The results of analysis of shotgun proteomics mass spectrometry data can be greatly affected by the selection of the reference protein sequence database against which the spectra are matched. For many species there are multiple sources from which somewhat different sequence sets can be obtained. This can lead to confusion about which database is best in which circumstancesa problem especially acute in human sample analysis. All sequence databases are genome-based, with sequences for the predicted gene and their protein translation products compiled. Our goal is to create a set of primary sequence databases that comprise the union of sequences from many of the different available sources and make the result easily available to the community. We have compiled a set of four sequence databases of varying sizes, from a small database consisting of only the ∼20,000 primary isoforms plus contaminants to a very large database that includes almost all nonredundant protein sequences from several sources. This set of tiered, increasingly complete human protein sequence databases suitable for mass spectrometry proteomics sequence database searching is called the Tiered Human Integrated Search Proteome set. In order to evaluate the utility of these databases, we have analyzed two different data sets, one from the HeLa cell line and the other from normal human liver tissue, with each of the four tiers of database complexity. The result is that approximately 0.8%, 1.1%, and 1.5% additional peptides can be identified for Tiers 2, 3, and 4, respectively, as compared with the Tier 1 database, at substantially increasing computational cost. This increase in computational cost may be worth bearing if the identification of sequence variants or the discovery of sequences that are not present in the reviewed knowledge base entries is an important goal of the study. We find that it is useful to search a data set against a simpler database, and then check the uniqueness of the discovered peptides against a more complex database. We have set up an automated system that downloads all the source databases on the first of each month and automatically generates a new set of search databases and makes them available for download at http://www.peptideatlas.org/thisp/.

Protein Data Bank (PDB) contains data on the spatial protein structures and their biologically active sites (i.e., ligand binding regions, enzyme catalytic centers, regions subjected to biochemical modifications, etc.). However, neither of the well known systems searching PDB does not provide the user with possibility to make the queries related with the active sites. A database PDBSITE storing the data on biologically active sites contained in the PDB database has been developed. PDBSITE accumulates amino acid content, structure features calculated by spatial protein structures, and physicochemical properties of sites and their spatial surroundings.

Protein sequence and structure data

This data set contains data from Uniprot (in the files called protein_sequence, protein_synonyms, protein_names, organism_synonyms) and PDB (in the files called PDB and PDB_chain) as used by the Aquaria web resource at the time of download (2022-02-08).

The PSSH2 data set

PSSH2 is a database of protein sequence-to-structure homologies based on HHblits, an alignment method employing iterative comparisons of hidden Markov models (HMMs). To ensure the highest possible final alignment quality for matches in Aquaria using HHblits, we first calculate HMM profiles for each unique PDB sequence (PDB_full) and also for each unique Swiss-Prot sequence. We generated PSSH2 using HHblits to find similarities between HMMs from PDB and HMMs from UniProt sequences.

Calculating PSSH2

The Swissprot and PDB data was downloaded in November 2021. Generating PSSH2: We used UniRef30_2021_03 (originally called UniRef30_2021_06) from HH-suite, a database of non-redundant UniProt sequence clusters in which the highest pairwise sequence identity between clusters was 30%. The HHblits code and the code for running the calculations was retrieved from git (https://github.com/soedinglab/hh-suite.git and https://github.com/aschafu/PSSH2.git respectively) at the respective time of calculation in the timeframe until December 2021.

PDB based sequence-to-structure alignments

In addition to the PSSH2 data, new PDB structures were retrieved based on the primary accession of the proteins, by querying for all chains in all PDB entries with exact matches using the sequence cross references records given in PDB. Sequence-to-structure alignments were then created, again based on information provided in each PDB entry. These are contained in the PDBchain data.

This data covers sequences and PDB structures in the timeframe until February 2022.

