Sequence-based Approach Research Articles

A genotype-phenotype correlation in split-hand/foot malformation type 1: further refinement of the phenotypic subregions within the 7q21.3 locus.

Background: Split-hand/foot malformation type 1 (SHFM1) refers to the group of rare congenital limb disorders defined by the absence or hypoplasia of the central rays of the autopods with or without accompanying anomalies, such as hearing loss, craniofacial malformation, and ectodermal dysplasia. Consequently, the condition is characterized by clinical variability that hinders diagnostic and counseling procedures. SHFM1 is caused by pathogenic variants affecting the DLX5/6 genes and/or their tissue-specific enhancers at the 7q21.3 locus. Herein, we report on seven patients from five unrelated Polish families affected by variable symptoms of the SHFM1 spectrum, all harboring 7q21.3 or 7q21.2-q21.3 rearrangements, and provide a genotype-phenotype correlation in the studied cohort. Methods: We applied GTG banding, array-based comparative genomic hybridization (aCGH), and whole-genome sequencing (WGS) in order to identify the causative aberrations in all affected patients. Results: The identified pathogenic structural variants included deletions and/or translocations involving the 7q21.3 locus, i.e., t(7;10)(q21.3;q22.2) and t(7;12)(q21.3;q21.2) in all affected individuals. Interestingly, a sporadic carrier of the latter aberration presented the SHFM1 phenotype with additional features overlapping with Baker-Gordon syndrome (BAGOS), which resulted from the translocation breakpoint at chromosome 12 within the SYT1 gene. Conclusion: Clinical variability of the studied cohort reflects the composition of the DLX5/6 regulatory elements that were dislocated from their target genes by chromosomal rearrangements. The correlation of our data with the previously published observations enabled us to update the phenotypic subregions and regulatory units within the SHFM1 locus. In addition, we present the first case of SHFM1 and BAGOS-like phenotype that resulted from translocation breakpoints at chromosomes 7 and 12, both of which were pathogenic, and consequently, we show the first evidence that BAGOS can also result from the regulatory loss-of-function SYT1 mutations. In this paper, we emphasize the utility of sequence-based approaches in molecular diagnostics of disorders caused by regulatory structural variants.

Metagenomics-assembled analysis revealed the characteristics of antibiotic resistome and community coalescence in the soils irrigated with different irrigation materials

Antibiotic resistance has received widespread attention in recent years. Soil irrigation and fertilization are routine agricultural practices, but also lead to the spread of antibiotic resistance genes (ARGs) in soil-crop system such as via resistome coalescence. Despite community coalescence being ubiquitous and important in natural ecosystems, little research has been done to investigate resistome coalescence during soil irrigation activities. In this study, the characteristics of antibiotic resistome and community coalescence in the soils irrigated with different irrigation materials (wastewater, wastewater-river water, and wastewater-manure) have been revealed by utilizing microcosm experiments and high-throughput sequencing-based metagenomic assembly approaches. Results showed irrigation and coalescence changed soil quality and resistome. Totally, 789 unique ARGs were identified in the irrigation system, including some emerging ARGs. The abundance and diversity of ARGs increased in the coalesced soils, mainly due to the newly imported ARGs from irrigation materials. Relatively, the soils irrigated with wastewater and manure showed higher level of ARGs. Irrigation with the mixtures containing river water caused greater loss of indigenous taxa, while the community structure of mixing treatment with manure changed more dramatically. Interestingly, the succession of community in coalesced soils was influenced by transient competition for resources and ecological niche width, and the highest abundance and diversity of microorganisms and ARGs were found in the initial phase of coalescence, followed by a gradual succession towards the original community. With increasement of wastewater in the irrigation materials, the soil community showed a stepwise change rather than linear change. Notably, natural deposit of irrigation materials reduced their impacts on the ARGs in the coalesced soils. Findings provide new insights into the resistome coalescence during agricultural practices for reducing the spread risks of ARGs.

Tracking gut microbiome and bloodstream infection in critically ill adults.

