Conformational Flexibility in the Transmembrane Protein TSPO

The translocator protein (TSPO) is an integral membrane protein that interacts with a wide variety of endogenous ligands, such as cholesterol and porphyrins, and is also the target for several small molecules with substantial in vivo efficacy. When complexed with the TSPO‐specific radioligand (R)‐PK11195, TSPO folds into a rigid five‐helix bundle. However, little is known about the structure and dynamics of TSPO in the absence of high‐affinity ligands. By means of NMR spectroscopy, we show that TSPO exchanges between multiple conformations in the absence of (R)‐PK11195. Extensive motions on time scales from pico‐ to microseconds occur all along the primary sequence of the protein, leading to a loss of stable tertiary interactions and local unfolding of the helical structure in the vicinity of the ligand‐binding site. The flexible nature of TSPO highlights the importance of conformational plasticity in integral membrane proteins.     

Identification of New Lead Molecules Against UBE2NL Enzyme for Cancer Therapy

Cancer is characterized by abnormal growth of cells. Targeting ubiquitin proteins in the discovery of new anticancer therapeutics is an attractive strategy. The present study uses the structure-based drug discovery methods to identify new lead structures, which are selective to the putative ubiquitin-conjugating enzyme E2N-like (UBE2NL). The 3D structure of the UBE2NL was evaluated using homology modeling techniques. The model was validated using standard in silico methods. The hydrophobic pocket of UBE2NL that aids in binding with its natural receptor ubiquitin-conjugating enzyme E2 variant (UBE2V) was identified through protein-protein docking study. The binding site region of the UBE2NL was identified using active site prediction tools. The binding site of UBE2NL which is responsible for cancer cell progression is considered for docking study. Virtual screening study with the small molecular structural database was carried out against the active site of UBE2NL. The ligand molecules that have shown affinity towards UBE2NL were considered for ADME prediction studies. The ligand molecules that obey the Lipinski’s rule of five and Jorgensen’s rule of three pharmacokinetic properties like human oral absorption etc. are prioritized. The resultant ligand molecules can be considered for the development of potent UBE2NL enzyme inhibitors for cancer therapy.     

A water-swap reaction coordinate for the calculation of absolute protein-ligand binding free energies

The accurate prediction of absolute protein-ligand binding free energies is one of the grand challenge problems of computational science. Binding free energy measures the strength of binding between a ligand and a protein, and an algorithm that would allow its accurate prediction would be a powerful tool for rational drug design. Here we present the development of a new method that allows for the absolute binding free energy of a protein-ligand complex to be calculated from first principles, using a single simulation. Our method involves the use of a novel reaction coordinate that swaps a ligand bound to a protein with an equivalent volume of bulk water. This water-swap reaction coordinate is built using an identity constraint, which identifies a cluster of water molecules from bulk water that occupies the same volume as the ligand in the protein active site. A dual topology algorithm is then used to swap the ligand from the active site with the identified water cluster from bulk water. The free energy is then calculated using replica exchange thermodynamic integration. This returns the free energy change of simultaneously transferring the ligand to bulk water, as an equivalent volume of bulk water is transferred back to the protein active site. This, directly, is the absolute binding free energy. It should be noted that while this reaction coordinate models the binding process directly, an accurate force field and sufficient sampling are still required to allow for the binding free energy to be predicted correctly. In this paper we present the details and development of this method, and demonstrate how the potential of mean force along the water-swap coordinate can be improved by calibrating the soft-core Coulomb and Lennard-Jones parameters used for the dual topology calculation. The optimal parameters were applied to calculations of protein-ligand binding free energies of a neuraminidase inhibitor (oseltamivir), with these results compared to experiment. These results demonstrate that the water-swap coordinate provides a viable and potentially powerful new route for the prediction of protein-ligand binding free energies.     

Covalent‐Allosteric Inhibitors to Achieve Akt Isoform‐Selectivity

Isoforms of protein kinase Akt are involved in essential processes including cell proliferation, survival, and metabolism. However, their individual roles in health and disease have not been thoroughly evaluated. Thus, there is an urgent need for perturbation studies, preferably mediated by highly selective bioactive small molecules. Herein, we present a structure‐guided approach for the design of structurally diverse and pharmacologically beneficial covalent‐allosteric modifiers, which enabled an investigation of the isoform‐specific preferences and the important residues within the allosteric site of the different isoforms. The biochemical, cellular, and structural evaluations revealed interactions responsible for the selective binding profiles. The isoform‐selective covalent‐allosteric Akt inhibitors that emerged from this approach showed a conclusive structure–activity relationship and broke ground in the development of selective probes to delineate the isoform‐specific functions of Akt kinases.     

