Shape shifting: The multiple conformational substates of the PTEN N-terminal PIP2 -binding domain

IRTKS promotes osteogenic differentiation by inhibiting PTEN phosphorylation

Insulin stimulates osteoblast proliferation and differentiation as an anabolic agent in bone. Insulin Receptor Tyrosine Kinase Substrate (IRTKS) is involved in insulin signaling as an adapter for insulin receptors (IR). Here, we showed that IRTKS levels were significantly decreased in bone marrow mesenchymal stem cells (BMSCs) derived from the bone marrow of patients with osteoporosis. Based on relevant experiments, we observed that IRTKS promoted the proliferation, migration, and osteoblast differentiation of BMSCs and MC3T3-E1 cells. In addition, we identified a Phosphatase and Tensin homolog deleted on chromosome 10 (PTEN) as a potential active substrate of IRTKS. We demonstrated a direct interaction between IRTKS and PTEN using co-immunoprecipitation. Subsequently, we confirmed that the SH3 domain of IRTKS directly binds to the C-terminal tail of PTEN. Further experimental results demonstrated that PTEN attenuated the promoting effects of IRTKS on the proliferation, migration, and osteoblast differentiation of BMSCs and MC3T3-E1 cells. In conclusion, this study suggests that IRTKS contributes to osteogenic differentiation by inhibiting PTEN phosphorylation and provides a potential therapeutic target for osteoporosis patients.

Hybrid computational methods combining experimental information with molecular dynamics

A goal of structural biology is to understand how macromolecules carry out their biological roles by identifying their metastable states, mechanisms of action, pathways leading to conformational changes, and the thermodynamic and kinetic relationships between those states. Integrative modeling brings structural insights into systems where traditional structure determination approaches cannot help. We focus on the synergies and challenges of integrative modeling combining experimental data with molecular dynamics simulations.

AlphaFold2 and its applications in the fields of biology and medicine

AlphaFold2 (AF2) is an artificial intelligence (AI) system developed by DeepMind that can predict three-dimensional (3D) structures of proteins from amino acid sequences with atomic-level accuracy. Protein structure prediction is one of the most challenging problems in computational biology and chemistry, and has puzzled scientists for 50 years. The advent of AF2 presents an unprecedented progress in protein structure prediction and has attracted much attention. Subsequent release of structures of more than 200 million proteins predicted by AF2 further aroused great enthusiasm in the science community, especially in the fields of biology and medicine. AF2 is thought to have a significant impact on structural biology and research areas that need protein structure information, such as drug discovery, protein design, prediction of protein function, et al. Though the time is not long since AF2 was developed, there are already quite a few application studies of AF2 in the fields of biology and medicine, with many of them having preliminarily proved the potential of AF2. To better understand AF2 and promote its applications, we will in this article summarize the principle and system architecture of AF2 as well as the recipe of its success, and particularly focus on reviewing its applications in the fields of biology and medicine. Limitations of current AF2 prediction will also be discussed.

Comparative Protein Structural Network Analysis Reveals C-Terminal Tail Phosphorylation Structural Communication Fingerprint in PTEN-Associated Mutations in Autism and Cancer

PTEN (phosphatase and tensin homolog deleted on chromosome 10) is a tightly regulated dual-specificity phosphatase and key regulator of the PI3K/AKT/mTOR signaling pathway. PTEN phosphorylation at its carboxy-terminal tail (CTT) serine/threonine cluster negatively regulates its tumor suppressor function by inducing a stable, closed, and inactive conformation. Germline PTEN mutations predispose individuals to PTEN hamartoma tumor syndrome (PHTS), a rare inherited cancer syndrome and, intriguingly, one of the most common causes of autism spectrum disorder (ASD). However, the mechanistic details that govern phosphorylated CTT catalytic conformational dynamics in the context of PHTS-associated mutations are unknown. Here, we utilized a comparative protein structure network (PSN)-based approach to investigate PTEN CTT phosphorylation-induced conformational dynamics specific to PTEN-ASD compared to PTEN-cancer phenotypes. Results from our study show differences in structural flexibility, inter-residue contacts, and allosteric communication patterns mediated by CTT phosphorylation, differentiating PTEN-ASD and PTEN-cancer phenotypes. Further, we identified perturbations among global metapaths and community network connections within the active site and inter-domain regions, indicating the significance of these regions in transmitting information across the PSN. Together, our studies provide a mechanistic underpinning of allosteric regulation through the coupled interplay of CTT phosphorylation conformational dynamics in PTEN-ASD and PTEN-cancer mutations. Importantly, the detailed atomistic interactions and structural consequences of PTEN variants reveal potential allosteric druggable target sites as a viable and currently unexplored treatment approach for individuals with different PHTS-associated mutations.

New opportunities in integrative structural modeling

Integrative structural modeling enables structure determination of macromolecules and their complexes by integrating data from multiple sources. It has been successfully used to characterize macromolecular structures when a single structural biology technique was insufficient. Recent developments in cellular structural biology, including in-cell cryo-electron tomography and artificial intelligence-based structure prediction, have created new opportunities for integrative structural modeling. Here, we will review these opportunities along with the latest developments in integrative modeling methods and their applications. We also highlight open challenges and directions for further development.

Structural and Dynamic Effects of PTEN C-Terminal Tail Phosphorylation

The phosphatase and tensin homologue deleted on chromosome 10 (PTEN) tumor suppressor gene encodes a tightly regulated dual-specificity phosphatase that serves as the master regulator of PI3K/AKT/mTOR signaling. The carboxy-terminal tail (CTT) is key to regulation and harbors multiple phosphorylation sites (Ser/Thr residues 380-385). CTT phosphorylation suppresses the phosphatase activity by inducing a stable, closed conformation. However, little is known about the mechanisms of phosphorylation-induced CTT-deactivation dynamics. Using explicit solvent microsecond molecular dynamics simulations, we show that CTT phosphorylation leads to a partially collapsed conformation, which alters the secondary structure of PTEN and induces long-range conformational rearrangements that encompass the active site. The active site rearrangements prevent localization of PTEN to the membrane, precluding lipid phosphatase activity. Notably, we have identified phosphorylation-induced allosteric coupling between the interdomain region and a hydrophobic site neighboring the active site in the phosphatase domain. Collectively, the results provide a mechanistic understanding of CTT phosphorylation dynamics and reveal potential druggable allosteric sites in a previously believed clinically undruggable protein.