Epistatic models predict mutable sites in SARS-CoV-2 proteins and epitopes.

The emergence of new variants of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is a major concern given their potential impact on the transmissibility and pathogenicity of the virus as well as the efficacy of therapeutic interventions. Here, we predict the mutability of all positions in SARS-CoV-2 protein domains to forecast the appearance of unseen variants. Using sequence data from other coronaviruses, preexisting to SARS-CoV-2, we build statistical models that not only capture amino acid conservation but also more complex patterns resulting from epistasis. We show that these models are notably superior to conservation profiles in estimating the already observable SARS-CoV-2 variability. In the receptor binding domain of the spike protein, we observe that the predicted mutability correlates well with experimental measures of protein stability and that both are reliable mutability predictors (receiver operating characteristic areas under the curve ∼0.8). Most interestingly, we observe an increasing agreement between our model and the observed variability as more data become available over time, proving the anticipatory capacity of our model. When combined with data concerning the immune response, our approach identifies positions where current variants of concern are highly overrepresented. These results could assist studies on viral evolution and future viral outbreaks and, in particular, guide the exploration and anticipation of potentially harmful future SARS-CoV-2 variants.

OTUB1 stabilizes mismatch repair protein MSH2 by blocking ubiquitination.

DNA mismatch repair (MMR) maintains genome stability primarily by correcting replication errors. MMR deficiency can lead to cancer development and bolsters cancer cell resistance to chemotherapy. However, recent studies have shown that checkpoint blockade therapy is effective in MMR-deficient cancers, thus the ability to identify cancer etiology would greatly benefit cancer treatment. MutS homolog 2 (MSH2) is an obligate subunit of mismatch recognition proteins MutSα (MSH2-MSH6) and MutSß (MSH2-MSH3). Precise regulation of MSH2 is critical, as either over- or underexpression of MSH2 results in an increased mutation frequency. The mechanism by which cells maintain MSH2 proteostasis is unknown. Using functional ubiquitination and deubiquitination assays, we show that the ovarian tumor (OTU) family deubiquitinase ubiquitin aldehyde binding 1 (OTUB1) inhibits MSH2 ubiquitination by blocking the E2 ligase ubiquitin transfer activity. Depleting OTUB1 in cells promotes the ubiquitination and subsequent degradation of MSH2, leading to greater mutation frequency and cellular resistance to genotoxic agents, including the common chemotherapy agents N-methyl-N'-nitro-N-nitrosoguanidine and cisplatin. Taken together, our data identify OTUB1 as an important regulator of MSH2 stability and provide evidence that OTUB1 is a potential biomarker for cancer etiology and therapy.

De Novo Mutation Rate Variation and Its Determinants in Chlamydomonas.

De novo mutations are central for evolution, since they provide the raw material for natural selection by regenerating genetic variation. However, studying de novo mutations is challenging and is generally restricted to model species, so we have a limited understanding of the evolution of the mutation rate and spectrum between closely related species. Here, we present a mutation accumulation (MA) experiment to study de novo mutation in the unicellular green alga Chlamydomonas incerta and perform comparative analyses with its closest known relative, Chlamydomonas reinhardtii. Using whole-genome sequencing data, we estimate that the median single nucleotide mutation (SNM) rate in C. incerta is µ = 7.6 × 10-10, and is highly variable between MA lines, ranging from µ = 0.35 × 10-10 to µ = 131.7 × 10-10. The SNM rate is strongly positively correlated with the mutation rate for insertions and deletions between lines (r > 0.97). We infer that the genomic factors associated with variation in the mutation rate are similar to those in C. reinhardtii, allowing for cross-prediction between species. Among these genomic factors, sequence context and complexity are more important than GC content. With the exception of a remarkably high C→T bias, the SNM spectrum differs markedly between the two Chlamydomonas species. Our results suggest that similar genomic and biological characteristics may result in a similar mutation rate in the two species, whereas the SNM spectrum has more freedom to diverge.

