Efficient modelling and validation of cryptic protein binding sites for drug discovery
Lead Research Organisation:
University College London
Department Name: Chemistry
Abstract
Over 75% of disease-involved proteins cannot be readily targeted by conventional chemical biology approaches. New approaches are needed to increase the scope of molecular medicine.
Cryptic binding pockets, i.e. pockets that transiently form in a folded protein, but are not apparent in the crystal structure of the unliganded apo-form, offer outstanding opportunities to target proteins otherwise deemed 'undruggable' and are thus of considerable interest in academia and the pharmaceutical industry. Unfortunately, not only they are notoriously difficult to identify, but also the molecular mechanism by which they form is still debated. The aim of this collaborative project is to address the knowledge gaps and develop an efficient computational platform based on atomistic molecular simulations to systematically detect druggable cryptic pockets in targets of biopharmaceutical interest. The platform will build on our successful experience in developing and applying enhanced-sampling simulation algorithms to molecular recognition, and will be extensively tested on validated drug targets harbouring cryptic sites. The computational results will be further validated on novel targets by a combination of experiments in collaboration with an industrial partner (UCB).
Cryptic binding pockets, i.e. pockets that transiently form in a folded protein, but are not apparent in the crystal structure of the unliganded apo-form, offer outstanding opportunities to target proteins otherwise deemed 'undruggable' and are thus of considerable interest in academia and the pharmaceutical industry. Unfortunately, not only they are notoriously difficult to identify, but also the molecular mechanism by which they form is still debated. The aim of this collaborative project is to address the knowledge gaps and develop an efficient computational platform based on atomistic molecular simulations to systematically detect druggable cryptic pockets in targets of biopharmaceutical interest. The platform will build on our successful experience in developing and applying enhanced-sampling simulation algorithms to molecular recognition, and will be extensively tested on validated drug targets harbouring cryptic sites. The computational results will be further validated on novel targets by a combination of experiments in collaboration with an industrial partner (UCB).
Planned Impact
Drug discovery is a costly and risky undertaking, and the attrition rate of new leads and drug candidates is extremely high and worsening, leading to a significant escalation of drug-development costs (Scannell et al. Nat Rev Drug Discov 2012, 11, 191.). The average costs to develop a new drug increased from the $802 million in 2003 to $2.6 billion in 2014. This leads to lengthy delays in developing new treatments for complex conditions such as cancer, autoimmune and neurodegenerative diseases and huge costs for pharmaceutical companies and the national health service. This proposal will develop and demonstrate a new computational platform addressing a fundamental problem in rational drug design: targeting difficult ("undruggable") disease-involved biomolecules. In so doing it will contribute to greatly expand the number of tractable targets for small molecule therapeutic intervention but also provide a valid alternative to classical substrate-competitive drug design strategies. Our project will lead not only to the development of a unified, user-friendly suite of computational tools for this challenging problem but also to a better understanding of molecular recognition, allosterism and the role of flexibility in ligand-target binding. The approach we propose has the potential to significantly change the rational drug discovery pipeline increasing the predictive value of computational models, the accuracy of free energy calculations and extending their applicability of very complex systems well beyond the capability of current tools. This in turn will speed up the design of more effective and less toxic drugs.
Accordingly, the proposed work has the potential for impact on public healthcare costs and on the speed at which optimal treatments for complex conditions and drug-resistant microorganisms (AMR) can be developed and become available. In addition, the knowledge acquired on allosteric regulation will address a major open issue in structural biology and biophysics, while the platform developed will also be applicable in other fields where flexible molecular recognition is of importance, such as in self-assembled bio-inspired material and industrial biotechnology. In developing accurate chemical models for complex biological systems, this proposal addresses the core questions of the EPSRC CBBC priority area. The novel, ambitious and interdisciplinary approach that we propose will help drug discovery and the EPSRC "Developing Future Therapies" challenge in several ways: 1) the accurate prediction of drug binding mechanisms to cryptic binding sites and the associated binding free energies will enable the early selection of the best candidates in terms of safety and effectiveness; 2) the in-depth understanding of cryptic pocket formation will facilitate the rational design of allosteric inhibitors targeting "undruggable" targets and/or increasing the selectivity (thus decreasing toxicity).
The impact of our novel approach will include the prediction and increased understanding of allosteric small-molecules binding to key biomedical targets in the short term, leading to a rationale for the development of allosteric modulators in the medium term, and thereby to faster and more cost-effective development of new drug treatments (as well as optimized biotechnological production of fine chemicals) in the long term. To achieve maximum impact, a wide uptake of the developed methods and models is imperative. We therefore will collaborate with UCB Slough research center (see support letter) to ensure the wide applicability of the platform and to validate it on pre-competitive (open) drug targets. We plan to make the computational platform freely available, and to spend significant time and effort to make it user-friendly (disseminate the models, methods and tools, as well as the outcomes of the initial applications, through a range of activities and actions, outlined in the specific objectives listed in the PtI.
