Sequences In Protein Data Bank Research Articles

Relevance of ARID1A Mutations in Endometrial Carcinomas.

Since the Cancer Genome Atlas (TCGA) project identified four distinct groups based on molecular alterations, mutation analyses have been integrated into the characterization of endometrial carcinomas (ECs). ARID1A seems to be the subunit more involved in the loss of function of the SWI/SNF complex in ECs. The aim of this study is to define the relevance of ARID1A alterations in a cohort of EC, studying the possible associations between DNA mutation (genomic level), RNA expression (transcriptomic level), and protein expression (proteomic level). A total of 50 endometrial carcinomas were characterized for ARID1A mutations (using targeted DNA next-generation sequencing—NGS), ARID1A gene expression (using RNAseq and qRT-PCR), and ARID1A protein expression (using immunohistochemistry—IHC). Moreover, we have investigated if ARID1A mutations may alter the protein structure, using the Protein Data Bank sequence. We found a good correlation between ARID1A mutations and protein immunostaining, even if we did not find statistically significant differences in the ARID1A expression levels. In conclusion, our data demonstrated that the molecular characterization of ARID1A should be associated with IHC analysis, mainly in those cases harboring “novel” ARID1A mutations or in those alterations with “uncertain” pathogenic significance.

A massively parallel speciation‐based differential evolution algorithm applied to the 3D‐AB protein structure prediction

AbstractOne of the most challenging problems in Bioinformatics is the finding of a protein conformation and it is known as the Protein Structure Prediction (PSP) problem. The main feature present in the AB off‐lattice model is the use of polarity as the main driving force to guide the optimization process. The present work proposes an adaptive evolutionary algorithm based on GPU that is applied to the 3D‐AB off‐lattice PSP problem. The proposed approach is named cuDSMjDE, and is composed of a Dynamic Speciation‐based Mutation Strategies Differential Evolution that employs the jDE mechanism to control F and CR parameters. A crowding strategy is also adopted, increasing competition between individuals and maintaining the diversity of solutions. All routines of the proposed algorithm are developed to run in GPU. During the experiments, eight Protein Data Bank sequences are employed. Results obtained concerning potential energy are competitive when compared with state‐of‐the‐art algorithms. The use of a massively parallel architecture promoted the necessary scalability with speedups up to 708.78.

PDBrenum: A webserver and program providing Protein Data Bank files renumbered according to their UniProt sequences.

The Protein Data Bank (PDB) was established at Brookhaven National Laboratories in 1971 as an archive for biological macromolecular crystal structures. In mid 2021, the database has almost 180,000 structures solved by X-ray crystallography, nuclear magnetic resonance, cryo-electron microscopy, and other methods. Many proteins have been studied under different conditions, including binding partners such as ligands, nucleic acids, or other proteins; mutations, and post-translational modifications, thus enabling extensive comparative structure-function studies. However, these studies are made more difficult because authors are allowed by the PDB to number the amino acids in each protein sequence in any manner they wish. This results in the same protein being numbered differently in the available PDB entries. For instance, some authors may include N-terminal signal peptides or the N-terminal methionine in the sequence numbering and others may not. In addition to the coordinates, there are many fields that contain structural and functional information regarding specific residues numbered according to the author. Here we provide a webserver and Python3 application that fixes the PDB sequence numbering problem by replacing the author numbering with numbering derived from the corresponding UniProt sequences. We obtain this correspondence from the SIFTS database from PDBe. The server and program can take a list of PDB entries or a list of UniProt identifiers (e.g., "P04637" or "P53_HUMAN") and provide renumbered files in mmCIF format and the legacy PDB format for both asymmetric unit files and biological assembly files provided by PDBe.

PDBcle – An online tool for extracting chain structure and sequence of macromolecule and small molecule structure from the Protein Data Bank

