Surely you are joking, Mr Docking!

In the wake of recent COVID-19 pandemics scientists around the world rushed to deliver numerous CADD (Computer-Aided Drug Discovery) methods and tools that could be reliably used to discover novel drug candidates against the SARS-CoV-2 virus. With that, there emerged a trend of a significant democratization of CADD that contributed to the rapid development of various COVID-19 drug candidates currently undergoing different stages of validation. On the other hand, this democratization also inadvertently led to the surge rapidly performed molecular docking studies to nominate multiple scores of novel drug candidates supported by computational arguments only. Albeit driven by best intentions, most of such studies also did not follow best practices in the field that require experience and expertise learned through multiple rigorously designed benchmarking studies and rigorous experimental validation. In this Viewpoint we reflect on recent disbalance between small number of rigorous and comprehensive studies and the proliferation of purely computational studies enabled by the ease of docking software availability. We further elaborate on the hyped oversale of CADD methods' ability to rapidly yield viable drug candidates and reiterate the critical importance of rigor and adherence to the best practices of CADD in view of recent emergence of AI and Big Data in the field.

Beware of R(2): Simple, Unambiguous Assessment of the Prediction Accuracy of QSAR and QSPR Models.

The statistical metrics used to characterize the external predictivity of a model, i.e., how well it predicts the properties of an independent test set, have proliferated over the past decade. This paper clarifies some apparent confusion over the use of the coefficient of determination, R(2), as a measure of model fit and predictive power in QSAR and QSPR modeling. R(2) (or r(2)) has been used in various contexts in the literature in conjunction with training and test data for both ordinary linear regression and regression through the origin as well as with linear and nonlinear regression models. We analyze the widely adopted model fit criteria suggested by Golbraikh and Tropsha ( J. Mol. Graphics Modell. 2002 , 20 , 269 - 276 ) in a strict statistical manner. Shortcomings in these criteria are identified, and a clearer and simpler alternative method to characterize model predictivity is provided. The intent is not to repeat the well-documented arguments for model validation using test data but rather to guide the application of R(2) as a model fit statistic. Examples are used to illustrate both correct and incorrect uses of R(2). Reporting the root-mean-square error or equivalent measures of dispersion, which are typically of more practical importance than R(2), is also encouraged, and important challenges in addressing the needs of different categories of users such as computational chemists, experimental scientists, and regulatory decision support specialists are outlined.

Molecular simulations of beta-sheet twisting.

Twisted conformations of two- and three-stranded antiparallel beta-sheet models containing alanine, glycine and valine with three or five residues per strand have been studied by molecular dynamics simulations. Free molecular dynamics and free energy simulations have been carried out to characterize the dynamics and energetics of the conformational change from a flat sheet to a twisted sheet. By altering the charges on the model in the free energy simulations, we have been able to analyze the contributions to the twist from electrostatic and van der Waals interactions. We have found that alanine and valine beta-sheets prefer conformations with a right-handed twist. In contrast, model glycine sheets do not have a pronounced preference to twist. Single beta-strands are found to be easily twisted, but to not have a strong preference for twisted conformations. Hence, the driving forces for the right-handed twist of beta-sheets must come principally from interactions between strands. These results disagree with several previous theoretical studies and constitute a different paradigm of the origin of beta-sheet twist observed in proteins.

Four-body potentials reveal protein-specific correlations to stability changes caused by hydrophobic core mutations.

Mutational experiments show how changes in the hydrophobic cores of proteins affect their stabilities. Here, we estimate these effects computationally, using four-body likelihood potentials obtained by simplicial neighborhood analysis of protein packing (SNAPP). In this procedure, the volume of a known protein structure is tiled with tetrahedra having the center of mass of one amino acid side-chain at each vertex. Log-likelihoods are computed for the 8855 possible tetrahedra with equivalent compositions from structural databases and amino acid frequencies. The sum of these four-body potentials for tetrahedra present in a given protein yields the SNAPP score. Mutations change this sum by changing the compositions of tetrahedra containing the mutated residue and their related potentials. Linear correlation coefficients between experimental mutational stability changes, Delta(DeltaG(unfold)), and those based on SNAPP scoring range from 0.70 to 0.94 for hydrophobic core mutations in five different proteins. Accurate predictions for the effects of hydrophobic core mutations can therefore be obtained by virtual mutagenesis, based on changes to the total SNAPP likelihood potential. Significantly, slopes of the relation between Delta(DeltaG(unfold)) and DeltaSNAPP for different proteins are statistically distinct, and we show that these protein-specific effects can be estimated using the average SNAPP score per residue, which is readily derived from the analysis itself. This result enhances the predictive value of statistical potentials and supports previous suggestions that "comparable" mutations in different proteins may lead to different Delta(DeltaG(unfold)) values because of differences in their flexibility and/or conformational entropy.

