Tense and aspect in Zhuang : a study of a set of tense and aspect markers

In spite of numerous efforts spent on the study tense and aspect, the nature of this linguistic phenomenon is still a topic of much theoretical concern. The present thesis is intended as a contribution to this topic by investigating the tense and aspect system in a particular language-- Zhuang. The research methodology adopted in the present study is that of Anna Wierzbicka's conceptual analysis, although other approaches are taken into consideration. Seven selected aspectualisers are studied in some depth, which cover the entire tense and aspect system. Of the aspectualisers discussed, special emphasis is given to four: lieu, day, kwa and pay. Each item has a distinct semantic feature and favours verbs of different semantic types, which reflects speaker's conceptualization of the event or situation in the real world. The syntax of these aspectualisers has offered insight into, as well as problems for, the analysis of verb phrase. The intricate interaction between the syntax and semantics of each aspectualiser calls for an integrated account of verbal meaning, sentence meaning and discourse meaning. Among points of theoretical interest being discussed are actualization, termination, experiential perfect, structural change of verb serialization involving the scope of temporal-apectual operators and above all, quantification. Quantification is seen as an indispensable component of Zhuang aspect. It is manifested most typically through the use of lexical items --the aspectualiser. The interpretation of aspect in a number of construction types involves the notion of quantification. This indicates that quantification should be included in the domain of aspect To our knowledge, no previous treatment of tense and aspect has ever seriously addressed this point

Understanding and Fine Tuning the Propensity of ATP-Driven Liquid-Liquid Phase Separation with Oligolysine

Liquid-Liquid Phase Separation (LLPS) plays pivotal roles in the organization and functionality of living cells. It is imperative to understand the underlying driving forces behind LLPS and to control its occurrence. In this study, we employed coarse-grained (CG) simulations as a research tool to investigate systems comprising oligolysine and adenosine triphosphate (ATP) under conditions of various ionic concentrations and oligolysine lengths. Consistent with experimental observations, our CG simulations captured the formation of LLPS upon the addition of ATP and tendency of dissociating under high ionic concentration. The primary driving force behind this phenomenon is the electrostatic interaction between oligolysine and ATP. An in-depth analysis on the structural properties of LLPS was conducted, where the oligolysine structure remained unchanged with increased ionic concentration and the addition of ATP led to a more pronounced curvature, aligning with the observed enhancement of $\alpha$-helical secondary structure in experiments. In terms of the dynamic properties, the introduction of ATP led to a significant reduction in translational and vibrational degrees of freedom but not rotational degrees of freedom. Through keeping the total number of charged residues constant and varying their entropic effects, we constructed two systems of similar biochemical significance and further validated the entropy effects on the LLPS formation. These findings provide a deeper understanding of LLPS formation and shed lights on the development of novel bioreactor and primitive artificial cells for synthesizing key chemicals for certain diseases

Grid-Robust Efficient Neural Interface Model for Universal Molecule Surface Construction from Point Clouds.

Molecular surfaces play a pivotal role in elucidating the properties and functions of biological complexes. While various surfaces have been proposed for specific scenarios, their widespread adoption faces challenges due to limited efficiency stemming from hand-crafted modeling designs. In this work, we proposed a general framework that incorporates both the point cloud concept and neural networks. The use of matrix multiplication in this framework enables efficient implementation across diverse platforms and libraries. We applied this framework to develop the GENIUSES (Grid-robust Efficient Neural Interface for Universal Solvent-Excluded Surface) model for constructing SES. GENIUSES demonstrates high accuracy and efficiency across data sets with varying conformations and complexities. Compared to the classical implementation of SES in the AMBER software package, our framework achieved a 26-fold speedup while retaining ∼95% accuracy when ported to the GPU platform using CUDA. Greater speedups can be obtained in large-scale systems. Importantly, our model exhibits robustness against variations in the grid spacing. We have integrated this infrastructure into AMBER to enhance accessibility for research in drug screening and related fields, where efficiency is of paramount importance

Targeting SHP2 Cryptic Allosteric Sites for Effective Cancer Therapy.

SHP2, a pivotal component downstream of both receptor and non-receptor tyrosine kinases, has been underscored in the progression of various human cancers and neurodevelopmental disorders. Allosteric inhibitors have been proposed to regulate its autoinhibition. However, oncogenic mutations, such as E76K, convert SHP2 into its open state, wherein the catalytic cleft becomes fully exposed to its ligands. This study elucidates the dynamic properties of SHP2 structures across different states, with a focus on the effects of oncogenic mutation on two known binding sites of allosteric inhibitors. Through extensive modeling and simulations, we further identified an alternative allosteric binding pocket in solution structures. Additional analysis provides insights into the dynamics and stability of the potential site. In addition, multi-tier screening was deployed to identify potential binders targeting the potential site. Our efforts to identify a new allosteric site contribute to community-wide initiatives developing therapies using multiple allosteric inhibitors to target distinct pockets on SHP2, in the hope of potentially inhibiting or slowing tumor growth associated with SHP2

