Differences between high- and low-affinity complexes of enzymes and nonenzymes

A novel tetratricopeptide-repeat protein, TTP1, forms complexes with glutamyl-tRNA reductase and protochlorophyllide oxidoreductase during tetrapyrrole biosynthesis

The biosynthesis of the tetrapyrrole end-products chlorophyll and heme depends on a multifaceted control mechanism that acts primarily at the post-translational level upon the rate-limiting step of 5-aminolevulinic acid synthesis and upon light-dependent protochlorophyllide oxidoreductase (POR). These regulatory processes require auxiliary factors that modulate the activity, stability, complex formation, and subplastidal localization of the relevant proteins. Together, they ensure optimal metabolic flow during the day and at night. As an Arabidopsis homolog of the POR-interacting tetratricopeptide-repeat protein (Pitt) first reported in Synechocystis, we characterize tetrapyrrole biosynthesis-regulating tetratricopeptide-repeat protein1 (TTP1). TTP1 is a plastid-localized, membrane-bound factor that interacts with POR, the Mg protoporphyrin monomethylester cyclase CHL27, glutamyl-tRNA reductase (GluTR), GluTR-binding protein, and FLUORESCENCE IN BLUE LIGHT. Lack of TTP1 leads to accumulation of GluTR, enhanced 5-aminolevulinic acid synthesis and lower levels of POR. Knockout mutants show enhanced sensitivity to reactive oxygen species and a slower greening of etiolated seedlings. Based on our studies, the interaction of TTP1 with GluTR and POR does not directly inhibit their enzymatic activity and contribute to the control of 5-aminolevulinic acid synthesis. Instead, we propose that TTP1 sequesters a fraction of these proteins on the thylakoid membrane, and contributes to their stability.

Comparative structural study of selective and non-selective NSAIDs against the enzyme cyclooxygenase-2 through real-time molecular dynamics linked to post-dynamics MM-GBSA and e-pharmacophores mapping

BACKGROUND

Inflammation-provoked disorders including cancer are arbitrated by cyclooxygenase-2 (COX-2). Celecoxib and niflumic acid are among the potent and selective inhibitors of this enzyme while aspirin (acetylsalicylic acid) and sodium salicylate are its non-selective and lesser potent inhibitors. Despite these proven studies, the comparative structural study of these selective and non-selective molecules at atomistic scale in complex state with COX-2 that may answer this differential inhibitory behavior has not been accomplished spotlighting the imperative need of additional research in this area. Thus, this study was framed to provide a strong explanation for the enigma of higher inhibitory activity of celecoxib-niflumic acid duo in comparison to aspirin and sodium salicylate towards COX-2.

METHODS

A contemporary approach including advanced molecular docking against COX2, molecular dynamics of receptor-ligand complexes, simulation-trajectory-backed MMGBSA for different time points, radius of gyration (Rg) calculations, and e-pharmacophores approach was employed to attain a rational conclusion.

RESULTS

Our findings demonstrated the higher binding affinity of celecoxib and niflumic acid over aspirin and sodium salicylate against COX-2. Although both selective and non-selective COX-2 inhibitors manifested nearly the same stability in the active site of this enzyme but the e-pharmocophoric features found in the case of selective inhibitors scored over non-selective ones. Thus, our findings excluded the differential stability to be the cause of stronger potency of selective inhibitors but attributed their potency to greater number of complementary features present in these inhibitors against the active site of inflammation engendering COX-2.

