Function analyses of genes with and without accumulation of non-optimal codons.

<p><b>(a)</b> Proportion of differentially expressed genes in cancers. The red box represents the proportion of differentially expressed genes in the genes with accumulation of non-optimal codons, the blue box represents the proportion of the proportion of differentially expressed genes in genes without accumulation of non-optimal codons. The p-values were estimated by <i>Chi-square</i>, <i>two-tail test</i>. <b>(b)</b> Number of cancer types for differentially expressed genes. The red box represents the number of differentially expressed cancer types for the genes with accumulation of non-optimal codons. The p-values were estimated by <i>Mann–Whitney U</i>, <i>two-tail test</i>. <b>(c)</b> Proportion of proto-oncogenes and tumor-repressors in the genes without accumulation of non-optimal codons, and the genes with accumulation of non-optimal codons. The red box represents the proportion of annotated genes in the genes with accumulation of non-optimal codons, the blue box represents the proportion of the proportion of annotated genes in genes without accumulation of non-optimal codons. The p-values were estimated by <i>Chi-square</i>, <i>two-tail test</i>. (d) Functional enrichment analysis of genes with and without accumulation of non-optimal codons annotated with GO terms under molecular function. The red box represents the proportion of annotated genes in the genes with accumulation of non-optimal codons, the blue box represents the proportion of the proportion of annotated genes in genes without accumulation of non-optimal codons. The p-values were estimated by <i>Hypergeometric test</i> and <i>Benjamini</i> corrected. (e) Functional enrichment analysis of genes with and without accumulation of non-optimal codons annotated with GO terms under biological process. The red box represents the proportion of annotated genes in the genes with accumulation of non-optimal codons, the blue box represents the proportion of the proportion of annotated genes in genes without accumulation of non-optimal codons. The p-values were estimated by <i>Hypergeometric test</i> and <i>Benjamini</i> corrected.</p

Prevalent Accumulation of Non-Optimal Codons through Somatic Mutations in Human Cancers

<div><p>Cancer is characterized by uncontrolled cell growth, and the cause of different cancers is generally attributed to checkpoint dysregulation of cell proliferation and apoptosis. Recent studies have shown that non-optimal codons were preferentially adopted by genes to generate cell cycle-dependent oscillations in protein levels. This raises the intriguing question of how dynamic changes of codon usage modulate the cancer genome to cope with a non-controlled proliferative cell cycle. In this study, we comprehensively analyzed the somatic mutations of codons in human cancers, and found that non-optimal codons tended to be accumulated through both synonymous and non-synonymous mutations compared with other types of genomic substitution. We further demonstrated that non-optimal codons were prevalently accumulated across different types of cancers, amino acids, and chromosomes, and genes with accumulation of non-optimal codons tended to be involved in protein interaction/signaling networks and encoded important enzymes in metabolic networks that played roles in cancer-related pathways. This study provides insights into the dynamics of codons in the cancer genome and demonstrates that accumulation of non-optimal codons may be an adaptive strategy for cancerous cells to win the competition with normal cells. This deeper interpretation of the patterns and the functional characterization of somatic mutations of codons will help to broaden the current understanding of the molecular basis of cancers.</p></div

Schematic representation of the dual roles of O->N transformations during the tumorigenesis.

<p>Schematic representation of the dual roles of O->N transformations during the tumorigenesis.</p

The frequencies of O->N and N->O transformations.

<p>The p-values were estimated by <i>Chi-square</i>, <i>two-tail test</i>.</p

O->N transformations enriched in different cancers, amino acids and chromosomes.

