Subtitle

"Synthetic protein dataset with sequences, physical properties, and functional classification for machine learning tasks."

Description

Introduction

This synthetic dataset was created to explore and develop machine learning models in bioinformatics. It contains 20,000 synthetic proteins, each with an amino acid sequence, calculated physicochemical properties, and a functional classification.

Columns Included

• ID_Protein: Unique identifier for each protein.
• Sequence: String of amino acids.
• Molecular_Weight: Molecular weight calculated from the sequence.
• Isoelectric_Point: Estimated isoelectric point based on the sequence composition.
• Hydrophobicity: Average hydrophobicity calculated from the sequence.
• Total_Charge: Sum of the charges of the amino acids in the sequence.
• Polar_Proportion: Percentage of polar amino acids in the sequence.
• Nonpolar_Proportion: Percentage of nonpolar amino acids in the sequence.
• Sequence_Length: Total number of amino acids in the sequence.
• Class: The functional class of the protein, one of five categories: Enzyme, Transport, Structural, Receptor, Other.

Inspiration and Sources

While this is a simulated dataset, it was inspired by patterns observed in real protein datasets, such as: - UniProt: A comprehensive database of protein sequences and annotations. - Kyte-Doolittle Scale: Calculations of hydrophobicity. - Biopython: A tool for analyzing biological sequences.

Proposed Uses

This dataset is ideal for: - Training classification models for proteins. - Exploratory analysis of physicochemical properties of proteins. - Building machine learning pipelines in bioinformatics.

How This Dataset Was Created

1. Sequence Generation: Amino acid chains were randomly generated with lengths between 50 and 300 residues.
2. Property Calculation: Physicochemical properties were calculated using the Biopython library.
3. Class Assignment: Classes were randomly assigned for classification purposes.

Limitations

• The sequences and properties do not represent real proteins but follow patterns observed in natural proteins.
• The functional classes are simulated and do not correspond to actual biological characteristics.

Data Split

The dataset is divided into two subsets: - Training: 16,000 samples (proteinas_train.csv). - Testing: 4,000 samples (proteinas_test.csv).

Acknowledgment

This dataset was inspired by real bioinformatics challenges and designed to help researchers and developers explore machine learning applications in protein analysis.

According to Cognitive Market Research, the Global Bioinformatics Services Market Size will be USD XX Billion in 2023 and is set to achieve a market size of USD XX Billion by the end of 2031 growing at a CAGR of XX% from 2024 to 2031.

• The global Bioinformatics services Market will expand significantly by XX% CAGR between 2024 and 2031.

• Based on technology, Because of the growing number of platform applications and the need for improved tools for drug development, the bioinformatics platforms segment dominated the market.

• In terms of service type, The sequencing services segment held the largest share and is anticipated to grow over the coming years

• Based on application, The genomic segment dominated the bioinformatics market

• Based on End-user, academic institutes and research centers segment hold the largest share.

• Based on speciality segment, The medical bioinformatics segment holds the large share and is anticipated to expand at a substantial CAGR during the forecast period.

• The North America region accounted for the highest market share in the Global Bioinformatics Services Market. CURRENT SCENARIO OF THE BIOINFORMATICS SERVICES

Driving Factors of the Bioinformatics Services Market

Expansive uses of bioinformatics across multiple sectors is propelling the market's growth.

Several industries, such as the food, bioremediation, agriculture, forensics, and consumer industries, are also using bioinformatics services to improve the quality of their products and supply chain processes. Companies in a variety of sectors are rapidly utilizing bioinformatics services such as data integration, manipulation, lead generation, data management, in silico analysis, and advanced knowledge discovery.

• Bioinformatics Approaches in Food Sciences

In order to meet the needs of food production, food processing, enhancing the quality and nutritional content of food sources, and many other areas, bioinformatics plays a significant role in forecasting and evaluating the intended and undesired impacts of microorganisms on food, genomes, and proteomics research. Furthermore, bioinformatics techniques can be applied to produce crops with high yields and resistance to disease, among other desirable qualities. Additionally, there are numerous databases with information about food, including its components, nutritional value, chemistry, and biology.

