Molecular mechanisms within the checkpoint receptor PD-1 are essential for its activation by PD-L1 as well as for blocking such an activation via checkpoint inhibitors. We use molecular dynamics to scrutinize patterns of atomic motion in PD-1 without a ligand. Molecular dynamics is performed for the whole extracellular domain of PD-1, and the analysis focuses on its CC’-loop and some adjacent Cα-atoms. We extend previous work by applying common nearest neighbor clustering (Cnn) and compare the performance of this method with Daura clustering as well as UMAP dimension reduction and subsequent agglomerative linkage clustering. As compared to Daura clustering, we found Cnn less sensitive to cutoff selection and better able to return representative clusters for sets of different 3D atomic conformations. Interestingly, Cnn yields results quite similar to UMAP plus linkage clustering.

Dengue fever (DF) and dengue haemorrhagic fever (DHF) are among the most common tropical diseases affecting humans. To analyze the risk of clinical and transmission of DF/DHF in Shenzhen, the surveillance on patients of all-age patients with dengue virus (DENV) infections was conducted. Our findings revealed that the majority of DENV-infected patients are young to middle-aged males, and the development of the disease is accompanied by abnormal changes in the percentages of neutrophils, lymphocytes, and basophils. Demographic analysis revealed that these patients is concentrated in areas such as Futian District, which may be due to the higher mosquito density and temperature than that in other area. Subsequent, mosquito infection experiments confirmed that the effect of temperature shift on DENV proliferation and transmission. Not only that, constant temperatures can enhance the spread of DENV, even increase the risk of epidemic. Thus, the role of innate immune response should be highlighted in the prediction of severe severity of DENV-infected patients, and temperature should be taken into account in the prevention and control of DENV.IntroductionDengue fever (DF) and dengue haemorrhagic fever (DHF) are among the most common tropical diseases affecting humans, and which caused by the four dengue virus serotypes (DENV 1–4).ObjectivesTo analyze the risk of clinical and transmission of DF/DHF in Shenzhen.MethodsThe surveillance on patients of all-age patients with dengue virus (DENV) infections was conducted.ResultsOur findings revealed that the majority of DENV-infected patients are young to middle-aged males, and the development of the disease is accompanied by abnormal changes in the percentages of neutrophils, lymphocytes, and basophils. Demographic analysis revealed that these patients is concentrated in areas such as Futian District, which may be due to the higher mosquito density and temperature than that in other area. Subsequent, mosquito infection experiments confirmed that the effect of temperature shift on DENV proliferation and transmission. Not only that, constant temperatures can enhance the spread of DENV, even increase the risk of epidemic.Conclusion1.Elevated levels of neutrophils, lymphocytes, basophils, and temperature are all significant risk factors for dengue transmission and pathogenesis; 2. Temperature increasing is associated with a higher risk of dengue transmission; 3.Fluctuations in temperature around 28°C (28 ± 5°C) would increase dengue transmission.

Trichodesmium is the dominant photoautotrophic dinitrogen (N2) fixer (diazotroph) in the ocean. Iron is an important factor limiting growth of marine diazotrophs including Trichodesmium mainly because of high iron content of its N2-fixing enzyme, nitrogenase. However, it still lacks a quantitative understanding of how dynamic iron allocation among physiological processes acts to regulate growth and N2 fixation in Trichodesmium. Here, we constructed a model of Trichodesmium trichome in which intracellular iron could be dynamically re-allocated in photosystems and nitrogenase during the daytime. The results demonstrate that the dynamic iron allocation enhances modeled N2 fixation and growth rates of Trichodesmium, especially in iron-limited conditions, albeit having a marginal impact under high iron concentrations. Although the reuse of iron during a day is an apparent cause that dynamic iron allocation can benefit Trichodesmium under iron limitation, our model reveals two important mechanisms. First, the release of iron from photosystems downregulates the intracellular oxygen (O2) production and reduces the demand of respiratory protection, a process that Trichodesmium wastefully respires carbohydrates to create a lower O2 window for N2 fixation. Hence, more carbohydrates can be used in growth. Second, lower allocation of iron to nitrogenase during early daytime, a period when photosynthesis is active and intracellular O2 is high, reduces the amount of iron that is trapped in the inactivated nitrogenase induced by O2. This mechanism further increases the iron use efficiency in Trichodesmium. Overall, our study provides mechanistic and quantitative insight into the diurnal iron allocation that can alleviate iron limitation to Trichodesmium.

