Specific Protein-ligand Interactions Research Articles

Distance plus attention for binding affinity prediction

Protein-ligand binding affinity plays a pivotal role in drug development, particularly in identifying potential ligands for target disease-related proteins. Accurate affinity predictions can significantly reduce both the time and cost involved in drug development. However, highly precise affinity prediction remains a research challenge. A key to improve affinity prediction is to capture interactions between proteins and ligands effectively. Existing deep-learning-based computational approaches use 3D grids, 4D tensors, molecular graphs, or proximity-based adjacency matrices, which are either resource-intensive or do not directly represent potential interactions. In this paper, we propose atomic-level distance features and attention mechanisms to capture better specific protein-ligand interactions based on donor-acceptor relations, hydrophobicity, and π\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\pi $$\\end{document}-stacking atoms. We argue that distances encompass both short-range direct and long-range indirect interaction effects while attention mechanisms capture levels of interaction effects. On the very well-known CASF-2016 dataset, our proposed method, named Distance plus Attention for Affinity Prediction (DAAP), significantly outperforms existing methods by achieving Correlation Coefficient (R) 0.909, Root Mean Squared Error (RMSE) 0.987, Mean Absolute Error (MAE) 0.745, Standard Deviation (SD) 0.988, and Concordance Index (CI) 0.876. The proposed method also shows substantial improvement, around 2% to 37%, on five other benchmark datasets. The program and data are publicly available on the website https://gitlab.com/mahnewton/daap.Scientific Contribution StatementThis study innovatively introducesdistance-based features to predict protein-ligand binding affinity, capitalizing onunique molecular interactions. Furthermore, the incorporation of protein sequencefeatures of specific residues enhances the model’s proficiency in capturing intricatebinding patterns. The predictive capabilities are further strengthened through theuse of a deep learning architecture with attention mechanisms, and an ensembleapproach, averaging the outputs of five models, is implemented to ensure robustand reliable predictions.

Development of a β-lactamase activity assay for detecting ligand–protein interactions using an engineered split intein and β-lactamase

Abstract Several synthetic compounds bind to proteins of interest and inhibit protein–protein interactions. To develop a detection method for the interactions between the synthetic compounds and the target proteins, we used an engineered split intein derived from Nostoc punctiforme PCC73102 (Npu) DnaE and TEM-1 β-lactamase as reporter proteins. We constructed synthetic ligands bearing a 6-residue C-terminal peptide from Npu DnaE and Cys-Trp as the C-extein, and target proteins bearing the N-terminal region of the engineered Npu DnaE and residues 24–284 of β-lactamase. Specific ligand–protein interactions such as phosphopeptide–Src homology domain 2 (SH2) of c-Src and imatinib–quinone reductase 2 (NQO2) increased the protein trans-splicing (PTS) reaction rates and yields. The PTS product showed the enhanced β-lactamase activity compared with the starting materials. The PTS-based β-lactamase activity assay was used for the quantitative analysis of the ligand–protein interactions. The signal sequence and 9-residue N-terminal sequence of Escherichia coli (E. coli) lipoprotein (Lpp) and residues 46–159 of outer membrane protein A (OmpA) (LppOmpA) were conjugated with the target proteins bearing split intein and β-lactamase to display them on live E. coli cell surfaces. PTS on live E. coli surfaces provided enhanced resistance to carbenicillin.

Fingerprinting Interactions between Proteins and Ligands for Facilitating Machine Learning in Drug Discovery.

Molecular recognition is fundamental in biology, underpinning intricate processes through specific protein-ligand interactions. This understanding is pivotal in drug discovery, yet traditional experimental methods face limitations in exploring the vast chemical space. Computational approaches, notably quantitative structure-activity/property relationship analysis, have gained prominence. Molecular fingerprints encode molecular structures and serve as property profiles, which are essential in drug discovery. While two-dimensional (2D) fingerprints are commonly used, three-dimensional (3D) structural interaction fingerprints offer enhanced structural features specific to target proteins. Machine learning models trained on interaction fingerprints enable precise binding prediction. Recent focus has shifted to structure-based predictive modeling, with machine-learning scoring functions excelling due to feature engineering guided by key interactions. Notably, 3D interaction fingerprints are gaining ground due to their robustness. Various structural interaction fingerprints have been developed and used in drug discovery, each with unique capabilities. This review recapitulates the developed structural interaction fingerprints and provides two case studies to illustrate the power of interaction fingerprint-driven machine learning. The first elucidates structure-activity relationships in β2 adrenoceptor ligands, demonstrating the ability to differentiate agonists and antagonists. The second employs a retrosynthesis-based pre-trained molecular representation to predict protein-ligand dissociation rates, offering insights into binding kinetics. Despite remarkable progress, challenges persist in interpreting complex machine learning models built on 3D fingerprints, emphasizing the need for strategies to make predictions interpretable. Binding site plasticity and induced fit effects pose additional complexities. Interaction fingerprints are promising but require continued research to harness their full potential.

