Structural Database Research Articles

Traits.build: A data model, workflow and R package for building harmonised ecological trait databases

Trait databases have proliferated over the past decades, facilitating research on the ecology, evolution, and conservation of taxa across the Tree of Life. Typically, teams of independent researchers build these databases, and each must develop their own workflow and output structure. This divests research hours from downstream tasks such as trait-based analysis and interpretation and the resultant datasets are often difficult to integrate due to disparate database structures. Here we introduce the {traits.build} R-package, which offers a generalised workflow for building trait databases. {traits.build} contains bespoke functions for propagating metadata files, extensive tutorials, and sample configuration files, allowing researchers to efficiently build a new trait database using open source tools. In addition, the {traits.build} output structure is fully documented by a data model, ensuring the meaning of each variable and semantic relationship between variables is transparent and consistent. The data standard links to terms in previously published data standards, drawing strongly on DarwinCore and the Ecological Trait-data Standard, but also includes the ability to fully map location and context properties absent from these vocabularies. It is the first published database-building workflow that adheres to the Extensible Observation Ontology. Simultaneously developing a generalised workflow and publishing a data standard for the workflow provides {traits.build} users a straightforward pathway to build a new trait database that achieves the FAIR principles. The meaning of all variables in a {traits.build} database are already documented, allowing further integration with either other {traits.build} databases or indeed any other database with a documented data model. This follows the vision of the Open Traits Network to build trait databases whose data can be easily integrated for further analysis.

Is blockchain cost-effective in construction project management? A systematic review from the perspective of transaction cost

PurposeBlockchain technology (BCT) is considered a promising tool to improve the productivity of construction project management. Existing research has studied its potential costs and benefits for the construction industry. However, the potential costs and benefits of BCT failed to be compared as actual costs and benefits of specific applications for stakeholders. To fill this gap, this study seeks to analyze the cost-effectiveness of BCT-based applications in construction project management.Design/methodology/approachThis study is conducted with a customized systematic literature review based on transaction cost theory to enable qualitative comparison. With a deliberately designed structure confining extraneous variables, the costs and benefits of BCT-based applications are identified and compared. The inherent dependent relations of processes and the evolution relations of functions are identified. The cost-effectiveness of blockchain adoption is then analyzed.FindingsSeven functions and six challenges are identified within five processes. The result suggests all identified functions are cost-effective except for manual instruction (coding smart contracts manually). The smart contracts require explicit definition and logic to be effective. However, the construction projects essentially require the institution to be flexible due to unpredictability. The adoption of smart contracts and corresponding additional requirements can increase the transaction cost of bounded rationality.Research limitations/implicationsAs manual instruction is fundamental to realize other functions, and its advanced substitute relies on its broad adoption, its cost-effectiveness must be improved for applications to be acceptable to stakeholders. The establishment of a universal smart contract model and a universal, legitimate and efficient database structure are recommended to minimize the cost and maximize the effect of applications.Originality/valueThis study contributes to the knowledge by providing a comprehensive analysis of BCT adoption’s cost-effectiveness in construction project management. The adopted review structure can be extended to analyze the qualitative benefits and challenges of management automation in the early stages.

Enhancing Superconductor Critical Temperature Prediction: A Novel Machine Learning Approach Integrating Dopant Recognition.

Doping plays a crucial role in determining the critical temperature (Tc) of superconductors, yet accurately predicting its effects remains a significant challenge. Here, we introduce a novel doping descriptor that captures the complex influence of dopants on superconductivity. By integrating the doping descriptor with elemental and physical features within a Mixture of Experts (MoE) model, we achieve a remarkable R2 of 0.962 for Tc prediction, surpassing all published prediction models. Our approach successfully identifies optimal doping levels in the Bi2-xPbxSr2Ca2-yCuyOz system, with predictions closely aligning with experimental results. Leveraging this model, we screen compounds from the Inorganic Crystal Structure Database and employ a generative approach to explore new doped superconductors. This process reveals 40 promising candidates for high Tc superconductivity among existing and hypothetical doped materials. By explicitly accounting for doping effects, our method offers a powerful tool for guiding the experimental discovery of new superconductors, potentially accelerating progress in high-temperature superconductivity research and opening new avenues for material design.

