Local recognition of affine rank 3 graphs

Optimizing recombinant antibody function in SPR immunosensing

The Drug Design Data Resource (D3R) ran Grand Challenge 2 (GC2) from September 2016 through February 2017. This challenge was based on a dataset of structures and affinities for the nuclear receptor farnesoid X receptor (FXR), contributed by F. Hoffmann-La Roche. The dataset contained 102 IC50 values, spanning six orders of magnitude, and 36 high-resolution co-crystal structures with representatives of four major ligand classes. Strong global participation was evident, with 49 participants submitting 262 prediction submission packages in total. Procedurally, GC2 mimicked Grand Challenge 2015 (GC2015), with a Stage 1 subchallenge testing ligand pose prediction methods and ranking and scoring methods, and a Stage 2 subchallenge testing only ligand ranking and scoring methods after the release of all blinded co-crystal structures. Two smaller curated sets of 18 and 15 ligands were developed to test alchemical free energy methods. This overview summarizes all aspects of GC2, including the dataset details, challenge procedures, and participant results. We also consider implications for progress in the field, while highlighting methodological areas that merit continued development. Similar to GC2015, the outcome of GC2 underscores the pressing need for methods development in pose prediction, particularly for ligand scaffolds not currently represented in the Protein Data Bank ( http://www.pdb.org ), and in affinity ranking and scoring of bound ligands.

The 2016 D3R Grand Challenge 2 includes both pose and affinity or ranking predictions. This article is focused exclusively on affinity predictions submitted to the D3R challenge from a collaborative effort of the modeling and informatics group. Our submissions include ranking of 102 ligands covering 4 different chemotypes against the FXR ligand binding domain structure, and the relative binding affinity predictions of the two designated free energy subsets of 15 and 18 compounds. Using all the complex structures prepared in the same way allowed us to cover many types of workflows and compare their performances effectively. We evaluated typical workflows used in our daily structure-based design modeling support, which include docking scores, force field-based scores, QM/MM, MMGBSA, MD-MMGBSA, and MacroModel interaction energy estimations. The best performing methods for the two free energy subsets are discussed. Our results suggest that affinity ranking still remains very challenging; that the knowledge of more structural information does not necessarily yield more accurate predictions; and that visual inspection and human intervention are considerably important for ranking. Knowledge of the mode of action and protein flexibility along with visualization tools that depict polar and hydrophobic maps are very useful for visual inspection. QM/MM-based workflows were found to be powerful in affinity ranking and are encouraged to be applied more often. The standardized input and output enable systematic analysis and support methodology development and improvement for high level blinded predictions.

Fluorine NMR has recently gained high popularity in drug discovery as it allows efficient and sensitive screening of large numbers of ligands. However, the positive hits found in screening must subsequently be ranked according to their affinity in order to prioritize them for follow-up chemistry. Unfortunately, the primary read-out from the screening experiments, namely the increased relaxation rate upon binding, is not proportional to the affinity of the ligand, as it is polluted by effects such as exchange broadening. Here we present the method CSAR (Chemical Shift-anisotropy-based Affinity Ranking) for reliable ranking of fluorinated ligands by NMR, without the need of isotope labeled protein, titrations or setting up a reporter format. Our strategy is to produce relaxation data that is directly proportional to the binding affinity. This is achieved by removing all other contributions to relaxation as follows: (i) exchange effects are efficiently suppressed by using high power spin lock pulses, (ii) dipolar relaxation effects are approximately subtracted by measuring at two different magnetic fields and (iii) differences in chemical shift anisotropy are normalized using calculated values. A similar ranking can be obtained with the simplified approach FastCSAR that relies on a measurement of a single relaxation experiment at high field (preferably > 600MHz). An affinity ranking obtained in this simple way will enable prioritizing ligands and thus improve the efficiency of fragment-based drug design.