Structural Changes, Biological Consequences, and Repurposing of Colchicine Site Ligands

Microtubule-targeting agents (MTAs) bind to one of several distinct sites in the tubulin dimer, the subunit of microtubules. The binding affinities of MTAs may vary by several orders of magnitude, even for MTAs that specifically bind to a particular site. The first drug binding site discovered in tubulin was the colchicine binding site (CBS), which has been known since the discovery of the tubulin protein. Although highly conserved throughout eukaryotic evolution, tubulins show diversity in their sequences between tubulin orthologs (inter-species sequence differences) and paralogs (intraspecies differences, such as tubulin isotypes). The CBS is promiscuous and binds to a broad range of structurally distinct molecules that can vary in size, shape, and affinity. This site remains a popular target for the development of new drugs to treat human diseases (including cancer) and parasitic infections in plants and animals. Despite the rich knowledge about the diversity of tubulin sequences and the structurally distinct molecules that bind to the CBS, a pattern has yet to be found to predict the affinity of new molecules that bind to the CBS. In this commentary, we briefly discuss the literature evidencing the coexistence of the varying binding affinities for drugs that bind to the CBS of tubulins from different species and within species. We also comment on the structural data that aim to explain the experimental differences observed in colchicine binding to the CBS of β-tubulin class VI (TUBB1) compared to other isotypes.

In silico study of potential SARS-CoV-2 antagonist from Clitoria ternatea

Objectives

In this study, we implemented a structure-based virtual screening protocol in search of natural bioactive compounds in Clitoria ternatea that could inhibit the viral Mpro.

Methods

A library of twelve main bioactive compounds in C. ternatea was created from PubChem database by minimizing ligand structure in PyRx software to increase the ligand flexibility. Molecular docking studies were performed by targeting Mpro (PDB ID: 6lu7) via Discovery Studio Visualiser and PyRx platforms. Top hits compounds were then selected to study their Adsorption, distribution, metabolism, excretion, and toxicity (ADMET) and drug likeness properties through pkCSM pharmacokinetics tool to understand the stability, interaction, conformational changes, and pharmaceutical relevant parameters.

Results

This investigation found that, in the molecular docking simulation, four bioactive compounds (procyanidin A2 [-9.3 kcal/mol], quercetin-3-rutinoside [-8.9 kcal/mol], delphinidin-3-O-glucoside [-8.3 kcal/mol], and ellagic acid [-7.4 kcal/mol]) showed producing the strongest binding affinity to the Mpro of severe acute respiratory syndrome coronavirus 2, as compared to positive control (N3 inhibitor) (-7.5 kcal/mol). These binding energies were found to be favorable for an efficient docking and resultant. In addition, the stability of quercetin-3-rutinoside and ellagic acid is higher without any unfavorable bond. The ADMET and drug likeness of these two compounds were found that they are considered an effective and safe coronavirus disease 2019 (COVID-19) inhibitors through Lipinski's Rule, absorption, distribution, metabolism, and toxicity properties.

Conclusion

From these results, it was concluded that C. ternatea possess potential therapeutic properties against COVID-19.

Sunsetting Binding MOAD with its last data update and the addition of 3D-ligand polypharmacology tools

Binding MOAD is a database of protein-ligand complexes and their affinities with many structured relationships across the dataset. The project has been in development for over 20 years, but now, the time has come to bring it to a close. Currently, the database contains 41,409 structures with affinity coverage for 15,223 (37%) complexes. The website BindingMOAD.org provides numerous tools for polypharmacology exploration. Current relationships include links for structures with sequence similarity, 2D ligand similarity, and binding-site similarity. In this last update, we have added 3D ligand similarity using ROCS to identify ligands which may not necessarily be similar in two dimensions but can occupy the same three-dimensional space. For the 20,387 different ligands present in the database, a total of 1,320,511 3D-shape matches between the ligands were added. Examples of the utility of 3D-shape matching in polypharmacology are presented. Finally, plans for future access to the project data are outlined.

