Molecular docking programs successfully predict the binding of a beta-lactamase inhibitory protein to TEM-1 beta-lactamase

Insight into Structure-Function Relationships of β-Lactamase and BLIPs Interface Plasticity using Protein-Protein Interactions

BACKGROUND

Mostly BLIPs are identified in soil bacteria Streptomyces and originally isolated from Streptomyces clavuligerus and can be utilized as a model system for biophysical, structural, mutagenic and computational studies. BLIP possess homology with two proteins viz., BLIP-I (Streptomyces exofoliatus) and BLP (beta-lactamase inhibitory protein like protein from S. clavuligerus). BLIP consists of 165 amino acid, possessing two homologues domains comprising helix-loop-helix motif packed against four stranded beta-sheet resulting into solvent exposed concave surface with extended four stranded beta-sheet. BLIP-I is a 157 amino acid long protein obtained from S. exofoliatus having 37% sequence identity to BLIP and inhibits beta-lactamase.

METHODS

This review is intended to briefly illustrate the beta-lactamase inhibitory activity of BLIP via proteinprotein interaction and aims to open up a new avenue to combat antimicrobial resistance using peptide based inhibition.

RESULTS

D49A mutation in BLIP-I results in a decrease in affinity for TEM-1 from 0.5 nM to 10 nM (Ki). It is capable of inhibiting TEM-1 and bactopenemase and differs from BLIP only in modulating cell wall synthesis enzyme. Whereas, BLP is a 154 amino acid long protein isolated from S. clavuligerus via DNA sequencing analysis of Cephamycin-Clavulanate gene bunch. It shares 32% sequence similarity with BLIP and 42% with BLIP-I. Its biological function is unclear and lacks beta-lactamase inhibitory activity.

CONCLUSION

Protein-protein interactions mediate a significant role in regulation and modulation of cellular developments and processes. Specific biological markers and geometric characteristics are manifested by active site binding clefts of protein surfaces which determines the specificity and affinity for their targets. TEM1.BLIP is a classical model to study protein-protein interaction. β-Lactamase inhibitory proteins (BLIPs) interacts and inhibits various β-lactamases with extensive range of affinities.

Machine Learning Classification Model for Functional Binding Modes of TEM-1 β-Lactamase

TEM family of enzymes is one of the most commonly encountered β-lactamases groups with different catalytic capabilities against various antibiotics. Despite the studies investigating the catalytic mechanism of TEM β-lactamases, the binding modes of these enzymes against ligands in different functional catalytic states have been largely overlooked. But the binding modes may play a critical role in the function and even the evolution of these proteins. In this work, a newly developed machine learning analysis approach to the recognition of protein dynamics states was applied to compare the binding modes of TEM-1 β-lactamase with regard to penicillin in different catalytic states. While conventional analysis methods, including principal components analysis (PCA), could not differentiate TEM-1 in different binding modes, the application of a machine learning method led to excellent classification models differentiating these states. It was also revealed that both reactant/product states and apo/product states are more differentiable than the apo/reactant states. The feature importance generated by the training procedure of the machine learning model was utilized to evaluate the contribution from residues at active sites and in different secondary structures. Key active site residues, Ser70 and Ser130, play a critical role in differentiating reactant/product states, while other active site residues are more important for differentiating apo/product states. Overall, this study provides new insights into the different dynamical function states of TEM-1 and may open a new venue for β-lactamases functional and evolutional studies in general.

A non-beta-lactam antibiotic inhibitor for enterohemorrhagic Escherichia coli O104:H4

The overuse of antibiotics has caused an increased prevalence of drug-resistant bacteria. Bacterial resistance in E. coli is regulated via production of β-lactam-hydrolyzing β-lactamases enzymes. Escherichia coli O104: H4 is a multi-drug resistant strain known to resist β-lactam as well as several other antibiotics. Here, we report a molecular dynamic simulation-combined docking approach to identify, screen, and verify active pharmacophores against enterohemorrhagic Escherichia coli (EHEC). Experimental studies revealed a boronic acid cyclic monomer (BACM), a non-β-lactam compound, to inhibit the growth of E. coli O104: H4. In vitro Kirby Bauer disk diffusion susceptibility testing coupled interaction analysis suggests BACM inhibits E. coli O104:H4 growth by not only inhibiting the β-lactamase pathway but also via direct inhibition of the penicillin-binding protein. These results suggest that BACM could be used as a lead compound to develop potent drugs targeting beta-lactam resistant Gram-negative bacterial strains. KEY MESSAGES: • An in silico approach was reported to identify pharmacophores against E. coli O104: H4. • In vitro studies revealed a non-β-lactam compound to inhibit the growth of E. coli O104: H4. • This non-β-lactam compound could be used as a lead compound for targeting beta-lactam strains.

Tackling the Antibiotic Resistance Caused by Class A β-Lactamases through the Use of β-Lactamase Inhibitory Protein

β-Lactams are the most widely used and effective antibiotics for the treatment of infectious diseases. Unfortunately, bacteria have developed several mechanisms to combat these therapeutic agents. One of the major resistance mechanisms involves the production of β-lactamase that hydrolyzes the β-lactam ring thereby inactivating the drug. To overcome this threat, the small molecule β-lactamase inhibitors (e.g., clavulanic acid, sulbactam and tazobactam) have been used in combination with β-lactams for treatment. However, the bacterial resistance to this kind of combination therapy has evolved recently. Therefore, multiple attempts have been made to discover and develop novel broad-spectrum β-lactamase inhibitors that sufficiently work against β-lactamase producing bacteria. β-lactamase inhibitory proteins (BLIPs) (e.g., BLIP, BLIP-I and BLIP-II) are potential inhibitors that have been found from soil bacterium Streptomyces spp. BLIPs bind and inhibit a wide range of class A β-lactamases from a diverse set of Gram-positive and Gram-negative bacteria, including TEM-1, PC1, SME-1, SHV-1 and KPC-2. To the best of our knowledge, this article represents the first systematic review on β-lactamase inhibitors with a particular focus on BLIPs and their inherent properties that favorably position them as a source of biologically-inspired drugs to combat antimicrobial resistance. Furthermore, an extensive compilation of binding data from β-lactamase–BLIP interaction studies is presented herein. Such information help to provide key insights into the origin of interaction that may be useful for rationally guiding future drug design efforts.

