Amino Acid Sequence Patterns Research Articles

Automatic generation of functional peptides with desired bioactivity and membrane permeability using Bayesian optimization.

Peptides are potentially useful modalities of drugs; however, cell membrane permeability is an obstacle in peptide drug discovery. The identification of bioactive peptides for a therapeutic target is also challenging because of the huge amino acid sequence patterns of peptides. In this study, we propose a novel computational method, PEptide generation system using Neural network Trained on Amino acid sequence data and Gaussian process-based optimizatiON (PENTAGON), to automatically generate new peptides with desired bioactivity and cell membrane permeability. In the algorithm, we mapped peptide amino acid sequences onto the latent space constructed using a variational autoencoder and searched for peptides with desired bioactivity and cell membrane permeability using Bayesian optimization. We used our proposed method to generate peptides with cell membrane permeability and bioactivity for each of the nine therapeutic targets, such as the estrogen receptor (ER). Our proposed method outperformed a previously developed peptide generator in terms of similarity to known active peptide sequences and the length of generated peptide sequences.

Unique Patterns in Amino Acid Sequences of Aging-Related Proteins.

Aging has strong genetic components and the list of genes that may regulate the aging process is collected in the GenAge database. There may be characteristic patterns in the amino acid sequences of aging-related proteins that distinguish them from other proteins and this information will lead to a better understanding of the aging process. To test this hypothesis, human protein sequences are extracted from the UniProt database and the relative frequency of every amino acid residue in aging-related proteins and the remaining proteins is calculated. The main observation is that the mean relative frequency of aspartic acid (D) is consistently higher, while the mean relative frequencies of tryptophan (W) and leucine (L) are consistently lower in aging-related proteins compared to the non-aging-related proteins for the human and four examined model organisms. It is also observed that the mean relative frequency of aspartic acid is higher, while the mean relative frequency of tryptophan is lower in pro-longevity proteins compared to anti-longevity proteins in model organisms. Finally, it is found that aging-related proteins tend to be longer than non-aging-related proteins. It is hoped that this analysis initiates further computational and experimental research to explore the underlying mechanisms of these findings.

Review and perspective on bioinformatics tools using machine learning and deep learning for predicting antiviral peptides.

Viruses constitute a constant threat to global health and have caused millions of human and animal deaths throughout human history. Despite advances in the discovery of antiviral compounds that help fight these pathogens, finding a solution to this problem continues to be a task that consumes time and financial resources. Currently, artificial intelligence (AI) has revolutionized many areas of the biological sciences, making it possible to decipher patterns in amino acid sequences that encode different functions and activities. Within the field of AI, machine learning, and deep learning algorithms have been used to discover antimicrobial peptides. Due to their effectiveness and specificity, antimicrobial peptides (AMPs) hold excellent promise for treating various infections caused by pathogens. Antiviral peptides (AVPs) are a specific type of AMPs that have activity against certain viruses. Unlike the research focused on the development of tools and methods for the prediction of antimicrobial peptides, those related to the prediction of AVPs are still scarce. Given the significance of AVPs as potential pharmaceutical options for human and animal health and the ongoing AI revolution, we have reviewed and summarized the current machine learning and deep learning-based tools and methods available for predicting these types of peptides.

TEMPROT: protein function annotation using transformers embeddings and homology search

BackgroundAlthough the development of sequencing technologies has provided a large number of protein sequences, the analysis of functions that each one plays is still difficult due to the efforts of laboratorial methods, making necessary the usage of computational methods to decrease this gap. As the main source of information available about proteins is their sequences, approaches that can use this information, such as classification based on the patterns of the amino acids and the inference based on sequence similarity using alignment tools, are able to predict a large collection of proteins. The methods available in the literature that use this type of feature can achieve good results, however, they present restrictions of protein length as input to their models. In this work, we present a new method, called TEMPROT, based on the fine-tuning and extraction of embeddings from an available architecture pre-trained on protein sequences. We also describe TEMPROT+, an ensemble between TEMPROT and BLASTp, a local alignment tool that analyzes sequence similarity, which improves the results of our former approach.ResultsThe evaluation of our proposed classifiers with the literature approaches has been conducted on our dataset, which was derived from CAFA3 challenge database. Both TEMPROT and TEMPROT+ achieved competitive results on F_{max }, S_{min }, AuPRC and IAuPRC metrics on Biological Process (BP), Cellular Component (CC) and Molecular Function (MF) ontologies compared to state-of-the-art models, with the main results equal to 0.581, 0.692 and 0.662 of F_{max } on BP, CC and MF, respectively.ConclusionsThe comparison with the literature showed that our model presented competitive results compared the state-of-the-art approaches considering the amino acid sequence pattern recognition and homology analysis. Our model also presented improvements related to the input size that the model can use to train compared to the literature methods.

