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Zhou P, Wen L, Lin J, Mei L, Liu Q, Shang S, Li J, Shu J. Integrated unsupervised-supervised modeling and prediction of protein-peptide affinities at structural level. Brief Bioinform 2022; 23:6555404. [PMID: 35352094 DOI: 10.1093/bib/bbac097] [Citation(s) in RCA: 23] [Impact Index Per Article: 11.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/04/2022] [Revised: 02/15/2022] [Accepted: 02/23/2022] [Indexed: 12/24/2022] Open
Abstract
Cell signal networks are orchestrated directly or indirectly by various peptide-mediated protein-protein interactions, which are normally weak and transient and thus ideal for biological regulation and medicinal intervention. Here, we develop a general-purpose method for modeling and predicting the binding affinities of protein-peptide interactions (PpIs) at the structural level. The method is a hybrid strategy that employs an unsupervised approach to derive a layered PpI atom-residue interaction (ulPpI[a-r]) potential between different protein atom types and peptide residue types from thousands of solved PpI complex structures and then statistically correlates the potential descriptors with experimental affinities (KD values) over hundreds of known PpI samples in a supervised manner to create an integrated unsupervised-supervised PpI affinity (usPpIA) predictor. Although both the ulPpI[a-r] potential and usPpIA predictor can be used to calculate PpI affinities from their complex structures, the latter seems to perform much better than the former, suggesting that the unsupervised potential can be improved substantially with a further correction by supervised statistical learning. We examine the robustness and fault-tolerance of usPpIA predictor when applied to treat the coarse-grained PpI complex structures modeled computationally by sophisticated peptide docking and dynamics simulation. It is revealed that, despite developed solely based on solved structures, the integrated unsupervised-supervised method is also applicable for locally docked structures to reach a quantitative prediction but can only give a qualitative prediction on globally docked structures. The dynamics refinement seems not to change (or improve) the predictive results essentially, although it is computationally expensive and time-consuming relative to peptide docking. We also perform extrapolation of usPpIA predictor to the indirect affinity quantities of HLA-A*0201 binding epitope peptides and NHERF PDZ binding scaffold peptides, consequently resulting in a good and moderate correlation of the predicted KD with experimental IC50 and BLU on the two peptide sets, with Pearson's correlation coefficients Rp = 0.635 and 0.406, respectively.
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Affiliation(s)
- Peng Zhou
- Center for Informational Biology, School of Life Science and Technology, University of Electronic Science and Technology of China (UESTC), Chengdu 611731, China
| | - Li Wen
- Center for Informational Biology, School of Life Science and Technology, University of Electronic Science and Technology of China (UESTC), Chengdu 611731, China
| | - Jing Lin
- Center for Informational Biology, School of Life Science and Technology, University of Electronic Science and Technology of China (UESTC), Chengdu 611731, China
| | - Li Mei
- Institute of Culinary, Sichuan Tourism University, Chengdu 610100, China
| | - Qian Liu
- Center for Informational Biology, School of Life Science and Technology, University of Electronic Science and Technology of China (UESTC), Chengdu 611731, China
| | - Shuyong Shang
- of Ecological Environment Protection, Chengdu Normal University, Chengdu 611130, China
| | - Juelin Li
- Center for Informational Biology, School of Life Science and Technology, University of Electronic Science and Technology of China (UESTC), Chengdu 611731, China
| | - Jianping Shu
- Center for Informational Biology, School of Life Science and Technology, University of Electronic Science and Technology of China (UESTC), Chengdu 611731, China
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Kizhedath A, Karlberg M, Glassey J. Cross-Interaction Chromatography-Based QSAR Model for Early-Stage Screening to Facilitate Enhanced Developability of Monoclonal Antibody Therapeutics. Biotechnol J 2019; 14:e1800696. [PMID: 30810283 DOI: 10.1002/biot.201800696] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/26/2018] [Revised: 01/19/2019] [Indexed: 01/13/2023]
Abstract
Monoclonal antibodies (mAbs) constitute a rapidly growing biopharmaceutical sector. However, their growth is impeded by developability issues such as polyspecificity and lack of solubility, which leads to attrition as well as manufacturing failures. In this study a multitool hybrid quantitative structure-activity relationship (QSAR) model development framework is described. This framework uses four novel datasets derived from the primary sequences of IgG1-κ-humanized mAbs with varying degrees of resolutions. Unsupervised pattern recognition is first performed on the descriptor sets to visualize any intrinsic property-based clustering, followed by regression of descriptors against cross-interaction chromatography (CIC) retention times. Model optimization is performed via unsupervised variable reduction followed by supervised variable selection. Finally, the models and datasets are benchmarked based on the regression model performance metrics such as R2 , Q2 , and RMSE. The results show that datasets containing localized descriptors rather than averaged value over the entire protein have better predictive performance of CIC retention behavior with R2 > 0.8 and RMSE < 0.3. Furthermore, the results indicate the physicochemical, electronic, and topological properties of hypervariable regions of antibodies that contribute most to the CIC retention times. The results of these studies could contribute to early-stage screening and better design of mAbs.