A survey of essential anatomy from the perspective of anesthesiology, emergency medicine, obstetrics and gynecology, and orthopedics resident physicians

BackgroundRecent changes in anatomy curricula in undergraduate medical education (UME), including pedagogical changes and reduced time, pose challenges for foundational learning. Consequently, it is important to ask clinicians what anatomical content is important for their clinical specialty, which when taken collectively, can inform curricular development.MethodsThis study surveyed 55 non-primary care residents in anesthesiology (AN; N = 6), emergency medicine (EM; N = 15), obstetrics and gynecology (OB; N = 13), and orthopedics (OR; N = 21) to assess the importance of 907 anatomical structures across all anatomical regions. Survey ratings by participants were converted into a post-hoc classification system to provide end-users of this data with an intuitive and useful classification system for categorizing individual anatomical structures (i.e., essential, more important, less important, not important).ResultsSignificant variability was observed in the classifications of essential anatomy: 29.1% of all structures were considered essential by OB residents, 37.6% for AN residents, 41.6% for EM residents, and 72.0% for OR residents. Significant differences (with large effect sizes) were also observed between residency groups: OR residents rated anatomy of the back, limbs, and pelvis and perineum anatomy common to both sexes significantly higher, whereas OB residents rated the pelvis and perineum anatomy common to both sexes and anatomy for individuals assigned female at birth highest. Agreement in classifications of importance among residents was observed for selected anatomical structures in the thorax, abdomen, pelvis and perineum (assigned male at birth-specific anatomy), and head and neck. As with the ratings of anatomical structures, OR residents had the highest classification across all nine tissue types (p < 0.01).ConclusionsThe present study created a database of anatomical structures assessed from a clinical perspective that may be considered when determining foundational anatomy for UME curriculum, as well as for graduate medical education.

Quantum chemical calculation dataset for representative protein folds by the fragment molecular orbital method

The function of a biomacromolecule is not only determined by its three-dimensional structure but also by its electronic state. Quantum chemical calculations are promising non-empirical methods available for determining the electronic state of a given structure. In this study, we used the fragment molecular orbital (FMO) method, which applies to biopolymers such as proteins, to provide physicochemical property values on representative structures in the SCOP2 database of protein families, a subset of the Protein Data Bank. Our dataset was constructed by over 5,000 protein structures, including over 200 million inter-fragment interaction energies (IFIEs) and their energy components obtained by pair interaction energy decomposition analysis (PIEDA) using FMO-MP2/6-31 G*. Moreover, three basis sets, 6-31 G*, 6-31 G**, and cc-pVDZ, were used for the FMO calculations of each structure, making it possible to compare the energies obtained with different basis functions for the same fragment pair. The total data size is approximately 6.7 GB. Our dataset will be useful for functional analyses and machine learning based on the physicochemical property values of proteins.

A Spatiotemporal Empowerment Framework for China’s National 3D Mapping Program 3dRGLM

Abstract. China has launched a national 3D mapping program to build 3D Realistic geospatial Landscape Model (3dRGLm) over the whole country, covering both cities and countryside. The 3dRGLM is a digital description and representation of the real 3D geospatial spaces, and is defined as a new generation geospatial data product with 3D structures, realistic scenes, and geospatial entities. Such a national or local 3dRGLm will set up a digital 3D realistic geospatial space which can facilitate the vital connection with the real geospatial space, and will serve as a new generation of geospatial information infrastructure for our society. With the acquisition of complex 3D spatial data continues to advance, managing and utilizing the massive volumes of 3D data to provide spatiotemporal empowerment applications has become a significant technological challenge. This paper analyzes the technical characteristics of 3d realistic geospatial landscape model, and then proposes the main content and basic structure of a 3d realistic geospatial landscape model database, as well as the spatiotemporal service empowerment methods based on the database. Finally, a case analysis is provided to serve as a reference for the construction and application of 3d realistic geospatial landscape.

Proteome-scale structural prediction of the giant Marseillevirus reveals conserved folds and putative homologs of the hypothetical proteins.

A significant proportion of the highly divergent and novel proteins of giant viruses are termed "hypothetical" due to the absence of detectable homologous sequences in the existing databases. The quality of genome and proteome annotations often relies on the identification of signature sequences and motifs in order to assign putative functions to the gene products. These annotations serve as the first set of information for researchers to develop workable hypotheses for further experimental research. The structure-function relationship of proteins suggests that proteins with similar functions may also exhibit similar folding patterns. Here, we report the first proteome-wide structure prediction of the giant Marseillevirus. We use AlphaFold-predicted structures and their comparative analysis with the experimental structures in the PDB database to preliminarily annotate the viral proteins. Our work highlights the conservation of structural folds in proteins with highly divergent sequences and reveals potentially paralogous relationships among them. We also provide evidence for gene duplication and fusion as contributing factors to giant viral genome expansion and evolution. With the easily accessible AlphaFold and other advanced bioinformatics tools for high-confidence de novo structure prediction, we propose a combined sequence and predicted-structure-based proteome annotation approach for the initial characterization of novel and complex organisms or viruses.