Effects of perceived scarcity on COVID-19 consumer stimulus spending: The roles of ontological insecurity and mutability in predicting prosocial outcomes.

In 2021, the United States government provided a third economic impact payment (EIP) for those designated as experiencing greater need due to the COVID-19 pandemic. With a particular focus on scarcity and ontological insecurity, we collected time-separated data prior to, and following, the third EIP to examine how these variables shape consumer allocation of stimulus funds. We find that scarcity is positively associated with feelings of ontological insecurity, which, interestingly, correlates to a greater allocation of stimulus funds toward charitable giving. We further find evidence that mutability moderates the relationship between ontological insecurity and allocations to charitable giving. In other words, it is those who feel most insecure, but perceive that their resource situation is within their control, who allocated more to charity giving. We discuss the implications of these findings for theory, policy-makers, and the transformative consumer research (TCR) movement.

Using Mutability Landscapes To Guide Enzyme Thermostabilization.

Thermostabilizing enzymes while retaining their activity and enantioselectivity for applied biocatalysis is an important topic in protein engineering. Rational and computational design strategies as well as directed evolution have been used successfully to thermostabilize enzymes. Herein, we describe an alternative mutability-landscape approach that identified three single mutations (R11Y, R11I and A33D) within the enzyme 4-oxalocrotonate tautomerase (4-OT), which has potential as a biocatalyst for pharmaceutical synthesis, that gave rise to significant increases in apparent melting temperature Tm (up to 20 °C) and in half-life at 80 °C (up to 111-fold). Introduction of these beneficial mutations in an enantioselective but thermolabile 4-OT variant (M45Y/F50A) afforded improved triple-mutant enzyme variants showing an up to 39 °C increase in Tm value, with no reduction in catalytic activity or enantioselectivity. This study illustrates the power of mutability-landscape-guided protein engineering for thermostabilizing enzymes.

Tuning Enzyme Activity for Nonaqueous Solvents: Engineering an Enantioselective "Michaelase" for Catalysis in High Concentrations of Ethanol.

Enzymes have evolved to function under aqueous conditions and may not exhibit features essential for biocatalytic application, such as the ability to function in high concentrations of an organic solvent. Consequently, protein engineering is often required to tune an enzyme for catalysis in non-aqueous solvents. In this study, we have used a collection of nearly all single mutants of 4-oxalocrotonate tautomerase, which promiscuously catalyzes synthetically useful Michael-type additions of acetaldehyde to various nitroolefins, to investigate the effect of each mutation on the ability of this enzyme to retain its "Michaelase" activity in elevated concentrations of ethanol. Examination of this mutability landscape allowed the identification of two hotspot positions, Ser30 and Ala33, at which mutations are beneficial for catalysis in high ethanol concentrations. The "hotspot" position Ala33 was then randomized in a highly enantioselective, but ethanol-sensitive 4-OT variant (L8F/M45Y/F50A) to generate an improved enzyme variant (L8F/A33I/M45Y/F50A) that showed great ethanol stability and efficiently catalyzes the enantioselective addition of acetaldehyde to nitrostyrene in 40 % ethanol (permitting high substrate loading) to give the desired γ-nitroaldehyde product in excellent isolated yield (89 %) and enantiopurity (ee=98 %). The presented work demonstrates the power of mutability-landscape-guided enzyme engineering for efficient biocatalysis in non-aqueous solvents.

Sequence intrinsic somatic mutation mechanisms contribute to affinity maturation of VRC01-class HIV-1 broadly neutralizing antibodies.

