Variety Of Designs Research Articles

PLMC: Language Model of Protein Sequences Enhances Protein Crystallization Prediction.

X-ray diffraction crystallography has been most widely used for protein three-dimensional (3D) structure determination for which whether proteins are crystallizable is a central prerequisite. Yet, there are a number of procedures during protein crystallization, including protein material production, purification, and crystal production, which take turns affecting the crystallization outcome. Due to the expensive and laborious nature of this multi-stage process, various computational tools have been developed to predict protein crystallization propensity, which is then used to guide the experimental determination. In this study, we presented a novel deep learning framework, PLMC, to improve multi-stage protein crystallization propensity prediction by leveraging a pre-trained protein language model. To effectively train PLMC, two groups of features of each protein were integrated into a more comprehensive representation, including protein language embeddings from the large-scale protein sequence database and a handcrafted feature set consisting of physicochemical, sequence-based and disordered-related information. These features were further separately embedded for refinement, and then concatenated for the final prediction. Notably, our extensive benchmarking tests demonstrate that PLMC greatly outperforms other state-of-the-art methods by achieving AUC scores of 0.773, 0.893, and 0.913, respectively, at the aforementioned individual stages, and 0.982 at the final crystallization stage. Furthermore, PLMC is shown to be superior for predicting the crystallization of both globular and membrane proteins, as demonstrated by an AUC score of 0.991 for the latter. These results suggest the significant potential of PLMC in assisting researchers with the experimental design of crystallizable protein variants.

M-ARM: An automated systematic approach for generating new variant design options from an existing product family

Companies with established product families can meet emerging market needs by extending their product families over time, that is, by introducing new product variants sequentially. Starting points for each new design can be generated by integrating design solutions from existing products in the same family. This article introduces an automated systematic approach to derive all possible ways of combining existing designs to produce a new variant with a desired new combination of functionality, based on algorithmic analysis of product models. The approach considers the functions in existing products and how they are realised in specific CAD parts and the geometric features defining them. By automatically generating and evaluating all the options, the new approach helps identify the most efficient way to combine existing parts and features from a product family to generate a new product variant with distinct functionality. The approach has been applied to product families of mechanical and mechatronic consumer products. It was evaluated by manually implementing the automatically-generated redesign instructions in CAD for several product variants, which built confidence in the approach’s accuracy and performance. From the evaluation, opportunities for further work are suggested.

Cryo-EM structures of a bathy phytochrome histidine kinase reveal a unique light-dependent activation mechanism

Phytochromes are photoreceptor proteins in plants, fungi, and bacteria. They can adopt two photochromic states with differential biochemical responses. The structural changes transducing the signal from the chromophore to the biochemical output modules are poorly understood due to challenges in capturing structures of the dynamic, full-length protein. Here, we present cryoelectron microscopy (cryo-EM) structures of the phytochrome from Pseudomonas aeruginosa (PaBphP) in its resting (Pfr) and photoactivated (Pr) state. The kinase-active Pr state has an asymmetric, dimeric structure, whereas the kinase-inactive Pfr state opens up. This behavior is different from other known phytochromes and we explain it with the unusually short connection between the photosensory and output modules. Multiple sequence alignment of this region suggests evolutionary optimization for different modes of signal transduction in sensor proteins. The results establish a new mechanism for light-sensing by phytochrome histidine kinases and provide input for the design of optogenetic phytochrome variants.

Neural network extrapolation to distant regions of the protein fitness landscape

Machine learning (ML) has transformed protein engineering by constructing models of the underlying sequence-function landscape to accelerate the discovery of new biomolecules. ML-guided protein design requires models, trained on local sequence-function information, to accurately predict distant fitness peaks. In this work, we evaluate neural networks’ capacity to extrapolate beyond their training data. We perform model-guided design using a panel of neural network architectures trained on protein G (GB1)-Immunoglobulin G (IgG) binding data and experimentally test thousands of GB1 designs to systematically evaluate the models’ extrapolation. We find each model architecture infers markedly different landscapes from the same data, which give rise to unique design preferences. We find simpler models excel in local extrapolation to design high fitness proteins, while more sophisticated convolutional models can venture deep into sequence space to design proteins that fold but are no longer functional. We also find that implementing a simple ensemble of convolutional neural networks enables robust design of high-performing variants in the local landscape. Our findings highlight how each architecture’s inductive biases prime them to learn different aspects of the protein fitness landscape and how a simple ensembling approach makes protein engineering more robust.