HIV-1 associated colorectal cancer (HA-CRC) is one of the most understudied non-AIDS-defining cancers. In this study, we analyzed the proteome of HA-CRC and the paired remote tissues (HA-RT) through data-independent acquisition mass spectrometry (MS). The quantified proteins could differentiate the HA-CRC and HA-RT groups per PCA or cluster analyses. As a background comparison, we reanalyzed the MS data of non-HIV-1 infected CRC (non-HA-CRC) published by CPTAC. According to the GSEA results, we found that HA-CRC and non-HA-CRC shared similarly over-represented KEGG pathways. Hallmark analysis suggested that terms of antiviral response could only be significantly enriched in HA-CRC. The network and molecular system analysis centered the crosstalk of IFN-associated antiviral response and cancerous pathways, which was favored significant up-regulation of ISGylated proteins as detected in the HA-CRC tissues. We further proved that defective HIV-1 reservoir cells as represented by the 8E5 cells could activate the IFN pathway in human macrophages via horizonal transfer of cell-associated HIV-1 RNA (CA-HIV RNA) carried by extracellular vesicles (EVs). In conclusion, HIV-1 reservoir cells secreted CA-HIV RNA-containing EVs can induce IFN pathway activation in macrophages that contributes to one of the mechanistic explanations of the systems crosstalk between antiviral response and cancerous pathways in HA-CRC.

The influence of adaptive evolution on disease susceptibility has drawn attention; however, the extent of the influence, whether favored mutations also influence drug responses, and whether the associations between the three are population-specific remain unknown. Using a reported deep learning network to integrate seven statistical tests for detecting selection signals, we predicted favored mutations in the genomes of 17 human populations and integrated these favored mutations with reported GWAS sites and drug response-related variants into the database PopTradeOff (http://www.gaemons.net/PopFMIntro). The database also contains genome annotation information on the SNP, sequence, gene, and pathway levels. The preliminary data analyses suggest that substantial associations exist between adaptive evolution, disease susceptibility, and drug responses and that the associations are highly population-specific. The database may be valuable for disease studies, drug development, and personalized medicine.

The central function of the large subunit of the ribosome is to catalyze peptide bond formation. This biochemical reaction is conducted at the peptidyl transferase center (PTC). Experimental evidence shows that the catalytic activity is affected by the electrostatic environment around the peptidyl transferase center. Here, we set up a minimal geometrical model fitting the available x-ray solved structures of the ribonucleic cavity around the catalytic center of the large subunit of the ribosome. The purpose of this phenomenological model is to estimate quantitatively the electrostatic potential and electric field that are experienced during the peptidyl transfer reaction. At least two reasons motivate the need for developing this quantification. First, we inquire whether the electric field in this particular catalytic environment, made only of nucleic acids, is of the same order of magnitude as the one prevailing in catalytic centers of the proteic enzymes counterparts. Second, the protein synthesis rate is dependent on the nature of the amino acid sequentially incorporated in the nascent chain. The activation energy of the catalytic reaction and its detailed kinetics are shown to be dependent on the mechanical work exerted on the amino acids by the electric field, especially when one of the four charged amino acid residues (R, K, E, D) has previously been incorporated at the carboxy-terminal end of the peptidyl-tRNA. Physical values of the electric field provide quantitative knowledge of mechanical work, activation energy and rate of the peptide bond formation catalyzed by the ribosome. We show that our theoretical calculations are consistent with two independent sets of previously published experimental results. Experimental results for E.coli in the minimal case of the dipeptide bond formation when puromycin is used as the final amino acid acceptor strongly support our theoretically derived reaction time courses. Experimental Ribo-Seq results on E. coli and S. cerevisiae comparing the residence time distribution of ribosomes upon specific codons are also well accounted for by our theoretical calculations. The statistical queueing time theory was used to model the ribosome residence time per codon during nascent protein elongation and applied for the interpretation of the Ribo-Seq data. The hypo-exponential distribution fits the residence time observed distribution of the ribosome on a codon. An educated deconvolution of this distribution is used to estimate the rates of each elongation step in a codon specific manner. Our interpretation of all these results sheds light on the functional role of the electrostatic profile around the PTC and its impact on the ribosome elongation cycle.