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Affiliation(s)
- Arathi Kizhedath
- School of Engineering, Newcastle University, Newcastle upon Tyne, NE17RU, UK
| | - Micael Karlberg
- School of Engineering, Newcastle University, Newcastle upon Tyne, NE17RU, UK
| | - Jarka Glassey
- School of Engineering, Newcastle University, Newcastle upon Tyne, NE17RU, UK
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Antunes DA, Abella JR, Devaurs D, Rigo MM, Kavraki LE. Structure-based Methods for Binding Mode and Binding Affinity Prediction for Peptide-MHC Complexes. Curr Top Med Chem 2018; 18:2239-2255. [PMID: 30582480 PMCID: PMC6361695 DOI: 10.2174/1568026619666181224101744] [Citation(s) in RCA: 38] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/27/2018] [Revised: 11/29/2018] [Accepted: 12/08/2018] [Indexed: 12/26/2022]
Abstract
Understanding the mechanisms involved in the activation of an immune response is essential to many fields in human health, including vaccine development and personalized cancer immunotherapy. A central step in the activation of the adaptive immune response is the recognition, by T-cell lymphocytes, of peptides displayed by a special type of receptor known as Major Histocompatibility Complex (MHC). Considering the key role of MHC receptors in T-cell activation, the computational prediction of peptide binding to MHC has been an important goal for many immunological applications. Sequence- based methods have become the gold standard for peptide-MHC binding affinity prediction, but structure-based methods are expected to provide more general predictions (i.e., predictions applicable to all types of MHC receptors). In addition, structural modeling of peptide-MHC complexes has the potential to uncover yet unknown drivers of T-cell activation, thus allowing for the development of better and safer therapies. In this review, we discuss the use of computational methods for the structural modeling of peptide-MHC complexes (i.e., binding mode prediction) and for the structure-based prediction of binding affinity.