Background: Protein Data Bank (PDB) is the most popular structure database that contains experimentally determined three-dimensional (3D) structures of biological macromolecules and small molecules. The rich features of PDB are keyword assisted advanced text search, structure search by sequence alignment, sequence motif search, ligand to target-ligand complex search through SMILES substructure search, JSON API query search, structure alignment, structure quality assessment, genome viewer, and 3D structure viewer. It is widely used in molecular modelling and computer-aided drug design. PDBcle is a simple tool to extract chain sequence of protein/nucleotide and 3D structure of protein/ nucleotide/ ligand from the PDB. Objectives: To construct an online tool for separating molecule-wise chain sequence and structure of polymers and non-polymer structures in a macromolecule. Moreover, the separated sequences and structures are produced to moleculespecific standard file format. Methods: The graphical web-interface of PDBcle tool has been designed using PHP, CSS, and pure JavaScript. Parsing the atomic coordinate records and sequence records from the PDBML/XML file and/or PDBx/mmJSON file through the API of PDB was done through PHP server script. Findings: The PDBcle tool retrieves and generates separate structure/sequence files for each amino acid/RNA chain, and pair of chains for DNA base pairs with/without ligand complex from the PDB. The ligand molecules are separated and sorted from the chains and produced to an SDF file. Applications: PDBcle tool is publicly accessible at https://www.biogem.org/tool/pdbcle/. Keywords: PDBcle; PDB; PDB Chain and Ligand Extractor; PDB Sequence Extractor; PDBML

Chameleon sequences in neurodegenerative diseases.

Chameleon sequences can adopt either alpha helix sheet or a coil conformation. Defining chameleon sequences in PDB (Protein Data Bank) may yield to an insight on defining peptides and proteins responsible in neurodegeneration. In this research, we benefitted from the large PDB and performed a sequence analysis on Chameleons, where we developed an algorithm to extract peptide segments with identical sequences, but different structures. In order to find new chameleon sequences, we extracted a set of 8315 non-redundant protein sequences from the PDB with an identity less than 25%. Our data was classified to “helix to strand (HE)”, “helix to coil (HC)” and “strand to coil (CE)” alterations. We also analyzed the occurrence of singlet and doublet amino acids and the solvent accessibility in the chameleon sequences; we then sorted out the proteins with the most number of chameleon sequences and named them Chameleon Flexible Proteins (CFPs) in our dataset. Our data revealed that Gly, Val, Ile, Tyr and Phe, are the major amino acids in Chameleons. We also found that there are proteins such as Insulin Degrading Enzyme IDE and GTP-binding nuclear protein Ran (RAN) with the most number of chameleons (640 and 405 respectively). These proteins have known roles in neurodegenerative diseases. Therefore it can be inferred that other CFP's can serve as key proteins in neurodegeneration, and a study on them can shed light on curing and preventing neurodegenerative diseases.

Protein molecular modeling of genetic markers for thyroid cancer

Introduction: The advances in thyroid molecular biology studies provide not only insight into thyroid diseases but accurate diagnosis of thyroid cancer. Objective: Design a tutorial on protein molecular modeling of genetic markers for thyroid cancer. Methods: The proteins were selected using the Protein Data Bank sequence and the basic local alignment search tool (BLAST) algorithm. The obtained sequences were aligned with the Clustal W multiple alignment algorithms. For the molecular modeling, three-dimensional structures were generated from this set of constraints with the SWISS-MODEL, which is a fully automated protein structure homology-modeling server, accessible via the ExPASy web server. Results: We demonstrated protein analysis, projection of the molecular structure and protein homology of the following molecular markers of thyroid cancer: receptor tyrosine kinase (RET) proto-oncogene; neurotrophic tyrosine kinase receptor 1 (NTRK1) proto-oncogene; phosphatase and tensin homolog (PTEN); tumor protein p53 (TP53) gene; phosphoinositide 3-kinase/threonine protein kinase (PI3K/AKT); catenin beta 1 (CTNNB1); paired box 8-peroxisome proliferator-activated receptor gamma (PAX8-PPARG); rat sarcoma viral oncogene (RAS); B-raf proto-oncogene, serine/threonine kinase (BRAF); and thyroid-stimulating hormone receptor (TSHR). Conclusion: This study shows the importance of understanding the molecular structure of the markers for thyroid cancer through bioinformatics, and consequently, the development of more effective new molecules as alternative tools for thyroid cancer treatment.

MUFOLD-DB: a processed protein structure database for protein structure prediction and analysis.

