MOTIVATION: G-quadruplex-binding proteins (G4BPs) play key roles in RNA metabolism and stress response, yet their identification remains experimentally challenging. Here, we present a deep learning (DL) framework for the prediction of RNA G4BPs (RG4BPs), integrating diverse encoding strategies and neural architectures. Our best-performing model, which includes ESM-2 protein language model embeddings and consists of an LSTM architecture, achieved 86% accuracy in distinguishing RG4BPs from non-binder proteins. The application of this model to the human proteome uncovered 2160 high-confidence RG4BP candidates, many of which display intrinsically disordered regions (IDRs) and enrichment in stress granule organelles. These findings reveal a potential link between G-quadruplex recognition and cellular stress responses. To enable easy and broad access to the framework, we developed G4REP, a web server for RG4BP prediction and analysis. Overall, an effective approach to explore the RG4BPs landscape and uncover novel players in RNA regulation is provided. AVAILABILITY: Source code for the G4REP Model training and evaluation is available at: https://github.com/G4REP/G4REPmodel and at https://doi.org/10.5281/zenodo.17963046. G4REP Server is hosted at: https://schubert.bio.uniroma1.it/g4/.

MOTIVATION: Protein contact networks (PCNs) represent the 3D structure of a protein using network formalism. Inter-residue contacts are described as binary adjacency matrices, which are derived from the graph representation of residues (as α-carbons, β-carbons or centroids) and Euclidean distances according to defined thresholds. Functional characterization algorithms are computed on binary adjacency matrices to unveil allosteric, dynamic, and interaction mechanisms in proteins. Such strategies are usually applied in a combinatorial manner, although rarely in seamless and user-friendly implementations. RESULTS: PyPCN is a plugin for PyMOL wrapping more than twenty PCN algorithms and metrics in an easy-to-use graphical user interface, to support PCN analysis. The plugin accepts 3D structures from the Protein Data Bank, user-provided PDBs, or precomputed adjacency matrices. The results are directly mapped to 3D protein structures and organized into interactive diagrams for their visualization. A dedicated graphical user interface combined with PyMOL visual support makes analysis more intuitive and easier, extending the applicability of PCNs. AVAILABILITY AND IMPLEMENTATION: https://github.com/pcnproject/PyPCN.

MOTIVATION: The primary strategy for predicting the binding mode of small molecules to their receptors and for performing receptor-based virtual screening studies is protein-ligand docking, which is undoubtedly the most popular and successful approach in computer-aided drug discovery. The increased popularity of docking has resulted in the development of different docking algorithms and scoring functions. Nonetheless, it is unlikely that a single approach outperforms the others in terms of reproducibility and precision. In this ground, consensus docking techniques are taking hold. RESULTS: We have developed DockingPie, an open source PyMOL plugin for individual, as well as consensus docking analyses. Smina, AutoDock Vina, ADFR and RxDock are the four docking engines that DockingPie currently supports in an easy and extremely intuitive way, thanks to its integrated docking environment and its GUI, fully integrated within PyMOL. AVAILABILITY AND IMPLEMENTATION: https://github.com/paiardin/DockingPie. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

SUMMARY: The PyMod project is designed to act as a fully integrated interface between the popular molecular graphics viewer PyMOL, and some of the most frequently used tools for structural bioinformatics, e.g. BLAST, HMMER, Clustal, MUSCLE, PSIPRED, DOPE and MODELLER. Here we report its latest release, PyMod 3, which has been completely renewed with a graphical interface written in PyQt, to make it compatible with the most recent PyMOL versions, and has been extended with a large set of new functionalities compared to its predecessor, i.e. PyMod 2. Starting from the amino acid sequence of a target protein, users can take advantage of PyMod 3 to carry out all the steps of the homology modeling process (i.e. template searching, target-template sequence alignment, model building and quality assessment). Additionally, the integrated tools in PyMod 3 may also be used alone, in order to extend PyMOL with a wide range of capabilities. Sequence similarity searches, multiple sequence/structure alignment building, phylogenetic trees and evolutionary conservation analyses, domain parsing, single/multiple chains and loop modeling can be performed in the PyMod 3/PyMOL environment. AVAILABILITY AND IMPLEMENTATION: A cross-platform PyMod 3 installer package for Windows, Linux and Mac OS X and a complete user guide with tutorials, are available at https://github.com/pymodproject/pymod. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

