Interpreting the Dynamics of Binding Interactions of snRNA and U1A Using a Coarse-Grained Model

Selected humanization of yeast U1 snRNP leads to global suppression of pre-mRNA splicing and mitochondrial dysfunction in the budding yeast

The recognition of the 5' splice site (5' ss) is one of the earliest steps of pre-mRNA splicing. To better understand, the mechanism and regulation of 5' ss recognition, we selectively humanized components of the yeast U1 (yU1) snRNP to reveal the function of these components in 5' ss recognition and splicing. We targeted U1C and Luc7, two proteins that interact with and stabilize the yU1 snRNA and the 5' ss RNA duplex. We replaced the zinc-finger (ZnF) domain of yeast U1C (yU1C) with its human counterpart, which resulted in a cold-sensitive growth phenotype and moderate splicing defects. We next added an auxin-inducible degron to yeast Luc7 (yLuc7) protein (to mimic the lack of Luc7Ls in human U1 snRNP). We found that Luc7-depleted yU1 snRNP resulted in the concomitant loss of Prp40 and Snu71 (two other essential yU1 snRNP proteins), and further biochemical analyses suggest a model of how these three proteins interact with each other in the U1 snRNP. The loss of these proteins resulted in a significant growth retardation accompanied by a global suppression of pre-mRNA splicing. The splicing suppression led to mitochondrial dysfunction as revealed by a release of Fe2+ into the growth medium and an induction of mitochondrial reactive oxygen species. Together, these observations indicate that the human U1C ZnF can substitute that of yeast, Luc7 is essential for the incorporation of the Luc7-Prp40-Snu71 trimer into yU1 snRNP, and splicing plays a major role in the regulation of mitochondrial function in yeast.

Insights into Activation Dynamics and Functional Sites of Inwardly Rectifying Potassium Channel Kir3.2 by an Elastic Network Model Combined with Perturbation Methods

The inwardly rectifying potassium channel Kir3.2, a member of the inward rectifier potassium (Kir) channel family, exerts important biological functions through transporting potassium ions outside of the cell, during which a large-scale synergistic movement occurs among its different domains. Currently, it is not fully understood how the binding of the ligand to the Kir3.2 channel leads to the structural changes and which key residues are responsible for the channel gating and allosteric dynamics. Here, we construct the Gaussian network model (GNM) of the Kir3.2 channel with the secondary structure and covalent interaction information considered (sscGNM), which shows a better performance in reproducing the channel's flexibility compared with the traditional GNM. In addition, the sscANM-based perturbation method is used to simulate the channel's conformational transition caused by the activator PIP2's binding. By applying certain forces to the PIP2 binding pocket, the coarse-grained calculations generate the similar conformational changes to the experimental observation, suggesting that the topology structure as well as PIP2 binding are crucial to the allosteric activation of the Kir3.2 channel. We also utilize the sscGNM-based thermodynamic cycle method developed by us to identify the key residues whose mutations significantly alter the channel's binding free energy with PIP2. We identify not only the residues important for the specific binding but also the ones critical for the allosteric transition coupled with PIP2 binding. This study is helpful for understanding the working mechanism of Kir3.2 channels and can provide important information for related drug design.

Selected humanization of yeast U1 snRNP leads to global suppression of pre-mRNA splicing and mitochondrial dysfunction in the budding yeast

The recognition of 5' splice site (5' ss) is one of the earliest steps of pre-mRNA splicing. To better understand the mechanism and regulation of 5' ss recognition, we selectively humanized components of the yeast U1 snRNP to reveal the function of these components in 5' ss recognition and splicing. We targeted U1C and Luc7, two proteins that interact with and stabilize the yeast U1 (yU1) snRNA and the 5' ss RNA duplex. We replaced the Zinc-Finger (ZnF) domain of yU1C with its human counterpart, which resulted in cold-sensitive growth phenotype and moderate splicing defects. Next, we added an auxin-inducible degron to yLuc7 protein and found that Luc7-depleted yU1 snRNP resulted in the concomitant loss of PRP40 and Snu71 (two other essential yeast U1 snRNP proteins), and further biochemical analyses suggest a model of how these three proteins interact with each other in the U1 snRNP. The loss of these proteins resulted in a significant growth retardation accompanied by a global suppression of pre-mRNA splicing. The splicing suppression led to mitochondrial dysfunction as revealed by a release of Fe 2+ into the growth medium and an induction of mitochondrial reactive oxygen species. Together, these observations indicate that the human U1C ZnF can substitute that of yeast, Luc7 is essential for the incorporation of the Luc7-Prp40-Snu71 trimer into yeast U1 snRNP, and splicing plays a major role in the regulation of mitochondria function in yeast.