We present the performances of our mathematical deep learning (MathDL) models for D3R Grand Challenge 4 (GC4). This challenge involves pose prediction, affinity ranking, and free energy estimation for beta secretase 1 (BACE) as well as affinity ranking and free energy estimation for Cathepsin S (CatS). We have developed advanced mathematics, namely differential geometry, algebraic graph, and/or algebraic topology, to accurately and efficiently encode high dimensional physical/chemical interactions into scalable low-dimensional rotational and translational invariant representations. These representations are integrated with deep learning models, such as generative adversarial networks (GAN) and convolutional neural networks (CNN) for pose prediction and energy evaluation, respectively. Overall, our MathDL models achieved the top place in pose prediction for BACE ligands in Stage 1a. Moreover, our submissions obtained the highest Spearman correlation coefficient on the affinity ranking of 460 CatS compounds, and the smallest centered root mean square error on the free energy set of 39 CatS molecules. It is worthy to mention that our method on docking pose predictions has significantly improved from our previous ones.

Induced fit or protein flexibility can make a given structure less useful for docking and/or scoring. The 2015 Drug Design Data Resource (D3R) Grand Challenge provided a unique opportunity to prospectively test optimal strategies for virtual screening in these type of targets: heat shock protein 90 (HSP90), a protein with multiple ligand-induced binding modes; and mitogen-activated protein kinase kinase kinase kinase 4 (MAP4K4), a kinase with a large flexible pocket. Using previously known co-crystal structures, we tested predictions from methods that keep the receptor structure fixed and used (a) multiple receptor/ligand co-crystals as binding templates for minimization or docking ("close"), (b) methods that align or dock to a single receptor ("cross"), and (c) a hybrid approach that chose from multiple bound ligands as initial templates for minimization to a single receptor ("min-cross"). Pose prediction using our "close" models resulted in average ligand RMSDs of 0.32 and 1.6Å for HSP90 and MAP4K4, respectively, the most accurate models of the community-wide challenge. On the other hand, affinity ranking using our "cross" methods performed well overall despite the fact that a fixed receptor cannot model ligand-induced structural changes,. In addition, "close" methods that leverage the co-crystals of the different binding modes of HSP90 also predicted the best affinity ranking. Our studies suggest that analysis of changes on the receptor structure upon ligand binding can help select an optimal virtual screening strategy.

Advanced mathematics, such as multiscale weighted colored subgraph and element specific persistent homology, and machine learning including deep neural networks were integrated to construct mathematical deep learning models for pose and binding affinity prediction and ranking in the last two D3R Grand Challenges in computer-aided drug design and discovery. D3R Grand Challenge 2 focused on the pose prediction, binding affinity ranking and free energy prediction for Farnesoid X receptor ligands. Our models obtained the top place in absolute free energy prediction for free energy set 1 in stage 2. The latest competition, D3R Grand Challenge 3 (GC3), is considered as the most difficult challenge so far. It has five subchallenges involving Cathepsin S and five other kinase targets, namely VEGFR2, JAK2, p38-α, TIE2, and ABL1. There is a total of 26 official competitive tasks for GC3. Our predictions were ranked 1st in 10 out of these 26 tasks.

The Drug Design Data Resource aims to test and advance the state of the art in protein-ligand modeling by holding community-wide blinded, prediction challenges. Here, we report on our third major round, Grand Challenge 3 (GC3). Held 2017-2018, GC3 centered on the protein Cathepsin S and the kinases VEGFR2, JAK2, p38-α, TIE2, and ABL1, and included both pose-prediction and affinity-ranking components. GC3 was structured much like the prior challenges GC2015 and GC2. First, Stage 1 tested pose prediction and affinity ranking methods; then all available crystal structures were released, and Stage 2 tested only affinity rankings, now in the context of the available structures. Unique to GC3 was the addition of a Stage 1b self-docking subchallenge, in which the protein coordinates from all of the cocrystal structures used in the cross-docking challenge were released, and participants were asked to predict the pose of CatS ligands using these newly released structures. We provide an overview of the outcomes and discuss insights into trends and best-practices.