The gut microbiome is believed to contribute to bloodstream infection (BSI) via translocation of dominant gut bacteria in vulnerable patient populations. However, conclusively linking gut and blood organisms requires stringent approaches to establish strain-level identity. We enrolled a convenience cohort of critically ill patients and investigated 86 bloodstream infection episodes that occurred in 57 patients. Shotgun metagenomic sequencing was used to define constituents of their gut microbiomes, and whole genome sequencing and assembly was done on 23 unique bloodstream isolates that were available from 21 patients. Whole genome sequences were downloaded from public databases and used to establish sequence-identity distribution and define thresholds for unrelated genomes of BSI species. Gut microbiome reads were then aligned to whole genome sequences of the cognate bloodstream isolate and unrelated database isolates to assess identity. Gut microbiome constituents matching the bloodstream infection species were present in half of BSI episodes, and represented >30% relative abundance of gut sequences in 10% of episodes. Among the 23 unique bloodstream organisms that were available for whole genome sequencing, 14 were present in gut at the species level. Sequence alignment applying defined thresholds for identity revealed that 6 met criteria for identical strains in blood and gut, but 8 did not. Sequence identity between BSI isolates and gut microbiome reads was more likely when the species was present at higher relative abundance in gut. In assessing potential gut source for BSI, stringent sequence-based approaches are essential to determine if organisms responsible for BSI are identical to those in gut: of 14 evaluable patients in which the same species was present in both sites, they were identical in 6/14, but were non-identical in 8/14 and thus inconsistent with gut source. This report demonstrates application of sequencing as a key tool to investigate infection tracking within patients.

Prediction of Protein Ion-Ligand Binding Sites with ELECTRA.

Interactions between proteins and ions are essential for various biological functions like structural stability, metabolism, and signal transport. Given that more than half of all proteins bind to ions, it is becoming crucial to identify ion-binding sites. The accurate identification of protein-ion binding sites helps us to understand proteins' biological functions and plays a significant role in drug discovery. While several computational approaches have been proposed, this remains a challenging problem due to the small size and high versatility of metals and acid radicals. In this study, we propose IonPred, a sequence-based approach that employs ELECTRA (Efficiently Learning an Encoder that Classifies Token Replacements Accurately) to predict ion-binding sites using only raw protein sequences. We successfully fine-tuned our pretrained model to predict the binding sites for nine metal ions (Zn2+, Cu2+, Fe2+, Fe3+, Ca2+, Mg2+, Mn2+, Na+, and K+) and four acid radical ion ligands (CO32-, SO42-, PO43-, NO2-). IonPred surpassed six current state-of-the-art tools by over 44.65% and 28.46%, respectively, in the F1 score and MCC when compared on an independent test dataset. Our method is more computationally efficient than existing tools, producing prediction results for a hundred sequences for a specific ion in under ten minutes.

Prediction of Protein-Protein Interactions Using Vision Transformer and Language Model.

The knowledge of protein-protein interaction (PPI) helps us to understand proteins' functions, the causes and growth of several diseases, and can aid in designing new drugs. The majority of existing PPI research has relied mainly on sequence-based approaches. With the availability of multi-omics datasets (sequence, 3D structure) and advancements in deep learning techniques, it is feasible to develop a deep multi-modal framework that fuses the features learned from different sources of information to predict PPI. In this work, we propose a multi-modal approach utilizing protein sequence and 3D structure. To extract features from the 3D structure of proteins, we use a pre-trained vision transformer model that has been fine-tuned on the structural representation of proteins. The protein sequence is encoded into a feature vector using a pre-trained language model. The feature vectors extracted from the two modalities are fused and then fed to the neural network classifier to predict the protein interactions. To showcase the effectiveness of the proposed methodology, we conduct experiments on two popular PPI datasets, namely, the human dataset and the S. cerevisiae dataset. Our approach outperforms the existing methodologies to predict PPI, including multi-modal approaches. We also evaluate the contributions of each modality by designing uni-modal baselines. We perform experiments with three modalities as well, having gene ontology as the third modality.

New technologies provide innovative opportunities to enhance understanding of major virus diseases threatening global food security.

