Optimality of the genetic code with respect to protein stability and amino-acid frequencies

Proteome-Wide Covalent Targeting of Acidic Residues with Tunable N-Aryl Aziridines

Carboxylate side chains in aspartic and glutamic acids play critical roles in protein structure and function due to their polarity and negative charge. These acidic residues, which are abundant in high-value drug targets, represent attractive yet underexplored hotspots for covalent inhibitor discovery. In this study, we introduce N-aryl aziridines as a systematically tunable, chemoselective scaffold for covalent targeting of carboxylates across the proteome. Using a library of N-pyridinium aziridine-based fragments combined with chemoproteomics-enabled target deconvolution, we identified lead hits for aspartates and glutamates in proteins such as mitochondrial carrier homologue 2 (MTCH2), RUN and FYVE domain-containing protein 1 (RUFY1), and delta(24)-sterol reductase (DHCR24). Modular build-and-couple synthetic logic enabled fragment evolution via Ni-catalyzed cross-coupling to access N-aryl aziridines with enhanced affinities for MTCH2 and RUFY1. Notably, N-aryl aziridine 5b selectively modified RUFY1 at E502, disrupting its interactions within the endosomal trafficking network and impairing receptor recycling. This work establishes N-aryl aziridines as versatile carboxylate-targeting covalent inhibitor scaffolds, broadening the scope of covalent ligand discovery.

Protein sequence modelling with Bayesian flow networks

Exploring the vast and largely uncharted territory of amino acid sequences is crucial for understanding complex protein functions and the engineering of novel therapeutic proteins. Whilst generative machine learning has advanced protein sequence modelling, no existing approach is proficient in both unconditional and conditional generation. In this work, we propose that Bayesian Flow Networks (BFNs), a recently introduced framework for generative modelling, can address these challenges. We present ProtBFN, a 650M parameter model trained on protein sequences curated from UniProtKB, which generates natural-like, diverse, structurally coherent, and novel protein sequences, significantly outperforming leading autoregressive and discrete diffusion models. Further, we fine-tune ProtBFN on heavy chains from the Observed Antibody Space to obtain an antibody-specific model, AbBFN, which we use to evaluate zero-shot conditional generation capabilities. AbBFN is found to be competitive with or better than antibody-specific BERT-style models when applied to predicting individual framework or complimentary determining regions.

Gag HIV-1 Virus-like Particles and Extracellular Vesicles Functionalization with Spike Epitopes of SARS-CoV-2 Using a Copper-Free Click Chemistry Approach

Enveloped nanoparticles such as extracellular vesicles (EVs) and virus-like particles (VLPs) have emerged as promising nanocarriers capable of transporting bioactive molecules for drug delivery and vaccination. Optimized functionalization methodologies are required to increase the functionalization levels of these nanoparticles, enhancing their performance. Here, a bioorthogonal copper-free strain-promoted azide-alkyne cycloaddition (SPAAC) reaction has been optimized to functionalize human immunodeficiency virus type 1 (HIV-1) Gag-based VLPs and EVs. The optimization process has been carried out through reaction kinetics and design of experiments (DoE) using Cy5 as a reporter molecule. The functionalization of both VLPs and EVs has been studied using super-resolution fluorescence microscopy (SRFM), revealing remarkable differences between Gag-VLPs and coproduced EVs. EVs produced by mock transfection and cell growth have been functionalized achieving a mean of 3618.63 ± 48.91 and 6498.75 ± 352.71 Cy5 molecules covalently linked per particle (Cy5cov/particle), respectively. Different nanoparticles have been functionalized with two linear B-cell epitopes from the Spike protein of SARS-CoV-2, S315-338 TSNFRVQPTESIVRFPNITNLCPF and S648-663 GCLIGAEHVNNSYECD, and analyzed by an immunoassay with sera from COVID-19 patients. The obtained results validate the selected B-cell epitopes and highlight the potential of the optimized functionalization approach for the development of nanoparticle-based vaccines.

Toward Absolute Quantification of Soluble Proteins via Proton Nuclear Magnetic Resonance Spectroscopy: Total Protein Concentration in Blood Plasma

For absolute protein quantification using nuclear magnetic resonance (NMR) spectroscopy, we considered proteins as homopolymers and effective amino acid (AA) residues (AAREff) as monomer units. For diverse classes of proteins, we determined the AAREff molecular weight as 111.5 ± 3.2 Da and the number of hydrogens per AA as 7.8 ± 0.2. Their ratio of 14.3 ± 0.3 (g/LP)/(mol/LH) remains constant across various protein classes and is equivalent to Kjeldahl's nitrogen-to-protein conversion constant of 5.78 ± 0.29 gN/gP. By analogy to the Kjeldahl method, we suggest that the total integral of a 1H NMR solution protein spectrum could be used for total protein quantification. We synthesized low-resolution protein spectra from the weighted sums of individual AA spectra and compared them with experimental spectra. In the methyl region, the ratio of the protein mass to the total number of protons in the synthetic spectra (corrected for the chemical shift mismatch) was ∼1 (mg/mL)/mM, which agrees with an earlier reported experimental ratio for urine (1.05 ± 0.06 (mg/mL)/mM). For human blood plasma, in the methyl region, we found empirical ratios of 1.115 ± 0.006 (mg/mL)/mM (using 96 patient samples) and 1.121 ± 0.011 (mg/mL)/mM for the NIST plasma standard. This numerical agreement points to universal conversion constants, i.e., protein mixtures with unknown compositions could be quantified without the need for calibration standards by measuring the millimolar proton concentration within the methyl region of the NMR spectrum using the same conversion constant.