Consensus combining outcomes of multiple ensemble dockings: examples using dDAT crystalized complexes

AutoDock Vina (Vina) achieved a very high docking-success rate, p ̂, but give a rather low correlation coefficient, R, for binding affinity with respect to experiments. This low correlation can be an obstacle for ranking of ligand-binding affinity, which is the main objective of docking simulations. In this context, we evaluated the dependence of Vina R coefficient upon its empirical parameters. R is affected more by changing the gauss2 and rotation than other terms. The docking-success rate p ̂ is sensitive to the alterations of the gauss1, gauss2, repulsion, and hydrogen bond parameters. Based on our benchmarks, parameter set1 has been suggested to be the most optimal. The testing study over 800 complexes indicated that the modified Vina provided higher correlation with experiment R_set1=0.556±0.025 compared with R_Default=0.493±0.028 obtained by the original Vina and R_(Vina 1.2)=0.503±0.029 by Vina version 1.2. Besides, the modified Vina can be also applied more widely, giving R≥0.500 for 32/48 targets, compared with the default package, giving R≥0.500 for 31/48 targets. In addition, validation calculations for 1036 complexes obtained from version 2019 of PDBbind refined structures showed that the set1 of parameters gave higher correlation coefficient (R_set1=0.621±0.016) than the default package (R_Default=0.552±0.018) and Vina version 1.2 (R_(Vina 1.2)=0.549±0.017). The version of Vina with set1 of parameters can be downloaded at https://github.com/sontungngo/mvina. The outcomes would enhance the ranking of ligand-binding affinity using Autodock Vina.

AutoDock Vina (Vina) achieved a very high docking-success rate, , but give a rather low correlation coefficient, , for binding affinity with respect to experiments. This low correlation can be an obstacle for ranking of ligand-binding affinity, which is the main objective of docking simulations. In this context, we evaluated the dependence of Vina R coefficient upon its empirical parameters. is affected more by changing the gauss2 and rotation than other terms. The docking-success rate is sensitive to the alterations of the gauss1, gauss2, repulsion, and hydrogen bond parameters. Based on our benchmarks, the parameter set1 has been suggested to be the most optimal. The testing study over 800 complexes indicated that the modified Vina provided higher correlation with experiment compared with obtained by the original Vina and by Vina version 1.2. Besides, the modified Vina can be also applied more widely, giving for 32/48 targets, compared with the default package, giving for 31/48 targets. In addition, validation calculations for 1036 complexes obtained from version 2019 of PDBbind refined structures showed that the set1 of parameters gave higher correlation coefficient ( ) than the default package ( ) and Vina version 1.2 ( ). The version of Vina with set1 of parameters can be downloaded at https://github.com/sontungngo/mvina. The outcomes would enhance the ranking of ligand-binding affinity using Autodock Vina.

According to the previous benchmark, Autodock Vina (Vina) achieved a very high successful-docking rate, p ̂, but give a rather a low correlation coefficient, R, for binding affinity with respect to experiment. This low correlation can be an obstacle for ranking of ligand-binding affinity, which is a main objective of docking simulations. The accuracy of Vina likely depends on the empirical parameters, which include the Gaussian steric interaction, repulsion, hydrophobic, hydrogen bond, and rotation metrics. In this context, we evaluated the dependence of Vina accuracy upon empirical parameters. Although changing of six parameters alters the obtained R values, the gauss2 and rotation terms form more effects. The p ̂ terms are sensitive to the alterations of the gauss1, gauss2, repulsion, and hydrogen bond parameters. Therefore, three sets of empirical parameters were proposed as well as more to modestly focus on R (set1), modestly focus on p ̂ (set3), and keep a tradeoff between R and p ̂. The testing study over 800 complexes indicated that the Vina with proposed sets of parameters can provide more accurate results since forming the larger R value (R_set1=0.556±0.025) compared with the original one (R_Default=0.493±0.028) and Vina version 1.2 (R_(Vina 1.2)=0.503±0.029). Besides, the testing study over 48 biological targets indicated that the modified Vina can be applied more widely compared with the default package. These newly proposed parameters achieved a higher correlation coefficient and reasonable correlation coefficients (R>0.500) for at least 32 targets, whereas the default parameters provided by the original Vina gave only 31 targets with at least 0.500 correlation. In addition, validation calculations for 1315 complexes obtained from the version 2019 of PDBbind refined structures suggested that set1 of parameters are more appropriate than the other parameters (R_set1=0.621±0.016) compared with the default package (R_Default=0.552±0.018) and Vina version 1.2 (R_(Vina 1.2)=0.549±0.017). The version of Vina with set1 of parameters can be downloaded at https://github.com/sontungngo/mvina. The outcomes probably enhance the ranking of ligand-binding affinity using Autodock Vina.