Variable regions of Ig chains provide the antigen recognition portion of B-cell receptors and derivative antibodies. Ig heavy-chain variable region exons are assembled developmentally from V, D, J gene segments. Each variable region contains three antigen-contacting complementarity-determining regions (CDRs), with CDR1 and CDR2 encoded by the V segment and CDR3 encoded by the V(D)J junction region. Antigen-stimulated germinal center (GC) B cells undergo somatic hypermutation (SHM) of V(D)J exons followed by selection for SHMs that increase antigen-binding affinity. Some HIV-1-infected human subjects develop broadly neutralizing antibodies (bnAbs), such as the potent VRC01-class bnAbs, that neutralize diverse HIV-1 strains. Mature VRC01-class bnAbs, including VRC-PG04, accumulate very high SHM levels, a property that hinders development of vaccine strategies to elicit them. Because many VRC01-class bnAb SHMs are not required for broad neutralization, high overall SHM may be required to achieve certain functional SHMs. To elucidate such requirements, we used a V(D)J passenger allele system to assay, in mouse GC B cells, sequence-intrinsic SHM-targeting rates of nucleotides across substrates representing maturation stages of human VRC-PG04. We identify rate-limiting SHM positions for VRC-PG04 maturation, as well as SHM hotspots and intrinsically frequent deletions associated with SHM. We find that mature VRC-PG04 has low SHM capability due to hotspot saturation but also demonstrate that generation of new SHM hotspots and saturation of existing hotspot regions (e.g., CDR3) does not majorly influence intrinsic SHM in unmutated portions of VRC-PG04 progenitor sequences. We discuss implications of our findings for bnAb affinity maturation mechanisms.

Boosting the efficiency of site-saturation mutagenesis for a difficult-to-randomize gene by a two-step PCR strategy.

Site-saturation mutagenesis (SSM) has been used in directed evolution of proteins for a long time. As a special form of saturation mutagenesis, it involves individual randomization at a given residue with formation of all 19 amino acids. To date, the most efficient embodiment of SSM is a one-step PCR-based approach using NNK codon degeneracy. However, in the case of difficult-to-randomize genes, SSM may not deliver all of the expected 19 mutants, which compels the user to invest further efforts by applying site-directed mutagenesis for the construction of the missing mutants. To solve this problem, we developed a two-step PCR-based technique in which a mutagenic primer and a non-mutagenic (silent) primer are used to generate a short DNA fragment, which is recovered and then employed as a megaprimer to amplify the whole plasmid. The present two-step and older one-step (partially overlapped primer approach) procedures were compared by utilizing cytochrome P450-BM3, which is a "difficult-to-randomize" gene. The results document the distinct superiority of the new method by checking the library quality on DNA level based on massive sequence data, but also at amino acid level. Various future applications in biotechnology can be expected, including the utilization when constructing mutability landscapes, which provide semi-rational information for identifying hot spots for protein engineering and directed evolution.

The Generation and Exploitation of Protein Mutability Landscapes for Enzyme Engineering.

The increasing number of enzyme applications in chemical synthesis calls for new engineering methods to develop the biocatalysts of the future. An interesting concept in enzyme engineering is the generation of large-scale mutational data in order to chart protein mutability landscapes. These landscapes allow the important discrimination between beneficial mutations and those that are neutral or detrimental, thus providing detailed insight into sequence-function relationships. As such, mutability landscapes are a powerful tool with which to identify functional hotspots at any place in the amino acid sequence of an enzyme. These hotspots can be used as targets for combinatorial mutagenesis to yield superior enzymes with improved catalytic properties, stability, or even new enzymatic activities. The generation of mutability landscapes for multiple properties of one enzyme provides the exciting opportunity to select mutations that are beneficial either for one or for several of these properties. This review presents an overview of the recent advances in the construction of mutability landscapes and discusses their importance for enzyme engineering.

Protein engineering using mutability landscapes: Controlling site-selectivity of P450-catalyzed steroid hydroxylation.

Directed evolution and rational design have been used widely in engineering enzymes for their application in synthetic organic chemistry and biotechnology. With stereoselectivity playing a crucial role in catalysis for the synthesis of valuable chemical and pharmaceutical compounds, rational design has not achieved such wide success in this specific area compared to directed evolution. Nevertheless, one bottleneck of directed evolution is the laborious screening efforts and the observed trade-offs in catalytic profiles. This has motivated researchers to develop more efficient protein engineering methods. As a prime approach, mutability landscaping avoids such trade-offs by providing more information of sequence-function relationships. Here, we describe an application of this efficient protein engineering method to improve the regio-/stereoselectivity and activity of P450BM3 for steroid hydroxylation, while keeping the mutagenesis libraries small so that they will require only minimal screening.