QSAR modeling of datasets with enantioselective compounds using chirality sensitive molecular descriptors.

Shape descriptors used in 3D QSAR studies naturally take into account chirality; however, for flexible and structurally diverse molecules such studies require extensive conformational searching and alignment. QSAR modeling studies of two datasets of fragrance compounds with complex stereochemistry using simple alignment-free chirality sensitive descriptors developed in our laboratories are presented. In the first investigation, 44 alpha-campholenic derivatives with sandalwood odor were represented as derivatives of several common structural templates with substituents numbered according to their relative spatial positions in the molecules. Both molecular and substituent descriptors were used as independent variables in MLR calculations, and the best model was characterized by the training set q2 of 0.79 and external test set r2 of 0.95. In the second study, several types of chirality descriptors were employed in combinatorial QSAR modeling of 98 ambergris fragrance compounds. Among 28 possible combinations of seven types of descriptors and four statistical modeling techniques, k nearest neighbor classification with CoMFA descriptors was initially found to generate the best models with the internal and external accuracies of 76 and 89%, respectively. The same dataset was then studied using novel atom pair chirality descriptors (cAP). The cAP are based on a modified definition of the atomic chirality, in which the seniority of the substituents is defined by their relative partial charge values: higher values correspond to higher seniorities. The resulting models were found to have higher predictive power than those developed with CoMFA descriptors; the best model was characterized by the internal and external accuracies of 82 and 94%, respectively. The success of modeling studies using simple alignment free chirality descriptors discussed in this paper suggests that they should be applied broadly to QSAR studies of many datasets when compound stereochemistry plays an important role in defining their activity.

Rational design of a three-heptad coiled-coil protein and comparison by molecular dynamics simulation with the GCN4 coiled coil: presence of interior three-center hydrogen bonds.

alpha-Helical coiled coils have a 7-residue repeating pattern (abcdefg) where a and d are usually hydrophobic. We have designed a 2-stranded 44-residue coiled-coil protein (P44) consisting of 2 22-residue alpha-helices linked by 2 terminal disulfide groups to test whether the disulfide bridges could stabilize a 3-heptad coiled coil. P44 should be stabilized by intrahelical hydrogen bonds, interhelical disulfide and salt bridges, and interior hydrophobic interactions. A computer model of P44 was built and its stability was studied by molecular dynamics simulation with explicit water. This doubly crosslinked 3-heptad coiled coil did not unfold during a 300-ps simulation with explicit water. This doubly crosslinked 3-heptad coiled coil did not unfold during a 300-ps simulation. But reduced P44 with 4 thiol groups did unfold. For comparison, the 62-residue crystal structure of the 4-heptad coiled coil of transcription activator GCN4 did not unfold during a 300-ps simulation. Thus P44 may be a stable folded protein in aqueous solution. These simulations revealed the presence of 2 local hydrogen bond networks involving intra-helical 3-center hydrogen bonds in the hydrophobic interior of the coiled coils of GCN4 and P44. The NH hydrogen at d makes a 3-center hydrogen bond whose major component is to the i - 4 C = O oxygen at g and minor component is to the solvent-inaccessible i - 3 C = O oxygen at a. Likewise, the NH hydrogen at g makes a 3-center hydrogen bond with the i - 4 C = O oxygen at c and the buried i - 3 C = O oxygen at d.

Pseudotorsional OCCO backbone angle as a single descriptor of protein secondary structure.

