Bioinformatics Protocols Research Articles

Recommendations for Uniform Variant Calling of SARS-CoV-2 Genome Sequence across Bioinformatic Workflows.

Genomic sequencing of clinical samples to identify emerging variants of SARS-CoV-2 has been a key public health tool for curbing the spread of the virus. As a result, an unprecedented number of SARS-CoV-2 genomes were sequenced during the COVID-19 pandemic, which allowed for rapid identification of genetic variants, enabling the timely design and testing of therapies and deployment of new vaccine formulations to combat the new variants. However, despite the technological advances of deep sequencing, the analysis of the raw sequence data generated globally is neither standardized nor consistent, leading to vastly disparate sequences that may impact identification of variants. Here, we show that for both Illumina and Oxford Nanopore sequencing platforms, downstream bioinformatic protocols used by industry, government, and academic groups resulted in different virus sequences from same sample. These bioinformatic workflows produced consensus genomes with differences in single nucleotide polymorphisms, inclusion and exclusion of insertions, and/or deletions, despite using the same raw sequence as input datasets. Here, we compared and characterized such discrepancies and propose a specific suite of parameters and protocols that should be adopted across the field. Consistent results from bioinformatic workflows are fundamental to SARS-CoV-2 and future pathogen surveillance efforts, including pandemic preparation, to allow for a data-driven and timely public health response.

Methods: A bioinformatic protocol for rapid analysis of zebrafish embryo photo-motory responses (PMR) in neurotoxicity testing

Chemobehavioural phenotyping presents unique opportunities for analyzing neurotoxicants and discovering behavior-modifying neuroceuticals in small aquatic model organisms such as zebrafish (Danio rerio). A recently popularized approach in this field involves the utilization of zebrafish embryos for a photo-motor response (PMR) bioassay. The PMR bioassay entails stimulating zebrafish embryos between 24 and 36 h post fertilization (hpf) with a high-intensity light stimulus, inducing a transient increase in the frequency of photo-induced embryo body flexions. These flexions can be computationally analyzed to derive behavioral signatures, enabling the categorization of neuromodulating chemicals.Despite the significant advantages of the PMR bioassay, its widespread implementation is hindered by lack of well described and straightforward high-throughput bioinformatic analysis of behavioral data. In this methods article, we present an easily implementable bioinformatics protocol specifically designed for rapid behavioral analysis of large cohorts of zebrafish specimens in PMR bioassays. We also address common pitfalls encountered during PMR analysis, discuss its limitations, and propose future directions for developing next-generation biometric analysis techniques in chemobehavioural assays utilizing zebrafish embryos.

Systematic comparison of variant calling pipelines of target genome sequencing cross multiple next-generation sequencers.

Targeted genomic sequencing (TS) greatly benefits precision oncology by rapidly detecting genetic variations with better accuracy and sensitivity owing to its high sequencing depth. Multiple sequencing platforms and variant calling tools are available for TS, making it excruciating for researchers to choose. Therefore, benchmarking study across different platforms and pipelines available for TS is imperative. In this study, we performed a TS of Reference OncoSpan FFPE (HD832) sample enriched by TSO500 panel using four commercially available sequencers, and analyzed the output 50 datasets using five commonly-used bioinformatics pipelines. We systematically investigated the sequencing quality and variant detection sensitivity, expecting to provide optimal recommendations for future research. Four sequencing platforms returned highly concordant results in terms of base quality (Q20 > 94%), sequencing coverage (>97%) and depth (>2000×). Benchmarking revealed good concordance of variant calling across different platforms and pipelines, among which, FASTASeq 300 platform showed the highest sensitivity (100%) and precision (100%) in high-confidence variants calling when analyzed by SNVer and VarScan 2 algorithms. Furthermore, this sequencer demonstrated the shortest sequencing time (∼21h) at the sequencing mode PE150. Through the intersection of 50 datasets generated in this study, we recommended a novel set of variant genes outside the truth set published by HD832, expecting to replenish HD832 for future research on tumor variant diagnosis. Besides, we applied these five tools to another panel (TargetSeq One) for Twist cfDNA Pan-cancer Reference Standard, comprehensive consideration of SNP and InDel sensitivity, SNVer and VarScan 2 performed best among them. Furthermore, SNVer and VarScan 2 also performed best for six cancer cell lines samples regarding SNP and InDel sensitivity. Considering the dissimilarity of variant calls across different pipelines for datasets from the same platform, we recommended an integration of multiple tools to improve variant calling sensitivity and accuracy for the cancer genome. Illumina and GeneMind technologies can be used independently or together by public health laboratories performing tumor TS. SNVer and VarScan 2 perform better regarding variant detection sensitivity for three typical tumor samples. Our study provides a standardized target sequencing resource to benchmark new bioinformatics protocols and sequencing platforms.

