Target 2035 – update on the quest for a probe for every protein

Müller, Susanne; Ackloo, Suzanne; Chawaf, Arij Al; Al‐Lazikani, Bissan; Antolín, Albert A.; Baell, Jonathan B.; Beck, Hartmut; Beedie, Shaunna; Betz, Ulrich; Bezerra, G.A.; Brennan, P.E.; Brown, David A.; Brown, Peter J.; Bullock, Alex N.; Carter, Adrian J.; Chaikuad, A.; Chaineau, Mathilde; Ciulli, Alessio; Collins, Ian; Dreher, Jan; Drewry, David H.; Edfeldt, Kristina; Edwards, A.M.; Egner, Ursula; Frye, Stephen V.; Fuchs, Stephen M.; Hall, Matthew D.; Hartung, Ingo V.; Hillisch, Alexander; Hitchcock, Stephen; Homan, Evert; Kannan, Natarajan; Kiefer, James R.; Knapp, Stefan; Kostić, Milka; Kubicek, Stefan; Leach, Andrew R.; Lindemann, Sven; Marsden, Brian D.; Matsui, Hisanori; Meier, Jordan L.; Merk, Daniel; Michel, Maurice; Morgan, Max; Mueller‐Fahrnow, Anke; Owen, Dafydd R.; Perry, Benjamin; Rosenberg, Saul H.; Saikatendu, Kumar Singh; Schapira, Matthieu; Scholten, Cora; Sharma, Sujata; Simeonov, Anton; Sundström, M.; Superti‐Furga, Giulio; Todd, Matthew H.; Tredup, Claudia; Vedadi, Masoud; Delft, F. von; Willson, Timothy M.; Workman, Paul; Arrowsmith, C.H.

doi:10.1039/d1md00228g

Cited by 52 publications

(60 citation statements)

References 34 publications

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“…This explains the similar performance of the best performing models on CASF-2016, which are likely close to the theoretical limit. Ventures like the Critical Assessment of Computational Hitfinding Experiments (CACHE) (Müller et al, 2022) will play an important role to validate computational methods in the future and generate a larger corpus of very high-quality data.…”

Section: Discussionmentioning

confidence: 99%

Scoring Functions for Protein-Ligand Binding Affinity Prediction Using Structure-based Deep Learning: A Review

2022

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The rapid and accurate in silico prediction of protein-ligand binding free energies or binding affinities has the potential to transform drug discovery. In recent years, there has been a rapid growth of interest in deep learning methods for the prediction of protein-ligand binding affinities based on the structural information of protein-ligand complexes. These structure-based scoring functions often obtain better results than classical scoring functions when applied within their applicability domain. Here we review structure-based scoring functions for binding affinity prediction based on deep learning, focussing on different types of architectures, featurization strategies, data sets, methods for training and evaluation, and the role of explainable artificial intelligence in building useful models for real drug-discovery applications.

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Section: Discussionmentioning

confidence: 99%

Scoring Functions for Protein-Ligand Binding Affinity Prediction Using Structure-based Deep Learning: A Review

2022

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“…As SLCs are eminently druggable, and we have hundreds of assays and validation tools aligned, a plan for the systematic identification of specific chemical probes for each transporter is within reach. 10 Given the integral role of SLC in human physiology and disease, a focus on the top 10-20% of all SLCs in terms of disease target attractivity should represent a priority focus of the drug discovery community to match the rate of successfully exploited top targets known from other target classes as GPCRs, kinases, channels, and proteases. We are confident that the long-term impact of this dedicated research effort on SLCs will ultimately result in more approved drugs targeting SLCs.…”

Section: Fertile Soil For Further Cultivationmentioning

confidence: 99%

“…Now that the end of this large, cumbersome effort on providing the basic knowledge and tools to “unlock” this target class is within sight, future work should aim at leveraging the enhanced knowledge of SLC function and regulation to design effective therapeutics targeting SLCs. As SLCs are eminently druggable, and we have hundreds of assays and validation tools aligned, a plan for the systematic identification of specific chemical probes for each transporter is within reach 10 . Given the integral role of SLC in human physiology and disease, a focus on the top 10–20% of all SLCs in terms of disease target attractivity should represent a priority focus of the drug discovery community to match the rate of successfully exploited top targets known from other target classes as GPCRs, kinases, channels, and proteases.…”

Section: Fertile Soil For Further Cultivationmentioning

confidence: 99%

Accelerating SLC Transporter Research: Streamlining Knowledge and Validated Tools

Wiedmer

Inglés-Prieto

Goldmann

et al. 2022

Clin Pharma and Therapeutics

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A modern, rational approach to a large class of proteins broadly relevant for pharmacology is enabling both from a postgenomic perspective and for reasons of economy of scale. The international public-private consortia RESOLUTE (https://re-solute.eu/) and REsolution (https://re-solute.eu/resol ution) are generating a systematic functional assignment of each of the 451 different human solute carrier (SLC) proteins and, at the same time, trying to catalog and interpret the associated main genetic variants.

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“…Out of 65% of proteomes that are characterized, less than 5% of the proteome is focused on drug discovery programs. 1 However, there is renewed interest to study the entire human proteome in an unbiased manner. The development of new proteomic technologies would greatly facilitate annotating the function of a plethora of proteins.…”

Section: Introductionmentioning

confidence: 99%

Development of Non-invasive ABPP-BRET Platform Technology Enable Real-Time Detection of Post-Translational Modification and Target Engagement with Absolute Specificity in Living Systems

Bathla

Sandanaraj

2022

Preprint

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Non-invasive, real-time, longitudinal imaging of protein functions in living systems with unprecedented specificity is one of the critical challenges of modern biomedical research. Despite several advancements, it is estimated that nearly 35% of the human proteome is not completely characterized. Therefore, the development of new technologies is imperative for shining more light on so-called “dark proteomes”. Towards that goal, here we report a platform fusion technology called activity-based protein profiling-bioluminescence resonance energy transfer (ABPP-BRET). This method provides an opportunity to study the post-translational modification of a target protein in real-time in living systems in a longitudinal manner with a high spatio-temporal resolution. This semi-synthetic BRET biosensor method is used for target engagement studies and further for inhibitor profiling in live cells. The simplicity of this method coupled with the critical physical distance dependent BRET read-out turned out to be a powerful method, thus pushing the activity-based protein profiling technology to the next level.

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Target 2035 – update on the quest for a probe for every protein

Abstract: Twenty years after the publication of the first draft of the human genome, our knowledge of the human proteome is still fragmented. Target 2035 aims to develop a pharmacological modulator for every protein in the human proteome to fill this gap.

Cited by 52 publications

References 34 publications

Scoring Functions for Protein-Ligand Binding Affinity Prediction Using Structure-based Deep Learning: A Review

Scoring Functions for Protein-Ligand Binding Affinity Prediction Using Structure-based Deep Learning: A Review

Accelerating SLC Transporter Research: Streamlining Knowledge and Validated Tools

Development of Non-invasive ABPP-BRET Platform Technology Enable Real-Time Detection of Post-Translational Modification and Target Engagement with Absolute Specificity in Living Systems

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