Showing papers on "Mutation (genetic algorithm) published in 2021"

N501Y mutation of spike protein in SARS-CoV-2 strengthens its binding to receptor ACE2.

Abstract: SARS-CoV-2 has been spreading around the world for the past year. Recently, several variants such as B.1.1.7 (alpha), B.1.351 (beta), and P.1 (gamma), which share a key mutation N501Y on the receptor-binding domain (RBD), appear to be more infectious to humans. To understand the underlying mechanism, we used a cell surface-binding assay, a kinetics study, a single-molecule technique, and a computational method to investigate the interaction between these RBD (mutations) and ACE2. Remarkably, RBD with the N501Y mutation exhibited a considerably stronger interaction, with a faster association rate and a slower dissociation rate. Atomic force microscopy (AFM)-based single-molecule force microscopy (SMFS) consistently quantified the interaction strength of RBD with the mutation as having increased binding probability and requiring increased unbinding force. Molecular dynamics simulations of RBD-ACE2 complexes indicated that the N501Y mutation introduced additional π-π and π-cation interactions that could explain the changes observed by force microscopy. Taken together, these results suggest that the reinforced RBD-ACE2 interaction that results from the N501Y mutation in the RBD should play an essential role in the higher rate of transmission of SARS-CoV-2 variants, and that future mutations in the RBD of the virus should be under surveillance.

Covid-19: The E484K mutation and the risks it poses.

Abstract: The mutation E484K, first identified in the South African SARS-CoV-2 variant, has now been identified in the UK fast-spreading variant, prompting fears the virus is evolving further and could become resistant to vaccines. Jacqui Wise looks at what we know so far

An improved differential evolution algorithm and its application in optimization problem

Abstract: The selection of the mutation strategy for differential evolution (DE) algorithm plays an important role in the optimization performance, such as exploration ability, convergence accuracy and convergence speed. To improve these performances, an improved differential evolution algorithm with neighborhood mutation operators and opposition-based learning, namely NBOLDE, is developed in this paper. In the proposed NBOLDE, the new evaluation parameters and weight factors are introduced into the neighborhood model to propose a new neighborhood strategy. On this basis, a new neighborhood mutation strategy based on DE/current-to-best/1, namely DE/neighbor-to-neighbor/1, is designed in order to replace large-scale global mutation by local neighborhood mutation with high search efficiency. Then, a generalized opposition-based learning is employed to optimize the initial population and select the better solution between the current solution and reverse solution in order to approximate global optimal solution, which can amend the convergence direction, accelerate convergence, improve efficiency, enhance the stability and avoid premature convergence. Finally, the proposed NBOLDE is compared with four state-of-the-art DE variants by 12 benchmark functions with low-dimension and high-dimension. The experiment results indicate that the proposed NBOLDE has a faster convergence speed, higher convergence accuracy, and better optimization capabilities in solving high-dimensional complex functions.

Acquisition of the L452R mutation in the ACE2-binding interface of Spike protein triggers recent massive expansion of SARS-Cov-2 variants

Abstract: The recent rise in mutational variants of SARS-CoV-2, especially with changes in the Spike protein, is of significant concern due to the potential ability for these mutations to increase viral infectivity, virulence and/or ability to escape protective antibodies. Here, we investigated genetic variations in a 414-583 amino acid region of the Spike protein, partially encompassing the ACE2 receptor-binding domain (RBD), across a subset of 570 nasopharyngeal samples isolated between April 2020 and February 2021, from Washington, California, Arizona, Colorado, Minnesota and Illinois. We found that samples isolated since November have an increased number of amino acid mutations in the region, with L452R being the dominant mutation. This mutation is associated with a recently discovered CAL.20C viral variant from clade 20C, lineage B.1.429, that since November-December 2020 is associated with multiple outbreaks and is undergoing massive expansion across California. In some samples, however, we found a distinct L452R-carrying variant of the virus that, upon detailed analysis of the GISAID database genomes, is also circulating primarily in California, but emerged even more recently. The newly identified variant derives from the clade 20A (lineage B.1.232) and is named CAL.20A. We also found that the SARS-CoV-2 strain that caused the only recorded case of infection in an ape - gorillas in the San Diego Zoo, reported in January 2021 - is CAL.20A. In contrast to CAL.20C that carries two additional to L452R mutations in the Spike protein, L452R is the only mutation found in CAL.20A. According to the phylogenetic analysis, however, emergence of CAL.20C was also specifically triggered by acquisition of the L452R mutation. Further analysis of GISAID-deposited genomes revealed that several independent L452R-carrying lineages have recently emerged across the globe, with over 90% of the isolates reported between December 2020 - February 2021. Taken together, these results indicate that the L452R mutation alone is of significant adaptive value to SARS-CoV-2 and, apparently, the positive selection for this mutation became particularly strong only recently, possibly reflecting viral adaptation to the containment measures or increasing population immunity. While the functional impact of L452R has not yet been extensively evaluated, leucine-452 is positioned in the receptor-binding motif of RBD, in the interface of direct contact with the ACE2 receptor. Its replacement with arginine is predicted to result in both a much stronger binding to the receptor and escape from neutralizing antibodies. If true, this in turn might lead to significantly increased infectivity of the L452R variants, warranting their close surveillance and in-depth functional studies.