Modular Structure and Polymerization Status of GABAA Receptors Illustrated with EM Analysis and AlphaFold2 Prediction

Type-A γ-aminobutyric acid (GABAA) receptors are channel proteins crucial to mediating neuronal balance in the central nervous system (CNS). The structure of GABAA receptors allows for multiple binding sites and is key to drug development. Yet the formation mechanism of the receptor's distinctive pentameric structure is still unknown. This study aims to investigate the role of three predominant subunits of the human GABAA receptor in the formation of protein pentamers. Through purifying and refolding the protein fragments of the GABAA receptor α1, β2, and γ2 subunits, the particle structures were visualised with negative staining electron microscopy (EM). To aid the analysis, AlphaFold2 was used to compare the structures. Results show that α1 and β2 subunit fragments successfully formed homo-oligomers, particularly homopentameric structures, while the predominant heteropentameric GABAA receptor was also replicated through the combination of the three subunits. However, homopentameric structures were not observed with the γ2 subunit proteins. A comparison of the AlphaFold2 predictions and the previously obtained cryo-EM structures presents new insights into the subunits' modular structure and polymerization status. By performing experimental and computational studies, a deeper understanding of the complex structure of GABAA receptors is provided. Hopefully, this study can pave the way to developing novel therapeutics for neuropsychiatric diseases.

Chemoselective Reagents for the Traceless Bioreversible Modification of Native Proteins

Nature utilizes bioreversible post-translational modifications (PTMs) to spatiotemporally diversify protein function. Mimicking Nature's approach, chemists have developed a variety of chemoselective regents for traceless, bioreversible modification of native proteins. These strategies have found utility in the development of reversible covalent inhibitors and degraders as well as the synthesis of functional protein conjugates for delivery into cells. This Viewpoint provides a snapshot of such tools, which currently cover Cys, Ser, Thr, Lys, Asp, and Glu residues and the N terminus. Additionally, we explore how bioreversible reagents, originally developed by research communities with differing objectives, can be utilized synergistically. Looking forward, we discuss the need for developing bioreversible reagents for labeling His, Tyr, Arg, Trp, Asn, Gln, and Met residues and the C-terminus as well as the installation of dynamic PTMs. Finally, to broaden the applicability of these tools, we point out the importance of developing modular release scaffolds with tunable release times and responsiveness to multiple endogenous triggers. We anticipate that this Viewpoint will catalyze further research and technological breakthroughs in this rapidly evolving field.

Precision engineering of antibodies: A review of modification and design in the Fab region

The binding of functional groups to antibodies is crucial for disease treatment, diagnosis, and basic scientific research. Traditionally, antibody modifications have focused on the Fc region to maintain antigen-antibody binding activity. However, such modifications may impact critical antibody functions, including immune cell surface receptor activation, cytokine release, and other immune responses. In recent years, modifications targeting the antigen-binding fragment (Fab) region have garnered increasing attention. Precise modifications of the Fab region not only maximize the retention of antigen-antibody binding capacity but also enhance numerous physicochemical properties of antibodies. This paper reviews the chemical, biological, biochemical, and computer-assisted methods for modifying the Fab region of antibodies, discussing their advantages, limitations, recent advances, and future trends.

Late-stage peptide modification and macrocyclization enabled by tertiary amine catalyzed tryptophan allylation

Late-stage modification of peptides could potentially endow peptides with significant bioactivity and physicochemical properties, and thereby provide novel opportunities for peptide pharmaceutical studies. Since tryptophan (Trp) bears a unique indole ring residue and plays various critical functional roles in peptides, the modification methods for tryptophan were preliminarily developed with considerable progress via transition-metal mediated C-H activation. Herein, we report an unprecedented tertiary amine catalyzed peptide allylation via the SN2'-SN2' pathway between the N1 position of the indole ring of Trp and Morita-Baylis-Hillman (MBH) carbonates. Using this method that proceeds under mild conditions, we demonstrated an extremely broad scope of Trp-containing peptides and MBH carbonates to prepare a series of peptide conjugates and cyclic peptides. The reaction is amenable to either solid-phase (on resin) or solution-phase conditions. In addition, the modified peptides can be further conjugated with other biomolecules at Trp, providing a new handle for bioconjugation.

Photoredox C(2)-Arylation of Indole- and Tryptophan-Containing Biomolecules

We introduce a novel and straightforward methodology for photoredox arylation of an indole scaffold using aryldiazonium salts under mild and metal-free conditions. Our approach enables the regioselective and chemoselective introduction of several aryl groups to the C(2) position of indoles and tryptophan, even in competition with other amino acids. This approach extends to the late-stage functionalization of peptides and lysozyme, heralding the unprecedented arylation of tryptophan residues in wild-type proteins and offering broad utility in chemical biology.

Robust genetic codes enhance protein evolvability

The standard genetic code defines the rules of translation for nearly every life form on Earth. It also determines the amino acid changes accessible via single-nucleotide mutations, thus influencing protein evolvability-the ability of mutation to bring forth adaptive variation in protein function. One of the most striking features of the standard genetic code is its robustness to mutation, yet it remains an open question whether such robustness facilitates or frustrates protein evolvability. To answer this question, we use data from massively parallel sequence-to-function assays to construct and analyze 6 empirical adaptive landscapes under hundreds of thousands of rewired genetic codes, including those of codon compression schemes relevant to protein engineering and synthetic biology. We find that robust genetic codes tend to enhance protein evolvability by rendering smooth adaptive landscapes with few peaks, which are readily accessible from throughout sequence space. However, the standard genetic code is rarely exceptional in this regard, because many alternative codes render smoother landscapes than the standard code. By constructing low-dimensional visualizations of these landscapes, which each comprise more than 16 million mRNA sequences, we show that such alternative codes radically alter the topological features of the network of high-fitness genotypes. Whereas the genetic codes that optimize evolvability depend to some extent on the detailed relationship between amino acid sequence and protein function, we also uncover general design principles for engineering nonstandard genetic codes for enhanced and diminished evolvability, which may facilitate directed protein evolution experiments and the bio-containment of synthetic organisms, respectively.