Phenylboronic Acid Derivatives as Validated Leads Active in Clinical Strains Overexpressing KPC-2: A Step against Bacterial Resistance

The emergence and dissemination of multidrug resistant (MDR) pathogens resistant to nearly all available antibiotics poses a significant threat in clinical therapy. Among them, Klebsiella pneumoniae clinical isolates overexpressing KPC-2 carbapenemase are the most worrisome, extending bacterial resistance to last-resort carbapenems. In this study, we investigate the molecular recognition requirements in the KPC-2 active site by small phenylboronic acid derivatives. Four new phenylboronic acid derivatives were designed and tested against KPC-2. For the most active, despite their simple chemical structures, nanomolar affinity was achieved. The new derivatives restored susceptibility to meropenem in clinical strains overexpressing KPC-2. Moreover, no cytotoxicity was detected in cell-viability assays, which further validated the designed leads. Two crystallographic binary complexes of the best inhibitors binding KPC-2 were obtained at high resolution. Kinetic descriptions of slow binding, time-dependent inhibition, and interaction geometries in KPC-2 were fully investigated. This study will ultimately lead toward the optimization and development of more-effective KPC-2 inhibitors.

Microscopic Characterization of Membrane Transporter Function by In Silico Modeling and Simulation

Membrane transporters mediate one of the most fundamental processes in biology. They are the main gatekeepers controlling active traffic of materials in a highly selective and regulated manner between different cellular compartments demarcated by biological membranes. At the heart of the mechanism of membrane transporters lie protein conformational changes of diverse forms and magnitudes, which closely mediate critical aspects of the transport process, most importantly the coordinated motions of remotely located gating elements and their tight coupling to chemical processes such as binding, unbinding and translocation of transported substrate and cotransported ions, ATP binding and hydrolysis, and other molecular events fueling uphill transport of the cargo. An increasing number of functional studies have established the active participation of lipids and other components of biological membranes in the function of transporters and other membrane proteins, often acting as major signaling and regulating elements. Understanding the mechanistic details of these molecular processes require methods that offer high spatial and temporal resolutions. Computational modeling and simulations technologies empowered by advanced sampling and free energy calculations have reached a sufficiently mature state to become an indispensable component of mechanistic studies of membrane transporters in their natural environment of the membrane. In this article, we provide an overview of a number of major computational protocols and techniques commonly used in membrane transporter modeling and simulation studies. The article also includes practical hints on effective use of these methods, critical perspectives on their strengths and weak points, and examples of their successful applications to membrane transporters, selected from the research performed in our own laboratory.

Applying mass spectrometry to study non-covalent biomolecule complexes

Non-covalent interactions are essential for the structural organization of biomacromolecules and play an important role in molecular recognition processes, such as the interactions between proteins, glycans, lipids, DNA, and RNA. Mass spectrometry (MS) is a powerful tool for studying of non-covalent interactions, due to the low sample consumption, high sensitivity, and label-free nature. Nowadays, native-ESI MS is heavily used in studies of non-covalent interactions and to understand the architecture of biomolecular complexes. However, MALDI-MS is also becoming increasingly useful. It is challenging to detect the intact complex without fragmentation when analyzing non-covalent interactions with MALDI-MS. There are two methodological approaches to do so. In the first approach, different experimental and instrumental parameters are fine-tuned in order to find conditions under which the complex is stable, such as applying non-acidic matrices and collecting first-shot spectra. In the second approach, the interacting species are "artificially" stabilized by chemical crosslinking. Both approaches are capable of studying non-covalently bound biomolecules even in quite challenging systems, such as membrane protein complexes. Herein, we review and compare native-ESI and MALDI MS for the study of non-covalent interactions.

Receptor-based virtual screening protocol for drug discovery

Computational aided drug design (CADD) is presently a key component in the process of drug discovery and development as it offers great promise to drastically reduce cost and time requirements. In the pharmaceutical arena, virtual screening is normally regarded as the top CADD tool to screen large libraries of chemical structures and reduce them to a key set of likely drug candidates regarding a specific protein target. This chapter provides a comprehensive overview of the receptor-based virtual screening process and of its importance in the present drug discovery and development paradigm. Following a focused contextualization on the subject, the main stages of a virtual screening campaign, including its strengths and limitations, are the subject of particular attention in this review. In all of these stages special consideration will be given to practical issues that are normally the Achilles heel of the virtual screening process.

Probing the binding mechanism of novel dual NF-κB/AP-1 inhibitors by 3D-QSAR, docking and molecular dynamics simulations

Potent dual NF-κB/AP-1 inhibitors could effectively treat immunoinflammatory diseases. An integrated computational study was carried out to identify the most favourable binding sites, the structural features and the interaction mechanisms.

Studies of synthetic chalcone derivatives as potential inhibitors of secretory phospholipase A2, cyclooxygenases, lipoxygenase and pro-inflammatory cytokines

Arachidonic acid metabolism leads to the generation of key lipid mediators which play a fundamental role during inflammation. The inhibition of enzymes involved in arachidonic acid metabolism has been considered as a synergistic anti-inflammatory effect with enhanced spectrum of activity. A series of 1,3-diphenyl-2-propen-1-one derivatives were investigated for anti-inflammatory related activities involving inhibition of secretory phospholipase A2, cyclooxygenases, soybean lipoxygenase, and lipopolysaccharides-induced secretion of interleukin-6 and tumor necrosis factor-alpha in mouse RAW264.7 macrophages. The results from the above mentioned assays exhibited that the synthesized compounds were effective inhibitors of pro-inflammatory enzymes and cytokines. The results also revealed that the chalcone derivatives with 4-methlyamino ethanol substitution seem to be significant for inhibition of enzymes and cytokines. Molecular docking experiments were carried out to elucidate the molecular aspects of the observed inhibitory activities of the investigated compounds. Present findings increase the possibility that these chalcone derivatives might serve as a beneficial starting point for the design and development of improved anti-inflammatory agents.

