Docking rigid macrocycles using Convex-PL, AutoDock Vina, and RDKit in the D3R Grand Challenge 4

International audienceThe D3R Grand Challenge 4 provided a brilliant opportunity to test macrocyclic docking protocols on a diverse high-quality experimental data. We participated in both pose and affinity prediction exercises. Overall, we aimed to use an automated structure-based docking pipeline built around a set of tools developed in our team. This exercise again demonstrated a crucial importance of the correct local ligand geometry for the overall success of docking. Starting from the second part of the pose prediction stage, we developed a stable pipeline for sampling macrocycle conformers. This resulted in the subangstrom average precision of our pose predictions. In the affinity prediction exercise we obtained average results. However, we could improve these when using docking poses submitted by the best predictors. Our docking tools including the Convex-PL scoring function are available at https://team.inria.fr/nano-d/software/

Simultaneous segmentation and grading of anatomical structures for patient's classification: application to Alzheimer's Disease

Data used in the preparation of this article were obtained from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database (www.loni.ucla.edu/ADNI).In this paper, we propose an innovative approach to robustly and accurately detect Alzheimer's disease (AD) based on the distinction of specific atrophic patterns of anatomical structures such as hippocampus (HC) and entorhinal cortex (EC). The proposed method simultaneously performs segmentation and grading of structures to efficiently capture the anatomical alterations caused by AD. Known as SNIPE (Scoring by Non-local Image Patch Estimator), the novel proposed grading measure is based on a nonlocal patch-based frame-work and estimates the similarity of the patch surrounding the voxel under study with all the patches present in different training populations. In this study, the training library was composed of two populations: 50 cognitively normal subjects (CN) and 50 patients with AD, randomly selected from the ADNI database. During our experiments, the classification accuracy of patients (CN vs. AD) using several biomarkers was compared: HC and EC volumes, the grade of these structures and finally the combination of their volume and their grade. Tests were completed in a leave-one-out framework using discriminant analysis. First, we showed that biomarkers based on HC provide better classification accuracy than biomarkers based on EC. Second, we demonstrated that structure grading is a more powerful measure than structure volume to distinguish both populations with a classification accuracy of 90%. Finally, by adding the ages of subjects in order to better separate age-related structural changes from disease-related anatomical alterations, SNIPE obtained a classification accuracy of 93%Data collection and sharing for this project were funded by the Alzheimer's Disease Neuroimaging Initiative (ADNI) (National Institutes of Health Grant U01 AG024904). ADNI is funded by the National Insti- tute on Aging, the National Institute of Biomedical Imaging and Bioengineering, and through generous contributions from the following: Abbott, AstraZeneca AB, Bayer Schering Pharma AG, Bristol-Myers Squibb, Eisai Global Clinical Development, Elan Corporation, Genentech, GE Healthcare, GlaxoSmithKline, Innogenetics, Johnson and Johnson, Eli Lilly and Co., Medpace, Inc., Merck and Co., Inc., Novartis AG, Pfizer Inc, F. Hoffman-La Roche, Schering-Plough, Synarc, Inc., as well as non-profit partners the Alzheimer's Association and Alzheimer's Drug Discovery Foundation, with participation from the U.S. Food and Drug Administration. Private sector contributions to ADNI are facilitated by the Foundation for the National Institutes of Health (www.fnih.org). The grantee organization is the Northern California Institute for Research and Education, and the study is coordinated by the Alzheimer's Disease Cooperative Study at the University of California, San Diego. ADNI data are disseminated by the Laboratory for Neuro Imaging at the University of California, Los Angeles. This research was also supported by NIH grants P30AG010129, K01 AG030514, and the Dana Foundation.Coupé, P.; Eskildsen, SF.; Manjón Herrera, JV.; Fonov, VS.; Collins, DL.; Alzheimer's Dis Neuroimaging (2012). Simultaneous segmentation and grading of anatomical structures for patient's classification: application to Alzheimer's Disease. NeuroImage. 59(4):3736-3747. https://doi.org/10.1016/j.neuroimage.2011.10.080S3736374759