Soil health and root-associated microbiome are interconnected factors involved in plant health. The use of manure amendment on agricultural fields exerts a direct benefit on soil nutrient content and water retention, among others. However, little is known about the impact of manure amendment on the root-associated microbiome, particularly in woody species. In this study, we aimed to evaluate the effects of ovine manure on the microbial communities of the olive rhizosphere and root endosphere. Two adjacent orchards subjected to conventional (CM) and organic (OM) management were selected. We used metabarcoding sequencing to assess the bacterial and fungal communities. Our results point out a clear effect of manure amendment on the microbial community. Fungal richness and diversity were increased in the rhizosphere. The fungal biomass in the rhizosphere was more than doubled, ranging from 1.72 × 106 ± 1.62 × 105 (CM) to 4.54 × 106 ± 8.07 × 105 (OM) copies of the 18 S rRNA gene g-1 soil. Soil nutrient content was also enhanced in the OM orchard. Specifically, oxidable organic matter, total nitrogen, nitrate, phosphorous, potassium and sulphate concentrations were significantly increased in the OM orchard. Moreover, we predicted a higher abundance of bacteria in OM with metabolic functions involved in pollutant degradation and defence against pathogens. Lastly, microbial co-occurrence network showed more positive interactions, complexity and shorter geodesic distance in the OM orchard. According to our results, manure amendment on olive orchards represents a promising tool for positively modulating the microbial community in direct contact with the plant.

The manuscript discusses apossible hypothesis about the transformation of healthy cells into cancer cells as a result of modification of the molecular structure of intracellular water from normal hexagonal to abnormal cubic phase (which may be caused by radiation, chemical, viral, mechanical and microbiological factors) and the possibility of returning to its original state under the influence of microwave radiation. The authors are not aware of any relevant experimental and theoretical support for this hypothesis in other literature.Our hypothesis is based on a completely unexpected experimental fact that we have received. It turned out that the radio spectra of cancer-affected tissues and the cubic phase of water are identicalwhich confirms that these tissues really contain a cubic phase of water. It should be expected that the use of radiation of “therapeutic” frequencies may lead to regression of tumor growth. This assumption is based on another experimental fact confirming the possibility of the transition of the molecular structure of water from the cubic phase to the hexagonal phase (which is contained in healthy tissues) when irradiated with therapeutic frequencies.The conducted experiments demonstrate the real possibilities of structural-phase and spectral mutual transformations of the water medium under the influence of extremely low intensity flows of microwaves at “therapeutic” frequencies of 1000 MHz and 985 MHz or “pathologic” frequencies of 990 MHz and 51 GHz. The aim of this study was to experimentally verify a possible causal relationship between the violation of the molecular structure of intracellular water in healthy tissues and carcinogenesis.

Human gait is a complex and unique biological process that can offer valuable insights into an individual’s health and well-being. In this work, we leverage a machine learning-based approach to model individual gait signatures and identify factors contributing to inter-individual variability in gait patterns. We provide a comprehensive analysis of gait individuality by (1) demonstrating the uniqueness of gait signatures in a large-scale dataset and (2) highlighting the gait characteristics that are most distinctive to each individual. We utilized the data from three publicly available datasets comprising 5,368 bilateral ground reaction force recordings during level overground walking from 671 distinct healthy individuals. Our results show that individuals can be identified with a prediction accuracy of 99.3% by using the bilateral signals of all three ground reaction force components, with only 10 out of 1342 recordings in our test set being misclassified. This indicates that the combination of bilateral ground reaction force signals with all three components provides a more comprehensive and accurate representation of an individual’s gait signature. The highest accuracy was achieved by (linear) Support Vector Machines (99.3%), followed by Random Forests (98.7%), Convolutional Neural Networks (95.8%), and Decision Trees (82.8%). The proposed approach provides a powerful tool to better understand biological individuality and has potential applications in personalized healthcare, clinical diagnosis, and therapeutic interventions.

The importance of genes amplifications in evolution is more and more recognized. Yet, tools to study multi-copy gene families are still scarce, and many such families are overlooked using common sequencing methods. Haplotype reconstruction is even harder for polymorphic multi-copy gene families. Here, we show that all variants (or haplotypes) of a multi-copy gene family present in a single genome, can be obtained using Oxford Nanopore Technologies sequencing of PCR products, followed by steps of mapping, SNP calling and haplotyping.As a proof of concept, we acquired the sequences of highly similar variants of the cidA and cidB genes present in the genome of the Wolbachia wPip, a bacterium infecting Culex pipiens mosquitoes. Our method relies on a wide database of cid genes, previously acquired by cloning and Sanger sequencing. We addressed problems commonly faced when using mapping approaches for multi-copy gene families with highly similar variants. In addition, we confirmed that PCR amplification causes frequent chimeras which have to be carefully considered when working on families of recombinant genes. We tested the robustness of the method using a combination of bioinformatics (read simulations) and molecular biology approaches (sequence acquisitions through cloning and Sanger sequencing, specific PCRs and digital droplet PCR).When different haplotypes present within a single genome cannot be reconstructed from short reads sequencing, this pipeline confers a high throughput acquisition, gives reliable results as well as insights of the relative copy numbers of the different variants.

