Receptor-specific scoring functions derived from quantum chemical models improve affinity estimates for in-silico drug discovery

The adaptation of forcefield-based scoring function to specific receptors remains an important challenge for in-silico drug discovery. Here we compare binding energies of forcefield-based scoring functions with models that are reparameterized on the basis of large-scale quantum calculations of the receptor. We compute binding energies of eleven ligands to the human estrogen receptor subtype alpha (ERalpha) and four ligands to the human retinoic acid receptor of isotype gamma (RARgamma). Using the FlexScreen all-atom receptor-ligand docking approach, we compare docking simulations parameterized by quantum-mechanical calculation of a large protein fragment with purely forcefield-based models. The use of receptor flexibility in the FlexScreen permits the treatment of all ligands in the same receptor model. We find a high correlation between the classical binding energy obtained in the docking simulation and quantum mechanical binding energies and a good correlation with experimental affinities R=0.81 for ERalpha and R=0.95 for RARgamma using the quantum derived scoring functions. A significant part of this improvement is retained, when only the receptor is treated with quantum-based parameters, while the ligands are parameterized with a purely classical model.     

Molecular basis for anti-insomnia drug design from structure of lemborexant-bound orexin 2 receptor

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DynaDock: A new molecular dynamics‐based algorithm for protein–peptide docking including receptor flexibility

Molecular docking programs play an important role in drug development and many well‐established methods exist. However, there are two situations for which the performance of most approaches is still not satisfactory, namely inclusion of receptor flexibility and docking of large, flexible ligands like peptides. In this publication a new approach is presented for docking peptides into flexible receptors. For this purpose a two step procedure was developed: first, the protein–peptide conformational space is scanned and approximate ligand poses are identified and second, the identified ligand poses are refined by a new molecular dynamics‐based method, optimized potential molecular dynamics (OPMD). The OPMD approach uses soft‐core potentials for the protein–peptide interactions and applies a new optimization scheme to the soft‐core potential. Comparison with refinement results obtained by conventional molecular dynamics and a soft‐core scaling approach shows significant improvements in the sampling capability for the OPMD method. Thus, the number of starting poses needed for successful refinement is much lower than for the other methods. The algorithm was evaluated on 15 protein–peptide complexes with 2–16mer peptides. Docking poses with peptide RMSD values <2.10 Å from the equilibrated experimental structures were obtained in all cases. For four systems docking into the unbound receptor structures was performed, leading to peptide RMSD values <2.12 Å. Using a specifically fitted scoring function in 11 of 15 cases the best scoring poses featured a peptide RMSD ≤2.10 Å. Proteins 2010. © 2009 Wiley‐Liss, Inc.     

Induced fit in liver X receptor beta: a molecular dynamics-based investigation

Ligand induced fit phenomenon occurring at the ligand binding domain of the liver X receptor beta (LXRbeta) was investigated by means of molecular dynamics. Reliability of a 4-ns trajectory was tested from two distinct LXRbeta crystal complexes 1PQ6B/GW and 1PQ9B/T09 characterized by an open and a closed state of the pocket, respectively. Crossed complexes 1PQ6B/T09 and 1PQ9B/GW were then submitted to the same molecular dynamic conditions, which were able to recover LXRbeta conformations similar to the original crystallography data. Analysis of "open to closed" and "closed to open" conformational transitions pointed out the dynamic role of critical residues lining the ligand binding pocket involved in the local remodeling upon ligand binding (e.g., Phe271, Phe329, Phe340, Arg319, Glu281). Altogether, the present study indicates that the molecular dynamic protocol is a consistent approach for managing LXRbeta-related induced fit process. This protocol could therefore be used for refining ligand docking solutions of a structure-based design strategy.     

Score_set: A CAPRI benchmark for scoring protein complexes

Critical Assessment of PRedicted Interactions (CAPRI) has proven to be a catalyst for the development of docking algorithms. An essential step in docking is the scoring of predicted binding modes in order to identify stable complexes. In 2005, CAPRI introduced the scoring experiment, where upon completion of a prediction round, a larger set of models predicted by different groups and comprising both correct and incorrect binding modes, is made available to all participants for testing new scoring functions independently from docking calculations. Here we present an expanded benchmark data set for testing scoring functions, which comprises the consolidated ensemble of predicted complexes made available in the CAPRI scoring experiment since its inception. This consolidated scoring benchmark contains predicted complexes for 15 published CAPRI targets. These targets were subjected to 23 CAPRI assessments, due to existence of multiple binding modes for some targets. The benchmark contains more than 19,000 protein complexes. About 10% of the complexes represent docking predictions of acceptable quality or better, the remainder represent incorrect solutions (decoys). The benchmark set contains models predicted by 47 different predictor groups including web servers, which use different docking and scoring procedures, and is arguably as diverse as one may expect, representing the state of the art in protein docking. The data set is publicly available at the following URL: http://cb.iri.univ‐lille1.fr/Users/lensink/Score_set . Proteins 2014; 82:3163–3169. © 2014 Wiley Periodicals, Inc.     