Multi-species phase separation of intrinsically disordered proteins in analytical and field-theoretic models.

Biological condensates are now widely considered to be of fundamental biological, biomedical and biophysical importance. Numerous experimental studies suggest that bio-condensates like nucleolus, stress-granules, p-bodies etc. are physically underpinned to a significant degree by liquid-liquid phase separation (LLPS) of proteins driven by amino acid sequence pattern dependent multivalent interactions e.g. electrostatics, hydrophobicity, cation-Pi, Pi-Pi etc. The discovery of sequence dependent protein LLPS has led to the development of field-theoretic polymer physics models because mean field theories do not account fully for sequence specificity.

Cell-Based Manufacturing Technology Increases Antigenic Match of Influenza Vaccine and Results in Improved Effectiveness.

To ensure that vaccination offers the best protection against an infectious disease, sequence identity between the vaccine and the circulating strain is paramount. During replication of nucleic acid, random mutations occur due to the level of polymerase fidelity. In traditional influenza vaccine manufacture, vaccine viruses are propagated in fertilized chicken eggs, which can result in egg-adaptive mutations in the antigen-encoding genes. Whilst this improves infection and replication in eggs, mutations may reduce the effectiveness of egg-based influenza vaccines against circulating human viruses. In contrast, egg-adaptive mutations are avoided when vaccine viruses are propagated in Madin-Darby canine kidney (MDCK) cell lines during manufacture of cell-based inactivated influenza vaccines. The first mammalian cell-only strain was included in Flucelvax® Quadrivalent in 2017. A sequence analysis of the viruses selected for inclusion in this vaccine (n = 15 vaccine strains, containing both hemagglutinin and neuraminidase) demonstrated that no mutations occur in the antigenic sites of either hemagglutinin or neuraminidase, indicating that cell adaptation does not occur during production of this cell-based vaccine. The development of this now entirely mammalian-based vaccine system, which incorporates both hemagglutinin and neuraminidase, ensures that the significant protective antigens are equivalent to the strains recommended by the World Health Organization (WHO) in both amino acid sequence and glycosylation pattern. The inclusion of both proteins in a vaccine may provide an advantage over recombinant vaccines containing hemagglutinin alone. Findings from real world effectiveness studies support the use of cell-based influenza vaccines.

A Uniquely Stable Trimeric Model of SARS-CoV-2 Spike Transmembrane Domain.

Understanding fusion mechanisms employed by SARS-CoV-2 spike protein entails realistic transmembrane domain (TMD) models, while no reliable approaches towards predicting the 3D structure of transmembrane (TM) trimers exist. Here, we propose a comprehensive computational framework to model the spike TMD only based on its primary structure. We performed amino acid sequence pattern matching and compared the molecular hydrophobicity potential (MHP) distribution on the helix surface against TM homotrimers with known 3D structures and selected an appropriate template for homology modeling. We then iteratively built a model of spike TMD, adjusting “dynamic MHP portraits” and residue variability motifs. The stability of this model, with and without palmitoyl modifications downstream of the TMD, and several alternative configurations (including a recent NMR structure), was tested in all-atom molecular dynamics simulations in a POPC bilayer mimicking the viral envelope. Our model demonstrated unique stability under the conditions applied and conforms to known basic principles of TM helix packing. The original computational framework looks promising and could potentially be employed in the construction of 3D models of TM trimers for a wide range of membrane proteins.

