Discovery of a Novel Binding Trench in HIV Integrase

Equilibrium fluctuations of a single folded protein reveal a multitude of potential cryptic allosteric sites

Cryptic allosteric sites--transient pockets in a folded protein that are invisible to conventional experiments but can alter enzymatic activity via allosteric communication with the active site--are a promising opportunity for facilitating drug design by greatly expanding the repertoire of available drug targets. Unfortunately, identifying these sites is difficult, typically requiring resource-intensive screening of large libraries of small molecules. Here, we demonstrate that Markov state models built from extensive computer simulations (totaling hundreds of microseconds of dynamics) can identify prospective cryptic sites from the equilibrium fluctuations of three medically relevant proteins--β-lactamase, interleukin-2, and RNase H--even in the absence of any ligand. As in previous studies, our methods reveal a surprising variety of conformations--including bound-like configurations--that implies a role for conformational selection in ligand binding. Moreover, our analyses lead to a number of unique insights. First, direct comparison of simulations with and without the ligand reveals that there is still an important role for an induced fit during ligand binding to cryptic sites and suggests new conformations for docking. Second, correlations between amino acid sidechains can convey allosteric signals even in the absence of substantial backbone motions. Most importantly, our extensive sampling reveals a multitude of potential cryptic sites--consisting of transient pockets coupled to the active site--even in a single protein. Based on these observations, we propose that cryptic allosteric sites may be even more ubiquitous than previously thought and that our methods should be a valuable means of guiding the search for such sites.

Novel cruzain inhibitors for the treatment of Chagas' disease

The protozoan parasite Trypanosoma cruzi, the etiological agent of Chagas’ disease, affects millions of individuals and continues to be an important global health concern. The poor efficacy and unfavorable side effects of current treatments necessitate novel therapeutics. Cruzain, the major cysteine protease of T. cruzi, is one potential novel target. Recent advances in a class of vinyl sulfone inhibitors are encouraging; however, as most potential therapeutics fail in clinical trials and both disease progression and resistance call for combination therapy with several drugs, the identification of additional classes of inhibitory molecules is essential. Using an exhaustive virtual-screening and experimental validation approach, we identify several additional small-molecule cruzain inhibitors. Further optimization of these chemical scaffolds could lead to the development of novel drugs useful in the treatment of Chagas’ disease.

Using metadynamics and path collective variables to study ligand binding and induced conformational transitions

Large-scale conformational transitions represent both a challenge and an opportunity for computational drug design. Exploring the conformational space of a druggable target with sufficient detail is computationally demanding. However, if it were possible to fully account for target flexibility, one could exploit this knowledge to rationally design more potent and more selective drug candidates. Here, we discuss how molecular dynamics together with free energy algorithms based on Metadynamics and Path Collective Variables can be used to study both large-scale conformational transitions and ligand binding to flexible targets. We show real-life examples of how these methods have been applied in the case of cyclin-dependent kinases, a family of flexible targets that shows promise in cancer therapy.

In silico characterization of atypical kinase PFD0975w from Plasmodium kinome: a suitable target for drug discovery

RIO-2 kinase is known to regulate ribosome biogenesis and other cell cycle events. The 3D model of ATP bound and an unbound form of PFD0975w was generated using AfRIO-2 crystal structure 1TQI, 1ZAO as template employing Modeller9v7 program. Structural characterization identified N-terminal winged helix domain (1-84), C-terminal kinase domain (148-275), and presence of other critical residues known for ATP binding and kinase activity. Using Q-site and pocket finder, a number of well-defined substrate (peptide) binding regions were identified in the catalytic core of the protein. The peptide binding regions were further validated by molecular modeling a non-specific polyalanine peptide and a sequence-specific peptide2 into these sites to generate a stable PFD0975w/peptide complexes. Peptide fits well into identified pocket on PFD0975w and makes extensive interaction with the protein residues. These newly identified peptide binding sites potentially give opportunity to design a specific inhibitor against PFD0975w. There are subtle but significant differences between Plasmodium falciparum and human RIO-2 to exploit PFD0975w for drug development. In conclusion, our finding will let us to design effective chemotherapy against malaria parasite exploiting PFD0975w as a drug target.