The salivary microbiome as a diagnostic biomarker of periodontitis: a 16S multi-batch study before and after the removal of batch effects.

Microbiome-based clinical applications that improve diagnosis related to oral health are of great interest to precision dentistry. Predictive studies on the salivary microbiome are scarce and of low methodological quality (low sample sizes, lack of biological heterogeneity, and absence of a validation process). None of them evaluates the impact of confounding factors as batch effects (BEs). This is the first 16S multi-batch study to analyze the salivary microbiome at the amplicon sequence variant (ASV) level in terms of differential abundance and machine learning models. This is done in periodontally healthy and periodontitis patients before and after removing BEs. Saliva was collected from 124 patients (50 healthy, 74 periodontitis) in our setting. Sequencing of the V3-V4 16S rRNA gene region was performed in Illumina MiSeq. In parallel, searches were conducted on four databases to identify previous Illumina V3-V4 sequencing studies on the salivary microbiome. Investigations that met predefined criteria were included in the analysis, and the own and external sequences were processed using the same bioinformatics protocol. The statistical analysis was performed in the R-Bioconductor environment. The elimination of BEs reduced the number of ASVs with differential abundance between the groups by approximately one-third (Before=265; After=190). Before removing BEs, the model constructed using all study samples (796) comprised 16 ASVs (0.16%) and had an area under the curve (AUC) of 0.944, sensitivity of 90.73%, and specificity of 87.16%. The model built using two-thirds of the specimens (training=531) comprised 35 ASVs (0.36%) and had an AUC of 0.955, sensitivity of 86.54%, and specificity of 90.06% after being validated in the remaining one-third (test=265). After removing BEs, the models required more ASVs (all samples=200-2.03%; training=100-1.01%) to obtain slightly lower AUC (all=0.935; test=0.947), lower sensitivity (all=81.79%; test=78.85%), and similar specificity (all=91.51%; test=90.68%). The removal of BEs controls false positive ASVs in the differential abundance analysis. However, their elimination implies a significantly larger number of predictor taxa to achieve optimal performance, creating less robust classifiers. As all the provided models can accurately discriminate health from periodontitis, implying good/excellent sensitivities/specificities, the salivary microbiome demonstrates potential clinical applicability as a precision diagnostic tool for periodontitis.

Metatranscriptomic RNA-Seq Data Analysis of Virus-Infected Host Cells.

RNA sequencing (RNA-seq) analysis of virus-infected host cells enables researchers to study a wide range of phenomena involving host-virus interactions. This includes genomic analysis of the viral population itself, as well as analysis of the transcriptional dynamics of the virus and host during infection. In this chapter, we provide a guide for researchers interested in performing RNA-seq data analysis of virus-infected host cells or cell lines. We outline several bioinformatic protocols for quantifying viral abundance, assembling viral genomes from mixed samples, and performing differential expression analysis, among other common workflows. These workflows can be used as starting points for researchers aiming to analyze RNA-seq datasets of mixed samples containing both host and viral RNA, such as virus-infected cell lines or clinical samples.

Bioinformatics Methods for Prediction of Gene Families Encoding Extracellular Peptides.