Evaluating PSSH2

The resulting alignment data was analysed using CATH domain assignments downloaded from /cath/releases/all-releases/v4_2_0/cath-classification-data/ to define correct hits and false hits:

The set of query sequences is defined by the CATH non-redundant S40_overlap_60 dataset (ftp://orengoftp.biochem.ucl.ac.uk/cath/releases/all-releases/v4_2_0/non-redundant-data-sets/)

The set of all expected hits are all pdb structures containing a domain with the same CATH code if contained in the set of processed sequences (-> all) or only if also contained in the set of non redundant sequences (-> nr40).

The set of true positives is defined by sharing the same CATH code up to the level of homology ("CATH") or up to the level of topology ("CAT").

The data was evaluated with respect to false discovery rate (FDR) and recall (true positive rate TPR) by cumulatively considering all hits with an E-value below the threshold ("C") or in bins with an E-value between the threshold and one tenth of the threshold ("B"). This evaluation was carried out for the data obtained in November 2021 (202111) as well as previous data from October 2020 (202010), February 2020 (202002) and September 2017 (201709). The results are collected in PSSH CATH validation.csv.

Known errors

Due to processing error, the profile of pdb structure 5fia A / B (sequence md5 052667679fc644184f40063c7602c9e1) is incomplete in the pdb_full hhblits database which led to further errors in generating sequence based alignments for sequences for 1vtm P (sequence md5 c844aff103449363cb8489c78c58ebf1) and 434t A / B (sequence md5 d67aa1c3a36492c719cb48b5e7ecc624).

The Kabat Database determines the combining site of antibodies based on the available amino acid sequences. The precise delineation of complementarity determining regions (CDR) of both light and heavy chains provides the first example of how properly aligned sequences can be used to derive structural and functional information of biological macromolecules. The Kabat database now includes nucleotide sequences, sequences of T cell receptors for antigens (TCR), major histocompatibility complex (MHC) class I and II molecules, and other proteins of immunological interest. The Kabat Database searching and analysis tools package is an ASP.NET web-based portal containing lookup tools, sequence matching tools, alignment tools, length distribution tools, positional correlation tools and much more. The searching and analysis tools are custom made for the aligned data sets contained in both the SQL Server and ASCII text flat file formats. The searching and analysis tools may be run on a single PC workstation or in a distributed environment. The analysis tools are written in ASP.NET and C# and are available in Visual Studio .NET 2003/2005/2008 formats. The Kabat Database was initially started in 1970 to determine the combining site of antibodies based on the available amino acid sequences at that time. Bence Jones proteins, mostly from human, were aligned, using the now-known Kabat numbering system, and a quantitative measure, variability, was calculated for every position. Three peaks, at positions 24-34, 50-56 and 89-97, were identified and proposed to form the complementarity determining regions (CDR) of light chains. Subsequently, antibody heavy chain amino acid sequences were also aligned using a different numbering system, since the locations of their CDRs (31-35B, 50-65 and 95-102) are different from those of the light chains. CDRL1 starts right after the first invariant Cys 23 of light chains, while CDRH1 is eight amino acid residues away from the first invariant Cys 22 of heavy chains. During the past 30 years, the Kabat database has grown to include nucleotide sequences, sequences of T cell receptors for antigens (TCR), major histocompatibility complex (MHC) class I and II molecules and other proteins of immunological interest. It has been used extensively by immunologists to derive useful structural and functional information from the primary sequences of these proteins.

A database of protein subcellular localization containing proteins from primary protein database SWISS-PROT and PIR. By collecting the subcellular localization annotation, these information are classified and categorized by cross references to taxonomies and Gene Ontology database. Annotations were taken from primary protein databases, model organism genome projects and literature texts, and then were analyzed to dig out the subcellular localization features of the proteins. The proteins are also classified into different categories. Based on sequence alignment, nonredundant subsets of the database have been built, which may provide useful information for subcellular localization prediction. The database now contains >60 000 protein sequences including 30 000 protein sequences in the nonredundant data sets. Online download, SOAP server, Blast tools and prediction services are also available.

