Structures In Protein Data Bank Research Articles

Data Intensive Genome Level Analysis for Identifying Novel, Non-Toxic Drug Targets for Multi Drug Resistant Mycobacterium tuberculosis

We report the construction of a novel Systems Biology based virtual drug discovery model for the prediction of non-toxic metabolic targets in Mycobacterium tuberculosis (Mtb). This is based on a data-intensive genome level analysis and the principle of conservation of the evolutionarily important genes. In the 1623 sequenced Mtb strains, 890 metabolic genes identified through a systems approach in Mtb were evaluated for non-synonymous mutations. The 33 genes showed none or one variation in the entire 1623 strains, including 1084 Russian MDR strains. These invariant targets were further evaluated for their experimental and in silico essentiality as well as availability of their crystal structure in Protein Data Bank (PDB). Along with this, targets for the common existing antibiotics and the new Tb drug candidates were also screened for their variation across 1623 strains of Mtb for understanding the drug resistance. We propose that the reduced set of these reported targets could be a more effective starting point for medicinal chemists in generating new chemical leads. This approach has the potential of fueling the dried up Tuberculosis (Tb) drug discovery pipeline.

Pharmacophore searching: A potential solution for correcting unknown ligands (UNK) labelling errors in Protein Data Bank (PDB’S)

The Protein Data Bank (PDB) is the single most important repository of structural data for proteins and other biologically relevant molecules. Therefore, it is critically important to keep the PDB data, error-free as much as possible. In this study, we have critically examined PDB structures of 292 protein molecules which have been deposited in the repository along with potentially incorrect ligands labelled as Unknown ligands (UNK). Pharmacophores were generated for all the protein structures by using Discovery Studio Visualizer (DSV) and Accelrys, Catalyst®. The generated pharmacophores were subjected to the database search containing the reported ligand. Ligands obtained through Pharmacophore searching were then checked for fitting the observed electron density map by using Coot®. The predicted ligands obtained via Pharmacophore searching fitted well with the observed electron density map, in comparison to the ligands reported in the PDB's. Based on our study we have learned that till may 2016, among 292 submitted structures in the PDB, at least 20 structures have ligands with a clear electron density but have been incorrectly labelled as unknown ligands (UNK). We have demonstrated that Pharmacophore searching and Coot® can provide potential help to find suitable known ligands for these protein structures, the former for ligand search and the latter for electron density analysis. The use of these two techniques can facilitate the quick and reliable labelling of ligands where the electron density map serves as a reference.

Structural and functional characterization of a unique hypothetical protein (WP_003901628.1) of Mycobacterium tuberculosis: a computational approach

Mycobacterium tuberculosis, the causative agent of tuberculosis, has been a serious threat to public health. The recent emergence of extreme drug-resistant strains further worsened the situation and required more serious efforts to find a better cure for TB. Availability of the complete genome map of various Mycobacterium tuberculosis strains serves a basis to propose new therapeutic targets against Mycobacterium tuberculosis. The present study aimed to identify and characterize potential and novel drug target against Mycobacterium tuberculosis by using an in silico approach. The study consisted of three phases. In phase I, subtractive/comparative genomics was performed on genomic data of 17 strains of Mycobacterium tuberculosis comprised of 66,988 protein sequences. The analysis resulted in three identical hypothetical proteins of distinct origin. The shortlisted sequences were non-homologous to the host, a prerequisite characteristic of potential drug target. One of the sequences, i.e., WP_003901628.1 (the locus tag name is CFBR_RS17985) from the CDC5180 strain was further subjected to other bioinformatics methods in phase II. In phase II, the shortlisted protein sequence was qualitatively characterized for subcellular localization, functional family prediction and evolutionary relationships. The predicted subcellular localization of the query protein was found in cytoplasmic region. The functional family classification resulted in classifying the protein in transferase superfamily. Finally, in phase III of the study, the structure-based methods (i.e., homology modeling and docking) were performed to build the three-dimensional (3D) structure and to predict the putative binding site. The PDB-ID 3DR4 was found to be a suitable template having 30.1% sequence identity. The binding site of modeled protein was deciphered with DoGSite scorer tool. Later on, the ProBis tool was used to probe structurally similar binding sites in Protein Data Bank. Subsequently, the co-crystal ligands of the Protein Data Bank structures (prioritized by high-rank Z scores) were extracted and cross-docked into the binding site of the modeled protein. The cross-docking results revealed two ligands, i.e., 4′-deoxy-4′-aminopyridoxal-5′-phosphate and OG1 from two Protein Data Bank structures, i.e., 1MDO and 4K2M as best docked with RMSDs 2.8 and 2.5 A, respectively. It is inferred from the protein structure that the identified protein is alanine-rich with a PI < 7. Current study convincingly proposed that the shortlisted hypothetical protein (WP_003901628.1) is a potential drug target against which new drug candidates could be designed in a hope to cure tuberculosis.

