Random Mutations Research Articles

Screening Key Sites of Class 2 Integron Integrase that Impact Recombination Efficiency.

By capturing and expressing exogenous resistance gene cassettes through site-specific recombination, integrons play important roles in the horizontal transfer of antimicrobial resistant genes among bacteria. The characteristics of integron integrase make it to be a potential gene editing tool enzyme. In this study, a random mutation library using error-prone PCR was constructed, and amino acid residues mutants that impact on attI2 × attC or attC × attC recombination efficiency were screened and analyzed. Thirteen amino acid mutations were identified to be critical impacted on site-specific recombination of IntI2, including the predicted catalyzed site Y301. Nine of 13 mutated amino acid residues that have critically impacted on IntI2 activity were relative concentrated and near the predicted catalyzed site Y301 in the predicted three-dimensional structure indicated the importance of this area in maintain the activity of IntI2. No mutant with obviously increased recombination activity (more than four-fold as high as that of wild IntI2) was found in library screening, except P95S, R100K slightly increased (within two-fold) the excision activity of IntI2, and S243T slightly increased (within two-fold) both excision and integration activity of IntI2. These findings will provide clues for further specific modification of integron integrase to be a tool enzyme as well as establishing a new gene editing system and applied practically.

Novel hybrid classifier based on fuzzy type-III decision maker and ensemble deep learning model and improved chaos game optimization

AbstractThis paper presents a unique hybrid classifier that combines deep neural networks with a type-III fuzzy system for decision-making. The ensemble incorporates ResNet-18, Efficient Capsule neural network, ResNet-50, the Histogram of Oriented Gradients (HOG) for feature extraction, neighborhood component analysis (NCA) for feature selection, and Support Vector Machine (SVM) for classification. The innovative inputs fed into the type-III fuzzy system come from the outputs of the mentioned neural networks. The system’s rule parameters are fine-tuned using the Improved Chaos Game Optimization algorithm (ICGO). The conventional CGO’s simple random mutation is substituted with wavelet mutation to enhance the CGO algorithm while preserving non-parametricity and computational complexity. The ICGO was evaluated using 126 benchmark functions and 5 engineering problems, comparing its performance with well-known algorithms. It achieved the best results across all functions except for 2 benchmark functions. The introduced classifier is applied to seven malware datasets and consistently outperforms notable networks like AlexNet, ResNet-18, GoogleNet, and Efficient Capsule neural network in 35 separate runs, achieving over 96% accuracy. Additionally, the classifier’s performance is tested on the MNIST and Fashion-MNIST in 10 separate runs. The results show that the new classifier excels in accuracy, precision, sensitivity, specificity, and F1-score compared to other recent classifiers. Based on the statistical analysis, it has been concluded that the ICGO and propose method exhibit significant superiority compared to the examined algorithms and methods. The source code for ICGO is available publicly at https://nimakhodadadi.com/algorithms-%2B-codes. Graphical abstract

Non-Poissonian bursts in the arrival of phenotypic variation can strongly affect the dynamics of adaptation.

Modelling the rate at which adaptive phenotypes appear in a population is a key to predicting evolutionary processes. Given random mutations, should this rate be modelled by a simple Poisson process, or is a more complex dynamics needed? Here we use analytic calculations and simulations of evolving populations on explicit genotype-phenotype maps to show that the introduction of novel phenotypes can be 'bursty' or overdispersed. In other words, a novel phenotype either appears multiple times in quick succession, or not at all for many generations. These bursts are fundamentally caused by statistical fluctuations and other structure in the map from genotypes to phenotypes. Their strength depends on population parameters, being highest for 'monomorphic' populations with low mutation rates. They can also be enhanced by additional inhomogeneities in the mapping from genotypes to phenotypes. We mainly investigate the effect of bursts using the well-studied genotype-phenotype map for RNA secondary structure, but find similar behaviour in a lattice protein model and in Richard Dawkins's biomorphs model of morphological development. Bursts can profoundly affect adaptive dynamics. Most notably, they imply that fitness differences play a smaller role in determining which phenotype fixes than would be the case for a Poisson process without bursts.

