Molecular dynamics-based identification of binding pathways and two distinct high-affinity sites for succinate in succinate receptor 1/GPR91.

SUCNR1 is an auto- and paracrine sensor of the metabolic stress signal succinate. Using unsupervised molecular dynamics (MD) simulations (170.400 ns) and mutagenesis across human, mouse, and rat SUCNR1, we characterize how a five-arginine motif around the extracellular pole of TM-VI determines the initial capture of succinate in the extracellular vestibule (ECV) to either stay or move down to the orthosteric site. Metadynamics demonstrate low-energy succinate binding in both sites, with an energy barrier corresponding to an intermediate stage during which succinate, with an associated water cluster, unlocks the hydrogen-bond-stabilized conformationally constrained extracellular loop (ECL)-2b. Importantly, simultaneous binding of two succinate molecules through either a "sequential" or "bypassing" mode is a frequent endpoint. The mono-carboxylate NF-56-EJ40 antagonist enters SUCNR1 between TM-I and -II and does not unlock ECL-2b. It is proposed that occupancy of both high-affinity sites is required for selective activation of SUCNR1 by high local succinate concentrations.

Prediction of cancer driver genes and mutations: the potential of integrative computational frameworks.

The vast amount of available sequencing data allows the scientific community to explore different genetic alterations that may drive cancer or favor cancer progression. Software developers have proposed a myriad of predictive tools, allowing researchers and clinicians to compare and prioritize driver genes and mutations and their relative pathogenicity. However, there is little consensus on the computational approach or a golden standard for comparison. Hence, benchmarking the different tools depends highly on the input data, indicating that overfitting is still a massive problem. One of the solutions is to limit the scope and usage of specific tools. However, such limitations force researchers to walk on a tightrope between creating and using high-quality tools for a specific purpose and describing the complex alterations driving cancer. While the knowledge of cancer development increases daily, many bioinformatic pipelines rely on single nucleotide variants or alterations in a vacuum without accounting for cellular compartments, mutational burden or disease progression. Even within bioinformatics and computational cancer biology, the research fields work in silos, risking overlooking potential synergies or breakthroughs. Here, we provide an overview of databases and datasets for building or testing predictive cancer driver tools. Furthermore, we introduce predictive tools for driver genes, driver mutations, and the impact of these based on structural analysis. Additionally, we suggest and recommend directions in the field to avoid silo-research, moving towards integrative frameworks.

Acidic sphingomyelinase interactions with lysosomal membranes and cation amphiphilic drugs: A molecular dynamics investigation.

Lysosomes are pivotal in cellular functions and disease, influencing cancer progression and therapy resistance with Acid Sphingomyelinase (ASM) governing their membrane integrity. Moreover, cation amphiphilic drugs (CADs) are known as ASM inhibitors and have anti-cancer activity, but the structural mechanisms of their interactions with the lysosomal membrane and ASM are poorly explored. Our study, leveraging all-atom explicit solvent molecular dynamics simulations, delves into the interaction of glycosylated ASM with the lysosomal membrane and the effects of CAD representatives, i.e., ebastine, hydroxyebastine and loratadine, on the membrane and ASM. Our results confirm the ASM association to the membrane through the saposin domain, previously only shown with coarse-grained models. Furthermore, we elucidated the role of specific residues and ASM-induced membrane curvature in lipid recruitment and orientation. CADs also interfere with the association of ASM with the membrane at the level of a loop in the catalytic domain engaging in membrane interactions. Our computational approach, applicable to various CADs or membrane compositions, provides insights into ASM and CAD interaction with the membrane, offering a valuable tool for future studies.

ASM variants in the spotlight: A structure-based atlas for unraveling pathogenic mechanisms in lysosomal acid sphingomyelinase.

Lysosomal acid sphingomyelinase (ASM), a critical enzyme in lipid metabolism encoded by the SMPD1 gene, plays a crucial role in sphingomyelin hydrolysis in lysosomes. ASM deficiency leads to acid sphingomyelinase deficiency, a rare genetic disorder with diverse clinical manifestations, and the protein can be found mutated in other diseases. We employed a structure-based framework to comprehensively understand the functional implications of ASM variants, integrating pathogenicity predictions with molecular insights derived from a molecular dynamics simulation in a lysosomal membrane environment. Our analysis, encompassing over 400 variants, establishes a structural atlas of missense variants of lysosomal ASM, associating mechanistic indicators with pathogenic potential. Our study highlights variants that influence structural stability or exert local and long-range effects at functional sites. To validate our predictions, we compared them to available experimental data on residual catalytic activity in 135 ASM variants. Notably, our findings also suggest applications of the resulting data for identifying cases suited for enzyme replacement therapy. This comprehensive approach enhances the understanding of ASM variants and provides valuable insights for potential therapeutic interventions.