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Affiliation(s)
| | - Jayvee R. Abella
- Computer Science Department, Rice University, Houston, Texas, USA
| | - Didier Devaurs
- Computer Science Department, Rice University, Houston, Texas, USA
| | - Maurício M. Rigo
- School of Medicine, Pontifical Catholic University of Rio Grande do Sul, Porto Alegre, Rio Grande do Sul, Brazil
| | - Lydia E. Kavraki
- Computer Science Department, Rice University, Houston, Texas, USA
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Meric G, Robinson AS, Roberts CJ. Driving Forces for Nonnative Protein Aggregation and Approaches to Predict Aggregation-Prone Regions. Annu Rev Chem Biomol Eng 2017; 8:139-159. [DOI: 10.1146/annurev-chembioeng-060816-101404] [Citation(s) in RCA: 41] [Impact Index Per Article: 5.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Affiliation(s)
- Gulsum Meric
- Department of Chemical and Biomolecular Engineering, University of Delaware, Newark, Delaware 19716
| | - Anne S. Robinson
- Department of Chemical and Biomolecular Engineering, Tulane University, New Orleans, Louisiana 70118
| | - Christopher J. Roberts
- Department of Chemical and Biomolecular Engineering, University of Delaware, Newark, Delaware 19716
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Kizhedath A, Wilkinson S, Glassey J. Applicability of predictive toxicology methods for monoclonal antibody therapeutics: status Quo and scope. Arch Toxicol 2016; 91:1595-1612. [PMID: 27766364 PMCID: PMC5364268 DOI: 10.1007/s00204-016-1876-7] [Citation(s) in RCA: 20] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/19/2016] [Accepted: 10/12/2016] [Indexed: 12/31/2022]
Abstract
Biopharmaceuticals, monoclonal antibody (mAb)-based therapeutics in particular, have positively impacted millions of lives. MAbs and related therapeutics are highly desirable from a biopharmaceutical perspective as they are highly target specific and well tolerated within the human system. Nevertheless, several mAbs have been discontinued or withdrawn based either on their inability to demonstrate efficacy and/or due to adverse effects. Approved monoclonal antibodies and derived therapeutics have been associated with adverse effects such as immunogenicity, cytokine release syndrome, progressive multifocal leukoencephalopathy, intravascular haemolysis, cardiac arrhythmias, abnormal liver function, gastrointestinal perforation, bronchospasm, intraocular inflammation, urticaria, nephritis, neuropathy, birth defects, fever and cough to name a few. The advances made in this field are also impeded by a lack of progress in bioprocess development strategies as well as increasing costs owing to attrition, wherein the lack of efficacy and safety accounts for nearly 60 % of all factors contributing to attrition. This reiterates the need for smarter preclinical development using quality by design-based approaches encompassing carefully designed predictive models during early stages of drug development. Different in vitro and in silico methods are extensively used for predicting biological activity as well as toxicity during small molecule drug development; however, their full potential has not been utilized for biological drug development. The scope of in vitro and in silico tools in early developmental stages of monoclonal antibody-based therapeutics production and how it contributes to lower attrition rates leading to faster development of potential drug candidates has been evaluated. The applicability of computational toxicology approaches in this context as well as the pitfalls and promises of extending such techniques to biopharmaceutical development has been highlighted.
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Affiliation(s)
- Arathi Kizhedath
- Chemical Engineering and Advanced Materials, Newcastle University, Newcastle upon Tyne, NE17RU, UK. .,Medical Toxicology Centre, Institute of Cellular Medicine, Newcastle University, Newcastle upon Tyne, NE2 4AA, UK.
| | - Simon Wilkinson
- Medical Toxicology Centre, Institute of Cellular Medicine, Newcastle University, Newcastle upon Tyne, NE2 4AA, UK
| | - Jarka Glassey
- Chemical Engineering and Advanced Materials, Newcastle University, Newcastle upon Tyne, NE17RU, UK
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Eberhardt M, Lai X, Tomar N, Gupta S, Schmeck B, Steinkasserer A, Schuler G, Vera J. Third-Kind Encounters in Biomedicine: Immunology Meets Mathematics and Informatics to Become Quantitative and Predictive. Methods Mol Biol 2016; 1386:135-179. [PMID: 26677184 DOI: 10.1007/978-1-4939-3283-2_9] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/05/2023]
Abstract
The understanding of the immune response is right now at the center of biomedical research. There are growing expectations that immune-based interventions will in the midterm provide new, personalized, and targeted therapeutic options for many severe and highly prevalent diseases, from aggressive cancers to infectious and autoimmune diseases. To this end, immunology should surpass its current descriptive and phenomenological nature, and become quantitative, and thereby predictive.Immunology is an ideal field for deploying the tools, methodologies, and philosophy of systems biology, an approach that combines quantitative experimental data, computational biology, and mathematical modeling. This is because, from an organism-wide perspective, the immunity is a biological system of systems, a paradigmatic instance of a multi-scale system. At the molecular scale, the critical phenotypic responses of immune cells are governed by large biochemical networks, enriched in nested regulatory motifs such as feedback and feedforward loops. This network complexity confers them the ability of highly nonlinear behavior, including remarkable examples of homeostasis, ultra-sensitivity, hysteresis, and bistability. Moving from the cellular level, different immune cell populations communicate with each other by direct physical contact or receiving and secreting signaling molecules such as cytokines. Moreover, the interaction of the immune system with its potential targets (e.g., pathogens or tumor cells) is far from simple, as it involves a number of attack and counterattack mechanisms that ultimately constitute a tightly regulated multi-feedback loop system. From a more practical perspective, this leads to the consequence that today's immunologists are facing an ever-increasing challenge of integrating massive quantities from multi-platforms.In this chapter, we support the idea that the analysis of the immune system demands the use of systems-level approaches to ensure the success in the search for more effective and personalized immune-based therapies.