Selective Late-Stage Functionalization of Tryptophan-Containing Peptides To Facilitate Bioorthogonal Tetrazine Ligation

Site-selective modification of complex peptides and the functionalization of their C-H bonds hold great promise for expanding their use in therapeutics and biomedical research. Herein, we leverage the power of late-stage chemoenzymatic catalysis using an indole prenyltransferase (IPT) enzyme and alkyl diphosphates to specifically modify the indole ring of tryptophan in clinically relevant peptides. Furthermore, the installed handle enables bioorthogonal click chemistry through an inverse electron-demand Diels-Alder (IEDDA) reaction with a biotin-conjugated tetrazine probe.

Pinpointing Acidic Residues in Proteins

It is of great importance to pinpoint specific residues or sites of a protein in biological contexts to enable desired mechanism of action for small molecules or to precisely control protein function. In this regard, acidic residues including aspartic acid (Asp) and glutamic acid (Glu) hold great potential due to their great prevalence and unique function. To unlock the largely untapped potential, great efforts have been made recently by synthetic chemists, chemical biologists and pharmacologists. Herein, we would like to highlight the remarkable progress and particularly introduce the electrophiles that exhibit reactivity to carboxylic acids, the light-induced reactivities to carboxylic acids and the genetically encoded noncanonical amino acids that allow protein manipulations at acidic residues. We also comment on certain unresolved challenges, hoping to draw more attention to this rapidly developing area.

Moderating the neutralist-selectionist debate: exactly which propositions are we debating, and which arguments are valid?

Half a century after its foundation, the neutral theory of molecular evolution continues to attract controversy. The debate has been hampered by the coexistence of different interpretations of the core proposition of the neutral theory, the 'neutral mutation-random drift' hypothesis. In this review, we trace the origins of these ambiguities and suggest potential solutions. We highlight the difference between the original, the revised and the nearly neutral hypothesis, and re-emphasise that none of them equates to the null hypothesis of strict neutrality. We distinguish the neutral hypothesis of protein evolution, the main focus of the ongoing debate, from the neutral hypotheses of genomic and functional DNA evolution, which for many species are generally accepted. We advocate a further distinction between a narrow and an extended neutral hypothesis (of which the latter posits that random non-conservative amino acid substitutions can cause non-ecological phenotypic divergence), and we discuss the implications for evolutionary biology beyond the domain of molecular evolution. We furthermore point out that the debate has widened from its initial focus on point mutations, and also concerns the fitness effects of large-scale mutations, which can alter the dosage of genes and regulatory sequences. We evaluate the validity of neutralist and selectionist arguments and find that the tested predictions, apart from being sensitive to violation of underlying assumptions, are often derived from the null hypothesis of strict neutrality, or equally consistent with the opposing selectionist hypothesis, except when assuming molecular panselectionism. Our review aims to facilitate a constructive neutralist-selectionist debate, and thereby to contribute to answering a key question of evolutionary biology: what proportions of amino acid and nucleotide substitutions and polymorphisms are adaptive?

Silicon-Containing Thiol-Specific Bioconjugating Reagent

A new bioconjugation reagent containing silicon has been developed for the selective reaction with thiols. The inclusion of silicon significantly improves chemoselectivity and suppresses retro processes, thereby exceeding the capabilities of traditional reagents. The method is versatile and compatible with a broad range of thiols and unsaturated carbonyl compounds and yields moderate to high results. These reactions can be conducted under biocompatible conditions, thereby making them suitable for protein bioconjugation. The resulting conjugates display good stability in the presence of various biomolecules, which suggests their potential application for the synthesis of antibody-drug conjugates. Furthermore, the presence of a silicon moiety within the conjugated products opens up new avenues for drug release and bridging inorganics with other disciplines. This new class of silicon-containing thiol-specific bioconjugation reagents has significant implications for researchers working in bioanalytical science and medicinal chemistry and leads to innovative opportunities for advancing the field of bioconjugation research and medicinal chemistry.

Chemical technology principles for selective bioconjugation of proteins and antibodies

Proteins are multifunctional large organic compounds that constitute an essential component of a living system. Hence, control over their bioconjugation impacts science at the chemistry-biology-medicine interface. A chemical toolbox for their precision engineering can boost healthcare and open a gateway for directed or precision therapeutics. Such a chemical toolbox remained elusive for a long time due to the complexity presented by the large pool of functional groups. The precise single-site modification of a protein requires a method to address a combination of selectivity attributes. This review focuses on guiding principles that can segregate them to simplify the task for a chemical method. Such a disintegration systematically employs a multi-step chemical transformation to deconvolute the selectivity challenges. It constitutes a disintegrate (DIN) theory that offers additional control parameters for tuning precision in protein bioconjugation. This review outlines the selectivity hurdles faced by chemical methods. It elaborates on the developments in the perspective of DIN theory to demonstrate simultaneous regulation of reactivity, chemoselectivity, site-selectivity, modularity, residue specificity, and protein specificity. It discusses the progress of such methods to construct protein and antibody conjugates for biologics, including antibody-fluorophore and antibody-drug conjugates (AFCs and ADCs). It also briefs how this knowledge can assist in developing small molecule-based covalent inhibitors. In the process, it highlights an opportunity for hypothesis-driven routes to accelerate discoveries of selective methods and establish new targetome in the precision engineering of proteins and antibodies.

Mechanisms and pathology of protein misfolding and aggregation

Despite advances in machine learning-based protein structure prediction, we are still far from fully understanding how proteins fold into their native conformation. The conventional notion that polypeptides fold spontaneously to their biologically active states has gradually been replaced by our understanding that cellular protein folding often requires context-dependent guidance from molecular chaperones in order to avoid misfolding. Misfolded proteins can aggregate into larger structures, such as amyloid fibrils, which perpetuate the misfolding process, creating a self-reinforcing cascade. A surge in amyloid fibril structures has deepened our comprehension of how a single polypeptide sequence can exhibit multiple amyloid conformations, known as polymorphism. The assembly of these polymorphs is not a random process but is influenced by the specific conditions and tissues in which they originate. This observation suggests that, similar to the folding of native proteins, the kinetics of pathological amyloid assembly are modulated by interactions specific to cells and tissues. Here, we review the current understanding of how intrinsic protein conformational propensities are modulated by physiological and pathological interactions in the cell to shape protein misfolding and aggregation pathology.

