Genomic inference of a severe human bottleneck during the Early to Middle Pleistocene transition.

Population size history is essential for studying human evolution. However, ancient population size history during the Pleistocene is notoriously difficult to unravel. In this study, we developed a fast infinitesimal time coalescent process (FitCoal) to circumvent this difficulty and calculated the composite likelihood for present-day human genomic sequences of 3154 individuals. Results showed that human ancestors went through a severe population bottleneck with about 1280 breeding individuals between around 930,000 and 813,000 years ago. The bottleneck lasted for about 117,000 years and brought human ancestors close to extinction. This bottleneck is congruent with a substantial chronological gap in the available African and Eurasian fossil record. Our results provide new insights into our ancestry and suggest a coincident speciation event.

Identification of and Mechanistic Insights into SARS-CoV-2 Main Protease Non-Covalent Inhibitors: An In-Silico Study.

The indispensable role of the SARS-CoV-2 main protease (Mpro) in the viral replication cycle and its dissimilarity to human proteases make Mpro a promising drug target. In order to identify the non-covalent Mpro inhibitors, we performed a comprehensive study using a combined computational strategy. We first screened the ZINC purchasable compound database using the pharmacophore model generated from the reference crystal structure of Mpro complexed with the inhibitor ML188. The hit compounds were then filtered by molecular docking and predicted parameters of drug-likeness and pharmacokinetics. The final molecular dynamics (MD) simulations identified three effective candidate inhibitors (ECIs) capable of maintaining binding within the substrate-binding cavity of Mpro. We further performed comparative analyses of the reference and effective complexes in terms of dynamics, thermodynamics, binding free energy (BFE), and interaction energies and modes. The results reveal that, when compared to the inter-molecular electrostatic forces/interactions, the inter-molecular van der Waals (vdW) forces/interactions are far more important in maintaining the association and determining the high affinity. Given the un-favorable effects of the inter-molecular electrostatic interactions-association destabilization by the competitive hydrogen bond (HB) interactions and the reduced binding affinity arising from the un-compensable increase in the electrostatic desolvation penalty-we suggest that enhancing the inter-molecular vdW interactions while avoiding introducing the deeply buried HBs may be a promising strategy in future inhibitor optimization.

Mechanistic Origin of Different Binding Affinities of SARS-CoV and SARS-CoV-2 Spike RBDs to Human ACE2.

The receptor-binding domain (RBD) of the SARS-CoV-2 spike protein (RBDCoV2) has a higher binding affinity to the human receptor angiotensin-converting enzyme 2 (ACE2) than the SARS-CoV RBD (RBDCoV). Here, we performed molecular dynamics (MD) simulations, binding free energy (BFE) calculations, and interface residue contact network (IRCN) analysis to explore the mechanistic origin of different ACE2-binding affinities of the two RBDs. The results demonstrate that, when compared to the RBDCoV2-ACE2 complex, RBDCoV-ACE2 features enhanced dynamicsand inter-protein positional movements and increased conformational entropy and conformational diversity. Although the inter-protein electrostatic attractive interactions are the primary determinant for the high ACE2-binding affinities of both RBDs, the significantly enhanced electrostatic attractive interactions between ACE2 and RBDCoV2 determine the higher ACE2-binding affinity of RBDCoV2 than of RBDCoV. Comprehensive comparative analyses of the residue BFE components and IRCNs between the two complexes reveal that it is the residue changes at the RBD interface that lead to the overall stronger inter-protein electrostatic attractive force in RBDCoV2-ACE2, which not only tightens the interface packing and suppresses the dynamics of RBDCoV2-ACE2, but also enhances the ACE2-binding affinity of RBDCoV2. Since the RBD residue changes involving gain/loss of the positively/negatively charged residues can greatly enhance the binding affinity, special attention should be paid to the SARS-CoV-2 variants carrying such mutations, particularly those near or at the binding interfaces with the potential to form hydrogen bonds and/or salt bridges with ACE2.

Variances and covariances of linear summary statistics of segregating sites.

Each mutation in a population sample of DNA sequences can be classified by the number of sequences that inherit the mutant nucleotide, the resulting frequencies are known as mutations of different sizes or site frequency spectrum. Many summary statistics can be defined as a linear function of these frequencies. A flexible class of such linear summary statistics is explored analytically in this paper which include several well-known quantities, such as the number of segregating sizes and the mean number of nucleotide differences between two sequences. Some asymptotic variances and covariances are obtained while the analytical formulas for the variances and covariances of nine such linear summary statistics are derived, most of which are unknown to date. This study not only provides some theoretical foundations for exploring linear summary statistics, but also provides some newlinear summary statistics that may be utilized for analyzing sample polymorphism. Furthermore it is showed that a newly developed linear summary statistics has a smaller variance almost uniformly than Watterson's estimator, and that a class of linear summary statistics given too heavy weights on mutations of smaller sizes result in asymptotically non-zero variance.

