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Файл:Ординатура / Хирургия / Библиотека им академика М.И. Перельмана / Книга_5440_Библиотеки_им_академика_М_И_Перельмана.pdf
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protocol, employing ensemble docking to identify new selective agonists. RXR acts as a
heterodimeric partner for approximately one-third of the 48 human nuclear receptor superfamily
members, implying potential alterations in its ligand binding pocket upon interaction with different
heterodimerization partners. This heterodimerization significantly enhances DNA binding and
transcriptional activation, with reports indicating that increased expression of RXRα augments the
transcriptional response to ligand binding. Selective RXR agonists have demonstrated the ability to
enhance the antiproliferative and apoptotic responses of breast cancer cell lines to PPAR ligands.
Recent discoveries have unveiled RXR-selective ligands that not only inhibit the proliferation of
breast cancer cells resistant to all-trans RA in vitro but also induce regression of the disease in
animal models. Hence, RXR emerges as a promising therapeutic target for cancer treatment.
Considering the presence of multiple RXR receptor conformations due to binding with different
heterodimerization partners, our objective is to enhance the specificity of identified binders for a
given heterodimer partner by carefully selecting the RXR receptor structure for SBVS. Our method
entails selecting RXR conformations for SBVS based on four criteria:
● Conducting pairwise comparison of receptor conformations using RMSD calculations.
● Analyzing and clustering RXR structures based on binding-site shape and volume using SiteMap
(Figure 5.7).
● Docking a small database of known actives for a specific heterodimer partner to the resulting
shape-diverse subset of binding sites from steps (a) and (b) using Glide 5.8 SP and XP [73].
● Retrieving representative protein conformations for the structure of interest from MD simula-
tions using GROMACS [73].
Performing VS on three different subsets of RXR receptor conformations, arising from binding
to different heterodimerization partners selected as mentioned above, may increase the success
MD Simulations
conformer
generation
Binding site
prediction
Post-processing
Virtual
screening
Goal:
Discovery of
mutant specific inhibitors
for PI3Kα
Protocol evaluation
through in-vitro assays
10 compounds
4 actives
In vitro assaying
vdW filtering
pchem properties
clustering
Figure 5.6 Workflow for the discovery of mutant-specific PI3K inhibitors based on an SBVS protocol
involving conformer generation, binding site prediction, and compound post-processing.
114
rate of the process. Preprocessing for docking compound databases in this SBVS exercise involves
assigning protonation and tautomeric states as described previously. Compounds for assaying are
chosen based on the following criteria: Molecules that exhibit high scores when docked in the RXR
protein ensemble binding to the heterodimer partner of interest, while simultaneously displaying
low scores for RXR structures binding to heterodimer partners of no interest, are selected to
achieve selectivity. Subsequently, a post-processing step is applied to the top-scoring compounds
using ChemBioServer [74] and FAF-Drugs2 filtering tools [75] pharmacological property predic-
tion using the QikProp software. The workflow of this protocol is depicted in Figure 5.7.
5.11 Challenges and Future Directions
5.11.1 Limitations of Virtual Screening
VS is a computational technique used in drug discovery to identify potential drug candidates by
screening large chemical libraries, typically consisting of thousands to millions of compounds,
against a target of interest, such as a protein receptor implicated in a disease pathway. While VS has
become an integral part of the drug discovery process, it also comes with several limitations.
Scoring functions and accuracy: VS methods often rely on scoring functions to estimate the
binding affinity between a ligand and a target protein. However, these scoring functions may not
always accurately predict the binding affinity due to the complex and dynamic nature of
RMSD pairwise comparison
MD simulations
Binding site shape analysis
Docking of known agonists
Goal:
Protocol evaluation
through in-vitro
assays
Structural
ensemble selection
Ensemble
docking &
counterscreening
Compound
selection
Post-processing
Pharmacological
property
prediction
Discovery of
selective compounds
for
RXRa
Figure 5.7 Workflow for the discovery of selective RXR ligands based on an SBVS protocol of ED and
counter-screening.
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protein–ligand interactions. A study by Gohlke et al. [16] highlights the challenges in accurately
predicting binding affinities, particularly for flexible binding sites and conformational changes
upon ligand binding.
Ligand flexibility: Conformational flexibility of ligands and proteins adds another layer of
complexity to VS. Traditional rigid docking approaches may fail to account for the conformational
changes required for binding, leading to false negatives or inaccurate predictions. Flexible docking
methods aim to address this issue by considering the flexibility of both ligands and receptors.
Library size and diversity: While VS allows for the rapid screening of large chemical libraries,
the quality and diversity of the compounds in these libraries greatly influence the success of the
screening process. Limited structural diversity within the chemical library can result in a biased
representation of chemical space and may overlook potential drug candidates. An analysis high-
lights the importance of chemical diversity in compound libraries for successful VS campaigns.
Computational resources and speed: VS methods often require significant computational
resources, especially when using computationally intensive techniques such as molecular dynam-
ics simulations or ensemble docking. Limited computational resources and time constraints may
restrict the thoroughness of the screening process or necessitate the use of simplified models,
potentially compromising accuracy. A study by Kitchen et al. [7] discusses the impact of computa-
tional resources on VS strategies.
