Mechanosensitive Piezo1 and Piezo2 channels transduce various forms of mechanical forces into cellular signals that play vital roles in many important biological processes in vertebrate organisms. Besides mechanical forces, Piezo1 is selectively activated by micromolar concentrations of the small molecule Yoda1 through an unknown mechanism. Here, using a combination of all-atom molecular dynamics simulations, calcium imaging and electrophysiology, we identify an allosteric Yoda1 binding pocket located in the putative mechanosensory domain, approximately 40 Å away from the central pore. Our simulations further indicate that the presence of the agonist correlates with increased tension-induced motions of the Yoda1-bound subunit. Our results suggest a model wherein Yoda1 acts as a molecular wedge, facilitating force-induced conformational changes, effectively lowering the channel’s mechanical threshold for activation. The identification of an allosteric agonist binding site in Piezo1 channels will pave the way for the rational design of future Piezo modulators with clinical value.

Chemical fragment cosolvent sampling techniques have become a versatile tool in ligand-protein binding prediction. Site-Identification by Ligand Competitive Saturation (SILCS) is one such method that maps the distribution of chemical fragments on a protein as free energy fields called FragMaps. Ligands are then simulated via Monte Carlo techniques in the field of the FragMaps (SILCS-MC) to predict their binding conformations and relative affinities for the target protein. Application of SILCS-MC using a number of different scoring schemes and MC sampling protocols against multiple protein targets was undertaken to evaluate and optimize the predictive capability of the method. Seven protein targets and 551 ligands with broad chemical variability were used to evaluate and optimize the model to maximize Pearson's correlation coefficient, Pearlman's Predictive Index, correct relative binding affinity and root mean square error versus the absolute experimental binding affinities. Across the protein-ligand sets, the relative affinities of the ligands were predicted correctly an average of 69 % of the time for the highest overall SILCS protocol. Training the FragMap weighting factors using a Bayesian machine learning (ML) algorithm led to an increase to an average 75 % relative correct affinity predictions. Furthermore, once the optimal protocol is identified for a specific protein-ligand system average predictabilities of 76 % are achieved. The ML algorithm is successful with small training sets of data (30 or more compounds) due to the use of physically correct FragMap weights as priors. Notably, the 76 % correct relative prediction rate is similar to or better than free energy perturbation methods that are significantly computationally more expensive than SILCS. The results further support the utility of SILCS as a powerful and computationally accessible tool to support lead optimization and development in drug discovery.

The cell envelope of Gram-negative bacteria contains a lipopolysaccharide (LPS) rich outer membrane that acts as the first line of defense for bacterial cells in adverse physical and chemical environments. The LPS macromolecule has a negatively charged oligosaccharide domain that acts as an ionic brush, limiting the permeability of charged chemical agents through the membrane. Besides the LPS, the outer membrane has radially extending O-antigen polysaccharide chains and β-barrel membrane proteins that make the bacterial membrane physiologically unique compared to phospholipid cell membranes. Elucidating the interplay of these contributing macromolecular components and their role in the integrity of the bacterial outer membrane remains a challenge. To bridge the gap in our current understanding of the Gram-negative bacterial membrane, we have developed a coarse grained force field for outer membrane that is computationally affordable for simulating dynamical process over physiologically relevant time scales. The force field was benchmarked against available experimental and atomistic simulations data for properties such as membrane thickness, density profiles of the residues, area per lipid, gel to liquid-crystalline phase transition temperatures, and order parameters. More than 17 membrane compositions were studied with a combined simulation time of over 100 μs. A comparison of simulated structural and dynamical properties with corresponding experimental data shows that the developed force field reproduces the overall physiology of LPS rich membranes. The affordability of the developed model for long time scale simulations can be instrumental in determining the mechanistic aspects of the antimicrobial action of chemical agents as well as assist in designing antimicrobial peptides with enhanced outer membrane permeation properties.

Mechanosensitive Piezo1 channels are essential mechanotransduction proteins in eukaryotes. Their curved transmembrane domains, called arms, create a convex membrane deformation, or footprint, which is predicted to flatten in response to increased membrane tension. Here, using a hyperbolic tangent model, we show that, due to the intrinsic bending rigidity of the membrane, the overlap of neighboring Piezo1 footprints produces a flattening of the Piezo1 footprints and arms. Multiple all-atom molecular dynamics simulations of Piezo1 further reveal that this tension-independent flattening is accompanied by gating motions that open an activation gate in the pore. This open state recapitulates experimentally obtained ionic selectivity, unitary conductance, and mutant phenotypes. Tracking ion permeation along the open pore reveals the presence of intracellular and extracellular fenestrations acting as cation-selective sites. Simulations also reveal multiple potential binding sites for phosphatidylinositol 4,5-bisphosphate. We propose that the overlap of Piezo channel footprints may act as a cooperative mechanism to regulate channel activity.

