Fragmental modeling of hPepT2 and analysis of its binding features by docking studies and pharmacophore mapping

Over the last years, considerable progress has been made for the identification and characterization of drug transporters, and several modeling studies have been undertaken to predict their effects on ADME profiling. Thus, this study was focused on the peptide transporter hPepT2, which influences the regional pharmacokinetics in brain, the reabsorption from renal tubular fluid and the pulmonary delivery. A reliable model for hPepT2 was generated by fragments based on the resolved structure of the homologue lactose permease LacY and the structure is made available as Supplementary data. The interaction capacities of such a model were explored by docking a set of 75 known ligands. Docking results underlined the predilection of hPepT2 for highly hydrophobic ligands and the key role of ionic interactions elicited by both charged termini. The docking results were further verified developing a pharmacophore model which clarified the key features required for an optimal hPepT2 affinity and confirmed the main factors governing the hPepT2/hPepT1 selectivity. The soundness of the docking results and the agreement with the pharmacophore mapping afford an encouraging validation for the proposed hPepT2 model and suggest that it can be conveniently exploited to design peptide-like molecules with an improved affinity for this transporter.     

Shape variation in protein binding pockets and their ligands

A common assumption about the shape of protein binding pockets is that they are related to the shape of the small ligand molecules that can bind there. But to what extent is that assumption true? Here we use a recently developed shape matching method to compare the shapes of protein binding pockets to the shapes of their ligands. We find that pockets binding the same ligand show greater variation in their shapes than can be accounted for by the conformational variability of the ligand. This suggests that geometrical complementarity in general is not sufficient to drive molecular recognition. Nevertheless, we show when considering only shape and size that a significant proportion of the recognition power of a binding pocket for its ligand resides in its shape. Additionally, we observe a "buffer zone" or a region of free space between the ligand and protein, which results in binding pockets being on average three times larger than the ligand that they bind.     

Implicit flexibility in protein docking: cross-docking and local refinement

In previous CAPRI rounds (3-5) we showed that using MD-generated ensembles, as inputs for a rigid-body docking algorithm, increased our success rate, especially for targets exhibiting substantial amounts of induced fit. In recent rounds (6-11), our cross-docking was followed by a short MD-based local refinement for the subset of solutions with the lowest interaction energies after minimization. The above approach showed promising results for target 20, where we were able to recover 30% of native contacts for one of our submitted models. Further tests, performed a posteriori, revealed that cross-docking approach produces more near-native (NN) solutions but only for targets with large conformational changes upon binding. However, at the time of the blind docking experiment, these improved solutions were not chosen for the subsequent refinement, as their interaction energies after minimization ranked poorly compared with other solutions. This indicates deficiencies in the present scoring schemes that are based on interaction energies of minimized structures. Refinement MD simulations substantially increase the fraction of native contacts for NN docked solutions, but generally worsen interface and ligand RMSD. Further analysis shows that although MD simulations are able to improve sidechain packing across the interface, which results in an increased fraction of native contacts, they are not capable of improving interface and ligand backbone RMSD for NN structures beyond 1.5 and 3.5 A, respectively, even if explicit solvent is used.     

From complete cross-docking to partners identification and binding sites predictions

Proteins ensure their biological functions by interacting with each other. Hence, characterising protein interactions is fundamental for our understanding of the cellular machinery, and for improving medicine and bioengineering. Over the past years, a large body of experimental data has been accumulated on who interacts with whom and in what manner. However, these data are highly heterogeneous and sometimes contradictory, noisy, and biased. Ab initio methods provide a means to a “blind” protein-protein interaction network reconstruction. Here, we report on a molecular cross-docking-based approach for the identification of protein partners. The docking algorithm uses a coarse-grained representation of the protein structures and treats them as rigid bodies. We applied the approach to a few hundred of proteins, in the unbound conformations, and we systematically investigated the influence of several key ingredients, such as the size and quality of the interfaces, and the scoring function. We achieved some significant improvement compared to previous works, and a very high discriminative power on some specific functional classes. We provide a readout of the contributions of shape and physico-chemical complementarity, interface matching, and specificity, in the predictions. In addition, we assessed the ability of the approach to account for protein surface multiple usages, and we compared it with a sequence-based deep learning method. This work may contribute to guiding the exploitation of the large amounts of protein structural models now available toward the discovery of unexpected partners and their complex structure characterisation.     

