Combinatorial Coarse-Graining of Molecular Dynamics Simulations for Detecting Relationships between Local Configurations and Overall Conformations

K-Means Clustering Coarse-Graining (KMC-CG): A Next Generation Methodology for Determining Optimal Coarse-Grained Mappings of Large Biomolecules

Coarse-grained (CG) molecular dynamics (MD) has become a method of choice for simulating various large scale biomolecular processes; therefore, the systematic definition of the CG mappings for biomolecules remains an important topic. Appropriate CG mappings can significantly enhance the representability of a CG model and improve its ability to capture critical features of large biomolecules. In this work, we present a systematic and more generalized method called K-means clustering coarse-graining (KMC-CG), which builds on the earlier approach of essential dynamics coarse-graining (ED-CG). KMC-CG removes the sequence-dependent constraints of ED-CG, allowing it to explore a more extensive space and thus enabling the discovery of more physically optimal CG mappings. Furthermore, the implementation of the K-means clustering algorithm can variationally optimize the CG mapping with efficiency and stability. This new method is tested in three cases: ATP-bound G-actin, the HIV-1 CA pentamer, and the Arp2/3 complex. In these examples, the CG models generated by KMC-CG are seen to better capture the structural, dynamic, and functional domains. KMC-CG therefore provides a robust and consistent approach to generating CG models of large biomolecules that can then be more accurately parametrized by either bottom-up or top-down CG force fields.

Insight into the structures of Interleukin-18 systems

Structure-based molecular designs play a critical role in the context of next generation drug development. Besides their fundamental scientific aspects, the findings established in this approach have significant implications in the expansions of target-based therapies and vaccines. Interleukin-18 (IL-18), also known as interferon gamma (IFN-γ) inducing factor, is a pro-inflammatory cytokine. The IL-18 binds first to the IL-18α receptor and forms a lower affinity complex. Upon binding with IL-18β a hetero-trimeric complex with higher affinity is formed that initiates the signal transduction process. The present study, including structural and molecular dynamics simulations, takes a close look at the structural stabilities of IL-18 and IL-18 receptor-bound ligand structures as functions of time. The results help to identify the conformational changes of the ligand due to receptor binding, as well as the structural orders of the apo and holo IL-18 protein complexes.

Computational investigation of a ternary model of SnoN-SMAD3-SMAD4 complex

The transforming growth factor β (TGFβ) plays an essential role in the regulation of cellular processes such as cell proliferation, migration, differentiation, and apoptosis by association with SMAD transcriptional factors that are regulated by the transcriptional regulator SnoN. The crystal structure of SnoN-SMAD4 reveals that SnoN can adopt two binding modes, the open and closed forms, at the interfaces of SMAD4 subunits. Accumulating evidence indicates that SnoN can interact with both SMAD3 and SMAD4 to form a ternary SnoN-SMAD3-SMAD4 complex in the TGFβ signaling pathway. However, how the interaction of SnoN with the SMAD3 and SMAD4 remains unclear. Here, molecular dynamics (MD) simulations and molecular modeling methods were performed to figure out this issue. The simulations reveal that SnoN^open exists in two, open and semi-closed, conformations. Molecular modeling and MD simulation studies suggest that the SnoN^closed form interferes with the SMAD3-SMAD4 protein; in contract, the SnoN^open can form a stable SnoN-SMAD3-SMAD4 complex. These mechanistic mechanisms may help elucidate the detailed engagement of SnoN with two SMAD3 and SMAD4 transcriptional factors in the regulation of TGFβ signaling pathway.

Computational Structural Biology: Successes, Future Directions, and Challenges

Computational biology has made powerful advances. Among these, trends in human health have been uncovered through heterogeneous 'big data' integration, and disease-associated genes were identified and classified. Along a different front, the dynamic organization of chromatin is being elucidated to gain insight into the fundamental question of genome regulation. Powerful conformational sampling methods have also been developed to yield a detailed molecular view of cellular processes. when combining these methods with the advancements in the modeling of supramolecular assemblies, including those at the membrane, we are finally able to get a glimpse into how cells' actions are regulated. Perhaps most intriguingly, a major thrust is on to decipher the mystery of how the brain is coded. Here, we aim to provide a broad, yet concise, sketch of modern aspects of computational biology, with a special focus on computational structural biology. We attempt to forecast the areas that computational structural biology will embrace in the future and the challenges that it may face. We skirt details, highlight successes, note failures, and map directions.