Improving 3D Structure Prediction of Beta-Barrel Membrane Proteins

Beta-barrel membrane proteins are found in the outer membrane of gram-negative bacteria, mitochondria, and chloroplasts. They carry out diverse biological functions, including pore formation, membrane anchoring, enzyme activity, and bacterial virulence. In addition, beta-barrel membrane proteins increasingly serve as scaffolds for bacterial surface display and nanopore-based DNA sequencing. Due to difficulties in experimental structure determination, they are sparsely represented in the protein structure databank and computational methods are required to understand their biophysical principles. We have developed a novel 3D structural template for the construction of beta-barrel membrane proteins, which captures major geometric properties of these proteins. With the help of the prediction procedure of strand registers that we developed, we have achieved a high accuracy for the structure prediction of beta-barrel proteins. In addition, for the beta-barrel proteins with irregular barrel shape, we have further developed an elastic model, which characterizes bending and curvatures of the barrels, to improve the structure prediction of these proteins.

JET2 Viewer: a database of predicted multiple, possibly overlapping, protein-protein interaction sites for PDB structures.

The database JET2 Viewer, openly accessible at http://www.jet2viewer.upmc.fr/, reports putative protein binding sites for all three-dimensional (3D) structures available in the Protein Data Bank (PDB). This knowledge base was generated by applying the computational method JET2 at large-scale on more than 20 000 chains. JET2 strategy yields very precise predictions of interacting surfaces and unravels their evolutionary process and complexity. JET2 Viewer provides an online intelligent display, including interactive 3D visualization of the binding sites mapped onto PDB structures and suitable files recording JET2 analyses. Predictions were evaluated on more than 15 000 experimentally characterized protein interfaces. This is, to our knowledge, the largest evaluation of a protein binding site prediction method. The overall performance of JET2 on all interfaces are: Sen = 52.52, PPV = 51.24, Spe = 80.05, Acc = 75.89. The data can be used to foster new strategies for protein-protein interactions modulation and interaction surface redesign.

JET2 Viewer: a database of predicted multiple, possibly overlapping, protein–protein interaction sites for PDB structures

The database JET2 Viewer, openly accessible at http://www.jet2viewer.upmc.fr/, reports putative protein binding sites for all three-dimensional (3D) structures available in the Protein Data Bank (PDB). This knowledge base was generated by applying the computational method JET2 at large-scale on more than 20 000 chains. JET2 strategy yields very precise predictions of interacting surfaces and unravels their evolutionary process and complexity. JET2 Viewer provides an online intelligent display, including interactive 3D visualization of the binding sites mapped onto PDB structures and suitable files recording JET2 analyses. Predictions were evaluated on more than 15 000 experimentally characterized protein interfaces. This is, to our knowledge, the largest evaluation of a protein binding site prediction method. The overall performance of JET2 on all interfaces are: Sen = 52.52, PPV = 51.24, Spe = 80.05, Acc = 75.89. The data can be used to foster new strategies for protein–protein interactions modulation and interaction surface redesign.

Protein stays stable without its charges

Charge is a fundamental factor that helps dictate a protein’s structure and activity. Five out of the 20 amino acids commonly found in proteins are either positively or negatively charged under physiological conditions, and all known soluble proteins have at least a few of these residues. Now, in a surprising twist, researchers have mutated a protein to remove its charged amino acids and found that the protein retains its structure, solubility, and activity. The findings may one day help scientists build better algorithms for designing proteins with new functions (Biochemistry 2016, DOI: 10.1021/acs.biochem.6b00269). To make the uncharged protein, Jakob R. Winther of the University of Copenhagen and colleagues first searched a data bank of protein structures to find the least-charged protein or protein domain containing 100 or more residues. That turned out to be a cellulose-binding domain from the bacterium Cellulomonas fimi. Next, the researchers looked for ways to swap

