MOTIVATION: Identifying antibody binding sites, is crucial for developing vaccines and therapeutic antibodies, processes that are time-consuming and costly. Accurate prediction of the paratope's binding site can speed up the development by improving our understanding of antibody-antigen interactions. RESULTS: We present ParaSurf, a deep learning model that significantly enhances paratope prediction by incorporating both surface geometric and non-geometric factors. Trained and tested on three prominent antibody-antigen benchmarks, ParaSurf achieves state-of-the-art results across nearly all metrics. Unlike models restricted to the variable region, ParaSurf demonstrates the ability to accurately predict binding scores across the entire Fab region of the antibody. Additionally, we conducted an extensive analysis using the largest of the three datasets employed, focusing on three key components: (i) a detailed evaluation of paratope prediction for each complementarity-determining region loop, (ii) the performance of models trained exclusively on the heavy chain, and (iii) the results of training models solely on the light chain without incorporating data from the heavy chain. AVAILABILITY AND IMPLEMENTATION: Source code for ParaSurf, along with the datasets used, preprocessing pipeline, and trained model weights, are freely available at https://github.com/aggelos-michael-papadopoulos/ParaSurf.

Recent technological advances in the fields of data and computer science have improved significantly the everyday life of people. However, technological advances are also being adopted by criminals to facilitate and expand their illicit actions. The Deep Learning (DL) paradigm has shown a significant potential in analysing complex structured data. However, in the crime detection domain, a limited number of public datasets is available, constrained to specific tasks only, which hinders the research and development of accurate and robust DL-assisted tools. The goal of this work is to extend the well-known UCF-crime dataset to the case of video captioning. To the best of our knowledge, this is the first publicly available crime-related video captioning dataset. A new proposed video captioning approach is compared to a plethora of state-of- the-art-methods in this dataset, while qualitative and quantitative characteristics of the latter are presented.

MOTIVATION: The knowledge of potentially druggable binding sites on proteins is an important preliminary step toward the discovery of novel drugs. The computational prediction of such areas can be boosted by following the recent major advances in the deep learning field and by exploiting the increasing availability of proper data. RESULTS: In this article, a novel computational method for the prediction of potential binding sites is proposed, called DeepSurf. DeepSurf combines a surface-based representation, where a number of 3D voxelized grids are placed on the protein's surface, with state-of-the-art deep learning architectures. After being trained on the large database of scPDB, DeepSurf demonstrates superior results on three diverse testing datasets, by surpassing all its main deep learning-based competitors, while attaining competitive performance to a set of traditional non-data-driven approaches. AVAILABILITY AND IMPLEMENTATION: The source code of the method along with trained models are freely available at https://github.com/stemylonas/DeepSurf.git. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Drug discovery involves extremely costly and time consuming procedures and can be significantly benefited by computational approaches, such as virtual screening (VS). Structure-based VS relies on scoring functions which aim to evaluate the binding of a candidate compound (ligand) on a protein target. Over the last few years, the advancement of the deep learning field has led to the development of novel scoring functions based on convolutional neural networks (CNN), which have achieved state-of-the-art results. In this paper, we present an integrated end-to-end VS pipeline for application on real-world drug discovery scenarios. It combines multiple conformations of the ligand with a new CNN scoring function based on the ResNet architecture, called ResNetVS, which incorporates also the docking output score in its evaluation. After experiments on the DUD-E dataset, it has shown notable performance, especially in early enrichment, where it overcomes current benchmarks. The proposed pipeline is finally applied on the emerging case of COVID-19 pandemic, in a struggle to discover inhibitors for the viral spike protein-ACE2 interaction.

It is a challenge to aggregate and analyze data from heterogeneous social media sources not only for businesses and organizations but also for Law Enforcement Agencies. The latter’s core objectives are to monitor criminal and terrorist related activities and to identify the ”key players” in various networks. In this paper, a framework for homogenizing and exploiting data from multiple sources is presented. Moreover, as part of the framework, an ontology that reflects today’s social media perceptions is introduced. Data from multiple sources is transformed into a labeled property graph and stored in a graph database in a homogenized way based on the proposed ontology. The result is a cross-source analysis system where end-users can explore different scenarios and draw conclusions through a library of predefined query placeholders that focus on forensic investigation. The framework is evaluated on the Stormfront dataset, a radical right, web community. Finally, the benefits of applying the proposed framework to discover and visualize the relationships between the Stormfront profiles are presented.

