Initial Model Generation Research Articles

In silico characterization of the psilocybin biosynthesis pathway

Nearly all mushrooms of the Psilocybe genus contain the natural product psilocybin, which is a psychoactive alkaloid derived from l-tryptophan. Considering their use in ancient times, as well as their psychedelic properties, these mushrooms have re-emerged with psychotherapeutic potential for treating depression, which has triggered increased pharmaceutical interest. However, the psilocybin biosynthesis pathway was only recently defined and, as such, little exists in the way of structural data. Accordingly, the aim of this study was to structurally characterize this pathway by generating homology models for the four Psilocybe cubensis enzymes involved in psilocybin biosynthesis (PsiD, a decarboxylase; PsiH, a monooxygenase; PsiK, a phosphotransferase; PsiM, a methyltransferase). Following initial model generation and alignment with the identified structural templates, repeated refinement of the models was carried out using secondary structure prediction, geometry evaluation, energy minimization, and molecular dynamics simulations in water. The final models were then evaluated using molecular docking interactions with their substrates, i.e., psilocybin precursors (l-tryptophan, tryptamine, 4-hydroxytryptamine, and norbaeocystin/baeocystin), all of which generated feasible binding modes for the expected biotransformation. Further plausibility of the psilocybin → aeruginascin, 4-hydroxytryptamine → norpsilocin, and tryptamine → N,N-dimethyltryptamine conversions, all mediated by the generated model for PsiM, suggests valid routes of formation for these key secondary metabolites. The structural characterization of these enzymes and their binding modes which emerged from this study can lead to a better understanding of psilocybin synthesis, thereby paving the way for the development of novel substrates and selective inhibitors, as well as improved biotechnological manipulation and production of psilocybin in vitro.

Estimation of projection parameter distribution and initial model generation in single-particle analysis.

This study focused on the problem of projection parameter search in 3D reconstruction using single-particle analysis. We treated the sampling distribution for the parameter search as a prior distribution and designed a probabilistic model for efficient parameter estimation. Using our method, we showed that it is possible to perform 3D reconstruction from synthetic and actual electron microscope images using an initial model and to generate the initial model itself. We also examined whether the optimization function used in the stochastic gradient descent method can be applied with loose constraints to improve the convergence of initial model generation and confirmed the effect. In order to investigate the advantage of generating a smooth sampling distribution from the stochastic model, we compared the distribution of estimated projection directions with the conventional method of performing a global search using spherical gridding. As a result, our method, which is simple in both mathematical model and implementation, showed no algorithmic artifacts.

Prediction of the ocean water sound speeds via attribute-guided seismic waveform inversion

Oceanic temperature and salinity are important for studying ocean dynamics and marine ecosystems, and for developing climate prediction models. Physical oceanographers measure these properties using expendable instruments, providing accurate measurements at high vertical resolution at sparse lateral locations. Accuracy of these temperature and salinity data when laterally interpolated and used for ocean dynamics, marine biology, and weather-related studies is questionable. Marine seismic data indicate visible reflections within the water columns, which, in turn, are primarily controlled by the vertical and lateral variations of the ocean water sound speeds. Because these sound-speed variations are functions of temperature and salinity, water-column seismic reflection data have been inverted for sound speeds and used to estimate temperature and salinity. These seismically derived temperature and salinity data have lower vertical, but much finer lateral resolution than those measured by expendable instruments, and therefore are useful for physical oceanography, marine biology, and climate research. However, quality of the seismic inversion relies on a good initial model, which requires generating models at sparse locations over 2D or 3D seismic data volumes and interpolating them over the horizon, manually picked from the stacked seismic data. Because horizon picking is subject to human error and bias, replacing it with an automated process is desirable. We outline a new attribute-guided methodology for initial model generation where no horizon picking is necessary. Starting with this initial model, we run prestack waveform inversion based on genetic algorithm optimization and demonstrate that our method can predict reliable sound-speed profiles. We conclude that our method is a good way for estimating ocean water sound speeds and relating them to the temperature and salinity.

Life Cycle of a Digital Resource Twin: Meta-Modeling and Application Example

To provide a systematization for the emerging Digital Twin paradigm we recently introduced the Digital Twin Structure Model. In this contribution, we now utilize this model to elaborate further on the life cycle of a Digital Resource Twin: This covers the initial model generation, includes different simulation stages like software- and hardware-in-the-loop leading to virtual commissioning and the transition into serial production. The methodological considerations are accompanied by practical examples, which base on an automated assembly system. Thus, this contribution complements the Digital Twin Structure Model with an approach to design and operate a Digital Resource Twin.

SPREAD: A Fully Automated Toolkit for Single-Particle Cryogenic Electron Microscopy Data 3D Reconstruction with Image-Network-Aided Orientation Assignment.

For the past decade, cryogenic electron microscopy (cryo-EM) has become an important technology to determine three-dimensional (3D) structures of biomacromolecules. Many software tools have been developed for cryo-EM image processing and 3D reconstruction, covering various computational tasks in cryo-EM data analysis. Despite the recent progress, most of these software tools focus on a single task, such as automatic particle picking or image clustering, whereas software packages covering the whole pipeline of cryo-EM data processing are still few. In this study, we developed a fully automatic single-particle reconstruction and analysis toolkit for cryo-EM data, named SPREAD, which integrates 2D image classification, 3D initial model generation, model selection, and 3D refinement. In SPREAD, we adopt our previously proposed network-based clustering algorithm for 2D image classification, NCEM, and the reference-free resolution measurement method SRes to realize the automatic model ranking and selection procedure. Projection orientation assignment is one of the key steps in initial model generation and 3D refinement. In SPREAD, we use the network-based image similarity metric and introduce a new probabilistic-based orientation searching method, named peak finding, to enhance assignment of the projection orientations. For dealing with both the particle images and projection images in the 3D refinement using SPREAD, we build a mixture image network containing both of these types of images on the basis of the peak-finding results, and then similarities for node pairs are recomputed by a superposed random walk on the network. SPREAD achieves a fully automatic workflow in which nearly no expert domain knowledge and interactive manual operation are involved. Our software can accessed for free at http://www.csbio.sjtu.edu.cn/bioinf/SPREAD/ for academic use.