Accordingly, the proposed work has the potential for impact on public healthcare costs and on the speed at which optimal treatments for complex conditions and drug-resistant microorganisms (AMR) can be developed and become available. In addition, the knowledge acquired on allosteric regulation will address a major open issue in structural biology and biophysics, while the platform developed will also be applicable in other fields where flexible molecular recognition is of importance, such as in self-assembled bio-inspired material and industrial biotechnology. In developing accurate chemical models for complex biological systems, this proposal addresses the core questions of the EPSRC CBBC priority area. The novel, ambitious and interdisciplinary approach that we propose will help drug discovery and the EPSRC "Developing Future Therapies" challenge in several ways: 1) the accurate prediction of drug binding mechanisms to cryptic binding sites and the associated binding free energies will enable the early selection of the best candidates in terms of safety and effectiveness; 2) the in-depth understanding of cryptic pocket formation will facilitate the rational design of allosteric inhibitors targeting "undruggable" targets and/or increasing the selectivity (thus decreasing toxicity).
The impact of our novel approach will include the prediction and increased understanding of allosteric small-molecules binding to key biomedical targets in the short term, leading to a rationale for the development of allosteric modulators in the medium term, and thereby to faster and more cost-effective development of new drug treatments (as well as optimized biotechnological production of fine chemicals) in the long term. To achieve maximum impact, a wide uptake of the developed methods and models is imperative. We therefore will collaborate with UCB Slough research center (see support letter) to ensure the wide applicability of the platform and to validate it on pre-competitive (open) drug targets. We plan to make the computational platform freely available, and to spend significant time and effort to make it user-friendly (disseminate the models, methods and tools, as well as the outcomes of the initial applications, through a range of activities and actions, outlined in the specific objectives listed in the PtI.
Publications
Comitani F
(2018)
Exploring Cryptic Pockets Formation in Targets of Pharmaceutical Interest with SWISH.
in Journal of chemical theory and computation
Eelen G
(2018)
Role of glutamine synthetase in angiogenesis beyond glutamine synthesis.
in Nature
Estarellas C
(2019)
Modulating Ligand Dissociation through Methyl Isomerism in Accessory Sites: Binding of Retinol to Cellular Carriers.
in The journal of physical chemistry letters
Evans R
(2020)
Combining Machine Learning and Enhanced Sampling Techniques for Efficient and Accurate Calculation of Absolute Binding Free Energies.
in Journal of chemical theory and computation
Galdadas I
(2020)
Unravelling the effect of the E545K mutation on PI3Ka kinase.
in Chemical science
Galdadas I
(2018)
Defining the architecture of KPC-2 Carbapenemase: identifying allosteric networks to fight antibiotics resistance.
in Scientific reports
Haldar S
(2018)
A Multiscale Simulation Approach to Modeling Drug-Protein Binding Kinetics.
in Journal of chemical theory and computation
Hedges L
(2019)
BioSimSpace: An interoperable Python framework for biomolecular simulation
in Journal of Open Source Software
Hovan L
(2018)
Assessment of the model refinement category in CASP12.
in Proteins
Hovan L
(2019)
Defining an Optimal Metric for the Path Collective Variables.
in Journal of chemical theory and computation
Huggins D
(2018)
Biomolecular simulations: From dynamics and mechanisms to computational assays of biological activity
in WIREs Computational Molecular Science
Ilmjärv S
(2021)
Concurrent mutations in RNA-dependent RNA polymerase and spike protein emerged as the epidemiologically most successful SARS-CoV-2 variant.
in Scientific reports
Kuzmanic A
(2019)
Importance of the Force Field Choice in Capturing Functionally Relevant Dynamics in the von Willebrand Factor
in The Journal of Physical Chemistry Letters
Kuzmanic A
(2020)
Investigating Cryptic Binding Sites by Molecular Dynamics Simulations.
in Accounts of chemical research
Martin-Fernandez ML
(2019)
Structure and Dynamics of the EGF Receptor as Revealed by Experiments and Simulations and Its Relevance to Non-Small Cell Lung Cancer.
in Cells
Mattedi G
(2019)
Understanding Ligand Binding Selectivity in a Prototypical GPCR Family.
in Journal of chemical information and modeling
Mattedi G
(2020)
A combined activation mechanism for the glucagon receptor.
in Proceedings of the National Academy of Sciences of the United States of America
Noble Jesus C
(2021)
Amphiphilic Histidine-Based Oligopeptides Exhibit pH-Reversible Fibril Formation.
in ACS macro letters
Ordan M
(2018)
Intrinsically active MEK variants are differentially regulated by proteinases and phosphatases.