Internal Normal Mode Analysis Applied to RNA Flexibility and Conformational Changes

We investigated the capability of internal normal modes to reproduce RNA flexibility and predict observed RNA conformational changes and, notably, those induced by the formation of RNA-protein and RNA-ligand complexes. Here, we extended our iNMA approach developed for proteins to study RNA molecules using a simplified representation of the RNA structure and its potential energy. Three data sets were also created to investigate different aspects. Despite all the approximations, our study shows that iNMA is a suitable method to take into account RNA flexibility and describe its conformational changes opening the route to its applicability in any integrative approach where these properties are crucial.

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.

Allosteric mechanism for SL RNA recognition by polypyrimidine tract binding protein RRM1: An atomistic MD simulation and network-based study

Polypyrimidine tract-binding protein (PTB), an RNA-binding protein, is involved in the regulation of diverse processes in mRNA metabolism. However, the allosteric modulation of its binding with RNA remains unclear. We explore the dynamic characteristics of PTB RNA recognition motif 1 (RRM1) in its RNA-free and wild-type/mutant RNA-bound states to understand the issues using molecular dynamics (MD) simulation, perturbation response scanning (PRS) and protein structure network (PSN) models. It is found that RNA binding strengthens RRM1 stability, while L151G mutation in α3 helix far away from the interface makes the complex unstable. The latter is caused by long-distance dynamic couplings, which makes intermolecular electrostatic and entropy energies unfavorable. The weakened couplings between interface β sheets and C-terminal parts upon mutation reveal RNA recognition is co-regulated by these regions. Interestingly, PRS analysis reveals the allostery caused by the perturbation on α3 helix has already been pre-encoded in the equilibrium dynamics of the protein structure. PSN analysis shows the details of the allosteric signal transmission, revealing the necessity of strong couplings between α3 helix and interface for maintaining the high binding affinity. This study sheds light on the mechanisms of PTB allostery and RNA recognition and can provide important information for drug design.

aPRBind: protein-RNA interface prediction by combining sequence and I-TASSER model-based structural features learned with convolutional neural networks

MOTIVATION

Protein-RNA interactions play a critical role in various biological processes. The accurate prediction of RNA-binding residues in proteins has been one of the most challenging and intriguing problems in the field of computational biology. The existing methods still have a relatively low accuracy especially for the sequence-based ab-initio methods.

RESULTS

In this work, we propose an approach aPRBind, a convolutional neural network-based ab-initio method for RNA-binding residue prediction. aPRBind is trained with sequence features and structural ones (particularly including residue dynamics information and residue-nucleotide propensity developed by us) that are extracted from the predicted structures by I-TASSER. The analysis of feature contributions indicates the sequence features are most important, followed by dynamics information, and the sequence and structural features are complementary in binding site prediction. The performance comparison of our method with other peer ones on benchmark dataset shows that aPRBind outperforms some state-of-the-art ab-initio methods. Additionally, aPRBind can give a better prediction for the modeled structures with TM-score≥0.5, and meanwhile since the structural features are not very sensitive to the refined 3D structures, aPRBind has only a marginal dependence on the accuracy of the structure model, which allows aPRBind to be applied to the RNA-binding site prediction for the modeled or unbound structures.

AVAILABILITY AND IMPLEMENTATION

The source code is available at https://github.com/ChunhuaLiLab/aPRbind.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

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.

Study on functional sites in human multidrug resistance protein 1 (hMRP1)