Development of a Single-Chain Variable Fragment of CR3022 for a Plasmonic-Based Biosensor Targeting the SARS-CoV-2 Spike Protein

Two years after SARS-CoV-2 caused the first case of COVID-19, we are now in the "new normal" period, where people's activity has bounced back, followed by the easing of travel policy restrictions. The lesson learned is that the wide availability of accurate and rapid testing procedures is crucial to overcome possible outbreaks in the future. Therefore, many laboratories worldwide have been racing to develop a new point-of-care diagnostic test. To aid continuous innovation, we developed a plasmonic-based biosensor designed explicitly for portable Surface Plasmon Resonance (SPR). In this study, we designed a single chain variable fragment (scFv) from the CR3022 antibody with a particular linker that inserted a cysteine residue at the second position. It caused the linker to have a strong affinity to the gold surface through thiol-coupling and possibly become a ready-to-use bioreceptor toward a portable SPR gold chip without purification steps. The theoretical affinity of this scFv on spike protein was -64.7 kcal/mol, computed using the Molecular Mechanics Generalized Born Surface Area (MM/GBSA) method from the 100 ns molecular dynamics trajectory. Furthermore, the scFv was produced in Escherichia coli BL21 (DE3) as a soluble protein. The binding activity toward Spike Receptor Binding Domain (RBD) SARS-CoV-2 was confirmed with a spot-test, and the experimental binding free energy of -10.82 kcal/mol was determined using portable SPR spectroscopy. We hope this study will be useful in designing specific and low-cost bioreceptors, particularly early in an outbreak when the information on antibody capture is still limited.

Enhancement of DNAzymatic activity using iterative in silico maturation

Enhancement of DNZymatic activity using a combined iterative in silico and in vitro method as a cheaper and more stable alternative to antibodies or enzymes.

Discovery of potential inhibitors against New Delhi metallo-β-lactamase-1 from natural compounds: in silico-based methods

New Delhi metallo-β-lactamase variants and different types of metallo-β-lactamases have attracted enormous consideration for hydrolyzing almost all β-lactam antibiotics, which leads to multi drug resistance bacteria. Metallo-β-lactamases genes have disseminated in hospitals and all parts of the world and became a public health concern. There is no inhibitor for New Delhi metallo-β-lactamase-1 and other metallo-β-lactamases classes, so metallo-β-lactamases inhibitor drugs became an urgent need. In this study, multi-steps virtual screening was done over the NPASS database with 35,032 natural compounds. At first Captopril was extracted from 4EXS PDB code and use as a template for the first structural screening and 500 compounds obtained as hit compounds by molecular docking. Then the best ligand, i.e. NPC120633 was used as templet and 800 similar compounds were obtained. As a final point, ten compounds i.e. NPC171932, NPC100251, NPC18185, NPC98583, NPC112380, NPC471403, NPC471404, NPC472454, NPC473010 and NPC300657 had proper docking scores, and a 50 ns molecular dynamics simulation was performed for calculation binding free energy of each compound with New Delhi metallo-β-lactamase. Protein sequence alignment, 3D conformational alignment, pharmacophore modeling on all New Delhi metallo-β-lactamase variants and all types of metallo-β-lactamases were done. Quantum chemical perspective based on the fragment molecular orbital (FMO) method was performed to discover conserved and crucial residues in the catalytic activity of metallo-β-lactamases. These residues had similar 3D coordinates of spatial location in the 3D conformational alignment. So it is posibble that all types of metallo-β-lactamases can inhibit by these ten compounds. Therefore, these compounds were proper to mostly inhibit all metallo-β-lactamases in experimental studies.

Discovery of nanomolar ligands with novel scaffolds for the histamine H4 receptor by virtual screening

The involvement of histamine H4 receptor (H4R) in immune cells chemotaxis and mediator release makes it an attractive target for the treatment of inflammation disorders. A decade of medicinal chemistry efforts has led to several promising ligands, although the chemical structures described so far possesses a singular limited diversity. We report here the discovery of novel structures, belonging to completely different scaffolds. The virtual screening was planed as a two-steps process. First, using a "scout screening" methodology, we have experimentally probed the H4R ligand binding site using a small size chemical library with very diverse structures, and identified a hit that further assist us in refining a raw 3D homology model. Second, the refined 3D model was used to conduct a widened virtual screening. This two-steps strategy proved to be very successful, both in terms of structural diversity and hit rate (23%). Moreover, the hits have high affinity for the H4R, with most potent ligands in the nanomolar range.

