Fundamental amino acid mass distributions and entropy costs in proteomes

Large-scale ORF screening based on LC-MS to discover novel lncRNA-encoded peptides responding to ionizing radiation and microgravity

In the human genome, 98% of genes can be transcribed into non-coding RNAs (ncRNAs), among which lncRNAs and their encoded peptides play important roles in regulating various aspects of cellular processes and may serve as crucial factors in modulating the biological effects induced by ionizing radiation and microgravity. Unfortunately, there are few reports in space radiation biology on lncRNA-encoded peptides below 10kD due to limitations in detection techniques. To fill this gap, we integrated a variety of methods based on genomics and peptidomics, and discovered 22 lncRNA-encoded small peptides that are sensitive to space radiation and microgravity, which have never been reported before. We concurrently validated the transmembrane helix, subcellular localization, and biological function of these small peptides using bioinformatics and molecular biology techniques. More importantly, we found that these small peptides function independently of the lncRNAs that encode them. Our findings have uncovered a previously unknown human proteome encoded by 'non-coding' genes in response to space conditions and elucidated their involvement in biological processes, providing valuable strategies for individual protection mechanisms for astronauts who carry out deep space exploration missions in space radiation environments.

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

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

Easy Production of "Difficult Peptides" Using Cell-Free Protein Synthesis and a New Methionine Analogue as a Latent Peptide Cleavage Site

Aliphatic γ-chloro-α-amino acids incorporated in place of their canonical analogues through cell-free protein synthesis act as heat-labile linkers, offering a useful strategy for the straightforward production of target peptides as fusion proteins, from which the targets are readily released. Until now, the natural abundance of aliphatic amino acids in peptides has limited the scope of the method, as it leads to undesired cleavage sites in synthesized products, but here the authors report the development of a new cleavable chloro amino acid that incorporates in place of the relatively rare amino acid methionine, thus greatly expanding the scope of producible targets. This new strategy is employed for simplified peptide synthesis with a methionine-free fusion partner, allowing single-site incorporation of the cleavable linker for clean release and easy purification of the target peptide. Its utility is demonstrated through the straightforward preparation of two peptides reported to be challenging targets and not accessible through standard solid-phase chemical methodologies, as well as analogues.

A neoantigen fitness model predicts tumour response to checkpoint blockade immunotherapy

Checkpoint blockade immunotherapies enable the host immune system to recognize and destroy tumour cells. Their clinical activity has been correlated with activated T-cell recognition of neoantigens, which are tumour-specific, mutated peptides presented on the surface of cancer cells. Here we present a fitness model for tumours based on immune interactions of neoantigens that predicts response to immunotherapy. Two main factors determine neoantigen fitness: the likelihood of neoantigen presentation by the major histocompatibility complex (MHC) and subsequent recognition by T cells. We estimate these components using the relative MHC binding affinity of each neoantigen to its wild type and a nonlinear dependence on sequence similarity of neoantigens to known antigens. To describe the evolution of a heterogeneous tumour, we evaluate its fitness as a weighted effect of dominant neoantigens in the subclones of the tumour. Our model predicts survival in anti-CTLA-4-treated patients with melanoma and anti-PD-1-treated patients with lung cancer. Importantly, low-fitness neoantigens identified by our method may be leveraged for developing novel immunotherapies. By using an immune fitness model to study immunotherapy, we reveal broad similarities between the evolution of tumours and rapidly evolving pathogens.

Mass & secondary structure propensity of amino acids explain their mutability and evolutionary replacements

Why is an amino acid replacement in a protein accepted during evolution? The answer given by bioinformatics relies on the frequency of change of each amino acid by another one and the propensity of each to remain unchanged. We propose that these replacement rules are recoverable from the secondary structural trends of amino acids. A distance measure between high-resolution Ramachandran distributions reveals that structurally similar residues coincide with those found in substitution matrices such as BLOSUM: Asn ↔ Asp, Phe ↔ Tyr, Lys ↔ Arg, Gln ↔ Glu, Ile ↔ Val, Met → Leu; with Ala, Cys, His, Gly, Ser, Pro, and Thr, as structurally idiosyncratic residues. We also found a high average correlation (\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\overline{R}$$\end{document}R¯ = 0.85) between thirty amino acid mutability scales and the mutational inertia (I_X), which measures the energetic cost weighted by the number of observations at the most probable amino acid conformation. These results indicate that amino acid substitutions follow two optimally-efficient principles: (a) amino acids interchangeability privileges their secondary structural similarity, and (b) the amino acid mutability depends directly on its biosynthetic energy cost, and inversely with its frequency. These two principles are the underlying rules governing the observed amino acid substitutions.