Reconstruction and metabolic profiling of the genome-scale metabolic network model of Pseudomonas stutzeri A1501

Pseudomonas stutzeri A1501 is a non-fluorescent denitrifying bacteria that belongs to the gram-negative bacterial group. As a prominent strain in the fields of agriculture and bioengineering, there is still a lack of comprehensive understanding regarding its metabolic capabilities, specifically in terms of central metabolism and substrate utilization. Therefore, further exploration and extensive studies are required to gain a detailed insight into these aspects. This study reconstructed a genome-scale metabolic network model for P. stutzeri A1501 and conducted extensive curations, including correcting energy generation cycles, respiratory chains, and biomass composition. The final model, iQY1018, was successfully developed, covering more genes and reactions and having higher prediction accuracy compared with the previously published model iPB890. The substrate utilization ability of 71 carbon sources was investigated by BIOLOG experiment and was utilized to validate the model quality. The model prediction accuracy of substrate utilization for P. stutzeri A1501 reached 90 %. The model analysis revealed its new ability in central metabolism and predicted that the strain is a suitable chassis for the production of Acetyl CoA-derived products. This work provides an updated, high-quality model of P. stutzeri A1501for further research and will further enhance our understanding of the metabolic capabilities.

Chemoselective Late-Stage Functionalization of Peptides via Photocatalytic C2-Alkylation of Tryptophan

Across eukaryotic proteomes, tryptophan is the least abundant of the 20 canonical amino acids, which makes it an ideal chemical handle for the late-stage functionalization of peptide and protein scaffolds with minimal production of undesired isoforms. Herein, we report the photocatalytic C2-alkylation of tryptophan using bromodifluoroacetate/acetamide-derived radical precursors. This rapid visible-light-mediated reaction is additive-free, operationally simple, and tolerates diverse functionality. We demonstrate the late-stage modification of a variety of complex peptides, including examples of biological significance.

Rare-event sampling analysis uncovers the fitness landscape of the genetic code

The genetic code refers to a rule that maps 64 codons to 20 amino acids. Nearly all organisms, with few exceptions, share the same genetic code, the standard genetic code (SGC). While it remains unclear why this universal code has arisen and been maintained during evolution, it may have been preserved under selection pressure. Theoretical studies comparing the SGC and numerically created hypothetical random genetic codes have suggested that the SGC has been subject to strong selection pressure for being robust against translation errors. However, these prior studies have searched for random genetic codes in only a small subspace of the possible code space due to limitations in computation time. Thus, how the genetic code has evolved, and the characteristics of the genetic code fitness landscape, remain unclear. By applying multicanonical Monte Carlo, an efficient rare-event sampling method, we efficiently sampled random codes from a much broader random ensemble of genetic codes than in previous studies, estimating that only one out of every 1020 random codes is more robust than the SGC. This estimate is significantly smaller than the previous estimate, one in a million. We also characterized the fitness landscape of the genetic code that has four major fitness peaks, one of which includes the SGC. Furthermore, genetic algorithm analysis revealed that evolution under such a multi-peaked fitness landscape could be strongly biased toward a narrow peak, in an evolutionary path-dependent manner.

Virus-like particle-based nanocarriers as an emerging platform for drug delivery

New nanocarrier technologies are emerging, and they have great potential for improving drug delivery, targeting efficiency, and bioavailability. Virus-like particles (VLPs) are natural nanoparticles from animal and plant viruses and bacteriophages. Hence, VLPs present several great advantages, such as morphological uniformity, biocompatibility, reduced toxicity, and easy functionalisation. VLPs can deliver many active ingredients to the target tissue and have great potential as a nanocarrier to overcome the limitations associated with other nanoparticles. This review will focus primarily on the construction and applications of VLPs, particularly as a novel nanocarrier to deliver active ingredients. Herein, the main methods for the construction, purification, and characterisation of VLPs, as well as various VLP-based materials used in delivery systems are summarised. The biological distribution of VLPs in drug delivery, phagocyte-mediated clearance, and toxicity are also discussed.

Bioconjugation of Au25 Nanocluster to Monoclonal Antibody at Tryptophan

We report the first bioconjugation of Au₂₅ nanocluster to a monoclonal antibody at scarcely exposed tryptophan (Trp) residues toward the development of high-resolution probes for cryogenic electron microscopy (cryo-EM) and tomography (cryo-ET). To achieve this, we improved the Trp-selective bioconjugation using hydroxylamine (ABNOH) reagents instead of previously developed N-oxyl radicals (ABNO). This new protocol allowed for the application of Trp-selective bioconjugation to acid-sensitive proteins such as antibodies. We found that a two-step procedure utilizing first Trp-selective bioconjugation for the introduction of azide groups to the protein and then strain-promoted azide-alkyne cycloaddition (SPAAC) to attach a bicyclononyne (BCN)-presenting redox-sensitive Au₂₅ nanocluster was essential for a scalable procedure. Covalent labeling of the antibody with gold nanoclusters was confirmed by various analytical methods, including cryo-EM analysis of the Au₂₅ nanocluster conjugates.