A conformational rearrangement of the SARS-CoV-2 host protein sigma-1 is required for antiviral activity: insights from a combined in-silico/in-vitro approach

The development of effective drugs to treat coronavirus infections remains a significant challenge for the scientific community. Recent evidence reports on the sigma-1 receptor (S1R) as a key druggable host protein in the SARS-CoV-1 and SARS-CoV-2 interactomes and shows a potent antiviral activity against SARS-CoV-2 for the S1R antagonist PB28. To improve PB28 activity, we designed and tested a series of its analogues and identified a compound that is fourfold more potent against SARS-CoV-2 than PB28 itself. Interestingly, we found no direct correlation between S1R affinity and SARS-CoV-2 antiviral activity. Building on this, we employed comparative induced fit docking and molecular dynamics simulations to gain insights into the possible mechanism that occurs when specific ligand–protein interactions take place and that may be responsible for the observed antiviral activity. Our findings offer a possible explanation for the experimental observations, provide insights into the S1R conformational changes upon ligand binding and lay the foundation for the rational design of new S1R ligands with potent antiviral activity against SARS-CoV-2 and likely other viruses.

General Theory of Specific Binding: Insights from a Genetic-Mechano-Chemical Protein Model.

Proteins need to selectively interact with specific targets among a multitude of similar molecules in the cell. However, despite a firm physical understanding of binding interactions, we lack a general theory of how proteins evolve high specificity. Here, we present such a model that combines chemistry, mechanics, and genetics and explains how their interplay governs the evolution of specific protein-ligand interactions. The model shows that there are many routes to achieving molecular discrimination-by varying degrees of flexibility and shape/chemistry complementarity-but the key ingredient is precision. Harder discrimination tasks require more collective and precise coaction of structure, forces, and movements. Proteins can achieve this through correlated mutations extending far from a binding site, which fine-tune the localized interaction with the ligand. Thus, the solution of more complicated tasks is enabled by increasing the protein size, and proteins become more evolvable and robust when they are larger than the bare minimum required for discrimination. The model makes testable, specific predictions about the role of flexibility and shape mismatch in discrimination, and how evolution can independently tune affinity and specificity. Thus, the proposed theory of specific binding addresses the natural question of "why are proteins so big?". A possible answer is that molecular discrimination is often a hard task best performed by adding more layers to the protein.

A Review of In Silico Research, SARS-CoV-2, and Neurodegeneration: Focus on Papain-Like Protease.

Since the appearance of SARS-CoV-2 and the COVID-19 pandemic, the search for new approaches to treat this disease took place in the scientific community. The in silico approach has gained importance at this moment, once the methodologies used in this kind of study allow for the identification of specific protein–ligand interactions, which may serve as a filter step for molecules that can act as specific inhibitors. In addition, it is a low-cost and high-speed technology. Molecular docking has been widely used to find potential viral protein inhibitors for structural and non-structural proteins of the SARS-CoV-2, aiming to block the infection and the virus multiplication. The papain-like protease (PLpro) participates in the proteolytic processing of SARS-CoV-2 and composes one of the main targets studied for pharmacological intervention by in silico methodologies. Based on that, we performed a systematic review about PLpro inhibitors from the perspective of in silico research, including possible therapeutic molecules in relation to this viral protein. The neurological problems triggered by COVID-19 were also briefly discussed, especially relative to the similarities of neuroinflammation present in Alzheimer’s disease. In this context, we focused on two molecules, curcumin and glycyrrhizinic acid, given their PLpro inhibitory actions and neuroprotective properties and potential therapeutic effects on COVID-19.

Biased Docking for Protein-Ligand Pose Prediction.

