Validation of an allosteric binding site of Src kinase identified by unbiased ligand binding simulations

A comprehensive survey on protein-ligand binding site prediction

Protein-ligand binding site prediction is critical for protein function annotation and drug discovery. Biological experiments are time-consuming and require significant equipment, materials, and labor resources. Developing accurate and efficient computational methods for protein-ligand interaction prediction is essential. Here, we summarize the key challenges associated with ligand binding site (LBS) prediction and introduce recently published methods from their input features, computational algorithms, and ligand types. Furthermore, we investigate the specificity of allosteric site identification as a particular LBS type. Finally, we discuss the prospective directions for machine learning-based LBS prediction in the near future.

Allo-targeting of the kinase domain: Insights from in silico studies and comparison with experiments

The eukaryotic protein kinase domain has been a broadly explored target for drug discovery, despite limitations imposed by its high sequence conservation as a shared modular domain and the development of resistance to drugs. One way of addressing those limitations has been to target its potential allosteric sites, shortly called allo-targeting, in conjunction with, or separately from, its conserved catalytic/orthosteric site that has been widely exploited. Allosteric regulation has gained importance as an alternative to overcome the drawbacks associated with the indiscriminate effect of targeting the active site, and it turned out to be particularly useful for these highly promiscuous and broadly shared kinase domains. Yet, allo-targeting often faces challenges as the allosteric sites are not as clearly defined as its orthosteric sites, and the effect on the protein function may not be unambiguously assessed. A robust understanding of the consequence of site-specific allo-targeting on the conformational dynamics of the target protein is essential to design effective allo-targeting strategies. Recent years have seen important advances in in silico identification of druggable sites and distinguishing among them those sites expected to allosterically mediate conformational switches essential to signal transmission. The present opinion underscores the utility of such computational approaches applied to the kinase domain, with the help of comparison between computational predictions and experimental observations.

Grasping cryptic binding sites to neutralize drug resistance in the field of anticancer

Drug resistance is a significant obstacle to successful cancer treatment. The utilization and development of cryptic binding sites (CBSs) in proteins involved in cancer-related drug-resistance (CRDR) could help to overcome that drug resistance. However, there is no comprehensive review of the successful use of CBSs in addressing CRDR. Here, we have systematically summarized and analyzed the opportunities and challenges of using CBSs in addressing CRDR and revealed the key role that CBSs have in targeting CRDR. First, we have identified the CRDR targets and the corresponding CBSs. Second, we discuss the mechanisms by which CBSs can overcome CRDR. Finally, we have provided examples of successful CBS applications in addressing CRDR. We hope that this approach will provide guidance to biologists and chemists in effectively utilizing CBSs for the development of new drugs to alleviate CRDR.

Allosteric regulation and inhibition of protein kinases

The human genome encodes more than 500 different protein kinases: signaling enzymes with tightly regulated activity. Enzymatic activity within the conserved kinase domain is influenced by numerous regulatory inputs including the binding of regulatory domains, substrates, and the effect of post-translational modifications such as autophosphorylation. Integration of these diverse inputs occurs via allosteric sites that relate signals via networks of amino acid residues to the active site and ensures controlled phosphorylation of kinase substrates. Here, we review mechanisms of allosteric regulation of protein kinases and recent advances in the field.

AlphaFold, Artificial Intelligence (AI), and Allostery

AlphaFold has burst into our lives. A powerful algorithm that underscores the strength of biological sequence data and artificial intelligence (AI). AlphaFold has appended projects and research directions. The database it has been creating promises an untold number of applications with vast potential impacts that are still difficult to surmise. AI approaches can revolutionize personalized treatments and usher in better-informed clinical trials. They promise to make giant leaps toward reshaping and revamping drug discovery strategies, selecting and prioritizing combinations of drug targets. Here, we briefly overview AI in structural biology, including in molecular dynamics simulations and prediction of microbiota–human protein–protein interactions. We highlight the advancements accomplished by the deep-learning-powered AlphaFold in protein structure prediction and their powerful impact on the life sciences. At the same time, AlphaFold does not resolve the decades-long protein folding challenge, nor does it identify the folding pathways. The models that AlphaFold provides do not capture conformational mechanisms like frustration and allostery, which are rooted in ensembles, and controlled by their dynamic distributions. Allostery and signaling are properties of populations. AlphaFold also does not generate ensembles of intrinsically disordered proteins and regions, instead describing them by their low structural probabilities. Since AlphaFold generates single ranked structures, rather than conformational ensembles, it cannot elucidate the mechanisms of allosteric activating driver hotspot mutations nor of allosteric drug resistance. However, by capturing key features, deep learning techniques can use the single predicted conformation as the basis for generating a diverse ensemble.