Reinventing Therapeutic Proteins: Mining a Treasure of New Therapies
Abstract
:1. Introduction
2. Understanding Therapeutic Proteins
3. Reinvention Scope
4. Intellectual Property
5. Artificial Intelligence (AI) and Machine Learning (ML)
5.1. Structure Prediction
5.2. Target Identification
- AtomNet is a convolutional neural network-based tool that applies the concepts of feature locality and hierarchical composition extracted through protein sequence, structure, and function to model bioactivity and chemical interactions of potential drug targets [74]. AtomNet’s parent AtomWise has recently enabled the rapid discovery of drugs against 27 disease targets. DeepDTA is also a deep-learning-based model that uses only sequence information of targets and drugs to predict drug–target interaction binding affinities and potential small molecules as drug candidates from given biological data [75].
- A commercially available natural compounds database and search engine that operates using machine learning, MolPort, when used with quantitative-structure-activity relationship (qsar), analyze the chemical structure and predicts the biological activity of potential targets in the early stages of drug discovery [76].
- Pathway analysis also enables the identification of potential targets. Some crucial biological pathways are available on the Kegg Pathway database [77], which provides insight into a disease mechanism. TargetNet [78] uses this pathways data and protein interaction profiles to predict potential drug targets against a specific disease.
- DeepDock is the most recent AI-driven virtual screening platform with a vast library of small molecules. For example, DeepDock virtual screen results were used to identify 15% active molecules that led to the discovery of novel compounds against the Mpro protease of SARS-CoV2 [79].
5.3. Molecular Docking
- Higher binding affinity scores from an in-silico docking analysis of monoclonal antibodies (mAbs) against Alpha and Delta strains of SARS-CoV spike protein suggested that tixagevimab, regdanvimab, and cilgavimab can neutralize most Alpha strains efficiently and bamlanivimab, tixagevimab, and sotrovimab can be effective in suppressing the Delta strain [87]. Venetoclax [88], for treating chronic lymphocytic leukemia, was designed to target the overexpressed BCL-2 protein in cancer cells by binding to its hydrophobic groove. Its development involved optimizing the binding interactions between the drug and BCL-2 through in silico docking studies, highlighting the importance of docking in drug design.
- GOLD uses a genetic algorithm, and Autodock Vina uses a grid-based energy approach with a genetic algorithm.
- ICM [89] uses multiple stochastic runs.
- GLIDE SP [90] uses several sampling and scoring methods.
- DeepBSP, an ML-based sampling and evaluation tool, is very useful in generating and ranking profiles close to their respective native structures as a machine learning model-based pose sampling and evaluation [91].
- Identification of the correct view is crucial for higher binding affinity and lower steric hindrance, which can be efficiently achieved through precise AI-based tools. Structure prediction tools such as AlphaFold2 and trRosetta can be integrated with other ML-based approaches to identify and optimize potential poses. One such instance is identifying transition states between the active and inactive conformations of G-protein coupled receptors using multiple ML approaches [92].
- The effectiveness of interaction between the dynamic views and their binding partners can be weighted through scoring systems. Scoring functions are categorized into force-field-based, knowledge-based, and empirical scoring functions.
- Force-field-based scoring functions utilize molecular mechanics to evaluate complex energetic affinities based on their interactions, i.e., weak Van der Waals, electrostatic forces, bond stretching, bending, and torsional angles [93].
- Knowledge-based scoring functions include statistical analysis of distance-dependent atom-pair potentials of protein–ligand or protein–protein complexes generated directly from experimental structures [94,95]. Empirical scoring functions, e.g., LUDI [96], ID-Score [97], and GlideScore [90], are based on empirical data. They correlate binding free energies to weak Van der Waals energy, electrostatic energy, desolvation, entropy, enthalpy, H-bonding, rotational and translational degrees of freedom, polar and lipophilic effects, and hydrophobicity in the form of simple equations to reproduce experimental affinity data.
- These scores are used in combinations for better optimization, i.e., DockThor programs DockTScore [94,98] and blends empirical and force-field-based scoring methods, SMoG2016 [99] fuses empirical and knowledge-based scoring methods, and GalaxyDock BP2 Score [100] uses all three: force-field-based, knowledge-based, and empirical scoring methods [94].
- The recent integration of physics-based terms and ML in DockTScore has further enhanced binding energy prediction and conformation ranking [101].
- GNINA docking software, based on an ensemble of convolutional neural networks as a scoring function for scoring the sample view, has outperformed AutoDock Vina [102], once again proving that the paradigm shift from conventional methods to AI-based methods has significantly increased the impartial interpretations of scientific evidence leading to the discovery of targets.
5.4. Limitations
6. Structure Modifications
7. Drug Conjugates
8. Radioimmunoconjugates (RIC)
9. Regulatory Perspective
10. Regulatory Submission
10.1. Nonclinical Testing
10.2. Pharmacokinetics–Pharmacodynamics
10.3. Function Testing
10.4. Immunogenic Response
11. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
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Niazi, S.K.; Mariam, Z. Reinventing Therapeutic Proteins: Mining a Treasure of New Therapies. Biologics 2023, 3, 72-94. https://doi.org/10.3390/biologics3020005
Niazi SK, Mariam Z. Reinventing Therapeutic Proteins: Mining a Treasure of New Therapies. Biologics. 2023; 3(2):72-94. https://doi.org/10.3390/biologics3020005
Chicago/Turabian StyleNiazi, Sarfaraz K., and Zamara Mariam. 2023. "Reinventing Therapeutic Proteins: Mining a Treasure of New Therapies" Biologics 3, no. 2: 72-94. https://doi.org/10.3390/biologics3020005
APA StyleNiazi, S. K., & Mariam, Z. (2023). Reinventing Therapeutic Proteins: Mining a Treasure of New Therapies. Biologics, 3(2), 72-94. https://doi.org/10.3390/biologics3020005