Effects of Water Placement on Predictions of Binding Affinities for p38α MAP Kinase Inhibitors

Monte Carlo free energy perturbation (MC/FEP) calculations have been applied to compute the relative binding affinities of 17 congeneric pyridazo-pyrimidinone inhibitors of the protein p38α MAP kinase. Overall correlation with experiment was found to be modest when the complexes were hydrated using a traditional procedure with a stored solvent box. Significant improvements in accuracy were obtained when the MC/FEP calculations were repeated using initial solvent distributions optimized by the water placement algorithm JAWS. The results underscore the importance of accurate placement of water molecules in a ligand binding site for the reliable prediction of relative free energies of binding.     

The Interplay of Cholesterol and Ligand Binding in hTSPO from Classical Molecular Dynamics Simulations

The translocator protein (TSPO) is a 18kDa transmembrane protein, ubiquitously present in human mitochondria. It is overexpressed in tumor cells and at the sites of neuroinflammation, thus representing an important biomarker, as well as a promising drug target. In mammalian TSPO, there are cholesterol–binding motifs, as well as a binding cavity able to accommodate different chemical compounds. Given the lack of structural information for the human protein, we built a model of human (h) TSPO in the apo state and in complex with PK11195, a molecule routinely used in positron emission tomography (PET) for imaging of neuroinflammatory sites. To better understand the interactions of PK11195 and cholesterol with this pharmacologically relevant protein, we ran molecular dynamics simulations of the apo and holo proteins embedded in a model membrane. We found that: (i) PK11195 stabilizes hTSPO structural fold; (ii) PK11195 might enter in the binding site through transmembrane helices I and II of hTSPO; (iii) PK11195 reduces the frequency of cholesterol binding to the lower, N–terminal part of hTSPO in the inner membrane leaflet, while this impact is less pronounced for the upper, C–terminal part in the outer membrane leaflet, where the ligand binding site is located; (iv) very interestingly, cholesterol most frequently binds simultaneously to the so-called CRAC and CARC regions in TM V in the free form (residues L150–X–Y152–X(3)–R156 and R135–X(2)–Y138–X(2)–L141, respectively). However, when the protein is in complex with PK11195, cholesterol binds equally frequently to the CRAC–resembling motif that we observed in TM I (residues L17–X(2)–F20–X(3)–R24) and to CRAC in TM V. We expect that the CRAC–like motif in TM I will be of interest in future experimental investigations. Thus, our MD simulations provide insight into the structural features of hTSPO and the previously unknown interplay between PK11195 and cholesterol interactions with this pharmacologically relevant protein.     

PRO_SELECT: Combining structure-based drug design and combinatorial chemistry for rapid lead discovery. 1. Technology

This paper describes a novel methodology, PRO_SELECT, which combines elements of structure-based drug design and combinatorial chemistry to create a new paradigm for accelerated lead discovery. Starting with a synthetically accessible template positioned in the active site of the target of interest, PRO_SELECT employs database searching to generate lists of potential substituents for each substituent position on the template. These substituents are selected on the basis of their being able to couple to the template using known synthetic routes and their possession of the correct functionality to interact with specified residues in the active site. The lists of potential substituents are then screened computationally against the active site using rapid algorithms. An empirical scoring function, correlated to binding free energy, is used to rank the substituents at each position. The highest scoring substituents at each position can then be examined using a variety of techniques and a final selection is made. Combinatorial enumeration of the final lists generates a library of synthetically accessible molecules, which may then be prioritised for synthesis and assay. The results obtained using PRO_SELECT to design thrombin inhibitors are briefly discussed.     