Enzyme expression kinetics by Escherichia coli during transition from rich to minimal media depends on proteome reserves

Bacterial fitness depends on adaptability to changing environments. In rich growth medium, which is replete with amino acids, Escherichia coli primarily expresses protein synthesis machineries, which comprise ~40% of cellular proteins and are required for rapid growth. Upon transition to minimal medium, which lacks amino acids, biosynthetic enzymes are synthesized, eventually reaching ~15% of cellular proteins when growth fully resumes. We applied quantitative proteomics to analyse the timing of enzyme expression during such transitions, and established a simple positive relation between the onset time of enzyme synthesis and the fractional enzyme 'reserve' maintained by E. coli while growing in rich media. We devised and validated a coarse-grained kinetic model that quantitatively captures the enzyme recovery kinetics in different pathways, solely on the basis of proteomes immediately preceding the transition and well after its completion. Our model enables us to infer regulatory strategies underlying the 'as-needed' gene expression programme adopted by E. coli.

Chemical Conjugation to Less Targeted Proteinogenic Amino Acids

Protein bioconjugates are in high demand for applications in biomedicine, diagnostics, chemical biology and bionanotechnology. Proteins are large and sensitive molecules containing multiple different functional groups and in particular nucleophilic groups. In bioconjugation reactions it can therefore be challenging to obtain a homogeneous product in high yield. Numerous strategies for protein conjugation have been developed, of which a vast majority target lysine, cysteine and to a lesser extend tyrosine. Likewise, several methods that involve recombinantly engineered protein tags have been reported. In recent years a number of methods have emerged for chemical bioconjugation to other amino acids and in this review, we present the progress in this area.

The structure of the genetic code as an optimal graph clustering problem

The standard genetic code (SGC) is the set of rules by which genetic information is translated into proteins, from codons, i.e. triplets of nucleotides, to amino acids. The questions about the origin and the main factor responsible for the present structure of the code are still under a hot debate. Various methodologies have been used to study the features of the code and assess the level of its potential optimality. Here, we introduced a new general approach to evaluate the quality of the genetic code structure. This methodology comes from graph theory and allows us to describe new properties of the genetic code in terms of conductance. This parameter measures the robustness of codon groups against the potential changes in translation of the protein-coding sequences generated by single nucleotide substitutions. We described the genetic code as a partition of an undirected and unweighted graph, which makes the model general and universal. Using this approach, we showed that the structure of the genetic code is a solution to the graph clustering problem. We presented and discussed the structure of the codes that are optimal according to the conductance. Despite the fact that the standard genetic code is far from being optimal according to the conductance, its structure is characterised by many codon groups reaching the minimum conductance for their size. The SGC represents most likely a local minimum in terms of errors occurring in protein-coding sequences and their translation.

Frameshift and wild-type proteins are often highly similar because the genetic code and genomes were optimized for frameshift tolerance

Frameshift mutations have been considered of significant importance for the molecular evolution of proteins and their coding genes, while frameshift protein sequences encoded in the alternative reading frames of coding genes have been considered to be meaningless. However, functional frameshifts have been found widely existing. It was puzzling how a frameshift protein kept its structure and functionality while substantial changes occurred in its primary amino-acid sequence. This study shows that the similarities among frameshifts and wild types are higher than random similarities and are determined at different levels. Frameshift substitutions are more conservative than random substitutions in the standard genetic code (SGC). The frameshift substitutions score of SGC ranks in the top 2.0-3.5% of alternative genetic codes, showing that SGC is nearly optimal for frameshift tolerance. In many genes and certain genomes, frameshift-resistant codons and codon pairs appear more frequently than expected, suggesting that frameshift tolerance is achieved through not only the optimality of the genetic code but, more importantly, the further optimization of a specific gene or genome through the usages of codons/codon pairs, which sheds light on the role of frameshift mutations in molecular and genomic evolution.

Open Search-Based Proteomics Reveals Widespread Tryptophan Modifications Associated with Hypoxia in Lung Cancer

The tryptophan residue has a large hydrophobic surface that plays a unique role in the folded protein conformation and functions. Tryptophan modifications are presumably to be readily detected in proteins due to the vulnerability of the indole structure to electrophilic attacks. In this study, we report a systematic identification of sequence variations at tryptophan, termed tryptophan variants, from the proteome of patients with nonsmall cell lung cancer (NSCLC). Using shotgun proteomics and a modified open search algorithm, 25 tryptophan variants on 2481 sites in over 858 proteins were identified. Among these, 6 tryptophan variants are previously identified, 15 are newly annotated, and 4 are still unknown, most of which are involved in the cascade of oxidation in the blood microparticle. Remarkably, Trp313 of glyceraldehyde-3-phosphate dehydrogenase (GAPDH) was up-oxidized whereas Trp16 and Trp38 of hemoglobin (HBB) were down-oxidized in NCSLC tissues. The results were further supported by an independent cohort of 103 lung adenocarcinoma samples, reflecting a negative feedback and potential detoxification mechanism against tumor glycolysis and hypoxia. Overall, the study reports a quick approach to explore tryptophan variants at the proteomic scale. Our findings highlight the predominant role of tryptophan oxidation in regulating the redox balance of cancer cells and its potential role as prognostic biomarker for patients with NSCLC.

Mapping SP-C co-chaperone binding sites reveals molecular consequences of disease-causing mutations on protein maturation

BiP co-chaperones ERdj4, ERdj5, and GRP170 associate in cells with peptides predicted to be aggregation prone. Here, extending these findings to a full-length protein, we examine two Interstitial Lung Disease-associated mutants (ILD) of surfactant protein C (SP-C). The TANGO algorithm, which identifies sequences prone to formation of β strand aggregates, found three such regions in SP-C: the N-terminal transmembrane (TM) domain and two sites in the intermolecular chaperone BRICHOS domain. We show the ILD mutants disrupt di-sulfide bond formation in the BRICHOS domain and expose the aggregation-prone peptides leading to binding of ERdj4, ERdj5, and GRP170. The destabilized mutant BRICHOS domain fails to properly insert its TM region in the ER membrane, exposing part of the N-terminal TM domain site. Our studies with ILD-associated mutant proteins provide insights into the specificity of ERdj4, ERdj5, and GRP170, identify context-dependent differences in their binding, and reveal molecular consequences of disease-associated mutants on folding.

