Manifold Model Research Articles

LES of Delft Jet-in-Hot Coflow burner to investigate the effect of preferential diffusion on autoignition of [formula omitted] flames

This paper reports on numerical investigations of preferential diffusion effects in Large Eddy Simulation (LES) of turbulent lifted CH4/H2 flames. For this purpose, a combined LES and Flamelet Generated Manifolds (FGM) model is developed to simulate the Delft Jet-in-Hot Coflow (DJHC) burner. A novel type of flamelets, entitled “IML Flamelets”, has been used to tabulate the chemistry. IML flamelets are capable to incorporate preferential diffusion effects in autoigniting flames. The IML technique is coupled with LES to simulate the DJHC burner with CH4/H2 fuels where CH4 has been enriched with H2 ranging from 0% to 25% of the fuel volume. The significance of this study is to illustrate complex interactions of molecular diffusion, chemistry and turbulent transport. A good agreement has been found between LES and measurements for the velocity and OH fields. It turns out that preferential diffusion has a significant influence on the lift-off height and stabilization mechanism of the lifted H2-enriched turbulent flames. Predictions of the 0%H2 case indicate that inclusion of preferential diffusion in the combustion model modestly affects lift-off heights. However, for 5%H2, 10% H2 and 25%H2 cases, inclusion of preferential diffusion in the model affects strongly lift-off heights yielding much improved predictions compared to the unity Lewis number model. Predictions of lift-off heights and formation of ignition kernels agree very well with the measured instantaneous snapshots of OH chemiluminescence. It turns out that the combined FGM-IML approach can successfully capture main features of turbulent lifted flames such as formation of ignition kernels and stabilization mechanisms.

Adaptive edge weighting for graph-based learning algorithms

Graph-based learning algorithms including label propagation and spectral clustering are known as the effective state-of-the-art algorithms for a variety of tasks in machine learning applications. Given input data, i.e. feature vectors, graph-based methods typically proceed with the following three steps: (1) generating graph edges, (2) estimating edge weights and (3) running a graph based algorithm. The first and second steps are difficult, especially when there are only a few (or no) labeled instances, while they are important because the performance of graph-based methods heavily depends on the quality of the input graph. For the second step of the three-step procedure, we propose a new method, which optimizes edge weights through a local linear reconstruction error minimization under a constraint that edges are parameterized by a similarity function of node pairs. As a result our generated graph can capture the manifold structure of the input data, where each edge represents similarity of each node pair. To further justify this approach, we also provide analytical considerations for our formulation such as an interpretation as a cross-validation of a propagation model in the feature space, and an error analysis based on a low dimensional manifold model. Experimental results demonstrated the effectiveness of our adaptive edge weighting strategy both in synthetic and real datasets.

Implementation of electronically controlled pneumatic brake formulation in longitudinal train dynamics algorithms

The main goal of this investigation is to integrate an electronically controlled pneumatic (ECP) brake model with efficient longitudinal train force algorithms based on the trajectory coordinate formulations. The ECP brake model, developed in this investigation consists of the train line (cable), locomotive automatic brake valve, air brake pipe, and ECP manifold. The train line, which covers the entire length of the train, allows the brake commands to be received by the car simultaneously. While pneumatic pressure is used to generate the braking forces, the brake pipe is no longer used to provide the brake level commands. Instead, the brake pipes are used to provide a continuous supply of compressed air stored in a reservoir mounted on each railcar. Using the ECP system to apply the brakes uniformly and instantaneously gives better train control, shortens the stopping distances, and leads to a lower risk of derailment. In this investigation, the fluid continuity and momentum equations are used to develop the governing air pressure flow equations. These partial differential equations are converted to a set of ordinary differential equations using the finite element method leading to an air brake force model that accounts for the effect of the air flow in long train pipes as well as the effect of leakage and branch pipe flows. The car brake forces are applied to the wheels using the ECP manifold located in each car. The ECP manifold used in this investigation has four valves: cut-off valve, vent valve, auxiliary valve, and emergency valve. The ECP manifold is connected to three main pneumatic components: the auxiliary reservoir, the emergency reservoir, and the brake cylinder. The reservoirs serve as the main storage of the pressurized air, while the brake cylinder and other mechanical components such as the rigging and the brake shoes transmit the brake force to the wheels. In this investigation, a mathematical model of the ECP manifold and its components is developed. The relationship between the main components of the ECP brake system and the train dynamics is discussed, and the final set of differential equations that integrates the ECP brake and train dynamics is presented. Different simulation scenarios are considered in this study in order to investigate the effect of the brake forces on the train longitudinal dynamics in the case of different braking scenarios. The performance of the developed ECP brake system is compared with the Association of American Railroads safety and operation standards, and with experimental results published in the literature.