Quantum differential evolution with cooperative coevolution framework and hybrid mutation strategy for large scale optimization

Abstract: In order to overcome the low solution efficiency, insufficient diversity in the later search stage, slow convergence speed and a high search stagnation possibility of differential evolution(DE) algorithm, the quantum computing characteristics of quantum evolutionary algorithm(QEA) and the divide-and-conquer idea of cooperative coevolution evolutionary algorithm(CCEA) are combined to propose an improved differential evolution(HMCFQDE) in this paper. In the proposed HMCFQDE, a new hybrid mutation strategy based on the advantages of local neighborhood mutation and SaNSDE is designed. In the early stage of the search, the local neighborhood mutation strategy with high search efficiency is used to speed up the algorithm convergence. In the later stage of the search, the SaNSDE algorithm is used to adjust the search direction in order to avoid the search stagnation. The QEA is combined with the DE to make use of the quantum chromosome encoding to enhance the population diversity, the quantum rotation to speed up the convergence speed. The CC framework is used to divide the large-scale and high-dimensional complex optimization problem into several low-dimensional optimization sub-problems, and these sub-populations are solved by independent searching among sub-populations in order to improve the solution efficiency. By comparing with other 6 algorithms in solving 6 test functions from CEC’08 under the dimensions of 100, 500 and 1000, it is proved that the proposed HMCFQDE has higher convergence accuracy and stronger stability. In particular, it has a strong ability to optimize high-dimensional complex functions. Therefore, it provides a new method for solving large-scale optimization problem.

Acquisition of the L452R mutation in the ACE2-binding interface of Spike protein triggers recent massive expansion of SARS-CoV-2 variants.

Abstract: We report that there is a recent global expansion of numerous independent severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) variants with mutation L452R in the receptor-binding domain (RBD) of the spike protein. The massive emergence of L452R variants was first linked to lineage B.1.427/B.1.429 (clade 21C) that has been spreading in California since November and December 2020, originally named CAL.20C and currently variant of interest epsilon. By PCR amplification and Sanger sequencing of a 541-base fragment coding for amino acids 414 to 583 of the RBD from a collection of clinical specimens, we identified a separate L452R variant that also recently emerged in California but derives from the lineage B.1.232, clade 20A (named CAL.20A). Notably, CAL.20A caused an infection in gorillas in the San Diego Zoo, reported in January 2021. Unlike the epsilon variant that carries two additional mutations in the N-terminal domain of spike protein, L452R is the only mutation found in the spike proteins of CAL.20A. Based on genome-wide phylogenetic analysis, emergence of both viral variants was specifically triggered by acquisition of L452R, suggesting a strong positive selection for this mutation. Global analysis revealed that L452R is nearly omnipresent in a dozen independently emerged lineages, including the most recent variants of concern/interest delta, kappa, epsilon and iota, with the lambda variant carrying L452Q. L452 is in immediate proximity to the angiotensin-converting enzyme 2 (ACE2) interaction interface of RBD. It was reported that the L452R mutation is associated with immune escape and could result in a stronger cell attachment of the virus, with both factors likely increasing viral transmissibility, infectivity, and pathogenicity.

Chaos cloud quantum bat hybrid optimization algorithm

Abstract: The bat algorithm (BA) has fast convergence, a simple structure, and strong search ability. However, the standard BA has poor local search ability in the late evolution stage because it references the historical speed; its population diversity also declines rapidly. Moreover, since it lacks a mutation mechanism, it easily falls into local optima. To improve its performance, this paper develops a hybrid approach to improving its evolution mechanism, local search mechanism, mutation mechanism, and other mechanisms. First, the quantum computing mechanism (QCM) is used to update the searching position in the BA to improve its global convergence. Secondly, the X-condition cloud generator is used to help individuals with better fitness values to increase the rate of convergence, with the sorting of individuals after a particular number of iterations; the individuals with poor fitness values are used to implement a 3D cat mapping chaotic disturbance mechanism to increase population diversity and thereby enable the BA to jump out of a local optimum. Thus, a hybrid optimization algorithm—the chaotic cloud quantum bats algorithm (CCQBA)—is proposed. To test the performance of the proposed CCQBA, it is compared with alternative algorithms. The evaluation functions are nine classical comparative functions. The results of the comparison demonstrate that the convergent accuracy and convergent speed of the proposed CCQBA are significantly better than those of the other algorithms. Thus, the proposed CCQBA represents a better method than others for solving complex problems.