Structural plasticity in I-Ag7 links autoreactivity to hybrid insulin peptides in type I diabetes

We recently provided evidence for promiscuous recognition of several different hybrid insulin peptides (HIPs) by the highly diabetogenic, I-Ag7-restricted 4.1-T cell receptor (TCR). To understand the structural determinants of this phenomenon, we solved the structure of an agonistic HIP/I-Ag7 complex, both in isolation as well as bound to the 4.1-TCR. We find that HIP promiscuity of the 4.1-TCR is dictated, on the one hand, by an amino acid sequence pattern that ensures I-Ag7 binding and, on the other hand, by the presence of three acidic residues at positions P5, P7 and P8 that favor an optimal engagement by the 4.1-TCR’s complementary determining regions. Surprisingly, comparison of the TCR-bound and unbound HIP/I-Ag7 structures reveals that 4.1-TCR binding triggers several novel and unique structural motions in both the I-Ag7 molecule and the peptide that are essential for docking. This observation indicates that the type 1 diabetes-associated I-Ag7 molecule is structurally malleable and that this plasticity allows the recognition of multiple peptides by individual TCRs that would otherwise be unable to do so.

A human coronavirus OC43-derived polypeptide causes neuropathic pain.

Human coronaviruses have been recently implicated in neurological sequelae by insufficiently understood mechanisms. We here identify an amino acid sequence within the HCoV‐OC43 p65‐like protein homologous to the evolutionarily conserved motif of myelin basic protein (MBP). Because MBP‐derived peptide exposure in the sciatic nerve produces pronociceptive activity in female rodents, we examined whether a synthetic peptide derived from the homologous region of HCoV‐OC43 (OC43p) acts by molecular mimicry to promote neuropathic pain. OC43p, but not scrambled peptides, induces mechanical hypersensitivity in rats following intrasciatic injections. Transcriptome analyses of the corresponding spinal cords reveal upregulation of genes and signaling pathways with known nociception‐, immune‐, and cellular energy‐related activities. Affinity capture shows the association of OC43p with an Na+/K+‐transporting ATPase, providing a potential direct target and mechanistic insight into virus‐induced effects on energy homeostasis and the sensory neuraxis. We propose that HCoV‐OC43 polypeptides released during infection dysregulate normal nervous system functions through molecular mimicry of MBP, leading to mechanical hypersensitivity. Our findings might provide a new paradigm for virus‐induced neuropathic pain.

Emergence of XIII.2.2 genotype of Avian Avulavirus-1 with unique FPCS site in India

In the present study, two isolates namely D162 and D165 obtained from Newcastle disease outbreaks inbackyard poultry in Tamil Nadu were subjected to phylogenetic analysis using complete Fusion (F) gene sequenceto understand the circulation and evolution of Avian Avulavius-1 (AAv1). Further, these isolates were pathotypedbased on mean death time and intra-cerebral pathogenicity index. Pathotyping and genotyping of D162 and D165showed that these isolates belonged to velogenic pathotype and XIII.2.2 sub-genotype. In addition to this, the Fusion Protein Cleavage Site (FPCS) of F gene was analysed for amino acid sequence pattern. The amino acid sequence of FPCS of both D162 and D165 was 112RRRKRF117, which is unique among XIII.2.2 genotypes of AAv1, whereas it is common among Pigeon Paramyxovirus. It was hypothesized that cross-species infection by AAv1 has led to emergence of isolates with increased virulence.

Histone variants: The unsung guardians of the genome

Histones H2A, H2B, H3, H4 and H1 are highly conserved, positively charged proteins which form a disc-shaped protein core around which genomic DNA is wrapped to form a nucleosome. Immediately following DNA synthesis, replication-dependent canonical histones help package the DNA into nucleosomes to form compact chromatin fibers that can fit within the confines of the cell nucleus. Histone variants, which vary from the canonical histones in their primary amino acid sequence and expression patterns, replace their canonical counterparts throughout the cell cycle in important biological processes such as transcription, replication, DNA repair and heterochromatin formation. DNA damage is a continual threat to genomic stability and cell survival. Unrepaired DNA lesions are either lethal or can promote mutations if the damaged cells escape programmed cell death due to apoptosis. In order to repair DNA damage, cells use multiple DNA repair pathways, all of which require the recruitment of a multiple DNA damage signaling and repair factors. In order for these repair factors to be recruited efficiently and function properly at sites of DNA damage, the local chromatin environment surrounding the DNA lesion is often altered. Cells are able to regulate chromatin structure in the vicinity of DNA lesions through the addition of posttranslational modifications on histones and DNA, as well as through histone variant incorporation or removal. Recruitment or removal of histone variants at sites of DNA damage can alter the local chromatin structure by destabilizing it and making it more accessible to repair factors. Alternatively, some histone variants and their modifications may also provide specific binding sites for the recruitment of various DNA repair factors, thereby influencing repair pathway choice or repair efficiency, or both. This review seeks to provide an overview of our current understanding of the roles played by histone variants in DNA repair, especially in mammalian cells.

