Matteo Masetti

RNA molecules explore heterogeneous conformational ensembles that are essential for their biological function and molecular recognition, yet this intrinsic flexibility poses a major challenge for structure-based drug discovery. In particular, the absence of well-defined binding pockets in static structures limits the identification of ligandable sites. Here, we present an integrative ensemble-based approach that combines enhanced-sampling molecular dynamics simulations with Nuclear Magnetic Resonance data to characterize the conformational landscape of the HIV-1 TAR RNA at atomic resolution. Starting from extensive sampling, we refined the resulting conformational distribution through maximum-entropy reweighting to achieve quantitative agreement with experimental data. Analysis of the reweighted ensemble reveals a diverse set of conformational substates, including compact arrangements that exhibit pocket features compatible with ligand recognition and overlap with known ligand-bound structures. At the same time, highly ligandable conformations, which are only marginally populated, might nonetheless be critical for RNA recognition. Our results demonstrate that integrative ensemble modeling can reveal pharmacologically relevant RNA conformations that are not apparent from experimental static structures, providing a framework for ensemble-based strategies in RNA-targeted drug discovery.

Long noncoding RNAs (lncRNAs) are increasingly recognized as druggable targets due to their conserved secondary/tertiary structures and regulatory roles in disease. A prototypical example is the MALAT1 triple helix, whose stability supports transcript persistence and is implicated in oncogenesis. Here, we evaluate the ability of a structure-based drug discovery (SBDD) pipeline, integrating molecular dynamics (MD), pocket analysis, ensemble docking, and diverse scoring functions, to capture the binding behavior of 21 congeneric diminazene-based ligands targeting MALAT1. Conformational ensembles were generated using both conventional MD and Hamiltonian Replica Exchange MD, revealing two potential binding sites. Ensemble docking with AutoDock GPU and rDock across representative RNA conformations, followed by rescoring with force-field and machine-learning-based scoring functions, led to the identification of a binding mode with the best agreement across the series. Principal component analysis of interaction fingerprints within clustered poses was used to explain the experimentally observed affinity trends. Our findings highlight the promise and limitations of current SBDD pipelines for flexible RNA targets and offer a benchmark for future improvement in RNA-focused drug discovery.

Potassium channels exhibit high selectivity and conductance, yet the atomic details of ion permeation, particularly the involvement of water molecules, remain debated. Two main conduction mechanisms have been proposed: the hard knock-on, in which ions traverse the selectivity filter in direct contact, and the soft knock-on, which involves copermeation of water molecules. Using microsecond molecular dynamics simulations with the OPC water model, the AMBER19SB protein force field, and the 12-6-4 Sengupta et al. ion model, and an analysis strategy based on Markov State Models, we observed that both hard and soft knock-on mechanisms are accessible and, notably, can reversibly transition in the MthK and KcsA channels across all simulated membrane potentials. These reversible transitions contrast with previous observations using the TIP3P water model, where water entry either disrupted conduction or was expelled, favoring exclusive hard knock-on events. Our results suggest that the choice of the water model, force field, and ion parameters significantly influences the observed conduction mechanism. Importantly, the coexistence of hard and soft knock-on in these simulations provides a reconciliation between structural data supporting hard knock-on and streaming potential measurements demonstrating water copermeation. These findings reintroduce soft knock-on as a viable conduction mechanism and highlight the critical role of simulation parameters in reproducing potassium channel permeation behavior.

Long non-coding RNAs (lncRNAs) are increasingly recognized as druggable targets due to their conserved secondary/tertiary structures and regulatory roles in disease. A prototypical example is the MALAT1 triple helix, whose stability supports transcript persistence and is implicated in oncogenesis. Here, we evaluate the ability of a structure-based drug discovery (SBDD) pipeline, integrating molecular dynamics (MD), pocket analysis, ensemble docking, and diverse scoring functions, to capture the binding behavior of 21 congeneric diminazene-based ligands targeting MALAT1. Conformational ensembles were generated using both conventional MD and Hamiltonian Replica Exchange MD, revealing two potential binding sites. Ensemble docking with AutoDock GPU and rDock across representative RNA conformations, followed by rescoring with force-field and machine-learning-based scoring functions, led to the identification of a binding mode with the best agreement across the series. Principal component analysis of interaction fingerprints within clustered poses was used to explain the experimentally observed affinity trends. Our findings highlight the promise and limitations of current SBDD pipelines for flexible RNA targets and offer a benchmark for future improvement in RNA-focused drug discovery.