Molecular basis of secretin docking to its intact receptor using multiple photolabile probes distributed throughout the pharmacophore

The molecular basis of ligand binding and activation of family B G protein-coupled receptors is not yet clear due to the lack of insight into the structure of intact receptors. Although NMR and crystal structures of amino-terminal domains of several family members support consistency in general structural motifs that include a peptide-binding cleft, there are variations in the details of docking of the carboxyl terminus of peptide ligands within this cleft, and there is no information about siting of the amino terminus of these peptides. There are also no empirical data to orient the receptor amino terminus relative to the core helical bundle domain. Here, we prepared a series of five new probes, incorporating photolabile moieties into positions 2, 15, 20, 24, and 25 of full agonist secretin analogues. Each bound specifically to the receptor and covalently labeled single distinct receptor residues. Peptide mapping of labeled wild-type and mutant receptors identified that the position 15, 20, and 25 probes labeled residues within the distal amino terminus of the receptor, whereas the position 24 probe labeled the amino terminus adjacent to TM1. Of note, the position 2 probe labeled a residue within the first extracellular loop of the receptor, a region not previously labeled, providing an important new constraint for docking the amino-terminal region of secretin to its receptor core. These additional experimentally derived constraints help to refine our understanding of the structure of the secretin-intact receptor complex and provide new insights into understanding the molecular mechanism for activation of family B G protein-coupled receptors.     

Testing a flexible-receptor docking algorithm in a model binding site

Sampling receptor flexibility is challenging for database docking. We consider a method that treats multiple flexible regions of the binding site independently, recombining them to generate different discrete conformations. This algorithm scales linearly rather than exponentially with the receptor's degrees of freedom. The method was first evaluated for its ability to identify known ligands of a hydrophobic cavity mutant of T4 lysozyme (L99A). Some 200000 molecules of the Available Chemical Directory (ACD) were docked against an ensemble of cavity conformations. Surprisingly, the enrichment of known ligands from among a much larger number of decoys in the ACD was worse than simply docking to the apo conformation alone. Large decoys, accommodated in the larger cavity conformations sampled in the ensemble, were ranked better than known small ligands. The calculation was redone with an energy correction term that considered the cost of forming the larger cavity conformations. Enrichment improved, as did the balance between high-ranking large and small ligands. In a second retrospective test, the ACD was docked against a conformational ensemble of thymidylate synthase. Compared to docking against individual enzyme conformations, the flexible receptor docking approach improved enrichment of known ligands. Including a receptor conformational energy weighting term improved enrichment further. To test the method prospectively, the ACD database was docked against another cavity mutant of lysozyme (L99A/M102Q). A total of 18 new compounds predicted to bind this polar cavity and to change its conformation were tested experimentally; 14 were found to bind. The bound structures for seven ligands were determined by X-ray crystallography. The predicted geometries of these ligands all corresponded to the observed geometries to within 0.7A RMSD or better. Significant conformational changes of the cavity were observed in all seven complexes. In five structures, part of the observed accommodations were correctly predicted; in two structures, the receptor conformational changes were unanticipated and thus never sampled. These results suggest that although sampling receptor flexibility can lead to novel ligands that would have been missed when docking a rigid structure, it is also important to consider receptor conformational energy.     

