Competitive Chemical Reaction Kinetic Model of Nucleosome Assembly Using the Histone Variant H2A.Z and H2A In Vitro.

Nucleosomes not only serve as the basic building blocks for eukaryotic chromatin but also regulate many biological processes, such as DNA replication, repair, and recombination. To modulate gene expression in vivo, the histone variant H2A.Z can be dynamically incorporated into the nucleosome. However, the assembly dynamics of H2A.Z-containing nucleosomes remain elusive. Here, we demonstrate that our previous chemical kinetic model for nucleosome assembly can be extended to H2A.Z-containing nucleosome assembly processes. The efficiency of H2A.Z-containing nucleosome assembly, like that of canonical nucleosome assembly, was also positively correlated with the total histone octamer concentration, reaction rate constant, and reaction time. We expanded the kinetic model to represent the competitive dynamics of H2A and H2A.Z in nucleosome assembly, thus providing a novel method through which to assess the competitive ability of histones to assemble nucleosomes. Based on this model, we confirmed that histone H2A has a higher competitive ability to assemble nucleosomes in vitro than histone H2A.Z. Our competitive kinetic model and experimental results also confirmed that in vitro H2A.Z-containing nucleosome assembly is governed by chemical kinetic principles.

Intrinsic laws of k-mer spectra of genome sequences and evolution mechanism of genomes.

BACKGROUND: K-mer spectra of DNA sequences contain important information about sequence composition and sequence evolution. We want to reveal the evolution rules of genome sequences by studying the k-mer spectra of genome sequences. RESULTS: The intrinsic laws of k-mer spectra of 920 genome sequences from primate to prokaryote were analyzed. We found that there are two types of evolution selection modes in genome sequences, named as CG Independent Selection and TA Independent Selection. There is a mutual inhibition relationship between CG and TA independent selections. We found that the intensity of CG and TA independent selections correlates closely with genome evolution and G + C content of genome sequences. The living habits of species are related closely to the independent selection modes adopted by species genomes. Consequently, we proposed an evolution mechanism of genomes in which the genome evolution is determined by the intensities of the CG and TA independent selections and the mutual inhibition relationship. Besides, by the evolution mechanism of genomes, we speculated the evolution modes of prokaryotes in mild and extreme environments in the anaerobic age and the evolving process of prokaryotes from anaerobic to aerobic environment on earth as well as the originations of different eukaryotes. CONCLUSION: We found that there are two independent selection modes in genome sequences. The evolution of genome sequence is determined by the two independent selection modes and the mutual inhibition relationship between them.

Prediction of protein secondary structure using feature selection and analysis approach.

The prediction of the secondary structure of a protein from its amino acid sequence is an important step towards the prediction of its three-dimensional structure. However, the accuracy of ab initio secondary structure prediction from sequence is about 80% currently, which is still far from satisfactory. In this study, we proposed a novel method that uses binomial distribution to optimize tetrapeptide structural words and increment of diversity with quadratic discriminant to perform prediction for protein three-state secondary structure. A benchmark dataset including 2,640 proteins with sequence identity of less than 25% was used to train and test the proposed method. The results indicate that overall accuracy of 87.8% was achieved in secondary structure prediction by using ten-fold cross-validation. Moreover, the accuracy of predicted secondary structures ranges from 84 to 89% at the level of residue. These results suggest that the feature selection technique can detect the optimized tetrapeptide structural words which affect the accuracy of predicted secondary structures.

An evolutionary theory on virus mutation in COVID-19.

With the rapid evolution of SARS-CoV-2, the emergence of new strains is an intriguing question. This paper presents an evolutionary theory to analyze the mutations of the virus and identify the conditions that lead to the generation of new strains. We represent the virus variants using a 4-letter sequence based on amino acid mutations on the spike protein and employ an n-distance algorithm to derive a variant phylogenetic tree. We show that the theoretically-derived tree aligns with experimental data on virus evolution. Additionally, we propose an A-X model, utilizing the set of existing mutation sites (A) and a set of randomly generated sites (X), to calculate the emergence of new strains. Our findings demonstrate that a sufficient number of random iterations can predict the generation of new macro-lineages when the number of sites in X is large enough. These results provide a crucial theoretical basis for understanding the evolution of SARS-CoV-2.

Measurement of entropy production in living cells under an alternating electric field.