Homologous recombination (HR) is a critical repair pathway involving numerous proteins that ensure error-free DNA double-strand breaks (DSBs) repair. Dysfunction in HR components can compromise genome integrity. Despite advances, many aspects of HR remain poorly understood. Notably, even one of the earliest identified and most critical interactions, between RAD51 and BRCA2, remains incompletely characterized, mainly due to the lack of structural data. This study presents a comprehensive biophysical analysis of the RAD51–BRC repeats interaction, integrating computational and experimental approaches. Starting with assessing the correlation between the binding affinities of individual BRC repeats and their impact on RAD51 disassembly, our investigation extends to larger BRCA2 truncations, offering unprecedented insights into the molecular determinants of RAD51 recognition. As mutations in the BRC repeats impair RAD51 recruitment and are associated with cancer, these results provide a valuable framework for interpreting pathogenic variants and guiding precision medicine therapies. GRAPHICAL

Potassium channels exhibit high selectivity and conductance, yet the atomic details of ion permeation, particularly the involvement of water molecules, remain debated. Two main conduction mechanisms have been proposed: the hard knock-on, in which ions traverse the selectivity filter in direct contact, and the soft knock-on, which involves co-permeation of water molecules. Using microsecond molecular dynamics simulations with the OPC water model, the AMBER19SB protein force field, and the 12-6-4 Sengupta et al. ion model, we observed that both hard and soft knock-on mechanisms are accessible and, notably, can reversibly transition in the MthK and KcsA channels across all simulated membrane potentials. These reversible transitions contrast with previous observations using the TIP3P water model, where water entry either disrupted conduction or was expelled, favoring exclusive hard knock-on events. Our results suggest that the choice of the water model, force field, and ion parameters significantly influences the observed conduction mechanism. Importantly, the coexistence of hard and soft knock-on in these simulations provides a potential reconciliation between structural data supporting hard knock-on and streaming potential measurements demonstrating water co-permeation. These findings reintroduce soft knock-on as a viable conduction mechanism and highlight the critical role of simulation parameters in reproducing potassium channel permeation behavior.

The interaction between the RAD51 and BRCA2 proteins is central to homologous recombination (HR), a crucial pathway ensuring high-fidelity DNA repair. Recruitment of RAD51 involves eight highly conserved regions on BRCA2, named BRC repeats. The interaction between the fourth BRC repeat (BRC4) and the RAD51 C-terminal domain has been structurally characterized, while the complex of full-length RAD51 with the peptide remains elusive. This gap limits our understanding of cytosolic RAD51 recruitment driven by the BRCA2 BRC repeats, which is one of the first crucial steps in HR. Here, we report an integrative experimental and in silico approach to reconstruct the conformational ensemble in solution for full-length RAD51 in complex with BRC4. We combined AlphaFold2, cross-linking mass spectrometry, and small-angle X-ray scattering data with molecular dynamics simulations. Our results show that the full-length RAD51-BRC4 complex is a mixture of compact and elongated conformations and allow for the identification of key residues at the interface between the RAD51 N-terminus and BRC4, mediating complex conformational dynamics. Our evidence provides robust atomic-level insights into the RAD51-BRC4 interaction, shedding light on the molecular features that govern the recognition between these two proteins, while unveiling novel hotspots for developing novel anticancer agents.

Figure: GRAPHICAL ABSTRACT The interaction between the RAD51 and BRCA2 proteins is central for homologous recombination, a crucial pathway ensuring high-fidelity DNA repair. Recruitment of RAD51 involves eight highly conserved regions on BRCA2, named BRC repeats. To date, only the interaction between the fourth BRC repeat (BRC4) and the RAD51 C-terminal domain has been structurally characterized, while the complex of full-length RAD51 with the peptide still remains elusive. Here, we report an integrative experimental and in silico approach to reconstruct the conformational ensemble in solution for full length RAD51 in complex with BRC4. We combined AlphaFold2, crosslinking mass spectrometry (XL-MS) and small angle x-ray scattering (SAXS) data with molecular dynamics simulations (MD). These data show that the full-length RAD51-BRC4 complex is a mixture of compact and elongated conformations. Detailed analysis of the reweighted ensemble, achieved through the maximum entropy principle, identifies key residues at the N-terminal-BRC4 interface mediating complex conformational dynamics. Our evidence provides robust atomic-level insights into the interaction of RAD51 and BRC4. These findings are crucial for understanding the molecular features underlying the recognition between RAD51 and BRCA2, which are essential for developing therapeutic intervention strategies in cancer treatment.

In computational physics, chemistry, and biology, the implementation of new techniques in shared and open-source software lowers barriers to entry and promotes rapid scientific progress. However, effectively training new software users presents several challenges. Common methods like direct knowledge transfer and in-person workshops are limited in reach and comprehensiveness. Furthermore, while the COVID-19 pandemic highlighted the benefits of online training, traditional online tutorials can quickly become outdated and may not cover all the software’s functionalities. To address these issues, here we introduce “PLUMED Tutorials,” a collaborative model for developing, sharing, and updating online tutorials. This initiative utilizes repository management and continuous integration to ensure compatibility with software updates. Moreover, the tutorials are interconnected to form a structured learning path and are enriched with automatic annotations to provide broader context. This paper illustrates the development, features, and advantages of PLUMED Tutorials, aiming to foster an open community for creating and sharing educational resources.