Molecular docking and pharmacophore model studies of Rho kinase inhibitors

Molecular docking and pharmacophore model approaches were used to characterise the binding features of four different series of Rho kinase (ROCK) inhibitors. Docking simulation of 20 inhibitors with ROCK was performed. The binding conformations and binding affinities of these inhibitors were obtained using AutoDock 4.0 software. The predicted binding affinities correlate well with the activities of these inhibitors (R ² = 0.904). 3D pharmacophore models were generated for ROCK based on highly active inhibitors implemented in Catalyst 4.11 program. The best pharmacophore model consists of one hydrogen bond acceptor feature and two hydrophobic features, and they all seemed to be essential for inhibitors in terms of their binding activities. It is anticipated that the findings reported in this paper may provide very useful information for designing new ROCK inhibitors.     

A method for localizing ligand binding pockets in protein structures

The accurate identification of ligand binding sites in protein structures can be valuable in determining protein function. Once the binding site is known, it becomes easier to perform in silico and experimental procedures that may allow the ligand type and the protein function to be determined. For example, binding pocket shape analysis relies heavily on the correct localization of the ligand binding site. We have developed SURFNET-ConSurf, a modular, two-stage method for identifying the location and shape of potential ligand binding pockets in protein structures. In the first stage, the SURFNET program identifies clefts in the protein surface that are potential binding sites. In the second stage, these clefts are trimmed in size by cutting away regions distant from highly conserved residues, as defined by the ConSurf-HSSP database. The largest clefts that remain tend to be those where ligands bind. To test the approach, we analyzed a nonredundant set of 244 protein structures from the PDB and found that SURFNET-ConSurf identifies a ligand binding pocket in 75% of them. The trimming procedure reduces the original cleft volumes by 30% on average, while still encompassing an average 87% of the ligand volume. From the analysis of the results we conclude that for those cases in which the ligands are found in large, highly conserved clefts, the combined SURFNET-ConSurf method gives pockets that are a better match to the ligand shape and location. We also show that this approach works better for enzymes than for nonenzyme proteins.     

A flexible docking scheme to explore the binding selectivity of PDZ domains

Modeling of protein binding site flexibility in molecular docking is still a challenging problem due to the large conformational space that needs sampling. Here, we propose a flexible receptor docking scheme: A dihedral restrained replica exchange molecular dynamics (REMD), where we incorporate the normal modes obtained by the Elastic Network Model (ENM) as dihedral restraints to speed up the search towards correct binding site conformations. To our knowledge, this is the first approach that uses ENM modes to bias REMD simulations towards binding induced fluctuations in docking studies. In our docking scheme, we first obtain the deformed structures of the unbound protein as initial conformations by moving along the binding fluctuation mode, and perform REMD using the ENM modes as dihedral restraints. Then, we generate an ensemble of multiple receptor conformations (MRCs) by clustering the lowest replica trajectory. Using ROSETTA LIGAND , we dock ligands to the clustered conformations to predict the binding pose and affinity. We apply this method to postsynaptic density‐95/Dlg/ZO‐1 (PDZ) domains; whose dynamics govern their binding specificity. Our approach produces the lowest energy bound complexes with an average ligand root mean square deviation of 0.36 Å. We further test our method on (i) homologs and (ii) mutant structures of PDZ where mutations alter the binding selectivity. In both cases, our approach succeeds to predict the correct pose and the affinity of binding peptides. Overall, with this approach, we generate an ensemble of MRCs that leads to predict the binding poses and specificities of a protein complex accurately.     

Enhanced sampling of protein conformational states for dynamic cross-docking within the protein-protein docking server SwarmDock

The formation of specific protein-protein interactions is often a key to a protein's function. During complex formation, each protein component will undergo a change in the conformational state, for some these changes are relatively small and reside primarily at the sidechain level; however, others may display notable backbone adjustments. One of the classic problems in the protein-docking field is to be able to a priori predict the extent of such conformational changes. In this work, we investigated three protocols to find the most suitable input structure conformations for cross-docking, including a robust sampling approach in normal mode space. Counterintuitively, knowledge of the theoretically best combination of normal modes for unbound-bound transitions does not always lead to the best results. We used a novel spatial partitioning library, Aether Engine (see Supplementary Materials ), to efficiently search the conformational states of 56 receptor/ligand pairs, including a recent CAPRI target, in a systematic manner and selected diverse conformations as input to our automated docking server, SwarmDock, a server that allows moderate conformational adjustments during the docking process. In essence, here we present a dynamic cross-docking protocol, which when benchmarked against the simpler approach of just docking the unbound components shows a 10% uplift in the quality of the top docking pose.     