Challenges and advances in computational docking: 2009 in review

Docking is a computational technique that places a small molecule (ligand) in the binding site of its macromolecular target (receptor) and estimates its binding affinity. This review addresses methodological developments that have occurred in the docking field in 2009, with a particular focus on the more difficult, and sometimes controversial, aspects of this promising computational discipline. These developments aim to address the main challenges of docking: receptor representation (such aspects as structural waters, side chain protonation, and, most of all, flexibility (from side chain rotation to domain movement)), ligand representation (protonation, tautomerism and stereoisomerism, and the effect of input conformation), as well as accounting for solvation and entropy of binding. This review is strongly focused on docking advances in the context of drug design, specifically in virtual screening and fragment-based drug design.     

The 3A6-TCR/superagonist/HLA-DR2a complex shows similar interface and reduced flexibility compared to the complex with self-peptide

T-cell receptor (TCR) recognition of the myelin basic protein (MBP) peptide presented by major histocompatibility complex (MHC) protein HLA-DR2a, one of the MHC class II alleles associated with multiple sclerosis, is highly variable. Interactions in the trimolecular complex between the TCR of the MBP83-99-specific T cell clone 3A6 with the MBP-peptide/HLA-DR2a (abbreviated TCR/pMHC) lead to substantially different proliferative responses when comparing the wild-type decapeptide MBP90-99 and a superagonist peptide, which differs mainly in the residues that point toward the TCR. Here, we investigate the influence of the peptide sequence on the interface and intrinsic plasticity of the TCR/pMHC trimolecular and pMHC bimolecular complexes by molecular dynamics simulations. The intermolecular contacts at the TCR/pMHC interface are similar for the complexes with the superagonist and the MBP self-peptide. The orientation angle between TCR and pMHC fluctuates less in the complex with the superagonist peptide. Thus, the higher structural stability of the TCR/pMHC tripartite complex with the superagonist peptide, rather than a major difference in binding mode with respect to the self-peptide, seems to be responsible for the stronger proliferative response.     

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.     

Computational investigation of binding mechanism of substituted pyrazinones targeting corticotropin releasing factor-1 receptor deliberated for anti-depressant drug design

In spite of various research investigations towards anti-depressant drug discovery program, no one drug has not yet launched last 20 years. Corticotropin-releasing factor-1 (CRF-1) is one of the most validated targets for the development of antagonists against depression, anxiety and post-traumatic stress disorders. Various research studies suggest that pyrazinone based CRF-1 receptor antagonists were found to be highly potent and efficacious. In this research investigation, we identified the pharmacophore and binding pattern through 2D and 3D-QSAR and molecular docking respectively. Molecular dynamics studies were also performed to explore the binding pattern recognition. We establish the relationship between activity and pharmacophoric features to design new potent compounds. The best 2D-QSAR model was generated through multiple linear regression method with r² value of 0.97 and q² value of 0.89. Also 3D-QSAR model was obtained through k-nearest neighbor molecular field analysis method with q² value of 0.52 and q²_se value of 0.36. Molecular docking and binding energy were also evaluated to define binding patterns and pharmacophoric groups, including (i) hydrogen bond with residue Asp284, Glu305 and (ii) π–π stacking with residue Trp9. Compound 11i has the highest binding affinity compared to reference compounds, so this compound could be a potent drug for stress related disorders. Most of the compounds, including reference compounds were found within acceptable range of physicochemical parameters. These observations could be provided the leads for the design and optimization of novel CRF-1 receptor antagonists.

Communicated by Ramaswamy H. Sarma     

Novel computational biology methods and their applications to drug discovery

Computational biology methods are now firmly entrenched in the drug discovery process. These methods focus on modeling and simulations of biological systems to complement and direct conventional experimental approaches. Two important branches of computational biology include protein homology modeling and the computational biophysics method of molecular dynamics. Protein modeling methods attempt to accurately predict three-dimensional (3D) structures of uncrystallized proteins for subsequent structure-based drug design applications. Molecular dynamics methods aim to elucidate the molecular motions of the static representations of crystallized protein structures. In this review we highlight recent novel methodologies in the field of homology modeling and molecular dynamics. Selected drug discovery applications using these methods conclude the review.     