Moving objects pose a challenge to every video stabilization algorithm. We present a novel, efficient filtering technique that manages to remove outlier motion vectors caused from moving objects in a per-pixel smoothing setting. We leverage semantic information to change the calculation of optical flow, forcing the outliers to reside in the edges of our semantic mask. After a `content-preserving warping' and a smoothing step we manage to produce stable and artifact-free videos.

In this paper, a framework for shape-based similarity search of 3D molecular structures is presented. The proposed framework exploits simultaneously the discriminative capabilities of a global, a local, and a hybrid local-global shape feature to produce a geometric descriptor that achieves higher retrieval accuracy than each feature does separately. Global and hybrid features are extracted using pairwise computations of diffusion distances between the points of the molecular surface, while the local feature is based on accumulating pairwise relations among oriented surface points into local histograms. The local features are integrated into a global descriptor vector using the bag-of-features approach. Due to the intrinsic property of its constituting shape features to be invariant to articulations of the 3D objects, the framework is appropriate for similarity search of flexible 3D molecules, while at the same time it is also accurate in retrieving rigid 3D molecules. The proposed framework is evaluated in flexible and rigid shape matching of 3D protein structures as well as in shape-based virtual screening of large ligand databases with quite promising results.

In this article, a novel method for personalized item recommendation based on social tagging is presented. The proposed approach comprises a content-based tag propagation method to address the sparsity and “cold start” problems, which often occur in social tagging systems and decrease the quality of recommendations. The proposed method exploits (a) the content of items and (b) users' tag assignments through a relevance feedback mechanism in order to automatically identify the optimal number of content-based and conceptually similar items. The relevance degrees between users, tags, and conceptually similar items are calculated in order to ensure accurate tag propagation and consequently to address the issue of “learning tag relevance.” Moreover, the ternary relation among users, tags, and items is preserved by performing tag propagation in the form of triplets based on users' personal preferences and “cold start” degree. The latent associations among users, tags, and items are revealed based on a tensor factorization model in order to build personalized item recommendations. In our experiments with real-world social data, we show the superiority of the proposed approach over other state-of-the-art methods, since several problems in social tagging systems are successfully tackled. Finally, we present the recommendation methodology in the multimodal engine of I-SEARCH, where users' interaction capabilities are demonstrated.

In this paper, a framework for protein-protein docking is proposed, which exploits both shape and physicochemical complementarity to generate improved docking predictions. Shape complementarity is achieved by matching local surface patches. However, unlike existing approaches, which are based on single-patch or two-patch matching, we developed a new algorithm that compares simultaneously, groups of neighboring patches from the receptor with groups of neighboring patches from the ligand. Taking into account the fact that shape complementarity in protein surfaces is mostly approximate rather than exact, the proposed group-based matching algorithm fits perfectly to the nature of protein surfaces. This is demonstrated by the high performance that our method achieves especially in the case where the unbound structures of the proteins are considered. Additionally, several physicochemical factors, such as desolvation energy, electrostatic complementarity (EC), hydrophobicity (HP), Coulomb potential (CP), and Lennard-Jones potential are integrated using an optimized scoring function, improving geometric ranking in more than 60 percent of the complexes of Docking Benchmark 2.4.

This paper proposes a novel framework for 3-D object retrieval, taking into account most of the factors that may affect the retrieval performance. Initially, a novel 3-D model alignment method is introduced, which achieves accurate rotation estimation through the combination of two intuitive criteria, plane reflection symmetry and rectilinearity. After the pose normalization stage, a low-level descriptor extraction procedure follows, using three different types of descriptors, which have been proven to be effective. Then, a novel combination procedure of the above descriptors takes place, which achieves higher retrieval performance than each descriptor does separately. The paper provides also an in-depth study of the factors that can further improve the 3-D object retrieval accuracy. These include selection of the appropriate dissimilarity metric, feature selection/dimensionality reduction on the initial low-level descriptors, as well as manifold learning for re-ranking of the search results. Experiments performed on two 3-D model benchmark datasets confirm our assumption that future research in 3-D object retrieval should focus more on the efficient combination of low-level descriptors as well as on the selection of the best features and matching metrics, than on the investigation of the optimal 3-D object descriptor.

