Amino Acid Sequence Of Protein Research Articles

Bioinformatics-based Analysis of the Variability of MPOX Virus Proteins

Background: Previously restricted to remote areas of Central and Western Africa, the MPOX virus-based disease, also known as monkeypox, has now spread to more than 90 countries and has become endemic. As a consequence, the MPOX virus has become a global public health concern. Objective: The objective of this study was to conduct a computational-multiparametric study (at the genomic and proteomic levels) of the biological sequences that express the MPOX virus envelopes in order to fathom the physicochemical regularities of these proteins. Methods: Using computer programs, we determined the polarity index method (PIM) profile and protein intrinsic disorder predisposition (PIDP) for each studied protein. Results: The UniProt database was able to identify sequences similar to those of the MPOX virus expressed thanks to the computational regularities found in the virus' envelope sequences. Conclusion: The polarity index method and protein intrinsic disorder predisposition profiles could aid in elucidating the sequence-level structural regularities of the MPOX virus envelopes.

Re-engineering of a carotenoid-binding protein based on NMR structure.

Recently, a number of message passing neural network (MPNN)-based methods have been introduced that, based on backbone atom coordinates, efficiently recover native amino acid sequences of proteins and predict modifications that result in better expressing, more soluble, and stable variants. However, usually, X-ray structures, or artificial structures generated by algorithms trained on X-ray structures, were employed to define target backbone conformations. Here, we show that commonly used algorithms ProteinMPNN and SolubleMPNN display low sequence recovery on structures determined using NMR. We subsequently propose a computational approach that we successfully apply to re-engineer AstaP, a protein that natively binds a large hydrophobic ligand astaxanthin (C40H52O4), and for which only a structure determined using NMR is currently available. The engineered variants, designated NeuroAstaP, are 51 amino acid shorter than the 22 kDa parent protein, have 38%-42% sequence identity to it, exhibit good yields, are expressed in a soluble, mostly monomeric form, and demonstrate efficient binding of carotenoids in vitro and in cells. Altogether, our work further tests the limits of using machine learning for protein engineering and paves the way for MPNN-based modification of proteins based on NMR-derived structures.

Arachis mottle-associated virus, a new polerovirus infecting Pinto peanut.

A new polerovirus, named "arachis mottle-associated virus" (ArMoV), was identified by high-throughput sequencing in a Pinto peanut (Arachis pintoi) plant. The genome sequence was confirmed by Sanger sequencing and contains 5775 nucleotides and seven predicted open reading frames (ORFs), showing a typical polerovirus genome structure. All of the proteins encoded by ArMoV showed less than 90% amino acid sequence identity to those of other poleroviruses, the threshold to establish a new species in the genus. Phylogenetic analysis based on P1-P2 fusion protein and coat protein amino acid sequences showed that tobacco polerovirus 1 and chickpea chlorotic stunt virus, respectively, were the most closely related to ArMoV. These data suggest that ArMoV is a member of a new species of the genus Polerovirus, for which the binomial name "Polerovirus ARMOV " is proposed.

Annotation-free prediction of microbial dioxygen utilization.

Aerobes require dioxygen (O2) to grow; anaerobes do not. However, nearly all microbes-aerobes, anaerobes, and facultative organisms alike-express enzymes whose substrates include O2, if only for detoxification. This presents a challenge when trying to assess which organisms are aerobic from genomic data alone. This challenge can be overcome by noting that O2 utilization has wide-ranging effects on microbes: aerobes typically have larger genomes encoding distinctive O2-utilizing enzymes, for example. These effects permit high-quality prediction of O2 utilization from annotated genome sequences, with several models displaying ≈80% accuracy on a ternary classification task for which blind guessing is only 33% accurate. Since genome annotation is compute-intensive and relies on many assumptions, we asked if annotation-free methods also perform well. We discovered that simple and efficient models based entirely on genomic sequence content-e.g., triplets of amino acids-perform as well as intensive annotation-based classifiers, enabling rapid processing of genomes. We further show that amino acid trimers are useful because they encode information about protein composition and phylogeny. To showcase the utility of rapid prediction, we estimated the prevalence of aerobes and anaerobes in diverse natural environments cataloged in the Earth Microbiome Project. Focusing on a well-studied O2 gradient in the Black Sea, we found quantitative correspondence between local chemistry (O2:sulfide concentration ratio) and the composition of microbial communities. We, therefore, suggest that statistical methods like ours might be used to estimate, or "sense," pivotal features of the chemical environment using DNA sequencing data.IMPORTANCEWe now have access to sequence data from a wide variety of natural environments. These data document a bewildering diversity of microbes, many known only from their genomes. Physiology-an organism's capacity to engage metabolically with its environment-may provide a more useful lens than taxonomy for understanding microbial communities. As an example of this broader principle, we developed algorithms that accurately predict microbial dioxygen utilization directly from genome sequences without annotating genes, e.g., by considering only the amino acids in protein sequences. Annotation-free algorithms enable rapid characterization of natural samples, highlighting quantitative correspondence between sequences and local O2 levels in a data set from the Black Sea. This example suggests that DNA sequencing might be repurposed as a multi-pronged chemical sensor, estimating concentrations of O2 and other key facets of complex natural settings.