Protein secondary structure is conventionally identified using characteristic ranges of two backbone torsional angles phi and psi. We suggest that the secondary structure can be adequately characterized by a single descriptor, the Oi-1Ci-1CiOi (where i is the residue number) pseudotorsional backbone angle. A set of 102 structurally distinct protein chains from the Protein Data Bank was used to evaluate the adequacy of this descriptor. We find that a specific range of OCCO angles corresponds to each major secondary structure. The complete range of OCCO angles (-180 degrees to 179 degrees) was broken into 18 consecutive subranges of 20 degrees each, and each subrange was assigned a letter. Thus, the OCCO profiles for each protein in the database were "translated" into a sequence of letters. The Needleman-Wunsch primary sequence alignment algorithm was then used for secondary/tertiary structure comparison and alignment. Preliminary results indicate that this new approach has a significant potential for rapid identification of fold families in the Protein Data Bank.

Identification of the descriptor pharmacophores using variable selection QSAR: applications to database mining.

The pharmacophore concept is central to the rational drug design and discovery process. Traditionally, a pharmacophore is defined as a specific three-dimensional (3D) arrangement of chemical functional groups found in active molecules, which are characteristic of a certain pharmacological class of compounds. Herein, by analogy with 3D pharmacophores, a more general concept of descriptor pharmacophore is introduced. The descriptor pharmacophores are defined by the means of variable selection QSAR as a subset of molecular descriptors that afford the most statistically significant structure-activity correlation. The two variable selection QSAR methods developed in this laboratory are discussed; these include Genetic Algorithms--Partial Least Squares (GA-PLS) and K-Nearest Neighbors (KNN). Both methods employ multiple topological descriptors of chemical structures such as molecular connectivity indices or atom pairs (AP), and stochastic optimization algorithms to achieve a robust QSAR model, which is characterized by the highest value of cross-validated R2 (q2). By default, the descriptor pharmacophore represents an invariant selection of descriptor types however, descriptor values are generally different for different molecules. We demonstrate that chemical similarity searches using descriptor pharmacophores as opposed to using all descriptors afford more efficient mining of chemical databases or virtual libraries to discover compounds with a desired biological activity.

Delaunay tessellation of proteins: four body nearest-neighbor propensities of amino acid residues.

Delaunay tessellation is applied for the first time in the analysis of protein structure. By representing amino acid residues in protein chains by C alpha atoms, the protein is described as a set of points in three-dimensional space. Delaunay tessellation of a protein structure generates an aggregate of space-filling irregular tetrahedra, or Delaunay simplices. The vertices of each simplex define objectively four nearest neighbor C alpha atoms, i.e., four nearest-neighbor residues. A simplex classification scheme is introduced in which simplices are divided into five classes based on the relative positions of vertex residues in protein primary sequence. Statistical analysis of the residue composition of Delaunay simplices reveals nonrandom preferences for certain quadruplets of amino acids to be clustered together. This nonrandom preference may be used to develop a four-body potential that can be used in evaluating sequence-structure compatibility for the purpose of inverted structure prediction.

Relative binding free energies of peptide inhibitors of HIV-1 protease: the influence of the active site protonation state.

Hydrogen bonding plays an important role in the stabilization of complexes between HIV-1 protease (HIV-1 PR) and its inhibitors. The adequate treatment of the protease active site protonation state is important for accurate molecular simulations of the protonation state is important for accurate molecular simulations of the protease-inhibitor complexes. We have applied the free energy simulation/thermodynamic cycle approach to evaluate the relative binding affinities of the S vs R isomers of the U85548E inhibitor of the protease. Several mono- and diprotonation states of the catalytic aspartic acid residues of the protease active site were considered in the course of molecular simulations. The calculated difference in binding free energy of the S vs R isomers strongly depended on the location of proton(s), but in all cases the binding free energy of the S inhibitor was higher. On the basis of our calculations, we propose that in the HIV-1 PR-inhibitor complex only one catalytic aspartic acid residue is protonated and that the binding free energy of the S isomer is ca. 2.8 kcal/mol higher than that of the R isomer. The accuracy of these predictions shall be evaluated when binding affinities of both isomers become available.

Cross-validated R2-guided region selection for comparative molecular field analysis: a simple method to achieve consistent results.