Experimental validation and false positives: VS often generates a list of potential drug can-
didates that require experimental validation to confirm their biological activity and pharmacologi-
cal properties. False positives, where compounds exhibit activity in silico but fail in experimental
assays, can occur due to limitations in the scoring functions, incomplete representation of protein
flexibility, or neglect of relevant physicochemical properties. A study discusses the importance of
experimental validation in VS studies.
In conclusion, while VS is a valuable tool in drug discovery, researchers must be aware of its limi-
tations and continuously strive to improve methods and algorithms to overcome these challenges.
Addressing issues related to scoring accuracy, ligand flexibility, library diversity, computational
resources, and experimental validation will enhance the effectiveness of VS in identifying promis-
ing drug candidates.
5.11.2 Emerging Technologies and Trends
Emerging technologies like AI and ML are increasingly crucial in VS. Deep learning models, such
as CNNs, are gaining prominence for analyzing complex molecular structures. For example,
Alpha-Fold, a deep learning-based tool, has demonstrated remarkable accuracy in predicting pro-
tein structures. Integrating these technologies can significantly enhance the precision and effi-
ciency of VS.
5.11.3 Integration with High-throughput Experimentation
A promising trend is the integration of VS with high-throughput experimentation. In drug discov-
ery, VS can identify potential drug candidates quickly. These candidates are then subjected to high-
throughput experimental tests to validate their efficacy. For example, during the COVID-19
pandemic, VS identified existing drugs as potential treatments and high-throughput experiments
were used to rapidly confirm their effectiveness. This integration accelerates the drug discovery
process, especially in urgent situations like pandemics, where time is critical.
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5.12 Ethical and Regulatory Considerations
5.12.1 Intellectual Property and Patents
Importance: Intellectual property (IP) and patents are critical in drug discovery. Companies
invest heavily in research, and IP protection incentivizes innovation by granting exclusive rights to
develop and market new drugs for a specified period.
Example: Consider a pharmaceutical company that discovers a novel compound through VS. To
protect their investment, they file a patent for the compound. This patent gives them the exclusive
right to develop and commercialize the drug for a certain number of years. IP protection encour-
ages pharmaceutical companies to invest in expensive drug development processes.
5.12.2 Ethical Use of Computational Tools
Responsible research: Ethical considerations are vital in using computational tools. Researchers
must ensure that their methods and data sources are ethical and accurate. Misuse of data or algo-
rithms can lead to erroneous results or unintended consequences.
Example: When using VS to identify potential drug candidates, it is essential to ensure that the
data sources are reliable and that the research is conducted with transparency and integrity.
Misrepresenting results or manipulating data unethically can have serious consequences.
5.12.3 RegulatoryApproval Process
Safety and efficacy: Regulatory approval is a crucial step before a drug can be marketed and pre-
scribed to patients. Regulatory agencies like the FDA in the United States evaluate a drug’s safety
and efficacy based on clinical trial data.
Example: A pharmaceutical company that has successfully identified a lead compound through
VS must conduct extensive preclinical and clinical trials to demonstrate its safety and effective-
ness. The regulatory approval process ensures that drugs reaching the market meet rigorous stand-
ards and do not pose undue risks to patients.
5.13 Conclusion
VS and lead discovery have indeed revolutionized the drug discovery process by offering efficient
and cost-effective methods for identifying potential drug candidates. The integration of computa-
tional techniques with experimental approaches has significantly accelerated the early stages of
drug development, allowing researchers to sift through vast chemical libraries and prioritize com-
pounds with higher chances of success. Moreover, advancements in technology, such as increased
computational power, improved algorithms, and enhanced molecular modeling tools, have further
bolstered the capabilities of VS methods.
5.13.1 Future Prospects in Virtual Screening and Lead Discovery
The integration of advanced ML and AI is anticipated to significantly enhance the accuracy and
predictive capabilities of VS models, consequently leading to more successful lead discoveries.
Furthermore, the integration of Big Data and Omics data, including genomics, proteomics, and
References 117
metabolomics, into VS approaches promises to provide a deeper understanding of drug–target
interactions. VS’s potential role in personalized medicine, tailoring drug treatments to individual
patients based on their genetic profiles, is expected to increase drug efficacy and minimize side
effects. Additionally, VS will continue to be instrumental in drug repurposing efforts, identifying
existing drugs for new therapeutic uses and potentially expediting the availability of treatments for
various diseases. The advancement of high-throughput computational screening methods will
enable faster and more efficient screening of larger compound libraries, thus broadening the scope
of lead discovery endeavors.
5.13.2 Summary of Key Points
In summary, VS and lead discovery involve using computational techniques to identify potential
drug candidates, optimizing them, and bringing them to market. Key points include:
● Virtual screening: The process of using computer simulations to identify compounds with
potential therapeutic activity.
● Lead discovery: The process of refining initial hits from VS into promising lead compounds.
● SAR analysis: Studying the relationship between the chemical structure of compounds and
their biological activity.
●
ADME/Tox considerations: Evaluating how compounds are absorbed, distributed,
metabolized, excreted, and assessing toxicity.
● Ethical and regulatory considerations: Ensuring ethical research practices and adhering to
regulatory standards in drug development.
● Future prospects: Advancements in AI, big data integration, and personalized medicine are
expected to shape the future of VS and lead discovery.
VS and lead discovery continue to drive innovation in drug development, offering hope for the
discovery of novel therapies for a wide range of diseases.
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