Properties Of Amino Acids Research Articles

Structural and Vibrational Properties of Amino Acids from Composite Schemes and Double-Hybrid DFT: Hydrogen Bonding in Serine as a Test Case

The structures, relative stabilities, and vibrational wavenumbers of the two most stable conformers of serine, stabilized by the O-H···N, O-H···O═C and N-H···O-H intramolecular hydrogen bonds, have been evaluated by means of state-of-the-art composite schemes based on coupled-cluster (CC) theory. The so-called "cheap" composite approach (CCSD(T)/(CBS+CV)MP2) allowed determination of accurate equilibrium structures and harmonic vibrational wavenumbers, also pointing out significant corrections beyond the CCSD(T)/cc-pVTZ level. These accurate results stand as a reference for benchmarking selected hybrid and double-hybrid, dispersion-corrected DFT functionals. B2PLYP-D3 and DSDPBEP86 in conjunction with a triple-ζ basis set have been confirmed as effective methodologies for structural and spectroscopic studies of medium-sized flexible biomolecules, also showing intramolecular hydrogen bonding. These best performing double-hybrid functionals have been employed to simulate IR spectra by means of vibrational perturbation theory, also considering hybrid CC/DFT schemes. The best overall agreement with experiment, with mean absolute error of 8 cm-1, has been obtained by combining CCSD(T)/(CBS+CV)MP2 harmonic wavenumbers with B2PLYP-D3/maug-cc-pVTZ anharmonic corrections. Finally, a composite scheme entirely based on CCSD(T) calculations (CCSD(T)/CBS+CV) has been employed for energetics, further confirming that serine II is the most stable conformer, also when zero-point vibrational energy corrections are included.

Machine Learning-Based Ensemble Model for Zika Virus T-Cell Epitope Prediction.

Zika virus (ZIKV), the causative agent of Zika fever in humans, is an RNA virus that belongs to the genus Flavivirus. Currently, there is no approved vaccine for clinical use to combat the ZIKV infection and contain the epidemic. Epitope-based peptide vaccines have a large untapped potential for boosting vaccination safety, cross-reactivity, and immunogenicity. Though many attempts have been made to develop vaccines for ZIKV, none of these have proved to be successful. Epitope-based peptide vaccines can act as powerful alternatives to conventional vaccines due to their low production cost, less reactogenic, and allergenic responses. For designing an effective and viable epitope-based peptide vaccine against this deadly virus, it is essential to select the antigenic T-cell epitopes since epitope-based vaccines are considered safe. The in silico machine-learning-based approach for ZIKV T-cell epitope prediction would save a lot of physical experimental time and efforts for speedy vaccine development compared to in vivo approaches. We hereby have trained a machine-learning-based computational model to predict novel ZIKV T-cell epitopes by employing physicochemical properties of amino acids. The proposed ensemble model based on a voting mechanism works by blending the predictions for each class (epitope or nonepitope) from each base classifier. Predictions obtained for each class by the individual classifier are summed up, and the class with the majority vote is predicted upon. An odd number of classifiers have been used to avoid the occurrence of ties in the voting. Experimentally determined ZIKV peptide sequences data set was collected from Immune Epitope Database and Analysis Resource (IEDB) repository. The data set consists of 3,519 sequences, of which 1,762 are epitopes and 1,757 are nonepitopes. The length of sequences ranges from 6 to 30 meter. For each sequence, we extracted 13 physicochemical features. The proposed ensemble model achieved sensitivity, specificity, Gini coefficient, AUC, precision, F-score, and accuracy of 0.976, 0.959, 0.993, 0.994, 0.989, 0.985, and 97.13%, respectively. To check the consistency of the model, we carried out five-fold cross-validation and an average accuracy of 96.072% is reported. Finally, a comparative analysis of the proposed model with existing methods has been carried out using a separate validation data set, suggesting the proposed ensemble model as a better model. The proposed ensemble model will help predict novel ZIKV vaccine candidates to save lives globally and prevent future epidemic-scale outbreaks.