Amitosis as a strategy of cell division-Insight from the proliferation of Tetrahymena thermophila macronuclei.

Cell division is a necessity of life which can be either mitotic or amitotic. While both are fundamental, amitosis is sometimes considered a relic of little importance in biology. Nevertheless, eukaryotes often have polyploid cells, including cancer cells, which may divide amitotically. To understand how amitosis ensures the completion of cell division, we turn to the macronuclei of ciliates. The grand scheme governing the proliferation of the macronuclei of ciliate cells, which involves chromosomal replication and amitosis, is currently unknown, which is crucial for developing population genetics model of ciliate populations. Using a novel model that encompasses a wide range of mechanisms together with experimental data of the composition of mating types at different stages derived from a single karyonide of Tetrahymena thermophila, we show that the chromosomal replication of the macronucleus has a strong head-start effect, with only about five copies of chromosomes replicated at a time and persistent reuse of the chromosomes involved in the early replication. Furthermore the fission of a fully grown macronucleus is non-random with regard to chromosome composition, with a strong tendency to push chromosomes and their replications to the same daughter cell.

A Deep Learning Approach for Predicting Antigenic Variation of Influenza A H3N2.

Modeling antigenic variation in influenza (flu) virus A H3N2 using amino acid sequences is a promising approach for improving the prediction accuracy of immune efficacy of vaccines and increasing the efficiency of vaccine screening. Antigenic drift and antigenic jump/shift, which arise from the accumulation of mutations with small or moderate effects and from a major, abrupt change with large effects on the surface antigen hemagglutinin (HA), respectively, are two types of antigenic variation that facilitate immune evasion of flu virus A and make it challenging to predict the antigenic properties of new viral strains. Despite considerable progress in modeling antigenic variation based on the amino acid sequences, few studies focus on the deep learning framework which could be most suitable to be applied to this task. Here, we propose a novel deep learning approach that incorporates a convolutional neural network (CNN) and bidirectional long-short-term memory (BLSTM) neural network to predict antigenic variation. In this approach, CNN extracts the complex local contexts of amino acids while the BLSTM neural network captures the long-distance sequence information. When compared to the existing methods, our deep learning approach achieves the overall highest prediction performance on the validation dataset, and more encouragingly, it achieves prediction agreements of 99.20% and 96.46% for the strains in the forthcoming year and in the next two years included in an existing set of chronological amino acid sequences, respectively. These results indicate that our deep learning approach is promising to be applied to antigenic variation prediction of flu virus A H3N2.

Exploring the Cold-Adaptation Mechanism of Serine Hydroxymethyltransferase by Comparative Molecular Dynamics Simulations.

Cold-adapted enzymes feature a lower thermostability and higher catalytic activity compared to their warm-active homologues, which are considered as a consequence of increased flexibility of their molecular structures. The complexity of the (thermo)stability-flexibility-activity relationship makes it difficult to define the strategies and formulate a general theory for enzyme cold adaptation. Here, the psychrophilic serine hydroxymethyltransferase (pSHMT) from Psychromonas ingrahamii and its mesophilic counterpart, mSHMT from Escherichia coli, were subjected to µs-scale multiple-replica molecular dynamics (MD) simulations to explore the cold-adaptation mechanism of the dimeric SHMT. The comparative analyses of MD trajectories reveal that pSHMT exhibits larger structural fluctuations and inter-monomer positional movements, a higher global flexibility, and considerably enhanced local flexibility involving the surface loops and active sites. The largest-amplitude motion mode of pSHMT describes the trends of inter-monomer dissociation and enlargement of the active-site cavity, whereas that of mSHMT characterizes the opposite trends. Based on the comparison of the calculated structural parameters and constructed free energy landscapes (FELs) between the two enzymes, we discuss in-depth the physicochemical principles underlying the stability-flexibility-activity relationships and conclude that (i) pSHMT adopts the global-flexibility mechanism to adapt to the cold environment and, (ii) optimizing the protein-solvent interactions and loosening the inter-monomer association are the main strategies for pSHMT to enhance its flexibility.

Stairway Plot 2: demographic history inference with folded SNP frequency spectra.