Application of conformational clustering in protein-ligand docking

Protein-Ligand docking is a powerful technique routinely employed in structure-based drug design. Despite many reported success stories, docking is not always able to provide an accurate and easily interpretable prediction of the structure of the bound complex formed by a small organic molecule and a pharmacologically relevant target. Cluster analysis can represent a versatile and readily available postprocessing tool to be employed in combination with protein-ligand docking to simplify the evaluation of the results and help to overcome present limitations of docking protocols.

Interaction of ganoderic acid on HIV related target: molecular docking studies

Finding the ultimate HIV cure remain a challenging tasks for decades. Various active compounds have been tested against various components of the virus in the effort to halt the virus development in infected host. The idea of finding cure from known pharmacologically active natural occurring compounds is intriguing and practical. Ganoderma lucidum (Ling-Zhi or Reishi) is one of the most productive and pharmacologically active compounds found in Asian countries. It has been used traditionally for many years throughout different cultures. More than a decade ago, el-Mekkawy and co-workers (1998) have tested several active compounds found in this plant. They have successfully identified several active compounds with reasonable inhibitory activity against HIV protease however; no further studies were done on these compounds. This study aimed to elucidate interactions for one of the active compounds of Ganoderma lucidum namely ganoderic acid with HIV-1 protease using molecular docking simulation. This study revealed four hydrogen bonds formed between model34 of ganoderic acid B and 1HVR. Hydrogen bonds in 1HVR-Model34 complex were formed through ILE50, ILE50', ASP29 and ASP30 residues. Interestingly similar interactions were also observed in the native ligand in 1HVR. Furthermore, interactions involving ILE50 and ILE50' residues have been previously identified to play central roles in HIV-1 protease-ligand interactions.These observed interactions not only suggested HIV-1 protease in general is a suitable target for ganoderic acid B, they also indicated a huge potential for HIV drug discovery based on this compound.

DOCKING OF RALTEGRAVIR TO HIV-1 INTEGRASE STRUCTURE ENSEMBLE

Docking of the drug raltegravir to HIV-1 integrase (IN) was performed based on the established Relaxed Complex Scheme (RCS) method which accounts for the flexibility of both receptor and ligand in molecular docking. Two representative butterfly-like structures of raltegravir were identified and both of them mimicked the binding mode of 5CITEP with similar ligand-receptor interactions. Furthermore, the results that raltegravir interacted with magnesium by intermediate water molecules indicate the importance of water molecules at the binding site which has always been ignored in the docking studies of IN inhibitors. Taking these water molecules into consideration gives more insight into the design and development of the second generation IN inhibitors.

Molecular recognition in the case of flexible targets

A protein's flexibility is well recognized to underlie its capacity to engage in critical functions, such as signal transduction, biomolecular transport and biochemical reactivity. Molecular recognition is also tightly linked to the dynamics of the binding partners, yet protein flexibility has largely been ignored by the growing field of structure-based drug design (SBDD). In combination with experimentally determined structures, a number of computational methods have been proposed to model protein movements, which may be important for small molecule binding. Such techniques have the ability to expose new binding site conformations, which may in turn recognize and lead to the discovery of more potent and selective drugs through molecular docking. In this article, we discuss various methods and focus on the Relaxed Complex Scheme (RCS), which uses Molecular Dynamics (MD) simulations to model full protein flexibility and enhance virtual screening programmes. We review practical applications of the RCS and use a recent study of the HIV-1 reverse transcriptase to illustrate the various phases of the scheme. We also discuss some encouraging developments, aimed at addressing current weaknesses of the RCS.