Plant viruses pose a continuous and serious threat to crop production worldwide, while globalization and climate change are exacerbating the establishment and rapid spread of new viruses. Simultaneously, developments in genome sequencing technology, nucleic acid amplification methods, and epidemiological modeling are providing plant health specialists with unprecedented opportunities to confront these major threats to the food security and livelihoods of millions of resource-constrained smallholders. In this perspective, we have used recent examples of integrated application of these technologies to enhance understanding of the emergence of plant viral diseases of key food security crops in low- and middle-income countries. We highlight how international funding and collaboration have enabled high-throughput sequencing-based surveillance approaches, targeted field and lab-based diagnostic tools, and modeling approaches that can be effectively used to support surveillance and preparedness against existing and emerging plant viral threats. The importance of national and international collaboration and the future role of CGIAR in further supporting these efforts, including building capabilities to make optimal use of these technologies in low- and middle-income countries is discussed.

TROLLOPE: A novel sequence-based stacked approach for the accelerated discovery of linear T-cell epitopes of hepatitis C virus.

Hepatitis C virus (HCV) infection is a concerning health issue that causes chronic liver diseases. Despite many successful therapeutic outcomes, no effective HCV vaccines are currently available. Focusing on T cell activity, the primary effector for HCV clearance, T cell epitopes of HCV (TCE-HCV) are considered promising elements to accelerate HCV vaccine efficacy. Thus, accurate and rapid identification of TCE-HCVs is recommended to obtain more efficient therapy for chronic HCV infection. In this study, a novel sequence-based stacked approach, termed TROLLOPE, is proposed to accurately identify TCE-HCVs from sequence information. Specifically, we employed 12 different sequence-based feature descriptors from heterogeneous perspectives, such as physicochemical properties, composition-transition-distribution information and composition information. These descriptors were used in cooperation with 12 popular machine learning (ML) algorithms to create 144 base-classifiers. To maximize the utility of these base-classifiers, we used a feature selection strategy to determine a collection of potential base-classifiers and integrated them to develop the meta-classifier. Comprehensive experiments based on both cross-validation and independent tests demonstrated the superior predictive performance of TROLLOPE compared with conventional ML classifiers, with cross-validation and independent test accuracies of 0.745 and 0.747, respectively. Finally, a user-friendly online web server of TROLLOPE (http://pmlabqsar.pythonanywhere.com/TROLLOPE) has been developed to serve research efforts in the large-scale identification of potential TCE-HCVs for follow-up experimental verification.

Deep Learning-Based Geomagnetic Navigation Method Integrated with Dead Reckoning

Accurate location information has significant commercial and economic value as they are widely used in intelligent manufacturing, material localization and smart homes. Magnetic sequence-based approaches show great promise mainly due to their pervasiveness and stability. However, existing geomagnetic indoor localization methods are facing the problems of location ambiguity and feature extraction deficiency, which will lead to large localization errors. To address these issues, we propose a coarse-to-fine geomagnetic indoor localization method based on deep learning. First, a multidimensional geomagnetic feature extraction method is presented which can extract magnetic features from spatial and temporal aspects. Then, a hierarchical deep neural network model is devised to extract more accurate geomagnetic information and corresponding location clues for more accurate localization. Finally, localization is achieved through a particle filter combined with IMU localization. To evaluate the performance of the proposed methods, we carried out several experiments at three trial paths with two heterogeneous devices, Vivo X30 and Huawei Mate30. Experimental results demonstrate that the proposed algorithm can achieve more accurate localization performance than the state-of-the-art methods. Meanwhile, the proposed algorithm has low cost and good pervasiveness for different devices.

Best practices for genotype imputation from low-coverage sequencing data in natural populations.

Monitoring genetic diversity in wild populations is a central goal of ecological and evolutionary genetics and is critical for conservation biology. However, genetic studies of nonmodel organisms generally lack access to species-specific genotyping methods (e.g. array-based genotyping) and must instead use sequencing-based approaches. Although costs are decreasing, high-coverage whole-genome sequencing (WGS), which produces the highest confidence genotypes, remains expensive. More economical reduced representation sequencing approaches fail to capture much of the genome, which can hinder downstream inference. Low-coverage WGS combined with imputation using a high-confidence reference panel is a cost-effective alternative, but the accuracy of genotyping using low-coverage WGS and imputation in nonmodel populations is still largely uncharacterized. Here, we empirically tested the accuracy of low-coverage sequencing (0.1-10×) and imputation in two natural populations, one with a large (n = 741) reference panel, rhesus macaques (Macaca mulatta), and one with a smaller (n = 68) reference panel, gelada monkeys (Theropithecus gelada). Using samples sequenced to coverage as low as 0.5×, we could impute genotypes at >95% of the sites in the reference panel with high accuracy (median r2 ≥ 0.92). We show that low-coverage imputed genotypes can reliably calculate genetic relatedness and population structure. Based on these data, we also provide best practices and recommendations for researchers who wish to deploy this approach in other populations, with all code available on GitHub (https://github.com/mwatowich/LoCSI-for-non-model-species). Our results endorse accurate and effective genotype imputation from low-coverage sequencing, enabling the cost-effective generation of population-scale genetic datasets necessary for tackling many pressing challenges of wildlife conservation.