Targeting tribbles homolog 3 (TRIB3) protein against type 2 diabetes for the identification of potential inhibitors by in silico screening

Tribbles homolog 3 (TRIB3) protein is inhibiting the insulin signaling by directly binding to the Akt/PKB leading to insulin resistance in the pancreas causing type 2 diabetes mellitus. Hence, TRIB3 protein is considered as a possible drug target for the new lead identification against type 2 diabetes. In the present study, the homology model of TRIB3 protein was generated to explore its biochemical function and molecular interactions in the new lead identification. The energy minimization of TRIB3 protein was carried out and evaluated by validation protocols for structure reliability. The druggable binding site of TRIB3 protein was identified for the virtual screening and molecular docking studies. The Asinex-fragments library of 22634 small molecules was docked at TRIB3 active site using the Glide module to identify new chemical entities. A total of 9 molecules were identified as final hits from virtual screening and their potency was ranked using Glide score, Glide energies, and residues interactions. The 6 prioritized lead molecules were further optimized using AutoDock, Prime MM/GBSA, and percentage of human oral absorption for the identification of potential leads. The molecules L2, L5, and L6 are identified as lead inhibitors and are showing consistent interactions with key residues Glu194 and Lys196 of TRIB3 protein. The identified potential leads were analyzed by ADME properties for their drug likeness and HergIC50 values are predicted for the prevention of preclinical failures. The present work sheds light on the identification of the best lead molecules against TRIB3 protein and offers a route to design as novel potential drug candidates for T2DM.     

Structure-based prediction of free energy changes of binding of PTP1B inhibitors

The goals were (1) to understand the driving forces in the binding of small molecule inhibitors to the active site of PTP1B and (2) to develop a molecular mechanics-based empirical free energy function for compound potency prediction. A set of compounds with known activities was docked onto the active site. The related energy components and molecular surface areas were calculated. The bridging water molecules were identified and their contributions were considered. Linear relationships were explored between the above terms and the binding free energies of compounds derived based on experimental inhibition constants. We found that minimally three terms are required to give rise to a good correlation (0.86) with predictive power in five-group cross-validation test (q2 = 0.70). The dominant terms are the electrostatic energy and non-electrostatic energy stemming from the intra- and intermolecular interactions of solutes and from those of bridging water molecules in complexes.     

Computation of affinity and selectivity: Binding of 2,4-diaminopteridine and 2,4-diaminoquinazoline inhibitors to dihydrofolate reductases

Binding energy calculations for complexes of mutant and wild-type human dihydrofolate reductases with 2,4-diaminopteridine and 2,4-diaminoquinazoline inhibitors are reported. Quantitative insight into binding energetics of these molecules is obtained from calculations based on force field energy evaluation and thermal sampling by molecular dynamics simulations. The calculated affinity of methotrexate for wild-type and mutant enzymes is reasonably well reproduced. Truncation of the methotrexate glutamate tail results in a loss of affinity by several orders of magnitude. No major difference in binding strength is predicted between the pteridines and the quinazolines, while the N-methyl group present in methotrexate appears to confer significantly stronger binding. The recent improvement, which is used here, of our linear interaction energy method for binding affinity prediction, as well as problems with treating charged and flexible ligands are discussed. This approach should be suitable in a drug discovery context for prediction of binding energies of new inhibitors prior to their synthesis, when some information about the binding mode is available.     

基于虚拟筛选策略发现新型潜在JAK3小分子抑制剂

利用已知活性的分子采用基于配体的策略构建药效团模型,通过基于类药规则、药效团模型、多种精度的分子对接算法、MM/GBSA结合能预测以及ADMET筛选手段对含约250万个分子的数据库进行虚拟筛选。发现5种JAK3抑制剂的新型骨架,其中6个以1-苯基咪唑烷-2-酮为骨架的分子在与JAK3激酶的结合能以及分子的ADMET性质评价方面均表现优异,具备高JAK3抑制剂潜力,被认为是虚拟筛选的命中分子。     

Synthesis of Novel Halogenated Heterocycles Based on o-Phenylenediamine and Their Interactions with the Catalytic Subunit of Protein Kinase CK2

Protein kinase CK2 is a highly pleiotropic protein kinase capable of phosphorylating hundreds of protein substrates. It is involved in numerous cellular functions, including cell viability, apoptosis, cell proliferation and survival, angiogenesis, or ER-stress response. As CK2 activity is found perturbed in many pathological states, including cancers, it becomes an attractive target for the pharma. A large number of low-mass ATP-competitive inhibitors have already been developed, the majority of them halogenated. We tested the binding of six series of halogenated heterocyclic ligands derived from the commercially available 4,5-dihalo-benzene-1,2-diamines. These ligand series were selected to enable the separation of the scaffold effect from the hydrophobic interactions attributed directly to the presence of halogen atoms. In silico molecular docking was initially applied to test the capability of each ligand for binding at the ATP-binding site of CK2. HPLC-derived ligand hydrophobicity data are compared with the binding affinity assessed by low-volume differential scanning fluorimetry (nanoDSF). We identified three promising ligand scaffolds, two of which have not yet been described as CK2 inhibitors but may lead to potent CK2 kinase inhibitors. The inhibitory activity against CK2α and toxicity against four reference cell lines have been determined for eight compounds identified as the most promising in nanoDSF assay.     