In silico designing of multi-epitope vaccine against canine parvovirus using reverse vaccinology.

Canine parvovirus (CPV-2) is a highly contagious virus affecting dogs worldwide, posing a significant threat. The VP2 protein stands out as the predominant and highly immunogenic structural component of CPV-2. Soon after its emergence, CPV-2 was replaced by variants known as CPV-2a, 2b and 2c, marked by changes in amino acid residue 426 of VP2. Additional amino acid alterations have been identified within VP2, with certain modifications serving as signatures of emerging variants. In Brazil, CPV-2 outbreaks persist with diverse VP2 profiles. Vaccination is the main preventive measure against the virus. However, the emergence of substitutions presents challenges to conventional vaccine methods. Commercial vaccines are formulated with strains that usually do not match those currently circulating in the field. To address this, the study aimed to investigate CPV-2 variants in Brazil, predict epitopes, and design an in silico vaccine tailored to local variants employing reverse vaccinology. The methodology involved data collection, genetic sequence analysis, and amino acid comparison between field strains and vaccines, followed by the prediction of B and T cell epitope regions. The predicted epitopes were evaluated for antigenicity, allergenicity and toxicity. The final vaccine construct consisted of selected epitopes linked to an adjuvant and optimized for expression in Escherichia coli. Structural predictions confirmed the stability and antigenicity of the vaccine, while molecular docking demonstrated interaction with the canine toll-like receptor 4. Molecular dynamics simulations indicated a stable complex formation. In silico immune simulations demonstrated a progressive immune response post-vaccination, including increased antibody production and T-helper cell activity. The multi-epitope vaccine design targeted prevalent CPV-2 variants in Brazil and potentially other regions globally. However, experimental validation is essential to confirm our in silico findings.

Olivar: towards automated variant aware primer design for multiplex tiled amplicon sequencing of pathogens

Tiled amplicon sequencing has served as an essential tool for tracking the spread and evolution of pathogens. Over 15 million complete SARS-CoV-2 genomes are now publicly available, most sequenced and assembled via tiled amplicon sequencing. While computational tools for tiled amplicon design exist, they require downstream manual optimization both computationally and experimentally, which is slow and costly. Here we present Olivar, a first step towards a fully automated, variant-aware design of tiled amplicons for pathogen genomes. Olivar converts each nucleotide of the target genome into a numeric risk score, capturing undesired sequence features that should be avoided. In a direct comparison with PrimalScheme, we show that Olivar has fewer mismatches overlapping with primers and predicted PCR byproducts. We also compare Olivar head-to-head with ARTIC v4.1, the most widely used primer set for SARS-CoV-2 sequencing, and show Olivar yields similar read mapping rates (~90%) and better coverage to the manually designed ARTIC v4.1 amplicons. We also evaluate Olivar on real wastewater samples and found that Olivar has up to 3-fold higher mapping rates while retaining similar coverage. In summary, Olivar automates and accelerates the generation of tiled amplicons, even in situations of high mutation frequency and/or density. Olivar is available online as a web application at https://olivar.rice.edu and can be installed locally as a command line tool with Bioconda. Source code, installation guide, and usage are available at https://github.com/treangenlab/Olivar.

Exploring variations in the weight, size and shape of liquid hydrogen tanks for zero-emission fuel-cell vessels

We report an exploration of liquid hydrogen (LH2) tank technology, determining if improving LH2 tank weight, multiplicity or shape enables more hydrogen to be stored on hydrogen vessels. The improvements investigated are well beyond the current LH2 technology state-of-the-art. The platform for this study is a monohull H2 Baseline research vessel powered 100% by hydrogen.Regarding weight reductions, we found that the research vessel does not benefit significantly from large weight reductions of the LH2 tanks. A 50% reduction in mass of the inner pressure vessel led to a 3.1% reduction in the total research vessel weight. Reducing the LH2 tank weight to the asymptotic limit of zero results in a 7.1% reduction in the overall research vessel weight. This maritime application, while benefitting from a reduction in LH2 tank weight, is not a strong driver for LH2 tank weight improvements.The two large LH2 tanks of the H2 Baseline Vessel can be replaced with a multitude of smaller LH2 tanks. In two Multiplicity design variants, the total amount of stored LH2 was ∼5–9% less than that of the H2 Baseline Vessel, which does not motivate conducting the needed R&D that would drive the technology to smaller high-performing LH2 tanks.We did find a significant benefit to improving the shape of LH2 tanks, towards prismatic tanks that would better match the vessel hullform. Considering a “Shape Variant” that included space-consuming features such as tank connection spaces, manifolding and ventilation, we found a 26% improvement in stored LH2 compared to the H2 Baseline Vessel. The large improvement in storage capacity could warrant further LH2 tank R&D that can enable high performing prismatic LH2 tanks, such as pressure vessel steel developments allowing for higher strength/ductility at 20 K, improved insulation systems, methods for efficiently building prismatic tanks with insulation systems fit for 20 K service and flare-less techniques for managing heat leak (venting).The results are reviewed considering the current regulatory restrictions as well as prevailing limitations in LH2 tank manufacturing.