Entropy is a thermodynamic property toward equilibrium based on the dissipation of energy. Cells constitute such a thermodynamic system, in which entropy production is both inevitable and highly significant. Although the experimental measurement of entropy production in a cell is very difficult, a new method to accomplish this in living cells is reported herein. Through heating the sample by alternating electric fields and recording the heat flow from cells, the entropy production in two normal cell lines, MCF10A and HL-7702, and two cancerous cell lines, MDA-MB-231 and SMMC-7721, was measured and compared. The scaled electroinduced entropy production rate (SEEP) of cancer cells monotonically increases with electric field strength at 5-40 V/cm, while that of normal cells changes nonmonotonically with electric field strength, reaching a peak at 5-30 V/cm. For all cell lines, the cancerous-to-normal ratio of field-induced entropy production is clearly <1 in a large range of field strength from 5 to 25 V/cm. Therefore, this work presents an easy and effective strategy for experimentally investigating the thermodynamic properties of the cell, and gives deeper insight into the physical differences between normal and cancerous cells exposed to electric fields.

Mathematical Modelling of Virus Spreading in COVID-19.

A mathematical model is proposed to analyze the spreading dynamics of COVID-19. By using the parameters of the model, namely the basic reproduction number (R0) and the attenuation constant (k), the daily number of infections (DNI) and the cumulative number of infections (CNI) over time (m) are deduced and shown to be in good agreement with experimental data. This model effectively addresses three key issues: (1) inferring the conditions under which virus infections die out for a specific strain given R0; (2) explaining the occurrence of second waves of infection and developing preventive measures; and (3) understanding the competitive spread of two viruses within a region and devising control strategies. The findings highlight the potential of this simple mathematical framework in comprehensively addressing these challenges. The theoretical insights derived from this model can guide the evaluation of infection wave severity and the formulation of effective strategies for controlling and mitigating epidemic outbreaks.

Characterizing Cellular Differentiation Potency and Waddington Landscape via Energy Indicator.

The precise characterization of cellular differentiation potency remains an open question, which is fundamentally important for deciphering the dynamics mechanism related to cell fate transition. We quantitatively evaluated the differentiation potency of different stem cells based on the Hopfield neural network (HNN). The results emphasized that cellular differentiation potency can be approximated by Hopfield energy values. We then profiled the Waddington energy landscape of embryogenesis and cell reprogramming processes. The energy landscape at single-cell resolution further confirmed that cell fate decision is progressively specified in a continuous process. Moreover, the transition of cells from one steady state to another in embryogenesis and cell reprogramming processes was dynamically simulated on the energy ladder. These two processes can be metaphorized as the motion of descending and ascending ladders, respectively. We further deciphered the dynamics of the gene regulatory network (GRN) for driving cell fate transition. Our study proposes a new energy indicator to quantitatively characterize cellular differentiation potency without prior knowledge, facilitating the further exploration of the potential mechanism of cellular plasticity.

The organization of nucleosomes around splice sites.

The occupancy of nucleosomes along chromosome is a key factor for gene regulation. However, except promoter regions, genome-wide properties and functions of nucleosome organization remain unclear in mammalian genomes. Using the computational model of Increment of Diversity with Quadratic Discriminant (IDQD) trained from the microarray data, the nucleosome occupancy score (NOScore) was defined and applied to splice junction regions of constitutive, cassette exon, alternative 3' and 5' splicing events in the human genome. We found an interesting relation between NOScore and RNA splicing: exon regions have higher NOScores compared with their flanking intron sequences in both constitutive and alternative splicing events, indicating the stronger nucleosome occupation potential of exon regions. In addition, NOScore valleys present at approximately 25 bp upstream of the acceptor site in all splicing events. By defining folding diversity-to-energy ratio to describe RNA structural flexibility, we demonstrated that primary RNA transcripts from nucleosome occupancy regions are relatively rigid and those from nucleosome depleted regions are relatively flexible. The negative correlation between nucleosome occupation/depletion of DNA sequence and structural flexibility/rigidity of its primary transcript around splice junctions may provide clues to the deeper understanding of the unexpected role for nucleosome organization in the regulation of RNA splicing.

Nucleosome Assembly and Disassembly in vitro Are Governed by Chemical Kinetic Principles.

As the elementary unit of eukaryotic chromatin, nucleosomes in vivo are highly dynamic in many biological processes, such as DNA replication, repair, recombination, or transcription, to allow the necessary factors to gain access to their substrate. The dynamic mechanism of nucleosome assembly and disassembly has not been well described thus far. We proposed a chemical kinetic model of nucleosome assembly and disassembly in vitro. In the model, the efficiency of nucleosome assembly was positively correlated with the total concentration of histone octamer, reaction rate constant and reaction time. All the corollaries of the model were well verified for the Widom 601 sequence and the six artificially synthesized DNA sequences, named CS1-CS6, by using the salt dialysis method in vitro. The reaction rate constant in the model may be used as a new parameter to evaluate the nucleosome reconstitution ability with DNAs. Nucleosome disassembly experiments for the Widom 601 sequence detected by Förster resonance energy transfer (FRET) and fluorescence thermal shift (FTS) assays demonstrated that nucleosome disassembly is the inverse process of assembly and can be described as three distinct stages: opening phase of the (H2A-H2B) dimer/(H3-H4)2 tetramer interface, release phase of the H2A-H2B dimers from (H3-H4)2 tetramer/DNA and removal phase of the (H3-H4)2 tetramer from DNA. Our kinetic model of nucleosome assembly and disassembly allows to confirm that nucleosome assembly and disassembly in vitro are governed by chemical kinetic principles.