Aquaria Coverage map

In "SARS-CoV-2 structural coverage map reveals viral protein interactions, hijacking, and mimicry" we introduce a novel concept to visually organize a complex dataset of a large numbers of models: a one-stop visualization summarizing what is known - and not known - about the 3D structure of the viral proteome. This tailored visualization — called the SARS-CoV-2 structural coverage map — helps researchers find structural models related to specific research questions and can be viewed in the Aquaria-COVID resource. Aquaria_COVID_Coverage_Map.csv summarises the most basic information of this map, specifying dark and non-dark regions, as well as number of residues predicted to be disordered in these regions - predicted by Meta-Disorder (Schlessinger et al, 2006).

The PSSH2 data set

The sequence-to-structure alignments were generated using a modified version of the Aquaria sequence-to-structure processing pipeline (O’Donoghue et al, 2015), making up a subset of the PSSH2 database. PSSH2 is a database of protein sequence-to-structure homologies based on HHblits, an alignment method employing iterative comparisons of hidden Markov models (HMMs). To ensure the highest possible final alignment quality for matches in Aquaria using HHblits, we first calculate HMM profiles for each unique PDB sequence (PDB_full) and also for each unique Swiss-Prot sequence. We generated PSSH2 using HHblits to find similarities between HMMs from PDB and HMMs from UniProt sequences. seq_to_struc_alignemnts_PSSH2.csv.gz contains a subset of the usual PSSH2 database, including only the proteins relevant to visualise Sars-CoV-2 structures. Protein sequences and PDB structures are identified by the MD5 hashes of their respective sequences. PDB_chain_identifier_mappings.csv and swissprot_identifier_mappings.csv detail which entries in Swissprot and PDB chain are referred to by the MD5 hashes in the PSSH2 data set.

Calculating PSSH2

The main bunch of Swissprot and PDB data was downloaded in October 2020, but incremental updates, especially as related to Covid19 were added until April 2021. Generating PSSH2: We used Uniclust30 from HH-suite, a database of non-redundant UniProt sequence clusters in which the highest pairwise sequence identity between clusters was 30% (http://gwdu111.gwdg.de/~compbiol/uniclust/2020_03/UniRef30_2020_03_hhsuite.tar.gz). The HHblits code and the code for running the calculations was retrieved from git (https://github.com/soedinglab/hh-suite.git and https://github.com/aschafu/PSSH2.git respectively) at the respective time of calculation in the timeframe until April 2021.

PDB based sequence-to-structure alignments

In addition to the PSSH2 data, new PDB structures were retrieved based on the primary accession of the proteins, by querying for all chains in all PDB entries with exact matches using the sequence cross references records given in PDB. Sequence-to-structure alignments were then created, again based on information provided in each PDB entry. These alignments are summarised in PDB_chain_alignments_pssh2Format.csv.

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...].

