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In proteomics, a crucial aspect is to identify peptide sequences. De novo sequencing methods have been widely employed to identify peptide sequences, and numerous tools have been proposed over the past two decades. Recently, deep learning approaches have been introduced for de novo sequencing. Previous methods focused on encoding tandem mass spectra and predicting peptide sequences from the first amino acid onwards. However, when predicting peptides using tandem mass spectra, the peptide sequence can be predicted not only from the first amino acid but also from the last amino acid due to the coexistence of b-ion (or a- or c-ion) and y-ion (or x- or z-ion) fragments in the tandem mass spectra. Therefore, it is essential to predict peptide sequences bidirectionally. Our approach, called NovoB, utilizes a Transformer model to predict peptide sequences bidirectionally, starting with both the first and last amino acids. In comparison to Casanovo, our method achieved an improvement of the average peptide-level accuracy rate of approximately 9.8% across all species. Author summary: Understanding the characteristics of data is very important in deep learning methods. When predicting sentences, the transformer model naturally predicts from the first word. For this reason, previous methods predicted peptide sequences from the first amino acid. However, in tandem mass spectra, it is possible to predict peptide sequences bidirectionally. This method shows better results than previous approaches because it can better encode tandem mass spectra. We have demonstrated that good results can be achieved simply by understanding the characteristics of such data and using the model appropriately. We hope that this paper will help various readers improve the performance capabilities of their models. Furthermore, given that bidirectional peptide sequence prediction is crucial in de novo peptide sequence analysis, we hope that this approach will be applied to both existing and future methods utilizing deep learning techniques. [ABSTRACT FROM AUTHOR]

Today, the use of nutrients derived from natural bioactive compounds application in the food, pharmaceutical, and cosmetic industries is on the increase. This paper aimed to evaluate the amino acids profile (essential and non-essential) and pigments composition (chlorophyll a, carotenoids, and phycocyanin) of Arthrospira platensis (a blue-green microalga) cultivation in a flat-plate photobioreactor under various types of light-emitting diodes (red: 620–680 nm, white: 380–780 nm, yellow: 570–600nm, blue: 445–480 nm). The maximum biomass concentration (604.96 mg L−1) occurred when the red LED was applied for cultivation, and the minimum biomass concentration (279.39 mg L−1) was obtained under blue LED. The sequence of pigments and amino acids concentrations (mg L−1culture volume) was approximately in accordance with the biomass productivity. It means the red light produces the maximum concentration of pigments (chlorophyll a: 5.42, carotenoids: 2.92, phycocyanin: 67.54 mg L−1) and amino acids (essential amino acids: 110.47, nonessential amino acids: 179.10 mg L−1). Nevertheless, when these values were measured in mg per g of dry weight, the utmost contents were observed in microalgal products cultivated under blue LED. These consequences are due to the highest cell productivity and the most extended length of cells that occurred under red and blue LEDs, respectively. Highlights: Spectral light quality is an effective parameter on Spirulina platensis growth. Red light produces the highest biomass productivity. Pigment and amino acids concentrations have conformity with biomass productivity. Blue light causes the cells to be longer. Longer cells have more time to synthesize the intracellular compounds. [ABSTRACT FROM AUTHOR]

Fluorescence indicators capable of binding to human immunodeficiency virus-1 (HIV-1) trans-activation responsive (TAR) RNA are powerful tools for the exploratory studies of the identification of anti-HIV drug candidates. This work presents a new design strategy for fluorogenic indicators with a transactivator of transcription (Tat)-derived peptide based on the forced intercalation of thiazole orange (TO) dyes (FIT). The developed 9-mer FIT peptide (RKKRR-TO-RRR: named FiLuP) features the TO unit integrated onto a Dap (2,3-diaminopropionic acid) residue in the middle of the Tat peptide sequence; the Q (glutamic acid) residue in the Tat peptide (RKKRR-Q-RRR) is replaced with TO as if it were an amino acid surrogate. This facilitates a significant light-up response (450-fold at λem = 541 nm, Φfree = 0.0057, and Φbound = 0.61) upon binding to TAR RNA. The response of FiLuP is highly selective to TAR RNA over other non-cognate RNAs, and FiLuP maintains strong binding affinity (Kd = 1.0 ± 0.6 nM). Significantly, in contrast to previously developed Tat peptide-based FRET probes, FiLuP is able to discriminate between "competitive" and "noncompetitive" inhibitors when used in the fluorescence indicator displacement (FID) assay. The FID assay under stringent screening conditions is also possible, enabling super-strong competitive binders toward TAR RNA to be sieved out. [ABSTRACT FROM AUTHOR]