Specific and non-specific protein association in solution: computation of solvent effects and prediction of first-encounter modes for efficient configurational bias Monte Carlo simulations

Weak and ultraweak protein-protein association play a role in molecular recognition and can drive spontaneous self-assembly and aggregation. Such interactions are difficult to detect experimentally, and are a challenge to the force field and sampling technique. A method is proposed to identify low-population protein-protein binding modes in aqueous solution. The method is designed to identify preferential first-encounter complexes from which the final complex(es) at equilibrium evolve. A continuum model is used to represent the effects of the solvent, which accounts for short- and long-range effects of water exclusion and for liquid-structure forces at protein/liquid interfaces. These effects control the behavior of proteins in close proximity and are optimized on the basis of binding enthalpy data and simulations. An algorithm is described to construct a biasing function for self-adaptive configurational-bias Monte Carlo of a set of interacting proteins. The function allows mixing large and local changes in the spatial distribution of proteins, thereby enhancing sampling of relevant microstates. The method is applied to three binary systems. Generalization to multiprotein complexes is discussed.

F2Dock: fast Fourier protein-protein docking

The functions of proteins are often realized through their mutual interactions. Determining a relative transformation for a pair of proteins and their conformations which form a stable complex, reproducible in nature, is known as docking. It is an important step in drug design, structure determination, and understanding function and structure relationships. In this paper, we extend our nonuniform fast Fourier transform-based docking algorithm to include an adaptive search phase (both translational and rotational) and thereby speed up its execution. We have also implemented a multithreaded version of the adaptive docking algorithm for even faster execution on multicore machines. We call this protein-protein docking code F2Dock (F2 = Fast Fourier). We have calibrated F2Dock based on an extensive experimental study on a list of benchmark complexes and conclude that F2Dock works very well in practice. Though all docking results reported in this paper use shape complementarity and Coulombic-potential-based scores only, F2Dock is structured to incorporate Lennard-Jones potential and reranking docking solutions based on desolvation energy .

Computational redesign of the SHV-1 beta-lactamase/beta-lactamase inhibitor protein interface

Beta-lactamases are enzymes that catalyze the hydrolysis of beta-lactam antibiotics. beta-lactamase/beta-lactamase inhibitor protein (BLIP) complexes are emerging as a well characterized experimental model system for studying protein-protein interactions. BLIP is a 165 amino acid protein that inhibits several class A beta-lactamases with a wide range of affinities: picomolar affinity for K1; nanomolar affinity for TEM-1, SME-1, and BlaI; but only micromolar affinity for SHV-1 beta-lactamase. The large differences in affinity coupled with the availability of extensive mutagenesis data and high-resolution crystal structures for the TEM-1/BLIP and SHV-1/BLIP complexes make them attractive systems for the further development of computational design methodology. We used EGAD, a physics-based computational design program, to redesign BLIP in an attempt to increase affinity for SHV-1. Characterization of several of designs and point mutants revealed that in all cases, the mutations stabilize the interface by 10- to 1000-fold relative to wild type BLIP. The calculated changes in binding affinity for the mutants were within a mean absolute error of 0.87 kcal/mol from the experimental values, and comparison of the calculated and experimental values for a set of 30 SHV-1/BLIP complexes yielded a correlation coefficient of 0.77. Structures of the two complexes with the highest affinity, SHV-1/BLIP (E73M) and SHV-1/BLIP (E73M, S130K, S146M), are presented at 1.7 A resolution. While the predicted structures have much in common with the experimentally determined structures, they do not coincide perfectly; in particular a salt bridge between SHV-1 D104 and BLIP K74 is observed in the experimental structures, but not in the predicted design conformations. This discrepancy highlights the difficulty of modeling salt bridge interactions with a protein design algorithm that approximates side chains as discrete rotamers. Nevertheless, while local structural features of the interface were sometimes miscalculated, EGAD is globally successful in designing complexes with increased affinity.

Details of Toll-like receptor:adapter interaction revealed by germ-line mutagenesis

The immunovariant N-ethyl-N-nitrosourea-induced mutations Pococurante (Poc) and Lackadaisical were found to alter MyD88, creating striking receptor-selective effects. Poc, in particular, prevented sensing of all MyD88-dependent Toll-like receptor (TLR) ligands except diacyl lipopeptides. Furthermore, Poc-site and classical BB loop mutations caused equivalent phenotypes when engrafted into any TLR/IL-1 receptor/resistance (TIR) domain. These observations, complemented by data from docking studies and site-directed mutagenesis, revealed that BB loops and Poc sites interact homotypically across the receptor:adapter signaling interface, whereas the C-terminal alpha(E)-helices support adapter:adapter and receptor:receptor oligomerization. We have thus defined the TIR domain surface that mediates association between TLRs and MyD88 and the surface required for MyD88 or TLR oligomerization. Moreover, MyD88 engages individual TLRs differently, suggesting the feasibility of selective pharmacologic TIR domain receptor blockade.

Computational model for the IGF-II/IGF2r complex that is predictive of mutational and surface plasmon resonance data

Insulin-like growth factors (IGFs) are key regulators of cell proliferation, differentiation, and transformation, and are thus pivotal in cancer, especially breast, prostate, and colon neoplasm. Their potent mitogenic and anti-apoptotic actions depend primarily on their availability to bind to the signaling IGF cell surface receptors. One mechanism by which IGF-II availability is thought to be modulated is by binding to the nonsignaling IGF-II receptor (IGF2R). This binding is essentially mediated by domain 11 in the multidomain IGF2R extracellular region. The crystal structure of domain 11 of the human IGF-II receptor (IGF2R-d11) has identified a putative IGF-II binding site, and a nuclear magnetic resonance (NMR) solution structure for the IGF-II ligand has also been characterized. These structures have now been used to model in silico the protein-protein interaction between IGF-II and IGF2R-d11 using the program 3D-Dock. Because the IGF-II data comprise an ensemble of 20 structures, all of which satisfy the NMR constraints, the docking procedure was applied to each member of the ensemble. Only those models in which residue Ile1572 of IGF2R-d11, known to be essential for the binding of IGF-II, was at the interface were considered further. These plausible complexes were then critically assessed using an array of analysis techniques including consideration of additional mutagenesis data. One model was strongly supported by these analyses and is discussed here in detail. Furthermore, we demonstrate in vitro experimental support for this model by studying the binding of chimeras of IGF-I and IGF-II to IGF2R fragments.