Database of three-dimensional structures of macromolecules that allows the user to retrieve structures for specific molecule types as well as structures for genes and proteins of interest. Three main databases comprise Structure-The Molecular Modeling Database; Conserved Domains and Protein Classification; and the BioSystems Database. Structure also links to the PubChem databases to connect biological activity data to the macromolecular structures. Users can locate structural templates for proteins and interactively view structures and sequence data to closely examine sequence-structure relationships. * Macromolecular structures: The three-dimensional structures of biomolecules provide a wealth of information on their biological function and evolutionary relationships. The Molecular Modeling Database (MMDB), as part of the Entrez system, facilitates access to structure data by connecting them with associated literature, protein and nucleic acid sequences, chemicals, biomolecular interactions, and more. It is possible, for example, to find 3D structures for homologs of a protein of interest by following the Related Structure link in an Entrez Protein sequence record. * Conserved domains and protein classification: Conserved domains are functional units within a protein that act as building blocks in molecular evolution and recombine in various arrangements to make proteins with different functions. The Conserved Domain Database (CDD) brings together several collections of multiple sequence alignments representing conserved domains, in addition to NCBI-curated domains that use 3D-structure information explicitly to define domain boundaries and provide insights into sequence/structure/function relationships. * Small molecules and their biological activity: The PubChem project provides information on the biological activities of small molecules and is a component of NIH''''s Molecular Libraries Roadmap Initiative. PubChem includes three databases: PCSubstance, PCBioAssay, and PCCompound. The PubChem data are linked to other data types (illustrated example) in the Entrez system, making it possible, for example, to retrieve information about a compound and then Link to its biological activity data, retrieve 3D protein structures bound to the compound and interactively view their active sites, and find biosystems that include the compound as a component. * Biological Systems: A biosystem, or biological system, is a group of molecules that interact directly or indirectly, where the grouping is relevant to the characterization of living matter. The NCBI BioSystems Database provides centralized access to biological pathways from several source databases and connects the biosystem records with associated literature, molecular, and chemical data throughout the Entrez system. BioSystem records list and categorize components (illustrated example), such as the genes, proteins, and small molecules involved in a biological system. The companion FLink icon FLink tool, in turn, allows you to input a list of proteins, genes, or small molecules and retrieve a ranked list of biosystems.

The PSSH2 data set

PSSH2 is a database of protein sequence-to-structure homologies based on HHblits, an alignment method employing iterative comparisons of hidden Markov models (HMMs). To ensure the highest possible final alignment quality for matches in Aquaria using HHblits, we first calculate HMM profiles for each unique PDB sequence (PDB_full) and also for each unique Swiss-Prot sequence. We generated PSSH2 using HHblits to find similarities between HMMs from PDB and HMMs from UniProt sequences.

This dataset contains a subset of the usual PSSH2 database, including only the proteins relevant to visualise Sars-CoV-2 structures.
It contains Swissprot and PDB data used for generating PSSH2 along with the PSSH2 data itself. This consists of the sequence-to-structure alignments used in Aquaria (aquaria.ws) and also for the Covid19 resource of Aquaria (http://aquaria.ws/covid).

Calculating PSSH2

The main bunch of Swissprot and PDB data was downloaded in October 2020, but incremental updates, especially as related to Covid19 were added until April 2021.
Generating PSSH2: We used Uniclust30 from HH-suite, a database of non-redundant UniProt sequence clusters in which the highest pairwise sequence identity between clusters was 30% (http://gwdu111.gwdg.de/~compbiol/uniclust/2020_03/UniRef30_2020_03_hhsuite.tar.gz). The HHblits code and the code for running the calculations was retrieved from git (https://github.com/soedinglab/hh-suite.git and https://github.com/aschafu/PSSH2.git respectively) at the respective time of calculation in the timeframe until April 2021.

The AlphaFold Protein Structure Database is a collection of protein structure predictions made using the machine learning model AlphaFold. AlphaFold was developed by DeepMind , and this database was created in partnership with EMBL-EBI . For information on how to interpret, download and query the data, as well as on which proteins are included / excluded, and change log, please see our main dataset guide and FAQs . To interactively view individual entries or to download proteomes / Swiss-Prot please visit https://alphafold.ebi.ac.uk/ . The current release aims to cover most of the over 200M sequences in UniProt (a commonly used reference set of annotated proteins). The files provided for each entry include the structure plus two model confidence metrics (pLDDT and PAE). The files can be found in the Google Cloud Storage bucket gs://public-datasets-deepmind-alphafold-v4 with metadata in the BigQuery table bigquery-public-data.deepmind_alphafold.metadata . If you use this data, please cite: Jumper, J et al. Highly accurate protein structure prediction with AlphaFold. Nature (2021) Varadi, M et al. AlphaFold Protein Structure Database: massively expanding the structural coverage of protein-sequence space with high-accuracy models. Nucleic Acids Research (2021) This public dataset is hosted in Google Cloud Storage and is available free to use. Use this quick start guide to quickly learn how to access public datasets on Google Cloud Storage.

