Non-Canonical Allostery in Cyclic Nucleotide Dependent Kinases

Purine nucleosides replace cAMP in allosteric regulation of PKA in trypanosomatid pathogens

Cyclic nucleotide binding domains (CNB) confer allosteric regulation by cAMP or cGMP to many signaling proteins, including PKA and PKG. PKA of phylogenetically distant Trypanosoma is the first exception as it is cyclic nucleotide-independent and responsive to nucleoside analogues (Bachmaier et al., 2019). Here, we show that natural nucleosides inosine, guanosine and adenosine are nanomolar affinity CNB ligands and activators of PKA orthologs of the important tropical pathogens Trypanosoma brucei, Trypanosoma cruzi, and Leishmania. The sequence and structural determinants of binding affinity, -specificity and kinase activation of PKAR were established by structure-activity relationship (SAR) analysis, co-crystal structures and mutagenesis. Substitution of two to three amino acids in the binding sites is sufficient for conversion of CNB domains from nucleoside to cyclic nucleotide specificity. In addition, a trypanosomatid-specific C-terminal helix (αD) is required for high affinity binding to CNB-B. The αD helix functions as a lid of the binding site that shields ligands from solvent. Selectivity of guanosine for CNB-B and of adenosine for CNB-A results in synergistic kinase activation at low nanomolar concentration. PKA pulldown from rapid lysis establishes guanosine as the predominant ligand in vivo in T. brucei bloodstream forms, whereas guanosine and adenosine seem to synergize in the procyclic developmental stage in the insect vector. We discuss the versatile use of CNB domains in evolution and recruitment of PKA for novel nucleoside-mediated signaling.

Allosteric drugs: New principles and design approaches

Focusing on an important biomedical implication of allostery - design of allosteric drugs, we describe characteristics of allosteric sites, effectors, and their modes of actions distinguishing them from the orthosteric counterparts and calling for new principles and protocols in the quests for allosteric drugs. We show the importance of considering both binding affinity and allosteric signaling in establishing the structure-activity relationships (SARs) toward design of allosteric effectors, arguing that pairs of allosteric sites and their effector ligands - the site-effector pairs - should be generated and adjusted simultaneously in the framework of what we call directed design protocol. Key ideas and approaches for designing allosteric effectors including reverse perturbation, targeted and agnostic analysis are also discussed here. Several promising computational approaches are highlighted, along with the need for and potential advantages of utilizing generative models to facilitate discovery/design of new allosteric drugs.

Probing ligand selectivity in pathogens

Why does protein kinase A respond to purine nucleosides in certain pathogens, but not to the cyclic nucleotides that activate this kinase in most other organisms?

On the linkage of thermodynamics and pathogenicity

This review outlines the effect of disease-causing mutations on proteins' thermodynamics. Two major thermodynamics quantities, which are essential for structural integrity, the folding and binding free energy changes caused by missense mutations, are considered. It is emphasized that disease effects in case of complex diseases may originate from several mutations over several genes, while monogenic diseases are caused by mutation is a single gene. Nevertheless, in both cases it is shown that pathogenic mutations cause larger perturbations of the above-mentioned thermodynamics quantities as compared with the benign mutations. Recent works demonstrating the effect of pathogenic mutations on the above-mentioned thermodynamics quantities, as well as on structural dynamics and allosteric pathways, are reviewed.

Exploring and Learning the Universe of Protein Allostery Using Artificial Intelligence Augmented Biophysical and Computational Approaches

Allosteric mechanisms are commonly employed regulatory tools used by proteins to orchestrate complex biochemical processes and control communications in cells. The quantitative understanding and characterization of allosteric molecular events are among major challenges in modern biology and require integration of innovative computational experimental approaches to obtain atomistic-level knowledge of the allosteric states, interactions, and dynamic conformational landscapes. The growing body of computational and experimental studies empowered by emerging artificial intelligence (AI) technologies has opened up new paradigms for exploring and learning the universe of protein allostery from first principles. In this review we analyze recent developments in high-throughput deep mutational scanning of allosteric protein functions; applications and latest adaptations of Alpha-fold structural prediction methods for studies of protein dynamics and allostery; new frontiers in integrating machine learning and enhanced sampling techniques for characterization of allostery; and recent advances in structural biology approaches for studies of allosteric systems. We also highlight recent computational and experimental studies of the SARS-CoV-2 spike (S) proteins revealing an important and often hidden role of allosteric regulation driving functional conformational changes, binding interactions with the host receptor, and mutational escape mechanisms of S proteins which are critical for viral infection. We conclude with a summary and outlook of future directions suggesting that AI-augmented biophysical and computer simulation approaches are beginning to transform studies of protein allostery toward systematic characterization of allosteric landscapes, hidden allosteric states, and mechanisms which may bring about a new revolution in molecular biology and drug discovery.