Exploring the dynamics of RNA molecules with multiscale Gaussian network model

RNAfcg: RNA Flexibility Prediction Based on Topological Centrality and Global Features

The dynamics of RNAs are related intimately to their functions. Molecular flexibility, as a starting point for understanding their dynamics, has been utilized to predict many characteristics associated with their functions. Since the experimental measurement methods are time-consuming and labor-intensive, it is urgently needed to develop reliable theoretical methods to predict RNA flexibility. In this work, we develop an effective machine learning method, RNAfcg, to predict RNA flexibility, where the Random Forest (RF) is trained by features including the topological centralities, flexibility-rigidity index, and global characteristics first introduced by us, as well as some traditional sequence and structural features. The analyses show that the three types of features introduced first have significant contributions to RNA flexibility prediction, among which the topological type contributes the most, which indicates the importance of structural topology in determining RNA flexibility. The performance comparison indicates that RNAfcg outperforms the state-of-the-art machine learning methods and the commonly used Gaussian Network Model (GNM) models, achieving a much higher Pearson correlation coefficient (PCC) of 0.6619 on the test data set. This work is helpful for understanding RNA dynamics and can be used to predict RNA function information. The source code is available at https://github.com/ChunhuaLab/RNAfcg/.

Key Residues in δ Opioid Receptor Allostery Explored by the Elastic Network Model and the Complex Network Model Combined with the Perturbation Method

Opioid receptors, a kind of G protein-coupled receptors (GPCRs), mainly mediate an analgesic response via allosterically transducing the signal of endogenous ligand binding in the extracellular domain to couple to effector proteins in the intracellular domain. The δ opioid receptor (DOP) is associated with emotional control besides pain control, which makes it an attractive therapeutic target. However, its allosteric mechanism and key residues responsible for the structural stability and signal communication are not completely clear. Here we utilize the Gaussian network model (GNM) and amino acid network (AAN) combined with perturbation methods to explore the issues. The constructed fcfGNM_MD, where the force constants are optimized with the inverse covariance estimation based on the correlated fluctuations from the available DOP molecular dynamics (MD) ensemble, shows a better performance than traditional GNM in reproducing residue fluctuations and cross-correlations and in capturing functionally low-frequency modes. Additionally, fcfGNM_MD can consider implicitly the environmental effects to some extent. The lowest mode can well divide DOP segments and identify the two sodium ion (important allosteric regulator) binding coordination shells, and from the fastest modes, the key residues important for structure stabilization are identified. Using fcfGNM_MD combined with a dynamic perturbation-response method, we explore the key residues related to the sodium ion binding. Interestingly, we identify not only the key residues in sodium ion binding shells but also the ones far away from the perturbation sites, which are involved in binding with DOP ligands, suggesting the possible long-range allosteric modulation of sodium binding for the ligand binding to DOP. Furthermore, utilizing the weighted AAN combined with attack perturbations, we identify the key residues for allosteric communication. This work helps strengthen the understanding of the allosteric communication mechanism in δ opioid receptor and can provide valuable information for drug design.

Dynamics of binding interactions of TDP-43 and RNA: An equally weighted multiscale elastic network model study

Transactive response DNA binding protein 43 (TDP-43), an alternative-splicing regulator, can specifically bind long UG-rich RNAs, associated with a range of neurodegenerative diseases. Upon binding RNA, TDP-43 undergoes a large conformational change with two RNA recognition motifs (RRMs) connected by a long linker rearranged, strengthening the binding affinity of TDP-43 with RNA. We extend the equally weighted multiscale elastic network model (ewmENM), including its Gaussian network model (ewmGNM) and Anisotropic network model (ewmANM), with the multiscale effect of interactions considered, to the characterization of the dynamics of binding interactions of TDP-43 and RNA. The results reveal upon RNA binding a loss of flexibility occurs to TDP-43's loop3 segments rich in positively charged residues and C-terminal of high flexibility, suggesting their anchoring RNA, induced fit and conformational adjustment roles in recognizing RNA. Additionally, based on movement coupling analyses, it is found that RNA binding strengthens the interactions among intra-RRM β-sheets and between RRMs partially through the linker's mediating role, which stabilizes RNA binding interface, facilitating RNA binding efficiency. In addition, utilizing our proposed thermodynamic cycle method combined with ewmGNM, we identify the key residues for RNA binding whose perturbations induce a large change in binding free energy. We identify not only the residues important for specific binding, but also the ones critical for the conformational rearrangement between RRMs. Furthermore, molecular dynamics simulations are also performed to validate and further interpret the ENM-based results. The study demonstrates a useful avenue to utilize ewmENM to investigate the protein-RNA interaction dynamics characteristics.

"Dividing and Conquering" and "Caching" in Molecular Modeling

Molecular modeling is widely utilized in subjects including but not limited to physics, chemistry, biology, materials science and engineering. Impressive progress has been made in development of theories, algorithms and software packages. To divide and conquer, and to cache intermediate results have been long standing principles in development of algorithms. Not surprisingly, most important methodological advancements in more than half century of molecular modeling are various implementations of these two fundamental principles. In the mainstream classical computational molecular science, tremendous efforts have been invested on two lines of algorithm development. The first is coarse graining, which is to represent multiple basic particles in higher resolution modeling as a single larger and softer particle in lower resolution counterpart, with resulting force fields of partial transferability at the expense of some information loss. The second is enhanced sampling, which realizes "dividing and conquering" and/or "caching" in configurational space with focus either on reaction coordinates and collective variables as in metadynamics and related algorithms, or on the transition matrix and state discretization as in Markov state models. For this line of algorithms, spatial resolution is maintained but results are not transferable. Deep learning has been utilized to realize more efficient and accurate ways of "dividing and conquering" and "caching" along these two lines of algorithmic research. We proposed and demonstrated the local free energy landscape approach, a new framework for classical computational molecular science. This framework is based on a third class of algorithm that facilitates molecular modeling through partially transferable in resolution "caching" of distributions for local clusters of molecular degrees of freedom. Differences, connections and potential interactions among these three algorithmic directions are discussed, with the hope to stimulate development of more elegant, efficient and reliable formulations and algorithms for "dividing and conquering" and "caching" in complex molecular systems.

Insight into Shared Properties and Differential Dynamics and Specificity of Secretory Phospholipase A2 Family Members

Understanding generic mechanisms of functions shared by the secretory phospholipase A₂ (sPLA₂) family involved in the lipid metabolism and cell signaling and the molecular basis of function specificity for family members is an intriguing but challenging problem for biologists. Here, we explore the issue through extensive analyses using a combination of structure-based methods and bioinformatics tools on130 sPLA₂ family members. The principal component analysis of the structure ensemble reveals that the enzyme has an open-close motion which helps widen the substrate binding channel, facilitating its binding to phospholipid. Performing elastic network model and sequence analyses found that the residues critical for family functions, such as cysteine and catalytic residues, are highly conserved and undergo minimal movements, which is evolutionarily essential as their perturbation would impact the function, while the four residue regions involved in the association with the calcium ion/membrane are lowly conserved and of high mobility and large variations in low-to-intermediate frequency modes, which reflects the specificity of members. The analyses from perturbation response scanning also reveal that the above four regions with high sensitivity to an external perturbation are member-specific, suggesting their different roles in allosteric modulation, while the minimal sensitive residues are the shared characteristics across family members, which play an important role in maintaining structural stability as the folding core. This study is helpful for understanding how sequences, structures, and dynamics of sPLA₂ family members evolve to ensure their common and specific functions and can provide a guide for accurate design of proteins with finely tuned activities.