in Scientific reports
Perdios L
(2017)
Conformational transition of FGFR kinase activation revealed by site-specific unnatural amino acid reporter and single molecule FRET.
in Scientific reports
PLUMED Consortium
(2019)
Promoting transparency and reproducibility in enhanced molecular simulations.
in Nature methods
Saleh N
(2017)
Investigating allosteric effects on the functional dynamics of ß2-adrenergic ternary complexes with enhanced-sampling simulations.
in Chemical science
Saleh N
(2017)
An Efficient Metadynamics-Based Protocol To Model the Binding Affinity and the Transition State Ensemble of G-Protein-Coupled Receptor Ligands.
in Journal of chemical information and modeling
Tsuchiya Y
(2018)
Protein CoAlation and antioxidant function of coenzyme A in prokaryotic cells.
in The Biochemical journal
Yalinca H
(2019)
The Role of Post-translational Modifications on the Energy Landscape of Huntingtin N-Terminus.
in Frontiers in molecular biosciences
Yan R
(2019)
The Structure of the Pro-domain of Mouse proNGF in Contact with the NGF Domain.
in Structure (London, England : 1993)
Zanetti-Domingues LC
(2018)
The architecture of EGFR's basal complexes reveals autoinhibition mechanisms in dimers and oligomers.
in Nature communications
Description | Cryptic allosteric pockets are not visible in the crystal structure of apo-proteins as they only open when a specific ligand bind. Thus, they are intrinsically difficult to find by standard approaches, but offer outstanding opportunities to pharmaceutical targets deemed 'undruggable' by classic substrate-competitive inhibitors. We have found that most cryptic pockets only open up when small hydrophobic ligand bind to them. This has fundamental consequences with respect to effective methods to systematically find and target them. Our finding is being used to design drugs for difficult targets in cancer and autoimmune diseases. During the COVID19 pandemic our methods for cryptic pockets have been successfully used to locate draggable pockets on target such as COVID19 non-structural protein 1 (nsp1). The computational predictions have been validated by crystallography. This knowledge might be used to design Nsp1 inhibitors. |
Exploitation Route | Our findings are of great importance to the drug discovery community as they show a viable approach to design ligands for difficult targets. |
Sectors | Healthcare,Pharmaceuticals and Medical Biotechnology |
Description | The enhanced-sampling algorithm we developed (SWISH) is being used to understand the dynamics of cryptic pocket opening in proteins. Cryptic or hidden pockets are cavities that are not visible in the crystal structure of apo-proteins as they only open when a specific ligand bind. Thus, they are intrinsically difficult to find by standard experimental and computational approaches, but offer outstanding opportunities to target proteins deemed 'undruggable' by classic substrate-competitive inhibitors. Our research, in collaboration with UCB, addressed the knowledge gaps in the dynamics of cryptic pocket opening and allowed the detection of previously unknown druggable cryptic pockets in a target of biopharmaceutical interest. This fundamental knowledge is being used to design new lead compounds, some of which are now being developed as novel therapies. For instance, we used SWISH to detect a novel cryptic pocket in a SARS-CoV-2 target (Nsp1) that was subsequently validated by X-ray crystallography. The pocket is now being investigated for the development of antiviral drugs. |
First Year Of Impact | 2019 |
Sector | Healthcare,Pharmaceuticals and Medical Biotechnology |
Impact Types | Economic,Policy & public services |
Description | AstraZeneca CASE studentship |
Amount | £29,500 (GBP) |
Organisation | AstraZeneca |
Sector | Private |
Country | United Kingdom |
Start | 03/2017 |
End | 02/2021 |
Description | Industrial PhD studentship |
Amount | £30,000 (GBP) |
Organisation | Heptares Therapeutics Ltd |
Sector | Private |
Country | United Kingdom |
Start | 03/2017 |
End | 02/2020 |
Description | Collaboration with Johnson and Johnson |
Organisation | Johnson & Johnson |
Department | Janssen-Cilag |
Country | Global |
Sector | Private |
PI Contribution | We helped J & J implement a computational pipeline for cryptic binding pocket discovery and collaborated on looking for druggable cryptic binding pockets on targets such the IMPase. |
Collaborator Contribution | Provided interesting drug targets and experimental data, including NMR fragment screening. |
Impact | Found interesting cryptic pockets in IMPase |
Start Year | 2020 |
Description | Collaboration with UCB pharma on cryptic sites |
Organisation | UCB Pharma |
Country | United Kingdom |
Sector | Private |
PI Contribution | We contributed our new computational methods to find cryptic binding sites. |
Collaborator Contribution | Experimental validation, including crystal structures, surface plasmon resonance, new compounds. |
Impact | Helped develop new drug candidates. |
Start Year | 2018 |