Potential intermediate hosts of Angiostrongylus cantonensis in the European Mediterranean region (Mallorca, Spain)

Angiostrongylus cantonensis is the main etiological agent of eosinophilic meningitis, a disease that often leads to severe neurological manifestations in mammals and birds. In recent years, the prevalence of this zoonotic nematode has dramatically increased as it expands into new territories beyond its native range in Southeast Asia and the Pacific Basin.Its arrival to Europe, the last continent to be invaded, has raised many questions concerning the parasite's life cycle, particularly in the Mediterranean region, where it is now endemic. This study aims to collect evidence about intermediate hosts (snail and slug species) involved in the transmission of the A. cantonensis in Mallorca. We have conducted a systematic surveillance of gastropods within 2 km radius areas, surrounding the specific locations where infected hedgehogs were found. We employed a sequence-based detection approach that included a species-specific PCR amplification followed by DNA sequencing of the internal transcribed spacer I (ITS-1). This conservative approach was essential to avoid cross reactions with the numerous metastrongylid species known to be circulating in Mallorca. Overall, we collected and identified 398 gastropods belonging to 17 species, of which 11% tested positive for A. cantonensis. These gastropods were collected from touristic settlements to agricultural lands. Five gastropod species: Theba pisana, Cornu aspersum, Deroceras reticulatum, Milax gagates and Otala lactea, had been previously incriminated as A. cantonensis intermediate hosts, while 6 species: Papillifera papillaris, Cochlicella acuta, Eobania vermiculata, Ganula lanuginosa, Milax nigricans and Rumina decollata, are newly recorded hosts. The findings of this study have important epidemiological implications, and further measures are discussed to prevent neuroangiostrongylosis cases.

RNA Sequencing for Solid Tumor Fusion Gene Detection: Proficiency Testing Practice and Performance Comparison.

Next-generation sequencing-based approaches using RNA have increasingly been used by clinical laboratories for the detection of fusion genes, intragenic rearrangements, and exon-skipping events. Correspondingly, the College of American Pathologists (CAP) has advanced RNA sequencing proficiency testing (PT) to ensure optimal performance of these assays. To report on laboratory performance and practices of RNA sequencing for the detection of fusion genes, intragenic rearrangements, and exon-skipping events using CAP PT data from 8 mailings (2018-A through 2021-B). CAP PT RNA sequencing program results from 153 laboratories across 24 proficiency test specimens, interrogating 22 distinct engineered fusion transcripts, were analyzed for correct identification of the fusion event, associated performance variables, and laboratory practices. Overall, the 4-year program detection rate (sensitivity) was 95.5% (1486 of 1556 results). False-negative rates were 3.6% (53 of 1463) and 18.3% (17 of 93) for fusion gene and intragenic rearrangement/exon-skipping events, respectively. Only 19 false-positive results were reported among the 8 PT mailings, and most were likely the result of preanalytical or postanalytical errors. There were no practice characteristics (eg, instrumentation, sequencing method) significantly associated with the fusion detection results. These data reveal a high overall sensitivity and specificity for fusion gene detection by participating laboratories using clinical RNA sequencing. Performance was comparable across all laboratories, regardless of methodology. The fraction of false-negative results for intragenic rearrangement/exon-skipping events was greater than that for the chimeric fusion genes. False-negative results could not be attributed to any specific practice characteristics.

Computational Methods in Immunology and Vaccinology: Design and Development of Antibodies and Immunogens.

