Xiaoqin Zou

The dual modulation of Glycogen Synthase Kinase 3 Beta (GSK3β) and Sirtuin 1 (SIRT1) offers a promising therapeutic strategy for neurodegenerative diseases and cancer. In this study, we investigated, for the first time, the dual activity of known SIRT1 activators and GSK3β inhibitors using advanced in silico methods followed by experimental validation. Specifically, we employed multiple virtual screening (VS) approaches, including ligand- and structure-based methods, blind docking, and molecular dynamics (MD) simulations with binding free energy calculations. Using enzyme activity assays, we confirmed the dual activity of two compounds. SIRT1 activator SC22 (SIRT1 EC150 = 7 μM) exhibited moderate GSK3β inhibition with an IC50 = 7.65 μM. GSK3β inhibitor GC43 (GSK3β IC50 = 0.005 μM) inhibited SIRT1 with SIRT1 IC50 = 0.39 μM, demonstrating potential for pancreatic cancer treatment. These results underscore the importance of integrating computational and experimental methods to identify multifunctional agents, providing insights into the modulation of GSK3β and SIRT1 and their therapeutic implications. Finally, our findings highlight the limits of in silico techniques, particularly in predicting SIRT1 activation due to potential allosteric effects.

The I_Ks channel, composed of voltage-gated potassium channel KCNQ1 and regulatory subunit KCNE1, controls cardiac action potential durations. KCNQ1 and I_Ks activation requires PIP₂, and its depletion abolishes channel opening. KCNQ1 adopts both bent and straight conformations and can bind two PIP₂ molecules: one adjacent to VSD (V-PIP₂), and the other at the VSD-pore interface (C-PIP₂). Here we show that the two PIP₂ perform essential yet distinct roles: V-PIP₂ enables the bent-to-straight transition, whereas C-PIP₂ mediates VSD-pore coupling. VSD activation elevates the V-PIP₂ site, permitting the shift from the bent, intermediate open (IO) state associated with KCNQ1 to the straight, I_Ks-exclusive activated open (AO) state, which is further stabilized by C-PIP₂. Leveraging this mechanism, we develop a compound CA1, which selectively targets the V-PIP₂ site and modulates I_Ks channel activity without affecting KCNQ1, offering a promising conceptional path for specific and safe antiarrhythmic therapeutics.

Primary root growth, regulated by internal hormone signals, adapts to external factors such as water availability, soil compactness, and microbial interactions. An essential step in root growth consists of cell divisions in the meristem, with the outermost root cap layer thought to provide protection. However, recent studies reveal that lateral root cap (LRC) cells control meristem size and lateral root initiation. In this study, we identified an upper LRC-specific protein condensation mechanism involving SUPPRESSOR of rps4-RLD1 (SRFR1) that governs root growth and show that growth conditions and hormone treatment dynamically modulate condensate accumulation. SRFR1 condensate formation is driven by its plant-associated N-terminal tetratricopeptide repeat (PANT) polymerization domain and fine-tuned by the adjacent intrinsically disordered region 1 (IDR1). Mutational and biophysical analyses show that IDR1's zwitterionic nature is essential for its regulatory role, acting as a chaperone to promote PANT polymerization at low temperatures while preventing aggregation at high temperatures. This enables SRFR1 condensate formation across a wide temperature range. Notably, the zwitterionic IDR1 can be functionally substituted by zwitterionic dehydrins. Shifting IDR1 toward a negative state impairs, whereas a positive shift enhances SRFR1 condensation and further improves root growth. The association of zwitterionic IDRs with polymerization domains is common, suggesting that this mechanism broadly prevents irreversible aggregation and promotes physiological polymerization under varying temperatures.

The I_Ks channel complex, composed of the voltage-gated potassium channel KCNQ1 and its regulatory subunit KCNE1, is essential for the termination of cardiac action potentials. The function of KCNQ1 and I_Ks requires PIP₂, and its depletion abolishes channel opening. Previous studies revealed that KCNQ1 adopts both bent and straight conformations and can bind two PIP₂ molecules: one adjacent to VSD (V-PIP₂), and the other at the VSD-pore interface (C-PIP₂). Here we show that the two PIP₂ perform essential yet distinct roles: V-PIP₂ enables the bent-to-straight transition, whereas C-PIP₂ mediates VSD-pore coupling and stabilizes the straight conformation. Structure-function analysis and molecular dynamic simulations show that VSD activation elevates the V-PIP₂ site and weakens the CaM-VSD interaction, permitting the conformational shift from the bent, intermediate open (IO) state associated with KCNQ1 to the straight, I_Ks-exclusive activated open (AO) state, which is further stabilized by C-PIP₂. Leveraging this mechanism, we developed a compound CA1, which selectively targets the V-PIP₂ site and modulates I_Ks channel activity without affecting KCNQ1, offering a novel and promising conceptional path for specific and safe antiarrhythmic therapeutics.

