Ancestral sequence reconstructions evaluated by extant sequence cross-validation

Ancestral sequence reconstruction (ASR) has become widely used to analyze the properties of ancient biomolecules and to elucidate the mechanisms of molecular evolution. By recapitulating the structural, mechanistic, and functional changes of proteins during their evolution, ASR has been able to address many fundamental and challenging evolutionary questions where more traditional methods have failed. Despite the tangible successes of ASR, the accuracy of its reconstructions is currently unknown, because it is generally impossible to compare resurrected proteins to the true ancient ancestors that are now extinct. Which evolutionary models are the best for ASR? How accurate are the resulting inferences? Here we answer these questions by applying cross-validation (CV) to sets of aligned extant sequences. To assess the adequacy of a chosen evolutionary model for predicting extant sequence data, our column-wise CV method iteratively cross-validates each column in an alignment. Unlike other phylogenetic model selection criteria, this method does not require bias correction and does not make restrictive assumptions commonly violated by phylogenetic data. We find that column-wise CV generally provides a more conservative criterion than the AIC by preferring less complex models. To validate ASR methods, we also apply cross-validation to each sequence in an alignment by reconstructing the extant sequences using ASR methodology, a method we term extant sequence reconstruction (ESR). We can thus quantify the accuracy of ASR methodology by comparing ESR reconstructions to the corresponding true sequences. We find that a common measure of the quality of a reconstructed sequence, the average probability of the sequence, is indeed a good estimate of the fraction of the sequence that is correct when the evolutionary model is accurate or overparameterized. However, the average probability is a poor measure for comparing reconstructions, because more accurate phylogenetic models typically result in reconstructions with lower average probabilities. In contrast, the entropy of the reconstructed distribution is a reliable indicator of the quality of a reconstruction, as the entropy provides an accurate estimate of the log-probability of the true sequence. Both column-wise CV and ESR are useful methods to validate evolutionary models used for ASR and can be applied in practice to any phylogenetic analysis of real biological sequences.

An analysis of LysM domain function in LytE when fulfilling the D,L-endopeptidase requirement for viability in Bacillus subtilis

The D,L-endopeptidase requirement states that Bacillus subtilis requires either the activity of the LytE or the CwlO enzyme for viability, therefore proving that these two enzymes can complement for each other despite their very different N-terminal domains. Here, we show that another D,L-endopeptidase, LytF, can also fulfill the D,L-endopeptidase requirement for viability, when expressed from the cwlO promoter. Both LytE and LytF contain N-terminally located LysM domains, three and five respectively. However, cells expressing another very similar D,L-endopeptidase CwlS, with four LysM domains were not viable. This led us to investigate whether a LytE protein with any one of its three LysM domains permuted can fulfill the D,L-endopeptidase requirement for viability. We found that the three LysM domains are not functionally equivalent and that the N-terminally located LysM domain plays a greater role for functioning of the LytE enzyme than the subsequent domains. Based on an investigation of orthologous enzymes in 19 B. subtilis species we propose an evolutionary model describing the development of the LytE-, CwlS- and LytF-type D,L-endopeptidases and their LysM domain repeats. In summary, these results show that the LytE enzyme has been optimized to fulfill the D,L-endopeptidase requirement for cell viability of B. subtilis with regard to the number and properties of LysM domains that mediate peptidoglycan-binding.

Improved Handover Authentication in Fifth-Generation Communication Networks Using Fuzzy Evolutionary Optimisation with Nanocore Elements in Mobile Healthcare Applications

Authentication is a suitable form of restricting the network from different types of attacks, especially in case of fifth-generation telecommunication networks, especially in healthcare applications. The handover and authentication mechanism are one such type that enables mitigation of attacks in health-related services. In this paper, we model an evolutionary model that uses a fuzzy evolutionary model in maintaining the handover and key management to improve the performance of authentication in nanocore technology-based 5G networks. The model is designed in such a way that it minimizes the delays and complexity while authenticating the networks in 5G networks. The attacks are mitigated using an evolutionary model when it is trained with the relevant attack datasets, and the model is validated to mitigate the attacks. The simulation is conducted to test the efficacy of the model, and the results of simulation show that the proposed method is effective in improving the handling and authentication and mitigation against various types of attacks in mobile health applications.

