UMMS: constrained harmonic and anharmonic analyses of macromolecules based on elastic network models

UMass Morph Server (UMMS) has been developed for the broad impact on the study of molecular dynamics (MD). The elastic network model (ENM) of a given macromolecule has been proven as a useful tool for analyzing thermal behaviors locally and predicting folding pathways globally. UMMS utilizes coarse-grained ENMs at various levels. These simplifications remarkably save computation time compared with all-atom MD simulations so that one can bring down massive computational problems from a supercomputer to a PC. To improve computational efficiency and physical reality of ENMs, the symmetry-constrained, rigid-cluster, hybrid and chemical-bond ENMs have been developed and implemented at UMMS. One can request both harmonic normal mode analysis of a single macromolecule and anharmonic pathway generation between two conformations of a same molecule using elastic network interpolation at

Structural insights into the mechanism defining substrate affinity in Arabidopsis thaliana dUTPase: the role of tryptophan 93 in ligand orientation

Background: Deoxyuridine triphosphate nucleotidohydrolase (dUTPase) hydrolyzes dUTP to dUMP and pyrophosphate to maintain the cellular thymine-uracil ratio. dUTPase is also a target for cancer chemotherapy. However, the mechanism defining its substrate affinity remains unclear. Sequence comparisons of various dUTPases revealed that Arabidopsis thaliana dUTPase has a unique tryptophan at position 93, which potentially contributes to its degree of substrate affinity. To better understand the roles of tryptophan 93, A. thaliana dUTPase was studied. Results: Enzyme assays showed that A. thaliana dUTPase belongs to a high-affinity group of isozymes, which also includes the enzymes from Escherichia coli and Mycobacterium tuberculosis. Enzymes from Homo sapiens and Saccharomyces cerevisiae are grouped as low-affinity dUTPases. The structure of the homo-trimeric A. thaliana dUTPase showed three active sites, each with a different set of ligand interactions between the amino acids and water molecules. On an α-helix, tryptophan 93 appears to keep serine 89 in place via a water molecule and to specifically direct the ligand. Upon being oriented in the active site, the C-terminal residues close the active site to promote the reaction. Conclusions: In the high-affinity group, the prefixed direction of the serine residues was oriented by a positively charged residue located four amino acids away, while low-affinity enzymes possess small hydrophobic residues at the corresponding sites

Antiviral susceptibility of clade 2.3.4.4b highly pathogenic avian influenza A(H5N1) viruses isolated from birds and mammals in the United States, 2022

Clade 2.3.4.4b highly pathogenic avian influenza (HPAI) A(H5N1) viruses that are responsible for devastating outbreaks in birds and mammals pose a potential threat to public health. Here, we evaluated their susceptibility to influenza antivirals. Of 1,015 sequences of HPAI A(H5N1) viruses collected in the United States during 2022, eight viruses (∼0.8%) had a molecular marker of drug resistance to an FDA-approved antiviral: three adamantane-resistant (M2-V27A), four oseltamivir-resistant (NA-H275Y), and one baloxavir-resistant (PA-I38T). Additionally, 31 viruses contained mutations that may reduce susceptibility to inhibitors of neuraminidase (NA) (n = 20) or cap-dependent endonuclease (CEN) (n = 11). A panel of 22 representative viruses was tested phenotypically. Overall, clade 2.3.4.4b A(H5N1) viruses lacking recognized resistance mutations were susceptible to FDA-approved antivirals. Oseltamivir was least potent at inhibiting NA activity, while the investigational NA inhibitor AV5080 was most potent, including against NA mutants. A novel NA substitution T438N conferred 12-fold reduced inhibition by zanamivir, and in combination with the known marker N295S, synergistically affected susceptibility to all five NA inhibitors. In cell culture-based assays HINT and IRINA, the PA-I38T virus displayed 75- to 108-fold and 37- to 78-fold reduced susceptibility to CEN inhibitors, baloxavir and the investigational AV5116, respectively. Viruses with PA-I38M or PA-A37T showed 5- to 10-fold reduced susceptibilities. As HPAI A(H5N1) viruses continue to circulate and evolve, close monitoring of drug susceptibility is needed for risk assessment and to inform decisions regarding antiviral stockpiling

Diode Laser—Can It Replace the Electrical Current Used in Endoscopic Submucosal Dissection?

Background/Aims A new medical fiber-guided diode laser system (FDLS) is expected to offer high-precision cutting with simultaneous hemostasis. Thus, this study aimed to evaluate the feasibility of using the 1,940-nm FDLS to perform endoscopic submucosal dissection (ESD) in the gastrointestinal tract of an animal model. Methods In this prospective animal pilot study, gastric and colorectal ESD using the FDLS was performed in ex vivo and in vivo porcine models. The completeness of en bloc resection, the procedure time, intraprocedural bleeding, histological injuries to the muscularis propria (MP) layer, and perforation were assessed. Results The en bloc resection and perforation rates in the ex vivo study were 100% (10/10) and 10% (1/10), respectively; those in the in vivo study were 100% (4/4) and 0% for gastric ESD and 100% (4/4) and 25% (1/4) for rectal ESD, respectively. Deep MP layer injuries tended to occur more frequently in the rectal than in the gastric ESD cases, and no intraprocedural bleeding occurred in either group. Conclusions The 1,940-nm FDLS was capable of yielding high en bloc resection rates without intraprocedural bleeding during gastric and colorectal ESD in animal models