Interstitial Lung Disease (ILD)-associated mutations in surfactant protein C (SP-C) render the protein prone to aggregation. Here, the authors reveal their impact on protein maturation, provide insights into recognition of aggregation prone regions by chaperones, and address the autosomal dominant nature of ILD mutants.

Toward Compilation of Balanced Protein Stability Data Sets: Flattening the ΔΔG Curve through Systematic Enrichment

Often studies analyzing stability data sets and/or predictors ignore neutral mutations and use a binary classification scheme labeling only destabilizing and stabilizing mutations. Recognizing that highly concentrated neutral mutations interfere with data set quality, we have explored three protein stability data sets: S2648, PON-tstab, and the symmetric S^sym that differ in size and quality. A characteristic leptokurtic shape in the ΔΔG distributions of all three data sets including the curated and symmetric ones was reported due to concentrated neutral mutations. To further investigate the impact of neutral mutations on ΔΔG predictions, we have comprehensively assessed the performance of 11 predictors on the PON-tstab data set. Correlation and error analyses showed that all of the predictors performed the best on the neutral mutations, while their performance became gradually worse as the ΔΔG of the mutations departed further from the neutral zone regardless of the direction, implying a bias toward dense mutations. To this end, after unraveling the role of concentrated neutral mutations in biases of stability data sets, we described a systematic enrichment approach to balance the ΔΔG distributions. Before enrichment, mutations were clustered based on their biochemical and/or structural features, and then three mutations were selected from every 2 kcal/mol of each cluster. Upon implementation of this approach by distinct clustering schemes, we generated five subsets varying in size and ΔΔG distributions. All subsets showed improved ΔΔG and frequency distributions. We ultimately reported that the errors toward enriched subsets were higher than those toward the parent data sets, confirming the enrichment of difficult-to-predict mutations in the subsets. In summary, we elaborated the prediction bias toward a concentrated neutral zone and also implemented a rational strategy to tackle this and other forms of biases. Ultimately, this study equipping us with an extended view of shortcomings of stability data sets is a step taken toward development of an unbiased predictor.

Model of Genetic Code Structure Evolution under Various Types of Codon Reading

The standard genetic code (SGC) is a set of rules according to which 64 codons are assigned to 20 canonical amino acids and stop coding signal. As a consequence, the SGC is redundant because there is a greater number of codons than the number of encoded labels. This redundancy implies the existence of codons that encode the same genetic information. The size and organization of such synonymous codon blocks are important characteristics of the SGC structure whose evolution is still unclear. Therefore, we studied possible evolutionary mechanisms of the codon block structure. We conducted computer simulations assuming that coding systems at early stages of the SGC evolution were sets of ambiguous codon assignments with high entropy. We included three types of reading systems characterized by different inaccuracy and pattern of codon recognition. In contrast to the previous study, we allowed for evolution of the reading systems and their competition. The simulations performed under minimization of translational errors and reduction of coding ambiguity produced the coding system resistant to these errors. The reading system similar to that present in the SGC dominated the others very quickly. The survived system was also characterized by low entropy and possessed properties similar to that in the SGC. Our simulation show that the unambiguous SGC could emerged from a code with a lower level of ambiguity and the number of tRNAs increased during the evolution.

Little Evidence the Standard Genetic Code Is Optimized for Resource Conservation

Selection for resource conservation can shape the coding sequences of organisms living in nutrient-limited environments. Recently, it was proposed that selection for resource conservation, specifically for nitrogen and carbon content, has also shaped the structure of the standard genetic code, such that the missense mutations the code allows tend to cause small increases in the number of nitrogen and carbon atoms in amino acids. Moreover, it was proposed that this optimization is not confounded by known optimizations of the standard genetic code, such as for polar requirement or hydropathy. We challenge these claims. We show the proposed optimization for nitrogen conservation is highly sensitive to choice of null model and the proposed optimization for carbon conservation is confounded by the known conservative nature of the standard genetic code with respect to the molecular volume of amino acids. There is therefore little evidence the standard genetic code is optimized for resource conservation. We discuss our findings in the context of null models of the standard genetic code.

Causes and Consequences of Purifying Selection on SARS-CoV-2

Owing to a lag between a deleterious mutation's appearance and its selective removal, gold-standard methods for mutation rate estimation assume no meaningful loss of mutations between parents and offspring. Indeed, from analysis of closely related lineages, in SARS-CoV-2, the Ka/Ks ratio was previously estimated as 1.008, suggesting no within-host selection. By contrast, we find a higher number of observed SNPs at 4-fold degenerate sites than elsewhere and, allowing for the virus's complex mutational and compositional biases, estimate that the mutation rate is at least 49-67% higher than would be estimated based on the rate of appearance of variants in sampled genomes. Given the high Ka/Ks one might assume that the majority of such intrahost selection is the purging of nonsense mutations. However, we estimate that selection against nonsense mutations accounts for only ∼10% of all the "missing" mutations. Instead, classical protein-level selective filters (against chemically disparate amino acids and those predicted to disrupt protein functionality) account for many missing mutations. It is less obvious why for an intracellular parasite, amino acid cost parameters, notably amino acid decay rate, is also significant. Perhaps most surprisingly, we also find evidence for real-time selection against synonymous mutations that move codon usage away from that of humans. We conclude that there is common intrahost selection on SARS-CoV-2 that acts on nonsense, missense, and possibly synonymous mutations. This has implications for methods of mutation rate estimation, for determining times to common ancestry and the potential for intrahost evolution including vaccine escape.

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.