<p>(<b>a</b>) Synonymous mutations in different types of cancers. (<b>b</b>) Non-synonymous mutations in different types of cancers. (<b>c</b>) Synonymous mutation for each amino acid. (<b>d</b>) Non-synonymous mutations for each amino acid. (<b>e</b>) Synonymous mutations in each human chromosomes. (<b>f</b>) Non-synonymous mutations in each human chromosome. The p-values were estimated by the comparison between CSM and Ortholog-Poly (<i>Chi-square</i>, <i>two-tail test</i>), ** p-values ≤ 0.01; * p-values between 0.01 and 0.05.</p

Bridging the Missing Link between Structure and Fidelity of the RNA-Dependent RNA Polymerase from Poliovirus through Free Energy Simulations

RNA-dependent RNA polymerases (RdRps) are enzymes catalyzing RNA replication from a RNA template. Active-site closure in RdRps, normally induced by correct nucleotide triphosphate (NTP) binding, is a prerequisite for the cycle of nucleotide incorporation. So, a complete understanding of polymerase function (in particular polymerase fidelity) of a RdRp requires more complete knowledge of active-site closure in the RdRp. In this work, based on solved crystal structures, we have built different models for the RNA-dependent RNA polymerase from poliovirus (termed PV 3D^pol). Through MD simulations and free energy calculations of these PV 3D^pol models, we have revealed the dynamic correlation between motif A and motif D and between motif A and incoming NTP, have deepened our understanding of polymerase fidelity from dynamic aspects, and have provided an explanation to the puzzle that arises from different observations based on kinetic studies and structural data

Genes with accumulation of non-optimal codons tend to be involved in high flux reactions in metabolic network.

<p>(<b>a</b>) Comparison of metabolic flux. N1 represents the number of reactions not catalyzed by genes with accumulation of non-optimal codons in Recon 2, and the N2 represents the number of reactions catalyzed by genes with accumulation of non-optimal codons in Recon 2. (<b>b</b>) Comparison of metabolic flux after filtering out null-flux. N1 represents the number of reactions not catalyzed by genes with accumulation of non-optimal codons in Recon 2 after filtering out null-flux, and the N2 represents the number of reactions catalyzed by genes with accumulation of non-optimal codons in Recon 2 after filtering out null-flux. (<b>c</b>) The largest sub-network of human enzyme-enzyme metabolic networks Red nodes represent the enzymes encoded by genes with accumulation of non-optimal codons. (<b>d</b>) Comparison of in-degree. N1 represents the number of enzymes encoded by genes without accumulation of non-optimal codons in enzyme-enzyme metabolic networks, and the N2 represents the number of enzymes encoded by genes with accumulation of non-optimal codons in enzyme-enzyme metabolic networks. (<b>e</b>) Comparison of out-degree. N1 represents the number of enzymes encoded by genes without accumulation of non-optimal codons in enzyme-enzyme metabolic networks, and the N2 represents the number of enzymes encoded by genes with accumulation of non-optimal codons in enzyme-enzyme metabolic networks. The average flux value and in/out-degree were represented. The p-values were estimated by comparisons between the genes without accumulation of non-optimal codons and the genes with accumulation of non-optimal codons (<i>Mann–Whitney U</i>, <i>two-tail test</i>).</p

Flexibility of Binding Site is Essential to the Ca²⁺ Selectivity in EF-Hand Calcium-Binding Proteins

High binding affinity and selectivity of metal ions are essential to the function of metalloproteins. Thus, understanding the factors that determine these binding characteristics is of major interest for both fundamental mechanistic investigations and guiding of the design of novel metalloproteins. In this work, we perform QM cluster model calculations and quantum mechanics/molecular mechanics (QM/MM) free energy simulations to understand the binding selectivity of Ca2+ and Mg2+ in the wild-type carp parvalbumin and its mutant. While a nonpolarizable MM model (CHARMM36) does not lead to the correct experimental trend, treatment of the metal binding site with the DFTB3 model in a QM/MM framework leads to relative binding free energies (ΔΔGbind) comparable with experimental data. For the wild-type (WT) protein, the calculated ΔΔGbind is ∼6.6 kcal/mol in comparison with the experimental value of 5.6 kcal/mol. The good agreement highlights the value of a QM description of the metal binding site and supports the role of electronic polarization and charge transfer to metal binding selectivity. For the D51A/E101D/F102W mutant, different binding site models lead to considerable variations in computed binding affinities. With a coordination number of seven for Ca2+, which is shown by QM/MM metadynamics simulations to be the dominant coordination number for the mutant, the calculated relative binding affinity is ∼4.8 kcal/mol, in fair agreement with the experimental value of 1.6 kcal/mol. The WT protein is observed to feature a flexible binding site that accommodates a range of coordination numbers for Ca2+, which is essential to the high binding selectivity for Ca2+ over Mg2+. In the mutant, the E101D mutation reduces the flexibility of the binding site and limits the dominant coordination number of Ca2+ to be seven, thereby leading to reduced binding selectivity against Mg2+. Our results highlight that the binding selectivity of metal ions depends on both the structural and dynamical properties of the protein binding site