Genome Canada is proud to partner with five Institutes where there are five funding pools within this opportunity and Genome Canada is partnering on the Bioinformatics, Computational Biology and Health Data Sciences pool. (Source:https://genomecanada.ca/genome-canada-partners-with-cihr-to-launch-health-research-training-platform-2024-25/)

• Bioinformatics in agriculture

Bioinformatics is becoming more and more crucial in the gathering, storing, and processing of genomic data in the field of agricultural genomics, or agri-genomics. Generally referred to as agri-informatics, some of the various applications of bioinformatics tools and methods in agriculture focus on improving plant resistance against biotic and abiotic stressors as well as enhancing the nutritional quality in depleted soils. Beyond these uses, computer software-assisted gene discovery has enabled researchers to create focused strategies for seed quality enhancement, incorporate extra micronutrients into plants for improved human health, and create plants with phytoremediation potential.

India/UK-based Agri-Genomics startup, Piatrika Biosystems has raised $1.2 Million in a seed round led by Ankur Capital. The company is bringing sustainable seeds and agri chemicals to market faster and cheaper. The investment will be used to build a strong Product Development team, also for more profound research, and to accelerate the productionising and commercialization of MVP. (Source:https://pressroom.icrisat.org/agri-genomics-startup-piatrika-biosystems-raises-12-million-in-seed-funding-led-by-ankur-capital)

This expansion in the application areas of bioinformatics services is likely to drive the overall market growth. Bioinformatics services such as data integration, manipulation, lead discovery, data management, in silico analysis, and advanced knowledge discovery are increasingly being adopted by companies across various industries.&...

Data for sequence comparison of commamox genomes and genes identified. This dataset is associated with the following publication: Camejo, P., J. Santodomingo, K. McMahon, and D. Noguera. Genome-enabled insights into the ecophysiology of the comammox bacterium Ca. Nitrospira nitrosa. ENVIRONMENTAL SCIENCE & TECHNOLOGY. American Chemical Society, Washington, DC, USA, 2(5): 1-16, (2017).

Bioinformatics Services Market size was valued at USD 11.1 Billion in 2023 and is projected to reach USD 3.58 Billion by 2031, growing at a CAGR of 15.06% from 2024-2031.

Bioinformatics Services Market: Definition/ Overview

Bioinformatics services cover a wide range of computational tools and methods for managing, analyzing, and interpreting biological data. These services enable the integration of data from domains such as genomics, proteomics, transcriptomics, and metabolomics to provide insights into biological systems. Drug discovery, customized medicine, gene sequencing, and biological data management are some of the most important applications of bioinformatics. Researchers and healthcare professionals use these services to analyze big datasets, detect disease markers, and develop tailored medicines, considerably improving the precision and efficiency of life science research.

Snapshot img

Bioinformatics Market Size 2024-2028

The bioinformatics market size is forecast to increase by USD 13.2 billion at a CAGR of 16.59% between 2023 and 2028. The market is experiencing significant growth due to the reduction in the cost of genetic sequencing and the development of sophisticated bioinformatics tools for next-generation sequencing (NGS). These advancements are enabling the identification and analysis of disease biomarkers, leading to the discovery of new therapeutic strategies. The market is also driven by the increasing demand for database development and management systems to store and analyze the vast amounts of data generated from NGS. Furthermore, the potential of gene therapy and drug development in treating various diseases is fueling the market growth. However, the shortage of trained laboratory professionals poses a challenge to the market, as the analysis of complex genomic data requires specialized expertise.

What will be the Size of the Bioinformatics Market During the Forecast Period?

To learn more about the bioinformatics market report, Request Free Sample

Bioinformatics is a rapidly growing market, driven by advancements in genome sequencing and NGS technologies. Precision medicine, which utilizes genomic information for personalized healthcare, is a key application area. The market is witnessing a significant decrease in equipment costs, making genomics instruments more accessible to researchers and healthcare providers. Transcriptomics, which focuses on the study of RNA, is another emerging field. Virus research is a significant application area, with a focus on transmission chains, public health control, and containment measures. Virus variability and vaccine development are major challenges, driving the need for advanced diagnostic methods. Key players in the market include Illumina and Eurofins Scientific.

Moreover, companies are making strides in addressing this challenge by providing comprehensive solutions for bioinformatics analysis and data management. Big data is another key trend in the market, with the use of advanced algorithms and machine learning techniques to extract valuable insights from genomic data. Overall, the market is poised for strong growth, driven by technological advancements, increasing demand for personalized medicine, and the potential to revolutionize disease diagnosis and treatment. In addition, these companies provide a range of services, from DNA and RNA sequencing to bioinformatics analysis and diagnostic testing. The market is expected to grow significantly due to the increasing demand for accurate and timely diagnostic methods and the ongoing research in the field of genomics and transcriptomics.