Computational stabilization of a non-heme iron enzyme enables efficient evolution of new function.

Directed evolution has emerged as a powerful tool for engineering new biocatalysts. However, introducing new catalytic residues can be destabilizing, and it is generally beneficial to start with a stable enzyme parent. Here we show that the deep learning-based tool ProteinMPNN can be used to redesign Fe(II)/αKG superfamily enzymes for greater stability, solubility, and expression while retaining both native activity and industrially-relevant non-native functions. For the Fe(II)/αKG enzyme tP4H, we performed site-saturation mutagenesis with both the wild-type and stabilized design variant and screened for activity increases in a non-native C-H hydroxylation reaction. We observed substantially larger increases in non-native activity for variants obtained from the stabilized scaffold compared to those from the wild-type enzyme. ProteinMPNN is user-friendly and widely-accessible, and straightforward structural criteria were sufficient to obtain stabilized, catalytically-functional variants of the Fe(II)/αKG enzymes tP4H and GriE. Our work suggests that stabilization by computational sequence redesign could be routinely implemented as a first step in directed evolution campaigns for novel biocatalysts.

Balloonless spinning spindle head shape optimisation

Abstract The article presents a procedure for assessing and selecting the shape of the ring spinner spindle head that is optimum for yarn manufacturing using the balloonless system, considering multiple criteria. This procedure consists of two stages: the Pareto-optimal method and the distance function method. Twelve spindle head variants were designed and manufactured of 100Cr6 bearing steel composed of C (0.93–1.05%), Si (0.15–0.35%), Mn (0.25–0.45%) along with aluminium alloys EN AW-6060 (AlSi1MgMn) containing Si (0.70–1.3%), Mg (0.6–1.2%), Mn (0.4–1.0%) and EN AW-6060 (AlMgSi) containing Si (0.30–0.60%) and Mg (0.35–0.60%). The shapes of the heads were developed on the basis of the design solutions used previously by the leading ring-spinning machine and spinning spindle manufacturers. Four design variants of spindle heads, made of AlMgSi wrought aluminium alloy, have a unique shape that enables shaping their teeth (notches) by extrusion on a hydraulic press. The assessment criteria were production cost per unit, head weight, maximum surface hardness HV0.1, and four yarn technological quality criteria: maximum yarn tension values during the formation of the base, body and head of the package, and the tension amplitude. The assessment values for the criteria obtained through calculations and measurements were normalized. Then, a set of Pareto-optimal solutions was determined using seven criteria. Due to the fact that this set consisted of six variants, the distance function was used to select the best one. The best design variant of the head is the one corresponding to the lowest distance function value. In our case, the variant is a unique shape, made of AlMgSi alloy, with teeth thickened on the outer diameter of the head, ensuring the lowest yarn tension value at the height during the formation of the package.

A Novel Computational Paradigm for Reconstructing Solid CAD Features from a Segmented Manifold Triangular Mesh

We introduce a novel computational paradigm for reconstructing solid computer-aided design (CAD) features from the surface of a segmented manifold triangular mesh. This paradigm addresses the challenge of capturing high-level design semantics for manifold triangular meshes and facilitates parametric and variational design capabilities. We categorize four prevalent features, namely extrusion, rotation, sweep, and loft, as generalized swept bodies driven by cross-sectional sketches and feature paths, providing a unified mathematical representation for various feature types. The numerical optimization-based approach conducts geometric processing on the segmented manifold triangular mesh patch, extracting cross-sectional sketch curves and feature paths from its surface, and then reconstructing appropriate features using the Open CASCADE kernel. We employ the personalized three-dimensional (3D) printed model as a case study. Parametric and variant designs of the 3D-printed models are achieved through feature reconstruction of the manifold triangular mesh obtained via 3D scanning.