Differential gene expression analysis of RAGE-S100A6 complex for target selection and the design of novel inhibitors for anticancer drug discovery

Receptor for advanced glycation end products (RAGE), a member of the immunoglobulin family, interactions with its ligands trigger downstream signaling and induce an inflammatory response linked to diabetes, inflammation, carcinogenesis, cardiovascular disease, and a variety of other human disorders. The interaction of RAGE and S100A6 has been associated with a variety of malignancies. For the control of RAGE-related illnesses, there is a great demand for more specialized drug options. To identify the most effective target for combating human malignancies associated with RAGE-S100A6 complex, we conducted single and differential gene expression analyses of S100A6 and RAGE, comparing normal and malignant tissues. Further, a structure-based virtual screening was conducted using the ZINC15 database. The chosen compounds were then subjected to a molecular docking investigation on the RAGE active site region, recognized by the various cancer-related RAGE ligands. An optimized RAGE structure was screened against a library of drug-like molecules. The screening results suggested that three promising compounds were presented as the top acceptable drug-like molecules with a high binding affinity at the RAGE V-domain catalytic region. We depicted that these compounds may be potential RAGE inhibitors and could be used to produce a successful medication against human cancer and other RAGE-related diseases based on their various assorted parameters, binding energy, hydrogen bonding, ADMET characteristics, etc. MD simulation on a time scale of 50 ns was used to test the stability of the RAGE-inhibitor complexes. Therefore, targeting RAGE and its ligands using these drug-like molecules may be an effective therapeutic approach.     

Focused library design in GPCR projects on the example of 5-HT(2c) agonists: comparison of structure-based virtual screening with ligand-based search methods

The aim of this study was to investigate the usefulness of structure-based virtual screening (VS) for focused library design in G protein-coupled receptors (GPCR) projects on the example of 5-HT(2c) agonists. We compared the performance of structure-based VS against two different homology models using FRED for docking and ScreenScore, FlexX, and PMF for rescoring with the results of 12 ligand-based similarity searches using four different query compounds and three different similarity metrics (Daylight, FTree, Phacir). The result of the similarity search showed much variation, from an enrichment factor up to 3.2 to worse than random, whereas the structure-based VS gave a more stable result with a constant enrichment factor around 2. Additionally, actives retrieved by the structure-based approach were more diverse than the actives among the top scorers of the similarity searches. Based on these results, we suggest basing a focused library design for a GPCR project on a combination of a ligand-based similarity search and structure-based docking.     

Trehalose synthesis inhibitor: A molecular in silico drug design

Infectious diseases are serious public health problems, affecting a large portion of the world's population. A molecule that plays a key role in pathogenic organisms is trehalose and recently has been an interest in the metabolism of this molecule for drug development. The trehalose-6-phosphate synthase (TPS1) is an enzyme responsible for the biosynthesis of trehalose-6-phosphate (T6P) in the TPS1/TPS2 pathway, which results in the formation of trehalose. Studies carried out by our group demonstrated the inhibitory capacity of T6P in the TPS1 enzyme from Saccharomyces cerevisiae, preventing the synthesis of trehalose. By in silico techniques, we compiled sequences and experimentally determined structures of TPS1. Sequence alignments and molecular modeling were performed. The generated structures were submitted in validation of algorithms, aligned structurally and analyzed evolutionarily. Molecular docking methodology was applied to analyze the interaction between T6P and TPS1 and ADMET properties of T6P were analyzed. The results demonstrated the models created presented sequence and structural similarities with experimentally determined structures. With the molecular docking, a cavity in the protein surface was identified and the molecule T6P was interacting with the residues TYR-40, ALA-41, MET-42, and PHE-372, indicating the possible uncompetitive inhibition mechanism provided by this ligand, which can be useful in directing the molecular design of inhibitors. In ADMET analyses, T6P had acceptable risk values compared with other compounds from World Drug Index. Therefore, these results may present a promising strategy to explore to develop a broad-spectrum antibiotic of this specific target with selectivity, potency, and reduced side effects, leading to a new way to treat infectious diseases like tuberculosis and candidiasis.     