ZoomQA: residue-level protein model accuracy estimation with machine learning on sequential and 3D structural features.

MotivationThe Estimation of Model Accuracy problem is a cornerstone problem in the field of Bioinformatics. As of CASP14, there are 79 global QA methods, and a minority of 39 residue-level QA methods with very few of them working on protein complexes. Here, we introduce ZoomQA, a novel, single-model method for assessing the accuracy of a tertiary protein structure/complex prediction at residue level, which have many applications such as drug discovery. ZoomQA differs from others by considering the change in chemical and physical features of a fragment structure (a portion of a protein within a radius n}{}r of the target amino acid) as the radius of contact increases. Fourteen physical and chemical properties of amino acids are used to build a comprehensive representation of every residue within a protein and grade their placement within the protein as a whole. Moreover, we have shown the potential of ZoomQA to identify problematic regions of the SARS-CoV-2 protein complex.ResultsWe benchmark ZoomQA on CASP14, and it outperforms other state-of-the-art local QA methods and rivals state of the art QA methods in global prediction metrics. Our experiment shows the efficacy of these new features and shows that our method is able to match the performance of other state-of-the-art methods without the use of homology searching against databases or PSSM matrices.Availability http://zoomQA.renzhitech.com

Prediction and characterization of the T cell epitopes for the major soybean protein allergens using bioinformatics approaches.

Protein allergens is a health risk for consumption of soybeans. To understand allerginicity mechanism, T cell epitopes of 7 soybean allergens were predicted and screened by abilities to induce cytokine interleukin (IL) 4. The relationships among amino acid composition, properties, allergenicity, and pepsin hydrolysis sites were analyzed. Among the 138 T cell epitopes identified, YIKDVFRVIPSEVLS, KDVFRVIPSEVLSNS, DVFRVIPSEVLSNSY of Gly m 6.0501 (P04347), and AKADALFKAIEAYLL, ADALFKAIEAYLLAH of Gly m 4.0101 (P26987) were the most possible epitope candidates. In T cell epitopes pattern, the frequencies of amino acids Q, D, E, P, and G decreased, while F, I, N, V, K, H, A, L, and S increased. Hydrophobic residues at positions p1 and p2 and positively charged residues in positions p13 might contribute to allergenicity. Most of epitopes could be hydrolyzed by pepsin into small polypeptides within 12 residues length, and the anti-digestive epitope regions contained I, V, S, N, and Q residues. T cell epitopes EEQRQQEGVIVELSK from Gly m 5.03 (P25974) showed resistance to pepsin hydrolysis and would cause a higher Th2 cell response. This research provides basis for the development of hypoallergenic soybean products in the soybean industry as well as for the immunotherapy design for protein allergy.

Prioritizing the candidate genes related to cervical cancer using the moment of inertia tensor.

It is well known that cervical cancer poses the fourth most malignancy threat to women worldwide among all cancer types. There is a tremendous improvement in realizing the underlying molecular associations in cervical cancer. Several studies reported pieces of evidence for the involvement of various genes in the disease progression. However, with the ever-evolving bioinformatics tools, there has been an upsurge in predicting numerous genes responsible for cervical cancer progression and making it highly complex to target the genes for further evaluation. In this article, we prioritized the candidate genes based on the sequence similarity analysis with known cancer genes. For this purpose, we used the concept of the moment of inertia tensor, which reveals the similarities between the protein sequences more efficiently. Tensor for moment of inertia explores the similarity of the protein sequences based on the physicochemical properties of amino acids. From our analysis, we obtained 14 candidate cervical cancer genes, which are highly similar to known cervical cancer genes. Further, we analyzed the GO terms and prioritized these genes based on the number of hits with biological process, molecular functions, and their involvement in KEGG pathways. We also discussed the evidence-based involvement of the prioritized genes in other cancers and listed the available drugs for those genes.