Learning Expressionlets via Universal Manifold Model for Dynamic Facial Expression Recognition.

Facial expression is a temporally dynamic event which can be decomposed into a set of muscle motions occurring in different facial regions over various time intervals. For dynamic expression recognition, two key issues, temporal alignment and semantics-aware dynamic representation, must be taken into account. In this paper, we attempt to solve both problems via manifold modeling of videos based on a novel mid-level representation, i.e., expressionlet. Specifically, our method contains three key stages: 1) each expression video clip is characterized as a spatial-temporal manifold (STM) formed by dense low-level features; 2) a universal manifold model (UMM) is learned over all low-level features and represented as a set of local modes to statistically unify all the STMs; and 3) the local modes on each STM can be instantiated by fitting to the UMM, and the corresponding expressionlet is constructed by modeling the variations in each local mode. With the above strategy, expression videos are naturally aligned both spatially and temporally. To enhance the discriminative power, the expressionlet-based STM representation is further processed with discriminant embedding. Our method is evaluated on four public expression databases, CK+, MMI, Oulu-CASIA, and FERA. In all cases, our method outperforms the known state of the art by a large margin.

Fully coupled hydro-mechanical numerical manifold modeling of porous rock with dominant fractures

Coupled hydro-mechanical (HM) processes are significant in geological engineering such as oil and gas extraction, geothermal energy, nuclear waste disposal and for the safety assessment of dam foundations and rock slopes, where the geological media usually consist of fractured rock masses. In this study, we developed a model for the analysis of coupled hydro-mechanical processes in porous rock containing dominant fractures, by using the numerical manifold method (NMM). In the current model, the fractures are regarded as different material domains from surrounding rock, i.e., finite-thickness fracture zones as porous media. Compared with the rock matrix, these fractured porous media are characterized with nonlinear behavior of hydraulic and mechanical properties, involving not only direct (poroelastic) coupling but also indirect (property change) coupling. By combining the potential energy associated with mechanical responses, fluid flow and solid–fluid interactions, a new formulation for direct HM coupling in porous media is established. For indirect coupling associated with fracture opening/closure, we developed a new approach implicitly considering the nonlinear properties by directly assembling the corresponding strain energy. Compared with traditional methods with approximation of the nonlinear constitutive equations, this new formulation achieves a more accurate representation of the nonlinear behavior. We implemented the new model for coupled HM analysis in NMM, which has fixed mathematical grid and accurate integration, and developed a new computer code. We tested the code for direct coupling on two classical poroelastic problems with coarse mesh and compared the results with the analytical solutions, achieving excellent agreement, respectively. Finally, we tested for indirect coupling on models with a single dominant fracture and obtained reasonable results. The current poroelastic NNM model with a continuous finite-thickness fracture zone will be further developed considering thin fractures in a discontinuous approach for a comprehensive model for HM analysis in fractured porous rock masses.