In this paper, a novel framework for rich-media object retrieval is described. The searchable items are media representations consisting of multiple modalities, such as 2-D images, 3-D objects and audio files, which share a common semantic concept. The proposed method utilizes the low-level descriptors of each separate modality to construct a new low-dimensional feature space, where all media objects can be mapped irrespective of their constituting modalities. While most of the existing state-of-the-art approaches support queries of one single modality at a time, the proposed one allows querying with multiple modalities simultaneously, through efficient multimodal query formulation, and retrieves multimodal results of any available type. Finally, a multimedia indexing scheme is adopted to tackle the problem of large scale media retrieval. The present framework proposes significant advances over existing methods and can be easily extended to involve as many heterogeneous modalities as possible. Experiments performed on two multimodal datasets demonstrate the effectiveness of the proposed method in multimodal search and retrieval.

In this paper, a novel approach for fast protein-protein docking based on geometric complementarity is introduced. Complementarity matching is achieved using a rotation-invariant 3D shape descriptor, the Shape Impact Descriptor (SID). Rotation invariance enables matching of equally-sized surface patches without the need for initial alignment. The candidate poses are computed using an intuitive alignment method, which is much faster than exhaustively searching the translational and rotational space of the ligand. The method yields competitive results when compared to other well-known geometry-based, rigid-docking approaches.

In this paper, a novel framework for 3D object retrieval is presented. The paper focuses on the investigation of an accurate 3D model alignment method, which is achieved by combining two intuitive criteria, the plane reflection symmetry and rectilinearity. After proper positioning in a coordinate system, a set of 2D images (multi-views) are automatically generated from the 3D object, by taking views from uniformly distributed viewpoints. For each image, a set of flip-invariant shape descriptors is extracted. Taking advantage of both the pose estimation of the 3D objects and the flip-invariance property of the extracted descriptors, a new matching scheme for fast computation of 3D object dissimilarity is introduced. Experiments conducted in SHREC 2009 benchmark show the superiority of the pose estimation method over similar approaches, as well as the efficiency of the new matching scheme.

This paper presents a novel approach for fast rigid docking of proteins based on geometric complementarity. After extraction of the 3D molecular surface, a set of local surface patches is generated based on the local surface curvature. The shape complementarity between a pair of patches is calculated using an efficient shape descriptor, the Shape Impact Descriptor. The key property of the Shape Impact Descriptor is its rotation invariance, which obviates the need for taking an exhaustive set of rotations for each pair of patches. Thus, complementarity matching between two patches is reduced to a simple histogram matching. Finally, a condensed set of almost complementary pairs of surface patches is supplied as input to the final scoring step, where each pose is evaluated using a 3D distance grid. The experimental results prove that the proposed method demonstrates superior performance over other well-known geometry-based, rigid-docking approaches.

This paper presents a novel framework for 3-D object content-based search and retrieval, appropriate for both partial and global matching applications. The framework is based on a graph representation of a 3-D object which is enhanced by local geometric features. The 3-D object is decomposed into meaningful parts and an attributed graph is constructed based on the connectivity of the parts. Every 3-D part is approximated with a suitable superellipsoid and a novel 3-D shape descriptor, called a 3-D distance field descriptor, is computed and associated to the corresponding graph nodes. The matching process used is based on attributed graph matching algorithm appropriate for this application. The proposed method not only provides successful retrieval results in terms of geometric similarity but also is invariant to rotation, translation and scaling of an object as well as to the different poses of articulated objects. Finally, it can be effectively used for partial and global 3-D object retrieval.

In this paper, a 3D shape-based approach is presented for the efficient search, retrieval, and classification of protein molecules. The method relies primarily on the geometric 3D structure of the proteins, which is produced from the corresponding PDB files and secondarily on their primary and secondary structure. After proper positioning of the 3D structures, in terms of translation and scaling, the Spherical Trace Transform is applied to them so as to produce geometry-based descriptor vectors, which are completely rotation invariant and perfectly describe their 3D shape. Additionally, characteristic attributes of the primary and secondary structure of the protein molecules are extracted, forming attribute-based descriptor vectors. The descriptor vectors are weighted and an integrated descriptor vector is produced. Three classification methods are tested. A part of the FSSP/DALI database, which provides a structural classification of the proteins, is used as the ground truth in order to evaluate the classification accuracy of the proposed method. The experimental results show that the proposed method achieves more than 99 percent classification accuracy while remaining much simpler and faster than the DALI method.

In this paper, the 2D polar wavelet transform is proposed for content based search and retrieval of 3D objects. After the decomposition of a 3D object's volume into a set of planes, the 2D polar wavelet transform is applied to each of them, generating a set of 2D rotation invariant features. These features comprise the input to the spherical trace transform for the final descriptor extraction, which is used for the shape matching. The 2D wavelet transform demonstrated experimentally a high discriminative power, as compared to other existing methods.