CAPRI challenges offer a range of blind tests for biomolecule interaction prediction. This study evaluates the performance of our prediction protocols for the human group Zou and the server group MDockPP in CAPRI rounds 47–55, highlighting the impact of AlphaFold2 (AF2) and the effectiveness of massive sampling approaches. Prior to AlphaFold2's release, our methods relied on homology modeling and docking‐based protocols, achieving limited accuracy due to constraints in structural templates and inherent docking limitations. After AlphaFold2's public release, which demonstrated breakthrough accuracy in protein structure prediction, we integrated its multimer models and massive sampling techniques into our protocols. This integration significantly improved prediction accuracy, with human predictions increasing from 1 correct interface of 19 pre‐AlphaFold2 to 4 of 8 post‐AlphaFold2. The massive sampling approach further enhanced performance, particularly for targets T231 and T233, yielding medium‐quality models that default parameters could not achieve.

The field of therapeutic peptides is experiencing a surge, fueled by their advantageous features. These include predictable metabolism, enhanced safety profile, high selectivity, and reduced off-target effects compared to small molecule drugs. Despite progress in addressing limitations associated with peptide drugs, a significant bottleneck remains: the absence of a large-scale in silico screening method for a given protein target structure. Such methods have proven invaluable in accelerating small molecule drug discovery. The high flexibility of peptide structures and the large diversity of peptide sequences greatly hinder the development of urgently needed computational methods. Here, we report a method called MDockPeP2_VS to address these challenges. It integrates molecular docking with structural conservation between protein folding and protein-peptide binding. Briefly, we discovered that when the interfacial residues are conserved, a sequence fragment derived from a monomeric protein exhibits a high propensity to bind a target protein with a similar conformation. This valuable insight significantly reduces the search space for peptide conformations, resulting in a substantial reduction in computational time and making in silico peptide screening practical. We applied MDockPeP2_VS to develop peptide inhibitors targeting the TEM-1 β-lactamase of E. coli, a key mechanism behind antibiotic resistance in gram-negative bacteria. Among the top ten peptides selected from in silico screening, TF7 (KTYLAQAAATG) showed significant inhibition of β-lactamase activity with a K_i value of 1.37 ± 0.37 µM. This fully automated, large-scale structure-based in silico peptide screening software is available for free download at https://zougrouptoolkit.missouri.edu/mdockpep2_vs/download.html.

International Press of Boston - publishers of scholarly mathematical and scientific journals and books

We present the results for CAPRI Round 54, the 5th joint CASP‐CAPRI protein assembly prediction challenge. The Round offered 37 targets, including 14 homodimers, 3 homo‐trimers, 13 heterodimers including 3 antibody–antigen complexes, and 7 large assemblies. On average ~70 CASP and CAPRI predictor groups, including more than 20 automatics servers, submitted models for each target. A total of 21 941 models submitted by these groups and by 15 CAPRI scorer groups were evaluated using the CAPRI model quality measures and the DockQ score consolidating these measures. The prediction performance was quantified by a weighted score based on the number of models of acceptable quality or higher submitted by each group among their five best models. Results show substantial progress achieved across a significant fraction of the 60+ participating groups. High‐quality models were produced for about 40% of the targets compared to 8% two years earlier. This remarkable improvement is due to the wide use of the AlphaFold2 and AlphaFold2‐Multimer software and the confidence metrics they provide. Notably, expanded sampling of candidate solutions by manipulating these deep learning inference engines, enriching multiple sequence alignments, or integration of advanced modeling tools, enabled top performing groups to exceed the performance of a standard AlphaFold2‐Multimer version used as a yard stick. This notwithstanding, performance remained poor for complexes with antibodies and nanobodies, where evolutionary relationships between the binding partners are lacking, and for complexes featuring conformational flexibility, clearly indicating that the prediction of protein complexes remains a challenging problem.