Expression and diversity of the sialic acid-binding adhesin and its homologs associated with oral streptococcal infection.

Streptococcus gordonii, one of the early colonizers of oral biofilms, is involved in the development of dental caries, periodontal disease, and infective endocarditis. The Hsa adhesin of S. gordonii DL1 has the ability to bind strongly to the terminal sialic acid groups of host glycoproteins via the binding region, nonrepetitive region 2 (NR2), which is important for the pathogenicity of S. gordonii DL1. Low similarity with the NR2 of Hsa homologs among other streptococcal species has been reported. However, the reports have been limited to certain strains. This study attempted to assess frequency of the expression on the bacterial cell surface and to analyze the diversity of Hsa homologs among different wild strains of oral streptococci. We isolated 186 wild-type strains of oral streptococci from healthy volunteers and analyzed their hemagglutinating (HA) activity on human erythrocytes and their Hsa homologs and NR2 homologous regions by dot immunoblotting using anti-Hsa and anti-NR2 antisera, respectively. We found 30 strains reacted with anti-NR2 antiserum (NR2 positive) and determined the sequence of the NR2 regions. Many strains with high HA activity were also NR2 positive, suggesting that the NR2 region may be associated with HA activity. Among the NR2-positive strains, four different amino acid sequence patterns were observed, demonstrating diversity in the NR2 region. Notably, S. gordonii strains frequently possessed Hsa homologs and NR2-like antigens compared with other streptococci. It is speculated that the possessing frequency of Hsa homologs and the amino acid sequence of NR2 region may vary among streptococcal species.

Predicting TCR-Epitope Binding Specificity Using Deep Metric Learning and Multimodal Learning.

Understanding the recognition of specific epitopes by cytotoxic T cells is a central problem in immunology. Although predicting binding between peptides and the class I Major Histocompatibility Complex (MHC) has had success, predicting interactions between T cell receptors (TCRs) and MHC class I-peptide complexes (pMHC) remains elusive. This paper utilizes a convolutional neural network model employing deep metric learning and multimodal learning to perform two critical tasks in TCR-epitope binding prediction: identifying the TCRs that bind a given epitope from a TCR repertoire, and identifying the binding epitope of a given TCR from a list of candidate epitopes. Our model can perform both tasks simultaneously and reveals that inconsistent preprocessing of TCR sequences can confound binding prediction. Applying a neural network interpretation method identifies key amino acid sequence patterns and positions within the TCR, important for binding specificity. Contrary to common assumption, known crystal structures of TCR-pMHC complexes show that the predicted salient amino acid positions are not necessarily the closest to the epitopes, implying that physical proximity may not be a good proxy for importance in determining TCR-epitope specificity. Our work thus provides an insight into the learned predictive features of TCR-epitope binding specificity and advances the associated classification tasks.

Identification and characterization of two classes of G1 β-bulge.

In standard β-bulges, a residue in one strand of a β-sheet forms hydrogen bonds to two successive residues (`1' and `2') of a second strand. Two categories, `classic' and `G1' β-bulges, are distinguished by their dihedral angles: 1,2-αRβR (classic) or 1,2-αLβR (G1). It had previously been observed that G1 β-bulges are most often found as components of two quite distinct composite structures, suggesting that a basis for further differentiation might exist. Here, it is shown that two subtypes of G1 β-bulges, G1α and G1β, may be distinguished by their conformation (αR or βR) at residue `0' of the second strand. β-Bulges that are constituents of the composite structure named the β-bulge loop are of the G1α type, whereas those that are constituents of the composite structure named β-link here are of the G1β type. A small proportion of G1β β-bulges, but not G1α β-bulges, occur in other contexts. There are distinctive differences in amino-acid composition and sequence pattern between these two types of G1 β-bulge which may have practical application in protein design.