Mutation N501Y in RBD of Spike Protein Strengthens the Interaction between COVID-19 and its Receptor ACE2

Abstract: SARS-CoV-2 is spreading around the world for the past year. Enormous efforts have been taken to understand its mechanism of transmission. It is well established now that the receptor-binding domain (RBD) of the spike protein binds to the human angiotensin-converting enzyme 2 (ACE2) as its first step of entry. Being a single-stranded RNA virus, SARS-CoV-2 is evolving rapidly. Recently, several variants such as B.1.1.7, B.1.351, and P.1, with a key mutation N501Y on the RBD, appear to be more infectious to humans. To understand its mechanism, we combined cell surface binding assay, kinetics study, single-molecule technique, and computational method to investigate the interaction between these RBD (mutations) and ACE2. Remarkably, RBD with the N501Y mutation exhibited a considerably stronger interaction characterized from all these methodologies, while the other two mutations from B.1.351 contributed to a less effect. Fluorescence-activated cell scan (FACS) assays found that RBD N501Y mutations are of higher binding affinity to ACE2 than the wild type. Surface plasmon resonance further indicated that N501Y mutation had a faster association rate and slower dissociation rate. Consistent with the kinetics study, atomic force microscopy-based single-molecule force microscopy quantify their strength on living cells, showing a higher binding probability and unbinding force for the mutation. Finally, Steered Molecular Dynamics (SMD) simulations on the dissociation of RBD-ACE2 complexes revealed that the N501Y introduced additional π-π and π-cation interaction for the higher force/interaction. Taken together, we suggested that the reinforced interaction from N501Y mutation in RBD should play an essential role in the higher transmission of COVID-19 variants.

SARS-CoV-2 spike P681R mutation enhances and accelerates viral fusion

Abstract: Summary During the current SARS-CoV-2 pandemic, a variety of mutations have been accumulated in the viral genome, and at least five variants of concerns (VOCs) have been considered as the hazardous SARS-CoV-2 variants to the human society. The newly emerging VOC, the B.1.617.2 lineage (delta variant), closely associates with a huge COVID-19 surge in India in Spring 2021. However, its virological property remains unclear. Here, we show that the B.1.617 variants are highly fusogenic and form prominent syncytia. Bioinformatic analyses reveal that the P681R mutation in the spike protein is highly conserved in this lineage. Although the P681R mutation decreases viral infectivity, this mutation confers the neutralizing antibody resistance. Notably, we demonstrate that the P681R mutation facilitates the furin-mediated spike cleavage and enhances and accelerates cell-cell fusion. Our data suggest that the P681R mutation is a hallmark characterizing the virological phenotype of this newest VOC, which may associate with viral pathogenicity. Highlights P681R mutation is highly conserved in the B.1.617 lineages P681R mutation accelerates and enhances SARS-CoV-2 S-mediated fusion Promotion of viral fusion by P681R mutation is augmented by TMPRSS2

Mutation Rates and Selection on Synonymous Mutations in SARS-CoV-2.

Abstract: The COVID-19 pandemic has seen an unprecedented response from the sequencing community. Leveraging the sequence data from more than 140,000 SARS-CoV-2 genomes, we study mutation rates and selective pressures affecting the virus. Understanding the processes and effects of mutation and selection has profound implications for the study of viral evolution, for vaccine design, and for the tracking of viral spread. We highlight and address some common genome sequence analysis pitfalls that can lead to inaccurate inference of mutation rates and selection, such as ignoring skews in the genetic code, not accounting for recurrent mutations, and assuming evolutionary equilibrium. We find that two particular mutation rates, G →U and C →U, are similarly elevated and considerably higher than all other mutation rates, causing the majority of mutations in the SARS-CoV-2 genome, and are possibly the result of APOBEC and ROS activity. These mutations also tend to occur many times at the same genome positions along the global SARS-CoV-2 phylogeny (i.e., they are very homoplasic). We observe an effect of genomic context on mutation rates, but the effect of the context is overall limited. Although previous studies have suggested selection acting to decrease U content at synonymous sites, we bring forward evidence suggesting the opposite.

Genomic and proteomic mutation landscapes of SARS-CoV-2.