Similarity sets created for every amino acid pair to complement semi-conserved amino acid positions with other highly similar amino acids.

The prediction of gene functions is crucial for a large number of different life science areas. Faster high throughput sequencing techniques generate more and larger datasets. The manual annotation by classical wet-lab experiments is not suitable for these large amounts of data. We showed earlier that the automatic sequence pattern-based BrEPS protocol, based on manually curated sequences, can be used for the prediction of enzymatic functions of genes. The growing sequence databases provide the opportunity for more reliable patterns, but are also a challenge for the implementation of automatic protocols. We reimplemented and optimized the BrEPS pattern generation to be applicable for larger datasets in an acceptable timescale. Primary improvement of the new BrEPS protocol is the enhanced data selection step. Manually curated annotations from Swiss-Prot are used as reliable source for function prediction of enzymes observed on protein level. The pool of sequences is extended by highly similar sequences from TrEMBL and SwissProt. This allows us to restrict the selection of Swiss-Prot entries, without losing the diversity of sequences needed to generate significant patterns. Additionally, a supporting pattern type was introduced by extending the patterns at semi-conserved positions with highly similar amino acids. Extended patterns have an increased complexity, increasing the chance to match more sequences, without losing the essential structural information of the pattern. To enhance the usability of the database, we introduced enzyme function prediction based on consensus EC numbers and IUBMB enzyme nomenclature. BrEPS is part of the Braunschweig Enzyme Database (BRENDA) and is available on a completely redesigned website and as download. The database can be downloaded and used with the BrEPScmd command line tool for large scale sequence analysis. The BrEPS website and downloads for the database creation tool, command line tool and database are freely accessible at http://breps.tu-bs.de.

SBASE is a database of protein domain sequences collected from the literature, from protein sequence databases and from genomic databases. The protein domains are defined by their sequence boundaries given by the publishing authors or in one of the primary sequence databases (Swiss-Prot, PIR, TREMBL etc.). Domain groups are included if they have well defined sequence boundaries, and if they can be distinguished from other sequences using a similarity search technique. The SBASE database uses a set theoretical approach for representing similarities, which in practical terms is extremely simple. Sequences are considered similar if they are members of a similarity group in which all or most sequences are similar to each other and less similar to other members of the database. Sequences that have an above threshold BLAST similarity score to at least one member of the group is called the neighbourhood of the group. The below sketch shows such a neighborhood; the similarities within the group (self-similarities) and those pointing to non-member neighbours (non-self similarities) are shown in different colours., THIS RESOURCE IS NO LONGER IN SERVICE. Documented on September 16,2025.

Proteins perform essential cellular functions, which range from cell division and metabolism to DNA replication. Thus, decoding the mechanism of action of cells, requires understanding of the functioning and physicochemical properties of proteins [1]. While the genetic code encodes the primary structure of proteins, they undergo various modifications as part of their normal functioning including addition of modifying groups, such as acetyl, phosphoryl, glycosyl, and methyl, to one or more amino acids after translation, which is known as post-translational modification (PTM) [2, 3]. PTMs play an essential role in regulating protein functions by altering their physicochemical properties and understanding these reactions provides valuable insights regarding cell function. Advances in proteomics research have significantly deepened our understanding of PTMs and their impact on cellular functions and disease mechanisms. The study of PTMs is now at the forefront of research in molecular biology and biochemistry.

Many databases, software, and tools have been developed to enhance our understanding of the various PTMs that affect human plasma proteins and help to simplify the analysis of complex PTM data [4]. These PTM databases and tools contain significant information and are a valuable resource for the research community. Key databases include dbPTM, UniProt, and PubChem. Utilising these databases, protein-related information like substrate peptides, amino acid sequence numbers, and experimentally validated PTM sites can be identified and curated.