ChemTreeMap: an interactive map of biochemical similarity in molecular datasets

MOTIVATION

What if you could explain complex chemistry in a simple tree and share that data online with your collaborators? Computational biology often incorporates diverse chemical data to probe a biological question, but the existing tools for chemical data are ill-suited for the very large datasets inherent to bioinformatics. Furthermore, existing visualization methods often require an expert chemist to interpret the patterns. Biologists need an interactive tool for visualizing chemical information in an intuitive, accessible way that facilitates its integration into today's team-based biological research.

RESULTS

ChemTreeMap is an interactive, bioinformatics tool designed to explore chemical space and mine the relationships between chemical structure, molecular properties, and biological activity. ChemTreeMap synergistically combines extended connectivity fingerprints and a neighbor-joining algorithm to produce a hierarchical tree with branch lengths proportional to molecular similarity. Compound properties are shown by leaf color, size and outline to yield a user-defined visualization of the tree. Two representative analyses are included to demonstrate ChemTreeMap's capabilities and utility: assessing dataset overlap and mining structure-activity relationships.

AVAILABILITY AND IMPLEMENTATION

The examples from this paper may be accessed at http://ajing.github.io/ChemTreeMap/ Code for the server and client are available in the Supplementary Information, at the aforementioned github site, and on Docker Hub (https://hub.docker.com) with the nametag ajing/chemtreemap.

CONTACT

carlsonh@umich.eduSupplementary information: Supplementary data are available at Bioinformatics online.

State-of-the-art strategies for targeting protein-protein interactions by small-molecule inhibitors

Targeting protein-protein interactions (PPIs) has emerged as a viable approach in modern drug discovery. However, the identification of small molecules enabling us to effectively interrupt their interactions presents significant challenges. In the recent past, significant advances have been made in the development of new biological and chemical strategies to facilitate the discovery process of small-molecule PPI inhibitors. This review aims to highlight the state-of-the-art technologies and the achievements made recently in this field. The "hot spots" of PPIs have been proved to be critical for small molecules to bind. Three strategies including screening, designing, and synthetic approaches have been explored for discovering PPI inhibitors by targeting the "hot spots". Although the classic high throughput screening approach can be used, fragment screening, fragment-based drug design and newly improved virtual screening are demonstrated to be more effective in the discovery of PPI inhibitors. In addition to screening approaches, design strategies including anchor-based and small molecule mimetics of secondary structures involved in PPIs have become powerful tools as well. Finally, constructing new chemically spaced libraries with high diversity and complexity is becoming an important area of interest for PPI inhibitors. The successful cases from the recent five year studies are used to illustrate how these approaches are implemented to uncover and optimize small molecule PPI inhibitors and notably some of them have become promising therapeutics.

Recent improvements to Binding MOAD: a resource for protein-ligand binding affinities and structures

For over 10 years, Binding MOAD (Mother of All Databases; http://www.BindingMOAD.org) has been one of the largest resources for high-quality protein-ligand complexes and associated binding affinity data. Binding MOAD has grown at the rate of 1994 complexes per year, on average. Currently, it contains 23,269 complexes and 8156 binding affinities. Our annual updates curate the data using a semi-automated literature search of the references cited within the PDB file, and we have recently upgraded our website and added new features and functionalities to better serve Binding MOAD users. In order to eliminate the legacy application server of the old platform and to accommodate new changes, the website has been completely rewritten in the LAMP (Linux, Apache, MySQL and PHP) environment. The improved user interface incorporates current third-party plugins for better visualization of protein and ligand molecules, and it provides features like sorting, filtering and filtered downloads. In addition to the field-based searching, Binding MOAD now can be searched by structural queries based on the ligand. In order to remove redundancy, Binding MOAD records are clustered in different families based on 90% sequence identity. The new Binding MOAD, with the upgraded platform, features and functionalities, is now equipped to better serve its users.

