Matthieu Montes

This SHREC 2025 track dedicated to protein surface shape retrieval involved 9 participating teams. We evaluated the performance in retrieval of 15 proposed methods on a large dataset of 11,555 protein surfaces with calculated electrostatic potential (a key molecular surface descriptor). The performance in retrieval of the proposed methods was evaluated through different metrics (Accuracy, Balanced accuracy, F1 score, Precision and Recall). The best retrieval performance was achieved by the proposed methods that used the electrostatic potential complementary to molecular surface shape. This observation was also valid for classes with limited data which highlights the importance of taking into account additional molecular surface descriptors.

VTX is an open-source molecular visualization software designed to overcome the scaling limitations of existing real-time molecular visualization software when handling massive molecular datasets. VTX employs a meshless molecular graphics engine utilizing impostor-based techniques and adaptive level-of-detail (LOD) rendering. This approach significantly reduces memory usage and enables real-time visualization and manipulation of large molecular systems. Performance benchmarks against VMD, PyMOL, and ChimeraX using a 114-million-bead Martini minimal whole-cell model demonstrate VTX’s efficiency, maintaining consistent frame rates even under interactive manipulation on standard computer hardware. VTX incorporates features such as Screen-Space Ambient Occlusion (SSAO) for enhanced depth perception and free-fly navigation for intuitive exploration of large molecular systems. VTX is open-source and free for non commercial use. Binaries for Windows and Ubuntu Linux are available at http://vtx.drugdesign.fr. VTX source code is available at https://github.com/VTX-Molecular-Visualization.

Summary: VTX is a molecular visualization software capable to handle most molecular structures and dynamics trajectories file formats. It features a real-time high-performance molecular graphics engine, based on modern OpenGL, optimized for the visualization of massive molecular systems and molecular dynamics trajectories. VTX includes multiple interactive camera and user interaction features, notably free-fly navigation and a fully modular graphical user interface designed for increased usability. It allows the production of high-resolution images for presentations and posters with custom background. VTX design is focused on performance and usability for research, teaching, and educative purposes. Availability and implementation: VTX is open source and free for non-commercial use. Builds for Windows and Ubuntu Linux are available at http://vtx.drugdesign.fr. The source code is available at https://github.com/VTX-Molecular-Visualization.

Nuclear receptors form a family of proteins capable of accommodating a wide variety of small molecules in their ligand binding domain, ranging from therapeutic compounds to endocrine-disrupting chemicals. The rapid identification of these compounds, especially within the latter category, is of paramount importance. Using data extracted from the CompTox Dashboard, an Environmental Protection Agency initiative, we assessed the effectiveness of a combination of molecular docking and pharmacophore models in identifying ligands binding to six nuclear receptors: androgen receptor, estrogen receptor alpha, estrogen receptor beta, glucocorticoid receptor, peroxisome proliferator-activated receptor gamma, and thyroid hormone receptor alpha. For each nuclear receptor, we selected a specifically designed and optimized in silico protocol that, in conjunction with experimental assays, can prioritize compounds for further evaluation to detect any potential toxicological concerns.

Neuropilin-1 is henceforth a relevant target in cancer treatment; however, its way of action remains partly elusive, and the development of small inhibitory molecules is therefore required for its study. Here, we report that two small-sized neuropilin antagonists (NRPa-47 and NRPa-48), VEGF-A₁₆₅/NRP-1 binding inhibitors, are able to decrease VEGF-Rs phosphorylation and to modulate their downstream cascades in the triple-negative breast cancer cell line (MDA-MB-231). Nevertheless, NRPas exert a divergent pathway regulation of MAPK phosphorylation, such as JNK-1/-2/-3, ERK-1/-2, and p38β/γ/δ-kinases, as well as their respective downstream targets. However, NRPa-47 and NRPa-48 apply a common down-regulation of the p38α-kinase phosphorylation and their downstream targets, emphasising its central regulating role. More importantly, none of the 40 selected kinases, including SAPK2a/p38α, are affected in vitro by NRPas, strengthening their specificity. Taken together, NRPas induced cell death by the down-modulation of pro-apoptotic and anti-apoptotic proteins, cell death receptors and adaptors, heat shock proteins (HSP-27/-60/-70), cell cycle proteins (p21, p27, phospho-RAD17), and transcription factors (p53, HIF-1α). In conclusion, we showed for the first time how NRPas may alter tumour cell signalling and contribute to the down-modulation of the cancer therapeutic key factor p38α-kinase phosphorylation. Thus, the efficient association of NRPas and p38α-kinase inhibitor strengthened this hypothesis.

The Solvent-Excluded Surface (SES) is an essential representation of molecules which is massively used in molecular modeling and drug discovery since it represents the interacting surface between molecules. Based on its properties, it supports the visualization of both large scale shapes and details of molecules. While several methods targeted its computation, the ability to process large molecular structures to address the introduction of big complex analysis while leveraging the massively parallel architecture of GPUs has remained a challenge. This is mostly caused by the need for consequent memory allocation or by the complexity of the parallelization of its processing. In this paper, we leverage the last theoretical advances made for the depiction of the SES to provide fast analytical computation with low impact on memory. We show that our method is able to compute the complete surface while handling large molecular complexes with competitive computation time costs compared to previous works.

The study of protein molecular surfaces enables to better understand and predict protein interactions. Different methods have been developed in computer vision to compare surfaces that can be applied to protein molecular surfaces. The present work proposes a method using the Wave Kernel Signature: Protein LOcal Surficial Similarity Screening (PLO3S). The descriptor of the PLO3S method is a local surface shape descriptor projected on a unit sphere mapped onto a 2D plane and called Surface Wave Interpolated Maps (SWIM). PLO3S allows to rapidly compare protein surface shapes through local comparisons to filter large protein surfaces datasets in protein structures virtual screening protocols.

Protein–protein docking aims at predicting the geometry of protein interactions to gain insights into the mechanisms underlying these processes and develop new strategies for drug discovery. Interactive and user-oriented manipulation tools can support this task complementary to automated software. This article presents an interactive multi-body protein–protein docking software, UDock2, designed for research but also usable for teaching and popularization of science purposes due to its high usability. In UDock2, the users tackle the conformational space of protein interfaces using an intuitive real-time docking procedure with on-the-fly scoring. UDock2 integrates traditional computer graphics methods to facilitate the visualization and to provide better insight into protein surfaces, interfaces, and properties. UDock2 is open-source, cross-platform (Windows and Linux), and available at http://udock.fr. The code can be accessed at https://gitlab.com/Udock/Udock2.

The study of protein molecular surfaces enable to better understand and predict protein interactions. Different methods have been developed in computer vision to compare surfaces that can be applied to protein molecular surfaces. The present work proposes a method using the the Wave Kernel Signature : Protein LOcal Surficial Similarity Screening (PLO3S). The descriptor of the PLO3S method is a local surface shape descriptor projected on a unit sphere mapped onto a 2D plane and called Surface Wave Interpolated Maps (SWIM). PLO3S allows to rapidly compare protein surface shapes through local comparisons to filter large protein surfaces datasets in protein structures virtual screening protocols.