A structural perspective on protein-protein interactions

Structures of macromolecular complexes are necessary for a mechanistic description of biochemical and cellular processes. They can be solved by experimental methods, such as X-ray crystallography, NMR spectroscopy and electron microscopy, as well as by computational protein structure prediction, docking and bioinformatics. Recent advances and applications of these methods emphasize the need for hybrid approaches that combine a variety of data to achieve better efficiency, accuracy, resolution and completeness.

The impact of protein flexibility on protein-protein docking

Accounting for protein flexibility in protein-protein docking algorithms is challenging, and most algorithms therefore treat proteins as rigid bodies or permit side-chain motion only. While the consequences are obvious when there are large conformational changes upon binding, the situation is less clear for the modest conformational changes that occur upon formation of most protein-protein complexes. We have therefore studied the impact of local protein flexibility on protein-protein association by means of rigid body and torsion angle dynamics simulation. The binding of barnase and barstar was chosen as a model system for this study, because the complexation of these 2 proteins is well-characterized experimentally, and the conformational changes accompanying binding are modest. On the side-chain level, we show that the orientation of particular residues at the interface (so-called hotspot residues) have a crucial influence on the way contacts are established during docking from short protein separations of approximately 5 A. However, side-chain torsion angle dynamics simulations did not result in satisfactory docking of the proteins when using the unbound protein structures. This can be explained by our observations that, on the backbone level, even small (2 A) local loop deformations affect the dynamics of contact formation upon docking. Complementary shape-based docking calculations confirm this result, which indicates that both side-chain and backbone levels of flexibility influence short-range protein-protein association and should be treated simultaneously for atomic-detail computational docking of proteins.

On proteins, grids, correlations, and docking

The activity of a living cell can be portrayed as a network of interactions involving proteins and nucleic acids that transfer biological information. Intervention in cellular processes requires thorough understanding of the interactions between the molecules, which can be provided by docking techniques. Docking methods attempt to predict the structures of complexes given the structures of the component molecules. We focus hereby on protein-protein docking procedures that employ grid representations of the molecules, and use correlation for searching the solution space and evaluating putative complexes. Geometric surface complementarity is the dominant descriptor in docking. Inclusion of electrostatics often improves the results of geometric docking for soluble proteins, whereas hydrophobic complementarity is more important in construction of oligomers. Using binding-site information in the scan or as a filter helps to identify and up-rank nearly correct solutions.

Probability rule for chiral recognition

Molecular Chirality is of central interest in biological studies because enantiomeric compounds, while indistinguishable by most inanimate systems, show profoundly different properties in biochemical environments. Enantioselective separation methods, based on the differential recognition of two optical isomers by a chiral selector, have been amply documented. Also, great effort has been directed towards a theoretical understanding of the fundamental mechanisms underlying the chiral recognition process. Here we report a comprehensive data examination of enantio separation measurements for over 72000 chiral selector-select and pairs from the chiral selection compendium CHIRBASE. The distribution of alpha = k'(D)/k'(L) values was found to follow a power law, equivalent to an exponential decay for chiral differential free energies. This observation is experimentally relevant in terms of the number of different individual or combinatorial selectors that need to be screened in order to observe alpha values higher than a preset minimum. A string model for enantiorecognition (SMED) formalism is proposed to account for this observation on the basis of an extended Ogston three-point interaction model. Partially overlapping molecular interaction domains are analyzed in terms of a string complementarity model for ligand-receptor complementarity. The results suggest that chiral selection statistics may be interpreted in terms of more general concepts related to biomolecular recognition.

An all-atom solution-equilibrated model for human extrinsic blood coagulation complex (sTF-VIIa-Xa): a protein-protein docking and molecular dynamics refinement study

Tissue factor (TF)-bound factor (F)VIIa plays a critical role in activating FX, an event that rapidly results in blood coagulation. Despite recent advances in the structural information about soluble TF (sTF)-bound VIIa and Xa individually, the atomic details of the ternary complex are not known. As part of our long-term goal to provide a structural understanding of the extrinsic blood coagulation pathway, we built an all atom solution-equilibrated model of the human sTF-VIIa-Xa ternary complex using protein-protein docking and molecular dynamics (MD) simulations. The starting structural coordinates of sTF-VIIa and Xa were derived from dynamically equilibrated solution structures. Due to the flexible nature of the light-chain of the Xa molecule, a three-stage docking approach was employed in which SP (Arg195-Lys448)/EGF2 (Arg86-Arg139), EGF1 (Asp46-Thr85) and GLA (Ala1-Lys45) domains were docked in a sequential manner. The rigid-body docking approach of the FTDOCK method in conjunction with filtering based on biochemical knowledge from experimental site-specific mutagenesis studies provided the strategy. The best complex obtained from the docking experiments was further refined using MD simulations for 3 ns in explicit water. In addition to explaining most of the known experimental site-specific mutagenesis data pertaining to sTF-VIIa, our model also characterizes likely enzyme-binding exosites on FVIIa and Xa that may be involved in the ternary complex formation. According to the equilibrated model, the 140s loop of VIIa serves as the key recognition motif for complex formation. Stable interactions occur between the FVIIa 140s loop and the FXa -strand B2 region near the sodium-binding domain, the 160 s loop and the N-terminal activation loop regions. The helical-hydrophobic stack region that connects the GLA and EGF1 domains of VIIa and Xa appears to play a potential role in the membrane binding region of the ternary complex. The proposed model may serve as a reasonable structural basis for understanding the exosite-mediated substrate recognition of sTF-VIIa and to advance understanding of the TFPI-mediated regulatory pathway of the extrinsic blood coagulation cascade.