RF‐SVM: Identification ofDNA‐binding proteins based on comprehensive feature representation methods and support vector machine

Protein-DNA interactions play an important role in biological progress, such as DNA replication, repair, and modification processes. In order to have a better understanding of its functions, the one of the most important steps is the identification of DNA-binding proteins. We propose a DNA-binding protein predictor, namely, RF-SVM, which contains four types features, that is, pseudo amino acid composition (PseAAC), amino acid distribution (AAD), adjacent amino acid composition frequency (ACF) and Local-DPP. Random Forest algorithm is utilized for selecting top 174 features, which are established the predictor model with the support vector machine (SVM) on training dataset UniSwiss-Tr. Finally, RF-SVM method is compared with other existing methods on test dataset UniSwiss-Tst. The experimental results demonstrated that RF-SVM has accuracy of 84.25%. Meanwhile, we discover that the physicochemical properties of amino acids for OOBM770101(H), CIDH920104(H), MIYS990104(H), NISK860101(H), VINM940103(H), and SNEP660101(A) have contribution to predict DNA-binding proteins. The main code and datasets can gain in https://github.com/NiJianWei996/RF-SVM.

EightyDVec: a method for protein sequence similarity analysis using physicochemical properties of amino acids

ABSTRACT Similarity analysis of protein sequences can expose the evolutionary relationship among them. It is required to design effective computational algorithms that can compare the similarities among the colossal amount of sequences. Alignment-based approaches to this problem are often computationally expensive, especially when the number of sequences is large. This research aims to develop an efficient alignment-free tool in the field of protein sequence comparison and phylogenetic study. The proposed method, namely EightyDVec, performs a feature generation process based on the physiochemical properties of amino acids that best describe the evolutionary relationship among the species in a protein family. Using EightyDVec, protein sequences are transformed into 80-dimensional feature vectors and the comparisons between sequences are performed conveniently through these vectors. Four different datasets are used to validate the accuracy of EightyDVec, and the obtained results have shown the great effectiveness of the proposed method in the similarity analysis of protein sequences.

Bioinspired self-assembled nanoparticles with stable fluorescent properties in wide visible light region

Development of fluorescent materials with high photostability and biocompatibility is of great importance in bio-labeling and bio-imaging. Herein, we employ amphiphilic amino acids as basic building blocks to fabricate stable biology self-luminous nanoparticles with a uniform size distribution of 300 nm. The endogenous property of amino acids endows the nanoparticles with high biocompatibility. More importantly, the nanoparticles can hold the property of multiple fluorescence emission in a wide wavelength range of 400–670 nm with high photostability. The dynamic fluorescence shift is as high as 1.36779. These properties make the nanoparticles a potential candidate for broad biomedical applications.

Protein music of enhanced musicality by music style guided exploration of diverse amino acid properties

Inspired by the traceable analogies between protein sequences and music notes, protein music has been composed from amino acid sequences for popularizing science and sourcing melodies. Despite the continuous development of protein-to-music algorithms, the musicality of protein music lags far behind human music. Musicality may be enhanced by fine-tuned protein-to-music mapping to the features of a specific music style. We analyzed the features of a music style (Fantasy-Impromptu style), and used the quantized musical features to guide broad exploration of diverse amino acid properties (104 properties, sequence patterns and variations) for developing a novel protein-to-music algorithm of enhanced musicality. This algorithm was applied to 18 proteins of various biological functions. The derived music pieces consistently exhibited enhanced musicality with respect to existing protein music. Music style guided exploration of diverse amino acid properties enable protein music composition of enhanced musicality, which may be further developed and applied to a wider variety of music styles.

PYTHIA: Deep Learning Approach for Local Protein Conformation Prediction.