The interaction between a protein and its ligands is one of the basic and most important processes in biological chemistry. Docking methods aim to predict the molecular 3D structure of protein-ligand complexes starting from coordinates of the protein and the ligand separately. They are widely used in both industry and academia, especially in the context of drug development projects. AutoDock4 is one of the most popular docking tools and, as for any docking method, its performance is highly system dependent. Knowledge about specific protein-ligand interactions on a particular target can be used to successfully overcome this limitation. Here, we describe how to apply the AutoDock Bias protocol, a simple and elegant strategy that allows users to incorporate target-specific information through a modified scoring function that biases the ligand structure towards those poses (or conformations) that establish selected interactions. We discuss two examples using different bias sources. In the first, we show how to steer dockings towards interactions derived from crystal structures of the receptor with different ligands; in the second example, we define and apply hydrophobic biases derived from Molecular Dynamics simulations in mixed solvents. Finally, we discuss general concepts of biased docking, its performance in pose prediction, and virtual screening campaigns as well as other potential applications.

Self-Immolative Difluorophenyl Ester Linker for Affinity-Based Fluorescence Turn-on Protein Detection.

Currently most fluorogenic probes are developed for the analysis of enzymes, where a bond breaking or rearrangement reaction is required to transform a nonfluorescent enzymatic substrate into a fluorescent product. However, this approach cannot be used for proteins that do not possess enzymatic activities. In this article, we show that fluorogenic probes with a self-immolative difluorophenyl ester linker can mimic the bond disassembly processes of fluorogenic enzyme substrates for the rapid analysis of nonenzymatic proteins. Although numerous self-immolative reagents have shown promising applications in sensors, drug delivery systems, and material chemistry, all of them are triggered by either enzymes or small reactive molecules. In our strategy, the probe binds to the protein via a specific protein-ligand interaction, inducing a chemical reaction between the self-immolative linker and an amino acid of the protein, thereby triggering a cascade reaction that leads to the activation and release of the fluorogenic reporter. In contrast, a phenyl ester linker without the difluoro substituent cannot be triggered to release the fluorogenic reporter. With this probe design, live-cell imaging of extracellular and intracellular endogenous tumor marker proteins can be achieved with high selectivity and sensitivity.

Could Quantum Mechanical Properties Be Reflected on Classical Molecular Dynamics? The Case of Halogenated Organic Compounds of Biological Interest.

Essential to understanding life, the biomolecular phenomena have been an important subject in science, therefore a necessary path to be covered to make progress in human knowledge. To fully comprehend these processes, the non-covalent interactions are the key. In this review, we discuss how specific protein-ligand interactions can be efficiently described by low computational cost methods, such as Molecular Mechanics (MM). We have taken as example the case of the halogen bonds (XB). Albeit generally weaker than the hydrogen bonds (HB), the XBs play a key role to drug design, enhancing the affinity and selectivity toward the biological target. Along with the attraction between two electronegative atoms in XBs explained by the σ-hole model, important orbital interactions, as well as relief of Pauli repulsion take place. Nonetheless, such electronic effects can be only well-described by accurate quantum chemical methods that have strong limitations dealing with supramolecular systems due to their high computational cost. To go beyond the poor description of XBs by MM methods, reparametrizing the force-fields equations can be a way to keep the balance between accuracy and computational cost. Thus, we have shown the steps to be considered when parametrizing force-fields to achieve reliable results of complex non-covalent interactions at MM level for In Silico drug design methods.

De Novo Prediction of Binders and Nonbinders for T4 Lysozyme by gREST Simulations.

Molecular recognition underpins all specific protein-ligand interactions and is essential for biomolecular functions. The prediction of canonical binding poses and distinguishing binders from nonbinders are much sought after goals. Here, we apply the generalized replica exchange with solute tempering method, gREST, combined with a flat-bottom potential to evaluate binder and nonbinder interactions with a T4 lysozyme Leu99Ala mutant. The buried hydrophobic cavity and possibility of coupled conformational changes in this protein make binding predictions difficult. The present gREST simulations, enabling enhanced flexibilities of the ligand and protein residues near the binding site, sample bindings in multiple poses, and correct portrayal of X-ray structures. The free-energy profiles of binders (benzene, ethylbenzene, and n-hexylbenzene) are distinct from those of nonbinders (phenol and benzaldehyde). Bindings of the two larger molecules seem to be associated with a structural change toward an excited conformation of the protein, which agrees with experimental findings. The protocol is generally applicable to various proteins having buried cavities with limited access for ligands with different shapes, sizes, and chemical properties.

Conductance Changes in Bovine Serum Albumin Caused by Drug-Binding Triggered Structural Transitions.