Partial biochemical and immunological characterization of an isolated 52.1 kDa pregnancy-associated glycoprotein charge variant

Pregnancy-associated glycoproteins (PAGs) are synthesized in the placental cells of ruminants and are detectable in blood, milk, and urine. Many of these proteins have been obtained and characterized from placental extracts by precipitation with 80 % ammonium sulfate. The possibility of purifying PAGs by precipitation with other concentrations of ammonium sulfate remains unexplored. We aimed to study PAG proteins obtained from extracts of ovine placenta at 100 days of gestation through precipitation with 40 % ammonium sulfate (Extract 40). The main protein complex (130 kDa) was obtained after Extract 40 precipitation. Under reducing SDS-PAGE conditions, the 130 kDa complex dissociated in two PAG proteins with apparent molecular weights of 52.1 kDa and 26.1 kDa. The 130 kDa protein appeared to be a molecular complex consisting of two copies of the 52.1 kDa protein linked to one copy of the 26.1 kDa protein, presumably by disulfide bonds. Furthermore, the 52.1 kDa protein consisted of at least three isoforms with distinct isoelectric points. Amino acid microsequencing of the 52.1 kDa protein revealed a chimeric structure containing amino acid sequences of PAG1, PAG4, PAG6, and PAG1-like proteins. This procedure recovered a novel 130 kDa protein complex composed of 26.1 kDa and two 52.1 kDa PAGs. To the best of our knowledge, this has not been previously reported as heterologous polymeric molecules.

TC-DTA: Predicting Drug-Target Binding Affinity With Transformer and Convolutional Neural Networks.

Bioinformatics is a rapidly evolving field that applies computational methods to analyze and interpret biological data. A key task in bioinformatics is identifying novel drug-target interactions (DTIs), which plays a crucial role in drug discovery. Most computational approaches treat DTI prediction as a binary classification problem, determining whether drug-target pairs interact. However, with the growing availability of drug-target binding affinity data, this binary task can be reframed as a regression problem focused on drug-target affinity (DTA). DTA quantifies the strength of drug-target binding, offering more detailed insights than DTI and serving as a valuable tool for virtual screening in drug discovery. Accurately predicting compound interactions with targets can accelerate the drug development process. In this study, we introduce a deep learning model named TC-DTA for DTA prediction, leveraging convolutional neural networks (CNN) and the encoder module of the transformer architecture. We begin by extracting raw drug SMILES strings and protein amino acid sequences from the dataset, which are then represented using various encoding methods. Subsequently, we employ CNN and the transformer's encoder module to extract features from the drug SMILES strings and protein sequences, respectively. Finally, the feature information is concatenated and input into a multi-layer perceptron to predict binding affinity scores. We evaluated our model on two benchmark DTA datasets, Davis and KIBA, comparing it with methods such as KronRLS, SimBoost, and DeepDTA. Our model, TC-DTA, outperformed these baseline methods based on evaluation metrics like Mean Squared Error (MSE), Concordance Index (CI), and Regression towards the Mean Index ( rm2 ). These results highlight the effectiveness of the Transformer's encoder and CNN in extracting meaningful representations from sequences, thereby enhancing DTA prediction accuracy. This deep learning model can accelerate drug discovery by identifying drug candidates with high binding affinity to specific targets. Compared to traditional methods, machine learning technology offers a more effective and efficient approach to drug discovery.