Large scale free energy calculations for blind predictions of protein–ligand binding: the D3R Grand Challenge 2015

We describe binding free energy calculations in the D3R Grand Challenge 2015 for blind prediction of the binding affinities of 180 ligands to Hsp90. The present D3R challenge was built around experimental datasets involving Heat shock protein (Hsp) 90, an ATP-dependent molecular chaperone which is an important anticancer drug target. The Hsp90 ATP binding site is known to be a challenging target for accurate calculations of ligand binding affinities because of the ligand-dependent conformational changes in the binding site, the presence of ordered waters and the broad chemical diversity of ligands that can bind at this site. Our primary focus here is to distinguish binders from nonbinders. Large scale absolute binding free energy calculations that cover over 3000 protein–ligand complexes were performed using the BEDAM method starting from docked structures generated by Glide docking. Although the ligand dataset in this study resembles an intermediate to late stage lead optimization project while the BEDAM method is mainly developed for early stage virtual screening of hit molecules, the BEDAM binding free energy scoring has resulted in a moderate enrichment of ligand screening against this challenging drug target. Results show that, using a statistical mechanics based free energy method like BEDAM starting from docked poses offers better enrichment than classical docking scoring functions and rescoring methods like Prime MM-GBSA for the Hsp90 data set in this blind challenge. Importantly, among the three methods tested here, only the mean value of the BEDAM binding free energy scores is able to separate the large group of binders from the small group of nonbinders with a gap of 2.4 kcal/mol. None of the three methods that we have tested provided accurate ranking of the affinities of the 147 active compounds. We discuss the possible sources of errors in the binding free energy calculations. The study suggests that BEDAM can be used strategically to discriminate binders from nonbinders in virtual screening and to more accurately predict the ligand binding modes prior to the more computationally expensive FEP calculations of binding affinity.     

Computational identification of novel piperidine derivatives as potential HDM2 inhibitors designed by fragment-based QSAR,molecular docking and molecular dynamics simulations

Tumor suppressor protein p53 maintains integrity of genome and regulates the genes responsible for DNA repair mechanism, apoptosis as well as cell cycle and growth arrest. As with murine double minute 2 (MDM2), the human homolog HDM2 is a principal cellular antagonist of p53. In unstressed cells, cellular levels of p53 and HDM2 are maintained in an autoregulatory manner in which both mutually control cellular levels of each other. About half of the human cancers express wild-type p53 protein that is antagonized by over-expressed HDM2. Restoring p53 function via HDM2 antagonists is a leading therapeutic approach for treating a variety of tumors. In this study, we have developed a novel statistically sound group-based QSAR (GQSAR) model using piperidine-derived compounds that have been validated experimentally to inhibit p53–HDM2 interaction. On the basis of developed GQSAR model, a combinatorial library of molecules was prepared and its activity was predicted. These molecules were then docked to HDM2, and two top-scoring molecules possessing a binding energy of ?6.639 and ?6.305 kcal/mol were selected for further study. These molecules and their binding poses were analyzed further via molecular dynamic simulations. In this study, we report two lead compounds as potent HDM2 inhibitors and also provide an insight into mechanism of interaction of the lead compounds to HDM2 target.

     

Computational fragment-based design of Wee1 kinase inhibitors with tricyclic core scaffolds

Wee1 is cell cycle protein comprising a kinase domain and is a validated cancer target. We have designed molecules with variable tricyclic core scaffolds [6-6-5] system and extended them based on the chemical space available in the active site of Wee1 kinase using de novo drug design. The core scaffolds and linking fragments were extracted from pharmacophore-based virtual screening of ZINC and PubChem databases and Ludi library. These molecules bind the hinge region of kinase active site and form hydrogen bonds as confirmed from molecular docking, molecular dynamics simulations, and MM_PBSA calculations. When compared with reference inhibitors, AZD1775 and PHA-848125, the de novo designed molecules also show good docking scores and stability, retained non-covalent interactions, and high binding free energies contributed from active site residues.