SYNBIP 2.0: epitopes mapping, sequence expansion and scaffolds discovery for synthetic binding protein innovation.

Synthetic binding proteins (SBPs) represent a pivotal class of artificially engineered proteins, meticulously crafted to exhibit targeted binding properties and specific functions. Here, the SYNBIP database, a comprehensive resource for SBPs, has been significantly updated. These enhancements include (i) featuring 3D structures of 899 SBP-target complexes to illustrate the binding epitopes of SBPs, (ii) using the structures of SBPs in the monomer or complex forms with target proteins, their sequence space has been expanded five times to 12 025 by integrating a structure-based protein generation framework and a protein property prediction tool, (iii) offering detailed information on 78 473 newly identified SBP-like scaffolds from the RCSB Protein Data Bank, and an additional 16401555 ones from the AlphaFold Protein Structure Database, and (iv) the database is regularly updated, incorporating 153 new SBPs. Furthermore, the structural models of all SBPs have been enhanced through the application of the AlphaFold2, with their clinical statuses concurrently refreshed. Additionally, the design methods employed for each SBP are now prominently featured in the database. In sum, SYNBIP 2.0 is designed to provide researchers with essential SBP data, facilitating their innovation in research, diagnosis and therapy. SYNBIP 2.0 is now freely accessible at https://idrblab.org/synbip/.

Using of Machine Learning Capabilities to Predict Double Phosphate Structures for Biomedical Applications

In the rapidly developing field of biomedical research, the search for new materials with improved properties is crucial to moving the entire field forward. Double phosphates have generated significant interest in a wide range of applications, ranging from drug delivery systems to catalysts for biomedical reactions, and the fields of biomedicine and tissue engineering are no exception. In this article, we propose a method for finding new double phosphate materials based on machine learning, screening and applying data from structural databases, and we use this methodology combined with chemical knowledge to propose several promising materials for bone engineering. For the selected candidates, we develop a solid-phase synthesis procedure and apply physical characteristics to confirm the results. In addition, the role of microstructure, i.e. The porosity of frameworks based on these materials is discussed from a biomedical point of view, and several synthetic ways to adjust this parameter are proposed and investigated.

Restoring protein glycosylation with GlycoShape.

Despite ground-breaking innovations in experimental structural biology and protein structure prediction techniques, capturing the structure of the glycans that functionalize proteins remains a challenge. Here we introduce GlycoShape ( https://glycoshape.org ), an open-access glycan structure database and toolbox designed to restore glycoproteins to their native and functional form in seconds. The GlycoShape database counts over 500 unique glycans so far, covering the human glycome and augmented by elements from a wide range of organisms, obtained from 1 ms of cumulative sampling from molecular dynamics simulations. These structures can be linked to proteins with a robust algorithm named Re-Glyco, directly compatible with structural data in open-access repositories, such as the Research Collaboratory for Structural Bioinformatics Protein Data Bank (RCSB PDB) and AlphaFold Protein Structure Database, or own. The quality, performance and broad applicability of GlycoShape is demonstrated by its ability to predict N-glycosylation occupancy, scoring a 93% agreement with experiment, based on screening all proteins in the PDB with a corresponding glycoproteomics profile, for a total of 4,259 N-glycosylation sequons.

DiSSCo Flanders and ILVO-CMS: Unlocking and Enhancing the Management of Natural Science Collections in a Belgian Research Institute