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Affiliation(s)
- Martin Eberhardt
- Laboratory of Systems Tumor Immunology, Department of Dermatology, University Hospital Erlangen and Friedrich-Alexander University Erlangen-Nürnberg, Erlangen, Germany
- Department of Dermatology, University Hospital Erlangen and Friedrich-Alexander University Erlangen-Nürnberg, Erlangen, Germany
| | - Xin Lai
- Laboratory of Systems Tumor Immunology, Department of Dermatology, University Hospital Erlangen and Friedrich-Alexander University Erlangen-Nürnberg, Erlangen, Germany
- Department of Dermatology, University Hospital Erlangen and Friedrich-Alexander University Erlangen-Nürnberg, Erlangen, Germany
| | - Namrata Tomar
- Laboratory of Systems Tumor Immunology, Department of Dermatology, University Hospital Erlangen and Friedrich-Alexander University Erlangen-Nürnberg, Erlangen, Germany
- Department of Dermatology, University Hospital Erlangen and Friedrich-Alexander University Erlangen-Nürnberg, Erlangen, Germany
| | - Shailendra Gupta
- Department of Systems Biology and Bioinformatics, University of Rostock, Rostock, Germany
| | - Bernd Schmeck
- Department of Medicine, Pulmonary and Critical Care Medicine, University Medical Center Marburg, Philipps University, Marburg, Germany
- Systems Biology Platform, Institute for Lung Research/iLung, German Center for Lung Research, Universities of Giessen and Marburg Lung Centre, Philipps University Marburg, Marburg, Germany
| | - Alexander Steinkasserer
- Department of Immune Modulation at the Department of Dermatology, University Hospital Erlangen, Erlangen, Germany
| | - Gerold Schuler
- Department of Dermatology, University Hospital Erlangen and Friedrich-Alexander University Erlangen-Nürnberg, Erlangen, Germany
| | - Julio Vera
- Laboratory of Systems Tumor Immunology, Department of Dermatology, University Hospital Erlangen and Friedrich-Alexander University Erlangen-Nürnberg, Erlangen, Germany.
- Department of Dermatology, University Hospital Erlangen and Friedrich-Alexander University Erlangen-Nürnberg, Erlangen, Germany.