Ranking of gas-phase acidities and chloride affinities of monosaccharides and linkage specificity in collision-induced decompositions of negative ion electrospray-generated chloride adducts of oligosaccharides

The detection of molecules that can bind to active sites of protein targets and the measurement of their affinities is a promising application of NMR. The use of long-lived states (LLS) for fragment screening leads to a spectacular increase in contrast between free and bound ligands, and thus allows one to characterize binding of fragments with very weak affinities, with KD in the millimolar range, where most other biophysical techniques fail. Furthermore, the combination of LLS for screening with 1H dissolution-DNP to enhance the sensitivity is an interesting application for measuring LLS signals of weak ligands while using very low concentrations of ligands and proteins. Dramatic differences have been observed between the spectra of the ligand in the presence or absence of a protein, or in the presence of the protein combined with a stronger ligand. Moreover, LLS involving pairs of 19F nuclei have been explored to study binding phenomena. Fluorinated ligands can be used as spy molecules in competition experiments, which allowed the ranking of affinities and the estimation of the dissociation constants of arbitrary ligands that do not contain any fluorine.

The calcium channel and the 'calcium release channel' of muscle membrane of the cockroach Periplaneta americana have been characterized. Biological assays with calcium channel blockers and ryanodine on different insects and acari revealed pronounced insecticidal effects with ryanodine, but not with calcium channel blockers, at concentrations between 0.1 and 300 μg ml -1 . Skeletal muscle membranes derived either from the tubular network or from the sarcoplasmatic reticulum of P. americana were characterized with respect to the binding of the dihydropyridine (DHP) [ 3 H]isradipine (PN 200-110), the phenylalkylamine [ 3 H]verapamil and the alkaloid [ 3 H]ryanodine. Preliminary binding studies with the benzothiazepine [ 3 H]diltiazem suggest a low-affinity binding site with a IC 50 value of 3.3 μM. All binding sites tested were sensitive to treatment with proteinase K. Optimal conditions for binding of the radioligand ryanodine revealed the highest specific binding at pH 8 and at calcium chloride concentrations between 100 and 500 μM. EGTA at 10 μM abolished 95% of the ryanodine binding. Binding studies with calcium channel binding sites revealed a pronounced effect of low Ca 2+ concentrations on specific isradipine binding, whereas verapamil and diltiazem binding were only reduced by the presence of 200 μM EGTA. With respect to high Ca 2+ concentrations, specific binding of diltiazem, isradipine and verapamil was reduced by 73, 40 and 20%, respectively, at 5 mM Ca 2+ . Radioligand binding experiments showed high-affinity binding sites for ryanodine and isradipine. K D values of 0.95 nM (B max = 550 fmol mg -1 protein) and 0.75 nM (B max = 213 fmol mg -1 protein) were determined respectively. A lower-affinity binding site was identified in binding studies with verapamil (K D = 7.4 nM and B max = 27 fmol mg -1 protein). [ 3 H]isradipine displacement studies with several dihydropyridines revealed the following ranking of affinity : nitrendipine > isradipine > Bay K8664 >> nicardipine. Displacement of [ 3 H]verapamil binding by effectors of the phenylalkylamine binding site showed that bepridil and S(-)verapamil had the highest affinities of the compounds tested followed by (±)verapamil, nor-methylverapamil and R(+)verapamil.