Genes encoding small secreted peptides are widely distributed among plant genomes but their detection and annotation remains challenging. The bioinformatics protocol described here aims to identify as exhaustively as possible secreted peptide precursors belonging to a family of interest. First, homology searches are performed at the protein and genome levels. Next, multiple sequence alignments and predictions of a secretion signal are used to define a set of homologous proteins sharing features of secreted peptide precursors. These protein sequences are then used as input of motif detection and profile-based tools to build representative matrices and profiles that are used iteratively as guides to scan again the proteome and genome until family completion.

Microbiome depiction through user-adapted bioinformatic pipelines and parameters.

Introduction. The role of the microbiome in health and disease continues to be increasingly recognized. However, there is significant variability in the bioinformatic protocols for analysing genomic data. This, in part, has impeded the potential incorporation of microbiomics into the clinical setting and has challenged interstudy reproducibility. In microbial compositional analysis, there is a growing recognition for the need to move away from a one-size-fits-all approach to data processing.Gap Statement. Few evidence-based recommendations exist for setting parameters of programs that infer microbiota community profiles despite these parameters significantly impacting the accuracy of taxonomic inference.Aim. To compare three commonly used programs (DADA2, QIIME2, and mothur) and optimize them into four user-adapted pipelines for processing paired-end amplicon reads. We aim to increase the accuracy of compositional inference and help standardize microbiomic protocol.Methods. Two key parameters were isolated across four pipelines: filtering sequence reads based on a whole-number error threshold (maxEE) and truncating read ends based on a quality score threshold (QTrim). Closeness of sample inference was then evaluated using a mock community of known composition.Results. We observed that raw genomic data lost were proportionate to how stringently parameters were set. Exactly how much data were lost varied by pipeline. Accuracy of sample inference correlated with increased sequence read retention. Falsely detected taxa and unaccounted for microbial constituents were unique to pipeline and parameter. Implementation of optimized parameter values led to better approximation of the known mock community.Conclusions. Microbial compositions generated based on the 16S rRNA marker gene should be interpreted with caution. To improve microbial community profiling, bioinformatic protocols must be user-adapted. Analysis should be performed with consideration for the select target amplicon, pipelines and parameters used, and taxa of interest.

An efficient computational protocol for template-based design of peptides that inhibit interactions involving SARS-CoV-2 proteins.

The RNA-dependent RNA polymerase (RdRp) complex of SARS-CoV-2 lies at the core of its replication and transcription processes. The interfaces between holo-RdRp subunits are highly conserved, facilitating the design of inhibitors with high affinity for the interaction interface hotspots. We, therefore, take this as a model protein complex for the application of a structural bioinformatics protocol to design peptides that inhibit RdRp complexation by preferential binding at the interface of its core subunit nonstructural protein, nsp12, with accessory factor nsp7. Here, the interaction hotspots of the nsp7-nsp12 subunit of RdRp, determined from a long molecular dynamics trajectory, are used as a template. A large library of peptide sequences constructed from multiple hotspot motifs of nsp12 is screened in-silico to determine sequences with high geometric complementarity and interaction specificity for the binding interface of nsp7 (target) in the complex. Two lead designed peptides are extensively characterized using orthogonal bioanalytical methods to determine their suitability for inhibition of RdRp complexation. Binding affinity of these peptides to accessory factor nsp7, determined using a surface plasmon resonance (SPR) assay, is slightly better than that of nsp12: dissociation constant of 133nM and 167nM, respectively, compared to 473nM for nsp12. A competitive ELISA is used to quantify inhibition of nsp7-nsp12 complexation, with one of the lead peptides giving an IC50 of 25μM . Cell penetrability and cytotoxicity are characterized using a cargo delivery assay and MTT cytotoxicity assay, respectively. Overall, this work presents a proof-of-concept of an approach for rational discovery of peptide inhibitors of SARS-CoV-2 protein-protein interactions.

BNIP3-mediated autophagy via the mTOR/ULK1 pathway induces primordial follicle loss after ovarian tissue transplantation.