Comparative assessment of scoring functions on an updated benchmark: 1. Compilation of the test set

Scoring functions are often applied in combination with molecular docking methods to predict ligand binding poses and ligand binding affinities or to identify active compounds through virtual screening. An objective benchmark for assessing the performance of current scoring functions is expected to provide practical guidance for the users to make smart choices among available methods. It can also elucidate the common weakness in current methods for future improvements. The primary goal of our comparative assessment of scoring functions (CASF) project is to provide a high-standard, publicly accessible benchmark of this type. Our latest study, i.e., CASF-2013, evaluated 20 popular scoring functions on an updated set of protein-ligand complexes. This data set was selected out of 8302 protein-ligand complexes recorded in the PDBbind database (version 2013) through a fairly complicated process. Sample selection was made by considering the quality of complex structures as well as binding data. Finally, qualified complexes were clustered by 90% similarity in protein sequences. Three representative complexes were chosen from each cluster to control sample redundancy. The final outcome, namely, the PDBbind core set (version 2013), consists of 195 protein-ligand complexes in 65 clusters with binding constants spanning nearly 10 orders of magnitude. In this data set, 82% of the ligand molecules are "druglike" and 78% of the protein molecules are validated or potential drug targets. Correlation between binding constants and several key properties of ligands are discussed. Methods and results of the scoring function evaluation will be described in a companion work in this issue (doi: 10.1021/ci500081m ).

Potent and specific inhibition of glycosidases by small artificial binding proteins (affitins)

Glycosidases are associated with various human diseases. The development of efficient and specific inhibitors may provide powerful tools to modulate their activity. However, achieving high selectivity is a major challenge given that glycosidases with different functions can have similar enzymatic mechanisms and active-site architectures. As an alternative approach to small-chemical compounds, proteinaceous inhibitors might provide a better specificity by involving a larger surface area of interaction. We report here the design and characterization of proteinaceous inhibitors that specifically target endoglycosidases representative of the two major mechanistic classes; retaining and inverting glycosidases. These inhibitors consist of artificial affinity proteins, Affitins, selected against the thermophilic CelD from Clostridium thermocellum and lysozyme from hen egg. They were obtained from libraries of Sac7d variants, which involve either the randomization of a surface or the randomization of a surface and an artificially-extended loop. Glycosidase binders exhibited affinities in the nanomolar range with no cross-recognition, with efficient inhibition of lysozyme (Ki = 45 nM) and CelD (Ki = 95 and 111 nM), high expression yields in Escherichia coli, solubility, and thermal stabilities up to 81.1°C. The crystal structures of glycosidase-Affitin complexes validate our library designs. We observed that Affitins prevented substrate access by two modes of binding; covering or penetrating the catalytic site via the extended loop. In addition, Affitins formed salt-bridges with residues essential for enzymatic activity. These results lead us to propose the use of Affitins as versatile selective glycosidase inhibitors and, potentially, as enzymatic inhibitors in general.

Exploiting conformational dynamics in drug discovery: design of C-terminal inhibitors of Hsp90 with improved activities

The interaction that occurs between molecules is a dynamic process that impacts both structural and conformational properties of the ligand and the ligand binding site. Herein, we investigate the dynamic cross-talk between a protein and the ligand as a source for new opportunities in ligand design. Analysis of the formation/disappearance of protein pockets produced in response to a first-generation inhibitor assisted in the identification of functional groups that could be introduced onto scaffolds to facilitate optimal binding, which allowed for increased binding with previously uncharacterized regions. MD simulations were used to elucidate primary changes that occur in the Hsp90 C-terminal binding pocket in the presence of first-generation ligands. This data was then used to design ligands that adapt to these receptor conformations, which provides access to an energy landscape that is not visible in a static model. The newly synthesized compounds demonstrated antiproliferative activity at ∼150 nM concentration. The method identified herein may be used to design chemical probes that provide additional information on structural variations of Hsp90 C-terminal binding site.