Comparative and functional analysis of desaturase FADS1 (∆5) and FADS2 (∆6) orthologues of marine organisms

Fatty acid desaturases are key enzymes involved in unsaturated fatty acid biosynthesis, which insert double bonds at specific positions of fatty acids, playing a pivotal role in unsaturated fatty acid synthesis required for membrane lipid fluidity. The ∆5 and ∆6 desaturases are responsible for producing long chain-polyunsaturated fatty acids (LC-PUFA) through their precursors α-linolenic acid and linoleic acid in organisms lacking or with very low ability to synthesize LC-PUFA by themselves. Extensive studies of fatty acid desaturases are available in model organisms, such as humans and mouse; however, the diversity of these genes in the marine biodiversity is less known. This study performed an exhaustive analysis to identify the ∆5 and ∆6 desaturases in the available marine genomes in databases, as well as transcriptomes and EST databases, and their coding sequences were compared to the well-characterized ∆5 and ∆6 desaturases from humans. The FADS1 and FADS2 genetic structures are well conserved among all the organisms analyzed. A common amino acid pattern was identified to discriminate between ∆5 and ∆6 desaturases. The analysis of the conserved motif involved in catalysis showed that 20% of the desaturases, ∆5 and ∆6, have lost motifs required for catalysis. Additionally, bifunctional ∆5/∆6 desaturases were able to be identified by amino acid sequence patterns found in previously described enzymes. A revision of the expression profiles and functional activity on sequences in databases and scientific literature provided information regarding the function of these marine organism enzymes.

Circulating HIV-1 Integrase Genotypes in Tanzania: Implication on the Introduction of Integrase Inhibitors-Based Antiretroviral Therapy Regimen.

Tanzania has recently adapted World Health Organization antiretroviral guidelines that include integrase strand transfer inhibitors (INSTIs) in the first-line regimen. However, there is lack of evidence on integrase (IN) gene polymorphisms in viral strains circulating in Tanzania. In this study, we characterize IN gene polymorphisms in viral strains circulating in Dar es Salaam, Tanzania, before introduction of INSTIs. Plasma viral RNAs were prepared from 158 HIV-1-infected subjects, including 111 treated, but viremic (INSTI-naïve), subjects. A part of the pol gene encompassing the IN-coding region was amplified and directly sequenced by the Sanger sequencing method. Subtype analysis revealed that subtypes A1, C, and D and intersubtype recombinants were 42%, 38%, 11%, and 9%, respectively. Although multiple subtypes cocirculate, the IN gene exhibited a relatively conserved amino acid sequence pattern with an average Shannon entropy score of 0.16. No major INSTI resistance mutations were found; however, accessory resistance mutations at positions T97A, E157Q, G163E/K, and 128A/T were detected in 5% of subjects.

Molecular underpinnings of integrin binding to collagen-mimetic peptides containing vascular Ehlers–Danlos syndrome–associated substitutions

Collagens carry out critical extracellular matrix (ECM) functions by interacting with numerous cell receptors and ECM components. Single glycine substitutions in collagen III, which predominates in vascular walls, result in vascular Ehlers-Danlos syndrome (vEDS), leading to arterial, uterine, and intestinal rupture and an average life expectancy of <50 years. Collagen interactions with integrin α2β1 are vital for platelet adhesion and activation; however, how these interactions are impacted by vEDS-associated mutations and by specific amino acid substitutions is unclear. Here, we designed collagen-mimetic peptides (CMPs) with previously reported Gly → Xaa (Xaa = Ala, Arg, or Val) vEDS substitutions within a high-affinity integrin α2β1-binding motif, GROGER. We used these peptides to investigate, at atomic-level resolution, how these amino acid substitutions affect the collagen III-integrin α2β1 interaction. Using a multitiered approach combining biological adhesion assays, CD, NMR, and molecular dynamics (MD) simulations, we found that these substitutions differentially impede human mesenchymal stem cell spreading and integrin α2-inserted (α2I) domain binding to the CMPs and were associated with triple-helix destabilization. Although an Ala substitution locally destabilized hydrogen bonding and enhanced mobility, it did not significantly reduce the CMP-integrin interactions. MD simulations suggested that bulkier Gly → Xaa substitutions differentially disrupt the CMP-α2I interaction. The Gly → Arg substitution destabilized CMP-α2I side-chain interactions, and the Gly → Val change broke the essential Mg2+ coordination. The relationship between the loss of functional binding and the type of vEDS substitution provides a foundation for developing potential therapies for managing collagen disorders.