An Angular Parameter Estimation Method for Incoherently Distributed Sources Via Generalized Shift Invariance

This paper proposes a new algorithm to estimate the nominal direction-of-arrival (DOA) and the angular spread of multiple incoherently distributed (ID) sources. With the general array manifold (GAM) model, the nominal DOAs are first separated from the original array manifold. Then, a generalized shift invariance property inside the array manifold is identified, based on which the nominal DOAs are obtained when the rank of a trickily formulated matrix drops. Next, the angular spreads are estimated from the central moments of the angular distribution. We also derive a polynomial rooting based search-free method for nominal DOA estimation. This method can greatly reduce the computational complexity. As compared with the popular ESPRIT-ID algorithm, the proposed algorithm can achieve higher accuracy, can handle more sources, and applies to a more general array structure. Extensive simulations are provided to demonstrate the superior performance of the proposed algorithm over the existing works.

What Is the Validity Domain of Einstein’s Equations? Distributional Solutions over Singularities and Topological Links in Geometrodynamics

The existence of singularities alerts that one of the highest priorities of a centennial perspective on general relativity should be a careful re-thinking of the validity domain of Einstein’s field equations. We address the problem of constructing distinguishable extensions of the smooth spacetime manifold model, which can incorporate singularities, while retaining the form of the field equations. The sheaf-theoretic formulation of this problem is tantamount to extending the algebra sheaf of smooth functions to a distribution-like algebra sheaf in which the former may be embedded, satisfying the pertinent cohomological conditions required for the coordinatization of all of the tensorial physical quantities, such that the form of the field equations is preserved. We present in detail the construction of these distribution-like algebra sheaves in terms of residue classes of sequences of smooth functions modulo the information of singular loci encoded in suitable ideals. Finally, we consider the application of these distribution-like solution sheaves in geometrodynamics by modeling topologically-circular boundaries of singular loci in three-dimensional space in terms of topological links. It turns out that the Borromean link represents higher order wormhole solutions.

Using virtual topology operations to generate analysis topology

Virtual topology operations have been utilized to generate an analysis topology definition suitable for downstream mesh generation. Detailed descriptions are provided for virtual topology merge and split operations for all topological entities. Current virtual topology technology is extended to allow the virtual partitioning of volume cells and the topological queries required to carry out each operation are provided. Virtual representations are robustly linked to the underlying geometric definition through an analysis topology. The analysis topology and all associated virtual and topological dependencies are automatically updated after each virtual operation, providing the link to the underlying CAD geometry. Therefore, a valid description of the analysis topology, including relative orientations, is maintained. This enables downstream operations, such as the merging or partitioning of virtual entities, and interrogations, such as determining if a specific meshing strategy can be applied to the virtual volume cells, to be performed on the analysis topology description. As the virtual representation is a non-manifold description of the sub-divided domain the interfaces between cells are recorded automatically. This enables the advantages of non-manifold modelling to be exploited within the manifold modelling environment of a major commercial CAD system, without any adaptation of the underlying CAD model. A hierarchical virtual structure is maintained where virtual entities are merged or partitioned. This has a major benefit over existing solutions as the virtual dependencies are stored in an open and accessible manner, providing the analyst with the freedom to create, modify and edit the analysis topology in any preferred sequence, whilst the original CAD geometry is not disturbed. Robust definitions of the topological and virtual dependencies enable the same virtual topology definitions to be accessed, interrogated and manipulated within multiple different CAD packages and linked to the underlying geometry.

A novel dictionary-based approach for missing sample recovery of signals in manifold

Recovery of signal with randomly positioned missing samples is a difficult or impossible task. The aim of this paper is to accurately recover missing samples if prior information on domain of sparsity is known. This paper proposes a novel approach for recovery of signals lying in low-dimensional sub-manifold, embedded in high-dimensional signal space and heavily corrupted by arbitrarily positioned missing samples. The proposed simple and efficient algorithm is based on global manifold model and can recover the corrupted signal from the limited available samples without affecting the remaining samples. The proposed method is applicable to any type of data and can be extensively used in a wide variety of data processing techniques. Experimental results prove that the proposed method outperforms the counterparts without much computational complexity.