Emerging evidence suggests that due to the misuse of antibiotics, bacteriophage (phage) therapy has been recognized as one of the most promising strategies for treating human diseases infected by antibiotic-resistant bacteria. Identification of phage-host interactions (PHIs) can help to explore the mechanisms of bacterial response to phages and provide new insights into effective therapeutic approaches. Compared to conventional wet-lab experiments, computational models for predicting PHIs can not only save time and cost, but also be more efficient and economical. In this study, we developed a deep learning predictive framework called GSPHI to identify potential phage and target bacterium pairs through DNA and protein sequence information. More specifically, GSPHI first initialized the node representations of phages and target bacterial hosts via a natural language processing algorithm. Then a graph embedding algorithm structural deep network embedding (SDNE) was utilized to extract local and global information from the interaction network, and finally, a deep neural network (DNN) was applied to accurately detect the interactions between phages and their bacterial hosts. In the drug-resistant bacteria dataset ESKAPE, GSPHI achieved a prediction accuracy of 86.65%% and AUC of 0.9208 under the 5-fold cross-validation technique, significantly better than other methods. In addition, case studies in Gram-positive and negative bacterial species demonstrated that GSPHI is competent in detecting potential Phage-host interactions. Taken together, these results indicate that GSPHI can provide reasonable candidate sensitive bacteria to phages for biological experiments. The webserver of the GSPHI predictor is freely available at http://120.77.11.78/GSPHI/

[This corrects the article DOI: 10.1016/j.csbj.2022.10.003.].

We propose PreCanCell, a novel algorithm for predicting malignant and non-malignant cells from single-cell transcriptomes. PreCanCell first identifies the differentially expressed genes (DEGs) between malignant and non-malignant cells commonly in five common cancer types-associated single-cell transcriptome datasets. The five common cancer types include renal cell carcinoma (RCC), head and neck squamous cell carcinoma (HNSCC), melanoma, lung adenocarcinoma (LUAD), and breast cancer (BC). With each of the five datasets as the training set and the DEGs as the features, a single cell is classified as malignant or non-malignant by k-NN (k = 5). Finally, the single cell is determined as malignant or non-malignant by the majority vote of the five k-NN classification results. We tested the predictive performance of PreCanCell in 19 single-cell datasets, and reported classification accuracy, sensitivity, specificity, balanced accuracy (the average of sensitivity and specificity) and the area under the receiver operating characteristic curve (AUROC). In all these datasets, PreCanCell achieved above 0.8 accuracy, sensitivity, specificity, balanced accuracy and AUROC. Finally, we compared the predictive performance of PreCanCell with that of seven other algorithms, including CHETAH, SciBet, SCINA, scmap-cell, scmap-cluster, SingleR, and ikarus. Compared to these algorithms, PreCanCell displays the advantages of higher accuracy and simpler implementation. We have developed an R package for the PreCanCell algorithm, which is available at http://github.com/WangX-Lab/PreCanCell.

The biomass equation is a critical component in genome-scale metabolic models (GEMs): it is used as the de facto objective function in flux balance analysis (FBA). This equation accounts for the quantities of all known biomass precursors that are required for cell growth based on the macromolecular and monomer compositions measured at certain conditions. However, it is often reported that the macromolecular composition of cells could change across different environmental conditions and thus the use of the same single biomass equation in FBA, under multiple conditions, is questionable. Herein, we first investigated the qualitative and quantitative variations of macromolecular compositions of three representative host organisms, Escherichia coli, Saccharomyces cerevisiae and Cricetulus griseus, across different environmental/genetic variations. While macromolecular building blocks such as RNA, protein, and lipid composition vary notably, changes in fundamental biomass monomer units such as nucleotides and amino acids are not appreciable. We also observed that flux predictions through FBA is quite sensitive to macromolecular compositions but not the monomer compositions. Based on these observations, we propose ensemble representations of biomass equation in FBA to account for the natural variation of cellular constituents. Such ensemble representations of biomass better predicted the flux through anabolic reactions as it allows for the flexibility in the biosynthetic demands of the cells. The current study clearly highlights that certain component of the biomass equation indeed vary across different conditions, and the ensemble representation of biomass equation in FBA by accounting for such natural variations could avoid inaccuracies that may arise from in silico simulations.