Experimental study of Double-Elliptic-Ring-based thermomechanical metamaterials’ behaviour

In recent years there has been increased interest in 3D thermomechanical metamaterials. The current work reports the design, fabrication and testing of a novel anisotropic anepectic metamaterial obtained by grafting inserts of a second material into a known auxetic structure, the Double Elliptic Ring (DER). While auxetics have a Negative Poisson's Ratio (NPR), anepectics combine this with a negative coefficient of thermal expansion (NTE). To avoid unnecessary time and material expenditure during the additive manufacturing (AM) stage, mechanical and thermal finite element simulations (FEA) were used as a tool to screen the multitude of possible design variants under consideration, thus aiding in rapidly determining the more promising geometries. To further increase the efficiency during AM, the selected structures were then assembled from planar elements, joined together with an adhesive. In the end, through the combination of two conventional materials – PVA and Nylon – of positive Poisson's ratio (PR) and coefficient of thermal expansion (CTE) organized in the DER architecture, it was possible to achieve the desired NPR and CTE behaviour along some directions, coupled with positive values in other directions. Experimental results revealed a correlation between PR values and the number of cells in the structure and showed that, while NPR values are essentially determined by the metamaterial's geometry, the induction of NTE results from a subtle interaction of geometry and the properties mismatch between the constitutive materials.

Diversity of pedagogical feedback designs: results from a scoping review of feedback research in higher education

Feedback is a key factor for learning success and has therefore been widely studied in higher education. As feedback is a highly contextualized practice serving various learner needs, researchers have utilized a plethora of feedback designs in their intervention studies. This diversity in feedback conceptualizations and pedagogical designs often results in considerable variability of effect sizes in reviews and meta-analyses and thus reduces their explanatory power. To explore the breadth of possible design variants and contribute to a greater conceptual clarity in empirical studies, we conducted a scoping review of feedback interventions in higher education (n = 135). The scoping process revealed a rich variety of feedback practices and underscored the need to precisely describe feedback designs in primary studies. Moreover, the supplemental qualitative content analysis resulted in a comprehensive category system, consisting of seven main dimensions and 247 subcategories. With its hierarchical structure and guiding questions, this feedback taxonomy can serve as a valuable tool for researchers and educators when exploring and implementing different design options. Despite its comprehensive scope, the taxonomy exhibits gaps, notably with respect to emerging digital feedback practices. It should therefore be considered as a dynamic resource worth of further investigation.

An integrated method and tool for telescopic beams design in extendable undercarriages

Earth-moving machine builders require innovative design methods and tool to optimize structural performance while reducing production and design costs, particularly in crucial phases like undercarriage frame design and structural verification. After an in-depth description of the design flow normally followed in industry, the paper presents a computationally efficient method and tool to aid designers in dimensioning extendable tracked undercarriages, aiming to drastically reduce design time and efforts to optimize resources. The proposed tool is based on an analytical model established from in-depth analyses of the undercarriage Computer Aided Design (CAD) assembly and the expertise of the industrial partner. To address the 3D structural problem, a planar system is employed with proper corrective coefficients. These coefficients are meticulously evaluated through direct comparison with Finite Element Method (FEM) models by seamlessly integrating SolidWorks and ANSYS Workbench. The tool accepts as inputs geometric and material data, as well as specific user-defined load scenarios, providing outputs in the form of the deflected configuration of the undercarriage and stress levels. Direct comparison with the results obtained from FEM for three industrial undercarriage models demonstrates the validity of the approach, with errors consistently within the 10% range in almost all cases. This enables designers with no advanced skills in FEM to efficiently validate diverse design variants with minimal effort. Once validated, the tool is integrated with an optimizer in Matlab to conduct computationally efficient design optimization studies. The optimization problem, focused on minimizing the beam’s vertical size while maintaining structural integrity and limiting deflections, has been successfully resolved within a limited computational time, showcasing the benefits of the proposed approach for undercarriage design.