Comparative Molecular Field Analysis (CoMFA) is one of the most powerful modern tools for quantitative structure-activity relationship studies. The CoMFA predictability is conventionally characterized by a cross-validated correlation coefficient R2 (q2). Our CoMFA investigation of 4 datasets, including 7 cephalotaxine esters, 20 5-HT1A receptor ligands, 59 inhibitors of HIV protease, and 21 steroids reveals that the q2 value is sensitive to the overall orientation of superimposed molecules on a computer terminal and can vary by as much as 0.5q2 units when the orientation is varied by systematic rotation. To optimize CoMFA, we have developed a new routine, cross-validated R2-guided region selection (q2-GRS). We first subdivide the rectangular lattice obtained initially with conventional CoMFA into 125 small boxes and perform 125 independent analyses using probe atoms placed within each box with the step size of 1.0 A. We then select only those small boxes for which a q2 is higher than a specified optimal cutoff value. Finally, we repeat CoMFA with the union of small boxes selected at the previous step. Four datasets described above were used to validate this new q2-GRS routine. In each case we have obtained an orientation-independent, high q2, exceeding the one obtained with the conventional CoMFA. This method shall be used routinely in the future CoMFA studies to guarantee the reproducibility of the reported q2 values.

Structure-based alignment and comparative molecular field analysis of acetylcholinesterase inhibitors.

The method of comparative molecular field analysis (CoMFA) was used to develop quantitative structure-activity relationships for physostigmine, 9-amino-1,2,3,4-tetrahydroacridine (THA), edrophonium (EDR), and other structurally diverse inhibitors of acetylcholinesterase (AChE). The availability of the crystal structures of enzyme/inhibitor complexes (EDR/AChE, THA/AChE, and decamethonium (DCM)/AChE) (Harel, M.; et al. Quaternary ligand binding to aromatic residues in the active-site gorge of acetylcholinesterase. Proc. Natl. Acad. Sci. U.S.A. 1993, 90, 9031-9035) provided information regarding not only the active conformation of the inhibitors but also the relative mutual orientation of the inhibitors in the active site of the enzyme. Crystallographic conformations of EDR and THA were used as templates onto which additional inhibitors were superimposed. The application of cross-validated R2 guided region selection method, recently developed in this laboratory (Cho, S.J.; Tropsha, A. Cross-Validated R2 Guided Region Selection for Comparative Molecular Field Analysis (CoMFA): A Simple Method to Achieve Consistent Results. J. Med. Chem. 1995, 38, 1060-1066), to 60 AChE inhibitors led to a highly predictive CoMFA model with the q2 of 0.734.

Antitumor agents. 152. In vitro inhibitory activity of etoposide derivative NPF against human tumor cell lines and a study of its conformation by X-ray crystallography, molecular modeling, and NMR spectroscopy.

NPF, the title compound, was studied for its in vitro antitumor activity against 56 human tumor cell lines derived from seven cancer types. In general, NPF is about 100 times more active as compared to its parent compound, etoposide, toward all the tumor cell lines and can be considered as a lead structure for further development of anticancer agents. In order to facilitate future computer-assisted design of NPF analogs, NPF was characterized by X-ray crystallography. This crystal structure was used as the starting point for conformational analysis of this compound using several commercially available software packages, including SYBYL (Tripos Associates; Tripos force field), INSIGHT/DISCOVER (Biosym Technologies; CVFF force field), and semiempirical package MOPAC as implemented in SYBYL. The lowest energy conformation generated with the Tripos force field disagreed with the X-ray structure. On the other hand, semiempirical MOPAC/AM1 calculations showed that the X-ray structure had a lower energy than the Tripos lowest energy conformation. Subsequent NMR studies agreed well with the X-ray structure. Furthermore, conformational analysis of NPF using the DISCOVER force field identified the X-ray structure as the lowest energy conformation. Thus, the latter force field is adequate for future molecular modeling of NPF and its analogs.

Antitumor agents. 163. Three-dimensional quantitative structure-activity relationship study of 4'-O-demethylepipodophyllotoxin analogs using the modified CoMFA/q2-GRS approach.

Analogs of 4'O-demethylepipodophyllotoxin are considered as potential anticancer agents. We have applied comparative molecular field analysis (CoMFA) and a novel CoMFA/q2-GRS technique recently developed in our group to identify the essential structural requirements for increasing the ability of these compounds to form cellular protein-DNA complex. In addition, a new method to incorporate different types of probe atoms as part of q2-GRS routine has been developed. The best final model with 101 compounds using a combination of four different sets of probe atoms and charges [C (sp3, +1), C (sp3, 0), H (+1), and O (sp3, -1)] yielded a q 2 of 0.584 and the standard error of prediction of 0.660 at 5 principal components. The steric and electrostatic contour plots of the final model were compared with the DNA phosphate backbone environment of the DNA-4'O-demethylepipodophylltoxin analog complex, which was generated using the X-ray structure of the DNA-nogalamycin complex. The comparison reveals that the CoMFA steric and electrostatic fields are compatible with stereochemical properties of the DNA backbone. The results obtained from this study shall guide our future synthetic efforts.