Assessment of blind predictions of protein-protein interactions: current status of docking methods

The current status of docking procedures for predicting protein-protein interactions starting from their three-dimensional structure is assessed from a first major evaluation of blind predictions. This evaluation was performed as part of a communitywide experiment on Critical Assessment of PRedicted Interactions (CAPRI). Seven newly determined structures of protein-protein complexes were available as targets for this experiment. These were the complexes between a kinase and its protein substrate, between a T-cell receptor beta-chain and a superantigen, and five antigen-antibody complexes. For each target, the predictors were given the experimental structures of the free components, or of one free and one bound component in a random orientation. The structure of the complex was revealed only at the time of the evaluation. A total of 465 predictions submitted by 19 groups were evaluated. These groups used a wide range of algorithms and scoring functions, some of which were completely novel. The quality of the predicted interactions was evaluated by comparing residue-residue contacts and interface residues to those in the X-ray structures and by analyzing the fit of the ligand molecules (the smaller of the two proteins in the complex) or of interface residues only, in the predicted versus target complexes. A total of 14 groups produced predictions, ranking from acceptable to highly accurate for five of the seven targets. The use of available biochemical and biological information, and in one instance structural information, played a key role in achieving this result. It was essential for identifying the native binding modes for the five correctly predicted targets, including the kinase-substrate complex where the enzyme changes conformation on association. But it was also the cause for missing the correct solution for the two remaining unpredicted targets, which involve unexpected antigen-antibody binding modes. Overall, this analysis reveals genuine progress in docking procedures but also illustrates the remaining serious limitations and points out the need for better scoring functions and more effective ways for handling conformational flexibility.

Weighted geometric docking: incorporating external information in the rotation-translation scan

Weighted geometric docking is a prediction algorithm that matches weighted molecular surfaces. Each molecule is represented by a grid of complex numbers, storing information about the shape of the molecule in the real part and weight information in the imaginary part. The weights are based on experimental biochemical and biophysical data or on theoretical analyses of amino acid conservation or correlation patterns in multiple-sequence alignments of homologous proteins. Only a few surface residues on either one or both molecules are weighted. In contrast to methods that use postscan filtering based on biochemical information, our method incorporates the external data in the rotation-translation search, producing a different set of docking solutions biased toward solutions in which the up-weighted residues are at the interface. Similarly, interactions involving specified residues can be impeded. The weighted geometric algorithm was applied to five systems for which regular geometric docking of the unbound molecules gave poor results. We obtained much better ranking of the nearly correct prediction and higher statistical significance when weighted geometric docking was used. The method was successful even when the weighted portion of the surface corresponded only partially and approximately to the binding site.

ICM-DISCO docking by global energy optimization with fully flexible side-chains

The ICM-DISCO (Docking and Interface Side-Chain Optimization) protein-protein-docking method is a direct stochastic global energy optimization from multiple starting positions of the ligand. The first step is performed by docking of a rigid all-atom ligand molecule to a set of soft receptor potentials precalculated on a 0.5 A grid from realistic solvent-corrected force-field energies. This step finds the correct solution as the lowest energy conformation in almost 100% of the cases in which interfaces do not change on binding. The second step is needed to deal with the induced changes and includes the global optimization of the interface side-chains of up to 400 best solutions. The CAPRI predictions were performed fully automatically with this method. Available experimental information was included as a filtering step to favor expected docking surfaces. In three of the seven proposed targets, the ICM-DISCO method found a good solution (>50% of correct contacts) within the five submitted models. The procedure is global and fully automated. We demonstrate that the algorithm handles the induced changes of surface side-chains but is less successful if the backbone undergoes large-scale rearrangements.

CAPRI: a Critical Assessment of PRedicted Interactions

CAPRI is a communitywide experiment to assess the capacity of protein-docking methods to predict protein-protein interactions. Nineteen groups participated in rounds 1 and 2 of CAPRI and submitted blind structure predictions for seven protein-protein complexes based on the known structure of the component proteins. The predictions were compared to the unpublished X-ray structures of the complexes. We describe here the motivations for launching CAPRI, the rules that we applied to select targets and run the experiment, and some conclusions that can already be drawn. The results stress the need for new scoring functions and for methods handling the conformation changes that were observed in some of the target systems. CAPRI has already been a powerful drive for the community of computational biologists who development docking algorithms. We hope that this issue of Proteins will also be of interest to the community of structural biologists, which we call upon to provide new targets for future rounds of CAPRI, and to all molecular biologists who view protein-protein recognition as an essential process.

GAPDOCK: a Genetic Algorithm Approach to Protein Docking in CAPRI round 1

As part of the first Critical Assessment of PRotein Interactions, round 1, we predict the structure of two protein-protein complexes, by using a genetic algorithm, GAPDOCK, in combination with surface complementarity, buried surface area, biochemical information, and human intervention. Among the five models submitted for target 1, HPr phosphocarrier protein (B. subtilis) and the hexameric HPr kinase (L. lactis), the best correctly predicts 17 of 52 interprotein contacts, whereas for target 2, bovine rotavirus VP6 protein-monoclonal antibody, the best model predicts 27 of 52 correct contacts. Given the difficult nature of the targets, these predictions are very encouraging and compare well with those obtained by other methods. Nevertheless, it is clear that there is a need for improved methods for distinguishing between "correct" and "plausible but incorrect" complexes.