Models of genetic code structure evolution with variable number of coded labels

It is assumed that at the early stage of cell evolution its translation machinery was characterized by high noise, i.e. ambiguous assignment of codons to amino acids in the genetic code, which initially encoded only few amino acids. Next, during its evolution new amino acids were added to this code. Taking into account this facts, we investigated theoretical models of genetic code's structure, which evolved from a set of ambiguous codons assignments into a coding system with a low level of uncertainty. We considered three types of translational inaccuracies assuming a different number of fixed codon positions. We applied a modified version of evolutionary algorithm for finding the genetic codes that the most effectively reduced the initial uncertainty in the assignment of codons to encoded labels, i.e. amino acids and a stop translation signal. We examined codes with the number of labels from four to 22. Our results indicated that the quality of genetic code structure is strongly dependent on the number of encoded labels as well as the type of translational mechanism. The more strict assignments of codon to the labels was preferred by the codes encoding more number of labels. The results showed that a smaller degeneracy of codes evolved from a more tolerant coding with the stepwise addition of coded amino acids to the genetic code. The distribution of codon groups in the standard genetic code corresponds well to the translation model assuming two fixed codon positions, whereas the six-codon groups can be relics form previous stages of evolution when the code characterized by a greater uncertainty.

The Mutational Robustness of the Genetic Code and Codon Usage in Environmental Context: A Non-Extremophilic Preference?

The genetic code was evolved, to some extent, to minimize the effects of mutations. The effects of mutations depend on the amino acid repertoire, the structure of the genetic code and frequencies of amino acids in proteomes. The amino acid compositions of proteins and corresponding codon usages are still under selection, which allows us to ask what kind of environment the standard genetic code is adapted to. Using simple computational models and comprehensive datasets comprising genomic and environmental data from all three domains of Life, we estimate the expected severity of non-synonymous genomic mutations in proteins, measured by the change in amino acid physicochemical properties. We show that the fidelity in these physicochemical properties is expected to deteriorate with extremophilic codon usages, especially in thermophiles. These findings suggest that the genetic code performs better under non-extremophilic conditions, which not only explains the low substitution rates encountered in halophiles and thermophiles but the revealed relationship between the genetic code and habitat allows us to ponder on earlier phases in the history of Life.

PPalign: optimal alignment of Potts models representing proteins with direct coupling information

Background

To assign structural and functional annotations to the ever increasing amount of sequenced proteins, the main approach relies on sequence-based homology search methods, e.g. BLAST or the current state-of-the-art methods based on profile Hidden Markov Models, which rely on significant alignments of query sequences to annotated proteins or protein families. While powerful, these approaches do not take coevolution between residues into account. Taking advantage of recent advances in the field of contact prediction, we propose here to represent proteins by Potts models, which model direct couplings between positions in addition to positional composition, and to compare proteins by aligning these models. Due to non-local dependencies, the problem of aligning Potts models is hard and remains the main computational bottleneck for their use.

Methods

We introduce here an Integer Linear Programming formulation of the problem and PPalign, a program based on this formulation, to compute the optimal pairwise alignment of Potts models representing proteins in tractable time. The approach is assessed with respect to a non-redundant set of reference pairwise sequence alignments from SISYPHUS benchmark which have lowest sequence identity (between \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$3\%$$\end{document}3% and \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$20\%$$\end{document}20%) and enable to build reliable Potts models for each sequence to be aligned. This experimentation confirms that Potts models can be aligned in reasonable time (\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$1'37''$$\end{document}1′37′′ in average on these alignments). The contribution of couplings is evaluated in comparison with HHalign and independent-site PPalign. Although Potts models were not fully optimized for alignment purposes and simple gap scores were used, PPalign yields a better mean \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$F_1$$\end{document}F1 score and finds significantly better alignments than HHalign and PPalign without couplings in some cases.

Conclusions

These results show that pairwise couplings from protein Potts models can be used to improve the alignment of remotely related protein sequences in tractable time. Our experimentation suggests yet that new research on the inference of Potts models is now needed to make them more comparable and suitable for homology search. We think that PPalign’s guaranteed optimality will be a powerful asset to perform unbiased investigations in this direction.

Some theoretical aspects of reprogramming the standard genetic code

Reprogramming of the standard genetic code to include non-canonical amino acids (ncAAs) opens new prospects for medicine, industry, and biotechnology. There are several methods of code engineering, which allow us for storing new genetic information in DNA sequences and producing proteins with new properties. Here, we provided a theoretical background for the optimal genetic code expansion, which may find application in the experimental design of the genetic code. We assumed that the expanded genetic code includes both canonical and non-canonical information stored in 64 classical codons. What is more, the new coding system is robust to point mutations and minimizes the possibility of reversion from the new to old information. In order to find such codes, we applied graph theory to analyze the properties of optimal codon sets. We presented the formal procedure in finding the optimal codes with various number of vacant codons that could be assigned to new amino acids. Finally, we discussed the optimal number of the newly incorporated ncAAs and also the optimal size of codon groups that can be assigned to ncAAs.

nSite-Selective, Chemical Modification of Protein at Aromatic Side Chain and Their Emergent Applications

Site-selective chemical modification of protein side chain has probed enormous opportunities in the fundamental understanding of cellular biology and therapeutic applications. Primarily, in the field of biopharmaceutical where formulation of bioconjugates is found to be potential medicine than an individual constituent. In this regard, Lysine and Cysteine are the most widely used endogenous amino acid for these purposes. Recently, the aromatic side chain residues (Trp, Tyr, and His) that are low abundant in protein have gained more attention in therapeutic applications due to their advantages of chemical reactivity and specificity. This review discusses the site-selective bioconjugation methods for aromatic side chains (Trp, Tyr and His) and highlights the developed strategies in the last three years, along with their applications. Also, the review highlights the prevalent methods published earlier. We have examined that metal-catalyzed and photocatalytic reactions are gaining more attention for bioconjugation, though their practical operation is under development. The review has been summarized with the future perspective of protein and peptide conjugations contemplating therapeutic applications and challenges.