BackgroundProtein structure data in Protein Data Bank (PDB) are widely used in studies of protein function and evolution and in protein structure prediction. However, there are two main barriers in large-scale usage of PDB data: 1) PDB data are highly redundant in terms of sequence and structure similarity; and 2) many PDB files have issues due to inconsistency of data and standards as well as missing residues, so that automated retrieval and analysis are often difficult.DescriptionTo address these issues, we have created MUFOLD-DB http://mufold.org/mufolddb.php, a web-based database, to collect and process the weekly PDB files thereby providing users with non-redundant, cleaned and partially-predicted structure data. For each of the non-redundant sequences, we annotate the SCOP domain classification and predict structures of missing regions by loop modelling. In addition, evolutional information, secondary structure, disorder region, and processed three-dimensional structure are computed and visualized to help users better understand the protein.ConclusionsMUFOLD-DB integrates processed PDB sequence and structure data and multiple computational results, provides a friendly interface for users to retrieve, browse and download these data, and offers several useful functionalities to facilitate users' data operation.

RNA CoSSMos: Characterization of Secondary Structure Motifs--a searchable database of secondary structure motifs in RNA three-dimensional structures

RNA secondary structure is important for designing therapeutics, understanding protein–RNA binding and predicting tertiary structure of RNA. Several databases and downloadable programs exist that specialize in the three-dimensional (3D) structure of RNA, but none focus specifically on secondary structural motifs such as internal, bulge and hairpin loops. The RNA Characterization of Secondary Structure Motifs (RNA CoSSMos) database is a freely accessible and searchable online database and website of 3D characteristics of secondary structure motifs. To create the RNA CoSSMos database, 2156 Protein Data Bank (PDB) files were searched for internal, bulge and hairpin loops, and each loop's structural information, including sugar pucker, glycosidic linkage, hydrogen bonding patterns and stacking interactions, was included in the database. False positives were defined, identified and reclassified or omitted from the database to ensure the most accurate results possible. Users can search via general PDB information, experimental parameters, sequence and specific motif and by specific structural parameters in the subquery page after the initial search. Returned results for each search can be viewed individually or a complete set can be downloaded into a spreadsheet to allow for easy comparison. The RNA CoSSMos database is automatically updated weekly and is available at http://cossmos.slu.edu.

SeeMotif: exploring and visualizing sequence motifs in 3D structures.

Sequence motifs are important in the study of molecular biology. Motif discovery tools efficiently deliver many function related signatures of proteins and largely facilitate sequence annotation. As increasing numbers of motifs are detected experimentally or predicted computationally, characterizing the functional roles of motifs and identifying the potential synergetic relationships between them are important next steps. A good way to investigate novel motifs is to utilize the abundant 3D structures that have also been accumulated at an astounding rate in recent years. This article reports the development of the web service seeMotif, which provides users with an interactive interface for visualizing sequence motifs on protein structures from the Protein Data Bank (PDB). Researchers can quickly see the locations and conformation of multiple motifs among a number of related structures simultaneously. Considering the fact that PDB sequences are usually shorter than those in sequence databases and/or may have missing residues, seeMotif has two complementary approaches for selecting structures and mapping motifs to protein chains in structures. As more and more structures belonging to previously uncharacterized protein families become available, combining sequence and structure information gives good opportunities to facilitate understanding of protein functions in large-scale genome projects. Available at: http://seemotif.csie.ntu.edu.tw,http://seemotif.ee.ncku.edu.tw or http://seemotif.csbb.ntu.edu.tw.

The Jena Library of Biological Macromolecules - JenaLib

AbstractThe JenaLib database ("www.fli-leibniz.de/IMAGE.html":http://www.fli-leibniz.de/IMAGE.html) offers value-added information for all Protein Data Bank (PDB) and Nucleic Acid Database (NDB) entries. This includes:(1) atlas pages and entry lists, (2) PDB sequence information extracted from atomic coordinates, (3) PDB/UniProt sequence alignments that clearly indicate gaps, mutations, numbering irregularities and modified residues, (4) integration of data on single amino acid polymorphisms (SAPs), PROSITE motifs, exon structure and SCOP/CATH/Pfam domains with PDB, GO and taxonomy information, (5) display of these data in the sequence/alignment viewer and in the Jmol-based molecule viewer Jena3D ("jena3d.fli-leibniz.de":http://jena3d.fli-leibniz.de ); in the latter case both for asymmetric and biological units, (6) a QuickSearch option that allows searching for PDB/NDB code, UniProt ID/accession number and other search terms in one input field, (7) a sequence homology search (BLAST) and pattern search options and (8) SCOP/CATH/Pfam tree browsers.Offering all this information and analysis tools in one place makes JenaLib a unique resource for the dissemination of 3D structural information on biological macromolecules.