The bioinformatics market is expanding rapidly, driven by advancements in genomics data analysis, next-gen sequencing, and precision medicine. Cloud-based bioinformatics solutions and AI in bioinformatics are revolutionizing molecular diagnostics, drug discovery platforms, and protein analysis tools. The market emphasizes genomic data storage, personalized healthcare, and biomarker discovery. With bioinformatics software, computational biology, and integrative bioinformatics solutions, bioinformatics as a service plays a pivotal role in advancing modern healthcare.

Bioinformatics Market Segmentation

The bioinformatics market research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD billion' for the period 2024-2028, as well as historical data from 2018-2022 for the following segments.

Application

  Molecular phylogenetics
  Transcriptomic
  Proteomics
  Metabolomics


Product

  Platforms
  Tools
  Services


Geography

  North America

    Canada
    US


  Europe

    Germany
    UK
    France


  Asia



  Rest of World (ROW)

By Application Insights

The molecular phylogenetics segment is estimated to witness significant growth during the forecast period. Bioinformatics, a critical field in molecular biology, encompasses the application of computational tools and techniques to analyze biological data. One significant area within bioinformatics is molecular phylogenetics, which utilizes molecular data to explore evolutionary relationships among various species. This technique has transformed the biological landscape by offering more precise and comprehensive insights into the interconnections among living organisms. In the international market, molecular phylogenetics is a vital instrument in numerous research domains, such as clinical diagnostics, drug discovery, RNA-based therapeutics, and conservation biology. For instance, in the realm of viral research, molecular phylogenetics is extensively employed to examine the evolution of viruses.

In addition, by deciphering the molecular data of distinct strains of viruses, scientists can trace the origins and dissemination patterns of these pathoge

The Report Covers Global Bioinformatics Services Market Growth & Insights. The Market is Segmented by Products and Services (Knowledge Management Tools, Bioinformatics Platform, and Bioinformatics Services), Applications (Microbial Genome, Gene Engineering, Drug Development, Personalized Medicine, Omics, and Other Applications), and Geography (North America, Europe, Asia-Pacific, Middle East and Africa, and South America). The value is provided in (USD million) for the above segments.

Bioinformatics Software Market Outlook

The global bioinformatics software market size was valued at approximately USD 10 billion in 2023, and it is projected to reach around USD 25 billion by 2032, growing at a robust CAGR of 11% during the forecast period. This remarkable growth is fueled by the increased application of bioinformatics in drug discovery and development, the rising demand for personalized medicine, and the ongoing advancements in sequencing technologies. The convergence of biology and information technology has led to the optimization of biological data management, propelling the market's expansion as it transforms the landscape of biotechnology and pharmaceutical research. The rapid integration of artificial intelligence and machine learning techniques to process complex biological data further accentuates the growth trajectory of this market.

An essential growth factor for the bioinformatics software market is the burgeoning demand for sequencing technologies. The decreasing cost of sequencing has led to a massive increase in the volume of genomic data generated, necessitating advanced software solutions to manage and interpret this data efficiently. This demand is particularly evident in genomics and proteomics, where bioinformatics software plays a critical role in analyzing and visualizing large datasets. Additionally, the adoption of cloud computing in bioinformatics offers scalable resources and cost-effective solutions for data storage and processing, further fueling market growth. The increasing collaboration between research institutions and software companies to develop innovative bioinformatics tools is also contributing positively to market expansion.

Another significant driver is the growth of personalized medicine, which relies heavily on bioinformatics for the analysis of individual genetic information to tailor therapeutic strategies. As healthcare systems worldwide move towards precision medicine, the demand for bioinformatics software that can integrate genetic, phenotypic, and environmental data becomes more pronounced. This trend is not only transforming patient care but also significantly impacting drug development processes, as pharmaceutical companies aim to create more effective and targeted therapies. The strategic partnerships and collaborations between biotech firms and bioinformatics software providers are critical in advancing personalized medicine and enhancing patient outcomes.

The increasing prevalence of complex diseases such as cancer and neurological disorders necessitates comprehensive research efforts, driving the need for robust bioinformatics software. These diseases require multi-omics approaches for better understanding, diagnosis, and treatment, where bioinformatics tools are indispensable. The ongoing research and development activities in this area, supported by government funding and private investments, are fostering innovation in bioinformatics solutions. Furthermore, the development of user-friendly and intuitive software interfaces is expanding the market beyond specialized research labs to include clinical settings and hospitals, broadening the potential user base and enhancing market penetration.