Protein pKa prediction with machine learning

Protein pKa prediction is essential for the investigation of pH-associated relation ship between protein structure and function. In this work, we introduce a deep learning based protein pKa predictor DeepKa, which is trained and validated with the pKa val ues derived from continuous constant pH molecular dynamics (CpHMD) simulations of 279 soluble proteins. Here the CpHMD implemented in the Amber molecular dynamics package has been employed (Huang, Harris, and Shen J. Chem. Inf. Model. 2018, 58, 1372-1383). Notably, to avoid discontinuities at the boundary, grid charges are proposed to represent protein electrostatics. We show that the prediction accuracy by DeepKa is close to that by CpHMD benchmarking simulations, validating DeepKa as an efficient protein pKa predictor. In addition, the training and validation sets created in this study can be applied to the development of machine learning based protein pKa predictors in future. Finally, the grid charge representation is general and applicable to other topics, such as the protein-ligand binding affinity prediction

Targeting SHP2 Cryptic Allosteric Sites for Effective Cancer Therapy

SHP2, a pivotal component downstream of both receptor and non-receptor tyrosine kinases, has been underscored in the progression of various human cancers and neurodevelopmental disorders. Allosteric inhibitors have been proposed to regulate its autoinhibition. However, oncogenic mutations, such as E76K, convert SHP2 into its open state, wherein the catalytic cleft becomes fully exposed to its ligands. This study elucidates the dynamic properties of SHP2 structures across different states, with a focus on the effects of oncogenic mutation on two known binding sites of allosteric inhibitors. Through extensive modeling and simulations, we further identified an alternative allosteric binding pocket in solution structures. Additional analysis provides insights into the dynamics and stability of the potential site. In addition, multi-tier screening was deployed to identify potential binders targeting the potential site. Our efforts to identify a new allosteric site contribute to community-wide initiatives developing therapies using multiple allosteric inhibitors to target distinct pockets on SHP2, in the hope of potentially inhibiting or slowing tumor growth associated with SHP2

Streamlining and Optimizing Strategies of Electrostatic Parameterization

Accurate characterization of electrostatic interactions is crucial in molecular simulation. Various methods and programs have been developed to obtain electrostatic parameters for additive or polarizable models to replicate electrostatic properties obtained from experimental measurements or theoretical calculations. Electrostatic potentials (ESPs), a set of physically well-defined observables from quantum mechanical (QM) calculations, are well suited for optimization efforts due to the ease of collecting a large amount of conformation-dependent data. However, a reliable set of QM ESP computed at an appropriate level of theory and atomic basis set is necessary. In addition, despite the recent development of the PyRESP program for electrostatic parameterizations of induced dipole-polarizable models, the time-consuming and error-prone input file preparation process has limited the widespread use of these protocols. This work aims to comprehensively evaluate the quality of QM ESPs derived by eight methods, including wave function methods such as Hartree-Fock (HF), second-order Møller-Plesset (MP2), and coupled cluster-singles and doubles (CCSD), as well as five hybrid density functional theory (DFT) methods, used in conjunction with 13 different basis sets. The highest theory levels CCSD/aug-cc-pV5Z (a5z) and MP2/aug-cc-pV5Z (a5z) were selected as benchmark data over two homemade data sets. The results show that the hybrid DFT method, ωB97X-D, combined with the aug-cc-pVTZ (a3z) basis set, performs well in reproducing ESPs while taking both accuracy and efficiency into consideration. Moreover, a flexible and user-friendly program called PyRESP_GEN was developed to streamline input file preparation. The restraining strengths, along with strategies for polarizable Gaussian multipole (pGM) model parameterizations, were also optimized. These findings and the program presented in this work facilitate the development and application of induced dipole-polarizable models, such as pGM models, for molecular simulations of both chemical and biological significance

The Role of Heat Shock Protein 90B1 in Patients with Polycystic Ovary Syndrome.

Polycystic ovary syndrome (PCOS) is a heterogenetic disorder in women that is characterized by arrested follicular growth and anovulatory infertility. The altered protein expression levels in the ovarian tissues reflect the molecular defects in folliculogenesis. To identify aberrant protein expression in PCOS, we analyzed protein expression profiles in the ovarian tissues of patients with PCOS. We identified a total of 18 protein spots that were differentially expressed in PCOS compared with healthy ovarian samples. A total of 13 proteins were upregulated and 5 proteins were downregulated. The expression levels of heat shock protein 90B1 (HSP90B1) and calcium signaling activator calmodulin 1 (CALM1) were increased by at least two-fold. The expression levels of HSP90B1 and CALM1 were positively associated with ovarian cell survival and negatively associated with caspase-3 activation and apoptosis. Knock-down of HSP90B1 with siRNA attenuated ovarian cell survival and increased apoptosis. In contrast, ovarian cell survival was improved and cell apoptosis was decreased in cells over-expressing HSP90B1. These results demonstrated the pivotal role of HSP90B1 in the proliferation and survival of ovarian cells, suggesting a critical role for HSP90B1 in the pathogenesis of PCOS. We also observed a downregulation of anti-inflammatory activity-related annexin A6 (ANXA6) and tropomyosin 2 (TPM2) compared with the normal controls, which could affect cell division and folliculogenesis in PCOS. This is the first study to identify novel altered gene expression in the ovarian tissues of patients with PCOS. These findings may have significant implications for future diagnostic and treatment strategies for PCOS using molecular interventions