This dataset presents curated information regarding PTM-related changes in the physicochemical properties of the 16 most abundant plasma proteins [5], i.e., Serum Albumin, Serotransferrin, Antithrombin-III, Apolipoprotein A-I, Apolipoprotein A-IV, Apolipoprotein B-100, Apolipoprotein C-II, Apolipoprotein C-III, Apolipoprotein E, Clusterin, Complement C3, Haptoglobin, Histidine-rich glycoprotein, Mannose-binding protein C, Hemoglobin, and Fibrinogen alpha chain. The physicochemical properties studied, and the impact of different PTMs on the properties, include the protein molecular weight, isoelectric point, surface hydrophobicity, and solubility. The PTMs explored include phosphorylation, acetylation, glycosylation, methylation, ubiquitination, SUMOylation, lipidation, glutathionylation, nitrosylation, sulfoxidation, succinylation, neddylation, malonylation, hydroxylation, oxidation, and palmitoylation.

References

Alberts B, Johnson A, Lewis J, et al. Molecular Biology of the Cell. 4th edition. New York: Garland Science; 2002. Analyzing Protein Structure and Function.

Chen, H.; Venkat, S.; McGuire, P.; Gan, Q.; Fan, C. Recent Development of Genetic Code Expansion for Posttranslational Modification Studies. Molecules 2018, 23, 1662.

Marc Oeller, Ryan Kang, Hannah Bolt, Ana Gomes dos Santos, Annika Langborg Weinmann, Antonios Nikitidis, Pavol Zlatoidsky, Wu Su, Werngard Czechtizky, Leonardo De Maria,Pietro Sormanni, Michele Vendruscolo: Sequence-based prediction of the solubility of peptides containing non-natural amino acids [bioRiv].

Ramazi S, Zahiri J. Posttranslational modifications in proteins: resources, tools and prediction methods. Database (Oxford). 2021 Apr 7;2021:baab012.

THIS RESOURCE IS NO LONGER IN SERVICE, documented May 10, 2017. A pilot effort that has developed a centralized, web-based biospecimen locator that presents biospecimens collected and stored at participating Arizona hospitals and biospecimen banks, which are available for acquisition and use by researchers. Researchers may use this site to browse, search and request biospecimens to use in qualified studies. The development of the ABL was guided by the Arizona Biospecimen Consortium (ABC), a consortium of hospitals and medical centers in the Phoenix area, and is now being piloted by this Consortium under the direction of ABRC. You may browse by type (cells, fluid, molecular, tissue) or disease. Common data elements decided by the ABC Standards Committee, based on data elements on the National Cancer Institute''s (NCI''s) Common Biorepository Model (CBM), are displayed. These describe the minimum set of data elements that the NCI determined were most important for a researcher to see about a biospecimen. The ABL currently does not display information on whether or not clinical data is available to accompany the biospecimens. However, a requester has the ability to solicit clinical data in the request. Once a request is approved, the biospecimen provider will contact the requester to discuss the request (and the requester''s questions) before finalizing the invoice and shipment. The ABL is available to the public to browse. In order to request biospecimens from the ABL, the researcher will be required to submit the requested required information. Upon submission of the information, shipment of the requested biospecimen(s) will be dependent on the scientific and institutional review approval. Account required. Registration is open to everyone., documented June 24, 2013 as per the Miriam database (http://www.ebi.ac.uk/miriam/main/collections/MIR:00000021). The CluSTr database offers an automatic classification of UniProt Knowledgebase and IPI proteins into groups of related proteins. The clustering is based on analysis of all pairwise comparisons between protein sequences. The database provides links to InterPro, which integrates information on protein families, domains and functional sites from PROSITE, PRINTS, Pfam, ProDom, SMART, TIGRFAMs, Gene3D, SUPERFAMILY, PIR Superfamily and PANTHER. To date (2011), CluSTr contains the following information: * 9,450,285 sequences from UniProt Knowledgebase release 15.6 * 308,281 sequences from IPI * 3,636,831,744 similarities, with pairwise alignments generated on-the-fly * 17,616,060 clusters * Clustering for 972 organisms with completely sequenced genomes. For the full list of the genomes see Integr8 * Putative homologues predictions for the above species. For more information see Homologue Selection at Integr8

Decoys-R-Us is a database of decoys, computer-generated conformations of protein sequences that possess some characteristics of native proteins, but are not biologically real. The primary use of decoys is to test scoring, or energy, functions. All the decoys in the Decoys ''R'' Us database can be downloaded.