Protein Blocks (PBs) are a widely used structural alphabet describing local protein backbone conformation in terms of 16 possible conformational states, adopted by five consecutive amino acids. The representation of complex protein 3D structures as 1D PB sequences was previously successfully applied to protein structure alignment and protein structure prediction. In the current study, we present a new model, PYTHIA (predicting any conformation at high accuracy), for the prediction of the protein local conformations in terms of PBs directly from the amino acid sequence. PYTHIA is based on a deep residual inception-inside-inception neural network with convolutional block attention modules, predicting 1 of 16 PB classes from evolutionary information combined to physicochemical properties of individual amino acids. PYTHIA clearly outperforms the LOCUSTRA reference method for all PB classes and demonstrates great performance for PB prediction on particularly challenging proteins from the CASP14 free modelling category.

Genetic variability and molecular evolution of arabis mosaic virus based on the coat protein gene sequence

AbstractArabis mosaic virus (ArMV) is an important pathogen infecting a wide range of plant species worldwide. In the present study, we carried out exhaustive phylogenetic analyses based on the coat protein (CP) sequence of ArMV isolates originating from different host plants and geographic regions. Maximum‐likelihood reconstruction revealed the presence of three distinct, well‐supported phylogroups. BaTS analyses indicated that the diversity of ArMV isolates may be correlated with both host plant and geographic origin. Moreover, population genetic analyses showed significant differences in the level of genetic diversity among variants within ArMV that originated from rhubarb plants, as expected by quasispecies. Further analysis revealed that both selection and recombination are shaping the population structure of ArMV. In total 14 groups of residues were detected as coevolving for different amino acid properties.

Probing physical invariance between enantiomers: The magnetooptical and refractive properties of the simplest chiral amino acid – Alanine

Observation of magnetochiral (MCh) phenomena and their possible contributions to mechanisms of homochirality among living things has stimulated interest in amino acids (AAs). MCh studies of AAs remain rare and even conventional magnetooptical properties are scarce. Herein, we develop a magnetooptical spectrometer and use it to measure the Faraday rotation (FR) for alanine isomers and racemate probing physical invariance between enantiomers. The FRs and refractive indexes (n) for a wide range of AA aqueous solution concentrations and their wavelength and pH dependences are measured. The pH dependences of FR and n show titration curve–like behavior. Enantiomers of Ala exhibit identical FR magnitudes and signs. Small differences are correlated with compound purity and hydrophobicity. All results indicate physical invariance between enantiomers of alanine. The β–isomer exhibits similar behavior but its FR and n are larger in magnitude.

Adaptive Chaotic Image Encryption Algorithm Based on RNA and Pixel Depth

To prevent the leakage of image content, image encryption technology has received increasing attention. Most current algorithms are only suitable for the images of certain types and cannot update keys in a timely manner. To tackle such problems, we propose an adaptive chaotic image encryption algorithm based on RNA and pixel depth. Firstly, a novel chaotic system, two-dimensional improved Logistic-adjusted-Sine map is designed. Then, we propose a three-dimensional adaptive Arnold transform for scrambling. Secondly, keys are generated by the hash values of the plain image and current time to achieve one-image, one-key, and one-time pad simultaneously. Thirdly, we build a pre-permuted RNA cube for 3D adaptive scrambling by pixel depth, chaotic sequences, and adaptive RNA coding. Finally, selective diffusion combined with pixel depth and RNA operations is performed, in which the RNA operators are determined by the chemical structure and properties of amino acids. Pixel depth is integrated into the whole procedure of parameter generation, scrambling, and diffusion. Experiments and algorithm analyses show that our algorithm has strong security, desirable performance, and a broader scope of application.