Investigation of Single-Jet Combustor Near Lean Blowout Conditions Using Flamelet-Generated Manifold Combustion Model and Detailed Chemistry

Numerical simulation results of a single-jet premixed combustion system at atmospheric pressure are compared against comprehensive particle image velocimetry (PIV) flow measurements and Raman scattering temperature measurements for natural gas and hydrogen fuels. The simulations were performed on hexahedral meshes with 1–5 × 106 elements. Reynolds-averaged Navier–Stokes (RANS) calculations were carried out with the k–ε realizable turbulence model. Combustion was modeled using the flamelet-generated manifold model (FGM) and detailed chemistry. Both the flame position and flame liftoff predicted by the FGM were in reasonable agreement with experiments for both fuels and showed little sensitivity to heat transfer or radiation modeling. The detailed chemistry calculation predicts the temperature gradients along the jet centerline accurately and compares very closely with the Raman scattering measurements. The much closer agreement of the jet axial velocity and temperature profiles with experimental values, coupled with the significantly protracted presence of intermediates in the detailed chemistry predictions, indicates that the impact of nonequilibrium intermediates on very lean natural gas flames is significant.

Hyperbolic geometry of the ample cone of a hyperkähler manifold

Let M be a compact hyperkahler manifold with maximal holonomy (IHS). The group $$H^2(M, {\mathbb {R}})$$ H 2 ( M , R ) is equipped with a quadratic form of signature $$(3, b_2-3)$$ ( 3 , b 2 - 3 ) , called Bogomolov–Beauville–Fujiki form. This form restricted to the rational Hodge lattice $$H^{1,1}(M,{\mathbb {Q}})$$ H 1 , 1 ( M , Q ) has signature (1, k). This gives a hyperbolic Riemannian metric on the projectivization H of the positive cone in $$H^{1,1}(M,{\mathbb {Q}})$$ H 1 , 1 ( M , Q ) . Torelli theorem implies that the Hodge monodromy group $$\varGamma $$ Γ acts on H with finite covolume, giving a hyperbolic orbifold $$X=H/\varGamma $$ X = H / Γ . We show that there are finitely many geodesic hypersurfaces, which cut X into finitely many polyhedral pieces in such a way that each of these pieces is isometric to a quotient $$P(M')/{\text {Aut}}(M')$$ P ( M ′ ) / Aut ( M ′ ) , where $$P(M')$$ P ( M ′ ) is the projectivization of the ample cone of a birational model $$M'$$ M ′ of M, and $${\text {Aut}}(M')$$ Aut ( M ′ ) the group of its holomorphic automorphisms. This is used to prove the existence of nef isotropic line bundles on a hyperkahler birational model of a simple hyperkahler manifold of Picard number at least 5 and also illustrates the fact that an IHS manifold has only finitely many birational models up to isomorphism (cf. Markman and Yoshioka in Int. Math. Res. Not. 2015(24), 13563–13574, 2015).

Annotation-retrieval reinforcement by visual cognition modeling on manifold

While content-based image annotation and retrieval have been active research topics over the past decade, their correlation is not well exploited until recently. We argue that offline annotation and online retrieval models should be regarded as a unified long-term learner to reinforce each other. Based on this viewpoint, this paper presents an annotation-retrieval reinforcement framework, in which a dual-structured learning model is presented: the keyword-image association is learned by semantic manifold modeling while the semantic correlation between keywords is learned by Image-Word Net modeling. In keyword-image association modeling level, to effectively model keyword-image manifold, we present a manifold co-training algorithm to address the sample insufficiency problem. In manifold-based image annotation, we view annotation process as semantic feature reduction in keyword space, based on which a Biased Fisher Discriminant Analysis (BFDA) algorithm is presented for Eigen feature (keyword) selection. In semantic correlation modeling level, a novel Image-Word Net is learned from annotation training set and users' retrieval log for (1) irrelevant annotated keyword pruning; (2) semantic-level retrieval enhancement. In retrieval, our framework can effectively reveal user target by improving traditional content-based relevance feedback to linguistic-level interaction using annotation information, based on which BFDA is adopted for keyword selection. Finally, user interaction logs are adopted for manifold model updating and annotation refinement. As presented in our experiments, proposed method outperforms state-of-the-art annotation and retrieval algorithms in COREL Image databases with over 10,000 general-purpose images.