Prediction of subcellular location apoptosis proteins with ensemble classifier and feature selection

Apoptosis proteins have a central role in the development and the homeostasis of an organism. These proteins are very important for understanding the mechanism of programmed cell death. The function of an apoptosis protein is closely related to its subcellular location. It is crucial to develop powerful tools to predict apoptosis protein locations for rapidly increasing gap between the number of known structural proteins and the number of known sequences in protein databank. In this study, amino acids pair compositions with different spaces are used to construct feature sets for representing sample of protein feature selection approach based on binary particle swarm optimization, which is applied to extract effective feature. Ensemble classifier is used as prediction engine, of which the basic classifier is the fuzzy K-nearest neighbor. Each basic classifier is trained with different feature sets. Two datasets often used in prior works are selected to validate the performance of proposed approach. The results obtained by jackknife test are quite encouraging, indicating that the proposed method might become a potentially useful tool for subcellular location of apoptosis protein, or at least can play a complimentary role to the existing methods in the relevant areas. The supplement information and software written in Matlab are available by contacting the corresponding author.

SSMap: A new UniProt-PDB mapping resource for the curation of structural-related information in the UniProt/Swiss-Prot Knowledgebase

BackgroundSequences and structures provide valuable complementary information on protein features and functions. However, it is not always straightforward for users to gather information concurrently from the sequence and structure levels. The UniProt knowledgebase (UniProtKB) strives to help users on this undertaking by providing complete cross-references to Protein Data Bank (PDB) as well as coherent feature annotation using available structural information. In this study, SSMap – a new UniProt-PDB residue-residue level mapping – was generated. The primary objective of this mapping is not only to facilitate the two tasks mentioned above, but also to palliate a number of shortcomings of existent mappings. SSMap is the first isoform sequence-specific mapping resource and is up-to-date for UniProtKB annotation tasks. The method employed by SSMap differs from the other mapping resources in that it stresses on the correct reconstruction of the PDB sequence from structures, and on the correct attribution of a UniProtKB entry to each PDB chain by using a series of post-processing steps.ResultsSSMap was compared to other existing mapping resources in terms of the correctness of the attribution of PDB chains to UniProtKB entries, and of the quality of the pairwise alignments supporting the residue-residue mapping. It was found that SSMap shared about 80% of the mappings with other mapping sources. New and alternative mappings proposed by SSMap were mostly good as assessed by manual verification of data subsets. As for local pairwise alignments, it was shown that major discrepancies (both in terms of alignment lengths and boundaries), when present, were often due to differences in methodologies used for the mappings.ConclusionSSMap provides an independent, good quality UniProt-PDB mapping. The systematic comparison conducted in this study allows the further identification of general problems in UniProt-PDB mappings so that both the coverage and the quality of the mappings can be systematically improved for the benefit of the scientific community. SSMap mapping is currently used to provide PDB cross-references in UniProtKB.

Effects of mycotoxins on apoptosis of human immune system

It is crucial to develop powerful tools to predict apoptosis protein locations for rapidly increasing gap between the number of known structural proteins and the number of known sequences in protein databank. In this study, based on the concept of pseudo amino acid (PseAA) composition originally introduced by Chou, a novel approximate entropy (ApEn) based PseAA composition is proposed to represent apoptosis protein sequences. An ensemble classifier is introduced, of which the basic classifier is the FKNN (fuzzy K-nearest neighbor) one, as prediction engine. Each basic classifier is trained in different dimensions of PseAA composition of protein sequences. The immune genetic algorithm (IGA) is used to search the optimal weight factors in generating the PseAA composition for crucial of weight factors in PseAA composition. The results obtained by Jackknife test are quite encouraging, indicating that the proposed method might become a potentially useful tool for protein function, or at least can play a complimentary role to the existing methods in the relevant areas.