Proteins, due to their binding selectivity, are promising candidates for fabricating nanoscale bio-sensors. However, the influence of structural change on protein conductance caused by specific protein-ligand interactions and disease-induced degeneration still remains unknown. Here, we excavated the relationship between circular dichroism (CD) spectroscopy and conductive atomic force microscopy (CAFM) to reveal the effect of the protein secondary structures changes on conductance. The secondary structure of bovine serum albumin (BSA) was altered by the binding of drugs, like amoxicillin (Amox), cephalexin (Cefa), and azithromycin (Azit). The CD spectroscopy shows that the α-helical and β-sheet content of BSA, which varied according to the molar ratio between the drug and BSA, changed by up to 6%. The conductance of BSA monolayers in varying drug concentrations was further characterized via CAFM. We found that BSA conductance has a monotonic relation with α-helical content. Moreover, BSA conductance seems to be in connection with the binding ability of drugs and proteins. This work elucidates that protein conductance variations caused by secondary structure transitions are triggered by drug-binding and indicate that electrical methods are of potential application in protein secondary structure analysis.

Computational Characterization of the Binding of Non-Competitive Inhibitors to AMPA Receptors

Non-competitive inhibitors of AMPA receptors have attracted significant interest in the recent years as potential antiepileptic drugs. While a considerable number of small molecules have been identified and tested as AMPA receptor non-competitive antagonists, only pyridone perampanel (PMP) has been approved for clinical use as an antiepileptic drug. PMP, however, still causes side effects at higher doses, indicating the need for safer and more effective drugs. Recently resolved crystal structures of AMPA receptor complexes of GYKI 53655 (GYKI), CP 465022 (CP), and PMP provided new structural information on non-competitive inhibitor binding to AMPA receptors. While these structurally dissimilar inhibitors share a common binding site, each ligand interacts differently with individual amino acid residues in the binding pocket. However, due to the limited resolution of the crystal structures they do not provide a comprehensive atomistic picture of specific protein-ligand interactions that are important in inhibitor binding. In this study, molecular docking along with molecular dynamics (MD) simulations and binding energy calculations were carried out to assess the stability of crystallographically determined binding modes and explore potential alternative binding modes of inhibitors GYKI, CP, and PMP. Our MD simulation results provide structural insights into interactions of these inhibitors with AMPA receptors and highlight the features of the AMPA receptor non-competitive inhibitor binding pocket that are important in accommodating structurally different inhibitors. This information will aid in structure-based design of new non-competitive inhibitors that target AMPA receptors.

Ligand-protein target screening from cell matrices using reactive desorption electrospray ionization-mass spectrometry via a native-denatured exchange approach.

Native mass spectrometry has been recognized as a powerful tool for probing interactions between small molecules, such as drugs and natural products, and target proteins. However, the presence of heterogeneous proteins and metabolites in real biological systems can alter the conformations of target proteins or compete with candidate ligands, thus necessitating a method for measuring binding stoichiometries in matrices aside from the extensively used pure/recombinant protein systems. Furthermore, some small molecule-protein interactions have a transient and low-affinity nature and thus can be mis-assigned as nonspecific binding complexes that are often formed during the native ESI process. A native-denatured exchange (NDX) approach was recently developed using a reactive desorption electrospray ionization-mass spectrometer (DESI-MS) setup to screen specific interacting partners. The method works by gradually increasing the composition of denaturing solvents contained in the DESI spray and thus conferring a switch from a native to denatured ionization environment. This change impairs three-dimensional structures of target proteins and disrupts specific ligand-protein interactions, leading to decreased holo/apo ratios. In contrast, ligand-protein complexes exhibiting different trends are assigned as nonspecific interactions. Herein, we applied the NDX approach to probe specific ligand-protein interactions in biological matrices. We first used mixtures of model ligands and proteins to examine the use of reactive DESI-MS in recognizing ligand-target binding in mixtures. Subsequently, we used the NDX approach to analyze binding affinity curves of ligands to target proteins spiked in cell lysates with the aid of size exclusion chromatography and demonstrated its use in probing specific ligand-protein interactions from cell matrices.

