MOTIVATION: The stability of protein interfaces influences protein dynamics and unfolding cooperativity. Although in some cases the dynamics of proteins can be deduced from their topology, much of the stability of an interface is related to the complementarity of the interacting parts. It is also important to note that proteins that display non-cooperative unfolding cannot be rationally stabilized unless the regions that unfold first are known. Being able to identify protein interfaces that are significantly less stable would contribute to our understanding of protein dynamics and be very valuable in guiding the rational stabilization of proteins with non-two-state unfolding equilibria. RESULTS: We introduce ProteinLIPs, a web server that detects interfaces of high polarity and low packing density, termed LIPs. Each LIP consist of a continuous sequence segment (mLIP) plus its contacting residues (cLIP). ProteinLIPs scans monomeric and oligomeric proteins and provides graphical sequence profiles and interactive 3D visualizations of the detected LIPs. Statistical analysis of 53 protein domains from 10 superfamilies shows the two parts of a LIP present distinct characteristics. mLIPs are conserved, structurally unstable and enriched in polar residues, whereas cLIPs are more stable, less conserved, and enriched in apolar residues. Besides, cLIPs are enriched in small-molecule binding site residues, suggesting they play a role in ligand interaction, likely facilitated by instability of the associated mLIPs. ProteinLIPs provides a user-friendly platform for the automated identification and visualization of LIPs and can be used to guide the engineering of non-two-state proteins where LIPs constitute preferential targets for thermostabilization. AVAILABILITY AND IMPLEMENTATION: ProteinLIPs is publicly available at https://lips.bifi.es/.

Remote sensing technologies, such as LiDAR, produce billions of points that commonly exceed the storage capacity of the GPU, restricting their processing and rendering. Level of detail (LoD) techniques have been widely investigated, but building the LoD structures is also time-consuming. This study proposes a GPU-driven culling system focused on determining the number of points visible in every frame. It can manipulate point clouds of any arbitrary size while maintaining a low memory footprint in both the CPU and GPU. Instead of organizing point clouds into hierarchical data structures, these are split into groups of points sorted using the Hilbert encoding. This alternative alleviates the occurrence of anomalous groups found in Morton curves. Instead of keeping the entire point cloud in the GPU, points are transferred on demand to ensure real-time capability. Accordingly, our solution can manipulate huge point clouds even in commodity hardware with low memory capacities. Moreover, hole filling is implemented to cover the gaps derived from insufficient density and our LoD system. Our proposal was evaluated with point clouds of up to 18 billion points, achieving an average of 80 frames per second (FPS) without perceptible quality loss. Relaxing memory constraints further enhances visual quality while maintaining an interactive frame rate. We assessed our method on real-world data, comparing it against three state-of-the-art methods, demonstrating its ability to handle significantly larger point clouds.

One of the primary challenges inherent in utilizing deep learning models is the scarcity and accessibility hurdles associated with acquiring datasets of sufficient size to facilitate effective training of these networks. This is particularly significant in object detection, shape completion, and fracture assembly. Instead of scanning a large number of real-world fragments, it is possible to generate massive datasets with synthetic pieces. However, realistic fragmentation is computationally intensive in the preparation (e.g., pre-factured models) and generation. Otherwise, simpler algorithms such as Voronoi diagrams provide faster processing speeds at the expense of compromising realism. In this context, it is required to balance computational efficiency and realism. This paper introduces a GPU-based framework for the massive generation of voxelized fragments derived from high-resolution 3D models, specifically prepared for their utilization as training sets for machine learning models. This rapid pipeline enables controlling how many pieces are produced, their dispersion and the appearance of subtle effects such as erosion. We have tested our pipeline with an archaeological dataset, producing more than 1M fragmented pieces from 1,052 Iberian vessels ( Github ). Although this work primarily intends to provide pieces as implicit data represented by voxels, triangle meshes and point clouds can also be inferred from the initial implicit representation. To underscore the unparalleled benefits of CPU and GPU acceleration in generating vast datasets, we compared against a realistic fragment generator that highlights the potential of our approach, both in terms of applicability and processing time. We also demonstrate the synergies between our pipeline and realistic simulators, which frequently cannot select the number and size of resulting pieces. To this end, a deep learning model was trained over realistic fragments and our dataset, showing similar results. • GPU-based fragmentation of voxelized artefacts for simulating brittle fractures. • Generation of large datasets for training machine learning models. • Definition of how many fragments are produced, their dispersion and appearance. • Publication of a dataset comprising more than 1M fragments.