Inferring the demographic histories of populations has wide applications in population, ecological, and conservation genomics. We present Stairway Plot 2, a cross-platform program package for this task using SNP frequency spectra. It is based on a nonparametric method with the capability of handling folded SNP frequency spectra (that is, when the ancestral alleles of the SNPs are unknown) of thousands of samples produced with genotyping-by-sequencing technologies; therefore, it is particularly suitable for nonmodel organisms.

The Energetic Origin of Different Catalytic Activities in Temperature-Adapted Trypsins.

Psychrophilic enzymes were always observed to have higher catalytic activity (k cat) than their mesophilic homologs at room temperature, while the origin of this phenomenon remains obscure. Here, we used two different temperature-adapted trypsins, the psychrophilic Atlantic cod trypsin (ACT) and the mesophilic bovine trypsin (BT), as a model system to explore the energetic origin of their different catalytic activities using computational methods. The results reproduce the characteristic changing trends in the activation free energy, activation enthalpy, and activation entropy between the psychrophilic and mesophilic enzymes, where, in particular, the slightly decreased activation free energy of ACT is determined by its considerably reduced activation enthalpy rather than by its more negative activation entropy compared to BT. The calculated electrostatic contributions to the solvation free energies in the reactant state/ground sate (RS/GS) and transition state (TS) show that, going from BT to ACT, the TS stabilization has a predominant effect over the RS stabilization on lowering the activation enthalpy of ACT. Comparison between the solvation energy components reveals a more optimized electrostatic preorganization to the TS in ACT, which provides a larger stabilization to the TS through reducing the reorganization energy, thus resulting in the lower activation enthalpy and hence lower activation free energy of ACT. Thus, it can be concluded that it is the difference in the protein electrostatic environment, and hence its different stabilizing effects on the TS, that brings about the different catalytic activities of different temperature-adapted trypsins.

Mycobacterium tuberculosis clinical isolates carry mutational signatures of host immune environments.

Mycobacterium tuberculosis (Mtb) infection results in a spectrum of clinical and histopathologic manifestations. It has been proposed that the environmental and immune pressures associated with different contexts of infection have different consequences for the associated bacterial populations, affecting drug susceptibility and the emergence of resistance. However, there is little concrete evidence for this model. We prospectively collected sputum samples from 18 newly diagnosed and treatment-naïve patients with tuberculosis and sequenced 795 colony-derived Mtb isolates. Mutant accumulation rates varied considerably between different bacilli isolated from the same individual, and where high rates of mutation were observed, the mutational spectrum was consistent with reactive oxygen species-induced mutagenesis. Elevated bacterial mutation rates were identified in isolates from HIV-negative but not HIV-positive individuals, suggesting that they were immune-driven. These results support the model that mutagenesis of Mtb in vivo is modulated by the host environment, which could drive the emergence of variants associated with drug resistance in a host-dependent manner.

Molecular dynamics simulations reveal distinct differences in conformational dynamics and thermodynamics between the unliganded and CD4-bound states of HIV-1 gp120.

The entry of human immunodeficiency virus type I (HIV-1) into host cells is initiated by binding to the cell-surface receptor CD4, which induces a conformational transition of the envelope (Env) glycoprotein gp120 from the closed, unliganded state to the open, CD4-bound state. Despite many available structures in these two states, detailed aspects on the dynamics and thermodynamics of gp120 remain elusive. Here, we performed microsecond-scale (µs-scale) multiple-replica molecular dynamics (MD) simulations to explore the differences in the conformational dynamics, protein motions, and thermodynamics between the unliganded and CD4-bound/complexed forms of gp120. Comparative analyses of MD trajectories reveal that CD4 binding promotes the structural deviations/changes and conformational flexibility, loosens the structural packing, and complicates the molecular motions of gp120. Comparison of the constructed free energy landscapes (FELs) reveals that the CD4-complexed gp120 has more conformational substates, larger conformational entropy, and lower thermostability than the unliganded form. Therefore, the unliganded conformation represents a structurally and energetically stable "ground state" for the full-length gp120. The observed great increase in the mobility of V1/V2 and V3 along with their more versatile movement directions in the CD4-bound gp120 compared to the unliganded form suggests that their orientations with respect to each other and to the structural core determine the differences in the conformational dynamics and thermodynamics between the two gp120 forms. The results presented here provide a basis by which to better understand the functional and immunological properties of gp120 and, furthermore, to deploy appropriate strategies for the development of anti-HIV-1 drugs or vaccines.