Insufficient research on the material basis and functional product development of horse bones has led to the waste of resources. This paper, as the by-product of horse meat bone marrow was the object, aimed to discover the flavor and antioxidant activity of novel collagen peptides. Two peptides, HBMP-1-1 and HBMP-2-1, were isolated and purified from horse bone marrow collagen protein (HBMP) hydrolysates. The combination of spectroscopic and chromatographic methods including SDS-PAGE, UV, FT-IR, CD, and SEM identified the structures of the peptides. The amino acid composition, flavor characteristics, and antioxidant activity were also evaluated. SDS-PAGE analysis showed that the molecular weight of the two peptides was concentrated under 4.1 kDa. The total amino acid contents of the HBMP-1-1 was up to 570.779 mg/g. Leucine, alanine, and glutamicacid were the predominant amino acids in HBMP-1-1 with high nutritional value, and it contained 10.07% β-folding, 46.39% β-turn, and 43.54% random coil structure. The peptide sequences IDDPTDSKPE, LNGKLTGM, ELDEGYVPK, AFQEDPDKF, and FVGKVVDPTQK with molecular weights of 1116.51, 833.45, 1049.51, 1096.49, and 1217.69 Da were detected using LC-MS/MS. The results of the antioxidant activity test showed that separation and purification significantly improved the bioactivity of the protein. The half-inhibition rate of HBMP-1-1 against DPPH, ABTS, and hydroxyl radicals were 0.054, 0.050, and 0.137 mg/mL, respectively. This article provides a scientific basis for clarifying the material basis of horse bone marrow collagen peptides. Also, it provides new approaches and ideas for exploring efficient and safe bone-derived lead compounds and products. [ABSTRACT FROM AUTHOR]

Statistical analysis of multiple sequence alignments of homologous proteins has revealed groups of coevolving amino acids called sectors. These groups of amino-acid sites feature collective correlations in their amino-acid usage, and they are associated to functional properties. Modeling showed that nonlinear selection on an additive functional trait of a protein is generically expected to give rise to a functional sector. These modeling results motivated a principled method, called ICOD, which is designed to identify functional sectors, as well as mutational effects, from sequence data. However, a challenge for all methods aiming to identify sectors from multiple sequence alignments is that correlations in amino-acid usage can also arise from the mere fact that homologous sequences share common ancestry, i.e. from phylogeny. Here, we generate controlled synthetic data from a minimal model comprising both phylogeny and functional sectors. We use this data to dissect the impact of phylogeny on sector identification and on mutational effect inference by different methods. We find that ICOD is most robust to phylogeny, but that conservation is also quite robust. Next, we consider natural multiple sequence alignments of protein families for which deep mutational scan experimental data is available. We show that in this natural data, conservation and ICOD best identify sites with strong functional roles, in agreement with our results on synthetic data. Importantly, these two methods have different premises, since they respectively focus on conservation and on correlations. Thus, their joint use can reveal complementary information. Author summary: Proteins perform crucial functions in the cell. The biological function of a protein is encoded in its amino-acid sequence. Natural selection acts at the level of function, while mutations arise randomly on sequences. In alignments of sequences of homologous proteins, which share common ancestry and common function, the amino acid usages at different sites can be correlated due to functional constraints. In particular, groups of collectively correlated amino acids, termed sectors, tend to emerge due to selection on functional traits. However, correlations can also arise from the shared evolutionary history of homologous proteins, even without functional constraints. This may obscure the inference of functional sectors. By analyzing controlled synthetic data as well as natural protein sequence data, we show that two very different methods allow to identify sectors and mutational effects in a way that is most robust to phylogeny. We suggest that considering both of these methods allows a better identification of functionally important sites from protein sequences. These results have potential impact on the design of new functional sequences. [ABSTRACT FROM AUTHOR]