Probing specific ligand-protein interactions by native-denatured exchange mass spectrometry

Probing ligand-target protein interactions provides essential information for deep understanding of biochemical machinery and design of drug screening assays. Native electrospray ionization-mass spectrometry (ESI-MS) is promising for direct analysis of ligand-protein complexes. However, it lacks the ability to distinguish between specific and non-specific ligand-protein interactions, and to further recognize the specifically bound proteins as drug target candidates, which remains as a major challenge in the field of drug developments by far. Herein we report a native-denatured exchange (NDX) mass spectrometry (MS) acquisition approach using a liquid sample-desorption electrospray ionization (LS-DESI) setup, and demonstrate its capability in enabling a change from native detection of noncovalent ligand-protein complexes to denatured analysis using three model ligand-protein complexes including myoglobin, CDP-ribonuclease and N,N′,N″-triacetylchitotriose (NAG3)-lysozyme. Notably, we found the NDX-MS approach can readily discriminate specific ligand-protein interactions from nonspecific ones, as revealed by their distinct dynamic profiles of Kd as a function of the DESI spraying flow rate. Consequently, this NDX-MS approach holds promise for future applications to discovering specific protein targets for ligands of interest, and to screening compounds with high specificity to drug targets and thus eliminates off-target effects.

Chaperonin-Nanocaged Hemin as an Artificial Metalloenzyme for Oxidation Catalysis.

Taking inspiration from biology's effectiveness in functionalizing protein-based nanocages for chemical processes, we describe here a rational design of an artificial metalloenzyme for oxidations with the bacterial chaperonin GroEL, a nanocage for protein folding in nature, by supramolecular anchoring of catalytically active hemin in its hydrophobic central cavity. The promiscuity of the chaperonin cavity is an essential element of this design, which can mimic the hydrophobic binding pocket in natural metalloenzymes to accept cofactor and substrate without requiring specific ligand-protein interactions. The success of this approach is manifested in the efficient loading of multiple monomeric hemin cofactors to the GroEL cavity by detergent dialysis and good catalytic oxidation properties of the resulting biohybrid in tandem with those of the clean oxidant of H2O2. Investigation of the mechanism of hemin-GroEL-catalyzed oxidation of two-model substrates reveals that the kinetic behavior of the complex follows a ping-pong mechanism in both cases. Through comparison with horseradish peroxidase, the oxidative activity and stability of hemin-GroEL were observed to be similar to those found in natural peroxidases. Adenosine 5'-triphosphate (ATP)-regulated partial dissociation of the biohybrid, as assessed by the reduction of its catalytic activity with the addition of the nucleotide, raises the prospect that ATP may be used to recycle the chaperonin scaffold. Moreover, hemin-GroEL can be applied to the chromogenic detection of H2O2, which (or peroxide in general) is commonly contained in industrial wastes. Considering the rich chemistry of free metalloporphyrins and the ease of production of GroEL and its supramolecular complex with hemin, this work should seed the creation of many new artificial metalloenzymes with diverse reactivities.

Antibody–like Synthetic Molecular Recognition Thin Layers Fabricated by Molecular Imprinting Based on Specific Protein–ligand Interactions

Antibody–like Synthetic Molecular Recognition Thin Layers Fabricated by Molecular Imprinting Based on Specific Protein–ligand Interactions

Discovery of (1S,2R,3S,4S,5R,6R)-2-Amino-3-[(3,4-difluorophenyl)sulfanylmethyl]-4-hydroxy-bicyclo[3.1.0]hexane-2,6-dicarboxylic Acid Hydrochloride (LY3020371·HCl): A Potent, Metabotropic Glutamate 2/3 Receptor Antagonist with Antidepressant-Like Activity.

As part of our ongoing efforts to identify novel ligands for the metabotropic glutamate 2 and 3 (mGlu2/3) receptors, we have incorporated substitution at the C3 and C4 positions of the (1S,2R,5R,6R)-2-amino-bicyclo[3.1.0]hexane-2,6-dicarboxylic acid scaffold to generate mGlu2/3 antagonists. Exploration of this structure-activity relationship (SAR) led to the identification of (1S,2R,3S,4S,5R,6R)-2-amino-3-[(3,4-difluorophenyl)sulfanylmethyl]-4-hydroxy-bicyclo[3.1.0]hexane-2,6-dicarboxylic acid hydrochloride (LY3020371·HCl, 19f), a potent, selective, and maximally efficacious mGlu2/3 antagonist. Further characterization of compound 19f binding to the human metabotropic 2 glutamate (hmGlu2) site was established by cocrystallization of this molecule with the amino terminal domain (ATD) of the hmGlu2 receptor protein. The resulting cocrystal structure revealed the specific ligand-protein interactions, which likely explain the high affinity of 19f for this site and support its functional mGlu2 antagonist pharmacology. Further characterization of 19f in vivo demonstrated an antidepressant-like signature in the mouse forced-swim test (mFST) assay when brain levels of this compound exceeded the cellular mGlu2 IC50 value.