Exploring the composition of protein-ligand binding sites on a large scale

The residue composition of a ligand binding site determines the interactions available for diffusion-mediated ligand binding, and understanding general composition of these sites is of great importance if we are to gain insight into the functional diversity of the proteome. Many structure-based drug design methods utilize such heuristic information for improving prediction or characterization of ligand-binding sites in proteins of unknown function. The Binding MOAD database if one of the largest curated sets of protein-ligand complexes, and provides a source of diverse, high-quality data for establishing general trends of residue composition from currently available protein structures. We present an analysis of 3,295 non-redundant proteins with 9,114 non-redundant binding sites to identify residues over-represented in binding regions versus the rest of the protein surface. The Binding MOAD database delineates biologically-relevant “valid” ligands from “invalid” small-molecule ligands bound to the protein. Invalids are present in the crystallization medium and serve no known biological function. Contacts are found to differ between these classes of ligands, indicating that residue composition of biologically relevant binding sites is distinct not only from the rest of the protein surface, but also from surface regions capable of opportunistic binding of non-functional small molecules. To confirm these trends, we perform a rigorous analysis of the variation of residue propensity with respect to the size of the dataset and the content bias inherent in structure sets obtained from a large protein structure database. The optimal size of the dataset for establishing general trends of residue propensities, as well as strategies for assessing the significance of such trends, are suggested for future studies of binding-site composition.

Describing the general structure of protein binding sites is fundamentally important for guiding drug design and better understanding structure-function relationships. Here, we analyze small molecules bound to proteins within our large database, Binding MOAD (Mother of All Databases, pronounced like “mode” as a pun referring to ligand-binding modes). We focus on different contacts across the residues in the binding sites, and we normalize the data relative to the protein's entire surface. A key feature of this study is the use of a “control” where we compare real, functional binding sites to the random contacts seen for crystallographic additives against the protein surface. Controls are required in experimental biology, but they are ill-defined in many computational approaches. This allows us to describe how true binding sites are unique on the protein surface and distinct from random patches that attract common, small molecules.

Structural properties of non-traditional drug targets present new challenges for virtual screening

Traditional drug targets have historically included signaling proteins that respond to small molecules and enzymes that use small molecules as substrates. Increasing attention is now being directed toward other types of protein targets, in particular those that exert their function by interacting with nucleic acids or other proteins rather than small-molecule ligands. Here, we systematically compare existing examples of inhibitors of protein-protein interactions to inhibitors of traditional drug targets. While both sets of inhibitors bind with similar potency, we find that the inhibitors of protein-protein interactions typically bury a smaller fraction of their surface area upon binding to their protein targets. The fact that an average atom is less buried suggests that more atoms are needed to achieve a given potency, explaining the observation that ligand efficiency is typically poor for inhibitors of protein-protein interactions. We then carried out a series of docking experiments and found a further consequence of these relatively exposed binding modes is that structure-based virtual screening may be more difficult: such binding modes do not provide sufficient clues to pick out active compounds from decoy compounds. Collectively, these results suggest that the challenges associated with such non-traditional drug targets may not lie with identifying compounds that potently bind to the target protein surface, but rather with identifying compounds that bind in a sufficiently buried manner to achieve good ligand efficiency and, thus, good oral bioavailability. While the number of available crystal structures of distinct protein interaction sites bound to small-molecule inhibitors is relatively small at present (only 21 such complexes were included in this study), these are sufficient to draw conclusions based on the current state of the field; as additional data accumulate it will be exciting to refine the viewpoint presented here. Even with this limited perspective however, we anticipate that these insights, together with new methods for exploring protein conformational fluctuations, may prove useful for identifying the "low-hanging fruit" among non-traditional targets for therapeutic intervention.