L858R emerges as a potential biomarker predicting response of lung cancer models to anti-EGFR antibodies: Comparison of osimertinib vs. cetuximab.

EGFR-specific tyrosine kinase inhibitors (TKIs), especially osimertinib, have changed lung cancer therapy, but secondary mutations confer drug resistance. Because other EGFR mutations promote dimerization-independent active conformations but L858R strictly depends on receptor dimerization, we herein evaluate the therapeutic potential of dimerization-inhibitory monoclonal antibodies (mAbs), including cetuximab. This mAb reduces viability of cells expressing L858R-EGFR and blocks the FOXM1-aurora survival pathway, but other mutants show no responses. Unlike TKI-treated patient-derived xenografts, which relapse post osimertinib treatment, cetuximab completely prevents relapses of L858R+ tumors. We report that osimertinib's inferiority associates with induction of mutagenic reactive oxygen species, whereas cetuximab's superiority is due to downregulation of adaptive survival pathways (e.g., HER2) and avoidance of mutation-prone mechanisms that engage AXL, RAD18, and the proliferating cell nuclear antigen. These results identify L858R as a predictive biomarker, which may pave the way for relapse-free mAb monotherapy relevant to a large fraction of patients with lung cancer.

'Nothing new under the sun': Rethinking recycling in the past- Editorial.

Editorial for the Special Issue of Archaeometry 'Tackling Recycling in the Past'. The practice of recycling has undoubtedly become one of the most important strategies to build a long-term sustainable society in the modern world. However, both the perception and practice of recycling can be traced back to prehistory through various archaeological records. Objects made of stone, jade, mortar, textiles, pottery and bones display evidence of physical reshaping and repair, as do objects of metal and glass. Metal and glass, moreover, are materials which can be melted and recast, freeing ancient people from the limitations of the physical form of the original object. Illustrating and understanding patterns of recycling and the underlying social organization can significantly advance our knowledge of ancient people, their economic, political and cultural motivations for recycling, as well as the broad interaction between the social and material world. Though the issue of recycling is not novel in the discussions and debates of the archaeological circle, new theoretical frameworks, methodologies and archaeometric data encourage us to revisit the topic in this special issue. In this editorial, we consider what recycling means in the past, and why these papers are vital.

How Sequence Context-Dependent Mutability Drives Mutation Rate Variation in the Genome.

The rate of mutations varies >100-fold across the genome, altering the rate of evolution, and susceptibility to genetic diseases. The strongest predictor of mutation rate is the sequence itself, varying 75-fold between trinucleotides. The fact that DNA sequence drives its own mutation rate raises a simple but important prediction; highly mutable sequences will mutate more frequently and eliminate themselves in favor of sequences with lower mutability, leading to a lower equilibrium mutation rate. However, purifying selection constrains changes in mutable sequences, causing higher rates of mutation. We conduct a simulation using real human mutation data to test if 1) DNA evolves to a low equilibrium mutation rate and 2) purifying selection causes a higher equilibrium mutation rate in the genome's most important regions. We explore how this simple process affects sequence evolution in the genome, and discuss the implications for modeling evolution and susceptibility to DNA damage.

A novel statistical method predicts mutability of the genomic segments of the SARS-CoV-2 virus.