Affinity-guided protein conjugation: the trilogy of covalent protein labeling, assembly and inhibition

Specific and dynamic biological interactions pave the blueprint of signal networks in cell. For example, a great variety of specific protein-ligand interactions define how intracellular signals flow. Taking advantage of the specificity of these interactions, we postulate an “affinity-guided covalent conjugation” strategy to lock binding ligands through covalent reactions between the ligand and the receptor protein. The presence of a nucleophile close to the ligand binding site of a protein is sine qua none of this reaction. Specific noncovalent interaction of a ligand derivative (which contains an electrophile at a designed position) to the ligand binding site of the protein brings the electrophile to the close proximity of the nucleophile. Subsequently, a conjugation reaction spontaneously takes place between the nucleophile and the electrophile, and leads to an intermolecular covalent linkage. This strategy was first showcased in coiled coil peptides which include a cysteine mutation at a selected position. The short peptide sequence was used for covalent labeling of cell surface receptors. The same strategy was then used to guide the design of a set of protein Lego bricks for covalent assembly of protein complexes of unnatural geometry. We finally made “reactive peptides” for natural adaptor proteins that play significant roles in signal transduction. The peptides were designed to react with a single domain of the multidomain adaptor protein, delivered into the cytosol of neurons, and re-directed the intracellular signal of neuronal migration. The trilogy of protein labeling, assembly, and inhibition of intracellular signals, all through a specific covalent bond, fully demonstrated the generality and versatility of “affinity-guided covalent conjugation” in various applications.

Toward an Enhanced Sampling Molecular Dynamics Method for Studying Ligand-Induced Conformational Changes in Proteins.

Current enhanced sampling molecular dynamics methods for studying large conformational changes in proteins suffer from certain limitations. These include, among others, the need for user defined collective variables, the prerequisite of both start and end point structures of the conformational change, and the need for a priori knowledge of the amount by which to boost specific parts of the potential. In this paper, a framework is proposed for a molecular dynamics method for studying ligand-induced conformational changes, in which the nonbonded interactions between the ligand and the protein are used to calculate a biasing force. The method requires only a single input structure, and does not entail the use of collective variables. We provide a proof-of-concept for accelerating conformational changes in three simple test molecules, as well as promising results for two proteins known to undergo domain closure upon ligand binding. For the ribose-binding protein, backbone root-mean-square deviations as low as 0.75 Å compared to the crystal structure of the closed conformation are obtained within 50 ns simulations, whereas no domain closures are observed in unbiased simulations. A skewed closed structure is obtained for the glutamine-binding protein at high bias values, indicating that specific protein-ligand interactions might suppress important protein-protein interactions.

Nucleotide binding database NBDB--a collection of sequence motifs with specific protein-ligand interactions.

NBDB database describes protein motifs, elementary functional loops (EFLs) that are involved in binding of nucleotide-containing ligands and other biologically relevant cofactors/coenzymes, including ATP, AMP, ATP, GMP, GDP, GTP, CTP, PAP, PPS, FMN, FAD(H), NAD(H), NADP, cAMP, cGMP, c-di-AMP and c-di-GMP, ThPP, THD, F-420, ACO, CoA, PLP and SAM. The database is freely available online at http://nbdb.bii.a-star.edu.sg. In total, NBDB contains data on 249 motifs that work in interactions with 24 ligands. Sequence profiles of EFL motifs were derived de novo from nonredundant Uniprot proteome sequences. Conserved amino acid residues in the profiles interact specifically with distinct chemical parts of nucleotide-containing ligands, such as nitrogenous bases, phosphate groups, ribose, nicotinamide, and flavin moieties. Each EFL profile in the database is characterized by a pattern of corresponding ligand–protein interactions found in crystallized ligand–protein complexes. NBDB database helps to explore the determinants of nucleotide and cofactor binding in different protein folds and families. NBDB can also detect fragments that match to profiles of particular EFLs in the protein sequence provided by user. Comprehensive information on sequence, structures, and interactions of EFLs with ligands provides a foundation for experimental and computational efforts on design of required protein functions.