3-{4-[(E)-{4-[(E)-Phenyldiazenyl]phenyl}diazenyl]phenoxy}propane-1,2-diol

Title compound was designed to be a black quencher of pyrene fluorescence. It was made amphiphilic to serve as a membrane-bound probe. The synthesis is a two-step procedure. The first step is a Mitsunobu reaction of [{(phenyldiazenyl)phenyl}diazenyl]phenol with 1,2-O-isopropylideneglycerol. The second step is the cleavage of the isopropylidene protecting group. The title compound has the extinction coefficient 59,000 at λmax = 380 nm. The Forster distance between the title compound and the pyrene was found to be 37.8 Å

Convex-PL-R - Revisiting affinity predictions and virtual screening using physics-informed machine learning

Virtual screening is an essential part of the modern drug design pipeline, which significantly accelerates the discovery of new drug candidates. Structure-based virtual screening involves ligand conformational sampling, which is often followed by re-scoring of docking poses. A great variety of scoring functions have been designed for this purpose. The advent of structural and affinity databases and the progress in machinelearning methods have recently boosted scoring function performance. Nonetheless, the most successful scoring functions are typically designed for specific tasks or systems. All-purpose scoring functions still perform poorly on the virtual screening tests, compared to precision with which they are able to predict co-crystal binding poses. Another limitation is the low interpretability of the heuristics being used. We analyzed scoring functions' performance in the CASF benchmarks and discovered that the vast majority of them have a strong bias towards predicting larger binding interfaces. This motivated us to develop a physical model with additional entropic terms with the aim of penalizing such a preference. We parameterized the new model using affinity and structural data, solving a classification problem followed by regression. The new model, called Convex-PL-R , demonstrated high-quality results on multiple tests and a substantial improvement over its predecessor Convex-PL. Convex-PL-R can be used for molecular docking together with VinaCPL, our version of AutoDock Vina, with Convex-PL integrated as a scoring function. Convex-PL-R , Convex-PL, and VinaCPL are available at https://team.inria.fr/nano-d/convex-pl/

Convex-PL-R - Revisiting affinity predictions and virtual screening using physics-informed machine learning

Virtual screening is an essential part of the modern drug design pipeline, which significantly accelerates the discovery of new drug candidates. Structure-based virtual screening involves ligand conformational sampling, which is often followed by re-scoring of docking poses. A great variety of scoring functions have been designed for this purpose. The advent of structural and affinity databases and the progress in machinelearning methods have recently boosted scoring function performance. Nonetheless, the most successful scoring functions are typically designed for specific tasks or systems. All-purpose scoring functions still perform poorly on the virtual screening tests, compared to precision with which they are able to predict co-crystal binding poses. Another limitation is the low interpretability of the heuristics being used. We analyzed scoring functions' performance in the CASF benchmarks and discovered that the vast majority of them have a strong bias towards predicting larger binding interfaces. This motivated us to develop a physical model with additional entropic terms with the aim of penalizing such a preference. We parameterized the new model using affinity and structural data, solving a classification problem followed by regression. The new model, called Convex-PL-R , demonstrated high-quality results on multiple tests and a substantial improvement over its predecessor Convex-PL. Convex-PL-R can be used for molecular docking together with VinaCPL, our version of AutoDock Vina, with Convex-PL integrated as a scoring function. Convex-PL-R , Convex-PL, and VinaCPL are available at https://team.inria.fr/nano-d/convex-pl/

Some Peculiarities of Low-Frequency Hydroacoustic Signals Behavior in Tomographic Studies of “Sea-Land-Sea” System

In this paper, we analyzed the results of experimental data processing in the study of regularities of propagation and transformation of low-frequency harmonic signals at the boundary of the “sea−land−sea” system. Harmonic signals at a carrier frequency of 33 Hz were generated by a low-frequency hydroacoustic radiator in Vityaz Bay. Then, they passed along the shelf of decreasing depth, transformed into seismoacoustic signals of the upper layer of the Earth’s crust and the bedrocks of Shultz Cape and excited hydroacoustic signals at the corresponding frequency in the shelf waters in the open part of the Sea of Japan. When processing the experiment results, we obtained the vertical distributions of the pressure field, caused by an acoustic low-frequency signal passing through the upper layer of the Earth’s crust. We presented the distributions of hydroacoustic and seismoacoustic energies. The obtained experimental data were compared with the simulations by the model, developed strictly according to the experiment scheme and the geological structure of the area. In the discussion of the obtained results, we explained a probable mechanism of acoustic energy propagation and the nature of the vertical distributions of the pressure field formation