Multi‐constraint computational design suggests that native sequences of germline antibody H3 loops are nearly optimal for conformational flexibility

The limited size of the germline antibody repertoire has to recognize a far larger number of potential antigens. The ability of a single antibody to bind multiple ligands due to conformational flexibility in the antigen‐binding site can significantly enlarge the repertoire. Among the six complementarity determining regions (CDRs) that generally comprise the binding site, the CDR H3 loop is particularly variable. Computational protein design studies showed that predicted low energy sequences compatible with a given backbone structure often have considerable similarity to the corresponding native sequences of naturally occurring proteins, indicating that native protein sequences are close to optimal for their structures. Here, we take a step forward to determine whether conformational flexibility, believed to play a key functional role in germline antibodies, is also central in shaping their native sequence. In particular, we use a multi‐constraint computational design strategy, along with the Rosetta scoring function, to propose that the native sequences of CDR H3 loops from germline antibodies are nearly optimal for conformational flexibility. Moreover, we find that antibody maturation may lead to sequences with a higher degree of optimization for a single conformation, while disfavoring sequences that are intrinsically flexible. In addition, this computational strategy allows us to predict mutations in the CDR H3 loop to stabilize the antigen‐bound conformation, a computational mimic of affinity maturation, that may increase antigen binding affinity by preorganizing the antigen binding loop. In vivo affinity maturation data are consistent with our predictions. The method described here can be useful to design antibodies with higher selectivity and affinity by reducing conformational diversity. Proteins 2009. © 2008 Wiley‐Liss, Inc.     

Predicting flexible loop regions that interact with ligands: the challenge of accurate scoring

Flexible loop regions play a critical role in the biological function of many proteins and have been shown to be involved in ligand binding. In the context of structure-based drug design, using or predicting an incorrect loop configuration can be detrimental to the study if the loop is capable of interacting with the ligand. Three protein systems, each with at least one flexible loop region in close proximity to the known binding site, were selected for loop prediction using the CorLps program; a six residue loop region from phosphoribosylglycinamide formyltransferase (GART), two nine residue loop regions from cytochrome P450 (CYP) 119, and an 11 residue loop region from enolase were selected for loop prediction. The results of this study indicate that the statistically based DFIRE scoring function implemented in the CorLps program did not accurately rank native-like predicted loop configurations in any protein system. In an attempt to improve the ranking of the native-like predicted loop configurations, the MM/GBSA and the optimized MM/GBSA-dsr scoring functions were used to re-rank the predicted loops with and without bound ligand. In general, single snapshot MM/GBSA scoring provided the best ranking of native-like loop configurations. Based on the scoring function analyses presented, the optimal ranking of native-like loop configurations is still a difficult challenge and the choice of the "best" scoring function appears to be system dependent.     

Ligand-supported homology modeling of the human angiotensin II type 1 (AT(1)) receptor: insights into the molecular determinants of telmisartan binding

Angiotensin II type 1 (AT(1)) receptor belongs to the super-family of G-protein-coupled receptors, and antagonists of the AT(1) receptor are effectively used in the treatment of hypertension. To understand the molecular interactions of these antagonists, such as losartan and telmisartan, with the AT(1) receptor, a homology model of the human AT(1) (hAT(1)) receptor with all connecting loops was constructed from the 2.6 A resolution crystal structure (PDB i.d., 1L9H) of bovine rhodopsin. The initial model generated by MODELLER was subjected to a stepwise ligand-supported model refinement. This protocol involved initial docking of non-peptide AT(1) antagonists in the putative binding site, followed by several rounds of iterative energy minimizations and molecular dynamics simulations. The final model was validated based on its correlation with several structure-activity relationships and site-directed mutagenesis data. The final model was also found to be in agreement with a previously reported AT(1) antagonist pharmacophore model. Docking studies were performed for a series of non-peptide AT(1) receptor antagonists in the active site of the final hAT(1) receptor model. The docking was able to identify key molecular interactions for all the AT(1) antagonists studied. Reasonable correlation was observed between the interaction energy values and the corresponding binding affinities of these ligands, providing further validation for the model. In addition, an extensive unrestrained molecular dynamics simulation showed that the docking-derived bound pose of telmisartan is energetically stable. Knowledge gained from the present studies can be used in structure-based drug design for developing novel ligands for the AT(1) receptor.     

Restrained molecular dynamics simulations of HIV-1 protease: the first step in validating a new target for drug design