To explore the underlying mechanism of primordial follicle loss in the early period following ovarian tissue transplantation (OTT). BNIP3 was selected through bioinformatic protocols, as the hub gene related to autophagy during OTT. BNIP3 and autophagy in mice ovarian grafts and in hypoxia-mimicking KGN cells were detected using immunohistochemistry, transmission electron microscopy (TEM), western blotting, qPCR, and fluorescence staining. The regulatory role played by BNIP3 overexpression and the silencing of KGN cells in autophagy via the mTOR/ULK1 pathway was investigated. Ultrastructure examination showed that autophagic vacuoles increased after mice ovarian auto-transplantation. The BNIP3 and autophagy-related proteins (Beclin-1, LC3B, and SQSTM1/p62) in mice ovarian granulosa cells of primordial follicle from ovarian grafts were altered compared with the control. Administration of an autophagy inhibitor in mice decreased the depletion of primordial follicles. In vitro experiments indicated that BNIP3 and autophagy activity were upregulated in KGN cells treated with cobalt chloride (CoCl2). The overexpression of BNIP3 activated autophagy, whereas the silencing of BNIP3 suppressed it and reversed the autophagy induced by CoCl2 in KGN cells. Western blotting analysis showed the inhibition of mTOR and activation of ULK1 in KGN cells treated with CoCl2 and in the overexpression of BNIP3, and the opposite results following BNIP3 silencing. The activation of mTOR reversed the autophagy induced by BNIP3 overexpression. BNIP3-induced autophagy is crucial in primordial follicle loss during OTT procedure, and BNIP3 is a potential therapeutic target for primordial follicle loss after OTT.

Whole-genome sequencing of SARS-CoV-2 isolates from symptomatic and asymptomatic individuals in Tanzania.

Coronavirus Disease-2019 (COVID-19), caused by Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) accounts for considerable morbidity and mortality globally. Paucity of SARS-CoV-2 genetic data from Tanzania challenges in-country tracking of the pandemic. We sequenced SARS-CoV-2 isolated in the country to determine circulating strains, mutations and phylogenies and finally enrich international genetic databases especially with sequences from Africa. This cross-sectional study utilized nasopharyngeal swabs of symptomatic and asymptomatic adults with positive polymerase chain reaction tests for COVID-19 from January to May 2021. Viral genomic libraries were prepared using ARTIC nCoV-2019 sequencing protocol version three. Whole-genome sequencing (WGS) was performed using Oxford Nanopore Technologies MinION device. In silico genomic data analysis was done on ARTIC pipeline version 1.2.1 using ARTIC nCoV-2019 bioinformatics protocol version 1.1.0. Twenty-nine (42%) out of 69 samples qualified for sequencing based on gel electrophoretic band intensity of multiplex PCR amplicons. Out of 29 isolates, 26 were variants of concern [Beta (n = 22); and Delta (n = 4)]. Other variants included Eta (n = 2) and B.1.530 (n = 1). We found combination of mutations (S: D80A, S: D215G, S: K417N, ORF3a: Q57H, E: P71L) in all Beta variants and absent in other lineages. The B.1.530 lineage carried mutations with very low cumulative global prevalence, these were nsp13:M233I, nsp14:S434G, ORF3a:A99S, S: T22I and S: N164H. The B.1.530 lineage clustered phylogenetically with isolates first reported in south-east Kenya, suggesting regional evolution of SARS-CoV-2. We provide evidence of existence of Beta, Delta, Eta variants and a locally evolving lineage (B.1.530) from samples collected in early 2021 in Tanzania. This work provides a model for ongoing WGS surveillance that will be required to inform on emerging and circulating SARS-CoV-2 diversity in Tanzania and East Africa.

Concomitant Sequencing of Accessible Chromatin and Mitochondrial Genomes in Single Cells Using mtscATAC-Seq.