The design of new biomolecules able to harness immune mechanisms for the treatment of diseases is a prime challenge for computational and simulative approaches. For instance, in recent years, antibodies have emerged as an important class of therapeutics against a spectrum of pathologies. In cancer, immune-inspired approaches are witnessing a surge thanks to a better understanding of tumor-associated antigens and the mechanisms of their engagement or evasion from the human immune system. Here, we provide a summary of the main state-of-the-art computational approaches that are used to design antibodies and antigens, and in parallel, we review key methodologies for epitope identification for both B- and T-cell mediated responses. A special focus is devoted to the description of structure- and physics-based models, privileged over purely sequence-based approaches. We discuss the implications of novel methods in engineering biomolecules with tailored immunological properties for possible therapeutic uses. Finally, we highlight the extraordinary challenges and opportunities presented by the possible integration of structure- and physics-based methods with emerging Artificial Intelligence technologies for the prediction and design of novel antigens, epitopes, and antibodies.

High throughput human genotyping for variants associated with malarial disease outcomes using custom targeted amplicon sequencing

Malaria has exhibited the strongest known selective pressure on the human genome in recent history and is the evolutionary driving force behind genetic conditions, such as sickle-cell disease, glucose-6-phosphatase deficiency, and some other erythrocyte defects. Genomic studies (e.g., The 1000 Genomes project) have provided an invaluable baseline for human genetics, but with an estimated two thousand ethno-linguistic groups thought to exist across the African continent, our understanding of the genetic differences between indigenous populations and their implications on disease is still limited. Low-cost sequencing-based approaches make it possible to target specific molecular markers and genes of interest, leading to potential insights into genetic diversity. Here we demonstrate the versatility of custom dual-indexing technology and Illumina next generation sequencing to generate a genetic profile of human polymorphisms associated with malaria pathology. For 100 individuals diagnosed with severe malaria in Northeast Tanzania, variants were successfully characterised on the haemoglobin subunit beta (HBB), glucose-6-phosphate dehydrogenase (G6PD), atypical chemokine receptor 1 (ACKR1) genes, and the intergenic Dantu genetic blood variant, then validated using pre-existing genotyping data. High sequencing coverage was observed across all amplicon targets in HBB, G6PD, ACKR1, and the Dantu blood group, with variants identified at frequencies previously observed within this region of Tanzania. Sequencing data exhibited high concordance rates to pre-existing genotyping data (> 99.5%). Our work demonstrates the potential utility of amplicon sequencing for applications in human genetics, including to personalise medicine and understand the genetic diversity of loci linked to important host phenotypes, such as malaria susceptibility.

An intrinsically interpretable neural network architecture for sequence-to-function learning.

Sequence-based deep learning approaches have been shown to predict a multitude of functional genomic readouts, including regions of open chromatin and RNA expression of genes. However, a major limitation of current methods is that model interpretation relies on computationally demanding post hoc analyses, and even then, one can often not explain the internal mechanics of highly parameterized models. Here, we introduce a deep learning architecture called totally interpretable sequence-to-function model (tiSFM). tiSFM improves upon the performance of standard multilayer convolutional models while using fewer parameters. Additionally, while tiSFM is itself technically a multilayer neural network, internal model parameters are intrinsically interpretable in terms of relevant sequence motifs. We analyze published open chromatin measurements across hematopoietic lineage cell-types and demonstrate that tiSFM outperforms a state-of-the-art convolutional neural network model custom-tailored to this dataset. We also show that it correctly identifies context-specific activities of transcription factors with known roles in hematopoietic differentiation, including Pax5 and Ebf1 for B-cells, and Rorc for innate lymphoid cells. tiSFM's model parameters have biologically meaningful interpretations, and we show the utility of our approach on a complex task of predicting the change in epigenetic state as a function of developmental transition. The source code, including scripts for the analysis of key findings, can be found at https://github.com/boooooogey/ATAConv, implemented in Python.

Direct detection of drug-resistant Mycobacterium tuberculosis using targeted next generation sequencing.