Large eddy simulations of partially premixed ethanol dilute spray flames using the flamelet generated manifold model

The paper presents Large Eddy Simulations (LESs) for the Sydney ethanol piloted turbulent dilute spray flames ETF2, ETF6, and ETF7. The Flamelet Generated Manifold (FGM) approach is employed to predict mixing and burning of the evaporating fuel droplets. A methodology to match the experimental inflow spray profiles is presented. The spray statistical time-averaged results show reasonable agreement with mean and RMS data. The Particle Size Distribution (PSD) shows a good match downstream of the nozzle exit and up to x/D = 10. At x/D = 20 and 30 the PSD is under-predicted for droplets with mean diameter D10 > 20μm and over-predicted for the smaller size droplets. The simulations reasonably predict the reported mean flame structure and length. The effect of increasing the carrier velocity (ETF2–ETF7) or decreasing the liquid fuel injection mass flow rate (ETF2–ETF6) is found to result in a leaner, shorter flame and stronger spray–flow interactions. Higher tendency to local extinction is observed for ETF7 which is closer to blow-off compared to ETF2 and has higher scalar dissipation rates, higher range of Stokes number, and faster droplet response. The possible sources of LES-FGM deviations from the measurements are discussed and highlighted. In particular, the spray time-averaged statistical error contribution is quantified and the impact of the inflow uncertainty is studied. Sensitivity analysis to the pre-vaporized nozzle fuel mass fraction show that such small inflow perturbations (by ± 2% for the ETF2 flame) have a strong impact on the flame structure, and the droplets’ dynamics. Conditional scatter plots show that the flame exhibits wide range of mixing conditions and bimodal mixing lines particularly at upstream locations (x/D < 20), where the injected droplets are still penetrating the centerline. This is relaxed further downstream as droplets gradually evaporate and burn in a diffusion like mode.

Insight into the Conformational Variations in SoxYZ Protein Complex from Two Different Members of the β-Proteobacterial Family Involved in Sulfur Oxidation.

Sulfur anions serve as the important environmental pollutants. Microbes use hydrogen sulfide in different redox reactions and thus make the environment pollution-free. The sulfur redox processes are performed by a gene cluster called the sox operon, possessed by a diverse set of microorganisms. However, most of the previous studies were confined to α-proteobacteria. In this work, we tried to elucidate the mechanistic details of sulfur oxidation in β-proteobacteria. We compared the molecular mechanism of sulfur oxidation process using Dechloromonas aromatica and Thiobacillus denitrificans. Dechloromonas aromatica possesses the entire sox operon, whereas T. denitrificans lacks SoxCD. In both the organisms, SoxYZ complex formation is essential for thiosulfate oxidation. This SoxYZ protein complex interacts with SoxCD and SoxAX, respectively, for recycling the thiosulfate-bound SoxY protein. For this purpose, individual proteins were modeled via manifold modeling techniques. Protein-protein docking studies were executed to generate duo- and quadro-protein complexes. Different stability parameters such as free energy of folding, solvent accessibility area (for final complexes), and electrostatic surface potential (for SoxYZ complexes) were calculated and analyzed. Fifteen strengthening ionic interactions were accomplished in the SoxYZAX complex, whereas eight such interactions were observed in SoxYZCD complex. From the result, SoxYZAX complex was found to be more stable and interactive one. This study is the first of its kind that analyzes the comparative aspects of the binding interactions of the proteins involved in redox reactions of sulfur anions. This study may, therefore, be helpful in tailoring the microorganisms to function in a better way to remove the environmental sulfur pollutants.