Mitochondria are unique organelles of eukaryotic cells that carry their own multicopy number and circular genome. In most mammals, including humans and mice, the size of the chromosome is ~16,000 base pairs and unlike nuclear DNA, mitochondrial DNA (mtDNA) is not densely compacted. This results in mtDNA to be highly accessible for enzymes such as the Tn5 transposase, commonly used for accessible chromatin profiling of nuclear chromatinized DNA. Here, we describe a method for the concomitant sequencing of mtDNA and accessible chromatin in thousands of individual cells via the mitochondrial single-cell assay for transposase accessible chromatin by sequencing (mtscATAC-seq). Our approach extends the utility of existing scATAC-seq products and protocols as we (Nam et al, Nat Rev Genet 22:3-18, 2021) fix cells using formaldehyde to retain mitochondria and its mtDNA within its originating cell, (Buenrostro et al, Nat Methods 10:1213-1218, 2013) modify lysis conditions to permeabilize cells and mitochondria, and (Corces et al, Nat Methods 14:959-962, 2017) optimize bioinformatic processing protocols to collectively increase mitochondrial genome coverage for downstream analysis. Here, we discuss the essentials for the experimental and computational methodologies to generate and analyze thousands of multiomic profiles of single cells over the course of a few days, enabling the profiling of accessible chromatin and mtDNA genotypes to reconstruct clonal relationships and studies of mitochondrial genetics and disease.

The Long and Short of Genome Sequencing: Using a Hybrid Sequencing Strategy to Sequence Oral Microbial Genomes.

Since our chapter on genome sequencing using the GS-FLX pyrosequencer in the First Edition of this book, significant advances have been made in next-generation DNA sequencing (NGS) technology. Not only has the GS-FLX become extinct, but the more recent introduction and establishment of the so-called third-generation DNA sequencers by Pacific Biosciences (PacBio) and Oxford Nanopore Technologies (ONT) has revolutionized genomics yet again by generating ultra-long (>100,000 basepair) sequence reads concomitant with an incredible reduction in cost per sequenced basepair. Unfortunately, the ultra-high sequence yields of third-generation sequencers are compromised by their inherent sequencing error rates, prompting an alternative sequencing strategy, i.e., a hybrid sequencing strategy, which combines PacBio/ONT primary datasets with complementary datasets generated by mainstream short-read NGS platforms, e.g., Illumina or Ion Torrent. Although the concept of a hybrid sequencing strategy is not new, existing yields and accuracy of ultra-long and short-read sequencing technologies makes such a strategy achievable, resulting in complete genome sequences in one hit. In this chapter, we describe our updated laboratory and bioinformatic protocols that will allow the average research group to obtain complete oral microbial genome sequences assembled from a combination of DNA sequence data generated by NGS and third-generation platforms.

Somatic CNV Detection by Single-Cell Whole-Genome Sequencing in Postmortem Human Brain.

The evidence for a role of somatic mutations, including copy-number variants (CNVs), in neurodegeneration has increased in the last decade. However, the understanding of the types and origins of these mutations, and their exact contributions to disease onset and progression, is still in its infancy. The use of single-cell (or nuclear) whole-genome sequencing (scWGS) has emerged as a powerful tool to answer these questions. In the present chapter, we provide laboratory and bioinformatic protocols used successfully in our lab to detect megabase-scale CNVs in single cells from multiple system atrophy (MSA) human postmortem brains, using immunolabeling prior to selection of nuclei for whole-genome amplification (WGA). We also present an unpublished comparison of scWGS generated from the same control substantia nigra (SN) sample, using the latest versions of popular WGA chemistries, MDA and PicoPLEX. We have used this protocol to focus on brain cell types most relevant to synucleinopathies (dopaminergic [DA] neurons in Parkinson's disease [PD] and oligodendrocytes in MSA), but it can be applied to any tissue and/or cell type with appropriate markers.

GROP: A genomic information repository for oilplants.

Biomass energy is an essential component of the agriculture economy and represents an important and particularly significant renewable energy source in the fight against fossil fuel depletion and global warming. The recognition that many plants naturally synthesize hydrocarbons makes these oil plants indispensable resources for biomass energy, and the advancement of next-generation sequencing technology in recent years has now made available mountains of data on plants that synthesize oil. We have utilized a combination of bioinformatic protocols to acquire key information from this massive amount of genomic data and to assemble it into an oil plant genomic information repository, built through website technology, including Django, Bootstrap, and echarts, to create the Genomic Information Repository for Oil Plants (GROP) portal (http://grop.site/) for genomics research on oil plants. The current version of GROP integrates the coding sequences, protein sequences, genome structure, functional annotation information, and other information from 18 species, 22 genome assemblies, and 46 transcriptomes. GROP also provides BLAST, genome browser, functional enrichment, and search tools. The integration of the massive amounts of oil plant genomic data with key bioinformatics tools in a database with a user-friendly interface allows GROP to serve as a central information repository to facilitate studies on oil plants by researchers worldwide.