Using Chou’s pseudo amino acid composition to predict subcellular localization of apoptosis proteins: An approach with immune genetic algorithm-based ensemble classifier

It is crucial to develop powerful tools to predict apoptosis protein locations for rapidly increasing gap between the number of known structural proteins and the number of known sequences in protein databank. In this study, based on the concept of pseudo amino acid (PseAA) composition originally introduced by Chou, a novel approximate entropy (ApEn) based PseAA composition is proposed to represent apoptosis protein sequences. An ensemble classifier is introduced, of which the basic classifier is the FKNN (fuzzy K-nearest neighbor) one, as prediction engine. Each basic classifier is trained in different dimensions of PseAA composition of protein sequences. The immune genetic algorithm (IGA) is used to search the optimal weight factors in generating the PseAA composition for crucial of weight factors in PseAA composition. The results obtained by Jackknife test are quite encouraging, indicating that the proposed method might become a potentially useful tool for protein function, or at least can play a complimentary role to the existing methods in the relevant areas.

Intrinsic Disorder in the Protein Data Bank

The Protein Data Bank (PDB) is the preeminent source of protein structural information. PDB contains over 32,500 experimentally determined 3-D structures solved using X-ray crystallography or nuclear magnetic resonance spectroscopy. Intrinsically disordered regions fail to form a fixed 3-D structure under physiological conditions. In this study, we compare the amino-acid sequences of proteins whose structures are determined by X-ray crystallography with the corresponding sequences from the Swiss-Prot database. The analyzed dataset includes 16,370 structures, which represent 18,101 PDB chains and 5,434 different proteins from 910 different organisms (2,793 eukaryotic, 2,109 bacterial, 288 viral, and 244 archaeal). In this dataset, on average, each Swiss-Prot protein is represented by 7 PDB chains with 76% of the crystallized regions being represented by more than one structure. Intriguingly, the complete sequences of only ~7% of proteins are observed in the corresponding PDB structures, and only ~25% of the total dataset have >95% of their lengths observed in the corresponding PDB structures. This suggests that the vast majority of PDB proteins is shorter than their corresponding Swiss-Prot sequences and/or contain numerous residues, which are not observed in maps of electron density. To determine the prevalence of disordered regions in PDB, the residues in the Swiss-Prot sequences were grouped into four general categories, “Observed” (which correspond to structured regions), “Not observed” (regions with missing electron density, potentially disordered), “Uncharacterized,” and “Ambiguous,” depending on their appearance in the corresponding PDB entries. This non-redundant set of residues can be viewed as a ‘fragment’ or empirical domain database that contains a set of experimentally determined structured regions or domains and a set of experimentally verified disordered regions or domains. We studied the propensities and properties of residues in these four categories and analyzed their relations to the predictions of disorder using several algorithms. “Non-observed,” “Ambiguous,” and “Uncharacterized” regions were shown to possess the amino acid compositional biases typical of intrinsically disordered proteins. The application of four different disorder predictors (PONDR® VL-XT, VL3-BA, VSL1P, and IUPred) revealed that the vast majority of residues in the “Observed” dataset are ordered, and that the “Not observed” regions are mostly disordered. The “Uncharacterized” regions possess some tendency toward order, whereas the predictions for the short “Ambiguous” regions are really ambiguous. Long “Ambiguous” regions (>70 amino acid residues) are mostly predicted to be ordered, suggesting that they are likely to be “wobbly” domains. Overall, we showed that completely ordered proteins are not highly abundant in PDB and many PDB sequences have disordered regions. In fact, in the analyzed dataset ~10% of the PDB proteins contain regions of consecutive missing or ambiguous residues longer than 30 amino-acids and ~40% of the proteins possess short regions (≥10 and <30 amino-acid long) of missing and ambiguous residues.

Identification of putative domain linkers by a neural network – application to a large sequence database

BackgroundThe reliable dissection of large proteins into structural domains represents an important issue for structural genomics/proteomics projects. To provide a practical approach to this issue, we tested the ability of neural network to identify domain linkers from the SWISSPROT database (101602 sequences).ResultsOur search detected 3009 putative domain linkers adjacent to or overlapping with domains, as defined by sequence similarity to either Protein Data Bank (PDB) or Conserved Domain Database (CDD) sequences. Among these putative linkers, 75% were "correctly" located within 20 residues of a domain terminus, and the remaining 25% were found in the middle of a domain, and probably represented failed predictions. Moreover, our neural network predicted 5124 putative domain linkers in structurally un-annotated regions without sequence similarity to PDB or CDD sequences, which suggest to the possible existence of novel structural domains. As a comparison, we performed the same analysis by identifying low-complexity regions (LCR), which are known to encode unstructured polypeptide segments, and observed that the fraction of LCRs that correlate with domain termini is similar to that of domain linkers. However, domain linkers and LCRs appeared to identify different types of domain boundary regions, as only 32% of the putative domain linkers overlapped with LCRs.ConclusionOverall, our study indicates that the two methods detect independent and complementary regions, and that the combination of these methods can substantially improve the sensitivity of the domain boundary prediction. This finding should enable the identification of novel structural domains, yielding new targets for large scale protein analyses.