Molecular dynamics simulations and drug discovery

This review discusses the many roles atomistic computer simulations of macromolecular (for example, protein) receptors and their associated small-molecule ligands can play in drug discovery, including the identification of cryptic or allosteric binding sites, the enhancement of traditional virtual-screening methodologies, and the direct prediction of small-molecule binding energies. The limitations of current simulation methodologies, including the high computational costs and approximations of molecular forces required, are also discussed. With constant improvements in both computer power and algorithm design, the future of computer-aided drug design is promising; molecular dynamics simulations are likely to play an increasingly important role.

Novel allosteric sites on Ras for lead generation

Aberrant Ras activity is a hallmark of diverse cancers and developmental diseases. Unfortunately, conventional efforts to develop effective small molecule Ras inhibitors have met with limited success. We have developed a novel multi-level computational approach to discover potential inhibitors of previously uncharacterized allosteric sites. Our approach couples bioinformatics analysis, advanced molecular simulations, ensemble docking and initial experimental testing of potential inhibitors. Molecular dynamics simulation highlighted conserved allosteric coupling of the nucleotide-binding switch region with distal regions, including loop 7 and helix 5. Bioinformatics methods identified novel transient small molecule binding pockets close to these regions and in the vicinity of the conformationally responsive switch region. Candidate binders for these pockets were selected through ensemble docking of ZINC and NCI compound libraries. Finally, cell-based assays confirmed our hypothesis that the chosen binders can inhibit the downstream signaling activity of Ras. We thus propose that the predicted allosteric sites are viable targets for the development and optimization of new drugs.

Pocket-space maps to identify novel binding-site conformations in proteins

The identification of novel binding-site conformations can greatly assist the progress of structure-based ligand design projects. Diverse pocket shapes drive medicinal chemistry to explore a broader chemical space and thus present additional opportunities to overcome key drug discovery issues such as potency, selectivity, toxicity, and pharmacokinetics. We report a new automated approach to diverse pocket selection, PocketAnalyzer(PCA), which applies principal component analysis and clustering to the output of a grid-based pocket detection algorithm. Since the approach works directly with pocket shape descriptors, it is free from some of the problems hampering methods that are based on proxy shape descriptors, e.g. a set of atomic positional coordinates. The approach is technically straightforward and allows simultaneous analysis of mutants, isoforms, and protein structures derived from multiple sources with different residue numbering schemes. The PocketAnalyzer(PCA) approach is illustrated by the compilation of diverse sets of pocket shapes for aldose reductase and viral neuraminidase. In both cases this allows identification of novel computationally derived binding-site conformations that are yet to be observed crystallographically. Indeed, known inhibitors capable of exploiting these novel binding-site conformations are subsequently identified, thereby demonstrating the utility of PocketAnalyzer(PCA) for rationalizing and improving the understanding of the molecular basis of protein-ligand interaction and bioactivity. A Python program implementing the PocketAnalyzer(PCA) approach is available for download under an open-source license ( http://sourceforge.net/projects/papca/ or http://cpclab.uni-duesseldorf.de/downloads ).

Non-bisphosphonate inhibitors of isoprenoid biosynthesis identified via computer-aided drug design

The relaxed complex scheme, a virtual-screening methodology that accounts for protein receptor flexibility, was used to identify a low-micromolar, non-bisphosphonate inhibitor of farnesyl diphosphate synthase. Serendipitously, we also found that several predicted farnesyl diphosphate synthase inhibitors were low-micromolar inhibitors of undecaprenyl diphosphate synthase. These results are of interest because farnesyl diphosphate synthase inhibitors are being pursued as both anti-infective and anticancer agents, and undecaprenyl diphosphate synthase inhibitors are antibacterial drug leads.