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Gupta SK, Jaitly T, Schmitz U, Schuler G, Wolkenhauer O, Vera J. Personalized cancer immunotherapy using Systems Medicine approaches. Brief Bioinform 2015; 17:453-67. [DOI: 10.1093/bib/bbv046] [Citation(s) in RCA: 19] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/02/2015] [Indexed: 12/27/2022] Open
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Prediction of the complexation stabilities of La3+ ion with ionophores applied in lanthanoid sensors. J INCL PHENOM MACRO 2013. [DOI: 10.1007/s10847-013-0303-4] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
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Tian F, Lv Y, Yang L. Structure-based prediction of protein–protein binding affinity with consideration of allosteric effect. Amino Acids 2011; 43:531-43. [DOI: 10.1007/s00726-011-1101-1] [Citation(s) in RCA: 41] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/07/2011] [Accepted: 09/21/2011] [Indexed: 11/28/2022]
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10
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Simple and accurate approaches to predict the activity of benzothiadiazine derivatives as HCV inhibitors. Med Chem Res 2011. [DOI: 10.1007/s00044-011-9734-x] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
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Bremel RD, Homan EJ. An integrated approach to epitope analysis II: A system for proteomic-scale prediction of immunological characteristics. Immunome Res 2010; 6:8. [PMID: 21044290 PMCID: PMC2991286 DOI: 10.1186/1745-7580-6-8] [Citation(s) in RCA: 30] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/19/2010] [Accepted: 11/02/2010] [Indexed: 11/25/2022] Open
Abstract
Background Improving our understanding of the immune response is fundamental to developing strategies to combat a wide range of diseases. We describe an integrated epitope analysis system which is based on principal component analysis of sequences of amino acids, using a multilayer perceptron neural net to conduct QSAR regression predictions for peptide binding affinities to 35 MHC-I and 14 MHC-II alleles. Results The approach described allows rapid processing of single proteins, entire proteomes or subsets thereof, as well as multiple strains of the same organism. It enables consideration of the interface of diversity of both microorganisms and of host immunogenetics. Patterns of binding affinity are linked to topological features, such as extracellular or intramembrane location, and integrated into a graphical display which facilitates conceptual understanding of the interplay of B-cell and T-cell mediated immunity. Patterns which emerge from application of this approach include the correlations between peptides showing high affinity binding to MHC-I and to MHC-II, and also with predicted B-cell epitopes. These are characterized as coincident epitope groups (CEGs). Also evident are long range patterns across proteins which identify regions of high affinity binding for a permuted population of diverse and heterozygous HLA alleles, as well as subtle differences in reactions with MHCs of individual HLA alleles, which may be important in disease susceptibility, and in vaccine and clinical trial design. Comparisons are shown of predicted epitope mapping derived from application of the QSAR approach with experimentally derived epitope maps from a diverse multi-species dataset, from Staphylococcus aureus, and from vaccinia virus. Conclusions A desktop application with interactive graphic capability is shown to be a useful platform for development of prediction and visualization tools for epitope mapping at scales ranging from individual proteins to proteomes from multiple strains of an organism. The possible functional implications of the patterns of peptide epitopes observed are discussed, including their implications for B-cell and T-cell cooperation and cross presentation.
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Affiliation(s)
- Robert D Bremel
- 1ioGenetics LLC, 3591 Anderson Street, Madison, WI 53704, USA.
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QSAR study on melanocortin-4 receptors by support vector machine. Eur J Med Chem 2010; 45:1087-93. [DOI: 10.1016/j.ejmech.2009.12.003] [Citation(s) in RCA: 29] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/08/2008] [Revised: 09/02/2009] [Accepted: 12/04/2009] [Indexed: 11/19/2022]
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Wu J, Li X, Cheng W, Xie Q, Liu Y, Zhao C. Quantitative Structure Activity Relationship (QSAR) Approach to Multiple Drug Resistance (MDR) Modulators Based on Combined Hybrid System. ACTA ACUST UNITED AC 2009. [DOI: 10.1002/qsar.200860134] [Citation(s) in RCA: 8] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
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Current mathematical methods used in QSAR/QSPR studies. Int J Mol Sci 2009; 10:1978-1998. [PMID: 19564933 PMCID: PMC2695261 DOI: 10.3390/ijms10051978] [Citation(s) in RCA: 126] [Impact Index Per Article: 8.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/19/2009] [Accepted: 04/28/2009] [Indexed: 02/07/2023] Open
Abstract
This paper gives an overview of the mathematical methods currently used in quantitative structure-activity/property relationship (QASR/QSPR) studies. Recently, the mathematical methods applied to the regression of QASR/QSPR models are developing very fast, and new methods, such as Gene Expression Programming (GEP), Project Pursuit Regression (PPR) and Local Lazy Regression (LLR) have appeared on the QASR/QSPR stage. At the same time, the earlier methods, including Multiple Linear Regression (MLR), Partial Least Squares (PLS), Neural Networks (NN), Support Vector Machine (SVM) and so on, are being upgraded to improve their performance in QASR/QSPR studies. These new and upgraded methods and algorithms are described in detail, and their advantages and disadvantages are evaluated and discussed, to show their application potential in QASR/QSPR studies in the future.
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