The Parametric Optimization of Voltage Multiplier

The evolutionary model of voltage multiplier parametric optimization which includes 5 diodes and 5 capacitors is reviewed. It executes the transformation of alternating into constant voltage using a five times larger amplitude. The valve work is modelled according to the scheme of an ideal key. The original mathematical model of voltage multiplier which includes additional logical variables is deducted. It aссepts binary meanings 0 and 1, where 0 corresponds to closed valve status and 1 corresponds to open. In order to receive such a model, it is necessary to indicate the amount of open and closed valve combinations. Then for each of them, it is necessary to write the system of differential equations. Comparing them with each other the single differential equation system with additional logical variables is written as a generalization. The evolutional model is used in order to select the capacitor volume meaning. The goal function forecasts two conditions: maximum meaning of output voltage 1 kV and its minimal fluctuations in the stable regime.

PITA, a pre-bilaterian p75NTR, is the evolutionary ancestor of TNF receptors

Tumor necrosis factor receptors (TNFRs) regulate a diverse array of biological functions, including adaptive immunity, neurodevelopment, and many others. Although TNFRs are expressed in all metazoan phyla, a coherent model of the molecular origins of mammalian TNFRs—and how they relate to TNFRs in other phyla—has remained elusive. To address this, we executed a large-scale, systematic Basic Local Alignment Search Tool (BLAST)-based approach to trace the evolutionary ancestry of all 29 human TNFRs. We discovered that all human TNFRs are descendants of a single pre-bilaterian TNFR with strong sequence similarity to the p75 neurotrophin receptor (p75NTR), which we designate as PITA for ‘ p75NTR is the TNFR Ancestor’ . A distinct subset of human TNFRs—including EDAR, XEDAR and TROY—share a unique history as descendants of EDAR-XEDAR-TROY (EXT), which diverged from PITA in a bilaterian ancestor. Most PITA descendants possess a death domain (DD) within their intracellular domain (ICD) but EXTs do not. PITA descendants are expressed in all bilaterian phyla and Cnidaria, but not in non-planulozoan ParaHoxozoa, suggesting that PITA originated in an ancestral planulozoan. Drosophila melanogaster TNFRs (Wengen (Wgn) and Grindelwald (Grnd)) were identified as divergent PITA descendants, providing the first evolutionary link between this model TNFR system and the mammalian TNFR superfamily. This study reveals PITA as the ancestor to human and Drosophila TNFR systems and describes an evolutionary model that will facilitate deciphering TNF-TNFR functions in health and disease.

ModelRevelator: Fast phylogenetic model estimation via deep learning

Selecting the best model of sequence evolution for a multiple sequence alignment (MSA) constitutes the first step of phylogenetic tree reconstruction. Common approaches for inferring nucleotide models typically apply maximum likelihood (ML) methods, with discrimination between models determined by one of several information criteria. This requires tree reconstruction and optimisation which can be computationally expensive. We demonstrate that neural networks can be used to perform model selection, without the need to reconstruct trees, optimise parameters, or calculate likelihoods. We introduce ModelRevelator, a model selection tool underpinned by two deep neural networks. The first neural network, NNmodelfind, recommends one of six commonly used models of sequence evolution, ranging in complexity from JC to GTR. The second, NNalphafind, recommends whether or not a Γ--distributed rate heterogeneous model should be incorporated, and if so, provides an estimate of the shape parameter, ɑ. Users can simply input an MSA into ModelRevelator, and swiftly receive output recommending the evolutionary model, inclusive of the presence or absence of rate heterogeneity, and an estimate of ɑ. We show that ModelRevelator performs comparably with likelihood-based methods over a wide range of parameter settings, with significant potential savings in computational effort. Further, we show that this performance is not restricted to the alignments on which the networks were trained, but is maintained even on unseen empirical data. ModelRevelator will be made freely available in the forthcoming version of IQ-Tree (http://www.iqtree.org), and we expect it will provide a valuable alternative for phylogeneticists, especially where traditional methods of model selection are computationally prohibitive.

Latent Errors and Visible Earned Value: How the Evolutionary Model Integrates Earned Value Metrics with Project System Dynamics

A project model is presented that weaves together ideas from earned value project management and systems dynamics. It is able to adjust to increasingly unhealthy actual project behaviors in ways that preserve the signature pattern of the staffing histograms observed in the real world and provide a tool for managers to correct projects that are not meeting the plan. Starting from the planned staffing histogram and the project performance baseline, the model captures the delay and cost of experience dilution, includes the unplanned-for effort that is revealed in the typical pattern of the Cost Performance Index, assesses progress using the actual cost to date and the earned value to date, and adjusts staffing, scope, or both, to complete the project on schedule. A new method of approximating work remaining, called project-to-date, is shown to track the planned staffing histogram better than the commonly used fraction-complete method.