The most frequently used approach for protein structure prediction is currently homology modeling. The 3D model building phase of this methodology is critical for obtaining an accurate and biologically useful prediction. The most widely employed tool to perform this task is MODELLER. This program implements the "modeling by satisfaction of spatial restraints" strategy and its core algorithm has not been altered significantly since the early 1990s. In this work, we have explored the idea of modifying MODELLER with two effective, yet computationally light strategies to improve its 3D modeling performance. Firstly, we have investigated how the level of accuracy in the estimation of structural variability between a target protein and its templates in the form of σ values profoundly influences 3D modeling. We show that the σ values produced by MODELLER are on average weakly correlated to the true level of structural divergence between target-template pairs and that increasing this correlation greatly improves the program's predictions, especially in multiple-template modeling. Secondly, we have inquired into how the incorporation of statistical potential terms (such as the DOPE potential) in the MODELLER's objective function impacts positively 3D modeling quality by providing a small but consistent improvement in metrics such as GDT-HA and lDDT and a large increase in stereochemical quality. Python modules to harness this second strategy are freely available at https://github.com/pymodproject/altmod. In summary, we show that there is a large room for improving MODELLER in terms of 3D modeling quality and we propose strategies that could be pursued in order to further increase its performance.

Motivation: The recently released PyMod GUI integrates many of the individual steps required for protein sequence-structure analysis and homology modeling within the interactive visualization capabilities of PyMOL. Here we describe the improvements introduced into the version 2.0 of PyMod. Results: The original code of PyMod has been completely rewritten and improved in version 2.0 to extend PyMOL with packages such as Clustal Omega, PSIPRED and CAMPO. Integration with the popular web services ESPript and WebLogo is also provided. Finally, a number of new MODELLER functionalities have also been implemented, including SALIGN, modeling of quaternary structures, DOPE scores, disulfide bond modeling and choice of heteroatoms to be included in the final model. Availability and Implementation: PyMod 2.0 installer packages for Windows, Linux and Mac OS X and user guides are available at http://schubert.bio.uniroma1.it/pymod/index.html. The open source code of the project is hosted at https://github.com/pymodproject/pymod. Contact: [email protected] or [email protected]

BACKGROUND: In recent years, an exponential growing number of tools for protein sequence analysis, editing and modeling tasks have been put at the disposal of the scientific community. Despite the vast majority of these tools have been released as open source software, their deep learning curves often discourages even the most experienced users. RESULTS: A simple and intuitive interface, PyMod, between the popular molecular graphics system PyMOL and several other tools (i.e., [PSI-]BLAST, ClustalW, MUSCLE, CEalign and MODELLER) has been developed, to show how the integration of the individual steps required for homology modeling and sequence/structure analysis within the PyMOL framework can hugely simplify these tasks. Sequence similarity searches, multiple sequence and structural alignments generation and editing, and even the possibility to merge sequence and structure alignments have been implemented in PyMod, with the aim of creating a simple, yet powerful tool for sequence and structure analysis and building of homology models. CONCLUSIONS: PyMod represents a new tool for the analysis and the manipulation of protein sequences and structures. The ease of use, integration with many sequence retrieving and alignment tools and PyMOL, one of the most used molecular visualization system, are the key features of this tool.Source code, installation instructions, video tutorials and a user's guide are freely available at the URL http://schubert.bio.uniroma1.it/pymod/index.html.