Insights into the molecular mechanism underlying CD4-dependency and neutralization sensitivity of HIV-1: a comparative molecular dynamics study on gp120s from isolates with different phenotypes.

The envelope (Env) of HIV-1 plays critical roles in viral infection and immune evasion. Although structures of prefusion Env have been determined and phenotypes relevant to the CD4 dependency and the neutralization sensitivity for various HIV-1 isolates have been identified, the detailed structural dynamics and energetics underlying these two phenotypes have remained elusive. In this study, two unliganded structural models of gp120, one from the CD4-dependent, neutralization-resistant isolate H061.14 and the other from the CD4-independent, neutralization-sensitive R2 strain, were constructed, and subsequently were subjected to multiple-replica molecular dynamics (MD) simulations followed by free energy landscape (FEL) construction. Comparative analyses of MD trajectories reveal that during simulations R2-gp120 demonstrated larger structural fluctuations/deviations and higher global conformational flexibility than H061.14-gp120. Close comparison of local conformational flexibility shows that some of the structural regions involving direct interactions with gp41 and adjacent gp120 subunits in the context of the closed trimeric Env exhibit significantly higher flexibility in R2-gp120 than in H061.14-gp120, thus likely increasing the probability for R2-Env to open the trimer crown and prime gp41 fusogenic properties without induction by CD4. Collective motions derived from principal component analysis (PCA) reveal that R2-gp120 is prone to spontaneous transition to the neutralization-sensitive CD4-bound state while H061.14-gp120 tends to maintain the neutralization-resistant unliganded state. Finally, comparison between FELs reveals that R2-gp120 has larger conformational entropy, richer conformational diversity, and lower thermostability than H061.14-gp120, thus explaining why R2-gp120 is more structurally unstable and conformationally flexible, and has a higher propensity to transition to the CD4-bound state than H061.14-gp120. The present results reveal that the differences in dynamics and energetics between R2-gp120 and H061.14-gp120 impart Env trimers with distinct capacities to sample different states (i.e., R2-Env samples more readily the open state while H061.14-Env is more inclined to maintain the closed state), thus shedding light on the molecular mechanism underlying the HIV-1 phenotype associated with CD4 dependency/neutralization sensitivity.

Insights into the role of electrostatics in temperature adaptation: a comparative study of psychrophilic, mesophilic, and thermophilic subtilisin-like serine proteases.

To investigate the role of electrostatics in different temperature adaptations, we performed a comparative study on subtilisin-like serine proteases from psychrophilic Vibrio sp. PA-44 (VPR), mesophilic Engyodontium album (Tritirachium album) (PRK), and thermophilic Thermus aquaticus (AQN) using multiple-replica molecular dynamics (MD) simulations combined with continuum electrostatics calculations. The results reveal that although salt bridges are not a crucial factor in determining the overall thermostability of these three proteases, they on average provide the greatest, moderate, and least electrostatic stabilization to AQN, PRK, and VPR, respectively, at the respective organism growth temperatures. Most salt bridges in AQN are effectively stabilizing and thus contribute to maintaining the overall structural stability at 343 K, while nearly half of the salt bridges in VPR interconvert between being stabilizing and being destabilizing, likely aiding in enhancing the local conformational flexibility at 283 K. The individual salt bridges, salt-bridge networks, and calcium ions contribute differentially to local stability and flexibility of these three enzyme structures, depending on their spatial distributions and electrostatic strengths. The shared negatively charged surface potential at the active center of the three enzymes may provide the active-center flexibility necessary for nucleophilic attack and proton transfer. The differences in distributions of the electro-negative, electro-positive, and electro-neutral potentials, particularly over the back surfaces of the three proteases, may modulate/affect not only protein solubility and thermostability but also structural stability and flexibility/rigidity. These results demonstrate that electrostatics contributes to both heat and cold adaptation of subtilisin-like serine proteases through fine-tuning, either globally or locally, the structural stability and conformational flexibility/rigidity, thus providing a foundation for further engineering and mutagenesis studies.

Comparative thermal unfolding study of psychrophilic and mesophilic subtilisin-like serine proteases by molecular dynamics simulations.