TET2 and MSH6: A Novel Role for MMR in the Pathogenesis of Myeloid Malignancies

TET2 mutations (TET2MT) occur in up to 40% of myeloid neoplasms and increase linearly with age. That these lesions are founding hits can be concluded from clonal architecture and from the presence of subclinical TET2MT clones in healthy individuals at risk for subsequent MDS. Though TET2MT are heterogeneous, most disrupt TET2 catalytic domain dioxygenase activity; these widespread LOF mutations implicate TET2 as a bona fide tumor-suppressor gene (TSG). As downstream targets have not been identified as being associated with TET2MT, the precise mechanisms of TET2MT in leukemogenesis remains unclear. We studied whether TET2MT predispose cells to acquiring additional oncogenic mutations through faulty DNA repair to produce clonally acquired mutator phenotypes.In WES analyses of 435 myeloid neoplasia patient (pt) samples, we identified 95 with somatic TET2MT enriched vs controls in the ExAC database, and used 6 bioinformatics tools to predict the effect on protein function based on algorithms incorporating sequence homology, amino acid properties, sequence context, and similarity to Mendelian diseases. Most pts (63) were predicted to have damaging TET2MT in 3+ out of six of these tools.To validate TET2MT clinical relevance, we established an in-vitro 2D-UPLC-MS/MS assay to assess levels of the TET2-dependent DNA oxidation products 5hmC, 5fC, and 5CaC. While TET2 +/- knockout mouse samples had nonsignificant differences in 5hmC (1.2-fold lower levels vs TET2WT, p=.23) and 5fC (1.4-fold increase vs TET2WT, p=.07), they had significantly lower 5CaC (2.4-fold decrease vs TET2WT, p<.0001). TET2 -/- mouse samples had significantly lower 5hmC (1.6-fold reduction vs TET2WT, p<.0001), 5fC (1.3-fold reduction vs TET2WT, p=.011), and 5CaC (2.4-fold reduction vs TET2 WT, p<.0001). Human MOLM-13 TET2 -knockdown (TET2 kd) cells also had 1.6-fold lower levels of 5hmC (p<.0001). Reassuringly, eight samples from TET2MT patients also had lower 5hmC (5/8 samples, peak 2.9-fold reduction vs TET2WT, p<.0001), 5fC (6/8 samples, peak 8.2-fold reduction vs TET2WT, p<.0001), and 5CaC (5/8 samples, peak 2.6-fold reduction vs TET2 WT, p<.0001), and all 8/8 TET2MT pt samples had significant declines in at least one of the three TET2-dependent oxidation vs TET2WT samples.To test whether TET2MT predisposed cells to acquiring additional genomic mutations, we measured the total number of single nucleotide variants (SNV) in the entire exome in each pt sample, excluded pts with mutations in known DNA repair proteins, and compared the median WES SNV of TET2MT pts to TET2WT pts. Consistent with our theory, TET2MT pts had a 1.5-fold increase in median WES SNV (p<.0001), particularly at 5hmC sites. Pts with a TET 2MT variant allele frequency>50% had a 2.1-fold increase in median WES SNV (p=.03), consistent with a gene-dose effect.TET2 -/- knockout mice had 1.4-fold increased mutagenicity at TET2-dependent active demethylation sites, in a manner suggestive of defective mismatch repair (MMR). HeLa TET2 kd cells also had a 24-fold increase in spontaneous mutations, reversed with TET2 cDNA knock-in. MMR gene expression (MSH2, MSH3, MSH6, and MLH1) was equivalent among 20 TET2MT MDS pts, 71 TET2WT MDS pts, and 17 controls, indicating MMR downregulation was not responsible for the hypermutagenicity. Five pts with TET2MT were microsatellite-stable (MSS) at 5 TET2-independent poly-dA microsatellite loci, suggesting that hypermutagenicity was not driven by global MMR dysfunction and may occur only at CpG-containing microsatellites.By overexpressing Flag/V5-tagged TET2 in mouse-MEL cells and subjecting them to protein affinity purification, we identified MSH6 as a novel TET2 binding partner. TET2:MSH6 interactions were validated in co-immunofluorescence experiments in MEL and MOLM-13 cells, and TET2kd altered MSH6 nuclear co-localization. MOLM-13 TET2kd increased PARP inhibitor sensitivity by almost 3-fold, suggesting that TET2kd renders cells vulnerable to perturbations in DNA repair pathways.In sum, we uncovered novel connections among TET2, MSH6, epigenetic modifications, and genomic instability. Given that MSH6 is known to preferentially bind 5hmC, TET2 may target MSH6 to TET2-dependent DNA loci. Genomic instability due to TET2 dysfunction may allow therapeutic targeting of DNA repair proteins in the subset of hematologic malignancy pts with TET2MT. DisclosuresGreenberg:Oisin Biotechnologies: Consultancy; Acetylon Pharmaceuticals Inc.: Patents & Royalties: Treatment of Protein Degradation Disorders. Makishima:Yasuda Medical Foundation: Research Funding. Sekeres:Celgene: Membership on an entity's Board of Directors or advisory committees. Maciejewski:Alexion Pharmaceuticals, Inc.: Consultancy, Membership on an entity's Board of Directors or advisory committees, Other: Speaker Fees; Apellis Pharmaceuticals: Consultancy; Ra Pharma: Consultancy.