Genomic Landscape and Regulation of RNA Editing in Pekin Ducks Susceptible to Duck Hepatitis A Virus Genotype 3 Infection.

RNA editing is increasingly recognized as a post-transcriptional modification that directly affects viral infection by regulating RNA stability and recoding proteins. the duck hepatitis A virus genotype 3 (DHAV-3) infection is seriously detrimental to the Asian duck industry. However, the landscape and roles of RNA editing in the susceptibility and resistance of Pekin ducks to DHAV-3 remain unclear. Here, we profiled dynamic RNA editing events in liver tissue and investigated their potential functions during DHAV-3 infection in Pekin ducks. We identified 11,067 informative RNA editing sites in liver tissue from DHAV-3-susceptible and -resistant ducklings at three time points during virus infection. Differential RNA editing sites (DRESs) between S and R ducks were dynamically changed during infection, which were enriched in genes associated with vesicle-mediated transport and immune-related pathways. Moreover, we predicted and experimentally verified that RNA editing events in 3'-UTR could result in loss or gain of miRNA-mRNA interactions, thereby changing the expression of target genes. We also found a few DRESs in coding sequences (CDSs) that altered the amino acid sequences of several proteins that were vital for viral infection. Taken together, these data suggest that dynamic RNA editing has significant potential to tune physiological processes in response to virus infection in Pekin ducks, thus contributing to host differential susceptibility to DHAV-3.

Cloning and Expression of Pigeon-Derived Escherichia coli Type 1 Pilus Clusters and Analysis of Amino Acid Sequence Characteristics of Functional Proteins.

Type 1 pili, as an important virulence factor of E. coli, has certain homology between APEC and UPEC, but the homology degree is not clear enough. This study aims to compare the homology between them. The recombinant bacteria were constructed by homologous recombination. The pili were observed by TEM, and the hemagglutination characteristics were determined by MHSA. The complete gene sequence was determined by sequencing, and the amino acid sequences of the functional proteins of type 1 pili of APEC and UPEC were compared. TEM showed that they could express pili, which were slender, straight, and dense. Stable-pUC-fimBH has MHSA but stable-pUC-fimBG does not. The amino acid sequence similarity of FimB of NJ05 and UPEC was 98.8%, FimE was 99.4%, and the similarity between them was 51.5%. Compared with UPEC's type 1 pili FimC and FimD sequences, the similarity was 99.52% and 87.8%, respectively. The amino acid sequence of FimA of NJ05 was 89-96%, similar to UPEC, and the N-terminal and C-terminal amino acid sequences were exactly the same. The gene sequence and amino acid sequence similarity of FimH between them were both above 99%. The similarity of the pilus binding domain of FimH was 52.8%, but only 27.6% in the receptor binding domain. A few of the same amino acid residues were found in the corresponding regions of FimA, FimF, FimG, and FimH. The type 1 pili of APEC and UPEC come from the same origin, which is helpful to further reveal the pathogenic mechanism of E. coli infection in the poultry respiratory tract.

DeepDBS: Identification of DNA-binding sites in protein sequences by using deep representations and random forest

Interactions of biological molecules in organisms are considered to be primary factors for the lifecycle of that organism. Various important biological functions are dependent on such interactions and among different kinds of interactions, the protein DNA interactions are very important for the processes of transcription, regulation of gene expression, DNA repairing and packaging. Thus, keeping the knowledge of such interactions and the sites of those interactions is necessary to study the mechanism of various biological processes. As experimental identification through biological assays is quite resource-demanding, costly and error-prone, scientists opt for the computational methods for efficient and accurate identification of such DNA-protein interaction sites. Thus, herein, we propose a novel and accurate method namely DeepDBS for the identification of DNA-binding sites in proteins, using primary amino acid sequences of proteins under study. From protein sequences, deep representations were computed through a one-dimensional convolution neural network (1D-CNN), recurrent neural network (RNN) and long short-term memory (LSTM) network and were further used to train a Random Forest classifier. Random Forest with LSTM-based features outperformed the other models, as well as the existing state-of-the-art methods with an accuracy score of 0.99 for self-consistency test, 10-fold cross-validation, 5-fold cross-validation, and jackknife validation while 0.92 for independent dataset testing. It is concluded based on results that the DeepDBS can help accurate and efficient identification of DNA binding sites (DBS) in proteins.