The SARS-CoV-2 virus has made the largest pandemic of the 21st century, with hundreds of millions of cases and tens of millions of fatalities. Scientists all around the world are racing to develop vaccines and new pharmaceuticals to overcome the pandemic and offer effective treatments for COVID-19 disease. Consequently, there is an essential need to better understand how the pathogenesis of SARS-CoV-2 is affected by viral mutations and to determine the conserved segments in the viral genome that can serve as stable targets for novel therapeutics. Here, we introduce a text-mining method to estimate the mutability of genomic segments directly from a reference (ancestral) whole genome sequence. The method relies on calculating the importance of genomic segments based on their spatial distribution and frequency over the whole genome. To validate our approach, we perform a large-scale analysis of the viral mutations in nearly 80,000 publicly available SARS-CoV-2 predecessor whole genome sequences and show that these results are highly correlated with the segments predicted by the statistical method used for keyword detection. Importantly, these correlations are found to hold at the codon and gene levels, as well as for gene coding regions. Using the text-mining method, we further identify codon sequences that are potential candidates for siRNA-based antiviral drugs. Significantly, one of the candidates identified in this work corresponds to the first seven codons of an epitope of the spike glycoprotein, which is the only SARS-CoV-2 immunogenic peptide without a match to a human protein.

The Mutability of Yeast Prions.

Prions replicate by a self-templating mechanism. Infidelity in the process can lead to the emergence of new infectious structures, referred to as variants or strains. The question of whether prions are prone to mis-templating is not completely answered. Our previous experiments with 23 variants of the yeast [PSI+] prion do not support broad mutability. However, it became clear recently that the heat shock protein Hsp104 can restrict [PSI+] strain variation. This raises the possibility that many transmutable variants of the prion may have been mistaken as faithful-propagating simply because the mutant structure was too sturdy or too frail to take root in the wild-type cell. Here, I alter the strength of Hsp104 in yeast, overexpressing wild-type Hsp104 or expressing the hypo-active Hsp104T160M mutant, and check if the new environments enable the variants to mutate. Two variants hitherto thought of as faithful-propagating are discovered to generate different structures, which are stabilized with the hypo-active chaperone. In contrast, most transmutable variants discovered in cells overexpressing Hsp104 have been correctly identified as such previously in wild-type cells without the overexpression. The majority of transmutable variants only mis-template the structure of VH, VK, or VL, which are the most frequently observed variants and do not spontaneously mutate. There are four additional variants that never give rise to different structures in all cell conditions tested. Therefore, quite a few [PSI+] variants are faithful-propagating, and even the transmutable ones do not freely evolve but can only change to limited structural types.

Understanding the mutational frequency in SARS-CoV-2 proteome using structural features.

The prolonged transmission of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) virus in the human population has led to demographic divergence and the emergence of several location-specific clusters of viral strains. Although the effect of mutation(s) on severity and survival of the virus is still unclear, it is evident that certain sites in the viral proteome are more/less prone to mutations. In fact, millions of SARS-CoV-2 sequences collected all over the world have provided us a unique opportunity to understand viral protein mutations and develop novel computational approaches to predict mutational patterns. In this study, we have classified the mutation sites into low and high mutability classes based on viral isolates count containing mutations. The physicochemical features and structural analysis of the SARS-CoV-2 proteins showed that features including residue type, surface accessibility, residue bulkiness, stability and sequence conservation at the mutation site were able to classify the low and high mutability sites. We further developed machine learning models using above-mentioned features, to predict low and high mutability sites at different selection thresholds (ranging 5-30% of topmost and bottommost mutated sites) and observed the improvement in performance as the selection threshold is reduced (prediction accuracy ranging from 65 to 77%). The analysis will be useful for early detection of variants of concern for the SARS-CoV-2, which can also be applied to other existing and emerging viruses for another pandemic prevention.

Scan-Find-Scan-Model: Discrete Site-Targeted Suppressor Design Strategy for Amyloid-ß.