Hybrid elastic network model for macromolecular dynamics

Biological functions of macromolecules and their assemblages play critical roles in a living cell. Comprehending such a biological mechanism helps us understand better the biological phenomena of the human body, in part, the mysteries of its functions. More importantly, it enables us to propose many viable suggestions for improving human life in a variety of ways (e.g. medical innovation). Computational approaches such as molecular dynamics (MD) and various coarse-grained elastic network models (ENMs) have emerged as powerful tools to comprehend and analyze biological functions and malfunctions otherwise inaccessible, thereby contributing to understanding functional disorders which may eventually cause diseases in the human body. Despite their technical and theoretical contributions, some drawbacks such as a limitation of computational efficiency in MD or the loss of local dynamics information in coarse-grained ENMs have also been observed in utilizing those computational tools. In the study of molecular dynamics, functional conformational changes can be largely resolved into hinge and shear motions (Gerstein 1998) and they are associated with the collective behavior of rigid domains involved (i.e. secondary structures). Therefore, we hypothesize that the global dynamics of large macromolecules can be described by using only several DOFs strongly related to collective motions of the systems rather than bringing their full DOFs into play, which is computationally so expensive. From this hypothesis, we develop a reduced DOF model called hybrid ENM in which rigid domains are represented as rigid clusters while all of the flexible regions (i.e. hinge, loop, and etc) are modeled in atomic detail to present the local dynamics. Hybrid ENM enables us to not only handle very large macromolecules in a PC but also represent global and local dynamics efficiently without loss of generality. A variety of applications addressed in this study show its potential and effectiveness as an innovative tool for the study of macromolecular structure and dynamics. In this dissertation, the mathematical description for hybrid ENM is fully derived from conventional coarse-grained ENM and applied to 70S ribosome, a very large macromolecule containing over ten thousands residues, in order to understand the global ratchet like motions and the complete cycle of tRNA translocation. Hybrid ENM is also utilized to predict folding pathways of antithrombin (1E05). A nascent polypeptide chain of antithrombin which includes at least the first four Cysteine residues is modeled using hybrid ENM to investigate the topological role of disulfide bonds (SS) and the effect of Carbohydrate (CHO) structure in the co-translational folding of antithrombin. Hybrid ENM is also applicable to fracture mechanics. The interaction between polymer matrix and nano/micro size particles is modeled at atomic level in order to understand the physics of the deformation and the fracture processes at crack tips of polymer composites. Even a 2-D simple lattice model explains well the phenomena observed in many previous experiments. A 3-D ENM is also investigated to obtain more realistic simulation results. Another contribution of this dissertation is the development of UMass Morph Server (UMMS) which provides both harmonic and anharmonic analysis tools online for anyone who are not mathematically oriented to directly adopt these methodologies to his/her studies. UMMS cannot only cross-validate the NMA and/or pathway generated by other morph servers, but also provide several unique features such as symmetry-constrained, rigid-cluster, and hybrid ENMs. Consequently, hybrid ENM can play an important role in the study of macromolecular dynamics and fracture mechanics of polymer composites by achieving both computational efficiency and physical realism of the simulation

PDE-guided reservoir computing for image denoising with small data

While network-based techniques have shown outstanding performance in image denoising in the big data regime requiring massive datasets and expensive computation, mathematical understanding of their working principles is very limited. Not to mention, their relevance to traditional mathematical approaches has not attracted much attention. Therefore, we suggest how reservoir computing networks can be strengthened in combination with conventional partial differential equation (PDE) methods for image denoising, especially in the small data regime. Given image data, PDEs generate sequential datasets enhancing desired image features, which provide the network with a better guideline for training in reservoir computing. The proposed procedure, reservoir computing in collaboration with PDEs (RCPDE), offers a synergetic combination of data-driven network-based methods and mathematically well-established PDE methods. It turns out that RCPDE outperforms both the usual reservoir computing and existing PDE approaches in image denoising. Furthermore, RCPDE also excels deep neural networks such as a convolutional neural network both in quality and in time in the small data regime. We believe that RCPDE reveals the great potential of reservoir computing in collaboration with various mathematically justifiable dynamics for better performance as well as for better mathematical understanding

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UMMS: constrained harmonic and anharmoni

Reservoir concatenation and the spectrum distribution of concatenated reservoir state matrices

Reservoir computing, one of the state-of-the-art machine learning architectures, processes time-series data generated by dynamical systems. Nevertheless, we have realized that reservoir computing with the conventional single-reservoir structure suffers from capacity saturation. This leads to performance stagnation in practice. Therefore, we propose an extended reservoir computing architecture called reservoir concatenation to further delay such stagnation. Not only do we provide training error analysis and test error comparison of reservoir concatenation, but we also propose a crucial measure, which is the trace associated with a reservoir state matrix, that explains the level of responsiveness to reservoir concatenation. Two reservoir dynamics are compared in detail, one by using the echo state network and the other by using a synchronization model called an explosive Kuramoto model. The distinct eigenvalue distributions of the reservoir state matrices from the two models are well reflected in the trace values that are shown to account for the different reservoir capacity behaviors, determining the different levels of responsiveness