Recent advances in targeting protein kinases and pseudokinases in cancer biology

Riegel, Kristina; Vijayarangakannan, Parthiban; Kechagioglou, Petros; Bogucka, Katarzyna; Rajalingam, Krishnaraj

doi:10.3389/fcell.2022.942500

Cited by 10 publications

(6 citation statements)

References 171 publications

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“…The pseudokinases are the most extensively studied pseudoenzyme family and a major target for drug discovery [ 15 , 114 ]. The modes of action of pseudokinases have parallels in other pseudoenzyme families ( Table 1 ).…”

Section: Discussionmentioning

confidence: 99%

Emerging functions of pseudoenzymes

Goldberg

Sreelatha

2023

Biochemical Journal

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As sequence and structural databases grow along with powerful analysis tools, the prevalence and diversity of pseudoenzymes have become increasingly evident. Pseudoenzymes are present across the tree of life in a large number of enzyme families. Pseudoenzymes are defined as proteins that lack conserved catalytic motifs based on sequence analysis. However, some pseudoenzymes may have migrated amino acids necessary for catalysis, allowing them to catalyze enzymatic reactions. Furthermore, pseudoenzymes retain several non-enzymatic functions such as allosteric regulation, signal integration, scaffolding, and competitive inhibition. In this review, we provide examples of each mode of action using the pseudokinase, pseudophosphatase, and pseudo ADP-ribosyltransferase families. We highlight the methodologies that facilitate the biochemical and functional characterization of pseudoenzymes to encourage further investigation in this burgeoning field.

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Section: Discussionmentioning

confidence: 99%

Emerging functions of pseudoenzymes

Goldberg

Sreelatha

2023

Biochemical Journal

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“…Since the Davis dataset consists only of protein kinases, its analysis does not allow direct conclusions about ProSmith's ability to predict drug affinities for other protein families. However, most druggable proteins are kinases [45], and therefore kinase datasets are the most commonly used data for evaluating target inhibition predictions.…”

Section: Prosmith Outperforms Previous Models For Predictions Of Kina...mentioning

confidence: 99%

A multimodal Transformer Network for protein-small molecule interactions enhances predictions of kinase inhibition and enzyme-substrate relationships

Kroll,

Ranjan,

Lercher

2024

PLoS Comput Biol

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The activities of most enzymes and drugs depend on interactions between proteins and small molecules. Accurate prediction of these interactions could greatly accelerate pharmaceutical and biotechnological research. Current machine learning models designed for this task have a limited ability to generalize beyond the proteins used for training. This limitation is likely due to a lack of information exchange between the protein and the small molecule during the generation of the required numerical representations. Here, we introduce ProSmith, a machine learning framework that employs a multimodal Transformer Network to simultaneously process protein amino acid sequences and small molecule strings in the same input. This approach facilitates the exchange of all relevant information between the two molecule types during the computation of their numerical representations, allowing the model to account for their structural and functional interactions. Our final model combines gradient boosting predictions based on the resulting multimodal Transformer Network with independent predictions based on separate deep learning representations of the proteins and small molecules. The resulting predictions outperform recently published state-of-the-art models for predicting protein-small molecule interactions across three diverse tasks: predicting kinase inhibitions; inferring potential substrates for enzymes; and predicting Michaelis constants KM. The Python code provided can be used to easily implement and improve machine learning predictions involving arbitrary protein-small molecule interactions.

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“…This strongly indicates pseudokinases are generally poorly studied and more effort is needed to illuminate their biological functions and develop appropriate chemical tools for their study. Of late, the importance and promise of pseudokinases has been recognized, as reflected by both primary literature and review papers [ 65 , 66 ].…”

Section: Explored Vs Underexplored Kinasesmentioning

confidence: 99%

How many kinases are druggable? A review of our current understanding

Anderson,

Rosston,

Ong

et al. 2023

Biochemical Journal

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There are over 500 human kinases ranging from very well-studied to almost completely ignored. Kinases are tractable and implicated in many diseases, making them ideal targets for medicinal chemistry campaigns, but is it possible to discover a drug for each individual kinase? For every human kinase, we gathered data on their citation count, availability of chemical probes, approved and investigational drugs, PDB structures, and biochemical and cellular assays. Analysis of these factors highlights which kinase groups have a wealth of information available, and which groups still have room for progress. The data suggest a disproportionate focus on the more well characterized kinases while much of the kinome remains comparatively understudied. It is noteworthy that tool compounds for understudied kinases have already been developed, and there is still untapped potential for further development in this chemical space. Finally, this review discusses many of the different strategies employed to generate selectivity between kinases. Given the large volume of information available and the progress made over the past 20 years when it comes to drugging kinases, we believe it is possible to develop a tool compound for every human kinase. We hope this review will prove to be both a useful resource as well as inspire the discovery of a tool for every kinase.

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Recent advances in targeting protein kinases and pseudokinases in cancer biology

Cited by 10 publications

References 171 publications

Emerging functions of pseudoenzymes

Emerging functions of pseudoenzymes

A multimodal Transformer Network for protein-small molecule interactions enhances predictions of kinase inhibition and enzyme-substrate relationships

How many kinases are druggable? A review of our current understanding

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