Amino acid network and its scoring application in protein-protein docking

Protein-protein complex, composed of hydrophobic and hydrophilic residues, can be divided into hydrophobic and hydrophilic amino acid network structures respectively. In this paper, we are interested in analyzing these two different types of networks and find that these networks are of small-world properties. Due to the characteristic complementarity of the complex interfaces, protein-protein docking can be viewed as a particular network rewiring. These networks of correct docked complex conformations have much more increase of the degree values and decay of the clustering coefficients than those of the incorrect ones. Therefore, two scoring terms based on the network parameters are proposed, in which the geometric complementarity, hydrophobic-hydrophobic and polar-polar interactions are taken into account. Compared with a two-term energy function, a simple scoring function HPNet which includes the two network-based scoring terms shows advantages in two aspects, not relying on energy considerations and better discrimination. Furthermore, combing the network-based scoring terms with some other energy terms, a new multi-term scoring function HPNet-combine can also make some improvements to the scoring function of RosettaDock.     

Characterization of binding site of closed-state KCNQ1 potassium channel by homology modeling, molecular docking, and pharmacophore identification

This investigation was performed to assess the importance of interaction in the binding of blockers to KCNQ1 potassium using molecular modeling. This work could be considered made up by three main steps: (1) the construction of closed-state structure of KCNQ1 through homology modeling; (2) the automated docking of three blockers: IKS-142, L-735821, and BMS-IKS, using DOCK program; (3) the generation and validation of pharmacophore for KCNQ1 ligands using Catalyst/HypoGen. The obtained results highlight the hydrophobic or aromatic residues involved in S6 transmembrane domain and the base of the pore helix of KCNQ1, confirming the mutagenesis data and pharmacophore model, and giving new suggestions for the rational design of novel KCNQ1 ligands.     

Ligand-based 3-D pharmacophore generation and molecular docking of mTOR kinase inhibitors

The 3-D structure of the human mTOR kinase domain was modeled based on the crystal structure of PI3Kγ using comparative modeling methods, and the ATP-binding site of mTOR was characterized. The mTOR kinase 3-D model structure is similar to the structure of the PI3Kγ kinase domain, and exhibits great similarity to PI3Kγ at the active site of the kinase. Pharmacophore generation, the docking of mTOR inhibitors, and molecular dynamics (MD) simulations of mTOR–inhibitor docked complexes were carried out in this work. The best pharmacophore model generated from 27 ATP-competitive mTOR inhibitors comprised two hydrogen-bond acceptors, one aromatic ring, and one hydrophobic feature. These 27 inhibitors were docked into the ATP-binding site comprising the DFG motif, and the interactions in each protein–inhibitor complex were characterized. Mapping the pharmacophore model onto the docked inhibitors explained the specificity of the features of the pharmacophore and how they were arranged inside the active site of mTOR kinase. MD studies revealed important structural features, such as the large hydrophobic pocket “HP” and hydrophilic pocket “A1,” and the solvent-exposed hydrophilic pocket “A2” at the active site of mTOR. Our results provide structural models of mTOR–inhibitor complexes and clues that should aid in the design of better mTOR kinase inhibitors.     