English

Proteins are the building blocks of cells of living creatures of all kingdoms. Due to pivotal role of protein structures in understanding evolutionary relationships and inferring their functions, the need to have a publically available databank was recognized and this results in establishment of protein data bank. This is a repository of protein structures in form of text files. Understanding and accessing protein structures in plain text files is very cumbersome job and a little bit difficult to understand the data as well. Several struggles have been made to transform protein structures of protein data bank in relational databases but all available relational databases of protein structures till now either do not provide complete information about the protein structures or they are not convenient to. Relational Protein Databank, presented in this paper, is a repository of all protein structures of protein databank stored in tables. A web interface has been provided to access various elements of protein structures in much more user friendly way. The structure of relational protein database has made data selection, insertion, deletion and editing very easy. RPDB is developed in MySQL which is an open source relational database management system.

Design Principles of Membrane Protein Structures

Membrane proteins play key role in cellular signaling and ion transport. Statistical analysis of expanding database of high-resolution membrane protein structures in Protein Data Bank (PDB) provides useful information about membrane protein structure and function. We used RosettaMembrane software (Yarov-Yarovoy V et al (2006) Proteins) to analyze ∼300 unique alpha helical membrane protein structures in PDB and derive knowledge based energy function for membrane protein structure prediction, membrane protein-protein docking, and membrane protein design. The RosettaMembrane residue environment energy term is based on amino acid propensities in hydrophobic, interface, and water layers of the membrane and depends on the residue burial state - from being completely buried within a protein environment to being completely exposed either to the lipid or water environments. Residue buried state is determined from the number of residue neighbors within 6 and 10 A spheres. The RosettaMembrane residue-residue interaction term is based on the propensities of amino acid pairs to be in close proximity to each other within hydrophobic, interface, and water layers. Results of our statistical analysis reveal fine details of favorable and unfavorable environments for all amino acids types in all membrane layers and residue burial states. We find that large hydrophobic amino acids are favorable facing the hydrophobic core of the lipid bilayer. Small amino acids are favorable facing the protein core within the hydrophobic layer of the membrane. Aromatic or positively charged amino acids and favorable facing the lipid head groups. Residue-residue interactions are often favored between polar and charged amino acids and also between some of small and large hydrophobic amino acids inside of the protein core within the hydrophobic layer of the membrane. These data will be useful for rational design of novel membrane protein structures and functions.

Improved 3D Structure Prediction of Beta-Barrel Membrane Proteins by using Evolutionary Coupling Constraints, Reduced State Space and an Empirical Potential Function

Beta-barrel membrane proteins are found in the outer membrane of gram-negative bacteria, mitochondria, and chloroplasts. They carry out diverse biological functions, including pore formation, membrane anchoring, enzyme activity, and bacterial virulence. In addition, beta-barrel membrane proteins increasingly serve as scaffolds for bacterial surface display and nanopore-based DNA sequencing. Due to difficulties in experimental structure determination, they are sparsely represented in the protein structure databank and computational methods can help to understand their biophysical principles. We have developed a novel computational method to predict the 3D structure of beta-barrel membrane proteins using evolutionary coupling (EC) constraints and a reduced state space. Combined with an empirical potential function, we can successfully predict strand register at >80% accuracy for a set of 49 non-homologous proteins with known structures. This is a significant improvement from previous results using EC alone (44%) [1] and using empirical potential function alone (73%) [2]. Our method is general and can be applied to genome-wide structural prediction.[1] Sikander Hayat, Chris Sander, Debora S. Marks, and Arne Elofsson. All-atom 3D structure prediction of transmembrane β-barrel proteins from sequences. PNAS 2015 112 (17) 5413-5418.[2] Hammad Naveed, Yun Xu, Ronald Jackups, Jr., and Jie Liang. Predicting Three-Dimensional Structures of Transmembrane Domains of β-Barrel Membrane Proteins. Journal of the American Chemical Society 2012 134 (3), 1775-1781.