Discovery of diverse thyroid hormone receptor antagonists by high-throughput docking

Treatment of hyperthyroidism, a common clinical condition that can have serious manifestations in the elderly, has remained essentially unchanged for >30 years. Directly antagonizing the effect of the thyroid hormone at the receptor level may be a significant improvement for the treatment of hyperthyroid patients. We built a computer model of the thyroid hormone receptor (TR) ligand-binding domain in its predicted antagonist-bound conformation and used a virtual screening algorithm to select 100 TR antagonist candidates out of a library of >250,000 compounds. We were able to obtain 75 of the compounds selected in silico and studied their ability to act as antagonists by using cultured cells that express TR. Fourteen of these compounds were found to antagonize the effect of T3 on TR with IC50s ranging from 1.5 to 30 microM. A small virtual library of compounds, derived from the highest affinity antagonist (1-850) that could be rapidly synthesized, was generated. A second round of virtual screening identified new compounds with predicted increased antagonist activity. These second generation compounds were synthesized, and their ability to act as TR antagonists was confirmed by transfection and receptor binding experiments. The extreme structural diversity of the antagonist compounds shows how receptor-based virtual screening can identify diverse chemistries that comply with the structural rules of TR antagonism.

Genome-wide studies of protein-protein interaction

Recent large-scale studies of protein complexes in yeast have demonstrated that the wide majority of proteins exist in the cell as parts of multicomponent assemblies, mostly novel and of unknown function. The structural and functional analysis of these complexes should be a priority for structural biologists in coming years. In silico methods such as docking simulations, which may contribute to this analysis, are being tested in the CAPRI community-wide experiment, which assesses blind predictions of the structure of protein-protein complexes.

From words to literature in structural proteomics

Technical advances on several frontiers have expanded the applicability of existing methods in structural biology and helped close the resolution gaps between them. As a result, we are now poised to integrate structural information gathered at multiple levels of the biological hierarchy - from atoms to cells - into a common framework. The goal is a comprehensive description of the multitude of interactions between molecular entities, which in turn is a prerequisite for the discovery of general structural principles that underlie all cellular processes.

Effect of local shape modifications of molecular surfaces on rigid-body protein-protein docking

Geometric complementarity is the most dominant term in protein-protein docking and therefore, a good geometric representation of the molecules, which takes into account the flexibility of surface residues, is desirable. We present a modified geometric representation of the molecular surface that down-weighs the contribution of specified parts of the surface to the complementarity score. We apply it to the mobile ends of the most flexible side chains: lysines, glutamines and arginines (trimming). The new representation systematically reduces the complementarity scores of the false-positive solutions, often more than the scores of the correct solutions, thereby improving significantly our ability to identify nearly correct solutions in rigid-body docking of unbound structures. The effect of trimming lysine residues is larger than trimming of glutamine or arginine residues. It appears to be independent of the conformations of the trimmed residues but depends on the relative abundance of such residues at the interface and on the non-interacting surface. Combining the modified geometric representation with electrostatic complementarity further improves the docking results.

ICFF: a new method to incorporate implicit flexibility into an internal coordinate force field

We introduce a new method to accurately "project" a Cartesian force field onto an internal coordinate molecular model with fixed-bond geometry. The algorithm automatically generates the Internal Coordinate Force Field (ICFF), which is a close approximation of the "source" Cartesian force field. The ICFF method reduces the number of free variables in a model by at least 10-fold and facilitates the fast convergence of geometry optimizations, an advantage that is critical for many applications such as the docking of flexible ligands or conformational modeling of macromolecules. Although covalent geometry is fixed in an ICFF model, implicit flexibility is incorporated into the force field parameters in the following two ways. First, we formulate an empirical torsion energy term in ICFF as a sixfold Fourier series and develop a procedure to calculate the Fourier coefficients from the conformational energy profiles of the fully flexible Cartesian model. The ICFF torsion parameters thus represent not only torsion component of the source force field, but also bond bending, bond stretching, and "1-4" van der Waals interactions. Second, we use a soft polynomial repulsion function for "1-5" and "1-6" interactions to mimic the flexibility of bonds, connecting these atoms. Also, we suggest a way to use a local part of the Cartesian force field to automatically generate fixed covalent geometries, compatible with the ICFF energy function. Here, we present an implementation of the ICFF algorithm, which employs the MMFF94s Cartesian force field as a "source." Extensive benchmarking of ICFF with a representative set of organic molecules demonstrates that the implicit flexibility model accurately reproduces MMFF94s equilibrium conformational energy differences (RMSD approximately 0.64 kcal) and, most importantly, detailed torsion energy profiles (RMSD approximately 0.37 kcal). This accuracy is characteristic of the method, because all the ICFF parameters (except one scaling factor in the "1-5,1-6" repulsion term) are derived directly from the source Cartesian force field and do not depend on any particular molecular set. In contrast, the rigid geometry model with the MMFF94s energy function yields highly biased estimations in this test with the RMSD exceeding 1.2 kcal for the equilibrium energy comparisons and approximately 3.4 kcal for the torsion energy profiles.

Principles of docking: An overview of search algorithms and a guide to scoring functions

The docking field has come of age. The time is ripe to present the principles of docking, reviewing the current state of the field. Two reasons are largely responsible for the maturity of the computational docking area. First, the early optimism that the very presence of the "correct" native conformation within the list of predicted docked conformations signals a near solution to the docking problem, has been replaced by the stark realization of the extreme difficulty of the next scoring/ranking step. Second, in the last couple of years more realistic approaches to handling molecular flexibility in docking schemes have emerged. As in folding, these derive from concepts abstracted from statistical mechanics, namely, populations. Docking and folding are interrelated. From the purely physical standpoint, binding and folding are analogous processes, with similar underlying principles. Computationally, the tools developed for docking will be tremendously useful for folding. For large, multidomain proteins, domain docking is probably the only rational way, mimicking the hierarchical nature of protein folding. The complexity of the problem is huge. Here we divide the computational docking problem into its two separate components. As in folding, solving the docking problem involves efficient search (and matching) algorithms, which cover the relevant conformational space, and selective scoring functions, which are both efficient and effectively discriminate between native and non-native solutions. It is universally recognized that docking of drugs is immensely important. However, protein-protein docking is equally so, relating to recognition, cellular pathways, and macromolecular assemblies. Proteins function when they are bound to other molecules. Consequently, we present the review from both the computational and the biological points of view. Although large, it covers only partially the extensive body of literature, relating to small (drug) and to large protein-protein molecule docking, to rigid and to flexible. Unfortunately, when reviewing these, a major difficulty in assessing the results is the non-uniformity in the formats in which they are presented in the literature. Consequently, we further propose a way to rectify it here.