Arduino-Based automatic cutting tool for coconut shell charcoal briquettes

Coconut shell waste is underutilized due to the high demand for coconut in Indonesia. To maximize the use value of the material, coconut shells are converted into biomass energy, specifically charcoal briquettes. Due to the rapid advancement of technology today, it is necessary to use equipment that can run automatically to increase productivity, reduce production time, and lower production costs. This research was conducted to make an automatic cutting tool based on an Arduino microcontroller. The first process identified with literature studies related to automatic briquette cutting tools. The second process is conceptualised by assessing several design variants, then the highest value is taken. The third process is designing by making detailed drawings per component. The fourth process of completing the design by assembling all the components that have been made, and then testing the Arduino-based automatic cutting tool. The results obtained by the automatic cutting tool produce a cutting consistency of 77.5% and produce a residue of 22.5%. From the test data, the Arduino-based automatic cutting tool can work more effectively and efficiently to increase production capacity.

Rational design of Zeocin binding protein variants for antibiotic resistance studies

Antibiotic resistance marker was used in this investigation because they are selected;e markers and effective in different hosts. This study used Zeocin binding protein (ZBP) due to its known 3D structure and applicability in prokaryotic and eukaryotic hosts. A library of 22 mutations was developed through a rational design strategy. Subsequently, a selection strategy was used to identify destabilizing mutations in the ZBP. ZBP variants were expressed in E.coli using a leaky expression approach, and minimum inhibitory concentration (MIC) was calculated by cultivating different variants at various Zeocin concentrations. Zeocin resistance was drastically decreased in some ZBP variants. Positive controls (wild-type ZBP) exhibited high resistance, while negative controls (pET vector without ZBP) showed susceptibility. Two variants (ZBP P9E and ZBP R26F) displayed drastic resistance loss. The variants reported in this study may identify molecular chaperones and folding modulators affecting proteostasis. These variants can potentially discover chemical chaperones that improve the stability and solubility of destabilized ZBP variants.

LEVERAGING MISSION SOLUTION CONFIGURATION THROUGH MBSE AND TRADESPACE EXPLORATION

AbstractThales is a worldwide leader in innovative radar and mission solution systems used by naval ships. As the demand for personalized products increased through time, Thales shifted from a project‐oriented to a product‐oriented approach. This shift aims to capitalize on variants, minimize customization, and streamline operations. In this context, Thales established a mission solution configuration process (SCP) to facilitate the selection of product variants to compose a system during the bidding phase. However, the current SCP has limitations, constraining exploration and integration with engineering processes and system data. Consequently, the proposed systems sometimes fall short of the most optimal solution the client could get. Therefore, the objective of this work is to develop and validate an improved mission solution configuration process to streamline the creation and selection of product variants at Thales, particularly during the bidding phase, to better meet client needs and operational requirements. This method integrates Model‐Based Systems Engineering (MBSE) and Tradespace Exploration (TSE), utilizing ARCADIA as the methodology and Capella as the tool. A descriptive model is generated for analytical purposes within TSE, employing Multi‐Attribute‐Utility‐Theory (MAUT) and Pareto‐Optimization for evaluating and selecting optimal mission solution variants. Validation was conducted through a Coast Guard mission case study involving 125 solution variants, revealing Pareto‐optimal solutions balancing performance and cost. This method enhances the current configuration process by aligning client and operational needs with Thales's sales and product teams, ensuring accurate interpretation of requirements and minimizing information inconsistencies. The case study results identify technological gaps in variant designs, guiding research and development efforts towards subsystems or components with significant impact on system performance.

Optimizing MEP design in early AEC projects through generative design

The digital transformation of the AEC industry through BIM has improved productivity during detailed design and construction planning phases. Early design choices influence a project's success but have yet to benefit from BIM-based approaches. This paper investigates the feasibility and acceptance of employing Generative Design (GD) to optimize early Mechanical, Electrical, and Plumbing (MEP) designs in residential real estate for space efficiency. Interviews indicate the main issue is acceptance due to the belief that a GD approach needs to be more robust. BIM is integrated with GD, utilizing the architectural layout (IFC) as input to generate design variants tailored to minimize technical space while ensuring installation feasibility. Robustness is assessed via Monte Carlo Simulation, revealing an estimated success rate of 99% (81% with 95% confidence). These results quantify the robustness of the approach, paving the way to broader acceptance of GD in the early phases of AEC projects.