Flexibility of Binding Site is Essential to the Ca²⁺ Selectivity in EF-Hand Calcium-Binding Proteins

High binding affinity and selectivity of metal ions are essential to the function of metalloproteins. Thus, understanding the factors that determine these binding characteristics is of major interest for both fundamental mechanistic investigations and guiding of the design of novel metalloproteins. In this work, we perform QM cluster model calculations and quantum mechanics/molecular mechanics (QM/MM) free energy simulations to understand the binding selectivity of Ca2+ and Mg2+ in the wild-type carp parvalbumin and its mutant. While a nonpolarizable MM model (CHARMM36) does not lead to the correct experimental trend, treatment of the metal binding site with the DFTB3 model in a QM/MM framework leads to relative binding free energies (ΔΔGbind) comparable with experimental data. For the wild-type (WT) protein, the calculated ΔΔGbind is ∼6.6 kcal/mol in comparison with the experimental value of 5.6 kcal/mol. The good agreement highlights the value of a QM description of the metal binding site and supports the role of electronic polarization and charge transfer to metal binding selectivity. For the D51A/E101D/F102W mutant, different binding site models lead to considerable variations in computed binding affinities. With a coordination number of seven for Ca2+, which is shown by QM/MM metadynamics simulations to be the dominant coordination number for the mutant, the calculated relative binding affinity is ∼4.8 kcal/mol, in fair agreement with the experimental value of 1.6 kcal/mol. The WT protein is observed to feature a flexible binding site that accommodates a range of coordination numbers for Ca2+, which is essential to the high binding selectivity for Ca2+ over Mg2+. In the mutant, the E101D mutation reduces the flexibility of the binding site and limits the dominant coordination number of Ca2+ to be seven, thereby leading to reduced binding selectivity against Mg2+. Our results highlight that the binding selectivity of metal ions depends on both the structural and dynamical properties of the protein binding site

Multiscale Simulations on Spectral Tuning and the Photoisomerization Mechanism in Fluorescent RNA Spinach

Fluorescent RNA aptamer Spinach can bind and activate a green fluorescent protein (GFP)-like chromophore (an anionic DFHBDI chromophore) displaying green fluorescence. Spectroscopic properties, spectral tuning, and the photoisomerization mechanism in the Spinach-DFHBDI complex have been investigated by high-level QM and hybrid ONIOM­(QM:AMBER) methods (QM method: (TD)­DFT, SF-BHHLYP, SAC-CI, LT-DF-LCC2, CASSCF, or MS-CASPT2), as well as classical molecular dynamics (MD) simulations. First, our benchmark calculations have shown that TD-DFT and spin-flip (SF) TD-DFT (SF-BHHLYP) failed to give a satisfactory description of absorption and emission of the anionic DFHBDI chromophore. Comparatively, SAC-CI, LT-DF-LCC2, and MS-CASPT2 can give more reliable transition energies and are mainly used to further study the spectra of the anionic DFHBDI chromophore in Spinach. The RNA environmental effects on the spectral tuning and the photoisomerization mechanism have been elucidated. Our simulations show that interactions of the anionic <i>cis</i>-DFHBDI chromophore with two G-quadruplexes as well as a UAU base triple suppress photoisomerization of DFHBDI. In addition, strong hydrogen bonds between the anionic <i>cis</i>-DFHBDI chromophore and nearby nucleotides facilitate its binding to Spinach and further inhibit the <i>cis</i>-<i>trans</i> photoisomerization of DFHBDI. Solvent molecules, ions, and loss of key hydrogen bonds with nearby nucleotides could induce dissociation of the anionic <i>trans</i>-DFHBDI chromophore from the binding site. These results provide new insights into fluorescent RNA Spinach and may help rational design of other fluorescent RNAs