A long-standing goal of personalized and precision medicine is to enable accurate prediction of the outcomes of a given treatment regimen for patients harboring a disease. Currently, many clinical trials fail to meet their endpoints due to underlying factors in the patient population that contribute to either poor responses to the drug of interest or to treatment-related adverse events. Identifying these factors beforehand and correcting for them can lead to an increased success of clinical trials. Comprehensive and large-scale data gathering efforts in biomedicine by omics profiling of the healthy and diseased individuals has led to a treasure-trove of host, disease and environmental factors that contribute to the effectiveness of drugs aiming to treat disease. With increasing omics data, artificial intelligence allows an in-depth analysis of big data and offers a wide range of applications for real-world clinical use, including improved patient selection and identification of actionable targets for companion therapeutics for improved translatability across more patients. As a blueprint for complex drug-disease-host interactions, we here discuss the challenges of utilizing omics data for predicting responses and adverse events in cancer immunotherapy with immune checkpoint inhibitors (ICIs). The omics-based methodologies for improving patient outcomes as in the ICI case have also been applied across a wide-range of complex disease settings, exemplifying the use of omics for in-depth disease profiling and clinical use.

The gut-liver axis is a complex bidirectional communication pathway between the intestine and the liver in which microorganisms and their metabolites flow from the intestine through the portal vein to the liver and influence liver function. In a sterile environment, the phenotype or function of the liver is altered, but few studies have investigated the specific cellular and molecular effects of microorganisms on the liver. To this end, we constructed single-cell and spatial transcriptomic (ST) profiles of germ-free (GF) and specific-pathogen-free (SPF) mouse livers. Single-cell RNA sequencing (scRNA-seq) and single-nucleus RNA sequencing (snRNA-seq) revealed that the ratio of most immune cells was altered in the liver of GF mice; in particular, natural killer T (NKT) cells, IgA plasma cells (IgAs) and Kupffer cells (KCs) were significantly reduced in GF mice. Spatial enhanced resolution omics sequencing (Stereo-seq) confirmed that microorganisms mediated the accumulation of Kupffer cells in the periportal zone. Unexpectedly, IgA plasma cells were more numerous and concentrated in the periportal vein in liver sections from SPF mice but less numerous and scattered in GF mice. ST technology also enables the precise zonation of liver lobules into eight layers and three patterns based on the gene expression level in each layer, allowing us to further investigate the effects of microbes on gene zonation patterns and functions. Furthermore, untargeted metabolism experiments of the liver revealed that the propionic acid levels were significantly lower in GF mice, and this reduction may be related to the control of genes involved in bile acid and fatty acid metabolism. In conclusion, the combination of sc/snRNA-seq, Stereo-seq, and untargeted metabolomics revealed immune system defects as well as altered bile acid and lipid metabolic processes at the single-cell and spatial levels in the livers of GF mice. This study will be of great value for understanding host-microbiota interactions.

TMEM165 is a Golgi protein playing a crucial role in Mn2+ transport, and whose mutations in patients are known to cause Congenital Disorders of Glycosylation. Some of those mutations affect the highly-conserved consensus motifs E-φ-G-D-[KR]-[TS] characterizing the CaCA2/UPF0016 family, presumably important for the transport of Mn2+ which is essential for the function of many Golgi glycosylation enzymes. Others, like the G>R304 mutation, are far away from these motifs in the sequence. Until recently, the classical membrane protein topology prediction methods were unable to provide a clear picture of the organization of TMEM165 inside the cell membrane, or to explain in a convincing manner the impact of patient and experimentally-generated mutations on the transporter function of TMEM165. In this study, AlphaFold 2 was used to build a TMEM165 model that was then refined by molecular dynamics simulation with membrane lipids and water. This model provides a realistic picture of the 3D protein scaffold formed from a two-fold repeat of three transmembrane helices/domains where the consensus motifs face each other to form a putative acidic cation-binding site at the cytosolic side of the protein. It sheds new light on the impact of mutations on the transporter function of TMEM165, found in patients and studied experimentally in vitro, formerly and within this study. More particularly and very interestingly, this model explains the impact of the G>R304 mutation on TMEM165’s function. These findings provide great confidence in the predicted TMEM165 model whose structural features are discussed in the study and compared to other structural and functional TMEM165 homologs from the CaCA2/UPF0016 family and the LysE superfamily.