The association between HIV-1 Tat and Vif amino acid sequence variation, inflammation and Trp-Kyn metabolism: an exploratory investigation

BackgroundHIV-1 has well-established mechanisms to disrupt essential pathways in people with HIV, such as inflammation and metabolism. Moreover, diversity of the amino acid sequences in fundamental HIV-1 proteins including Tat and Vif, have been linked to dysregulating these pathways, and subsequently influencing clinical outcomes in people with HIV. However, the relationship between Tat and Vif amino acid sequence variation and specific immune markers and metabolites of the tryptophan-kynurenine (Trp-Kyn) pathway remains unclear. Therefore, this study aimed to investigate the relationship between Tat/Vif amino acid sequence diversity and Trp-Kyn metabolites (quinolinic acid (QUIN), Trp, kynurenic acid (KA), Kyn and Trp/Kyn ratio), as well as specific immune markers (sCD163, suPAR, IL-6, NGAL and hsCRP) in n = 67 South African cART-naïve people with HIV.MethodsSanger sequencing was used to determine blood-derived Tat/Vif amino acid sequence diversity. To measure Trp-Kyn metabolites, a LC–MS/MS metabolomics platform was employed using a targeted approach. To measure immune markers, Enzyme-linked immunosorbent assays and the Particle-enhanced turbidimetric assay was used.ResultsAfter adjusting for covariates, sCD163 (p = 0.042) and KA (p = 0.031) were higher in participants with Tat signatures N24 and R57, respectively, and amino acid variation at position 24 (adj R2 = 0.048, β = -0.416, p = 0.042) and 57 (adj R2 = 0.166, β = 0.535, p = 0.031) of Tat were associated with sCD163 and KA, respectively.ConclusionsThese preliminary findings suggest that amino acid variation in Tat may have an influence on underlying pathogenic HIV-1 mechanisms and therefore, this line of work merits further investigation.

Enhancing Drug-Target Binding Affinity Prediction through Deep Learning and Protein Secondary Structure Integration

Background: Conventional approaches to drug discovery are often characterized by lengthy and costly processes. To expedite the discovery of new drugs, the integration of artificial intelligence (AI) in predicting drug-target binding affinity (DTA) has emerged as a crucial approach. Despite the proliferation of deep learning methods for DTA prediction, many of these methods primarily concentrate on the amino acid sequence of proteins. Yet, the interactions between drug compounds and targets occur within distinct segments within the protein structures, whereas the primary sequence primarily captures global protein features. Consequently, it falls short of fully elucidating the intricate relationship between drugs and their respective targets. Objective: This study aims to employ advanced deep-learning techniques to forecast DTA while incorporating information about the secondary structure of proteins. Methods: In our research, both the primary sequence of protein and the secondary structure of protein were leveraged for protein representation. While the primary sequence played the role of the overarching feature, the secondary structure was employed as the localized feature. Convolutional neural networks and graph neural networks were utilized to independently model the intricate features of target proteins and drug compounds. This approach enhanced our ability to capture drugtarget interactions more effectively Results: We have introduced a novel method for predicting DTA. In comparison to DeepDTA, our approach demonstrates significant enhancements, achieving a 3.9% increase in the Concordance Index (CI) and a remarkable 34% reduction in Mean Squared Error (MSE) when evaluated on the KIBA dataset. Conclusion: In conclusion, our results unequivocally demonstrate that augmenting DTA prediction with the inclusion of the protein's secondary structure as a localized feature yields significantly improved accuracy compared to relying solely on the primary structure.

A novel antifungal peptide, SP1.2, from Rhodopseudomonas palustris against the rice blast pathogen.