The use of formic acid as a common solvent for electrospinning of hybrid PHB/Soy protein fibers

The use of polymers naturally derived from green sources meet the requirements for the development of new sustainable and biodegradable materials. Soy protein (SP) are one of the most processed plant proteins which contain essential amino acid and valuable functional properties which could essentially tailor the hydrophobic poly β-hydroxybutyrate (PHB) biodegradable matrix. Novel blend nanofibrous nonwoven from SP and PHB at different proportions and increased hydrophilicity were successfully obtained by electrospinning from formic acid (FA) as a common solvent. The morphology and structure of electrospun blend nanofibers were investigated by SEM, FT-IR and WAXD analysis.

Solubility Parameters of Amino Acids on Liquid-Liquid Phase Separation and Aggregation of Proteins.

The solution properties of amino acids determine the folding, aggregation, and liquid–liquid phase separation (LLPS) behaviors of proteins. Various indices of amino acids, such as solubility, hydropathy, and conformational parameter, describe the behaviors of protein folding and solubility both in vitro and in vivo. However, understanding the propensity of LLPS and aggregation is difficult due to the multiple interactions among different amino acids. Here, the solubilities of aromatic amino acids (SAs) were investigated in solution containing 20 types of amino acids as amino acid solvents. The parameters of SAs in amino acid solvents (PSASs) were varied and dependent on the type of the solvent. Specifically, Tyr and Trp had the highest positive values while Glu and Asp had the lowest. The PSAS values represent soluble and insoluble interactions, which collectively are the driving force underlying the formation of droplets and aggregates. Interestingly, the PSAS of a soluble solvent reflected the affinity between amino acids and aromatic rings, while that of an insoluble solvent reflected the affinity between amino acids and water. These findings suggest that the PSAS can distinguish amino acids that contribute to droplet and aggregate formation, and provide a deeper understanding of LLPS and aggregation of proteins.

MDCAN-Lys: A Model for Predicting Succinylation Sites Based on Multilane Dense Convolutional Attention Network

Lysine succinylation is an important post-translational modification, whose abnormalities are closely related to the occurrence and development of many diseases. Therefore, exploring effective methods to identify succinylation sites is helpful for disease treatment and research of related drugs. However, most existing computational methods for the prediction of succinylation sites are still based on machine learning. With the increasing volume of data and complexity of feature representations, it is necessary to explore effective deep learning methods to recognize succinylation sites. In this paper, we propose a multilane dense convolutional attention network, MDCAN-Lys. MDCAN-Lys extracts sequence information, physicochemical properties of amino acids, and structural properties of proteins using a three-way network, and it constructs feature space. For each sub-network, MDCAN-Lys uses the cascading model of dense convolutional block and convolutional block attention module to capture feature information at different levels and improve the abstraction ability of the network. The experimental results of 10-fold cross-validation and independent testing show that MDCAN-Lys can recognize more succinylation sites, which is consistent with the conclusion of the case study. Thus, it is worthwhile to explore deep learning-based methods for the recognition of succinylation sites.

