or more molecules, as well as on an external test for the PPI-Affinity service. As a result, we managed to achieve the best prediction quality on these data sets among all the analyzed models: on the internal test, Pearson correlation R = 0.6, MAE = 1.60, on the external test, R = 0.55, MAE = 1.75. The open-source code, the trained ProBAN model, and the collected dataset are freely available at the following link https://github.com/EABogdanova/ProBAN.
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Chaves EJF, Coêlho DF, Cruz CHB, Moreira EG, Simões JCM, Nascimento-Filho MJ, Lins RD. Structure-based computational design of antibody mimetics: challenges and perspectives. FEBS Open Bio 2024. [PMID: 38925955 DOI: 10.1002/2211-5463.13855] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/03/2024] [Revised: 05/17/2024] [Accepted: 06/19/2024] [Indexed: 06/28/2024] Open
Abstract
The design of antibody mimetics holds great promise for revolutionizing therapeutic interventions by offering alternatives to conventional antibody therapies. Structure-based computational approaches have emerged as indispensable tools in the rational design of those molecules, enabling the precise manipulation of their structural and functional properties. This review covers the main classes of designed antigen-binding motifs, as well as alternative strategies to develop tailored ones. We discuss the intricacies of different computational protein-protein interaction design strategies, showcased by selected successful cases in the literature. Subsequently, we explore the latest advancements in the computational techniques including the integration of machine and deep learning methodologies into the design framework, which has led to an augmented design pipeline. Finally, we verse onto the current challenges that stand in the way between high-throughput computer design of antibody mimetics and experimental realization, offering a forward-looking perspective into the field and the promises it holds to biotechnology.
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Affiliation(s)
- Elton J F Chaves
- Aggeu Magalhães Institute, Oswaldo Cruz Foundation, Recife, Brazil
| | - Danilo F Coêlho
- Department of Fundamental Chemistry, Federal University of Pernambuco, Recife, Brazil
| | - Carlos H B Cruz
- Institute of Structural and Molecular Biology, University College London, UK
| | | | - Júlio C M Simões
- Aggeu Magalhães Institute, Oswaldo Cruz Foundation, Recife, Brazil
- Department of Fundamental Chemistry, Federal University of Pernambuco, Recife, Brazil
| | - Manassés J Nascimento-Filho
- Aggeu Magalhães Institute, Oswaldo Cruz Foundation, Recife, Brazil
- Department of Fundamental Chemistry, Federal University of Pernambuco, Recife, Brazil
| | - Roberto D Lins
- Aggeu Magalhães Institute, Oswaldo Cruz Foundation, Recife, Brazil
- Department of Fundamental Chemistry, Federal University of Pernambuco, Recife, Brazil
- Fiocruz Genomics Network, Brazil
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Aguilera-Puga MDC, Plisson F. Structure-aware machine learning strategies for antimicrobial peptide discovery. Sci Rep 2024; 14:11995. [PMID: 38796582 PMCID: PMC11127937 DOI: 10.1038/s41598-024-62419-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/08/2024] [Accepted: 05/16/2024] [Indexed: 05/28/2024] Open
Abstract
Machine learning models are revolutionizing our approaches to discovering and designing bioactive peptides. These models often need protein structure awareness, as they heavily rely on sequential data. The models excel at identifying sequences of a particular biological nature or activity, but they frequently fail to comprehend their intricate mechanism(s) of action. To solve two problems at once, we studied the mechanisms of action and structural landscape of antimicrobial peptides as (i) membrane-disrupting peptides, (ii) membrane-penetrating peptides, and (iii) protein-binding peptides. By analyzing critical features such as dipeptides and physicochemical descriptors, we developed models with high accuracy (86-88%) in predicting these categories. However, our initial models (1.0 and 2.0) exhibited a bias towards α-helical and coiled structures, influencing predictions. To address this structural bias, we implemented subset selection and data reduction strategies. The former gave three structure-specific models for peptides likely to fold into α-helices (models 1.1 and 2.1), coils (1.3 and 2.3), or mixed structures (1.4 and 2.4). The latter depleted over-represented structures, leading to structure-agnostic predictors 1.5 and 2.5. Additionally, our research highlights the sensitivity of important features to different structure classes across models.