How complex are the rules by which a protein's sequence determines its function? High-order epistatic interactions among residues are thought to be pervasive, suggesting an idiosyncratic and unpredictable sequence-function relationship. But many prior studies may have overestimated epistasis, because they analyzed sequence-function relationships relative to a single reference sequence—which causes measurement noise and local idiosyncrasies to snowball into high-order epistasis—or they did not fully account for global nonlinearities. Here we present a reference-free method that jointly infers specific epistatic interactions and global nonlinearity using a bird's-eye view of sequence space. This technique yields the simplest explanation of sequence-function relationships and is more robust than existing methods to measurement noise, missing data, and model misspecification. We reanalyze 20 experimental datasets and find that context-independent amino acid effects and pairwise interactions, along with a simple nonlinearity to account for limited dynamic range, explain a median of 96% of phenotypic variance and over 92% in every case. Only a tiny fraction of genotypes are strongly affected by higher-order epistasis. Sequence-function relationships are also sparse: a miniscule fraction of amino acids and interactions account for 90% of phenotypic variance. Sequence-function causality across these datasets is therefore simple, opening the way for tractable approaches to characterize proteins' genetic architecture. Understanding protein sequence-function relationships is complicated by high order epistatic interactions among residues, although the extent of these interactions remains uncertain. Here, the authors present a reference-free method which suggests that sequence-function relationships are relatively simple, with little influence from high order epistatic interactions. [ABSTRACT FROM AUTHOR]

TORC1 (target of rapamycin complex 1) is a highly conserved protein kinase that plays a central role in regulating cell growth. Given the role of mammalian TORC1 (mTORC1) in metabolism and disease, understanding mTORC1 downstream signaling and feedback loops is important. mTORC1 recognizes some of its substrates via a five amino acid binding sequence called the TOR signaling (TOS) motif. mTORC1 binding to a TOS motif facilitates phosphorylation of a distinct, distal site. Here, we show that LST2, also known as ZFYVE28, contains a TOS motif (amino acids 401 to 405) and is directly phosphorylated by mTORC1 at serine 670 (S670). mTORC1-mediated S670 phosphorylation promotes LST2 monoubiquitination on lysine 87 (K87). Monoubiquitinated LST2 is stable and displays a broad reticular distribution. When mTORC1 is inactive, unphosphorylated LST2 is degraded by the proteasome. The absence of LST2 enhances EGFR (epidermal growth factor receptor) signaling. We propose that mTORC1 negatively feeds back on its upstream receptor EGFR via LST2. [ABSTRACT FROM AUTHOR]

The molecular machines of life, proteins, are made up of twenty kinds of amino acids, each with distinctive side chains. We present a geometrical analysis of the protrusion statistics of side chains in more than 4000 high-resolution protein structures. We employ a coarse-grained representation of the protein backbone viewed as a linear chain of Cα atoms and consider just the heavy atoms of the side chains. We study the large variety of behaviors of the amino acids based on both rudimentary structural chemistry as well as geometry. Our geometrical analysis uses a backbone Frenet coordinate system for the common study of all amino acids. Our analysis underscores the richness of the repertoire of amino acids that is available to nature to design protein sequences that fit within the putative native state folds. [ABSTRACT FROM AUTHOR]

The construction of peptides to mimic heterogeneous proteins such as type I collagen plays a pivotal role in deciphering their function and pathogenesis. However, progress in the field has been severely hampered by the lack of capability to create stable heterotrimers with desired functional sequences and without the effect of homotrimers. We have herein developed a set of triblock peptides that can assemble into collagen mimetic heterotrimers with desired amino acids and are free from the interference of homotrimers. The triblock peptides comprise a central collagen-like block and two oppositely charged N-/C-terminal blocks, which display inherent incompetency of homotrimer formation. The favorable electrostatic attraction between two paired triblock peptides with complementary terminal charged sequences promptly leads to stable heterotrimers with controlled chain composition. The independence of the collagen-like block from the two terminal blocks endows this system with the adaptability to incorporate desired amino acid sequences while maintaining the heterotrimer structure. The triblock peptides provide a versatile and robust tool to mimic the composition and function of heterotrimer collagen and may have great potential in the design of innovative peptides mimicking heterogeneous proteins. [ABSTRACT FROM AUTHOR]