To test the anticorrelated relationship that was recently displayed in conventional molecular dynamics (MD) simulations, several different restrained MD simulations on a wild type and on the V82F/I84V drug-resistant mutant of HIV-1 protease were performed. This anticorrelated relationship refers to the observation that compression of the peripheral ear-to-cheek region of HIV protease (i.e., the elbow of the flap to the fulcrum and the cantilever) occurred as the active site flaps were opening, and, conversely, expansion of that ear-to-cheek region occurred as both flaps were closing. An additional examination of this anticorrelated relationship was necessary to determine whether it can be harnessed in a useful manner. Consequently, six different MD experiments were performed that incorporated pairwise distance restraints in that ear-to-cheek region (i.e., the distance between the alpha-carbons of Gly40 and Gln61 was restrained to either 7.7 or 10.5 A, in both monomers). Pushing the backbones of the ear and the cheek regions away from each other slightly did force the flaps that guard the active site to remain closed in both the wild type and the mutant systems-even though there were no ligands in the active sites. Thus, these restrained MD simulations provided evidence that the anticorrelated relationship can be exploited to affect the dynamic behavior of the flaps that guard the active site of HIV-1 protease. These simulations supported our hypothesis of the mechanism governing flap motion, and they are the first step towards validating that peripheral surface as a new target for drug design.     

Exploring the structural determinants of novel xanthine derivatives as A2B adenosine receptor antagonists: a computational study

Adenosine is a ubiquitous endogenous nucleoside that controls numerous physiological functions via interacting with its specific G-coupled receptors. Activation of adenosine receptors (AdoRs), particularly A_2B AdoRs promotes the release of inflammatory cytokines; reduces vascular permeabilization and induces angiogenesis, thereby making A_2B AdoR becomes a potentially pharmacological target for drug development. Presently, for investigating the structural determinants of 164 xanthine derivatives as A_2B AdoR antagonists, we performed an in silico study integrating with 3D-QSAR, docking and molecular dynamics (MD) simulation. The obtained optimal model shows strong predictability (Q²?=?0.647, R²_ncv?=?0.955, and R²_pred?=?0.848). Additionally, to explore the binding mode of the ligand with A_2B AdoR and to understand their binding mechanism, docking analysis, MD simulations (20?ns), and the calculation of binding free energy were also carried out. Finally, the structural determinants of these xanthine derivatives were identified and a total of 20 novel A_2B AdoR antagonists with improved potency were computationally designed, and their synthetic feasibility and selectivity were also evaluated. The information derived from the present study offers a better appreciation for exploring the interaction mechanism of the ligand with A_2B AdoR, which could be helpful for designing novel potent A_2B AdoR antagonists.

Communicated by Ramaswamy H. Sarma     

Conformational flexibility in calcitonin: The dynamic properties of human and salmon calcitonin in solution

We have studied the dynamic properties of human (h) and salmon (s) calcitonin (CT) in solution. For both hormones, distance geometry in torsion-angle space has been used to generate three-dimensional structures consistent with NMR data obtained in sodium dodecyl sulfate micelles. For sCT and hCT we used, respectively, 356 and 275 interproton distances together with hydrogen-bonds as restraints. To better characterize their flexibility and dynamic properties two fully unrestrained 1100-ps molecular dynamics (MD) simulations in methanol were performed on the lowest-energy structures of both hormones. Statistical analyses of average geometric parameters and of their fluctuations performed in the last 1000 ps of the MD run show typical helical values for residues 9–19 of sCT during the whole trajectory. For hCT a shorter helix was observed involving residues 13–21, with a constant helical region in the range 13–19. Angular order parameters S() and S() indicate that hCT exhibits a higher flexibility, distributed along the whole chain, including the helix, while the only flexible amino acid residues in sCT connect three well-defined domains. Finally, our study shows that simulated annealing in torsion-angle space can efficiently be extended to NMR-based three-dimensional structure calculations of helical polypeptides. Furthermore, provided that a sufficient number of NMR restraints describes the system, the method allows the detection of equilibria in solution. This identification occurs through the generation of 'spurious' high-energy structures, which, for right-handed -helices, are likely to be represented by left-handed -helices.     

KAISO inhibition: an atomic insight

In today’s world, the pursuit of a novel anti-cancer agent remains top priority because of the fact that the global burden of this malady is continuously increasing. Our work is no different from others in searching for new therapeutic solutions. To achieve this, we are looking into Epigenetics, the phenomenon governed by hypermethylation and hypomethylation of tumor suppressor genes and oncogenes, respectively. Our target for this study is an important intermediary methyl-CpG binding protein named kaiso. In our study, we have used the X-ray crystallographic structure of Kaiso for virtual screening and molecular dynamics simulations to study the binding modes of possible inhibitors. The C2H2 domain comprising LYS539 was used for screening the inter bio screen Database having 48,531 natural compounds. Our approach of using computer-aided drug designing methods helped us to remove the execrable compounds and narrowed our focus on a selected few for molecular simulation studies. The top ranked compound (chem. ID 28127) exhibited the highest binding affinity and was also found to be stable throughout the 20 ns timeframe. This compound is therefore a good starting point for developing strong inhibitors.