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Affiliation(s)
- Mariana D C Aguilera-Puga
- Department of Biotechnology and Biochemistry, Center for Research and Advanced Studies of the National Polytechnic Institute (CINVESTAV-IPN), Irapuato Unit, 36824, Irapuato, Guanajuato, Mexico
| | - Fabien Plisson
- Department of Biotechnology and Biochemistry, Center for Research and Advanced Studies of the National Polytechnic Institute (CINVESTAV-IPN), Irapuato Unit, 36824, Irapuato, Guanajuato, Mexico.
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Yi C, Taylor ML, Ziebarth J, Wang Y. Predictive Models and Impact of Interfacial Contacts and Amino Acids on Protein-Protein Binding Affinity. ACS OMEGA 2024; 9:3454-3468. [PMID: 38284090 PMCID: PMC10809705 DOI: 10.1021/acsomega.3c06996] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/13/2023] [Revised: 12/11/2023] [Accepted: 12/14/2023] [Indexed: 01/30/2024]
Abstract
Protein-protein interactions (PPIs) play a central role in nearly all cellular processes. The strength of the binding in a PPI is characterized by the binding affinity (BA) and is a key factor in controlling protein-protein complex formation and defining the structure-function relationship. Despite advancements in understanding protein-protein binding, much remains unknown about the interfacial region and its association with BA. New models are needed to predict BA with improved accuracy for therapeutic design. Here, we use machine learning approaches to examine how well different types of interfacial contacts can be used to predict experimentally determined BA and to reveal the impact of the specific amino acids at the binding interface on BA. We create a series of multivariate linear regression models incorporating different contact features at both residue and atomic levels and examine how different methods of identifying and characterizing these properties impact the performance of these models. Particularly, we introduce a new and simple approach to predict BA based on the quantities of specific amino acids at the protein-protein interface. We found that the numbers of specific amino acids at the protein-protein interface were correlated with BA. We show that the interfacial numbers of amino acids can be used to produce models with consistently good performance across different data sets, indicating the importance of the identities of interfacial amino acids in underlying BA. When trained on a diverse set of complexes from two benchmark data sets, the best performing BA model was generated with an explicit linear equation involving six amino acids. Tyrosine, in particular, was identified as the key amino acid in controlling BA, as it had the strongest correlation with BA and was consistently identified as the most important amino acid in feature importance studies. Glycine and serine were identified as the next two most important amino acids in predicting BA. The results from this study further our understanding of PPIs and can be used to make improved predictions of BA, giving them implications for drug design and screening in the pharmaceutical industry.
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Affiliation(s)
- Carey
Huang Yi
- Department of Chemistry, The University of Memphis, Memphis, Tennessee 38152, United States
| | - Mitchell Lee Taylor
- Department of Chemistry, The University of Memphis, Memphis, Tennessee 38152, United States
| | - Jesse Ziebarth
- Department of Chemistry, The University of Memphis, Memphis, Tennessee 38152, United States
| | - Yongmei Wang
- Department of Chemistry, The University of Memphis, Memphis, Tennessee 38152, United States
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Jarończyk M. Software for Predicting Binding Free Energy of Protein-Protein Complexes and Their Mutants. Methods Mol Biol 2024; 2780:139-147. [PMID: 38987468 DOI: 10.1007/978-1-0716-3985-6_9] [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] [Indexed: 07/12/2024]
Abstract
Protein-protein binding affinity prediction is important for understanding complex biochemical pathways and to uncover protein interaction networks. Quantitative estimation of the binding affinity changes caused by mutations can provide critical information for protein function annotation and genetic disease diagnoses. The binding free energies of protein-protein complexes can be predicted using several computational tools. This chapter is a summary of software developed for the prediction of binding free energies for protein-protein complexes and their mutants.