Molecular dynamics (MD) simulations of a subtilisin-like serine protease VPR from the psychrophilic marine bacterium Vibrio sp. PA-44 and its mesophilic homologue, proteinase K (PRK), have been performed for 20 ns at four different temperatures (300, 373, 473, and 573 K). The comparative analyses of MD trajectories reveal that at almost all temperatures, VPR exhibits greater structural fluctuations/deviations, more unstable regular secondary structural elements, and higher global flexibility than PRK. Although these two proteases follow similar unfolding pathways at high temperatures, VPR initiates unfolding at a lower temperature and unfolds faster at the same high temperatures than PRK. These observations collectively indicate that VPR is less stable and more heat-labile than PRK. Analyses of the structural/geometrical properties reveal that, when compared to PRK, VPR has larger radius of gyration (Rg), less intramolecular contacts and hydrogen bonds (HBs), more protein-solvent HBs, and smaller burial of nonpolar area and larger exposure of polar area. These suggest that the increased flexibility of VPR would be most likely caused by its reduced intramolecular interactions and more favourable protein-solvent interactions arising from the larger exposure of the polar area, whereas the enhanced stability of PRK could be ascribed to its increased intramolecular interactions arising from the better optimized hydrophobicity. The factors responsible for the significant differences in local flexibility between these two proteases were also analyzed and ascertained. This study provides insights into molecular basis of thermostability of homologous serine proteases adapted to different temperatures.

Large numbers of vertebrates began rapid population decline in the late 19th century.

Accelerated losses of biodiversity are a hallmark of the current era. Large declines of population size have been widely observed and currently 22,176 species are threatened by extinction. The time at which a threatened species began rapid population decline (RPD) and the rate of RPD provide important clues about the driving forces of population decline and anticipated extinction time. However, these parameters remain unknown for the vast majority of threatened species. Here we analyzed the genetic diversity data of nuclear and mitochondrial loci of 2,764 vertebrate species and found that the mean genetic diversity is lower in threatened species than in related nonthreatened species. Our coalescence-based modeling suggests that in many threatened species the RPD began ∼123 y ago (a 95% confidence interval of 20-260 y). This estimated date coincides with widespread industrialization and a profound change in global living ecosystems over the past two centuries. On average the population size declined by ∼25% every 10 y in a threatened species, and the population size was reduced to ∼5% of its ancestral size. Moreover, the ancestral size of threatened species was, on average, ∼22% smaller than that of nonthreatened species. Because the time period of RPD is short, the cumulative effect of RPD on genetic diversity is still not strong, so that the smaller ancestral size of threatened species may be the major cause of their reduced genetic diversity; RPD explains 24.1-37.5% of the difference in genetic diversity between threatened and nonthreatened species.

Effect of the Solvent Temperatures on Dynamics of Serine Protease Proteinase K.

To obtain detailed information about the effect of the solvent temperatures on protein dynamics, multiple long molecular dynamics (MD) simulations of serine protease proteinase K with the solute and solvent coupled to different temperatures (either 300 or 180 K) have been performed. Comparative analyses demonstrate that the internal flexibility and mobility of proteinase K are strongly dependent on the solvent temperatures but weakly on the protein temperatures. The constructed free energy landscapes (FELs) at the high solvent temperatures exhibit a more rugged surface, broader spanning range, and higher minimum free energy level than do those at the low solvent temperatures. Comparison between the dynamic hydrogen bond (HB) numbers reveals that the high solvent temperatures intensify the competitive HB interactions between water molecules and protein surface atoms, and this in turn exacerbates the competitive HB interactions between protein internal atoms, thus enhancing the conformational flexibility and facilitating the collective motions of the protein. A refined FEL model was proposed to explain the role of the solvent mobility in facilitating the cascade amplification of microscopic motions of atoms and atomic groups into the global collective motions of the protein.

Efficient Estimation of Mutation Rates during Individual Development by Minimization of Chi-Square.

Mutation primarily occurs when cells divide and it is highly desirable to have knowledge of the rate of mutations for each of the cell divisions during individual development. Recently, recessive lethal or nearly lethal mutations which were observed in a large mutation accumulation experiment using Drosophila melanogaster suggested that mutation rates vary significantly during the germline development of male Drosophila melanogaster. The analysis of the data was based on a combination of the maximum likelihood framework with numerical assistance from a newly developed coalescent algorithm. Although powerful, the likelihood based framework is computationally highly demanding which limited the scope of the inference. This paper presents a new estimation approach by minimizing chi-square statistics which is asymptotically consistent with the maximum likelihood method. When only at most one mutation in a family is considered the minimization of chi-square is simplified to a constrained weighted minimum least square method which can be solved easily by optimization theory. The new methods effectively eliminates the computational bottleneck of the likelihood. Reanalysis of the published Drosophila melanogaster mutation data results in similar estimates of mutation rates. The new method is also expected to be applicable to the analysis of mutation data generated by next-generation sequencing technology.