A new protein binding pocket similarity measure based on comparison of clouds of atoms in 3D: application to ligand prediction

<p>Abstract</p> <p>Background</p> <p>Predicting which molecules can bind to a given binding site of a protein with known 3D structure is important to decipher the protein function, and useful in drug design. A classical assumption in structural biology is that proteins with similar 3D structures have related molecular functions, and therefore may bind similar ligands. However, proteins that do not display any overall sequence or structure similarity may also bind similar ligands if they contain similar binding sites. Quantitatively assessing the similarity between binding sites may therefore be useful to propose new ligands for a given pocket, based on those known for similar pockets.</p> <p>Results</p> <p>We propose a new method to quantify the similarity between binding pockets, and explore its relevance for ligand prediction. We represent each pocket by a cloud of atoms, and assess the similarity between two pockets by aligning their atoms in the 3D space and comparing the resulting configurations with a convolution kernel. Pocket alignment and comparison is possible even when the corresponding proteins share no sequence or overall structure similarities. In order to predict ligands for a given target pocket, we compare it to an ensemble of pockets with known ligands to identify the most similar pockets. We discuss two criteria to evaluate the performance of a binding pocket similarity measure in the context of ligand prediction, namely, area under ROC curve (AUC scores) and classification based scores. We show that the latter is better suited to evaluate the methods with respect to ligand prediction, and demonstrate the relevance of our new binding site similarity compared to existing similarity measures.</p> <p>Conclusions</p> <p>This study demonstrates the relevance of the proposed method to identify ligands binding to known binding pockets. We also provide a new benchmark for future work in this field. The new method and the benchmark are available at <url>http://cbio.ensmp.fr/paris/</url>.</p

Quantifying Performance Bias in Label Fusion

Classification systems are employed to remotely assess whether an element of interest falls into a target class or non-target class. These systems have uses in fields as far ranging as biostatistics to search engine keyword analysis. The performance of the system is often summarized as a trade-off between the proportions of elements correctly labeled as target plotted against the number of elements incorrectly labeled as target. These are empirical estimates of the true positive and false positive rates. These rates are often plotted to create a receiver operating characteristic (ROC) curve that acts as a visual tool to assess classification system performance. The research contained in this thesis focuses on the label fusion technique and the bias that can occur when using incorrect assumptions regarding the partitioning of the event set. This partitioning may be defined in terms of what will be called within and across label fusion. The major goals of this work are the formulaic development and quantification of performance bias between different types of across and within label fusion and analysis of the effects of individual classification system performance, correlation, and target environment on the magnitude of bias between these two types of label fusion

Local Geometry Processing for Deformations of Non-Rigid 3D Shapes

Geometry processing and in particular spectral geometry processing deal with many different deformations that complicate shape analysis problems for non-rigid 3D objects. Furthermore, pointwise description of surfaces has increased relevance for several applications such as shape correspondences and matching, shape representation, shape modelling and many others. In this thesis we propose four local approaches to face the problems generated by the deformations of real objects and improving the pointwise characterization of surfaces. Differently from global approaches that work simultaneously on the entire shape we focus on the properties of each point and its local neighborhood. Global analysis of shapes is not negative in itself. However, having to deal with local variations, distortions and deformations, it is often challenging to relate two real objects globally. For this reason, in the last decades, several instruments have been introduced for the local analysis of images, graphs, shapes and surfaces. Starting from this idea of localized analysis, we propose both theoretical insights and application tools within the local geometry processing domain. In more detail, we extend the windowed Fourier transform from the standard Euclidean signal processing to different versions specifically designed for spectral geometry processing. Moreover, from the spectral geometry processing perspective, we define a new family of localized basis for the functional space defined on surfaces that improve the spatial localization for standard applications in this field. Finally, we introduce the discrete time evolution process as a framework that characterizes a point through its pairwise relationship with the other points on the surface in an increasing scale of locality. The main contribute of this thesis is a set of tools for local geometry processing and local spectral geometry processing that could be used in standard useful applications. The overall observation of our analysis is that localization around points could factually improve the geometry processing in many different applications

Feature Extraction and Design in Deep Learning Models

The selection and computation of meaningful features is critical for developing good deep learning methods. This dissertation demonstrates how focusing on this process can significantly improve the results of learning-based approaches. Specifically, this dissertation presents a series of different studies in which feature extraction and design was a significant factor for obtaining effective results. The first two studies are a content-based image retrieval system (CBIR) and a seagrass quantification study in which deep learning models were used to extract meaningful high-level features that significantly increased the performance of the approaches. Secondly, a method for change detection is proposed where the multispectral channels of satellite images are combined with different feature indices to improve the results. Then, two novel feature operators for mesh convolutional networks are presented that successfully extract invariant features from the faces and vertices of a mesh, respectively. The novel feature operators significantly outperform the previous state of the art for mesh classification and segmentation and provide two novel architectures for applying convolutional operations to the faces and vertices of geometric 3D meshes. Finally, a novel approach for automatic generation of 3D meshes is presented. The generative model efficiently uses the vertex-based feature operators proposed in the previous study and successfully learns to produce shapes from a mesh dataset with arbitrary topology