Generalized Allostery in Thrombin

Thrombin is an attractive target for antithrombotic therapy due to its central role in thrombosis and hemostasis. A single-stranded DNA aptamer under G-quadruplex structure binds thrombin at exosite I and inhibits the conversion of soluble fibrinogen to insoluble fibrin and thus prevent the formation of blood clots. To investigate thrombin's response to the aptamer-binding at the molecular level, thermodynamic properties of free and aptamer-bound thrombin were studied using microsecond-scaled all-atom MD simulations. We found the presence of bound-aptamer restricts thrombin's conformational space. The free energy landscape of the free thrombin covers not only structural ensembles of the aptamer-bound thrombin but also nearly all available thrombin structures in RCSB protein data bank. Our study suggests conformational selection is the dominant mechanism of thrombin's recognition of the bound aptamer and the binding event can result in a generalized allosteric regulation on a multifunctional protease, which should be of use for further antithrombotic drug discovery.

BLAST-based structural annotation of protein residues using Protein Data Bank.

BackgroundIn the era of next-generation sequencing where thousands of genomes have been already sequenced; size of protein databases is growing with exponential rate. Structural annotation of these proteins is one of the biggest challenges for the computational biologist. Although, it is easy to perform BLAST search against Protein Data Bank (PDB) but it is difficult for a biologist to annotate protein residues from BLAST search.ResultsA web-server StarPDB has been developed for structural annotation of a protein based on its similarity with known protein structures. It uses standard BLAST software for performing similarity search of a query protein against protein structures in PDB. This server integrates wide range modules for assigning different types of annotation that includes, Secondary-structure, Accessible surface area, Tight-turns, DNA-RNA and Ligand modules. Secondary structure module allows users to predict regular secondary structure states to each residue in a protein. Accessible surface area predict the exposed or buried residues in a protein. Tight-turns module is designed to predict tight turns like beta-turns in a protein. DNA-RNA module developed for predicting DNA and RNA interacting residues in a protein. Similarly, Ligand module of server allows one to predicted ligands, metal and nucleotides ligand interacting residues in a protein.ConclusionsIn summary, this manuscript presents a web server for comprehensive annotation of a protein based on similarity search. It integrates number of visualization tools that facilitate users to understand structure and function of protein residues. This web server is available freely for scientific community from URL http://crdd.osdd.net/raghava/starpdb.ReviewersThis article was reviewed by Prof Michael Gromiha, Prof. Thomas Dandekar and Dr. I. King Jordan.Electronic supplementary materialThe online version of this article (doi:10.1186/s13062-016-0106-9) contains supplementary material, which is available to authorized users.

Resolving the ambiguity: Making sense of intrinsic disorder when PDB structures disagree.

Missing regions in X-ray crystal structures in the Protein Data Bank (PDB) have played a foundational role in the study of intrinsically disordered protein regions (IDPRs), especially in the development of in silico predictors of intrinsic disorder. However, a missing region is only a weak indication of intrinsic disorder, and this uncertainty is compounded by the presence of ambiguous regions, where more than one structure of the same protein sequence "disagrees" in terms of the presence or absence of missing residues. The question is this: are these ambiguous regions intrinsically disordered, or are they the result of static disorder that arises from experimental conditions, ensembles of structures, or domain wobbling? A novel way of looking at ambiguous regions in terms of the pattern between multiple PDB structures has been demonstrated. It was found that the propensity for intrinsic disorder increases as the level of ambiguity decreases. However, it is also shown that ambiguity is more likely to occur as the protein region is placed within different environmental conditions, and even the most ambiguous regions as a set display compositional bias that suggests flexibility. The results suggested that ambiguity is a natural result for many IDPRs crystallized under different conditions and that static disorder and wobbling domains are relatively rare. Instead, it is more likely that ambiguity arises because many of these regions were conditionally or partially disordered.

UET: a database of evolutionarily-predicted functional determinants of protein sequences that cluster as functional sites in protein structures

The structure and function of proteins underlie most aspects of biology and their mutational perturbations often cause disease. To identify the molecular determinants of function as well as targets for drugs, it is central to characterize the important residues and how they cluster to form functional sites. The Evolutionary Trace (ET) achieves this by ranking the functional and structural importance of the protein sequence positions. ET uses evolutionary distances to estimate functional distances and correlates genotype variations with those in the fitness phenotype. Thus, ET ranks are worse for sequence positions that vary among evolutionarily closer homologs but better for positions that vary mostly among distant homologs. This approach identifies functional determinants, predicts function, guides the mutational redesign of functional and allosteric specificity, and interprets the action of coding sequence variations in proteins, people and populations. Now, the UET database offers pre-computed ET analyses for the protein structure databank, and on-the-fly analysis of any protein sequence. A web interface retrieves ET rankings of sequence positions and maps results to a structure to identify functionally important regions. This UET database integrates several ways of viewing the results on the protein sequence or structure and can be found at http://mammoth.bcm.tmc.edu/uet/.