From a regional perspective, North America currently leads the bioinformatics software market, thanks to its advanced technological infrastructure, significant investment in healthcare R&D, and the presence of numerous key market players. The region accounted for the largest market share in 2023 and is expected to maintain its dominance throughout the forecast period. Meanwhile, the Asia Pacific region is anticipated to exhibit the highest CAGR, driven by increasing investments in biotechnology and pharmaceutical research, expanding healthcare infrastructure, and the rising adoption of bioinformatics in emerging economies like China and India. Europe's market growth is also significant, supported by substantial funding for genomic research and a strong focus on precision medicine initiatives.

Lifesciences Data Mining and Visualization are becoming increasingly vital in the bioinformatics software market. As the volume of biological data continues to grow exponentially, the need for sophisticated tools to mine and visualize this data is paramount. These tools enable researchers to uncover hidden patterns and insights from complex datasets, facilitating breakthroughs in genomics, proteomics, and other life sciences fields. The integration of advanced data mining techniques with visualization capabilities allows for a more intuitive

THIS RESOURCE IS NO LONGER IN SERVICE, documented on 8/12/13. An expanded version of the Alternative Splicing Annotation Project (ASAP) database with a new interface and integration of comparative features using UCSC BLASTZ multiple alignments. It supports 9 vertebrate species, 4 insects, and nematodes, and provides with extensive alternative splicing analysis and their splicing variants. As for human alternative splicing data, newly added EST libraries were classified and included into previous tissue and cancer classification, and lists of tissue and cancer (normal) specific alternatively spliced genes are re-calculated and updated. They have created a novel orthologous exon and intron databases and their splice variants based on multiple alignment among several species. These orthologous exon and intron database can give more comprehensive homologous gene information than protein similarity based method. Furthermore, splice junction and exon identity among species can be valuable resources to elucidate species-specific genes. ASAP II database can be easily integrated with pygr (unpublished, the Python Graph Database Framework for Bioinformatics) and its powerful features such as graph query, multi-genome alignment query and etc. ASAP II can be searched by several different criteria such as gene symbol, gene name and ID (UniGene, GenBank etc.). The web interface provides 7 different kinds of views: (I) user query, UniGene annotation, orthologous genes and genome browsers; (II) genome alignment; (III) exons and orthologous exons; (IV) introns and orthologous introns; (V) alternative splicing; (IV) isoform and protein sequences; (VII) tissue and cancer vs. normal specificity. ASAP II shows genome alignments of isoforms, exons, and introns in UCSC-like genome browser. All alternative splicing relationships with supporting evidence information, types of alternative splicing patterns, and inclusion rate for skipped exons are listed in separate tables. Users can also search human data for tissue- and cancer-specific splice forms at the bottom of the gene summary page. The p-values for tissue-specificity as log-odds (LOD) scores, and highlight the results for LOD >= 3 and at least 3 EST sequences are all also reported.

RNA expression analysis was performed on the corpus luteum tissue at five time points after prostaglandin F2 alpha treatment of midcycle cows using an Affymetrix Bovine Gene v1 Array. The normalized linear microarray data was uploaded to the NCBI GEO repository (GSE94069). Subsequent statistical analysis determined differentially expressed transcripts ± 1.5-fold change from saline control with P ≤ 0.05. Gene ontology of differentially expressed transcripts was annotated by DAVID and Panther. Physiological characteristics of the study animals are presented in a figure. Bioinformatic analysis by Ingenuity Pathway Analysis was curated, compiled, and presented in tables. A dataset comparison with similar microarray analyses was performed and bioinformatics analysis by Ingenuity Pathway Analysis, DAVID, Panther, and String of differentially expressed genes from each dataset as well as the differentially expressed genes common to all three datasets were curated, compiled, and presented in tables. Finally, a table comparing four bioinformatics tools' predictions of functions associated with genes common to all three datasets is presented. These data have been further analyzed and interpreted in the companion article "Early transcriptome responses of the bovine mid-cycle corpus luteum to prostaglandin F2 alpha includes cytokine signaling". Resources in this dataset:Resource Title: Supporting information as Excel spreadsheets and tables. File Name: Web Page, url: http://www.sciencedirect.com/science/article/pii/S2352340917304031?via=ihub#s0070

Introduction

The Global Bioinformatics Services Market is poised for substantial growth, projected to increase from USD 2.9 billion in 2023 to USD 10.7 billion by 2033, achieving a compound annual growth rate (CAGR) of 13.9%. This market expansion is fueled by several key factors including technological advancements in genomics and the increasing complexity of biological datasets, which necessitate advanced computational technologies for efficient data management, analysis, and interpretation. These technologies are crucial for advancing medical research and improving patient care, particularly through personalized treatment plans and precision medicine.