Rice blast has a significant detrimental impact on rice yields, so developing efficient biological control technologies is an effective means for rice blast prevention and control. The GroEL protein has proven to be effective at preventing and managing the pathogenicity of rice blast. Here, we analyzed the amino acid sequence of the GroEL protein and synthesized the '60 kDa chaperonin signature' (350-373 amino acids) peptide SP1.2, which has potent antifungal activity. Notably, the SP1.2 peptide exhibited potent fungicidal activity against Magnaporthe oryzae, effectively inhibiting appressorium germination. Electron microscopy revealed that SP1.2 disrupted the fungal plasma membrane and bound to multiple bioactive phosphoinositides in vitro, triggering the production of reactive oxygen species. Furthermore, it also caused an increase in the acetylation of M. oryzae and induced autophagy in cells. The spray application of SP1.2 significantly reduced the number of disease spots caused by the fungal pathogen M. oryzae in rice, enhancing the defense response of rice plants. Field trials showed that the control effect was 64.59% after spraying SP1.2. Our study illustrates the antifungal activity of the structurally unique SP1.2 peptide against plant fungal pathogens and paves the way for the future development of this class of peptides as antifungal agents. © 2024 Society of Chemical Industry.

Molecular and phylogenetic analysis of transmissible gastroenteritis virus strain VET-16, isolated from piglets in Vietnam.

Porcine transmissible gastroenteritis virus (TGEV) is a major pathogen that causes viral enteritis and severe diarrhea in newborn piglets. TGEV strains have been isolated in the USA, Europe, and China, and their molecular characteristics are well known. However, there have been few reports of molecular analysis of TGEV strains isolated in Southeast Asia. In 2016, we isolated TGEV strain VET-16 from fecal samples collected from piglets in Vietnam and determined its complete genome sequence by Sanger sequencing. We found that, while the full genome of the VET-16 strain was 92.4-99.9% identical to those of other TGEV strains, the ORF3 gene showed very little sequence similarity. Phylogenetic analysis suggested that the VET-16 strain belongs to the Purdue subgroup. Comparison of the predicted amino acid (aa) sequence of the spike protein of strain VET-16 with those of other TGEV strains revealed three aa substitutions (V378L, S379T, and D380N) and a 3-aa insertion (F383_F387insWEK) in antigenic site D of the VET-16 strain. Also, a single aa deletion (∆F1413) was found in the transmembrane domain of the spike gene of VET-16. Like the ORF3 gene from the TGEV Miller M60 vaccine strain, the VET-16 strain has a large deletion (∆725 nt) in the ORF3 gene. Previous studies have suggested that these mutations in the spike and ORF3 genes might be associated with a reduction in pathogenicity. The data from this study will facilitate further genetic analysis and research into the evolution of TGEV in pigs in Vietnam.

Biological factors associated with long COVID and comparative analysis of SARS-CoV-2 spike protein variants: a retrospective study in Thailand.

Post-acute COVID-19 syndrome (long COVID) refers to the persistence of COVID-19 symptoms or exceptional symptoms following recovery. Even without conferring fatality, it represents a significant global public health burden. Despite many reports on long COVID, the prevalence and data on associated biological factors remain unclear and limited. This research aimed to determine the prevalence of long COVID during the two distinct epidemic periods in Thailand, due to the Delta and Omicron variants of SARS-CoV-2, and to investigate the biological factors associated with long COVID. In addition, the spike protein amino acid sequences of the Delta and Omicron variants were compared to determine the frequency of mutations and their potential biological implications. A retrospective cross-sectional study was established to recruit confirmed COVID-19 participants at Maharat Nakhon Ratchasima Hospital who had recovered for at least three months and were infected between June 2021 and August 2022. The demographic data and long COVID experience were collected via telephone interview. The biological factors were analyzed through binary logistic regression. The datasets of the SARS-CoV-2 spike protein amino acid sequence of the Delta and Omicron variants in Thailand were retrieved from GIDSAID to determine mutation frequencies and to identify possible roles of the mutations based on published data. Data was collected from a total of 247 participants comprising 106 and 141 participants of the Delta and Omicron epidemic periods, respectively. Apart from the COVID-19 severity and health status, the baseline participant data of the two time periods were remarkably similar. The prevalence of long COVID observed in the Omicron period was higher than in the Delta period (74.5% vs. 66.0%). The biological factors associated with long COVID were epidemic variant, age, treatment with symptomatic medicines, and vaccination status. When the spike protein sequence data of the two variants were compared, it was observed that the Omicron variant exhibited a greater quantity of amino acid changes in its receptor-binding domain (RBD) and receptor-binding motif (RBM). The critical changes of the Omicron variant within these regions had a significant function in enhancing virus transmissibility and host immune response resistance. This study revealed informative data associated with long COVID in Thailand. More attention should be given to long COVID caused by unique virus variants and other biological factors to prepare a healthcare management strategy for COVID-19 patients after recovery.