Synthesis, evaluation, and comparative molecular field analysis of 1-phenyl-3-amino-1,2,3,4-tetrahydronaphthalenes as ligands for histamine H(1) receptors.

A series of 1-phenyl-3-amino-1,2,3,4-tetrahydronaphthalenes (1-phenyl-3-aminotetralins, PATs) previously was found to modulate tyrosine hydroxylase activity and dopamine synthesis in rodent forebrain through interaction with a binding site labeled by [(3)H]-(-)-(1R,3S)-trans-H(2)-PAT. Recently, we have discovered that PATs also bind with high affinity to the [(3)H]mepyramine-labeled H(1) receptor in rat and guinea pig brain. Here, we report the synthesis and biological evaluation of additional PAT analogues in order to identify differences in binding at these two sites. Further molecular modifications involve the pendant phenyl ring as well as quaternary amine compounds. Comparison of about 38 PAT analogues, 10 structurally diverse H(1) ligands, and several other CNS-active compounds revealed no significant differences in affinity at [(3)H]-(-)-trans-H(2)-PAT sites versus [(3)H]mepyramine-labeled H(1) receptors. These results, together with previous autoradiographic brain receptor-mapping studies that indicate similar distribution of [(3)H]-(-)-trans-H(2)-PAT sites and [(3)H]mepyramine-labeled H(1) receptors, suggest that both radioligands label the same histamine H(1) receptors in rodent brain. We also report a revision of our previous comparative molecular field analysis (CoMFA) study of the PAT ligands that yields a highly predictive model for 66 compounds with a cross-validated R(2) (q(2)) value of 0.67. This model will be useful for the prediction of high-affinity ligands at radiolabeled H(1) receptors in mammalian brain.

Antitumor agents. 199. Three-dimensional quantitative structure-activity relationship study of the colchicine binding site ligands using comparative molecular field analysis.

Inhibitors of tubulin polymerization interacting at the colchicine binding site are potential anticancer agents. We have been involved in the synthesis of a number of colchicine site agents, such as thiocolchicinoids and allocolchicinoids, which are colchicine analogues, and 2-phenyl-quinolones and 2-aryl-naphthyridinones, which are the amino analogues of cytotoxic antimitotic flavonoids. The most cytotoxic of the latter compounds strongly inhibit binding of radiolabeled colchicine to tubulin, and these agents therefore probably bind in the colchicine site of tubulin. We have applied conventional CoMFA and q(2)-GRS CoMFA to identify the essential structural requirements for increasing the ability of these compounds to form tubulin complexes. The CoMFA model for the training set of 51 compounds yielded cross-validated R(2) (q(2)) values of 0.637 for conventional CoMFA and 0.692 for q(2)-GRS CoMFA. The predictive power of this model was confirmed by successful activity prediction for a test set of 53 compounds with known potencies as inhibitors of tubulin polymerization. The activities of 88% of the compounds were predicted with absolute value of residuals of less than 0.5. The predictive q(2) values were 0.546 for conventional CoMFA and 0.426 for q(2)-GRS CoMFA. The conventional CoMFA model with the highest predictive q(2) (0.546) was analyzed in detail in terms of underlying structure-activity relationships.

Conformational analysis of D1 dopamine receptor agonists: pharmacophore assessment and receptor mapping.