Assessing the reliability of point mutation as data augmentation for deep learning with genomic data

BackgroundDeep neural networks (DNNs) have the potential to revolutionize our understanding and treatment of genetic diseases. An inherent limitation of deep neural networks, however, is their high demand for data during training. To overcome this challenge, other fields, such as computer vision, use various data augmentation techniques to artificially increase the available training data for DNNs. Unfortunately, most data augmentation techniques used in other domains do not transfer well to genomic data.ResultsMost genomic data possesses peculiar properties and data augmentations may significantly alter the intrinsic properties of the data. In this work, we propose a novel data augmentation technique for genomic data inspired by biology: point mutations. By employing point mutations as substitutes for codons, we demonstrate that our newly proposed data augmentation technique enhances the performance of DNNs across various genomic tasks that involve coding regions, such as translation initiation and splice site detection.ConclusionSilent and missense mutations are found to positively influence effectiveness, while nonsense mutations and random mutations in non-coding regions generally lead to degradation. Overall, point mutation-based augmentations in genomic datasets present valuable opportunities for improving the accuracy and reliability of predictive models for DNA sequences.

Targeted genome-modification tools and their advanced applications in crop breeding.

Crop improvement by genome editing involves the targeted alteration of genes to improve plant traits, such as stress tolerance, disease resistance or nutritional content. Techniques for the targeted modification of genomes have evolved from generating random mutations to precise base substitutions, followed by insertions, substitutions and deletions of small DNA fragments, and are finally starting to achieve precision manipulation of large DNA segments. Recent developments in base editing, prime editing and other CRISPR-associated systems have laid a solid technological foundation to enable plant basic research and precise molecular breeding. In this Review, we systematically outline the technological principles underlying precise and targeted genome-modification methods. We also review methods for the delivery of genome-editing reagents in plants and outline emerging crop-breeding strategies based on targeted genome modification. Finally, we consider potential future developments in precise genome-editing technologies, delivery methods and crop-breeding approaches, as well as regulatory policies for genome-editing products.

On random genetic mutations and the environment that supported them

On random genetic mutations and the environment that supported them

Dynamic random mutation hybrid Harris hawk optimization and its application to training kernel extreme learning machine

Dynamic random mutation hybrid Harris hawk optimization and its application to training kernel extreme learning machine

Identification of Critical Amino Acid Residues of a Two-Component Sensor Protein for Signal Sensing in Porphyromonas gingivalis Fimbriation via Random Mutant Library Construction.

Porphyromonas gingivalis (Pg) utilizes FimA fimbriae to colonize the gingival sulcus and evade the host immune system. The biogenesis of all FimA-related components is positively regulated by the FimS-FimR two-component system, making the FimS sensory protein an attractive target for preventing Pg infection. However, the specific environmental signal received by FimS remains unknown. We constructed random Pg mutant libraries to identify critical amino acid residues for signal sensing by FimS. Optimized error-prone polymerase chain reaction (PCR) was used to introduce a limited number of random mutations in the periplasmic-domain-coding sequence of fimS, and expression vectors carrying various mutants were generated by inverse PCR. More than 500 transformants were obtained from the fimS-knockout Pg strain using the Escherichia coli-Pg conjugal transfer system, whereas only ~100 transformants were obtained using electroporation. Four and six transformant strains showed increased and decreased fimA expression, respectively. Six strains had single amino acid substitutions in the periplasmic domain, indicating critical residues for signal sensing by FimS. This newly developed strategy should be generally applicable and contribute to molecular genetics studies of Pg, including the elucidation of structure-function relationships of proteins of interest.

A Simple Differential Evolution with Random Mutation and Crossover Constants for Constrained Optimization

The article proposes a simple version of the differential evolution algorithm (abbreviated as sDE) in which the mutation factor and crossover constant are chosen randomly in the range (0,1) during the search for the optimal solution. The sDE is the same as the original version of the differential evolution algorithm, except the user does not have to choose the best values of mutation constant and crossover constant for each optimization problem. Therefore, the optimization process is now very simple as it remains only one parameter (i.e. the population size) in the algorithm, besides the stopping criterion (e.g. number of iterations). It also consumes less computation time than the original differential evolution as it is not necessary to tune the mutation and crossover constants. In this study, the proposed technique is applied to three constrained optimizations, three engineering design problems, and six planar and spatial trusses under frequency constraints. Despite the very simple characteristics of the proposed technique, sDE gives promising results in comparison with other results in the literature.

Improved Thermal Stability of a Novel Acidophilic Phytase.