The development of turn-based inhibitors of protein–protein interactions has attracted considerable attention in medicinal chemistry. Our group has synthesized a series of peptides derived from an amino-functionalized ferrocene to investigate their potential to mimic protein turn structures. Detailed DFT and spectroscopic studies (IR, NMR, CD) have shown that, for peptides, the backbone chirality and bulkiness of the amino acid side chains determine the hydrogen-bond pattern, allowing tuning of the size of the preferred hydrogen-bonded ring in turn-folded structures. However, their biological potential is more dependent on their lipophilicity. In addition, our pioneering work on the chiroptical properties of aminoferrocene-containing peptides enables the correlation of their geometry with the sign of the CD signal in the absorption region of the ferrocene chromophore. These studies have opened up the possibility of using aminoferrocene and its derivatives as chirooptical probes for the determination of various chirality elements, such as the central chirality of amino acids and the helicity of peptide sequences. [ABSTRACT FROM AUTHOR]

Motivation Protein language models (PLMs), which borrowed ideas for modelling and inference from natural language processing, have demonstrated the ability to extract meaningful representations in an unsupervised way. This led to significant performance improvement in several downstream tasks. Clustering amino acids based on their physical–chemical properties to achieve reduced alphabets has been of interest in past research, but their application to PLMs or folding models is unexplored. Results Here, we investigate the efficacy of PLMs trained on reduced amino acid alphabets in capturing evolutionary information, and we explore how the loss of protein sequence information impacts learned representations and downstream task performance. Our empirical work shows that PLMs trained on the full alphabet and a large number of sequences capture fine details that are lost in alphabet reduction methods. We further show the ability of a structure prediction model(ESMFold) to fold CASP14 protein sequences translated using a reduced alphabet. For 10 proteins out of the 50 targets, reduced alphabets improve structural predictions with LDDT-C α differences of up to 19%. Availability and implementation Trained models and code are available at github.com/Ieremie/reduced-alph-PLM. [ABSTRACT FROM AUTHOR]

Coacervation, or liquid-liquid phase separation (LLPS) of biomacromolecules, is increasingly recognized to play an important role both intracellularly and in the extracellular space. Central questions that remain to be addressed are the links between the material properties of coacervates (condensates) and both the primary and the secondary structures of their constitutive building blocks. Short LLPS-prone peptides, such as GY23 variants explored in this study, are ideal model systems to investigate these links because simple sequence modifications and the chemical environment strongly affect the viscoelastic properties of coacervates. Herein, a systematic investigation of the structure/property relationships of peptide coacervates was conducted using GY23 variants, combining biophysical characterization (plate rheology and surface force apparatus, SFA) with secondary structure investigations by infrared (IR) and circular dichroism (CD) spectroscopy. Mutating specific residues into either more hydrophobic or more hydrophilic residues strongly regulates the viscoelastic properties of GY23 coacervates. Furthermore, the ionic strength and kosmotropic characteristics (Hofmeister series) of the buffer in which LLPS is induced also significantly impact the properties of formed coacervates. Structural investigations by CD and IR indicate a direct correlation between variations in properties induced by endogenous (peptide sequence) or exogenous (ionic strength, kosmotropic characteristics, aging) factors and the β-sheet content within coacervates. These findings provide valuable insights to rationally design short peptide coacervates with programmable materials properties that are increasingly used in biomedical applications. Peptide-based coacervates display interesting properties for biomedical applications, however, the link between peptide structure and coacervate material properties remains unclear. Here, the authors report a direct correlation between the primary and secondary structures of the peptides and the viscoelastic properties of the coacervates. [ABSTRACT FROM AUTHOR]