Mycobacterium tuberculosis complex (MTBC) infections are treated with combinations of antibiotics; however, these regimens are not as efficacious against multidrug and extensively drug resistant MTBC. Phenotypic (growth-based) drug susceptibility testing on slow growing bacteria like MTBC requires many weeks to months to complete, whereas sequencing-based approaches can predict drug resistance (DR) with reduced turnaround time. We sought to develop a multiplexed, targeted next generation sequencing (tNGS) assay that can predict DR and can be performed directly on clinical respiratory specimens. A multiplex PCR was designed to amplify a group of thirteen full-length genes and promoter regions with mutations known to be involved in resistance to first- and second-line MTBC drugs. Long-read amplicon libraries were sequenced with Oxford Nanopore Technologies platforms and high-confidence resistance mutations were identified in real-time using an in-house developed bioinformatics pipeline. Sensitivity, specificity, reproducibility, and accuracy of the tNGS assay was assessed as part of a clinical validation study. In total, tNGS was performed on 72 primary specimens and 55 MTBC-positive cultures and results were compared to clinical whole genome sequencing (WGS) performed on paired patient cultures. Complete or partial susceptibility profiles were generated from 82% of smear positive primary specimens and the resistance mutations identified by tNGS were 100% concordant with WGS. In addition to performing tNGS on primary clinical samples, this assay can be used to sequence MTBC cultures mixed with other mycobacterial species that would not yield WGS results. The assay can be effectively implemented in a clinical/diagnostic laboratory with a two to three day turnaround time and, even if batched weekly, tNGS results are available on average 15 days earlier than culture-derived WGS results. This study demonstrates that tNGS can reliably predict MTBC drug resistance directly from clinical specimens or cultures and provide critical information in a timely manner for the appropriate treatment of patients with DR tuberculosis.

Using nanopore sequencing to identify fungi from clinical samples with high phylogenetic resolution

The study of microbiota has been revolutionized by the development of DNA metabarcoding. This sequence-based approach enables the direct detection of microorganisms without the need for culture and isolation, which significantly reduces analysis time and offers more comprehensive taxonomic profiles across broad phylogenetic lineages. While there has been an accumulating number of researches on bacteria, molecular phylogenetic analysis of fungi still remains challenging due to the lack of standardized tools and the incompleteness of reference databases limiting the accurate and precise identification of fungal taxa. Here, we present a DNA metabarcoding workflow for characterizing fungal microbiota with high taxonomic resolution. This method involves amplifying longer stretches of ribosomal RNA operons and sequencing them using nanopore long-read sequencing technology. The resulting reads were error-polished to generate consensus sequences with 99.5–100% accuracy, which were then aligned against reference genome assemblies. The efficacy of this method was explored using a polymicrobial mock community and patient-derived specimens, demonstrating the marked potential of long-read sequencing combined with consensus calling for accurate taxonomic classification. Our approach offers a powerful tool for the rapid identification of pathogenic fungi and has the promise to significantly improve our understanding of the role of fungi in health and disease.

PETCH-DB: a Portal forExploring Tissue-specific andComplex disease-associated 5-Hydroxymethylcytosines.

Epigenetic modifications play critical roles in gene regulation and disease pathobiology. Highly sensitive enabling technologies, including microarray- and sequencing-based approaches have allowed genome-wide profiling of cytosine modifications in DNAs in clinical samples to facilitate discovery of epigenetic biomarkers for disease diagnosis and prognosis. Historically, many previous studies, however, did not distinguish the most investigated 5-methylcytosines (5mC) from other modified cytosines, especially the biochemically stable5-hydroxymethylcytosines (5hmC), which have been shown to have a distinct genomic distribution and regulatory role from 5mC. Notably, during the past several years, the 5hmC-Seal, a highly sensitive chemical labeling technique, has been demonstrated to be a powerful tool for genome-wide profiling of 5hmC in clinically feasible biospecimens (e.g. a few milliliter of plasma or serum). The 5hmC-Seal technique has been utilized by our team in biomarker discovery for human cancers and other complex diseases using circulating cell-free DNA (cfDNA), as well as the characterization of the first 5hmC Human Tissue Map. Convenient access to the accumulating 5hmC-Seal data will allow the research community to validate and re-use these results, potentially providing novel insights into epigenetic contribution to a range of human diseases. Here we introduce the PETCH-DB, an integrated database that was implemented to provide 5hmC-related results generated using the 5hmC-Seal technique. We aim the PETCH-DB to be a central portal, which will be available to the scientific community with regularly updated 5hmC data in clinical samples to reflect current advances in this field. Database URL http://petch-db.org/.