Abstract: The ongoing pandemic caused by a novel coronavirus, Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2), affects thousands of people every day worldwide. Hence, drugs and vaccines effective against all variants of SARS-CoV-2 are crucial today. Viral genome mutations exist commonly which may impact the encoded proteins, possibly resulting to varied effectivity of detection tools and disease treatment. Thus, this study surveyed the SARS-CoV-2 genome and proteome and evaluated its mutation characteristics. Phylogenetic analyses of SARS-CoV-2 genes and proteins show three major clades and one minor clade (P6810S; ORF1ab). The overall frequency and densities of mutations in the genes and proteins of SARS-CoV-2 were observed. Nucleocapsid exhibited the highest mutation density among the structural proteins while the spike D614G was the most common, occurring mostly in genomes outside China and United States. ORF8 protein had the highest mutation density across all geographical areas. Moreover, mutation hotspots neighboring and at the catalytic site of RNA-dependent RNA polymerase were found that might challenge the binding and effectivity of remdesivir. Mutation coldspots may present as conserved diagnostic and therapeutic targets were found in ORF7b, ORF9b, and ORF14. These findings suggest that the virion's genotype and phenotype in a specific population should be considered in developing diagnostic tools and treatment options.

Emergence of SARS-COV-2 Spike Protein Escape Mutation Q493R after Treatment for COVID-19.

Abstract: We report in vivo selection of a severe acute respiratory syndrome coronavirus 2 spike mutation (Q493R) conferring simultaneous resistance to bamlanivimab and etesivimab. This mutation was isolated from a patient who had coronavirus disease and was treated with these drugs.

Using artificial intelligence techniques for COVID-19 genome analysis

Abstract: The genome of the novel coronavirus (COVID-19) disease was first sequenced in January 2020, approximately a month after its emergence in Wuhan, capital of Hubei province, China. COVID-19 genome sequencing is critical to understanding the virus behavior, its origin, how fast it mutates, and for the development of drugs/vaccines and effective preventive strategies. This paper investigates the use of artificial intelligence techniques to learn interesting information from COVID-19 genome sequences. Sequential pattern mining (SPM) is first applied on a computer-understandable corpus of COVID-19 genome sequences to see if interesting hidden patterns can be found, which reveal frequent patterns of nucleotide bases and their relationships with each other. Second, sequence prediction models are applied to the corpus to evaluate if nucleotide base(s) can be predicted from previous ones. Third, for mutation analysis in genome sequences, an algorithm is designed to find the locations in the genome sequences where the nucleotide bases are changed and to calculate the mutation rate. Obtained results suggest that SPM and mutation analysis techniques can reveal interesting information and patterns in COVID-19 genome sequences to examine the evolution and variations in COVID-19 strains respectively.

Genomic evidence for inbreeding depression and purging of deleterious genetic variation in Indian tigers.

Abstract: Increasing habitat fragmentation leads to wild populations becoming small, isolated, and threatened by inbreeding depression. However, small populations may be able to purge recessive deleterious alleles as they become expressed in homozygotes, thus reducing inbreeding depression and increasing population viability. We used whole-genome sequences from 57 tigers to estimate individual inbreeding and mutation load in a small-isolated and two large-connected populations in India. As expected, the small-isolated population had substantially higher average genomic inbreeding (F ROH = 0.57) than the large-connected (F ROH = 0.35 and F ROH = 0.46) populations. The small-isolated population had the lowest loss-of-function mutation load, likely due to purging of highly deleterious recessive mutations. The large populations had lower missense mutation loads than the small-isolated population, but were not identical, possibly due to different demographic histories. While the number of the loss-of-function alleles in the small-isolated population was lower, these alleles were at higher frequencies and homozygosity than in the large populations. Together, our data and analyses provide evidence of 1) high mutation load, 2) purging, and 3) the highest predicted inbreeding depression, despite purging, in the small-isolated population. Frequency distributions of damaging and neutral alleles uncover genomic evidence that purifying selection has removed part of the mutation load across Indian tiger populations. These results provide genomic evidence for purifying selection in both small and large populations, but also suggest that the remaining deleterious alleles may have inbreeding-associated fitness costs. We suggest that genetic rescue from sources selected based on genome-wide differentiation could offset any possible impacts of inbreeding depression.

DeepCrime: mutation testing of deep learning systems based on real faults

Abstract: Deep Learning (DL) solutions are increasingly adopted, but how to test them remains a major open research problem. Existing and new testing techniques have been proposed for and adapted to DL systems, including mutation testing. However, no approach has investigated the possibility to simulate the effects of real DL faults by means of mutation operators. We have defined 35 DL mutation operators relying on 3 empirical studies about real faults in DL systems. We followed a systematic process to extract the mutation operators from the existing fault taxonomies, with a formal phase of conflict resolution in case of disagreement. We have implemented 24 of these DL mutation operators into DeepCrime, the first source-level pre-training mutation tool based on real DL faults. We have assessed our mutation operators to understand their characteristics: whether they produce interesting, i.e., killable but not trivial, mutations. Then, we have compared the sensitivity of our tool to the changes in the quality of test data with that of DeepMutation++, an existing post-training DL mutation tool.