Institutions like the Mayo Clinic are significantly contributing to this growth by expanding their bioinformatics services to support translational research and enhance patient care through the integration of large multi-omics data sets. Additionally, prominent educational institutions such as Stanford and Georgetown University are advancing their bioinformatics programs to equip the next generation of professionals with the necessary skills to address complex biomedical challenges using computational and quantitative methods.

The sector is also witnessing a surge in demand within the healthcare and pharmaceutical industries, where bioinformatics tools are integral to drug discovery and disease diagnosis. This demand drives the development of therapeutic strategies and deepens the understanding of disease mechanisms, further boosting the market growth. Research initiatives and collaborations, such as those at Harvard Medical School’s Department of Biomedical Informatics and Stanford's Biomedical Informatics Research division, are key in transforming biomedical data into actionable insights for precision medicine.

In terms of recent industry developments, in January 2024, Qiagen announced a significant expansion of investments into its Qiagen Digital Insights (QDI) business. This expansion, fueled by robust sales of approximately $100 million in 2023, is set to enhance QDI's bioinformatics capabilities, including launching at least five new products and broadening the applications of Artificial Intelligence and Natural Language Processing within the sector.

Furthermore, in January 2023, Agilent Technologies unveiled a major investment of $725 million to double its manufacturing capacity for nucleic acid-based therapeutics, in response to the rapid growth in the therapeutic oligonucleotides market, projected to reach $2.4 billion by 2027. This expansion will introduce two new manufacturing lines to meet the escalating demand for siRNA, antisense, and CRISPR guide RNA molecules, reinforcing Agilent's market presence and capacity in this fast-evolving field.

Bioinformatics Platforms Market is segmented By Platform Type (Sequence Analysis Platforms, Sequence Alignment Platforms, Sequence Manipulation Platforms, Structural & Functional Analysis Platforms, and Others) and Application (Drug Development, Molecular Genomics, Personalized Medicine, Gene Therapy, Protein Function Analysis, and Others)

The BO1 subset of the scPDB database.

BO1 consists in 766 pairs of non-redundant binding-sites
(383 similar pairs, 383 dissimilar pairs).

BO1 was describbed in:
---
Eguida, M., & Rognan, D. (2020).
A computer vision approach to align and compare protein cavities:
application to fragment-based drug design.
Journal of Medicinal Chemistry, 63(13), 7127-7142.
https://doi.org/10.1021/acs.jmedchem.0c00422
---

The scPDB was recently describbed in:
---
Desaphy, J., Bret, G., Rognan, D., & Kellenberger, E. (2015).
sc-PDB: a 3D-database of ligandable binding sites—10 years on.
Nucleic acids research, 43(D1), D399-D404.
https://doi.org/10.1093/nar/gku928
---

The scPDB is available at:
http://bioinfo-pharma.u-strasbg.fr/scPDB/

We used the human genome reference sequence in its GRCh38.p13 version in order to have a reliable source of data in which to carry out our experiments. We chose this version because it is the most recent one available in Ensemble at the moment. However, the DNA sequence by itself is not enough, the specific TSS position of each transcript is needed. In this section, we explain the steps followed to generate the final dataset. These steps are: raw data gathering, positive instances processing, negative instances generation and data splitting by chromosomes.

First, we need an interface in order to download the raw data, which is composed by every transcript sequence in the human genome. We used Ensembl release 104 (Howe et al., 2020) and its utility BioMart (Smedley et al., 2009), which allows us to get large amounts of data easily. It also enables us to select a wide variety of interesting fields, including the transcription start and end sites. After filtering instances that present null values in any relevant field, this combination of the sequence and its flanks will form our raw dataset. Once the sequences are available, we find the TSS position (given by Ensembl) and the 2 following bases to treat it as a codon. After that, 700 bases before this codon and 300 bases after it are concatenated, getting the final sequence of 1003 nucleotides that is going to be used in our models. These specific window values have been used in (Bhandari et al., 2021) and we have kept them as we find it interesting for comparison purposes. One of the most sensitive parts of this dataset is the generation of negative instances. We cannot get this kind of data in a straightforward manner, so we need to generate it synthetically. In order to get examples of negative instances, i.e. sequences that do not represent a transcript start site, we select random DNA positions inside the transcripts that do not correspond to a TSS. Once we have selected the specific position, we get 700 bases ahead and 300 bases after it as we did with the positive instances.