Pyrone-based inhibitors of metalloproteinase types 2 and 3 may work as conformation-selective inhibitors

Matrix metalloproteinases are zinc-containing enzymes capable of degrading all components of the extracellular matrix. Owing to their role in human disease, matrix metalloproteinase have been the subject of extensive study. A bioinorganic approach was recently used to identify novel inhibitors based on a maltol zinc-binding group, but accompanying molecular-docking studies failed to explain why one of these inhibitors, AM-6, had approximately 2500-fold selectivity for MMP-3 over MMP-2. A number of studies have suggested that the matrix-metalloproteinase active site is highly flexible, leading some to speculate that differences in active-site flexibility may explain inhibitor selectivity. To extend the bioinorganic approach in a way that accounts for MMP-2 and MMP-3 dynamics, we here investigate the predicted binding modes and energies of AM-6 docked into multiple structures extracted from matrix-metalloproteinase molecular dynamics simulations. Our findings suggest that accounting for protein dynamics is essential for the accurate prediction of binding affinity and selectivity. Additionally, AM-6 and other similar inhibitors likely select for and stabilize only a subpopulation of all matrix-metalloproteinase conformations sampled by the apo protein. Consequently, when attempting to predict ligand affinity and selectivity using an ensemble of protein structures, it may be wise to disregard protein conformations that cannot accommodate the ligand.

Binding modes of diketo-acid inhibitors of HIV-1 integrase: a comparative molecular dynamics simulation study

HIV-1 integrase (IN) has become an attractive target since drug resistance against HIV-1 reverse transcriptase (RT) and protease (PR) has appeared. Diketo acid (DKA) inhibitors are potent and selective inhibitors of HIV-1 IN: however the action mechanism is not well understood. Here, to study the inhibition mechanism of DKAs we performed 10 ns comparative molecular dynamics simulations on HIV-1 IN bound with three most representative DKA inhibitors: Shionogi inhibitor, S-1360 and two Merck inhibitors L-731,988 and L-708,906. Our simulations show that the acidic part of S-1360 formed salt bridge and cation-π interactions with Lys159. In addition, the catalytic Glu152 in S-1360 was pushed away from the active site to form an ion-pair interaction with Arg199. The Merck inhibitors can maintain either one or both of these ion-pair interaction features. The difference in potencies of the DKA inhibitors is thus attributed to the different binding modes at the catalytic site. Such structural information at atomic level, not only demonstrates the action modes of DKA inhibitors but also provides a novel starting point for structural-based design of HIV-1 IN inhibitors.

Biomolecularmodeling and simulation: a field coming of age

We assess the progress in biomolecular modeling and simulation, focusing on structure prediction and dynamics, by presenting the field’s history, metrics for its rise in popularity, early expressed expectations, and current significant applications. The increases in computational power combined with improvements in algorithms and force fields have led to considerable success, especially in protein folding, specificity of ligand/biomolecule interactions, and interpretation of complex experimental phenomena (e.g. NMR relaxation, protein-folding kinetics and multiple conformational states) through the generation of structural hypotheses and pathway mechanisms. Although far from a general automated tool, structure prediction is notable for proteins and RNA that preceded the experiment, especially by knowledge-based approaches. Thus, despite early unrealistic expectations and the realization that computer technology alone will not quickly bridge the gap between experimental and theoretical time frames, ongoing improvements to enhance the accuracy and scope of modeling and simulation are propelling the field onto a productive trajectory to become full partner with experiment and a field on its own right.

Multilevel Parallelization of AutoDock 4.2

Background

Virtual (computational) screening is an increasingly important tool for drug discovery. AutoDock is a popular open-source application for performing molecular docking, the prediction of ligand-receptor interactions. AutoDock is a serial application, though several previous efforts have parallelized various aspects of the program. In this paper, we report on a multi-level parallelization of AutoDock 4.2 (mpAD4).

Results

Using MPI and OpenMP, AutoDock 4.2 was parallelized for use on MPI-enabled systems and to multithread the execution of individual docking jobs. In addition, code was implemented to reduce input/output (I/O) traffic by reusing grid maps at each node from docking to docking. Performance of mpAD4 was examined on two multiprocessor computers.