Polyethylene terephthalate (PET) is the largest produced polyester globally, and less than 30% of all the PET produced globally (~6 billion pounds annually) is currently recycled into lower-quality products. The major drawbacks in current recycling methods (mechanical and chemical), have inspired the exploration of potentially efficient and sustainable PET depolymerization using biological approaches. Researchers have discovered efficient PET hydrolyzing enzymes in the plastisphere and have demonstrated the selective degradation of PET to original monomers thus enabling biological recycling or upcycling. However, several significant hurdles such as the less efficiency of the hydrolytic reaction, low thermostability of the enzymes, and the inability of the enzyme to depolymerize crystalline PET must be addressed in order to establish techno-economically feasible commercial-scale biological PET recycling or upcycling processes. Researchers leverage a synthetic biology-based design; build, test, and learn (DBTL) methodology to develop commercially applicable efficient PET hydrolyzing enzymes through 1) high-throughput metagenomic and proteomic approaches to discover new PET hydrolyzing enzymes with superior properties: and, 2) enzyme engineering approaches to modify and optimize PET hydrolyzing properties. Recently, in-silico platforms including molecular mechanics and machine learning concepts are emerging as innovative tools for the development of more efficient and effective PET recycling through the exploration of novel mutations in PET hydrolyzing enzymes. In-silico-guided PET hydrolyzing enzyme engineering with DBTL cycles enables the rapid development of efficient variants of enzymes over tedious conventional enzyme engineering methods such as random or directed evolution. This review highlights the potential of in-silico-guided PET degrading enzyme engineering to create more efficient variants, including Ideonella sakaiensis PETase (IsPETase) and leaf-branch compost cutinases (LCC). Furthermore, future research prospects are discussed to enable a sustainable circular economy through the bioconversion of PET to original or high-value platform chemicals.

We expand studies of AlphaFold2 (AF2) in the context of intrinsic disorder prediction by comparing it against a broad selection of 20 accurate, popular and recently released disorder predictors. We use 25% larger benchmark dataset with 646 proteins and cover protein-level predictions of disorder content and fully disordered proteins. AF2-based disorder predictions secure a relatively high Area Under receiver operating characteristic Curve (AUC) of 0.77 and are statistically outperformed by several modern disorder predictors that secure AUCs around 0.8 with median runtime of about 20 seconds compared to 1200 seconds for AF2. Moreover, AF2 provides modestly accurate predictions of fully disordered proteins (F1=0.59 vs. 0.91 for the best disorder predictor) and disorder content (mean absolute error of 0.21 vs. 0.15). AF2 also generates statistically more accurate disorder predictions for about 20% of proteins that have relatively short sequences and a few disordered regions that tend to be located at the sequence termini, and which are absent of disordered protein-binding regions. Interestingly, AF2 and the most accurate disorder predictors rely on deep neural networks, suggesting that these models are useful for protein structure and disorder predictions.

Computational methods coupled with experimental validation play a critical role in the identification of novel inhibitory peptides that interact with viral antigenic determinants. The interaction between the receptor binding domain (RBD) of SARS-CoV-2 spike protein and the helical peptide of human angiotensin-converting enzyme-2 (ACE2) is a necessity for the initiation of viral infection. Herein, natural orthologs of ACE2 were evaluated for competitive inhibitory binding to the viral RBD by use of a computational approach, which was experimentally validated. A total of 624 natural ACE2 orthologous 32-amino acid long peptides were identified through a similarity search. Molecular docking was used to virtually screen and rank the peptides based on binding affinity metrics, benchmarked against human ACE2 docked to the RBD. Molecular dynamics (MD) simulations were done for the human reference and the Nipponia nippon peptide as it exhibited the highest binding affinity (Gibbs free energy; -14 kcal/mol) predicted from the docking results. The MD simulation confirmed the ability of the assessed peptide in the complex (-12.3 kcal/mol). The top three docked-peptides (from Chitinophaga sancti, Nipponia nippon, and Mus musculus) and the human reference were experimentally validated by use of surface plasmon resonance technology. The human reference exhibited the weakest binding affinity (Kd of 318-441 pM) among the peptides tested, in agreement with the docking prediction, while the peptide from Nipponia nippon was the best, with 267-538-fold higher affinity than the reference. The validated peptides merit further investigation. This work showcases that the approach herein can aid in the identification of inhibitory biosimilar peptides for other viruses.