How protein topology controls allosteric regulations

The Evolving Landscape of Protein Allostery: From Computational and Experimental Perspectives

Protein allostery is a fundamental biological regulatory mechanism that allows communication between distant locations within a protein, modifying its function in response to signals. Experimental techniques, such as NMR spectroscopy and cryo-electron microscopy (cryo-EM), are critical validation tools for computational predictions and provide valuable insights into dynamic conformational changes. Combining these approaches has greatly improved our understanding of classical conformational allostery and complex dynamic coupling mechanisms. Recent advances in machine learning and enhanced sampling methods have broadened the scope of allostery research, identifying cryptic allosteric sites and directing new drug discovery approaches. Despite progress, bridging static structural data with dynamic functional states remains challenging. This review underscores the importance of combining experimental and computational approaches to comprehensively understand protein allostery and its diverse applications in biology and medicine.

Macrocyclic catalysis mediated by water: opportunities and challenges

Nanospaces within enzymes play a crucial role in chemical reactions in biological systems, garnering significant attention from supramolecular chemists. Inspired by the highly efficient catalysis of enzymes, artificial supramolecular hosts have been developed and widely employed in various reactions, paving the way for innovative and selective catalytic processes and offering new insights into enzymatic catalytic mechanisms. In supramolecular macrocycle systems, weak non-covalent interactions are exploited to enhance substrate solubility, increase local concentration, and stabilize the transition state, ultimately accelerating reaction rates and improving product selectivity. In this review, we will focus on the opportunities and challenges associated with the catalysis of chemical reactions by supramolecular macrocycles in the aqueous phase. Key issues to be discussed include limitations in molecular interaction efficiency in aqueous media, product inhibition, and the incompatibility of catalysts or conditions in "one-pot" reactions.

Protein allosteric site identification using machine learning and per amino acid residue reported internal protein nanoenvironment descriptors

Allosteric regulation plays a crucial role in modulating protein functions and represents a promising strategy in drug development, offering enhanced specificity and reduced toxicity compared to traditional active site inhibition. Existing computational methods for predicting allosteric sites on proteins often rely on static protein surface pocket features, normal mode analysis or extensive molecular dynamics simulations encompassing both the protein function modulator and the protein itself. In this study, we introduce an innovative methodology that employs a per amino acid residue classifier to distinguish allosteric site-forming residues (AFRs) from non-allosteric, or free residues (FRs). Our model, STINGAllo, exhibits robust performance, achieving Distance Center Center (DCC) success rate when all AFRs were predicted within pockets identified by FPocket, overall DCC, F1 score and a Matthews correlation coefficient (MCC) of 78 %, 60 %, 64 % and 64 % respectively. Furthermore, we identified key descriptors that characterize the internal protein nanoenvironment of AFRs, setting them apart from FRs. These descriptors include the sponge effect, distance to the protein centre of geometry (cg), hydrophobic interactions, electrostatic potentials, eccentricity, and graph bottleneck features.

G protein-coupled receptors (GPCRs): advances in structures, mechanisms, and drug discovery

G protein-coupled receptors (GPCRs), the largest family of human membrane proteins and an important class of drug targets, play a role in maintaining numerous physiological processes. Agonist or antagonist, orthosteric effects or allosteric effects, and biased signaling or balanced signaling, characterize the complexity of GPCR dynamic features. In this study, we first review the structural advancements, activation mechanisms, and functional diversity of GPCRs. We then focus on GPCR drug discovery by revealing the detailed drug-target interactions and the underlying mechanisms of orthosteric drugs approved by the US Food and Drug Administration in the past five years. Particularly, an up-to-date analysis is performed on available GPCR structures complexed with synthetic small-molecule allosteric modulators to elucidate key receptor-ligand interactions and allosteric mechanisms. Finally, we highlight how the widespread GPCR-druggable allosteric sites can guide structure- or mechanism-based drug design and propose prospects of designing bitopic ligands for the future therapeutic potential of targeting this receptor family.

PASSerRank: Prediction of allosteric sites with learning to rank

Allostery plays a crucial role in regulating protein activity, making it a highly sought-after target in drug development. One of the major challenges in allosteric drug research is the identification of allosteric sites. In recent years, many computational models have been developed for accurate allosteric site prediction. Most of these models focus on designing a general rule that can be applied to pockets of proteins from various families. In this study, we present a new approach using the concept of Learning to Rank (LTR). The LTR model ranks pockets based on their relevance to allosteric sites, that is, how well a pocket meets the characteristics of known allosteric sites. After the training and validation on two datasets, the Allosteric Database (ASD) and CASBench, the LTR model was able to rank an allosteric pocket in the top three positions for 83.6% and 80.5% of test proteins, respectively. The model outperforms other common machine learning models with higher F1 scores (0.662 in ASD and 0.608 in CASBench) and Matthews correlation coefficients (0.645 in ASD and 0.589 in CASBench). The trained model is available on the PASSer platform (https://passer.smu.edu) to aid in drug discovery research.