WiFi-Based Human Activity Recognition Using Attention-Based BiLSTM

Recently, significant efforts have been made to explore human activity recognition (HAR) techniques that use information gathered by existing indoor wireless infrastructures through WiFi signals without demanding the monitored subject to carry a dedicated device. The key intuition is that different activities introduce different multi-paths in WiFi signals and generate different patterns in the time series of channel state information (CSI). In this paper, we propose and evaluate a full pipeline for a CSI-based human activity recognition framework for 12 activities in three different spatial environments using two deep learning models: ABiLSTM and CNN-ABiLSTM. Evaluation experiments have demonstrated that the proposed models outperform state-of-the-art models. Also, the experiments show that the proposed models can be applied to other environments with different configurations, albeit with some caveats. The proposed ABiLSTM model achieves an overall accuracy of 94.03%, 91.96%, and 92.59% across the 3 target environments. While the proposed CNN-ABiLSTM model reaches an accuracy of 98.54%, 94.25% and 95.09% across those same environments

Augmented Deep Representations for Unconstrained Still/Video-based Face Recognition

Face recognition is one of the active areas of research in computer vision and biometrics. Many approaches have been proposed in the literature that demonstrate impressive performance, especially those based on deep learning. However, unconstrained face recognition with large pose, illumination, occlusion and other variations is still an unsolved problem. Unconstrained video-based face recognition is even more challenging due to the large volume of data to be processed, lack of labeled training data and significant intra/inter-video variations on scene, blur, video quality, etc. Although Deep Convolutional Neural Networks (DCNNs) have provided discriminant representations for faces and achieved performance surpassing humans in controlled scenarios, modifications are necessary for face recognition in unconstrained conditions. In this dissertation, we propose several methods that improve unconstrained face recognition performance by augmenting the representation provided by the deep networks using correlation or contextual information in the data. For unconstrained still face recognition, we present an encoding approach to combine the Fisher vector (FV) encoding and DCNN representations, which is called FV-DCNN. The feature maps from the last convolutional layer in the deep network are encoded by FV into a robust representation, which utilizes the correlation between facial parts within each face. A VLAD-based encoding method called VLAD-DCNN is also proposed as an extension. Extensive evaluations on three challenging face recognition datasets show that the proposed FV-DCNN and VLAD-DCNN perform comparable to or better than many state-of-the-art face verification methods. For the more challenging video-based face recognition task, we first propose an automatic system and model the video-to-video similarity as subspace-to-subspace similarity, where the subspaces characterize the correlation between deep representations of faces in videos. In the system, a quality-aware subspace-to-subspace similarity is introduced, where subspaces are learned using quality-aware principal component analysis. Subspaces along with quality-aware exemplars of templates are used to produce the similarity scores between video pairs by a quality-aware principal angle-based subspace-to-subspace similarity metric. The method is evaluated on four video datasets. The experimental results demonstrate the superior performance of the proposed method. To utilize the temporal information in videos, a hybrid dictionary learning method is also proposed for video-based face recognition. The proposed unsupervised approach effectively models the temporal correlation between deep representations of video faces using dynamical dictionaries. A practical iterative optimization algorithm is introduced to learn the dynamical dictionary. Experiments on three video-based face recognition datasets demonstrate that the proposed method can effectively learn robust and discriminative representation for videos and improve the face recognition performance. Finally, to leverage contextual information in videos, we present the Uncertainty-Gated Graph (UGG) for unconstrained video-based face recognition. It utilizes contextual information between faces by conducting graph-based identity propagation between sample tracklets, where identity information are initialized by the deep representations of video faces. UGG explicitly models the uncertainty of the contextual connections between tracklets by adaptively updating the weights of the edge gates according to the identity distributions of the nodes during inference. UGG is a generic graphical model that can be applied at only inference time or with end-to-end training. We demonstrate the effectiveness of UGG with state-of-the-art results on the recently released challenging Cast Search in Movies and IARPA Janus Surveillance Video Benchmark datasets

A Contribution to Active Infrared Laser Spectroscopy for Remote Substance Detection

In this work we present a stand-off, long wave infrared (LWIR) spectroscopy system for remote detection of hazardous substances. The principle is based upon wavelength selective illumination using External Cavity Quantum Cascade Lasers, that are tunable in the LWIR wavelength range from 7.5 - 10 µm, in which most chemical substances exhibit a highly characteristic absorption behaviour

En route to CO2-based (a)cyclic carbonates and polycarbonates from alcohols substrates by direct and indirect approaches

This review is dedicated to the state‐of‐the art routes used for the synthesis of CO2 ‐based (a)cyclic carbonates and polycarbonates from alcohol substrates, with an emphasis on their respective main advantages and limitations. The first section reviews the synthesis of organic carbonates such as dialkyl carbonates or cyclic carbonates from the carbonation of alcohols. Many different synthetic strategies have been reported (dehydrative condensation, the alkylation route, the “leaving group” strategy, the carbodiimide route, the protected alcohols route, etc.) with various substrates (mono‐alcohols, diols, allyl alcohols, halohydrins, propargylic alcohols, etc.). The second section reviews the formation of polycarbonates via the direct copolymerization of CO2 with diols, as well as the ring‐opening polymerization route. Finally, polycondensation processes involving CO2-‐based dimethyl and diphenyl carbonates with aliphatic and aromatic diols are described

Biosensors: 10th Anniversary Feature Papers

Biosensors are analytical devices used for the detection of a chemical substance, or analyte, which combines a biological component with a physicochemical detector. Detection and quantification are based on the measurement of the biological interactions. The biological element of a biosensor may consist of tissues, microorganisms, organelles, cell receptors, enzymes, antibodies and nucleic acids. These devices have been shown to have a wide range of applications in a vast array of fields of research, including environmental monitoring, food analysis, drug detection and health and clinical assessment, and even security and safety. The current Special Issue, “Biosensors: 10th Anniversary Feature Papers”, addresses the existing knowledge gaps and aids the advancement of biosensing applications, in the form of six peer-reviewed research and review papers, detailing the most recent and innovative developments of biosensors