The ability of the 10–23 DNAzyme to specifically cleave RNA with high efficiency has fuelled expectation that this agent may have useful applications for targeted therapy. Here, we, for the first time, investigated the antitumor and radiosensitizing effects of a DNAzyme (DZ1) targeted to the Epstein-Barr virus (EBV)-LMP1 mRNA of nasopharyngeal carcinoma (NPC) in patients. Preclinical studies indicated that the DNAzyme was safe and well tolerated. A randomized and double-blind clinical study was conducted in 40 NPC patients who received DZ1 or saline intratumorally, in conjunction with radiation therapy. Tumor regression, patient survival, EBV DNA copy number and tumor microvascular permeability were assessed in a 3-month follow-up. The mean tumor regression rate at week 12 was significantly higher in DZ1 treated group than in the saline control group. Molecular imaging analysis showed that DZ1 impacted on tumor microvascular permeability as evidenced by a faster decline of the Ktrans in DZ1-treated patients. The percentage of the samples with undetectable level of EBV DNA copy in the DZ1 group was significantly higher than that in the control group. No adverse events that could be attributed to the DZ1 injection were observed in patients.

Delivery of poorly soluble anticancer drugs can be achieved by employing polymeric drug delivery systems, capable of forming stable self-assembled nanocarriers with drug encapsulated within their hydrophobic cores. Computational investigations can aid the design of efficient drug-delivery platforms; however, simulations of nanocarrier self-assembly process are challenging due to high computational cost associated with the large system sizes (millions of atoms) and long time scales required for equilibration. In this work, we overcome this challenge by employing a multiscale computational approach in conjunction with experiments to analyze the role of the individual building blocks in the self-assembly of a highly tunable linear poly(ethylene glycol)-b-dendritic oligo(cholic acid) block copolymer called telodendrimer. The multiscale approach involved developing a coarse grained description of the telodendrimer, performing simulations over several microseconds to capture the self-assembly process, followed by reverse mapping of the coarse grained system to atomistic representation for structural analysis. Overcoming the computational bottleneck allowed us to run multiple self-assembly simulations and determine average size, drug-telodendrimer micellar stoichiometry, optimal drug loading capacity, and atomistic details such hydrogen-bonding and solvent accessible area of the nanocarrier. Computed results are in agreement with the experimental data, highlighting the success of the multiscale approach applied here.

Reversible covalent inhibitors have drawn increasing attention in drug design, as they are likely more potent than noncovalent inhibitors and less toxic than covalent inhibitors. Despite those advantages, the computational prediction of reversible covalent binding presents a formidable challenge because the binding process consists of multiple steps and quantum mechanics (QM) level calculation is needed to estimate the covalent binding free energy. It has been shown that the dissociation rates and the equilibrium dissociation constants vary significantly even with similar warheads, due to noncovalent interactions. We have previously used a simplistic two-state model for predicting the relative binding selectivity of reversible covalent inhibitors (J. Am. Chem. Soc. 2017, 139, 17945). Here we go beyond binding selectivity and demonstrate that it is possible to use free energy perturbation (FEP) molecular dynamics (MD) to calculate the overall reversible covalent binding using a specially designed thermodynamic cycle. We show that FEP can predict the varying binding free energies of the analogs sharing a common α-ketoamide warhead. More importantly, our results revealed that the chemical modification away from warhead changes the binding affinity at both noncovalent and covalent binding states, and the computational prediction can be improved by considering the binding free energy of both states. Furthermore, we explored the possibility of using a more rapid computational method, Site-Identification by Ligand Competitive Saturation (SILCS), to rank the same set of reversible covalent inhibitors. We found that the fragment docking to a set of precomputed SILCS FragMaps produces a reasonable ranking. In conclusion, two independent approaches provided consistent results that the covalent binding state is suitable for the initial ranking of the reversible covalent drug candidates. For leadoptimization, the FEP approach described here can provide more rigorous and detailed information regarding how much the covalent and noncovalent binding states are contributing to the overall binding affinity, thus offering a new avenue for fine tuning the noncovalent interactions for optimizing reversible covalent drugs.

BackgroundThe evaluation of childhood trauma is essential for the treatment of schizophrenia. The short form of Childhood Trauma Questionnaire (CTQ-SF) is a widely used measure of the experience of childhood trauma in the general population. Nevertheless, data regarding the psychometric property of CTQ-SF for assessing childhood trauma of patients with schizophrenia are very limited.MethodsTwo hundred Chinese inpatients with schizophrenia completed the Chinese CTQ-SF, the Child Psychological Maltreatment Scale (CPMS), the Impact of Events Scale-Revised (IES-R), and the Dissociative Experiences Scale-II (DES-II). To assess test-retest reliability of the CTQ-SF, all patients completed the CTQ-SF again two weeks later. Concurrent and convergent validity was assessed by analyzing Pearson bivariate correlation coefficients between CTQ-SF and CPMS, IES-R, and DES-II.ResultsThe Cronbach’s α coefficient of the Chinese CTQ-SF was 0.81, and the two-week re-test reliability was 0.81 (P<0.01). The criterion-related validity coefficients of CTQ-SF with the CMPS, IES-R and DES-II were 0.61, 0.41, and 0.51, respectively.ConclusionThe Chinese CTQ-SF has satisfactory psychometric properties to measure childhood abuse or neglect in Chinese inpatients with schizophrenia.