Genetic diversity, variation and recombination among the human papillomaviruses (HPVs) genomes isolated in China: a comparative genomic and phylogenetic analysis

ABSTRACT Human papillomaviruses (HPVs) are widespread, sexually transmitted group of viruses that infect most individuals at some stage, causing genital warts and cancers. They are members of the Papillomaviridae family, which contains about 400 HPV types. China is among the high HPV burden countries with reported infections of multiple HPV types, accounting for 17.3% of global deaths and 18.2% of global new cases. Thus, understanding the genetic variation and geographic diversity characteristics of HPVs isolated in China is critical for global HPV prevention strategies. Thus, we analyzed the available HPV genome sequences isolated in China that grouped into two categories (alpha- and gamma-papillomaviruses) based on full-length genomes. The most common were HPV-16, −6, −58, and −52 respectively. In addition, four of the novel strains isolated in China, e.g. TG550, JDFY01, CH2, and L55 clustered with the HPV-mSK 159, 244, 201, and 200 respectively. Our phylogeographic network analysis indicated that the L55, TG550, and CH2 are genetically identical to the mSK 200, 046, and 201 respectively, while JDFY01 appeared separately, connected to the mSK-040 following five mutational steps. Also, we found ten recombination events among HPV-6/11 types within their E1, E2, E7, L1/L2 proteins, and Long Control Region ORFs. We achieved the consensus amino acid sequences of HPV proteins and found a conserved stretch of amino acids within E5A of all HPVs circulating in China. These findings offer valued insights into the genetic relationships, distribution, and evolution of the HPVs in China that may assist in adapting effective HPV preventive measures.

Efficacy of an AAV vector encoding a thermostable form of glucocerebrosidase in alleviating symptoms in a Gaucher disease mouse model

Almost all attempts to date at gene therapy approaches for monogenetic disease have used the amino acid sequences of the natural protein. In the current study, we use a designed, thermostable form of glucocerebrosidase (GCase), the enzyme defective in Gaucher disease (GD), to attempt to alleviate neurological symptoms in a GD mouse that models type 3 disease, i.e. the chronic neuronopathic juvenile subtype. Upon injection of an AAVrh10 (adeno-associated virus, serotype rh10) vector containing the designed GCase (dGCase) into the left lateral ventricle of Gba−/−;Gbatg mice, a significant improvement in body weight and life-span was observed, compared to injection of the same mouse with the wild type enzyme (wtGCase). Moreover, a reduction in levels of glucosylceramide (GlcCer), and an increase in levels of GCase activity were seen in the right hemisphere of Gba−/−;Gbatg mice, concomitantly with a significant improvement in motor function, reduction of neuroinflammation and a reduction in mRNA levels of various genes shown previously to be elevated in the brain of mouse models of neurological forms of GD. Together, these data pave the way for the possible use of modified proteins in gene therapy for lysosomal storage diseases and other monogenetic disorders.

A new protein glosaxin consisting of non-catalytic domains of type piii metalloproteinase from the venom of pit viper <i>Gloydius saxatilis</i> inhibits nicotinic acetylcholine receptor

Previously, we found that the venom of the pit viper Gloydius saxatilis inhibited the muscle-type nicotinic acetylcholine receptor (nAChR). Using liquid chromatography, a protein glosaxin was isolated from the venom that inhibited the binding of the α-bungarotoxin to the nAChR of muscle type from Torpedo californica. The amino acid sequence of the isolated protein was analyzed by high resolution mass spectrometry. Subsequent bioinformatic analysis showed that it is homologous to the amino acid sequences of disintegrin-like proteins, consisting of non-catalytic domains of type PIII metalloproteinases from the venom of pit vipers of genus Gloydius. A study of the biological activity of the isolated protein showed that it inhibits the binding of α-bungarotoxin to Torpedo californica nAChR with IC50 = 51 μM. The protein also inhibited acetylcholine-induced functional responses of the human neuronal nAChR of α3β2 subtype. This is the first evidence of the ability of proteins consisting of non-catalytic domains of snake venom type PIII metalloproteinase to inhibit the nAChR.