ProTarget: automatic prediction of protein structure novelty

ProTarget is a Web-based tool for the automatic prediction of fold novelty. It offers the structural genomics community a method for target selection by providing an online analysis of any new or pre-existing sequence for its relationship to any previously solved three-dimensional structure. ProTarget takes as input an amino acid sequence. Regions of this sequence that exhibit high similarity to an existing PDB (Protein Data Bank) sequence are removed, leaving one or more subsequences. Each of these subsequences is then analyzed against a clustering of the protein space to determine the likelihood of its representing a new structural superfamily. This likelihood is derived from the distance in the clustering between the (sub)sequence and sequences that have known structures. The output of ProTarget is a graphical visualization of the protein of interest together with the likelihood that a protein sequence represents a novel structural superfamily. ProTarget is updated regularly and currently covers over 160 000 protein sequences from the SwissProt and PDB databases. ProTarget is available at .

PISCES: recent improvements to a PDB sequence culling server

PISCES is a database server for producing lists of sequences from the Protein Data Bank (PDB) using a number of entry- and chain-specific criteria and mutual sequence identity. Our goal in culling the PDB is to provide the longest list possible of the highest resolution structures that fulfill the sequence identity and structural quality cut-offs. The new PISCES server uses a combination of PSI-BLAST and structure-based alignments to determine sequence identities. Structure alignment produces more complete alignments and therefore more accurate sequence identities than PSI-BLAST. PISCES now allows a user to cull the PDB by-entry in addition to the standard culling by individual chains. In this scenario, a list will contain only entries that do not have a chain that has a sequence identity to any chain in any other entry in the list over the sequence identity cut-off. PISCES also provides fully annotated sequences including gene name and species. The server allows a user to cull an input list of entries or chains, so that other criteria, such as function, can be used. Results from a search on the re-engineered RCSB's site for the PDB can be entered into the PISCES server by a single click, combining the powerful searching abilities of the PDB with PISCES's utilities for sequence culling. The server's data are updated weekly. The server is available at .

GDAP: a web tool for genome-wide protein disulfide bond prediction.

The Genomic Disulfide Analysis Program (GDAP) provides web access to computationally predicted protein disulfide bonds for over one hundred microbial genomes, including both bacterial and achaeal species. In the GDAP process, sequences of unknown structure are mapped, when possible, to known homologous Protein Data Bank (PDB) structures, after which specific distance criteria are applied to predict disulfide bonds. GDAP also accepts user-supplied protein sequences and subsequently queries the PDB sequence database for the best matches, scans for possible disulfide bonds and returns the results to the client. These predictions are useful for a variety of applications and have previously been used to show a dramatic preference in certain thermophilic archaea and bacteria for disulfide bonds within intracellular proteins. Given the central role these stabilizing, covalent bonds play in such organisms, the predictions available from GDAP provide a rich data source for designing site-directed mutants with more stable thermal profiles. The GDAP web application is a gateway to this information and can be used to understand the role disulfide bonds play in protein stability both in these unusual organisms and in sequences of interest to the individual researcher. The prediction server can be accessed at http://www.doe-mbi.ucla.edu/Services/GDAP.

Enlarged FAMSBASE: protein 3D structure models of genome sequences for 41 species.

Enlarged FAMSBASE is a relational database of comparative protein structure models for the whole genome of 41 species, presented in the GTOP database. The models are calculated by Full Automatic Modeling System (FAMS). Enlarged FAMSBASE provides a wide range of query keys, such as name of ORF (open reading frame), ORF keywords, Protein Data Bank (PDB) ID, PDB heterogen atoms and sequence similarity. Heterogen atoms in PDB include cofactors, ligands and other factors that interact with proteins, and are a good starting point for analyzing interactions between proteins and other molecules. The data may also work as a template for drug design. The present number of ORFs with protein 3D models in FAMSBASE is 183 805, and the database includes an average of three models for each ORF. FAMSBASE is available at http://famsbase.bio.nagoya-u.ac.jp/famsbase/.