Alzheimer's disease is undoubtedly the most well-studied neurodegenerative disease. Consequently, the amyloid-ß (Aß) protein ranks at the top in terms of getting attention from the scientific community for structural property-based characterization. Even after decades of extensive research, there is existing volatility in terms of understanding and hence the effective tackling procedures against the disease that arises due to the lack of knowledge of both specific target- and site-specific drugs. Here, we develop a multidimensional approach based on the characterization of the common static-dynamic-thermodynamic trait of the monomeric protein, which efficiently identifies a small target sequence that contains an inherent tendency to misfold and consequently aggregate. The robustness of the identification of the target sequence comes with an abundance of a priori knowledge about the length and sequence of the target and hence guides toward effective designing of the target-specific drug with a very low probability of bottleneck and failure. Based on the target sequence information, we further identified a specific mutant that showed the maximum potential to act as a destabilizer of the monomeric protein as well as enormous success as an aggregation suppressor. We eventually tested the drug efficacy by estimating the extent of modulation of binding affinity existing within the fibrillar form of the Aß protein due to a single-point mutation and hence provided a proof of concept of the entire protocol.

Allosteric perspective on the mutability and druggability of the SARS-CoV-2 Spike protein.

Recent developments in the SARS-CoV-2 pandemic point to its inevitable transformation into an endemic disease, urging both refinement of diagnostics for emerging variants of concern (VOCs) and design of variant-specific drugs in addition to vaccine adjustments. Exploring the structure and dynamics of the SARS-CoV-2 Spike protein, we argue that the high-mutability characteristic of RNA viruses coupled with the remarkable flexibility and dynamics of viral proteins result in a substantial involvement of allosteric mechanisms. While allosteric effects of mutations should be considered in predictions and diagnostics of new VOCs, allosteric drugs advantageously avoid escape mutations via non-competitive inhibition originating from alternative distal locations. The exhaustive allosteric signaling and probing maps presented herein provide a comprehensive picture of allostery in the spike protein, making it possible to locate potential mutations that could work as new VOC "drivers" and to determine binding patches that may be targeted by newly developed allosteric drugs.

Verb Metaphoric Extension Under Semantic Strain.

This paper explores the processes underlying verb metaphoric extension. Work on metaphor processing has largely focused on noun metaphor, despite evidence that verb metaphor is more common. Across three experiments, we collected paraphrases of simple intransitive sentences varying in semantic strain-for example, The motor complained → The engine made strange noises-and assessed the degree of meaning change for the noun and the verb. We developed a novel methodology for this assessment using word2vec. In Experiments 1 and 2, we found that (a) under semantic strain, verb meanings were more likely to be adjusted than noun meanings; (b) the degree of verb meaning adjustment-but not noun meaning adjustment-increased with semantic strain; and (c) verb meaning extension is primarily driven by online adjustment, although sense selection also plays a role. In Experiment 3, we replicated the word2vec results with an assessment using human subjects. The results further showed that nouns and verbs change meaning in qualitatively different ways, with verbs more likely to change meaning metaphorically and nouns more likely to change meaning taxonomically or metonymically. These findings bear on the origin and processing of verb metaphors and provide a link between online sentence processing and diachronic change over language evolution.

Mutational Asymmetries in the SARS-CoV-2 Genome May Lead to Increased Hydrophobicity of Virus Proteins.

The genomic diversity of SARS-CoV-2 has been a focus during the ongoing COVID-19 pandemic. Here, we analyzed the distribution and character of emerging mutations in a data set comprising more than 95,000 virus genomes covering eight major SARS-CoV-2 lineages in the GISAID database, including genotypes arising during COVID-19 therapy. Globally, the C>U transitions and G>U transversions were the most represented mutations, accounting for the majority of single-nucleotide variations. Mutational spectra were not influenced by the time the virus had been circulating in its host or medical treatment. At the amino acid level, we observed about a 2-fold excess of substitutions in favor of hydrophobic amino acids over the reverse. However, most mutations constituting variants of interests of the S-protein (spike) lead to hydrophilic amino acids, counteracting the global trend. The C>U and G>U substitutions altered codons towards increased amino acid hydrophobicity values in more than 80% of cases. The bias is explained by the existing differences in the codon composition for amino acids bearing contrasting biochemical properties. Mutation asymmetries apparently influence the biochemical features of SARS CoV-2 proteins, which may impact protein-protein interactions, fusion of viral and cellular membranes, and virion assembly.