Docking unbound proteins using shape complementarity, desolvation, and electrostatics

A comprehensive docking study was performed on 27 distinct protein-protein complexes. For 13 test systems, docking was performed with the unbound X-ray structures of both the receptor and the ligand. For the remaining systems, the unbound X-ray structure of only molecule was available; therefore the bound structure for the other molecule was used. Our method optimizes desolvation, shape complementarity, and electrostatics using a Fast Fourier Transform algorithm. A global search in the rotational and translational space without any knowledge of the binding sites was performed for all proteins except nine antibodies recognizing antigens. For these antibodies, we docked their well-characterized binding site-the complementarity-determining region defined without information of the antigen-to the entire surface of the antigen. For 24 systems, we were able to find near-native ligand orientations (interface C(alpha) root mean square deviation less than 2.5 A from the crystal complex) among the top 2,000 choices. For three systems, our algorithm could identify the correct complex structure unambiguously. For 13 other complexes, we either ranked a near-native structure in the top 20 or obtained 20 or more near-native structures in the top 2,000 or both. The key feature of our algorithm is the use of target functions that are highly tolerant to conformational changes upon binding. If combined with a post-processing method, our algorithm may provide a general solution to the unbound docking problem. Our program, called ZDOCK, is freely available to academic users (http://zlab.bu.edu/~rong/dock/).

Molecular modelling in structural biology

Molecular modelling is a powerful methodology for analysing the three dimensional structure of biological macromolecules. There are many ways in which molecular modelling methods have been used to address problems in structural biology. It is not widely appreciated that modelling methods are often an integral component of structure determination by NMR spectroscopy and X-ray crystallography. In this review we consider some of the numerous ways in which modelling can be used to interpret and rationalise experimental data and in constructing hypotheses that can be tested by experiment. Genome sequencing projects are producing a vast wealth of data describing the protein coding regions of the genome under study. However, only a minority of the protein sequences thus identified will have a clear sequence homology to a known protein. In such cases valuable three-dimensional models of the protein coding sequence can be constructed by homology modelling methods. Threading methods, which used specialised schemes to relate protein sequences to a library of known structures, have been shown to be able to identify the likely protein fold even in cases where there is no clear sequence homology. The number of protein sequences that cannot be assigned to a structural class by homology or threading methods, simply because they belong to a previously unidentified protein folding class, will decrease in the future as collaborative efforts in systematic structure determination begin to develop. For this reason, modelling methods are likely to become increasingly useful in the near future. The role of the blind prediction contests, such as the Critical Assessment of techniques for protein Structure Prediction (CASP), will be briefly discussed. Methods for modelling protein-ligand and protein-protein complexes are also described and examples of their applications given.

Electrostatics in protein-protein docking

A novel geometric-electrostatic docking algorithm is presented, which tests and quantifies the electrostatic complementarity of the molecular surfaces together with the shape complementarity. We represent each molecule to be docked as a grid of complex numbers, storing information regarding the shape of the molecule in the real part and information regarding the electrostatic character of the molecule in the imaginary part. The electrostatic descriptors are derived from the electrostatic potential of the molecule. Thus, the electrostatic character of the molecule is represented as patches of positive, neutral, or negative values. The potential for each molecule is calculated only once and stored as potential spheres adequate for exhaustive rotation/translation scans. The geometric-electrostatic docking algorithm is applied to 17 systems, starting form the structures of the unbound molecules. The results-in terms of the complementarity scores of the nearly correct solutions, their ranking in the lists of sorted solutions, and their statistical uniqueness-are compared with those of geometric docking, showing that the inclusion of electrostatic complementarity in docking is very important, in particular in docking of unbound structures. Based on our results, we formulate several "good electrostatic docking rules": The geometric-electrostatic docking procedure is more successful than geometric docking when the potential patches are large and when the potential extends away from the molecular surface and protrudes into the solvent. In contrast, geometric docking is recommended when the electrostatic potential around the molecules to be docked appears homogeneous, that is, with a similar sign all around the molecule.

Potential of mean force for protein-protein interaction studies

Calculating protein-protein interaction energies is crucial for understanding protein-protein associations. On the basis of the methodology of mean-field potential, we have developed an empirical approach to estimate binding free energy for protein-protein interactions. This knowledge-based approach has been used to derive distance-dependent free energies of protein complexes from a nonredundant training set in the Protein Data Bank (PDB), with a careful treatment of homology. We calculate atom pair potentials for 16 pair interactions, which can reflect the importance of hydrophobic interactions and specific hydrogen-bonding interactions. The derived potentials for hydrogen-bonding interactions show a valley of favorable interactions at a distance of approximately 3 A, corresponding to that of an established hydrogen bond. For the test set of 28 protein complexes, the calculated energies have a correlation coefficient of 0.75 compared with experimental binding free energies. The performance of the method in ranking the binding energies of different protein-protein complexes shows that the energy estimation can be applied to value binding free energies for protein-protein associations.

Soft protein-protein docking in internal coordinates

The association of two biological macromolecules is a fundamental biological phenomenon and an unsolved theoretical problem. Docking methods for ab initio prediction of association of two independently determined protein structures usually fail when they are applied to a large set of complexes, mostly because of inaccuracies in the scoring function and/or difficulties on simulating the rearrangement of the interface residues on binding. In this work we present an efficient pseudo-Brownian rigid-body docking procedure followed by Biased Probability Monte Carlo Minimization of the ligand interacting side-chains. The use of a soft interaction energy function precalculated on a grid, instead of the explicit energy, drastically increased the speed of the procedure. The method was tested on a benchmark of 24 protein-protein complexes in which the three-dimensional structures of their subunits (bound and free) were available. The rank of the near-native conformation in a list of candidate docking solutions was <20 in 85% of complexes with no major backbone motion on binding. Among them, as many as 7 out of 11 (64%) protease-inhibitor complexes can be successfully predicted as the highest rank conformations. The presented method can be further refined to include the binding site predictions and applied to the structures generated by the structural proteomics projects. All scripts are available on the Web.