Biophysical limits of protein-ligand binding

In classic work, Kuntz et al. (Proc. Nat. Acad. Sci. USA 1999, 96, 9997-10002) introduced the concept of ligand efficiency. Though that study focused primarily on drug-like molecules, it also showed that metal binding led to the greatest ligand efficiencies. Here, the physical limits of binding are examined across the wide variety of small molecules in the Binding MOAD database. The complexes with the greatest ligand efficiencies share the trait of being small, charged ligands bound in highly charged, well buried binding sites. The limit of ligand efficiency is -1.75 kcal/mol·atom for the protein-ligand complexes within Binding MOAD, and 95% of the set have efficiencies below a "soft limit" of -0.83 kcal/mol·atom. On the basis of buried molecular surface area, the hard limit of ligand efficiency is -117 cal/mol·Å(2), which is in surprising agreement with the limit of macromolecule-protein binding. Close examination of the most efficient systems reveals their incredibly high efficiency is dictated by tight contacts between the charged groups of the ligand and the pocket. In fact, a misfit of 0.24 Å in the average contacts inherently decreases the maximum possible efficiency by at least 0.1 kcal/mol·atom.

CSAR benchmark exercise of 2010: combined evaluation across all submitted scoring functions

As part of the Community Structure-Activity Resource (CSAR) center, a set of 343 high-quality, protein–ligand crystal structures were assembled with experimentally determined K_d or K_i information from the literature. We encouraged the community to score the crystallographic poses of the complexes by any method of their choice. The goal of the exercise was to (1) evaluate the current ability of the field to predict activity from structure and (2) investigate the properties of the complexes and methods that appear to hinder scoring. A total of 19 different methods were submitted with numerous parameter variations for a total of 64 sets of scores from 16 participating groups. Linear regression and nonparametric tests were used to correlate scores to the experimental values. Correlation to experiment for the various methods ranged R² = 0.58–0.12, Spearman ρ = 0.74–0.37, Kendall τ = 0.55–0.25, and median unsigned error = 1.00–1.68 pK_d units. All types of scoring functions—force field based, knowledge based, and empirical—had examples with high and low correlation, showing no bias/advantage for any particular approach. The data across all the participants were combined to identify 63 complexes that were poorly scored across the majority of the scoring methods and 123 complexes that were scored well across the majority. The two sets were compared using a Wilcoxon rank-sum test to assess any significant difference in the distributions of >400 physicochemical properties of the ligands and the proteins. Poorly scored complexes were found to have ligands that were the same size as those in well-scored complexes, but hydrogen bonding and torsional strain were significantly different. These comparisons point to a need for CSAR to develop data sets of congeneric series with a range of hydrogen-bonding and hydrophobic characteristics and a range of rotatable bonds.

Residue preference mapping of ligand fragments in the Protein Data Bank

The interaction between small molecules and proteins is one of the major concerns for structure-based drug design because the principles of protein-ligand interactions and molecular recognition are not thoroughly understood. Fortunately, the analysis of protein-ligand complexes in the Protein Data Bank (PDB) enables unprecedented possibilities for new insights. Herein, we applied molecule-fragmentation algorithms to split the ligands extracted from PDB crystal structures into small fragments. Subsequently, we have developed a ligand fragment and residue preference mapping (LigFrag-RPM) algorithm to map the profiles of the interactions between these fragments and the 20 proteinogenic amino acid residues. A total of 4032 fragments were generated from 71 798 PDB ligands by a ring cleavage (RC) algorithm. Among these ligand fragments, 315 unique fragments were characterized with the corresponding fragment-residue interaction profiles by counting residues close to these fragments. The interaction profiles revealed that these fragments have specific preferences for certain types of residues. The applications of these interaction profiles were also explored and evaluated in case studies, showing great potential for the study of protein-ligand interactions and drug design. Our studies demonstrated that the fragment-residue interaction profiles generated from the PDB ligand fragments can be used to detect whether these fragments are in their favorable or unfavorable environments. The algorithm for a ligand fragment and residue preference mapping (LigFrag-RPM) developed here also has the potential to guide lead chemistry modifications as well as binding residues predictions.