FEGS: a novel feature extraction model for protein sequences and its applications

BackgroundFeature extraction of protein sequences is widely used in various research areas related to protein analysis, such as protein similarity analysis and prediction of protein functions or interactions.ResultsIn this study, we introduce FEGS (Feature Extraction based on Graphical and Statistical features), a novel feature extraction model of protein sequences, by developing a new technique for graphical representation of protein sequences based on the physicochemical properties of amino acids and effectively employing the statistical features of protein sequences. By fusing the graphical and statistical features, FEGS transforms a protein sequence into a 578-dimensional numerical vector. When FEGS is applied to phylogenetic analysis on five protein sequence data sets, its performance is notably better than all of the other compared methods.ConclusionThe FEGS method is carefully designed, which is practically powerful for extracting features of protein sequences. The current version of FEGS is developed to be user-friendly and is expected to play a crucial role in the related studies of protein sequence analyses.

Mining the Biomarker Potential of the Urine Peptidome: From Amino Acids Properties to Proteases.

Native biofluid peptides offer important information about diseases, holding promise as biomarkers. Particularly, the non-invasive nature of urine sampling, and its high peptide concentration, make urine peptidomics a useful strategy to study the pathogenesis of renal conditions. Moreover, the high number of detectable peptides as well as their specificity set the ground for the expansion of urine peptidomics to the identification of surrogate biomarkers for extra-renal diseases. Peptidomics further allows the prediction of proteases (degradomics), frequently dysregulated in disease, providing a complimentary source of information on disease pathogenesis and biomarkers. Then, what does urine peptidomics tell us so far? In this paper, we appraise the value of urine peptidomics in biomarker research through a comprehensive analysis of all datasets available to date. We have mined > 50 papers, addressing > 30 different conditions, comprising > 4700 unique peptides. Bioinformatic tools were used to reanalyze peptide profiles aiming at identifying disease fingerprints, to uncover hidden disease-specific peptides physicochemical properties and to predict the most active proteases associated with their generation. The molecular patterns found in this study may be further validated in the future as disease biomarker not only for kidney diseases but also for extra-renal conditions, as a step forward towards the implementation of a paradigm of predictive, preventive and personalized (3P) medicine.

On the Origin of Frameshift-Robustness of the Standard Genetic Code.

The standard genetic code (SGC) has been extensively analyzed for the biological ramifications of its nonrandom structure. For instance, mismatch errors due to point mutation or mistranslation have an overall smaller effect on the amino acid polar requirement under the SGC than under random genetic codes (RGCs). A similar observation was recently made for frameshift errors, prompting the assertion that the SGC has been shaped by natural selection for frameshift-robustness—conservation of certain amino acid properties upon a frameshift mutation or translational frameshift. However, frameshift-robustness confers no benefit because frameshifts usually create premature stop codons that cause nonsense-mediated mRNA decay or production of nonfunctional truncated proteins. We here propose that the frameshift-robustness of the SGC is a byproduct of its mismatch-robustness. Of 564 amino acid properties considered, the SGC exhibits mismatch-robustness in 93–133 properties and frameshift-robustness in 55 properties, respectively, and that the latter is largely a subset of the former. For each of the 564 real and 564 randomly constructed fake properties of amino acids, there is a positive correlation between mismatch-robustness and frameshift-robustness across one million RGCs; this correlation arises because most amino acid changes resulting from a frameshift are also achievable by a mismatch error. Importantly, the SGC does not show significantly higher frameshift-robustness in any of the 55 properties than RGCs of comparable mismatch-robustness. These findings support that the frameshift-robustness of the SGC need not originate through direct selection and can instead be a site effect of its mismatch-robustness.