Therapeutic Application and Structural Features of Adeno-Associated Virus Vector.

Adeno-associated virus (AAV) is characterized by non-pathogenicity, long-term infection, and broad tropism and is actively developed as a vector virus for gene therapy products. AAV is classified into more than 100 serotypes based on differences in the amino acid sequence of the capsid protein. Endocytosis involves the uptake of viral particles by AAV and accessory receptors during AAV infection. After entry into the cell, they are transported to the nucleus through the nuclear pore complex. AAVs mainly use proteoglycans as receptors to enter cells, but the types of sugar chains in proteoglycans that have binding ability are different. Therefore, it is necessary to properly evaluate the primary structure of receptor proteins, such as amino acid sequences and post-translational modifications, including glycosylation, and the higher-order structure of proteins, such as the folding of the entire capsid structure and the three-dimensional (3D) structure of functional domains, to ensure the efficacy and safety of biopharmaceuticals. To further enhance safety, it is necessary to further improve the efficiency of gene transfer into target cells, reduce the amount of vector administered, and prevent infection of non-target cells.

De Novo Design of Peptide Binders to Conformationally Diverse Targets with Contrastive Language Modeling.

Designing binders to target undruggable proteins presents a formidable challenge in drug discovery, requiring innovative approaches to overcome the lack of putative binding sites. Recently, generative models have been trained to design binding proteins via three-dimensional structures of target proteins, but as a result, struggle to design binders to disordered or conformationally unstable targets. In this work, we provide a generalizable algorithmic framework to design short, target-binding linear peptides, requiring only the amino acid sequence of the target protein. To do this, we propose a process to generate naturalistic peptide candidates through Gaussian perturbation of the peptidic latent space of the ESM-2 protein language model, and subsequently screen these novel linear sequences for target-selective interaction activity via a CLIP-based contrastive learning architecture. By integrating these generative and discriminative steps, we create a Peptide Prioritization via CLIP (PepPrCLIP) pipeline and validate highly-ranked, target-specific peptides experimentally, both as inhibitory peptides and as fusions to E3 ubiquitin ligase domains, demonstrating functionally potent binding and degradation of conformationally diverse protein targets in vitro. Overall, our design strategy provides a modular toolkit for designing short binding linear peptides to any target protein without the reliance on stable and ordered tertiary structure, enabling generation of programmable modulators to undruggable and disordered proteins such as transcription factors and fusion oncoproteins.

Bioinformatics in Neonatal/Pediatric Medicine-A Literature Review.

Bioinformatics is a scientific field that uses computer technology to gather, store, analyze, and share biological data and information. DNA sequences of genes or entire genomes, protein amino acid sequences, nucleic acid, and protein-nucleic acid complex structures are examples of traditional bioinformatics data. Moreover, proteomics, the distribution of proteins in cells, interactomics, the patterns of interactions between proteins and nucleic acids, and metabolomics, the types and patterns of small-molecule transformations by the biochemical pathways in cells, are further data streams. Currently, the objectives of bioinformatics are integrative, focusing on how various data combinations might be utilized to comprehend organisms and diseases. Bioinformatic techniques have become popular as novel instruments for examining the fundamental mechanisms behind neonatal diseases. In the first few weeks of newborn life, these methods can be utilized in conjunction with clinical data to identify the most vulnerable neonates and to gain a better understanding of certain mortalities, including respiratory distress, bronchopulmonary dysplasia, sepsis, or inborn errors of metabolism. In the current study, we performed a literature review to summarize the current application of bioinformatics in neonatal medicine. Our aim was to provide evidence that could supply novel insights into the underlying mechanism of neonatal pathophysiology and could be used as an early diagnostic tool in neonatal care.