Prediction of protein-protein interactions by docking methods

Recently, developments have been made in predicting the structure of docked complexes when the coordinates of the components are known. The process generally consists of a stage during which the components are combined rigidly and then a refinement stage. Several rapid new algorithms have been introduced in the rigid docking problem and promising refinement techniques have been developed, based on modified molecular mechanics force fields and empirical measures of desolvation, combined with minimisations that switch on the short-range interactions gradually. There has also been progress in developing a benchmark set of targets for docking and a blind trial, similar to the trials of protein structure prediction, has taken place.

Protein therapeutics: promises and challenges for the 21st century

Recent advances in massively parallel experimental and computational technologies are leading to radically new approaches to the early phases of the drug production pipeline. The revolution in DNA microarray technologies and the imminent emergence of its analogue for proteins, along with machine learning algorithms, promise rapid acceleration in the identification of potential drug targets, and in high-throughput screens for subpopulation-specific toxicity. Similarly, advances in structural genomics in conjunction with in vitro and in silico evolutionary methods will rapidly accelerate the number of lead drug candidates and substantially augment their target specificity. Taken collectively, these advances will usher in an era of predictive medicine, which will move medical practice from reactive therapy after disease onset, to proactive prevention.

Structure-based design and in-parallel synthesis of inhibitors of AmpC beta-lactamase

BACKGROUND

Group I beta-lactamases are a major cause of antibiotic resistance to beta-lactams such as penicillins and cephalosporins. These enzymes are only modestly affected by classic beta-lactam-based inhibitors, such as clavulanic acid. Conversely, small arylboronic acids inhibit these enzymes at sub-micromolar concentrations. Structural studies suggest these inhibitors bind to a well-defined cleft in the group I beta-lactamase AmpC; this cleft binds the ubiquitous R1 side chain of beta-lactams. Intriguingly, much of this cleft is left unoccupied by the small arylboronic acids.

RESULTS

To investigate if larger boronic acids might take advantage of this cleft, structure-guided in-parallel synthesis was used to explore new inhibitors of AmpC. Twenty-eight derivatives of the lead compound, 3-aminophenylboronic acid, led to an inhibitor with 80-fold better binding (2; K(i) 83 nM). Molecular docking suggested orientations for this compound in the R1 cleft. Based on the docking results, 12 derivatives of 2 were synthesized, leading to inhibitors with K(i) values of 60 nM and with improved solubility. Several of these inhibitors reversed the resistance of nosocomial Gram-positive bacteria, though they showed little activity against Gram-negative bacteria. The X-ray crystal structure of compound 2 in complex with AmpC was subsequently determined to 2.1 A resolution. The placement of the proximal two-thirds of the inhibitor in the experimental structure corresponds with the docked structure, but a bond rotation leads to a distinctly different placement of the distal part of the inhibitor. In the experimental structure, the inhibitor interacts with conserved residues in the R1 cleft whose role in recognition has not been previously explored.

CONCLUSIONS

Combining structure-based design with in-parallel synthesis allowed for the rapid exploration of inhibitor functionality in the R1 cleft of AmpC. The resulting inhibitors differ considerably from beta-lactams but nevertheless inhibit the enzyme well. The crystal structure of 2 (K(i) 83 nM) in complex with AmpC may guide exploration of a highly conserved, largely unexplored cleft, providing a template for further design against AmpC beta-lactamase.

Identification and analysis of the acyl carrier protein (ACP) docking site on beta-ketoacyl-ACP synthase III

The molecular details that govern the specific interactions between acyl carrier protein (ACP) and the enzymes of fatty acid biosynthesis are unknown. We investigated the mechanism of ACP-protein interactions using a computational analysis to dock the NMR structure of ACP with the crystal structure of beta-ketoacyl-ACP synthase III (FabH) and experimentally tested the model by the biochemical analysis of FabH mutants. The activities of the mutants were assessed using both an ACP-dependent and an ACP-independent assay. The ACP interaction surface was defined by mutations that compromised FabH activity in the ACP-dependent assay but had no effect in the ACP-independent assay. ACP docked to a positively charged/hydrophobic patch adjacent to the active site tunnel on FabH, which included a conserved arginine (Arg-249) that was required for ACP docking. Kinetic analysis and direct binding studies between FabH and ACP confirmed the identification of Arg-249 as critical for FabH-ACP interaction. Our experiments reveal the significance of the positively charged/hydrophobic patch located adjacent to the active site cavities of the fatty acid biosynthesis enzymes and the high degree of sequence conservation in helix II of ACP across species.

Protein docking using continuum electrostatics and geometric fit

The computer program DOT quickly finds low-energy docked structures for two proteins by performing a systematic search over six degrees of freedom. A novel feature of DOT is its energy function, which is the sum of both a Poisson-Boltzmann electrostatic energy and a van der Waals energy, each represented as a grid-based correlation function. DOT evaluates the energy of interaction for many orientations of the moving molecule and maintains separate lists scored by either the electrostatic energy, the van der Waals energy or the composite sum of both. The free energy is obtained by summing the Boltzmann factor over all rotations at each grid point. Three important findings are presented. First, for a wide variety of protein-protein interactions, the composite-energy function is shown to produce larger clusters of correct answers than found by scoring with either van der Waals energy (geometric fit) or electrostatic energy alone. Second, free-energy clusters are demonstrated to be indicators of binding sites. Third, the contributions of electrostatic and attractive van der Waals energies to the total energy term appropriately reflect the nature of the various types of protein-protein interactions studied.