Phytase increases the availability of phosphate and trace elements by hydrolyzing the phospho-monoester bond in phytate present in animal feed. It is also an important enzyme from an environmental perspective because it not only promotes the growth of livestocks but also prevents phosphorus contamination released into the environment. Here we present a novel phytase derived from Turicimonas muris, TmPhy, which has distinctive structure and properties compared to other previously known phytases. TmPhy gene expressed in the Pichia system was confirmed to be 41 kDa in size and was used in purified form to evaluate optimal conditions for maximum activity. TmPhy has a dual optimum pH at pH3 and pH6.8 and exhibited the highest activity at 70℃. However, the heat tolerance of the wildtype was not satisfactory for feed application. Therefore, random mutation, disulfide bond introduction, and N-terminal mutation were performed to improve the thermostability of the TmPhy. Random mutation resulted in TmPhyM with about 45% improvement in stability at 60℃. Through further improvements, a total of three mutants were screened and their heat tolerance was evaluated. As a result, we obtained TmPhyMD1 with 46.5% residual activity, TmPhyMD2 with 74.1%, and TmPhyMD3 with 66.8% at 80℃ heat treatment without significant loss of or with increased activity.

Heat shock protein 71 restricts mutation of porcine reproductive and respiratory syndrome virus nsp2 in vitro

porcine reproductive and respiratory syndrome (PRRS), caused by porcine reproductive and respiratory syndrome virus (PRRSV) infection, is an important swine infectious disease that causes substantial losses worldwide each year. PRRSV is a positive-sense single-stranded RNA virus that is highly susceptible to mutation and recombination, making vaccine and drug research for the disease extremely difficult. In this study, the binding of PRRSV nsp2 to HSP71 protein was detected by using the IP/MS technique. And the inhibitory effect of HSP71 on nsp2 antagonistic activity was validated by measuring NF-kB luciferase reporter. According to stress from inhibitory effects, the amino acid variation profile of PRRSV nsp2 under HSP71 stress was further analyzed using second-generation sequencing. Surprisingly, the results indicated that HSP71 pressure limits the random mutations of PRRSV nsp2 and maintains the dominant PRRSV strain within the population. Mutant strain showed weaker antagonistic activity and replication capability in cell. These results imply the binding of HSP71 with PRRSV nsp2 may lead to maintain the stability of highly virulent strains of PRRSV.

An Approach for Engineering Peptides for Competitive Inhibition of the SARS-COV-2 Spike Protein.

SARS-CoV-2 is the virus responsible for a respiratory disease called COVID-19 that devastated global public health. Since 2020, there has been an intense effort by the scientific community to develop safe and effective prophylactic and therapeutic agents against this disease. In this context, peptides have emerged as an alternative for inhibiting the causative agent. However, designing peptides that bind efficiently is still an open challenge. Here, we show an algorithm for peptide engineering. Our strategy consists of starting with a peptide whose structure is similar to the interaction region of the human ACE2 protein with the SPIKE protein, which is important for SARS-COV-2 infection. Our methodology is based on a genetic algorithm performing systematic steps of random mutation, protein-peptide docking (using the PyRosetta library) and selecting the best-optimized peptides based on the contacts made at the peptide-protein interface. We performed three case studies to evaluate the tool parameters and compared our results with proposals presented in the literature. Additionally, we performed molecular dynamics (MD) simulations (three systems, 200 ns each) to probe whether our suggested peptides could interact with the spike protein. Our results suggest that our methodology could be a good strategy for designing peptides.

Evolution of a Human-Specific De Novo Open Reading Frame and Its Linked Transcriptional Silencer.

In the human genome, two short open reading frames (ORFs) separated by a transcriptional silencer and a small intervening sequence stem from the gene SMIM45. The two ORFs show different translational characteristics, and they also show divergent patterns of evolutionary development. The studies presented here describe the evolution of the components of SMIM45. One ORF consists of an ultra-conserved 68 amino acid (aa) sequence, whose origins can be traced beyond the evolutionary age of divergence of the elephant shark, ~462 MYA. The silencer also has ancient origins, but it has a complex and divergent pattern of evolutionary formation, as it overlaps both at the 68 aa ORF and the intervening sequence. The other ORF consists of 107 aa. It develops during primate evolution but is found to originate de novo from an ancestral non-coding genomic region with root origins within the Afrothere clade of placental mammals, whose evolutionary age of divergence is ~99 MYA. The formation of the complete 107 aa ORF during primate evolution is outlined, whereby sequence development is found to occur through biased mutations, with disruptive random mutations that also occur but lead to a dead-end. The 107 aa ORF is of particular significance, as there is evidence to suggest it is a protein that may function in human brain development. Its evolutionary formation presents a view of a human-specific ORF and its linked silencer that were predetermined in non-primate ancestral species. The genomic position of the silencer offers interesting possibilities for the regulation of transcription of the 107 aa ORF. A hypothesis is presented with respect to possible spatiotemporal expression of the 107 aa ORF in embryonic tissues.