IGNM 2.0: the Gaussian network model database for biomolecular structural dynamics.

Gaussian network model (GNM) is a simple yet powerful model for investigating the dynamics of proteins and their complexes. GNM analysis became a broadly used method for assessing the conformational dynamics of biomolecular structures with the development of a user-friendly interface and database, iGNM, in 2005. We present here an updated version, iGNM 2.0 http://gnmdb.csb.pitt.edu/, which covers more than 95% of the structures currently available in the Protein Data Bank (PDB). Advanced search and visualization capabilities, both 2D and 3D, permit users to retrieve information on inter-residue and inter-domain cross-correlations, cooperative modes of motion, the location of hinge sites and energy localization spots. The ability of iGNM 2.0 to provide structural dynamics data on the large majority of PDB structures and, in particular, on their biological assemblies makes it a useful resource for establishing the bridge between structure, dynamics and function.

Clustering and percolation in protein loop structures.

BackgroundHigh precision protein loop modelling remains a challenge, both in template based and template independent approaches to protein structure prediction.MethodWe introduce the concepts of protein loop clustering and percolation, to develop a quantitative approach to systematically classify the modular building blocks of loops in crystallographic folded proteins. These fragments are all different parameterisations of a unique kink solution to a generalised discrete nonlinear Schrödinger (DNLS) equation. Accordingly, the fragments are also local energy minima of the ensuing energy function.ResultsWe show how the loop fragments cover practically all ultrahigh resolution crystallographic protein structures in Protein Data Bank (PDB), with a 0.2 Ångström root-mean-square (RMS) precision. We find that no more than 12 different loop fragments are needed, to describe around 38 % of ultrahigh resolution loops in PDB. But there is also a large number of loop fragments that are either unique, or very rare, and examples of unique fragments are found even in the structure of a myoglobin.ConclusionsProtein loops are built in a modular fashion. The loops are composed of fragments that can be modelled by the kink of the DNLS equation. The majority of loop fragments are also common, which are shared by many proteins. These common fragments are probably important for supporting the overall protein conformation. But there are also several fragments that are either unique to a given protein, or very rare. Such fragments are probably related to the function of the protein. Furthermore, we have found that the amino acid sequence does not determine the structure in a unique fashion. There are many examples of loop fragments with an identical amino acid sequence, but with a very different structure.Electronic supplementary materialThe online version of this article (doi:10.1186/s12900-015-0049-x) contains supplementary material, which is available to authorized users.

PDB-Explorer: a web-based interactive map of the protein data bank in shape space

BackgroundThe RCSB Protein Data Bank (PDB) provides public access to experimentally determined 3D-structures of biological macromolecules (proteins, peptides and nucleic acids). While various tools are available to explore the PDB, options to access the global structural diversity of the entire PDB and to perceive relationships between PDB structures remain very limited.MethodsA 136-dimensional atom pair 3D-fingerprint for proteins (3DP) counting categorized atom pairs at increasing through-space distances was designed to represent the molecular shape of PDB-entries. Nearest neighbor searches examples were reported exemplifying the ability of 3DP-similarity to identify closely related biomolecules from small peptides to enzyme and large multiprotein complexes such as virus particles. The principle component analysis was used to obtain the visualization of PDB in 3DP-space.ResultsThe 3DP property space groups proteins and protein assemblies according to their 3D-shape similarity, yet shows exquisite ability to distinguish between closely related structures. An interactive website called PDB-Explorer is presented featuring a color-coded interactive map of PDB in 3DP-space. Each pixel of the map contains one or more PDB-entries which are directly visualized as ribbon diagrams when the pixel is selected. The PDB-Explorer website allows performing 3DP-nearest neighbor searches of any PDB-entry or of any structure uploaded as protein-type PDB file. All functionalities on the website are implemented in JavaScript in a platform-independent manner and draw data from a server that is updated daily with the latest PDB additions, ensuring complete and up-to-date coverage. The essentially instantaneous 3DP-similarity search with the PDB-Explorer provides results comparable to those of much slower 3D-alignment algorithms, and automatically clusters proteins from the same superfamilies in tight groups.ConclusionA chemical space classification of PDB based on molecular shape was obtained using a new atom-pair 3D-fingerprint for proteins and implemented in a web-based database exploration tool comprising an interactive color-coded map of the PDB chemical space and a nearest neighbor search tool. The PDB-Explorer website is freely available at www.cheminfo.org/pdbexplorer and represents an unprecedented opportunity to interactively visualize and explore the structural diversity of the PDB.ᅟ Graphical abstractᅟMaps of PDB in 3DP-space color-coded by heavy atom count and shape. Electronic supplementary materialThe online version of this article (doi:10.1186/s12859-015-0776-9) contains supplementary material, which is available to authorized users.