Effective management of natural science collections is crucial to maximizing their scientific value. They often contain invaluable items like type specimens and reference material, and data that contributes significantly to our understanding of biodiversity, ecology, and evolution. Research institutes often harbor several smaller collections, which are seldom managed properly, as collection management is not their core business, and because they are not directly connected to the natural science collection community. Recognizing these challenges, DiSSCo Flanders (Distributed System of Scientific Collections) has set out to develop a standardized infrastructure for managing natural science collections, ensuring their proper long-term conservation, and facilitating future reuse (Trekels 2022). Within the DiSSCo Flanders consortium of 14 Belgian institutes, we inventoried the collections utilizing the SYNTHESYS+ survey (Van Baelen 2022) and evaluated their state and management practices using the Collections Self-Assessment Tool (CSAT) (Van Baelen, in preparation). At the Flanders Research Institute for Agriculture, Fisheries and Food (ILVO, Belgium), 15 collections comprising over 558,000 items were cataloged. A significant portion of these, approximately 95%, are marine fish otoliths used for age determination and stock assessments of commercially important fish species. Additionally, ILVO has around 21,000 food- and plant-related items housed within microbial collections. These are instrumental in research related to plant pathology, food spoilage, and biocontrol methods. Furthermore, ILVO maintains several smaller, yet equally important collections. These include insect specimens, macrobenthos reference specimens, an array of seeds and azalea cultivars, marine and agricultural soil samples, and DNA samples from various of the aforementioned collections. To align with the FAIR principles (Findable, Accessible, Interoperable, Reusable) and to connect with the DiSSCo Europe Research Infrastructure, ILVO developed an in-house Collection Management System (CMS). This web application, utilizing a normalized relational database structure, generates dynamic and customizable web pages. It allows users to input, edit, search, and export data through a user-friendly, browser-based interface that supports multiple languages. Data can also be uploaded using custom Microsoft® Excel® templates that conform to the specific data model, ensuring that data is consistently formatted and easily integrable. The back end of the system is responsible for managing the collections and vocabularies (largely aligned with the Darwin Core standard (Darwin Core Task Group. 2009)), while the front end dynamically generates the multilingual user interface and collection-specific vocabularies. This flexibility provides a tailored experience for users based on the specific nature of the collections they are managing, which is particularly important for an institute like ILVO, with diverse collections that require different terminologies and data structures. ILVO-CMS also boasts features that extend beyond basic data management. Users can attach documents and pictures; they can create relationships between items; there are functionalities for referencing other databases; and the system is able to track transactions, such as loans. Though custom-developed for ILVO, the platform’s adaptability enables easy customization and implementation for other institutes and (smaller) collections globally. The usage of ILVO-CMS at ILVO will significantly improve the management, organization, accessibility, and exchangeability of the collections, enhancing their scientific value. By implementing such a comprehensive management tool, ILVO is better positioned to ensure that the collections are not only preserved for future generations but also readily available for ongoing and future research projects. One of the key future goals for ILVO-CMS is the incorporation of 3D images. These virtual representations of physical items will enhance the ability to study and share specimens, through their 3D images, without the need for physical handling, thereby reducing the risk of damage or loss. Additionally, we are committed to the further development of a user-friendly interface to facilitate the (re-)use of ILVO collections, both internally and externally. This includes integrating and updating features based on user feedback and linking a taxonomic backbone, such as that of the Global Biodiversity Information Facility (GBIF Secretariat 2023). ILVO’s experience demonstrates that through opportunities like DiSSCo Flanders, which bring in temporary funding and promote a culture of collaboration, research institutions without public-oriented collections may contribute to global natural science collections.

Zum Werdegang einer Fachzeitschrift: Dreißig Jahre Germanistische Beiträge (1993–2023)

Abstract The present article is dedicated to the articles of the journal Germanistische Beiträge, which celebrated its 30th anniversary in 2023, since its first issue in 1993 and has been published continuously for three decades under the editors-in-chief Prof. Dr. Horst Schuller and Prof. Dr. Maria Sass. The article outlines the specifics of the journal: its themes (language, literature, German culture, inter-culturality, etc.), structure, evaluation issues and indexing in international databases. The journal Germanistische Beiträge publishes studies and articles by authors from all centres of Germanic philology in the country, but also has contributors from abroad. It is a nationally and internationally recognised scientific journal.

A salt from biologically active compounds pyridine-2,3-dicarboxylic (quinolinic) acid and cytosine.

Biologically active compounds are highly sought-after materials for developing novel structures applicable to industry. Cytosine and pyridine-2,3-dicarboxylic acid (quinolinic acid) are notably significant environmentally. Cytosine, a pyrimidine derivative, features a six-membered ring with a ketone and an amino group, constituting a fundamental nitrogenous base found in deoxyribonucleic acid (DNA). The present synthesis yielded a salt of dipyridine-2,3-dicarboxylic acid with cytosine, wherein a proton was transferred from a carboxyl group of quinolinic acid to a ring N atom in the cytosine molecule giving the salt 6-amino-2-oxo-2,3-dihydropyrimidin-1-ium 3-carboxypyridine-2-carboxylate, C4H6N3O+·C7H4NO4-. A Hirshfeld surface analysis was conducted to examine the contribution of contacts within the salt. The structure of the salt was compared to other structures containing quinolinic acid in the Cambridge Structural Database (CSD).