Bias in the arrival of variation can dominate over natural selection in Richard Dawkins's biomorphs.

Biomorphs, Richard Dawkins's iconic model of morphological evolution, are traditionally used to demonstrate the power of natural selection to generate biological order from random mutations. Here we show that biomorphs can also be used to illustrate how developmental bias shapes adaptive evolutionary outcomes. In particular, we find that biomorphs exhibit phenotype bias, a type of developmental bias where certain phenotypes can be many orders of magnitude more likely than others to appear through random mutations. Moreover, this bias exhibits a strong preference for simpler phenotypes with low descriptional complexity. Such bias towards simplicity is formalised by an information-theoretic principle that can be intuitively understood from a picture of evolution randomly searching in the space of algorithms. By using population genetics simulations, we demonstrate how moderately adaptive phenotypic variation that appears more frequently upon random mutations can fix at the expense of more highly adaptive biomorph phenotypes that are less frequent. This result, as well as many other patterns found in the structure of variation for the biomorphs, such as high mutational robustness and a positive correlation between phenotype evolvability and robustness, closely resemble findings in molecular genotype-phenotype maps. Many of these patterns can be explained with an analytic model based on constrained and unconstrained sections of the genome. We postulate that the phenotype bias towards simplicity and other patterns biomorphs share with molecular genotype-phenotype maps may hold more widely for developmental systems.

CovEpiAb: a comprehensive database and analysis resource for immune epitopes and antibodies of human coronaviruses.

Coronaviruses have threatened humans repeatedly, especially COVID-19 caused by SARS-CoV-2, which has posed a substantial threat to global public health. SARS-CoV-2 continuously evolves through random mutation, resulting in a significant decrease in the efficacy of existing vaccines and neutralizing antibody drugs. It is critical to assess immune escape caused by viral mutations and develop broad-spectrum vaccines and neutralizing antibodies targeting conserved epitopes. Thus, we constructed CovEpiAb, a comprehensive database and analysis resource of human coronavirus (HCoVs) immune epitopes and antibodies. CovEpiAb contains information on over 60000 experimentally validated epitopes and over 12000 antibodies for HCoVs and SARS-CoV-2 variants. The database is unique in (1) classifying and annotating cross-reactive epitopes from different viruses and variants; (2) providing molecular and experimental interaction profiles of antibodies, including structure-based binding sites and around 70000 data on binding affinity and neutralizing activity; (3) providing virological characteristics of current and past circulating SARS-CoV-2 variants and in vitro activity of various therapeutics; and (4) offering site-level annotations of key functional features, including antibody binding, immunological epitopes, SARS-CoV-2 mutations and conservation across HCoVs. In addition, we developed an integrated pipeline for epitope prediction named COVEP, which is available from the webpage of CovEpiAb. CovEpiAb is freely accessible at https://pgx.zju.edu.cn/covepiab/.

Electrical stimulation of chicken embryo development supports the Inside story scenario of human development and evolution

Animal evolution is driven by random mutations at the genome level. However, it has long been suggested that there exist physical constraints which limit the set of possible outcomes. In craniate evolution, it has been observed that head features, notably in the genus homo, can be ordered in a morphological diagram such that, as the brain expands, the head rocks more forward, face features become less prognathous and the mouth tends to recede. One school of paleontologists suggests that this trend is wired somewhere structurally inside the anatomy, and that random modifications of genes push up or down animal forms along a pre-determined path. No actual experiment has been able to settle the dispute. I present here an experiment of electric stimulation of the head in the chicken embryo which is able to enhance the magnitude of tension forces during development. This experimental intervention causes a correlated brain shrinkage and rotatory movement of the head, congruent with tissue texture, which proves that head dilation and flexure are intimately linked. Numerical modelling explains why the brain curls when it dilates. This gives support to the idea that there exists, in the texture of the vertebrate embryo, a latent dynamic pattern for the observed paleontological trends in craniates towards homo, a concept known as Inside story.

Identification and characterization of Acinetobacter nosocomialis BfmRS, two-component regulatory system, essential for biofilm development.