A comparison of WHO-5 and ICC classifications in a series of myeloid neoplasms, considerations for hematopathologists and molecular pathologists

ObjectivesThe International Consensus Classification (ICC) and 5th Edition of the World Health Organization Classification (WHO-5) made substantive updates to the classification of myeloid neoplasms. This study compares the systems in a series of myeloid neoplasms with increased blasts, analyzing implications for diagnostic workflow and reporting. MethodsBone marrow biopsies categorized as myelodysplastic syndrome with excess blasts (MDS-EB) or acute myeloid leukemia (AML) by WHO-R4 were identified. Results of morphology review, karyotype, fluorescence in situ hybridization, and next-generation sequencing were compiled. Cases were retrospectively re-classified by WHO-5 and ICC. Results46 cases were reviewed. 28 cases (61 %) had ≥20 % blasts, with the remaining cases having 5–19.5 % blasts. The most common differences in classification were 1) the designation of MDS versus MDS/AML (10/46, 22 %) for cases with 10–19 % blasts and 2) the ICC's designation of TP53 variants as a separate classifier for AML (8/46, 17 %). Bi-allelic/multi-hit TP53 alterations were identified in 15 cases (33 %). Variants of potential germline significance were identified in 29 (63 %) cases. ConclusionsWhile terminology differences between WHO-5 and ICC exist, both systems invoke similar opportunities for improved reporting: standardized classification of pathogenic variants (notably TP53), streamlined systems to evaluate for potential germline variants, and integrated reporting of morphologic and genetic data.

Evaluation of Yield Stability and Adaptability of Oat Genotypes (Avena sativa L.)

This study’s main objectives were (i) to determine the stability of oat genotypes for the grain yield (t ha-1), (ii) to investigate the relationship of stability parameters with the grain yield in the analyzed set of genotypes, and (iii) to identify the high-yielding, stable, and adaptive oat genotypes for breeding purposes. The study was conducted during the 2015-22 period and involved fourteen winter oat genotypes maintained in the Bulgarian Seed Gene Bank. The trial was laid out in a randomized block design of variants in three replications and an experimental plot size of 10 m2. Sixteen grain yield stability parameters were determined. The AMMI stability value (ASV), the yield stability index (YSI), and the genotype selection index (GSI) were also calculated. A year and genotype x year interaction had an almost equally dominant effect on the grain yield per hectare. The Spearman’s rank correlation coefficients indicated that grain yield had significant positive associations with Thennarasu’s non-parametric statistics—the NP(2), NP(3), and NP(4), Kang’s rank-sum (KR), and the YSI. The yield stability parameters estimated the G11, G12, G13, and the G1 genotypes as the most stable. The ASV identified the G14, G8, and the G12 as the most stable genotypes, while the YSI detected the G14, G12, and G11, respectively. The GSI classified the G14, G12, G8, and the G11 as genotypes with the broadest adaptability to adverse climatic conditions. Thus, they could serve as a source material in winter oat breeding programs.

Bearing Steel Structures for Installation of Railway Strain Gauge Scales

Purpose. The main purpose of the publication is to compare the main technical and economic indicators of the two proposed structural variants of the support system for railway strain gauge scales for four-axle wide gauge railcars. This is due to the need to reconstruct and modernize the fixed assets of an industrial enterprise, in particular, to overhaul and replace old railway scales with modern electronic strain gauge scales. Also, the purpose of the work is related to the need to expand the range of railway cars for which the possibility of weighing is provided, including transport units of the foreign car fleet. Methodology. To achieve this goal, we analyzed the modern types of railroad cars for freight transportation, determined the main components of strain gauge scales for weighing the main types of cars, and developed a special support system to support the scale platform. The support system is provided in two design variants - with one spatial support frame and two separate flat support frames. Using the domestic design and computing complex SCAD, we built finite element models for both proposed design options. Based on the analysis of their stress-strain state, rational cross-sections were selected for each structural element, and the total mass was determined. Findings. A comparative analysis of the two developed structural solutions for the support system for modern railway strain gauges has revealed that the variant with two separate bearing frames has a lower weight by about 40 % compared to the variant with one spatial bearing frame. However, the manufacturability of its production is somewhat lower, since it involves 10 structural elements compared to 7 for variant No. 1. Originality. The numerical analysis made it possible to theoretically substantiate a more rational and efficient design solution for the support system for modern railway strain gauge scales. Practical value. A constructive variant of the steel support system has been developed and proposed for practical implementation, which is the most effective in terms of its technical and economic indicators for the existing conditions of an existing industrial enterprise. The developed design option was also coordinated with the project of major reconstruction of the building for weighing railroad cars.