Alpine freshwater fish biodiversity assessment: an inter-calibration test for metabarcoding method set up

The analysis of environmental DNA (eDNA) by high throughput sequencing (HTS) is proving to be a promising tool for freshwater fish biodiversity assessment in Europe within the Water Framework Directive (WFD, 2000/60/EC), especially for large rivers and lakes where current fish monitoring techniques have known shortcomings. These new biomonitoring methods based on eDNA show several advantages compared to classical morphological methods. The sampling procedures are easier and cheaper and eDNA metabarcoding is non-invasive and very sensitive, allowing for the detection of traces of DNA. However, eDNA metabarcoding methods need careful standardization to make the results of different surveys comparable. The aim of the EU project Eco-AlpsWater is to test and validate molecular biodiversity monitoring tools for aquatic ecosystems (i.e., eDNA metabarcoding) to improve the traditional WFD monitoring approaches in Alpine waterbodies. To this end, an inter-calibration test was performed using fish mock community samples containing either tissue-extracted DNA, eDNA collected from aquaculture tanks and eDNA samples collected from Lake Bourget (France). Samples were analysed using a DNA metabarcoding approach, relying on the amplification and HTS of a 12S rDNA marker, in two separate laboratories, to evaluate if different laboratory and bioinformatic protocols can provide a reliable and comparable description of the fish communities in both mock and natural samples. Our results highlight good replicability of the molecular laboratory protocols for HTS and good amplification success of selected primers, providing essential information concerning the taxonomic resolution of the 12S mitochondrial marker in describing the Alpine fish communities. Interestingly, different concentrations of species DNA in the mock samples were well represented by the relative DNA reads abundance. These tests confirm the reproducibility of eDNA metabarcoding analyses for the biomonitoring of freshwater fish inhabiting Alpine and peri-Alpine lakes and rivers.

Revealing the Satellite DNA History in Psalidodon and Astyanax Characid Fish by Comparative Satellitomics.

Eukaryotic genomes are usually enriched in repetitive DNA sequences, which can be classified as dispersed or tandemly repeated elements. Satellite DNAs are noncoding monomeric sequences organized in a head-to-tail fashion that are generally located on the subtelomeric and/or pericentromeric heterochromatin. In general, a single species incorporates a diverse group of satellite DNA families, which collection is called satellitome. Here, we characterized three new satellitomes from distinct characid fish (Psalidodon fasciatus, P. bockmanni, and Astyanax lacustris) using a combination of genomic, cytogenetic, and bioinformatic protocols. We also compared our data with the available satellitome of P. paranae. We described 57 satellite DNA (satDNA) families of P. fasciatus (80 variants), 50 of P. bockmanni (77 variants), and 33 of A. lacustris (54 variants). Our analyses demonstrated that several sequences were shared among the analyzed species, while some were restricted to two or three species. In total, we isolated 104 distinctive satDNA families present in the four species, of which 10 were shared among all four. Chromosome mapping revealed that the clustered satDNA was mainly located in the subtelomeric and pericentromeric areas. Although all Psalidodon species demonstrated the same pattern of clusterization of satDNA, the number of clusters per genome was variable, indicating a high dynamism of these sequences. In addition, our results expand the knowledge of the As51 satellite DNA family, revealing that P. bockmanni and P. paranae exhibited an abundant variant of 39 bp, while P. fasciatus showed a variant of 43 bp. The majority of satDNAs in the satellitomes analyzed here presented a common library repetitive sequence in Psalidodon and Astyanax, with abundance variations in each species, as expected for closely related groups. In addition, we concluded that the most abundant satDNA in Psalidodon (As51) passed through a diversification process in this group, resulting in new variants exclusive of Psalidodon.