Amino Acid Correlation Functions in Protein Structures.

Understanding the spatial folding of proteins from their amino acid sequences has an enormous potential in contemporary life sciences. The ability to predict secondary and tertiary structures from primary ones through the use of computers will enable a much faster and more efficient discovery of organic substances with therapeutic or otherwise bioactive potential, largely eliminating the need for synthesis and testing of large numbers of organic substances for physiological effects. Our manuscript presents an application of correlation function analysis, usually used to describe properties of liquids, to protein structures in order to elucidate statistically favored distances among amino acids. Pairwise distribution functions were calculated between C-alpha atoms of 20 amino acids in a large ensemble of Protein Data Bank structures. The correlation functions show characteristic distances in amino acid interactions. Different propensities for forming various secondary structure elements among all 210 possible amino acid pairs have been visualized and some have been interpreted. Notably, we found helices to be surprisingly common among certain pairs.

Dynamics of the BH3-Only Protein Binding Interface of Bcl-xL

The balance and interplay between pro-death and pro-survival members of the B-cell lymphoma-2 (Bcl-2) family proteins play key roles in regulation of the mitochondrial pathway of programmed cell death. Recent NMR and biochemical studies have revealed that binding of the proapoptotic BH3-only protein PUMA induces significant unfolding of antiapoptotic Bcl-xL at the interface, which in turn disrupts the Bcl-xL/p53 interaction to activate apoptosis. However, the molecular mechanism of such regulated unfolding of Bcl-xL is not fully understood. Analysis of the existing Protein Data Bank structures of Bcl-xL in both bound and unbound states reveal substantial intrinsic heterogeneity at its BH3-only protein binding interface. Large-scale atomistic simulations were performed in explicit solvent for six representative structures to further investigate the intrinsic conformational dynamics of Bcl-xL. The results support that the BH3-only protein binding interface of Bcl-xL is much more dynamic compared to the rest of the protein, both unbound and when bound to various BH3-only proteins. Such intrinsic interfacial conformational dynamics likely provides a physical basis that allows Bcl-xL to respond sensitively to detailed biophysical properties of the ligand. The ability of Bcl-xL to retain or even enhance dynamics at the interface in bound states could further facilitate the regulation of its interactions with various BH3-only proteins such as through posttranslational modifications.

ChSeq: A database of chameleon sequences.

Chameleon sequences (ChSeqs) refer to sequence strings of identical amino acids that can adopt different conformations in protein structures. Researchers have detected and studied ChSeqs to understand the interplay between local and global interactions in protein structure formation. The different secondary structures adopted by one ChSeq challenge sequence-based secondary structure predictors. With increasing numbers of available Protein Data Bank structures, we here identify a large set of ChSeqs ranging from 6 to 10 residues in length. The homologous ChSeqs discovered highlight the structural plasticity involved in biological function. When compared with previous studies, the set of unrelated ChSeqs found represents an about 20-fold increase in the number of detected sequences, as well as an increase in the longest ChSeq length from 8 to 10 residues. We applied secondary structure predictors on our ChSeqs and found that methods based on a sequence profile outperformed methods based on a single sequence. For the unrelated ChSeqs, the evolutionary information provided by the sequence profile typically allows successful prediction of the prevailing secondary structure adopted in each protein family. Our dataset will facilitate future studies of ChSeqs, as well as interpretations of the interplay between local and nonlocal interactions. A user-friendly web interface for this ChSeq database is available at prodata.swmed.edu/chseq.