Conclusions

Using MPI with OpenMP multithreading, mpAD4 scales with near linearity on the multiprocessor systems tested. In situations where I/O is limiting, reuse of grid maps reduces both system I/O and overall screening time. Multithreading of AutoDock's Lamarkian Genetic Algorithm with OpenMP increases the speed of execution of individual docking jobs, and when combined with MPI parallelization can significantly reduce the execution time of virtual screens. This work is significant in that mpAD4 speeds the execution of certain molecular docking workloads and allows the user to optimize the degree of system-level (MPI) and node-level (OpenMP) parallelization to best fit both workloads and computational resources.

Direct detection of structurally resolved dynamics in a multiconformation receptor-ligand complex

Structure-based drug design relies on static protein structures despite significant evidence for the need to include protein dynamics as a serious consideration. In practice, dynamic motions are neglected because they are not understood well enough to model, a situation resulting from a lack of explicit experimental examples of dynamic receptor-ligand complexes. Here, we report high-resolution details of pronounced ~1 ms time scale motions of a receptor-small molecule complex using a combination of NMR and X-ray crystallography. Large conformational dynamics in Escherichia coli dihydrofolate reductase are driven by internal switching motions of the drug-like, nanomolar-affinity inhibitor. Carr-Purcell-Meiboom-Gill relaxation dispersion experiments and NOEs revealed the crystal structure to contain critical elements of the high energy protein-ligand conformation. The availability of accurate, structurally resolved dynamics in a protein-ligand complex should serve as a valuable benchmark for modeling dynamics in other receptor-ligand complexes and prediction of binding affinities.

Computational databases, pathway and cheminformatics tools for tuberculosis drug discovery

We are witnessing the growing menace of both increasing cases of drug-sensitive and drug-resistant Mycobacterium tuberculosis strains and the challenge to produce the first new tuberculosis (TB) drug in well over 40 years. The TB community, having invested in extensive high-throughput screening efforts, is faced with the question of how to optimally leverage these data to move from a hit to a lead to a clinical candidate and potentially, a new drug. Complementing this approach, yet conducted on a much smaller scale, cheminformatic techniques have been leveraged and are examined in this review. We suggest that these computational approaches should be optimally integrated within a workflow with experimental approaches to accelerate TB drug discovery.

Free-energy-based methods for binding profile determination in a congeneric series of CDK2 inhibitors

Free-energy pathway methods show great promise in computing the mode of action and the free energy profile associated with the binding of small molecules with proteins, but are generally very computationally demanding. Here we apply a novel approach based on metadynamics and path collective variables. We show that this combination is able to find an optimal reaction coordinate and the free energy profile of binding with explicit solvent and full flexibility, while minimizing human intervention and computational costs. We apply it to predict the binding affinity of a congeneric series of 5 CDK2 inhibitors. The predicted binding free energy profiles are in accordance with experiment.

Characterization of a clinical polymer-drug conjugate using multiscale modeling

The molecular conformation of certain therapeutic agents has been shown to affect the ability to gain access to target cells, suggesting potential value in defining conformation of candidate molecules. This study explores how the shape and size of poly-γ-glutamyl-glutamate paclitaxel (PGG-PTX), an amphiphilic polymer-drug with potential chemotherapeutic applications, can be systematically controlled by varying hydrophobic and hydrophilic entities. Eighteen different formulations of PGG-PTX varying in three PTX loading fractions (f(PTX)) of 0.18, 0.24, and 0.37 and six spatial arrangements of PTX ('clusters', 'ends', 'even', 'middle', 'random', and 'side') were explored. Molecular dynamics (MD) simulations of all-atom (AA) models of PGG-PTX were run until a statistical equilibrium was reached at 100 ns and then continued as coarse-grained (CG) models until a statistical equilibrium was reached at an effective time of 800 ns. Circular dichroism spectroscopy was used to suggest initial modeling configurations. Results show that a PGG-PTX molecule has a strong tendency to form coil shapes, regardless of the PTX loading fraction and spatial PTX arrangement, although globular shapes exist at f(PTX) = 0.24. Also, less uniform PTX arrangements such as 'ends', 'middle', and 'side' produce coil geometries with more curvature. The prominence of coil shapes over globules suggests that PGG-PTX may confer a long circulation half-life and high propensity for accumulation to tumor endothelia. This multiscale modeling approach may be advantageous for the design of cancer therapeutic delivery systems. © 2010 Wiley Periodicals, Inc. Biopolymers 93: 936-951, 2010.