Human multidrug resistance protein 1 (hMRP1) is an important member of the ATP-binding cassette (ABC) transporter superfamily. It can extrude a variety of anticancer drugs and physiological organic anions across the plasma membrane, which is activated by substrate binding, and is accompanied by large-scale cooperative movements between different domains. Currently, it remains unclear completely about how the specific interactions between hMRP1 and its substrate are and which critical residues are responsible for allosteric signal transduction. To the end, we first construct an inward-facing state of hMRP1 using homology modeling method, and then dock substrate proinflammatory agent leukotriene C4 (LTC4) to hMRP1 pocket. The result manifests LTC4 interacts with two parts of hMRP1 pocket, namely the positively charged pocket (P pocket) and hydrophobic pocket (H pocket), similar to its binding mode with bMRP1 (bovine MRP1). Additionally, we use the Gaussian network model (GNM)-based thermodynamic method proposed by us to identify the key residues whose perturbations markedly alter their binding free energy. Here the conventional GNM is improved with covalent/non-covalent interactions and secondary structure information considered (denoted as sscGNM). In the result, sscGNM improves the flexibility prediction, especially for the nucleotide binding domains with rich kinds of secondary structures. The 46 key residue clusters located in different subdomains are identified which are highly consistent with experimental observations. Furtherly, we explore the long-range cooperation within the transporter. This study is helpful for strengthening the understanding of the work mechanism in ABC exporters and can provide important information to scientists in drug design studies.

Equally Weighted Multiscale Elastic Network Model and Its Comparison with Traditional and Parameter-Free Models

Dynamical properties of proteins play an essential role in their function exertion. The elastic network model (ENM) is an effective and efficient tool in characterizing the intrinsic dynamical properties encoded in biomacromolecule structures. The Gaussian network model (GNM) and anisotropic network model (ANM) are the two often-used ENM models. Here, we introduce an equally weighted multiscale ENM (equally weighted mENM) based on the original mENM (denoted as mENM), in which fitting weights of Kirchhoff/Hessian matrixes in mENM are removed since they neglect the details of pairwise interactions. Then, we perform its comparison with the mENM, traditional ENM, and parameter-free ENM (pfENM) in reproducing dynamical properties for the six representative proteins whose molecular dynamics (MD) trajectories are available in http://mmb.pcb.ub.es/MoDEL/. In the results, for B-factor prediction, mENM performs best, while the equally weighted mENM performs also well, better than the traditional ENM and pfENM models. As to the dynamical cross-correlation map calculation, mENM performs worst, while the results produced from the equally weighted mENM and pfENM models are close to those from MD trajectories with the latter a little better than the former. Furthermore, encouragingly, the equally weighted mANM displays the best performance in capturing the functional motional modes, followed by pfANM and traditional ANM models, while the mANM fails in all the cases. This work is helpful for strengthening the understanding of the elastic network model and provides a valuable guide for researchers to utilize the model to explore protein dynamics.

NCBP3: A Multifaceted Adaptive Regulator of Gene Expression

Eukaryotic cells have divided the steps of gene expression between their nucleus and cytoplasm. Protein-encoding genes generate mRNAs in the nucleus and mRNAs undergo transport to the cytoplasm for the purpose of producing proteins. Cap-binding protein (CBP)20 and its binding partner CBP80 have been thought to constitute the cap-binding complex (CBC) that is acquired co-transcriptionally by the precursors of all mRNAs. However, this principle has recently been challenged by studies of nuclear cap-binding protein 3 (NCBP3). Here we submit how NCBP3, as an alternative to CBP20, an accessory to the canonical CBP20-CBP80 CBC, and/or an RNA-binding protein - possibly in association with the exon-junction complex (EJC) - expands the capacity of cells to regulate gene expression.

A study on allosteric communication in U1A-snRNA binding interactions: network analysis combined with molecular dynamics data

The allosteric regulation during the binding interactions between small nuclear RNAs (snRNAs) and the associated protein factors is critical to the function of spliceosomes in alternative RNA splicing. Although network models combined with molecular dynamics simulations have shown to be powerful tools for the analysis of protein allostery, the atomic-level simulations are, however, too expensive and with limited accuracy for the large-size systems. In this work, we use a residual network model combined with a coarse-grained Gaussian network model (GNM) to investigate the binding interactions between the snRNA and the human U1A protein which is a major component of the spliceosomal U1 small nuclear ribonucleoprotein particle, and to identify the residues that play an important role in the allosteric communication in U1A during this process. We also utilize the Girvan-Newman method to detect the structural organization in U1A-snRNA recognition and interactions. Our results reveal that: (Ι) not only the residues at the binding sites that are traditionally considered to play a major role in U1A-snRNA association, but those residues that are far away from the RNA binding interface participate in the U1A's allosteric signal transmission induced by the RNA binding; (Π) the structure of U1A protein is well organized with different communities acting different roles for its RNA binding and allosteric regulation. The study demonstrates that the combination of the residual network and elastic network models is an effective and efficient method which can be readily extended to the investigation of the allosteric communication for other macromolecular interaction systems.