Three-dimensional model of the human urotensin-II receptor: docking of human urotensin-II and nonpeptide antagonists in the binding site and comparison with an antagonist pharmacophore model

Human urotensin-II (hU-II) is a cyclic peptide that plays a central role in cardiovascular homeostasis and is considered to be the most potent mammalian vasoconstrictor identified to date. It is a natural ligand of the human urotensin-II (hUT-II) receptor, a member of the family of rhodopsin-like G-protein-coupled receptors. To understand the molecular interactions of hU-II and certain antagonists with the hUT-II receptor, a model of the hUT-II receptor in an active conformation with all its connecting loops was constructed by homology modeling. The initial model was placed in a pre-equilibrated lipid bilayer and re-equilibrated by several procedures of energy minimization and molecular dynamics simulations. Docking studies were performed for hU-II and for a series of nonpeptide hUT-II receptor antagonists in the active site of the modeled receptor structure. Results of the hU-II docking study are in agreement with our previous work and with experimental data showing the contribution of the extracellular loops II and III to ligand recognition. The docking of hU-II nonpeptide antagonists allows identification of key molecular interactions and confirms a previously reported hU-II antagonist pharmacophore model. The results of the present studies will be used in structure-based drug design for developing novel antagonists for the hUT-II receptor.     

Geometric and chemical patterns of interaction in protein--ligand complexes and their application in docking

We present a new method for representing the binding site of a protein receptor that allows the use of the DOCK approach to screen large ensembles of receptor conformations for ligand binding. The site points are constructed from templates of what we called "attached points" (ATPTS). Each template (one for each type of amino acid) is composed of a set of representative points that are attached to side-chain and backbone atoms through internal coordinates, carry chemical information about their parent atoms and are intended to cover positions that might be occupied by ligand atoms when complexed to the protein. This method is completely automatic and proved to be extremely fast. With the aim of obtaining an experimental basis for this approach, the Protein Data Bank was searched for proteins in complex with small molecules, to study the geometry of the interactions between the different types of protein residues and the different types of ligand atoms. As a result, well-defined patterns of interaction were obtained for most amino acids. These patterns were then used for constructing a set of templates of attached points, which constitute the core of the ATPTS approach. The quality of the ATPTS representation was demonstrated by using this method, in combination with the DOCK matching and orientation algorithms, to generate correct ligand orientations for >1000 protein--ligand complexes.     

A new discriminant NMF algorithm and its application to the extraction of subtle emotional differences in speech

In this study we propose a new feature extraction algorithm, dNMF (discriminant non-negative matrix factorization), to learn subtle class-related differences while maintaining an accurate generative capability. In addition to the minimum representation error for the standard NMF (non-negative matrix factorization) algorithm, the dNMF algorithm also results in higher between-class variance for discriminant power. The multiplicative NMF learning algorithm has been modified to cope with this additional constraint. The cost function was carefully designed so that the extraction of feature coefficients from a single testing pattern with pre-trained feature vectors resulted in a quadratic convex optimization problem in non-negative space for uniqueness. It also resolves issues related to the previous discriminant NMF algorithms. The developed dNMF algorithm has been applied to the emotion recognition task for speech, where it needs to emphasize the emotional differences while de-emphasizing the dominant phonetic components. The dNMF algorithm successfully extracted subtle emotional differences, demonstrated much better recognition performance and showed a smaller representation error from an emotional speech database.     

A transformation clustering algorithm and its application in polyribosomes structural profiling

Improvements in cryo-electron tomography sample preparation, electron-microscopy instrumentations, and image processing algorithms have advanced the structural analysis of macromolecules in situ. Beyond such analyses of individual macromolecules, the study of their interactions with functionally related neighbors in crowded cellular habitats, i.e. ‘molecular sociology’, is of fundamental importance in biology. Here we present a NEighboring Molecule TOpology Clustering (NEMO-TOC) algorithm. We optimized this algorithm for the detection and profiling of polyribosomes, which play both constitutive and regulatory roles in gene expression. Our results suggest a model where polysomes are formed by connecting multiple nonstochastic blocks, in which translation is likely synchronized.