Compute-aided conformational analysis was used to characterize the agonist pharmacophore for D1 dopamine receptor recognition and activation. Dihydrexidine (DHX), a high-affinity full agonist with limited conformational flexibility, served as a structural template that aided in determining a molecular geometry that would be common for other more flexible, biologically active agonists. The intrinsic activity of the drugs at D1 receptors was assessed by their ability to stimulate adenylate cyclase activity in rat striatal homogenates (the accepted measure of D1 receptor activation). In addition, affinity data on 12 agonists including six purported full agonists (dopamine, dihydrexidine, SKF89626, SKF82958, A70108, and A77636), as well as six less efficacious structural analogs, were obtained from D1 dopamine radioreceptor-binding assays. The active analog approach to pharmacophore building was applied as implemented in the SYBYL software package. Conformational analysis and molecular mechanics calculations were used to determine the lowest energy conformation of the active analogs (i.e., full agonists), as well as the conformations of each compound that displayed a common pharmacophoric geometry. It is hypothesized that DHX and other full agonists may share a D1 pharmacophore made up of two hydroxy groups, the nitrogen atom (ca. 7 A from the oxygen of m-hydroxyl) and the accessory ring system characterized by the angle between its plane and that of the catechol ring (except for dopamine and A77636). For all full agonists (DHX, SKF89626, SKF82958, A70108, A77636, and dopamine), the energy difference between the lowest energy conformer and those that displayed a common pharmacophore geometry was relatively small (< 5 kcal/mol). The pharmacophoric conformations of the full agonists were also used to infer the shape of the receptor binding site. Based on the union of the van der Waals density maps of the active analogs, the excluded receptor volume was calculated. Various inactive analogs (partial agonists with D1 K0.5 > 300 nM) subsequently were used to define the receptor essential volume (i.e., sterically intolerable receptor regions). These volumes, together with the pharmacophore results, were integrated into a three-dimensional model estimating the D1 receptor active site topography.

Quantitative structure-activity relationship modeling of dopamine D(1) antagonists using comparative molecular field analysis, genetic algorithms-partial least-squares, and K nearest neighbor methods.

Several quantitative structure-activity relationship (QSAR) methods were applied to 29 chemically diverse D(1) dopamine antagonists. In addition to conventional 3D comparative molecular field analysis (CoMFA), cross-validated R(2) guided region selection (q(2)-GRS) CoMFA (see ref 1) was employed, as were two novel variable selection QSAR methods recently developed in one of our laboratories. These latter methods included genetic algorithm-partial least squares (GA-PLS) and K nearest neighbor (KNN) procedures (see refs 2-4), which utilize 2D topological descriptors of chemical structures. Each QSAR approach resulted in a highly predictive model, with cross-validated R(2) (q(2)) values of 0.57 for CoMFA, 0.54 for q(2)-GRS, 0.73 for GA-PLS, and 0.79 for KNN. The success of all of the QSAR methods indicates the presence of an intrinsic structure-activity relationship in this group of compounds and affords more robust design and prediction of biological activities of novel D(1) ligands.

Accurate prediction of the bound conformation of galanthamine in the active site of Torpedo californica acetylcholinesterase using molecular docking.

The alkaloid (-)-galanthamine is known to produce significant improvement of cognitive performances in patients with the Alzheimer's disease. Its mechanism of action involves competitive and reversible inhibition of acetylcholinesterase (AChE). Herein, we correctly predict the orientation and conformation of the galanthamine molecule in the active site of AChE from Torpedo californica (TcAChE) using a combination of rigid docking and flexible geometry optimization with a molecular mechanics force field. The quality of the predicted model is remarkable, as indicated by the value of the RMS deviation of approximately 0.5A when compared with the crystal structure of the TcAChE-galanthamine complex. A molecular model of the complex between TcAChE and a galanthamine derivative, SPH1107, with a long chain substituent on the nitrogen has been generated as well. The side chain of this ligand is predicted to extend along the enzyme active site gorge from the anionic subsite, at the bottom, to the peripheral anionic site, at the top. The docking procedure described in this paper can be applied to produce models of ligand-receptor complexes for AChE and other macromolecular targets of drug design.

[Structure-activity relationship in the series of muscarinic acetylcholine receptor agonists: three types of agonist-receptor complexes]. / Vzaimosviaz' struktury i aktivnosti f riadu agonistov muskarinovogo atsetilkholinovogo retseptora: tri tipa kompleksov agonistov s retseptorom.

A new procedure has been applied to analyze the data available in literature on the structure-activity relationships for agonists of the muscarinic acetylcholine receptor. The agonists were subdivided into three groups, each containing the compounds with the specific structural element. The binding constant logarithms, characterizing the agonist association with the receptor, increase linearly with the increase in the partition coefficient logarithms describing the distribution in the water-octanol system. The performed analysis suggests the existence of three different types of agonist-receptor complexes.