An experimental and computational investigation of the cyclopentene-containing peptide-derived compounds: focus on pseudo-cyclic motifs via intramolecular interactions.

Conformational flexibility is one of the main disadvantages of peptide-based compounds. We focus on their molecular 'chameleonicity' related to forming pseudo-cyclic motifs via modulation of weak intramolecular interactions. It is an appealing strategy for controlling equilibrium between the polar open and the nonpolar closed conformations. Within this context, we report here the crystal structure of the (R)-(2-tert-butoxycarbonyl)amino-1-oxo-3-phenyl)propyl)-1-cyclopentene (1), synthesis of which in high yield was achieved by a facile multi-step protocol. Our Cambridge Structural Database (CSD) overview for the peptide-based crystals revealed the exclusivity of this compound from the viewpoint of the unusual pseudo-bicyclic system via C-H…O and C-O…π interactions, in which cyclopentene shields the amide bond. Notably, cyclopentene as a bioisostere of proline is an appealing scaffold in medicinal chemistry. An extensive combined experimental and computational study provided more profound insight into the supramolecular landscape of 1 with respect to similar derivatives deposited in the CSD, including the tendency of cyclopentene for the generation of pseudo-cyclic motifs through weak H-bonding and π-based intramolecular interactions. These weak interactions have been examined by either the quantum theory of 'atoms-in-molecules' (QTAIM) or complex Hirshfeld surface methodology, including enrichment ratios, molecular electrostatic potential surfaces and energy frameworks. In all analysed crystals, all types of H-bonded motifs involving cyclopentene are formed at all levels of supramolecular architecture. A library of cyclopentene-based H-bonding synthons is provided. A molecular docking study depicted vital interactions of cyclopentene with key amino acid residues inside the active sites of two prominent protein kinases, uncovering the therapeutic potential of 1 against breast cancer. To a large extent, dispersion forces have significance in stabilizing the supramolecular structure of both ligand and bio-complex ligand-protein. Finally, the satisfactory in silico bio-pharmacokinetic profile of 1 related to drug-likeness and blood-brain barrier permeation was also revealed.

Comparative analysis of the Express.js and ElysiaJS frameworks in the context of web application development

JavaScript-based programming frameworks allow more and more web applications to be built, aiming to become the fastest platform for a particular application. The aim of this paper is, among other things, to conduct study to obtain performance results of two server-based programming libraries in the application of an online transaction database structure. The database structure, the TPC-C benchmark queries and the Bombardier HTTP(S) query measurement tool were used to perform the tests. The research thesis "The Express.js framework has better performance than ElysiaJS in terms of TPS measurement tests in TCP-C structure" was set. The results obtained did confirm the thesis.

TCR3d 2.0: expanding the T cell receptor structure database with new structures, toolsand interactions.

Recognition of antigens by T cell receptors (TCRs) is a key component of adaptive immunity. Understanding the structures of these TCR interactions provides major insights into immune protection and diseases, and enables design of therapeutics, vaccinesand predictive modeling algorithms. Previously, we released TCR3d, a database and resource for structures of TCRs and their recognition. Due to the growth of available structures and categories of complexes, the content of TCR3d has expanded substantially in the past 5 years. This expansion includes new tables dedicated to TCR mimic antibody complex structures, TCR-CD3 complexesand annotated Class I and II peptide-MHC complexes. Additionally, tools are available for users to calculate docking geometries for input TCR and TCR mimic complex structures. The core tables of TCR-peptide-MHC complexes have grown by 50%, and include binding affinity data for experimentally determined structures. These major content and feature updates enhance TCR3d as a resource for immunology, therapeuticsand structural biology research, and enable advanced approaches for predictive TCR modeling and design. TCR3d is available at: https://tcr3d.ibbr.umd.edu.