Prediction of cell wall lytic enzymes using Chou's amphiphilic pseudo amino acid composition.

Discriminating cell wall lytic enzymes from non lytic enzymes is a very important task for curing bacterial infections. In this paper, based on Chou's amphiphilic pseudo amino acid composition, we develop fisher-discriminant based classifier to predict cell wall lytic enzymes. Experiments show that 66.7% sensitivity with 88.6% specificity is obtained. The method is further able to predict endolysin and autolysin with an overall accuracy of 92.9%. Results demonstrated that our method can provide highly useful information for further bacterial control research.

Using estimative reaction free energy to predict splice sites and their flanking competitors.

A crucial part in the gene structure prediction is to identify the accurate splice sites, not only constitutive but also alternative ones. Here, we use the maximum information principle (MIP) to analyze the conservative segments around splice sites. According to the MIP, a reaction free energy (RFE) expression is deduced, which can be employed to estimate the free energy change during splicing reaction involving a donor or acceptor site. The expression contains not only the background probability factors, but also all kinds of dependencies among both adjacent and non-adjacent bases. We apply the RFE expression to recognize splice sites and their flanking competitors in human genes, the results show high sensitivity and specificity, so the RFE expression accords well with the splicing reaction process. Moreover, the RFE expression is better than previous methods for predicting competitors of splice sites, and it outperforms the reaction free energy subtraction (RFES), that implies RFE competition between a given splice site and its flanking competitor may not be an only primary factor for alternative splice site selection. The work is helpful to not only the understanding of splicing reaction from its relation to MIP, but also the research on computational recognition of splicing sites and alternative splice events.

Use of tetrapeptide signals for protein secondary-structure prediction.

This paper develops a novel sequence-based method, tetra-peptide-based increment of diversity with quadratic discriminant analysis (TPIDQD for short), for protein secondary-structure prediction. The proposed TPIDQD method is based on tetra-peptide signals and is used to predict the structure of the central residue of a sequence fragment. The three-state overall per-residue accuracy (Q (3)) is about 80% in the threefold cross-validated test for 21-residue fragments in the CB513 dataset. The accuracy can be further improved by taking long-range sequence information (fragments of more than 21 residues) into account in prediction. The results show the tetra-peptide signals can indeed reflect some relationship between an amino acid's sequence and its secondary structure, indicating the importance of tetra-peptide signals as the protein folding code in the protein structure prediction.

Distance conservation of transcription regulatory motifs in human promoters.

To understanding the interaction network among transcription-regulation elements in human is an immediate challenge for modern molecular biology. Here a central problem is how to extract evolutionary information and search the evolutionary conservation from the comparison of promoters of closely related species. Through the comparative studies of k-mer distribution in human and mouse transcription factor binding site (TFBS) sequences we have discovered that the average distance between a pair of transcription regulatory 7-mer motifs is conservative in human-mouse promoters. The distance conservation is a new kind of evolutionary conservation, not based on the strict location of bases in genome sequence. By utilizing the conservation of k-mer distance it will be helpful to propose a non-alignment-based approach for fast genome-wide discovery of transcription regulatory motifs. We demonstrated the distance conservation by genome-wide searching of conservative regulatory 7-mer motifs with successful rate 90%. Then, after defining human-mouse pair-distance divergence parameter we studied the tissue-specific motif pairs and found that the parameter for motif pairs is 11-16 times smaller than for their controls for 28 tissues and these pairs can be clearly differentiated on two-dimensional parameter plane. Finally, the mechanism of distance conservation was discussed briefly which is supposed to be related to the module structure of TFBSs.

Splice site prediction with quadratic discriminant analysis using diversity measure.