Regarding the positive to negative ratio, in a similar problem, but studying TIS instead of TSS (Zhang135
et al., 2017), a ratio of 10 negative instances to each positive one was found optimal. Following this136
idea, we select 10 random positions from the transcript sequence of each positive codon and label them137
as negative instances. After this process, we end up with 1,122,113 instances: 102,488 positive and 1,019,625 negative sequences. In order to validate and test our models, we need to split this dataset into three parts: train, validation and test. We have decided to make this differentiation by chromosomes, as it is done in (Perez-Rodriguez et al., 2020). Thus, we use chromosome 16 as validation because it is a good example of a chromosome with average characteristics. Then we selected samples from chromosomes 1, 3, 13, 19 and 21 to be part of the test set and used the rest of them to train our models. Every step of this process can be replicated using the scripts available in https://github.com/JoseBarbero/EnsemblTSSPrediction.

Hydrophobic-hydrophilic interactions have a strong impact on the three-dimensional structure a protein will adopt. Because structure, not amino acid sequence order, carry out certain functions it is important to understand how these forces affect the protein folding process. In recent years, a lot of focus has been dedicated towards ab initio protein folding prediction, which tries to predict a proteins native conformation from its sequence alone. To aid this type of prediction sub-conformations from already known proteins are used to limit the free energy conformational search space. In this paper we looked into the sub-conformations’ hydrophobic-hydrophilic nature by incorporating a HP approach and proposed a way of evaluating how these type of forces affect the protein folding process. By doing this, we can gain insight into how hydrophobic-hydrophilic interactions affect protein structural similarity, and thus aid us in picking more suitable sub-conformations based off their HP shape for use in protein structure prediction. PRIB 2008 proceedings found at: http://dx.doi.org/10.1007/978-3-540-88436-1

Contributors: Monash University. Faculty of Information Technology. Gippsland School of Information Technology ; Chetty, Madhu ; Ahmad, Shandar ; Ngom, Alioune ; Teng, Shyh Wei ; Third IAPR International Conference on Pattern Recognition in Bioinformatics (PRIB) (3rd : 2008 : Melbourne, Australia) ; Coverage: Rights: Copyright by Third IAPR International Conference on Pattern Recognition in Bioinformatics. All rights reserved.

Per-site transition rates for sequences.

A bioinformatics platform that is a joint project of several South of France laboratories with available services based on their expertise, issued from their research activities which involve phylogenetics, population genetics, molecular evolution, genome dynamics, comparative and functional genomics, and transcriptome analysis. Most of the software and databases on ATGC are (co)authored by researchers from South of France teams. Some are widely used and highly cited. South of France laboratories: * CRBM (transcriptomes and stem cells). * IBC (computational biology). * MiVEGEC (evolution and phylogeny). * LGDP (plant genomics). * LIRMM (computer science). * South Green (plant genomics).

The global bioinformatics market size reached USD 13.9 Billion in 2024. Looking forward, IMARC Group expects the market to reach USD 39.4 Billion by 2033, exhibiting a growth rate (CAGR) of 11.69% during 2025-2033. Rapid technological advancements, increasing genomic sequencing, surging demand for personalized medicine, data analytics growth, investment in research and development (R&D), expanding biological databases, and the rising focus on preventive care are some of the factors fostering the market growth.

IMARC Group provides an analysis of the key trends in each segment of the market, along with forecasts at the global, regional, and country levels for 2025-2033. Our report has categorized the market based on the product and service, application, and end-use sector.

PROSITE consists of documentation entries describing protein domains, families and functional sites as well as associated patterns and profiles to identify them [More... / References / Commercial users ]. PROSITE is complemented by ProRule , a collection of rules based on profiles and patterns, which increases the discriminatory power of profiles and patterns by providing additional information about functionally and/or structurally critical amino acids [More...].