BackgroundDespite the remarkable progress of bioinformatics, how the primary structure of a protein leads to a three-dimensional fold, and in turn determines its function remains an elusive question. Alignments of sequences with known function can be used to identify proteins with the same or similar function with high success. However, identification of function-related and structure-related amino acid positions is only possible after a detailed study of every protein. Folding pattern diversity seems to be much narrower than sequence diversity, and the amino acid sequences of natural proteins have evolved under a selective pressure comprising structural and functional requirements acting in parallel.Principal FindingsThe approach described in this work begins by generating a large number of amino acid sequences using ROSETTA [Dantas G et al. (2003) J Mol Biol 332:449–460], a program with notable robustness in the assignment of amino acids to a known three-dimensional structure. The resulting sequence-sets showed no conservation of amino acids at active sites, or protein-protein interfaces. Hidden Markov models built from the resulting sequence sets were used to search sequence databases. Surprisingly, the models retrieved from the database sequences belonged to proteins with the same or a very similar function. Given an appropriate cutoff, the rate of false positives was zero. According to our results, this protocol, here referred to as Rd.HMM, detects fine structural details on the folding patterns, that seem to be tightly linked to the fitness of a structural framework for a specific biological function.ConclusionBecause the sequence of the native protein used to create the Rd.HMM model was always amongst the top hits, the procedure is a reliable tool to score, very accurately, the quality and appropriateness of computer-modeled 3D-structures, without the need for spectroscopy data. However, Rd.HMM is very sensitive to the conformational features of the models' backbone.

Protein-Protein, Genetic, and Chemical Interactions for Kewalramani N (2023):State-of-the-art computational methods to predict protein-protein interactions with high accuracy and coverage. curated by BioGRID (https://thebiogrid.org); ABSTRACT: Prediction of protein-protein interactions (PPIs) commonly involves a significant computational component. Rapid recent advances in the power of computational methods for protein interaction prediction motivate a review of the state-of-the-art. We review the major approaches, organized according to the primary source of data utilized: protein sequence, protein structure, and protein co-abundance. The advent of deep learning (DL) has brought with it significant advances in interaction prediction, and we show how DL is used for each source data type. We review the literature taxonomically, present example case studies in each category, and conclude with observations about the strengths and weaknesses of machine learning methods in the context of the principal sources of data for protein interaction prediction.

The HUGE protein database has been created to publicize the Human cDNA project at the Kazusa DNA Research Institute. This project will sequence and analyze long (>4 kb) human cDNAs and establish methods by using the sequence data how to predict the primary structure of proteins of various biological activities. Currently, it focuses on the analysis of cDNA clones encoding particularly large proteins (>50 kDa). The HUGE protein database contains various types of information derived from the predicted primary structure data of newly identified human proteins. The HUGE protein database are expected to cover various sets of large human proteins of hitherto unidentified functions. They are likely to be involved in cellular structure/motility (such as cytoskeleton, membrane skeleton, and motor proteins), gene expression and nucleic acid metabolism, cell signaling/communication (such as cellular adhesion, signal transduction, channels, and receptors), and so on.

Decoys-R-Us is a database of decoys, computer-generated conformations of protein sequences that possess some characteristics of native proteins, but are not biologically real. The primary use of decoys is to test scoring, or energy, functions. All the decoys in the Decoys ''R'' Us database can be downloaded.