Single electron transfer-based peptide/protein bioconjugations driven by biocompatible energy input

Bioconjugation reactions play a central facilitating role in engendering modified peptides and proteins. Early progress in this area was inhibited by challenges such as the limited range of substrates and the relatively poor biocompatibility of bioconjugation reagents. However, the recent developments in visible-light induced photoredox catalysis and electrochemical catalysis reactions have permitted significant novel reactivities to be developed in the field of synthetic and bioconjugation chemistry. This perspective describes recent advances in the use of biocompatible energy input for the modification of peptides and proteins mainly, via the single electron transfer (SET) process, as well as key future developments in this area.

Large-scale in silico mutagenesis experiments reveal optimization of genetic code and codon usage for protein mutational robustness

BACKGROUND

How, and the extent to which, evolution acts on DNA and protein sequences to ensure mutational robustness and evolvability is a long-standing open question in the field of molecular evolution. We addressed this issue through the first structurome-scale computational investigation, in which we estimated the change in folding free energy upon all possible single-site mutations introduced in more than 20,000 protein structures, as well as through available experimental stability and fitness data.

RESULTS

At the amino acid level, we found the protein surface to be more robust against random mutations than the core, this difference being stronger for small proteins. The destabilizing and neutral mutations are more numerous in the core and on the surface, respectively, whereas the stabilizing mutations are about 4% in both regions. At the genetic code level, we observed smallest destabilization for mutations that are due to substitutions of base III in the codon, followed by base I, bases I+III, base II, and other multiple base substitutions. This ranking highly anticorrelates with the codon-anticodon mispairing frequency in the translation process. This suggests that the standard genetic code is optimized to limit the impact of random mutations, but even more so to limit translation errors. At the codon level, both the codon usage and the usage bias appear to optimize mutational robustness and translation accuracy, especially for surface residues.

CONCLUSION

Our results highlight the non-universality of mutational robustness and its multiscale dependence on protein features, the structure of the genetic code, and the codon usage. Our analyses and approach are strongly supported by available experimental mutagenesis data.

Chemical modifications of tryptophan residues in peptides and proteins

Chemical protein modifications facilitate the investigation of natural posttranslational protein modifications and allow the design of proteins with new functions. Proteins can be modified at a late stage on amino acid side chains by chemical methods. The indole moiety of tryptophan residues is an emerging target of such chemical modification strategies because of its unique reactivity and low abundance. This review provides an overview of the recently developed methods of tryptophan modification at the peptide and protein levels.

Basic principles of the genetic code extension

Compounds including non-canonical amino acids (ncAAs) or other artificially designed molecules can find a lot of applications in medicine, industry and biotechnology. They can be produced thanks to the modification or extension of the standard genetic code (SGC). Such peptides or proteins including the ncAAs can be constantly delivered in a stable way by organisms with the customized genetic code. Among several methods of engineering the code, using non-canonical base pairs is especially promising, because it enables generating many new codons, which can be used to encode any new amino acid. Since even one pair of new bases can extend the SGC up to 216 codons generated by a six-letter nucleotide alphabet, the extension of the SGC can be achieved in many ways. Here, we proposed a stepwise procedure of the SGC extension with one pair of non-canonical bases to minimize the consequences of point mutations. We reported relationships between codons in the framework of graph theory. All 216 codons were represented as nodes of the graph, whereas its edges were induced by all possible single nucleotide mutations occurring between codons. Therefore, every set of canonical and newly added codons induces a specific subgraph. We characterized the properties of the induced subgraphs generated by selected sets of codons. Thanks to that, we were able to describe a procedure for incremental addition of the set of meaningful codons up to the full coding system consisting of three pairs of bases. The procedure of gradual extension of the SGC makes the whole system robust to changing genetic information due to mutations and is compatible with the views assuming that codons and amino acids were added successively to the primordial SGC, which evolved minimizing harmful consequences of mutations or mistranslations of encoded proteins.

Induction of ADCC by a folic acid–mAb conjugate prepared by tryptophan-selective reaction toward folate-receptor-positive cancer cells

We developed conjugates between monoclonal antibody (mAb) and folic acid (FA) by using a tryptophan (Trp)-selective reaction, which yields relatively homogenous products compared to conventional methods. The obtained mAb–FA conjugates showed significant cellular cytotoxicity toward folate receptor-expressing cancer cells, demonstrating that the conjugates retained the Fc region's original function.

mAb–folic acid conjugates were prepared by a tryptophan-selective reaction using an organic radical under ambient conditions, which showed significant induction ability of antibody-dependent cellular cytotoxicity.

De novoformation of citrate-based fluorophores on N-termini of peptides and proteins in cells and tissues

Citrate-based fluorophores are generated inde novomanner on proteins in biological samples under mild conditions.

A metric space for semantic containment: Towards the implementation of genetic firewalls

Analysing or engineering the genetic code has mainly been considered as an approach to reduce or increase the mutational robustness of the genetic code, i.e. the error tolerance in DNA mutations, or to enable the incorporation of non-canonical amino acids. The approach of "semantic containment", however, is less interested in altering the mutational tolerance of the standard code, but to create synthetic alternative genetic codes that limit or all together impede horizontal gene transfer between a natural and genomically recoded organisms (GRO). A major claim or conjecture of semantic containment is: "the farther, the safer", meaning, the less similarity there is between two codes, the less chance of a horizontal gene transfer, and the stronger the genetic firewall. So far, no metrics were available to measure and quantify the "genetic distance" between different genetic codes. Such a metric, however, is iis paramount to allow the experimental testing and evaluation of the validity of semantic biocontainment for the first time. Here, we introduce a metric space to measure exactly the distance (dissimilarity) between different genetic codes, in order to provide a framework to evaluate the relation between distance and strength of a genetic firewall. Results are presented that incorporate bespoken metrics when producing alternative genetic codes according to predefined goals, specifications and limitations. Finally, as an outlook, implications and challenges for genetic firewall(s) are discussed for dual- and multi-code systems.