Computer-aided drug-discovery techniques that account for receptor flexibility

Protein flexibility plays a critical role in ligand binding to both orthosteric and allosteric sites. We here review some of the computer-aided drug-design techniques currently used to account for protein flexibility, ranging from methods that probe local receptor flexibility in the region of the protein immediately adjacent to the binding site, to those that account for general flexibility in all protein regions.

Emerging methods for ensemble-based virtual screening

Ensemble based virtual screening refers to the use of conformational ensembles from crystal structures, NMR studies or molecular dynamics simulations. It has gained greater acceptance as advances in the theoretical framework, computational algorithms, and software packages enable simulations at longer time scales. Here we focus on the use of computationally generated conformational ensembles and emerging methods that use these ensembles for discovery, such as the Relaxed Complex Scheme or Dynamic Pharmacophore Model. We also discuss the more rigorous physics-based computational techniques such as accelerated molecular dynamics and thermodynamic integration and their applications in improving conformational sampling or the ranking of virtual screening hits. Finally, technological advances that will help make virtual screening tools more accessible to a wider audience in computer aided drug design are discussed.

Computer-aided identification of Trypanosoma brucei uridine diphosphate galactose 4'-epimerase inhibitors: toward the development of novel therapies for African sleeping sickness

Trypanosoma brucei, the causative agent of human African trypanosomiasis, affects tens of thousands of sub-Saharan Africans. As current therapeutics are inadequate due to toxic side effects, drug resistance, and limited effectiveness, novel therapies are urgently needed. UDP-galactose 4′-epimerase (TbGalE), an enzyme of the Leloir pathway of galactose metabolism, is one promising T. brucei drug target. We here use the relaxed complex scheme, an advanced computer-docking methodology that accounts for full protein flexibility, to identify inhibitors of TbGalE. An initial hit rate of 62% was obtained at 100 μM, ultimately leading to the identification of 14 low-micromolar inhibitors. Thirteen of these inhibitors belong to a distinct series with a conserved binding motif that may prove useful in future drug design and optimization.

Mapping the druggable allosteric space of G-protein coupled receptors: a fragment-based molecular dynamics approach

To address the problem of specificity in G-protein coupled receptor (GPCR) drug discovery, there has been tremendous recent interest in allosteric drugs that bind at sites topographically distinct from the orthosteric site. Unfortunately, structure-based drug design of allosteric GPCR ligands has been frustrated by the paucity of structural data for allosteric binding sites, making a strong case for predictive computational methods. In this work, we map the surfaces of the β₁ (β₁AR) and β₂ (β₂AR) adrenergic receptor structures to detect a series of five potentially druggable allosteric sites. We employ the FTMAP algorithm to identify ‘hot spots’ with affinity for a variety of organic probe molecules corresponding to drug fragments. Our work is distinguished by an ensemble-based approach, whereby we map diverse receptor conformations taken from molecular dynamics (MD) simulations totaling approximately 0.5 μs. Our results reveal distinct pockets formed at both solvent-exposed and lipid-exposed cavities, which we interpret in light of experimental data and which may constitute novel targets for GPCR drug discovery. This mapping data can now serve to drive a combination of fragment-based and virtual screening approaches for the discovery of small molecules that bind at these sites and which may offer highly selective therapies.