Asymptotic Properties of Local Sampling on Manifold

In many applications, the real high-dimensional data occupy only a very small part in the high dimensional ‘observation space’ whose intrinsic dimension is small. The most popular model of such data is Manifold model which assumes that the data lie on or near an unknown manifold Data Manifold, (DM) of lower dimensionality embedded in an ambient high-dimensional input space (Manifold assumption about high-dimensional data). Manifold Learning is a Dimensionality Reduction problem under the Manifold assumption about the processed data and its goal is to construct a low-di-mensional parameterization of the DM (global low-dimensional coordinates on the DM) from a finite dataset sampled from the DM. Manifold assumption means that local neighborhood of each manifold point is equivalent to an area of low-dimensional Euclidean space. Because of this, most of Manifold Learning algorithms include two parts: ‘local part’ in which certain characteristics reflecting low-dimensional local structure of neighborhoods of all sample points are constructed and ‘global part’ in which global low-dimensional coordinates on the DM are constructed by solving certain convex optimization problem for specific cost function depending on the local characteristics. Statistical properties of ‘local part’ are closely connected with local sampling on the manifold, which is considered in the study.

Postoperative 3D spine reconstruction by navigating partitioning manifolds.

The postoperative evaluation of scoliosis patients undergoing corrective treatment is an important task to assess the strategy of the spinal surgery. Using accurate 3D geometric models of the patient's spine is essential to measure longitudinal changes in the patient's anatomy. On the other hand, reconstructing the spine in 3D from postoperative radiographs is a challenging problem due to the presence of instrumentation (metallic rods and screws) occluding vertebrae on the spine. This paper describes the reconstruction problem by searching for the optimal model within a manifold space of articulated spines learned from a training dataset of pathological cases who underwent surgery. The manifold structure is implemented based on a multilevel manifold ensemble to structure the data, incorporating connections between nodes within a single manifold, in addition to connections between different multilevel manifolds, representing subregions with similar characteristics. The reconstruction pipeline was evaluated on x-ray datasets from both preoperative patients and patients with spinal surgery. By comparing the method to ground-truth models, a 3D reconstruction accuracy of 2.24 ± 0.90 mm was obtained from 30 postoperative scoliotic patients, while handling patients with highly deformed spines. This paper illustrates how this manifold model can accurately identify similar spine models by navigating in the low-dimensional space, as well as computing nonlinear charts within local neighborhoods of the embedded space during the testing phase. This technique allows postoperative follow-ups of spinal surgery using personalized 3D spine models and assess surgical strategies for spinal deformities.

Modeling of spray jet flame under MILD condition with non-adiabatic FGM and a new conditional droplet injection model