Prediction of Organ Toxicity Endpoints by QSAR Modeling Based on Precise Chemical‐Histopathology Annotations

The ability to accurately predict the toxicity of drug candidates from their chemical structure is critical for guiding experimental drug discovery toward safer medicines. Under the guidance of the MetaTox consortium (Thomson Reuters, CA, USA), which comprised toxicologists from the pharmaceutical industry and government agencies, we created a comprehensive ontology of toxic pathologies for 19 organs, classifying pathology terms by pathology type and functional organ substructure. By manual annotation of full-text research articles, the ontology was populated with chemical compounds causing specific histopathologies. Annotated compound-toxicity associations defined histologically from rat and mouse experiments were used to build quantitative structure-activity relationship models predicting subcategories of liver and kidney toxicity: liver necrosis, liver relative weight gain, liver lipid accumulation, nephron injury, kidney relative weight gain, and kidney necrosis. All models were validated using two independent test sets and demonstrated overall good performance: initial validation showed 0.80-0.96 sensitivity (correctly predicted toxic compounds) and 0.85-1.00 specificity (correctly predicted non-toxic compounds). Later validation against a test set of compounds newly added to the database in the 2 years following initial model generation showed 75-87% sensitivity and 60-78% specificity. General hepatotoxicity and nephrotoxicity models were less accurate, as expected for more complex endpoints.

Validation of the orthogonal tilt reconstruction method with a biological test sample

Electron microscopy of frozen-hydrated samples (cryo-EM) can yield high resolution structures of macromolecular complexes by accurately determining the orientation of large numbers of experimental views of the sample relative to an existing 3D model. The "initial model problem", the challenge of obtaining these orientations ab initio, remains a major bottleneck in determining the structure of novel macromolecules, chiefly those lacking internal symmetry. We previously proposed a method for the generation of initial models--orthogonal tilt reconstruction (OTR)--that bypasses limitations inherent to the other two existing methods, random conical tilt (RCT) and angular reconstitution (AR). Here we present a validation of OTR with a biological test sample whose structure was previously solved by RCT: the complex between the yeast exosome and the subunit Rrp44. We show that, as originally demonstrated with synthetic data, OTR generates initial models that do not exhibit the "missing cone" artifacts associated with RCT and show an isotropic distribution of information when compared with the known structure. This eliminates the need for further user intervention to solve these artifacts and makes OTR ideal for automation and the analysis of heterogeneous samples. With the former in mind, we propose a set of simple quantitative criteria that can be used, in combination, to select from a large set of initial reconstructions a subset that can be used as reliable references for refinement to higher resolution.

MULTIDIMENSIONAL MODELING OF TYPE I X-RAY BURSTS. I. TWO-DIMENSIONAL CONVECTION PRIOR TO THE OUTBURST OF A PURE4He ACCRETOR

We present multidimensional simulations of the early convective phase preceding ignition in a Type I X-ray burst using the low Mach number hydrodynamics code, MAESTRO. A low Mach number approach is necessary in order to perform long-time integration required to study such phenomena. Using MAESTRO, we are able to capture the expansion of the atmosphere due to large-scale heating while capturing local compressibility effects such as those due to reactions and thermal diffusion. We also discuss the preparation of one-dimensional initial models and the subsequent mapping into our multidimensional framework. Our method of initial model generation differs from that used in previous multidimensional studies, which evolved a system through multiple bursts in one dimension before mapping onto a multidimensional grid. In our multidimensional simulations, we find that the resolution necessary to properly resolve the burning layer is an order of magnitude greater than that used in the earlier studies mentioned above. We characterize the convective patterns that form and discuss their resulting influence on the state of the convective region, which is important in modeling the outburst itself.

MD-Based Initial Model Generation for Inverse Renormalized MD

In Renormalization Group MD (RGMD) performed using atomic-cluster-based particles, Inverse Renormalized MD (IRMD) is carried out on specified occasions to observe atomic motion in clusters. Then, on the begining of the IRMD, it is necessary to determine initial positions and velocities of atoms in clusters such that physical and/or mechanical coditions of clusters are consistent with those of member atoms. This paper proposes a new method, in which the intial model of the IRMD is generated taking into account the equilibrium of forces in and between clusters, incremental strain state and temperature in clusters. Initial model generation based on the new method has been carried out, for a case of Mode I crack propagation, using an RMD model extracted from MD simulation. It is shown that an initial model containing amorphous state around a crack tip can be generated using the proposed method.

MDによる逆繰り込み変換MDの為の初期モデル生成(S09-2 計算ナノ・マイクロテクノロジーの新展開(2),S09 計算ナノ・マクロテクノロジーの新展開)

MDによる逆繰り込み変換MDの為の初期モデル生成(S09-2 計算ナノ・マイクロテクノロジーの新展開(2),S09 計算ナノ・マクロテクノロジーの新展開)