Communications: Electron polarization critically stabilizes the Mg2+ complex in the catalytic core domain of HIV-1 integrase

In this paper, we present a detailed dynamics study of the catalytic core domain (CCD) of HIV-1 integrase using both polarized and nonpolarized force fields. The numerical results reveal the critical role of protein polarization in stabilizing Mg(2+) coordination complex in CCD. Specifically, when nonpolarized force field is used, a remarkable drift of the Mg(2+) complex away from its equilibrium position is observed, which causes the binding site blocked by the Mg(2+) complex. In contrast, when polarized force field is employed in MD simulation, HIV-1 integrase CCD structure is stabilized and both the position of the Mg(2+) complex and the binding site are well preserved. The detailed analysis shows the transition of alpha-helix to 3(10)-helix adjacent to the catalytic loop (residues 139-147), which correlates with the dislocation of the Mg(2+) complex. The current study demonstrates the importance of electronic polarization of protein in stabilizing the metal complex in the catalytic core domain of HIV-1 integrase.

Computational identification of uncharacterized cruzain binding sites

Chagas disease, caused by the unicellular parasite Trypanosoma cruzi, claims 50,000 lives annually and is the leading cause of infectious myocarditis in the world. As current antichagastic therapies like nifurtimox and benznidazole are highly toxic, ineffective at parasite eradication, and subject to increasing resistance, novel therapeutics are urgently needed. Cruzain, the major cysteine protease of Trypanosoma cruzi, is one attractive drug target. In the current work, molecular dynamics simulations and a sequence alignment of a non-redundant, unbiased set of peptidase C1 family members are used to identify uncharacterized cruzain binding sites. The two sites identified may serve as targets for future pharmacological intervention.

Chagas disease, an infection that afflicts millions of people in Central and South America, is caused by the unicellular parasite Trypanosoma cruzi. In the chronic stage of the disease, patients' hearts are adversely affected. Chagas is the leading cause of infectious heart disease in the world. The current drugs used to treat Chagas disease are highly toxic, unable to eradiate the parasite, and subject to increasing drug resistance. Consequently, researchers are actively looking for new treatments. One attractive drug target is a Chagas protein called cruzain, which is required for the parasite's survival. Drugs that can inhibit the correct functioning of cruzain within the parasite may one day serve as powerful treatments in the fight against this devastating tropical disease. To design drugs that will be effective against cruzain, we need to know what portions of the protein are crucial for its functionality. For example, portions of the protein that bind to other proteins or to small molecules are likely to be critical. These regions are called “binding sites.” In the current work, we identify two uncharacterized cruzain binding sites. With this knowledge in hand, future researchers may be able to design drugs that target these sites.

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.

Including receptor flexibility and induced fit effects into the design of MMP-2 inhibitors

Matrix metalloproteinases (MMPs) comprise a class of flexible proteins required for normal tissue remodeling. Overexpression of MMPs is associated with a wide range of pathophysiological processes, including vascular disease, multiple sclerosis, Alzheimer's disease, and cancer. Nearly all MMP inhibitors have failed in clinical trials, in part due to lack of specificity. Due to the highly dynamic molecular motions of the MMP-2 binding pockets, the rational drug design of MMP inhibitors has been very challenging. To address these challenges, in the current study we combine computer docking with molecular dynamics (MD) simulations in order to incorporate receptor-flexibility and induced-fit effects into the drug-design process. Our strategy identifies molecular fragments predicted to target multiple MMP-2 binding pockets.

Potential drug-like inhibitors of Group 1 influenza neuraminidase identified through computer-aided drug design

Pandemic (H1N1) influenza poses an imminent threat. Nations have stockpiled inhibitors of the influenza protein neuraminidase in hopes of protecting their citizens, but drug-resistant strains have already emerged, and novel therapeutics are urgently needed. In the current work, the computer program AutoGrow is used to generate novel predicted neuraminidase inhibitors. Given the great flexibility of the neuraminidase active site, protein dynamics are also incorporated into the computer-aided drug-design process. Several potential inhibitors are identified that are predicted to bind to neuraminidase better than currently approved drugs.