All scripts for predictions and analysis are available from https://github.com/ElofssonLab/bioinfo-toolbox/trRosetta/Details for each run are available from https://github.com/ElofssonLab/bioinfo-toolbox/benchmark5/benchmark4.3/.All models joined alignments, and evaluation results are available from a figshare repository[44].The data is organized as follows1) One diretora (N*/ as well as ./) contains all the results and data for one set of parameters2) In each directory the following subdirectories are included2a) seq/ (all sequences)2b) pdb/ (all orginal pdb files) 2c) dimer/ all merged msa files2d) pymodel/ all models generated and the measuremenst (in csv files) to evalute their performance.3) In the director Figures/ all figures, scripts to generat them as well as summary of all predictions in a csv files is included

FTDMP docking results for protein-protein, protein-DNA, protein-RNA benchmarks.

FTDMP is a software system for running docking experiments and scoring/ranking multimeric models. This dataset contains FTDMP docking results for protein-protein, protein-DNA, protein-RNA benchmarks. The FTDMP framework itself is available at https://github.com/kliment-olechnovic/ftdmp.

Every *.tar.gz file in this dataset contains two folders: results for unbound-unbound and bound-bound docking. These folders contain results for the benchmark cases:

252 folders with results for the protein-protein docking benchmark cases [1].47 folders with results for the protein-DNA docking benchmark cases [2].42 folders with results for the protein-RNA docking benchmark cases [3-6].

Every folder is named according to the PDB ID of the complex. The folders contain:

1. A subfolder named relaxed_top_complexes. This subfolder contains 200 pdb files of relaxed [7] top docking models.2. A text file named scoring_results-ranks.txt. It contains the names of the models (that are in the relaxed_top_complexes folder) in the ranked order. This means that the first model in the file is considered to be the best prediction by the FTDMP framework.3. A text file named cad_scores.txt. It contains interface CAD-score and binding site CAD-score [8] results for every model.4. A text file named rmsd_results.txt, which is available only for protein-DNA and protein-RNA cases. The file contains ligand-RMSD values for the models, where the DNA/RNA is considered as the ligand.5. A text file named DockQ_results.txt, which is available only for the protein-protein docking cases. The file contains DockQ [9] results for every model, as well as model accuracy based on CAPRI criteria (Incorrect, Acceptable, Medium, High)6. A text file named binding_site_CAD-scores.txt, which contains the binding site CAD-score from the protein side for RNA and DNA docking. This binding site CAD-score shows how accurately the ligand (DNA/RNA) was docked to the protein without taking the orientation of the ligand into consideration. In the case of protein-protein docking the binding site CAD-score file is available only for antibody-antigen docking targets and contains the binding site (epitope) CAD-score for the antigen.

The ligand-RMSD, CAD-scores, and DockQ scores were all calculated by comparing the models to the corresponding targets. The target structures are available at https://zenodo.org/records/10517524. These target structures have the same residue numbering as the models available here.

REFERENCES

[1] Guest, J. D., et al. (2021). An expanded benchmark for antibody-antigen docking and affinity prediction reveals insights into antibody recognition determinants. Structure, 29(6), 606–621.e5.[2] van Dijk, M., Bonvin, A.M. (2008). A protein-DNA docking benchmark. Nucleic Acids Res, 36, e88. [3] Perez-Cano, L., et. Al. (2012). A protein-RNA docking benchmark (II): extended set from experimental and homology modeling data. Proteins, 80(7): 1872-1882. [4] Huang, S.Y., Zou, X. (2013). A nonredundant structure dataset for benchmarking protein-RNA computational docking. J Comput Chem, 34(4): 311-318. [5] Nithin, C., et. al. (2017). A non-redundant protein-RNA docking benchmark version 2.0. Proteins, 85(2) :256-267. [6] Zheng, J., et al. (2020). P3DOCK: a protein-RNA docking webserver based on template-based and template-free docking. Bioinformatics, 36(1), 96–103. [7] Eastman, P., et al.(2017). OpenMM 7: Rapid development of high performance algorithms for molecular dynamics. PLOS Comp. Biol., 13(7): e1005659. [8] Olechnovic, K., Venclovas, C. (2020). Contact area-based structural analysis of proteins and their complexes using CAD-score. Methods Mol Biol, 2112, 75.[9] Basu, S., Wallner, B. (2016). DockQ: A Quality Measure for Protein-Protein Docking Models. PLoS ONE 11(8): e0161879.