Some Peculiarities of Low-Frequency Hydroacoustic Signals Behavior in Tomographic Studies of “Sea-Land-Sea” System

In this paper, we analyzed the results of experimental data processing in the study of regularities of propagation and transformation of low-frequency harmonic signals at the boundary of the “sea−land−sea” system. Harmonic signals at a carrier frequency of 33 Hz were generated by a low-frequency hydroacoustic radiator in Vityaz Bay. Then, they passed along the shelf of decreasing depth, transformed into seismoacoustic signals of the upper layer of the Earth’s crust and the bedrocks of Shultz Cape and excited hydroacoustic signals at the corresponding frequency in the shelf waters in the open part of the Sea of Japan. When processing the experiment results, we obtained the vertical distributions of the pressure field, caused by an acoustic low-frequency signal passing through the upper layer of the Earth’s crust. We presented the distributions of hydroacoustic and seismoacoustic energies. The obtained experimental data were compared with the simulations by the model, developed strictly according to the experiment scheme and the geological structure of the area. In the discussion of the obtained results, we explained a probable mechanism of acoustic energy propagation and the nature of the vertical distributions of the pressure field formation

Study of Free Oscillations of Bays in the Northwestern Part of Posyet Bay

To study the specific features of free surface oscillations in the northwestern part of Posyet Bay (the Sea of Japan), a series of experimental works using an installation with a laser meter for measuring hydrosphere pressure variations were carried out in 2012 and 2014. In the course of the joint analysis, measurement results for oscillations with periods of 10–30 min and the results of calculations using a numerical model of shallow water with a difference approximation on an irregular triangular space grid, datasets of the space–time parameters for the resonance oscillations of the studied water area were obtained. The results of the numerical simulations confirm the manifestation of the resonance properties of Novgorodskaya, Expedicii, and Reyd Pallada Bays water areas on the oscillations singled out during the experimental studies. The positions of the peaks on the model resonance curves are consistent with the positions of the clearly pronounced peaks of the energy spectrum in the field data

Remote Sensing of Infrasound Signals of the “Voice of the Sea” during the Evolution of Typhoons

The article presents summarized results of the study of registered periods of microseisms of the “voice of the sea” infrasound oscillations, generated due to the influence of wind of tropical cyclones (typhoons) passing in the Sea of Japan. We compared the recorded signals and how they correspond to the microseismic background in the wind wave range. Using satellite monitoring data, we verified and identified the groups of cyclones that excite the “voice of the sea” microseisms with approximately the same characteristics. We found that the bandwidth of the “voice of the sea” range and its central frequency, with the maximum amplitude in this frequency range, are approximately the same for typhoons (cyclones) propagating along the same routes. Wind speed also determines the central frequency of the “voice of the sea” range for a particular typhoon (tropical cyclone) propagation route

Remote Sensing of Infrasound Signals of the “Voice of the Sea” during the Evolution of Typhoons

The article presents summarized results of the study of registered periods of microseisms of the “voice of the sea” infrasound oscillations, generated due to the influence of wind of tropical cyclones (typhoons) passing in the Sea of Japan. We compared the recorded signals and how they correspond to the microseismic background in the wind wave range. Using satellite monitoring data, we verified and identified the groups of cyclones that excite the “voice of the sea” microseisms with approximately the same characteristics. We found that the bandwidth of the “voice of the sea” range and its central frequency, with the maximum amplitude in this frequency range, are approximately the same for typhoons (cyclones) propagating along the same routes. Wind speed also determines the central frequency of the “voice of the sea” range for a particular typhoon (tropical cyclone) propagation route