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Danishuddin, Jamal MS, Song KS, Lee KW, Kim JJ, Park YM. Revolutionizing Drug Targeting Strategies: Integrating Artificial Intelligence and Structure-Based Methods in PROTAC Development. Pharmaceuticals (Basel) 2023; 16:1649. [PMID: 38139776 PMCID: PMC10747325 DOI: 10.3390/ph16121649] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/24/2023] [Revised: 11/20/2023] [Accepted: 11/22/2023] [Indexed: 12/24/2023] Open
Abstract
PROteolysis TArgeting Chimera (PROTAC) is an emerging technology in chemical biology and drug discovery. This technique facilitates the complete removal of the target proteins that are "undruggable" or challenging to target through chemical molecules via the Ubiquitin-Proteasome System (UPS). PROTACs have been widely explored and outperformed not only in cancer but also in other diseases. During the past few decades, several academic institutes and pharma companies have poured more efforts into PROTAC-related technologies, setting the stage for several major degrader trial readouts in clinical phases. Despite their promising results, the formation of robust ternary orientation, off-target activity, poor permeability, and binding affinity are some of the limitations that hinder their development. Recent advancements in computational technologies have facilitated progress in the development of PROTACs. Researchers have been able to utilize these technologies to explore a wider range of E3 ligases and optimize linkers, thereby gaining a better understanding of the effectiveness and safety of PROTACs in clinical settings. In this review, we briefly explore the computational strategies reported to date for the formation of PROTAC components and discuss the key challenges and opportunities for further research in this area.
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Affiliation(s)
- Danishuddin
- Department of Biotechnology, Yeungnam University, Gyeongsan 38541, Republic of Korea;
| | | | - Kyoung-Seob Song
- Department of Medical Science, Kosin University College of Medicine, 194 Wachi-ro, Yeongdo-gu, Busan 49104, Republic of Korea;
| | - Keun-Woo Lee
- Division of Life Science, Department of Bio & Medical Big-Data (BK4 Program), Research Institute of Natural Science (RINS), Gyeongsang National University (GNU), 501 Jinju-daero, Jinju 52828, Republic of Korea
- Angel i-Drug Design (AiDD), 33-3 Jinyangho-ro 44, Jinju 52650, Republic of Korea
| | - Jong-Joo Kim
- Department of Biotechnology, Yeungnam University, Gyeongsan 38541, Republic of Korea;
| | - Yeong-Min Park
- Department of Integrative Biological Sciences and Industry, Sejong University, 209, Neugdong-ro, Gwangjin-gu, Seoul 05006, Republic of Korea
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Yuan Y, Chen Q, Mao J, Li G, Pan X. DG-Affinity: predicting antigen-antibody affinity with language models from sequences. BMC Bioinformatics 2023; 24:430. [PMID: 37957563 PMCID: PMC10644518 DOI: 10.1186/s12859-023-05562-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/21/2023] [Accepted: 11/06/2023] [Indexed: 11/15/2023] Open
Abstract
BACKGROUND Antibody-mediated immune responses play a crucial role in the immune defense of human body. The evolution of bioengineering has led the progress of antibody-derived drugs, showing promising efficacy in cancer and autoimmune disease therapy. A critical step of this development process is obtaining the affinity between antibodies and their binding antigens. RESULTS In this study, we introduce a novel sequence-based antigen-antibody affinity prediction method, named DG-Affinity. DG-Affinity uses deep neural networks to efficiently and accurately predict the affinity between antibodies and antigens from sequences, without the need for structural information. The sequences of both the antigen and the antibody are first transformed into embedding vectors by two pre-trained language models, then these embeddings are concatenated into an ConvNeXt framework with a regression task. The results demonstrate the superiority of DG-Affinity over the existing structure-based prediction methods and the sequence-based tools, achieving a Pearson's correlation of over 0.65 on an independent test dataset. CONCLUSIONS Compared to the baseline methods, DG-Affinity achieves the best performance and can advance the development of antibody design. It is freely available as an easy-to-use web server at https://www.digitalgeneai.tech/solution/affinity .
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Affiliation(s)
- Ye Yuan
- Institute of Image Processing and Pattern Recognition, Shanghai Jiao Tong University, and Key Laboratory of System Control and Information Processing, Ministry of Education of China, Shanghai, 200240, China.
| | | | - Jun Mao
- DigitalGene, Ltd, Shanghai, 200240, China
| | - Guipeng Li
- DigitalGene, Ltd, Shanghai, 200240, China
| | - Xiaoyong Pan
- Institute of Image Processing and Pattern Recognition, Shanghai Jiao Tong University, and Key Laboratory of System Control and Information Processing, Ministry of Education of China, Shanghai, 200240, China.
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