This paper reports a numerical study on the Delft Spray-in-Hot-Coflow (DSHC) flame with a new OpenFOAM solver developed by the authors, in which the Flamelet Generated Manifolds (FGM) model has been implemented, and used to account for the Turbulence-Chemistry Interaction (TCI). The enthalpy loss effect due to droplet vaporization is considered by employing an additional controlling parameter in the FGM library. Different configurations for generating three dimensional non-adiabatic FGM library are tested. Analysis of the DSHC experimental data suggested that flash boiling influences the atomization of liquid fuel in the DSHC burner. This introduces new challenges for specifying precise spray boundary conditions. A conditional injection model is proposed to provide precise spray information at the injector exit plane (Z=0mm). In this conditional injection model, the droplets have an asymmetric distribution around the spray half angle, in agreement with experimental observations. Also, the possible range of droplet injection angle is conditioned upon the droplet size (mass). Droplet initial velocity magnitude is scaled such that it peaks at the spray half angle direction and reaches a minimum at the edge of the spray cloud. The results obtained show that this model significantly improves the prediction of all the properties examined compared to standard injection model. Comparison between Unsteady Reynolds Averaged Navier Stokes (URANS) and Large Eddy Simulation (LES) is made, and it is found that the LES predicted similar gas phase velocity and better temperature profiles compared to experimental data than URANS, mainly due to the better performance of the dynamic model for mixture fraction variance used in LES. The improvement of the temperature prediction by the non-adiabatic FGM was clearly demonstrated by comparing the results obtained with an adiabatic FGM table. Strong droplet–flame interaction was shown to exist in the DSHC flame. Large droplets have ballistic trajectories at the early stage of their lifetime, and can penetrate the flame and survive until far downstream outside the main reaction region. Movement of these large droplets strongly deform the shape of the flame. Rapid evaporation of droplets in the reaction region may also cause local quenching. Small droplets are quickly convected towards the central region and vanish soon, forming a fuel rich and high temperature condition in the center. These phenomena have been captured with the LES/non-adiabatic FGM modeling approach developed in this paper.

Cold Flow Simulation of Helical Intake Manifold for Single Cylinder CI Engine

In 21st century primary purpose of Internal Combustion (IC) engine inventors is to accomplish dual objectives of finest performance and reduced emission. For maximizing mass of air induced into cylinder during suction stroke intake manifold design should be optimized. Present work primarily incorporates the effect of helical intake manifold on air motion and turbulence inside the cylinder at different crank angles. Three dimensional models of intake manifold and cylinder are generated in Creo Parametric 3.0 and meshed using Ansys 16.0. The flow characteristics of air inside intake manifold as well as engine cylinder are analysed in FLUENT 16.0.

A new record excited state (3)MLCT lifetime for metalorganic iron(ii) complexes.

Herein we report the synthesis and time-resolved spectroscopic characterization of a homoleptic Fe(ii) complex exhibiting a record (3)MLCT lifetime of 26 ps promoted by benzimidazolylidene-based ligands. Time dependent density functional molecular modeling of the triplet excited state manifold clearly reveals that, at equilibrium geometries, the lowest (3)MC state lies higher in energy than the lowest (3)MLCT one. This unprecedented energetic reversal in a series of iron complexes, with the stabilization of the charge-transfer state, opens up new perspectives towards iron-made excitonic and photonic devices, hampering the deactivation of the excitation via metal centered channels.

Structural Exploration and Conformational Transitions in MDM2 upon DHFR Interaction fromHomo sapiens: A Computational Outlook for Malignancy via Epigenetic Disruption

Structural basis for exploration into MDM2 and MDM2-DHFR interaction plays a vital role in analyzing the obstruction in folate metabolism, nonsynthesis of purines, and further epigenetic regulation in Homo sapiens. Therefore, it leads to suppression of normal cellular behavior and malignancy. This has been earlier documented via yeast two-hybrid assays. So, with a novel outlook, this study explores the molecular level demonstration of the best satisfactory MDM2 model selection after performing manifold modeling techniques. Z-scores and other stereochemical features were estimated for comparison. Further, protein-protein docking was executed with MDM2 and the experimentally validated X-ray crystallographic DHFR. Residual disclosure from the best suited simulated protein complex disclosed 18 side chain and 3 ionic interactions to strongly accommodate MDM2 protein into the pocket-like zone in DHFR due to the positive environment by charged residues. Lysine residues from MDM2 played a predominant role. Moreover, evaluation from varied energy calculations, folding rate, and net area for solvent accessibility implied the active participation of MDM2 with DHFR. Fascinatingly, conformational transitions from coils to helices and β-sheets after interaction with DHFR affirm the conformational strength and firmer interaction of human MDM2-DHFR. Therefore, this probe instigates near-future clinical research and interactive computational investigations with mutations.