A dynamic model of HIV integrase inhibition and drug resistance

Human immunodeficiency virus type 1 (HIV-1) integrase is one of three virally encoded enzymes essential for replication and, therefore, a rational choice as a drug target for the treatment of HIV-1-infected individuals. In 2007, raltegravir became the first integrase inhibitor approved for use in the treatment of HIV-infected patients, more than a decade since the approval of the first protease inhibitor (saquinavir, Hoffman La-Roche, 1995) and two decades since the approval of the first reverse transcriptase inhibitor (retrovir, GlaxoSmithKline, 1987). The slow progress toward a clinically effective HIV-1 integrase inhibitor can at least in part be attributed to a poor structural understanding of this key viral protein. Here we describe the development of a restrained molecular dynamics protocol that produces a more accurate model of the active site of this drug target. This model provides an advance on previously described models as it ensures that the catalytic DDE motif makes correct, monodentate interactions with the two active-site magnesium ions. Dynamic restraints applied to this coordination state create models with the correct solvation sphere for the metal ion complex and highlight the coordination sites available for metal-binding ligands. Application of appropriate dynamic flexibility to the core domain allowed the inclusion of multiple conformational states in subsequent docking studies. These models have allowed us to (1) explore the effects of key drug resistance mutations on the dynamic flexibility and conformational preferences of HIV integrase and to (2) study raltegravir binding in the context of these dynamic models of both wild type and the G140S/Q148H drug-resistant enzyme.

Induced-fit docking approach provides insight into the binding mode and mechanism of action of HIV-1 integrase inhibitors

A three-dimensional model of a complex between HIV-1 integrase (IN), viral DNA, and metal ions that we recently built was used as a target for a docking method (induced-fit docking, IFD) that accurately predicts ligand binding modes and concomitant structural changes in the receptor. Six different well-known integrase strand transfer inhibitors (INSTIs): L-708,906, L-731,988, S-1360, L-870,810, raltegravir, and elvitegravir were thus used as ligands for our docking simulations. The obtained IFD results are consistent with the mechanism of action proposed for this class of IN inhibitors, that is, metal chelating/binding agents. This study affords new insight into the possible mechanism of inhibition and binding conformations for INSTIs. The impact on our hypothesis of specific mutations associated with IN inhibitor resistance was also evaluated. All these findings might have implications for integrase-directed HIV-1 drug discovery efforts.

Dynamics of the streptavidin-biotin complex in solution and in its crystal lattice: distinct behavior revealed by molecular simulations

We present a 250 ns simulation of the wild-type, biotin-liganded streptavidin tetramer in the solution phase and compare the trajectory to two previously published simulations of the protein in its crystal lattice. By performing both types of simulations, we are able to interpret the protein's behavior in solution in the context of its X-ray structure. We find that the rate of conformational sampling is increased in solution over the lattice environment, although the relevant conformational space in solution is also much larger, as indicated by overall fluctuations in the positions of backbone atoms. We also compare the distributions of chi1 angles sampled by side chains exposed to solvent in the lattice and in the solution phase, obtaining overall good agreement between the distributions obtained in our most rigorous lattice simulation and the crystallographic chi1 angles. We observe changes in the chi1 distributions in the solution phase, and note an apparent progression of the distributions as the environment changes from a tightly packed lattice filled with crystallization media to a bath of pure water. Finally, we examine the interaction of biotin and streptavidin in each simulation, uncovering a possible alternate conformation of the biotin carboxylate tail. We also note that a hydrogen bond observed to break transiently in previous solution-phase simulations is predominantly broken in this much longer solution-phase trajectory; in the lattice simulations, the lattice environment appears to help maintain the hydrogen bond, but more sampling will be needed to confirm whether the simulation model truly gives good agreement with the X-ray data in the lattice simulations. We expect that pairing solution-phase biomolecular simulations with crystal lattice simulations will help to validate simulation models and improve the interpretation of experimentally determined structures.