Structural differences between the ectodomains of murine and human CD98hc.

The CD98 heavy chain (CD98hc) constitutes both a promising cell surface target for the treatment of cancers and a transcytosis receptor potentially useful for the brain delivery of therapeutics. However, pharmacokinetic studies and safety assessment of cognate antibodies or nonimmunoglobulin binding proteins in rodents is hampered by cross-species variability of both amino acid sequence and glycosylation pattern. Here, we report the crystal structure of the murine CD98hc extracellular domain and a comprehensive comparison with its human ortholog, revealing only one conserved surface patch that is neither shielded by glycosylation nor by the cell membrane with an accessible surface area typical for an antibody epitope. Our results imply the necessity of a surrogate approach for CD98hc-specific binding proteins with predictive power for clinical investigations.

Structure and function of class III pistil-specific extensin-like protein in interspecific reproductive barriers

BackgroundThe transmitting tissue of the style is the pathway for pollen tube growth to the ovules and has components that function in recognizing and discriminating appropriate pollen genotypes. In Nicotiana tabacum, the class III pistil extensin-like (PELPIII) arabinogalactan protein is essential for the inhibition of N. obtusifolia pollen tube growth. The transmitting tissue-specific (TTS) arabinogalactan protein amino acid sequence and expression pattern is similar to PELPIII, but it facilitates self-pollinated N. tabacum. The TTS and PELPIII arabinogalactan protein can be divided into the less conserved N-terminal (NTD) and the more conserved C-terminal (CTD) domains. This research tested whether the NTD is the key domain in determining PELPIII function in the inhibition of interspecific pollen tube growth. Three variant PELPIII gene constructs were produced where the PELPIII NTD was exchanged with the TTS NTD and a single amino acid change (cysteine to alanine) was introduced into the PELPIII NTD. The PELPIII variants of N. tabacum were tested for activity by measuring the inhibition N. obtusifolia pollen tube growth by using them to complement a 3’UTR RNAi transgenic line with reduced PELPIII mRNA.ResultsThe RNAi N. tabacum line had reduced PELPIII mRNA accumulation and reduced inhibition of N. obtusifolia pollen tube growth, but had no effect on self-pollen tube growth or pollen tube growth of 12 other Nicotiana species. The NTD of PELPIII with either the PELPIII or TTS CTDs complemented the loss PELPIII activity in the RNAi transgenic line as measured by inhibition of N. obtusifolia pollen tube growth. The TTS NTD with the PELPIII CTD and a variant PELPIII with a cysteine to alanine mutation in its NTD failed to complement the loss of PELPIII activity and did not inhibit N. obtusifolia pollen tube growth.ConclusionThe NTD is a key determinant in PELPIII’s function in regulating interspecific pollen tube growth and is a first step toward understanding the mechanism of how PELPIII NTD regulates pollen tube growth.

Cloning and expression analysis of PtHDT903, a HD2-type histone deacetylase gene in Populus trichocarpa

Histone deacetylases (HDACs) catalyse the deacetylation of core histones and non-histone proteins. Histone deacetylases work in concert with histone acetyltransferases to modify the structure and function of chromatins, and regulate gene transcription. Histone deacetylases are critical enzymes involved in the regulation of multiple cellular processes, such as plant growth and development, stress responses and gene silencing. Plant HDACs are a supergene family and can be divided into three families, namely RPD3/HDA1, HD2 and SIR2. HD2 specifically occur in plants, not in fungi and animals. In this study, an HD2-type HDAC gene, PtHDT903, was cloned from Populus trichocarpa. Its amino acid sequence, subcellular localization and expression patterns under abiotic stresses were analysed. The results showed that PtHDT903 encodes a hydrophilic and acidic protein consisting of 305 amino acids. The predicted PtHDT903 protein has the conserved domains which found in the other HD2-type HDACs. The PtHDT903 protein was localized in the nucleus. The expression of the PtHDT903 gene was down-regulated by salt stress, whereas it was up-regulated by cold. The results provide valuable information for the further functional study of PtHDT903.