     

Prediction of new chromene-based inhibitors of tubulin using structure-based virtual screening and molecular dynamics simulation methods

Multidrug resistance (MDR) is one of the serious problems in cancer research that causes failure in chemotherapy. Chromene-based compounds have been proven to be the novel anti-MDR agents for inhibiting proliferation of tumor cells through tubulin polymerization inhibition of by binding at the colchicine binding site. In this study, we screened a chromene-based database of small molecules using physicochemical, ADMET properties and molecular docking to identify potential hit compounds. In order to validate our hit compounds, molecular dynamics simulations and related analysis were carried out and the results suggest that our hit compounds (PubChem CIDs: 16814409, 17594471, 57367244 and 69899719) can prove to be potential inhibitors of tubulin. The in silico results show that the present hits, like colchicine, effectively suppressed the dynamic instability of microtubules and induced microtubule-depolymerization and cell cycle arrest.     

Design, synthesis, and computational studies of inhibitors of Bcl-XL

One of the primary objectives in the design of protein inhibitors is to shape the three-dimensional structures of small molecules to be complementary to the binding site of a target protein. In the course of our efforts to discover potent inhibitors of Bcl-2 family proteins, we found a unique folded conformation adopted by tethered aromatic groups in the ligand that significantly enhanced binding affinity to Bcl-XL. This finding led us to design compounds that were biased by nonbonding interactions present in a urea tether to adopt this bioactive, folded motif. To characterize the key interactions that induce the desired conformational bias, a series of substituted N,N'-diarylureas were prepared and analyzed using X-ray crystallography and quantum mechanical calculations. Stabilizing pi-stacking interactions and destabilizing steric interactions were predicted to work in concert in two of the substitution patterns to promote the bioactive conformation as a global energy minimum and result in a high target binding affinity. Conversely, intramolecular hydrogen bonding present in the third substitution motif promotes a less active, extended conformer as the energetically favored geometry. These findings were corroborated when the inhibition constant of binding to Bcl-XL was determined for fully elaborated analogues bearing these structural motifs. Finally, we obtained the NMR solution structure of the disubstituted N,N'-diarylurea bound to Bcl-XL demonstrating the folded conformation of the urea motif engaged in extensive pi-interactions with the protein.     

Prediction of peptide binding to a major histocompatibility complex class I molecule based on docking simulation

Binding between major histocompatibility complex (MHC) class I molecules and immunogenic epitopes is one of the most important processes for cell-mediated immunity. Consequently, computational prediction of amino acid sequences of MHC class I binding peptides from a given sequence may lead to important biomedical advances. In this study, an efficient structure-based method for predicting peptide binding to MHC class I molecules was developed, in which the binding free energy of the peptide was evaluated by two individual docking simulations. An original penalty function and restriction of degrees of freedom were determined by analysis of 361 published X-ray structures of the complex and were then introduced into the docking simulations. To validate the method, calculations using a 50-amino acid sequence as a prediction target were performed. In 27 calculations, the binding free energy of the known peptide was within the top 5 of 166 peptides generated from the 50-amino acid sequence. Finally, demonstrative calculations using a whole sequence of a protein as a prediction target were performed. These data clearly demonstrate high potential of this method for predicting peptide binding to MHC class I molecules.     

Studies of the mechanism of selectivity of protein tyrosine phosphatase 1B (PTP1B) bidentate inhibitors using molecular dynamics simulations and free energy calculations

Bidentate inhibitors of protein tyrosine phosphatase 1B (PTP1B) are considered as a group of ideal inhibitors with high binding potential and high selectivity in treating type II diabetes. In this paper, the binding models of five bidentate inhibitors to PTP1B, TCPTP, and SHP-2 were investigated and compared by using molecular dynamics (MD) simulations and free energy calculations. The binding free energies were computed using the Molecular Mechanics/Poisson-Boltzmann Surface Area (MM/PBSA) methodology. The calculation results show that the predicted free energies of the complexes are well consistent with the experimental data. The Molecular Mechanics/Generalized Born Surface Area (MM/GBSA) free energy decomposition analysis indicates that the residues ARG24, ARG254, and GLN262 in the second binding site of PTP1B are essential for the high selectivity of inhibitors. Furthermore, the residue PHE182 close to the active site is also important for the selectivity and the binding affinity of the inhibitors. According to our analysis, it can be concluded that in most cases the polarity of the portion of the inhibitor that binds to the second binding site of the protein is positive to the affinity of the inhibitors while negative to the selectivity of the inhibitors. We expect that the information we obtained here can help to develop potential PTP1B inhibitors with more promising specificity.