Druggable pockets and binding site centric chemical space: a paradigm shift in drug discovery

Detection, comparison and analyses of binding pockets are pivotal to structure-based drug design endeavors, from hit identification, screening of exosites and de-orphanization of protein functions to the anticipation of specific and non-specific binding to off- and anti-targets. Here, we analyze protein-ligand complexes and discuss methods that assist binding site identification, prediction of druggability and binding site comparison. The full potential of pockets is yet to be harnessed, and we envision that better understanding of the pocket space will have far-reaching implications in the field of drug discovery, such as the design of pocket-specific compound libraries and scoring functions.

Computational approaches to identifying and characterizing protein binding sites for ligand design

Given the three-dimensional structure of a protein, how can one find the sites where other molecules might bind to it? Do these sites have the properties necessary for high affinity binding? Is this protein a suitable target for drug design? Here, we discuss recent developments in computational methods to address these and related questions. Geometric methods to identify pockets on protein surfaces have been developed over many years but, with new algorithms, their performance is still improving. Simulation methods show promise in accounting for protein conformational variability to identify transient pockets but lack the ease of use of many of the (rigid) shape-based tools. Sequence and structure comparison approaches are benefiting from the constantly increasing size of sequence and structure databases. Energetic methods can aid identification and characterization of binding pockets, and have undergone recent improvements in the treatment of solvation and hydrophobicity. The "druggability" of a binding site is still difficult to predict with an automated procedure. The methodologies available for this purpose range from simple shape and hydrophobicity scores to computationally demanding free energy simulations.

Non-peptidic thrombospondin-1 mimics as fibroblast growth factor-2 inhibitors: an integrated strategy for the development of new antiangiogenic compounds

Endogenous inhibitors of angiogenesis, such as thrombospondin-1 (TSP-1), are promising sources of therapeutic agents to treat angiogenesis-driven diseases, including cancer. TSP-1 regulates angiogenesis through different mechanisms, including binding and sequestration of the angiogenic factor fibroblast growth factor-2 (FGF-2), through a site located in the calcium binding type III repeats. We hypothesized that the FGF-2 binding sequence of TSP-1 might serve as a template for the development of inhibitors of angiogenesis. Using a peptide array approach followed by binding assays with synthetic peptides and recombinant proteins, we identified a FGF-2 binding sequence of TSP-1 in the 15-mer sequence DDDDDNDKIPDDRDN. Molecular dynamics simulations, taking the full flexibility of the ligand and receptor into account, and nuclear magnetic resonance identified the relevant residues and conformational determinants for the peptide-FGF interaction. This information was translated into a pharmacophore model used to screen the NCI2003 small molecule databases, leading to the identification of three small molecules that bound FGF-2 with affinity in the submicromolar range. The lead compounds inhibited FGF-2-induced endothelial cell proliferation in vitro and affected angiogenesis induced by FGF-2 in the chicken chorioallantoic membrane assay. These small molecules, therefore, represent promising leads for the development of antiangiogenic agents. Altogether, this study demonstrates that new biological insights obtained by integrated multidisciplinary approaches can be used to develop small molecule mimics of endogenous proteins as therapeutic agents.

Lessons for fragment library design: analysis of output from multiple screening campaigns

Over the past 8 years, we have developed, refined and applied a fragment based discovery approach to a range of protein targets. Here we report computational analyses of various aspects of our fragment library and the results obtained for fragment screening. We reinforce the finding of others that the experimentally observed hit rate for screening fragments can be related to a computationally defined druggability index for the target. In general, the physicochemical properties of the fragment hits display the same profile as the library, as is expected for a truly diverse library which probes the relevant chemical space. An analysis of the fragment hits against various protein classes has shown that the physicochemical properties of the fragments are complementary to the properties of the target binding site. The effectiveness of some fragments appears to be achieved by an appropriate mix of pharmacophore features and enhanced aromaticity, with hydrophobic interactions playing an important role. The analysis emphasizes that it is possible to identify small fragments that are specific for different binding sites. To conclude, we discuss how the results could inform further development and improvement of our fragment library.