Modeling market value of land plots using GIS

This work examines the regulatory framework for expert monetary valuation of land plots. Scientific research on the development and improvement of economic-statistical methods for mass monetary valuation of land plots is analyzed. The peculiarities and advantages of using GIS in the valuation procedure are considered. A structure of the valuation database is developed for the effective selection of comparable plots during valuation by comparing sales prices and market analysis. The valuation database is formed based on data on land plot sales. Price formation factors are analyzed in order to construct an economic-statistical model of market value for land plots in Rivne. Scales of indicator values are proposed for formalizing the assessment of price formation factors. Functions of the ArcGIS are used to establish indicator values. The market value of land plots is modeled using multiple regression analysis. Additionally, a surface of land plot values is constructed, which serves to determine the approximate market value. The model has been verified using GIS tools. An accuracy assessment of the developed model has been carried out.

Modeling protein-small molecule conformational ensembles with ChemNet.

Modeling the conformational heterogeneity of protein-small molecule systems is an outstanding challenge. We reasoned that while residue level descriptions of biomolecules are efficient for de novo structure prediction, for probing heterogeneity of interactions with small molecules in the folded state an entirely atomic level description could have advantages in speed and generality. We developed a graph neural network called ChemNet trained to recapitulate correct atomic positions from partially corrupted input structures from the Cambridge Structural Database and the Protein Data Bank; the nodes of the graph are the atoms in the system. ChemNet accurately generates structures of diverse organic small molecules given knowledge of their atom composition and bonding, and given a description of the larger protein context, and builds up structures of small molecules and protein side chains for protein-small molecule docking. Because ChemNet is rapid and stochastic, ensembles of predictions can be readily generated to map conformational heterogeneity. In enzyme design efforts described here and elsewhere, we find that using ChemNet to assess the accuracy and pre-organization of the designed active sites results in higher success rates and higher activities; we obtain a preorganized retroaldolase with a k cat / K M of 11000 M -1 min - 1 , considerably higher than any pre-deep learning design for this reaction. We anticipate that ChemNet will be widely useful for rapidly generating conformational ensembles of small molecule and small molecule-protein systems, and for designing higher activity preorganized enzymes.

Structural Deformations in Cucurbit[n]urils: Analysis, Host-Guest Dependence, and Automated Ellipticity Measurements Using ElliptiCB[n

Cucurbit[n]urils (CB[n]s) are cyclic macrocycles with rich host-guest chemistry. In many cases, guest binding in CB[n]s results in host structural deformations. Unfortunately, measuring such deformations remains a major challenge, with only a handful of manual estimations reported in the literature. To address this challenge, we have developed the public program ElliptiCB[n], which is available on GitHub, that provides a robust and automated method for measuring the elliptical deformations in CB[n] hosts. We outline the development and validation of this approach, apply ElliptiCB[n] to measure the ellipticity of the 1113 available CB[n] structures from the Cambridge Structural Database (CSD), and directly investigate the structural deformations of CB[5], CB[6], CB[7], CB[8], and CB[10] hosts. We also report the general landscape of accessible CB[n] elliptical deformations and compare ellipticity distributions across CB[n] hosts and host-guest complexes. We found that in almost all cases guest binding significantly impacts the distribution of host ellipticity distributions and that these distributions are dissimilar across host-guest complexes of differently sized CB[n]s. We anticipate that this work will provide a useful approach for understanding of the flexibility of CB[n] hosts and will also enable future measurement and standardization of ellipticity measurements of CB[n]s.

The Design and Implementation of Web Service for Laundry Service Payment at RAZ-Q Laundry Samarinda

The advancement of technology has facilitated access to information and various applications spanning desktop, web, and mobile platforms, becoming an integral part of everyday life. Among these platforms, websites are the most widely used. The web is widely used for business purposes. One of the common businesses is laundry services, which has become essential for individuals with busy schedules, such as workers and students. Busy daily activities make the need for clean clothes a top priority. However, the existence of many laundry service providers creates intense competition and difficulties for new consumers in finding the right service. This research examines a web services-based laundry service designed to help consumers find and book quality laundry services in their vicinity. It also provides process monitoring features and estimated time for clothes pickup. For laundry service owners, these web services offer opportunities to improve their business. This research uses the Waterfall method of software development, including the stages of analysis, design, coding, and testing. Use case diagrams were used to model the interaction between actors in the application, while class diagrams were used to describe the database structure. Activity and sequence diagrams were used to illustrate the flow of the application. The web interface is designed according to user needs. The result of this research is web services that can help consumers find and order laundry services easily and efficiently. Blackbox testing is conducted to ensure that all designed functions run properly. This application has the potential to help laundry service owners increase their income. Application development is done using Visual Studi Code with PHP Native programming language and MySQL as database management.