Biofilm development by bacteria is considered to be an essential stage in the bacterial infection. Acinetobacter nosocomialis is an important nosocomial pathogen causing a variety of human infections. However, characteristics and specific determinants of biofilm development have been poorly characterized in A. nosocomialis. The aim of this study was to investigate the factors involved in the biofilm development by A. nosocomialis. Library of random transposon mutants was constructed using the Tn5 mutagenesis. The mutant strains, in which the ability of biofilm formation was significantly impaired, were screened by gentian violet staining. The roles of BfmR and BfmS were determined by constructing a bfmR and bfmS deletion mutant and analyzing the effects of bfmR and bfmS mutation on the biofilm development and motility of A. nosocomialis. We identified a biofilm-defective mutant in which a transposon insertion inactivated an open reading frame encoding the BfmR in a two-component regulatory system consisting of BfmR and BfmS. The bfmR mutant revealed a significant reduction in biofilm formation and motility compared to wild-type strain. Deficiency in the biofilm formation and motility of the bfmR mutant was restored by single copy bfmR complementation. In contrast, the bfmS mutant had no effect on biofilm formation. A. nosocomialis has a two-component regulatory system, BfmRS. BfmR is a response regulator required for the initial attachment and maturation of biofilm during the biofilm development as well as the bacterial growth. BfmR could be a potential drug target for A. nosocomialis infection.

Enhanced multi-strategy bottlenose dolphin optimizer for UAVs path planning

The path planning of unmanned aerial vehicle is a complex practical optimization problem, which is an important part of unmanned aerial vehicle technology. For constrained path planning problem, the traditional path planning methods can not deal with the complex constraint conditions well, and the classical nature-inspired algorithms will find the local optimal solution due to the lack of optimization ability. In this paper, an enhanced multi-strategy bottlenose dolphin optimizer is proposed to solve the unmanned aerial vehicle path planning problem under threat environments. Firstly, the introduction of fish aggregating device strategy that simulates the living habits of sharks enriches the behavioral diversity of the population. Secondly, random mixed mutation strategy and chaotic opposition-based learning strategy expand the exploration range of the algorithm in the solution space by disturbing the positions of some individuals and generating the opposite population respectively. Finally, after balancing the exploration and exploitation ability of the algorithm more reasonably through the mutation factor and energy factor, this paper proposes a new swarm intelligence algorithm. After verifying the adaptability and efficiency of the proposed algorithm through different types of test functions, this paper further highlights the advantages of the proposed algorithm in finding the optimal feasible path in the unmanned aerial vehicle path planning model based on four constraints.

An improved RIME optimization algorithm for lung cancer image segmentation

Lung cancer is a prevalent form of cancer worldwide, necessitating early and accurate diagnosis for successful treatment. Within medical imaging processing, image segmentation plays a vital role in medical diagnosis. This study applies swarm intelligence algorithms to segment lung cancer pathological images at three levels. The original algorithm incorporates the Whales' search prey mechanism and a random mutation strategy, resulting in an improved version named WDRIME, which aims to enhance convergence speed and avoid local optima (LO). Additionally, the study introduces a multilevel image segmentation method for lung cancer based on the improved algorithm. WDRIME's performance is showcased by comparing it to the state-of-the-art algorithms in IEEE CEC2014. To design a framework for lung cancer image segmentation, this paper combines the WDRIME algorithm with the multilevel segmentation method. Evaluation of the segmentation results employs metrics such as PSNR, SSIM, and FSIM. Overall, the analysis confirms that the proposed algorithm supersedes others regarding convergence speed and accuracy. This model signifies a high-quality segmentation method and offers practical support for in-depth exploration of lung cancer pathological images.

Generating mutants of monotone affinity towards stronger protein complexes through adversarial learning

Despite breakthroughs achieved in protein sequence-to-structure and function-to-sequence predictions, the affinity-to-mutation prediction problem remains unsolved. Such a problem is of exponential complexity deemed to find a mutated protein or protein complex having a guaranteed binding-affinity change. Here we introduce an adversarial learning-based mutation method that creates optimal amino acid substitutions and changes the mutant’s affinity change significantly in a preset direction. The key aspect in our method is the adversarial training process that dynamically labels the real side of the protein data and generates fake pseudo-data accordingly to construct a deep learning architecture for guiding the mutation. The method is sufficiently flexible to generate both single- and multipointed mutations at the adversarial learning step to mimic the natural circumstances of protein evolution. Compared with random mutants, our mutated sequences have in silico exhibited more than one order of change in magnitude of binding free energy change towards stronger complexes in the case study of Novavax–angiotensin-converting enzyme-related carboxypeptidase vaccine construct optimization. We also applied the method iteratively each time, using the output as the input sequence of the next iteration, to generate paths and a landscape of mutants with affinity-increasing monotonicity to understand SARS-CoV-2 Omicron’s spike evolution. With these steps taken for effective generation of protein mutants of monotone affinity, our method will provide potential benefits to many other applications including protein bioengineering, drug design, antibody reformulation and therapeutic protein medication.