Definition of an Index Parameter To Screen Highly Functional Enzymes Derived from a Biochemical and Thermodynamic Analysis of Ancestral meso-Diaminopimelate Dehydrogenases.

Sequence-based protein design approaches are being adopted to generate highly functional enzymes; however, screening the enzymes remains a time-consuming task. In this study, by analyzing the enzymatic properties of four ancestral meso-2,6-diaminopimelate dehydrogenases (AncDAPDHs), AncDAPDH-N1, -N2, -N3, and -N4, we attempted to define a new index parameter that is helpful for efficiently screening the enzymes. Biochemical and thermodynamic analyses indicated that only AncDAPDH-N4 exhibited greater thermal stability than and activity similar to those of native DAPDHs. Structural and sequence comparisons between DAPDH from Corynebacterium glutamicum (CgDAPDH) and the AncDAPDHs suggested that "quality of mutations" is a potential index parameter. In fact, the mutations introduced from CgDAPDH to AncDAPDH-N4 correlated highly with the mutations accumulated during the evolution process from mesophiles to thermophiles. These results suggest that, although there are several exceptions, the correlation coefficient can be used as an index parameter for screening high-functioning enzymes from sequence data.

NHGNN-DTA: a node-adaptive hybrid graph neural network for interpretable drug-target binding affinity prediction.

Large-scale prediction of drug-target affinity (DTA) plays an important role in drug discovery. In recent years, machine learning algorithms have made great progress in DTA prediction by utilizing sequence or structural information of both drugs and proteins. However, sequence-based algorithms ignore the structural information of molecules and proteins, while graph-based algorithms are insufficient in feature extraction and information interaction. In this article, we propose NHGNN-DTA, a node-adaptive hybrid neural network for interpretable DTA prediction. It can adaptively acquire feature representations of drugs and proteins and allow information to interact at the graph level, effectively combining the advantages of both sequence-based and graph-based approaches. Experimental results have shown that NHGNN-DTA achieved new state-of-the-art performance. It achieved the mean squared error (MSE) of 0.196 on the Davis dataset (below 0.2 for the first time) and 0.124 on the KIBA dataset (3% improvement). Meanwhile, in the case of cold start scenario, NHGNN-DTA proved to be more robust and more effective with unseen inputs than baseline methods. Furthermore, the multi-head self-attention mechanism endows the model with interpretability, providing new exploratory insights for drug discovery. The case study on Omicron variants of SARS-CoV-2 illustrates the efficient utilization of drug repurposing in COVID-19. The source code and data are available at https://github.com/hehh77/NHGNN-DTA.

Asymmetric enzymatic hydration of unactivated terminal alkenes by two promiscuous oleate hydratases mined from marine metagenome

Enantiopure alcohols are very important chemicals as highly valuable building blocks in the synthesis of pharmaceuticals, agrochemicals and materials. The direct enantioselective hydration of unactivated 1-alkenes is a highly straightforward and atom economy approach to access chiral alcohols, though quite challenging in organic synthesis owing to low selectivity and many environmental concerns. Herein, two novel oleate hydratases (MmOAH1 and MmOAH2) were identified from marine metagenome via a sequence-based mining approach, and the two OAHs were functionally expressed in Escherichia coli T7, purified and characterized. Both of the two OAHs exhibited surprising highly promiscuous hydratase activity toward unactivated 1-alkenes when a carboxylic acid decoy molecule was involved in the reaction. Besides its natural substrate oleate acid, a series of unactivated 1-alkenes were converted to (S)-alcohols by using the resting cell of E. coli (MmOAH1) and E. coli (MmOAH2), moderate to good conversions (6–90%) and excellent ee (>98%) were obtained. Preparative-scale experiments were also demonstrated with 1-decene and 1-tridecene, affording (S)-2-decanol and (S)-2-tridecanol in good yields and excellent ee (>99%). This study expands the biocatalytic toolbox of available enzymes for asymmetric hydration reactions and proved that the OAHs mined from marine metagenome is promising for biotechnology application in production of chiral alcohols.