Based on the conservation of nucleotides at splicing sites and the features of base composition and base correlation around these sites we use the method of increment of diversity combined with quadratic discriminant analysis (IDQD) to study the dependence structure of splicing sites and predict the exons/introns and their boundaries for four model genomes: Caenorhabditis elegans, Arabidopsis thaliana, Drosophila melanogaster and human. The comparison of compositional features between two sequences and the comparison of base dependencies at adjacent or non-adjacent positions of two sequences can be integrated automatically in the increment of diversity (ID). Eight feature variables around a potential splice site are defined in terms of ID. They are integrated in a single formal framework given by IDQD. In our calculations 7 (8) base region around the donor (acceptor) sites have been considered in studying the conservation of nucleotides and sequences of 48 bp on either side of splice sites have been used in studying the compositional and base-correlating features. The windows are enlarged to 16 (donor), 29 (acceptor) and 80 bp (either side) to improve the prediction for human splice sites. The prediction capability of the present method is comparable with the leading splice site detector--GeneSplicer.

Shannon information in complete genomes.

Shannon information in the genomes of all completely sequenced prokaryotes and eukaryotes are measured in word lengths of two to ten letters. It is found that in a scale-dependent way, the Shannon information in complete genomes are much greater than that in matching random sequences--thousands of times greater in the case of short words. Furthermore, with the exception of the 14 chromosomes of Plasmodium falciparum, the Shannon information in all available complete genomes belong to a universality class given by an extremely simple formula. The data are consistent with a model for genome growth composed of two main ingredients: random segmental duplications that increase the Shannon information in a scale-independent way, and random point mutations that preferentially reduces the larger-scale Shannon information. The inference drawn from the present study is that the large-scale and coarse-grained growth of genomes was selectively neutral and this suggests an independent corroboration of Kimura's neutral theory of evolution.

Sequence-dependent flexibility in promoter sequences.

The non-neighbor interactions between base-pairs were taken into account to calculate the angular parameters (Omega, rho and tau) describing the orientation of successive base-pair planes and the translation parameters (D(y)) along the long axis of base-pair steps for 36 independent tetramers. A statistical mechanical model was proposed to predict the DNA flexibility that is mainly related to the thermal fluctuations at individual base-pair steps. The DNA flexibility can be described by the root-mean-square deviation of the end-to-end distance of DNA helical structure. The present model was then used to investigate the extreme flexible pattern in prokaryotic and eukaryotic promoter sequences. The results demonstrated several extreme flexible regions related to functionally important elements exist both in prokaryotic promoters and in eukaryotic promoters, DNA flexibility and AT content are highly correlated. The probabilities finding flexibility pattern in promoter sequences were also estimated statistically. The biological implications were discussed briefly.

Construction of genetic code from evolutionary stability.

The construction of the genetic code is investigated based on a stability principle. The concept and formulation of mutational deterioration (MD) of the genetic code is proposed. It is proved that the degeneracies of codon multiplets obey the rule to best resist MD. The MD for each ideal multiplet of codons is expressed by four parameters and it takes on a minimum value for real distributions of codons in the multiplet. Then the global mutational deterioration (GMD) of code table is calculated and the minimal code is deduced. The domain-like distribution of hydrophobic and hydrophilic amino acids on the genetic code is explained from the minimization of GMD. It is demonstrated that the standard code is approximately GMD-minimal. By introducing some constraints that are related to the initial condition of the system, we have deduced the standard genetic code from the minimization of GMD. The minimization shows the general trend of evolutionary process to some stable state while the constraints reflect a 'frozen accident.' Many deviant codon assignments are also explained through MD minimization assuming the changeable degrees of degeneracies for some multiplets. So, a possible answer to the question of "Why are synonymous codons and amino acids distributed in the code table just as they are?" is given.

[The relation between translation speed and protein secondary structure].

Based on the statistical analysis of 119 human and 92 E. coli proteins it was found that for both human and E. coli, the mRNA sequences consisting of tri-codon and tetra-codon with high translation speed preferably code for alpha helices more than for coils. For beta strand, the preference/avoidance oscillates with the translation speed. Moreover, the non-homogeneous usages of tri-codon and tetra-codon with different translation speeds in a given secondary structure have also been found. These results cannot be simply explained by the effect of stochastic fluctuation.

Statistical analyses of protein folding rates from the view of quantum transition.

Understanding protein folding rate is the primary key to unlock the fundamental physics underlying protein structure and its folding mechanism. Especially, the temperature dependence of the folding rate remains unsolved in the literature. Starting from the assumption that protein folding is an event of quantum transition between molecular conformations, we calculated the folding rate for all two-state proteins in a database and studied their temperature dependencies. The non-Arrhenius temperature relation for 16 proteins, whose experimental data had previously been available, was successfully interpreted by comparing the Arrhenius plot with the first-principle calculation. A statistical formula for the prediction of two-state protein folding rate was proposed based on quantum folding theory. The statistical comparisons of the folding rates for 65 two-state proteins were carried out, and the theoretical vs. experimental correlation coefficient was 0.73. Moreover, the maximum and the minimum folding rates given by the theory were consistent with the experimental results.