The Use of Next-generation Sequencing in the Diagnosis of Rare Inherited Anaemias: A Joint BSH/EHA Good Practice Paper.

The Use of Next-generation Sequencing in the Diagnosis of Rare Inherited Anaemias: A Joint BSH/EHA Good Practice Paper.

Deciphering the conformational transitions of LIMK2 active and inactive states to ponder specific druggable states through microsecond scale molecular dynamics simulation.

LIMK2 inhibitors are one of the potential therapeutic modalities for treating various diseases. In the current scenario, there is a paucity of effective LIMK inhibitors that are highly specific with minimal off-target effects. To date, the conformational transitions of LIMK2 from DFGinαCin (CIDI) (active) to DFGoutαCout (CODO) (inactive) states are yet to be probed and are essential for capturing the unique, druggable conformations. Therefore, this study was intended to capture the diverse conformational states of LIMK2 for accelerating the rational identification of conformation specific inhibitors through high-end structural bioinformatics protocols. Hence, in this study, molecular modelling followed by an extensive microsecond timescale of molecular dynamics simulation was performed encompassing perturbation response scanning, metapath, and community analysis towards the conformational sampling of LIMK2. Overall this study precisely identifies the conformational ensemble of LIMK2 the intermediate inactive states namely, CIDO, CinterDinter, CIDinter, CinterDI, CinterDO, CODI, CODinter apart from CIDI and CODO. This also facilitated observing that β8 preceding XDFG, αC (F373, L374), and αD (L413) as the major effectors that may facilitate the regulation of varying conformational transitions among the states. Additionally, the conserved βsheets and the loops namely, C.l, b.l, and G/P.loop were observed to be involved in the metapath for allosteric communication among the intermediates with CIDI and CODO state. Moreover, only the CODO state was observed to have closed type A.l, while the CIDI and other intermediate states except for CIDO were observed to have open-DFG out type A.l, thereby enabling the binding of substrate. Apart from these, the druggable site analysis inferred that the CIDI and CODO states harbor prominent druggable sites spanning the conserved N-lobe, while the intermediates were observed to have unraveled allosteric druggable sites distal from the ATP binding site, majorly spanning the C-lobe of LIMK2. Thus, this study provides potential insights into the intermediate conformational druggable states of LIMK2 and also the druggable conformations, especially the inactive states of LIMK2, as a specific therapeutic targeting mode. Thus, providing a widened avenue to ponder the allosteric sites or the isoform selectivity conformations for targeting LIMK2 in various disease conditions.

Computational Saturation Mutagenesis to Investigate the Effects of Neurexin-1 Mutations on AlphaFold Structure.

Neurexin-1 (NRXN1) is a membrane protein essential in synapse formation and cell signaling as a cell-adhesion molecule and cell-surface receptor. NRXN1 and its binding partner neuroligin have been associated with deficits in cognition. Recent genetics research has linked NRXN1 missense mutations to increased risk for brain disorders, including schizophrenia (SCZ) and autism spectrum disorder (ASD). Investigation of the structure–function relationship in NRXN1 has proven difficult due to a lack of the experimental full-length membrane protein structure. AlphaFold, a deep learning-based predictor, succeeds in high-quality protein structure prediction and offers a solution for membrane protein model construction. In the study, we applied a computational saturation mutagenesis method to analyze the systemic effects of missense mutations on protein functions in a human NRXN1 structure predicted from AlphaFold and an experimental Bos taurus structure. The folding energy changes were calculated to estimate the effects of the 29,540 mutations of AlphaFold model on protein stability. The comparative study on the experimental and computationally predicted structures shows that these energy changes are highly correlated, demonstrating the reliability of the AlphaFold structure for the downstream bioinformatics analysis. The energy calculation revealed that some target mutations associated with SCZ and ASD could make the protein unstable. The study can provide helpful information for characterizing the disease-causing mutations and elucidating the molecular mechanisms by which the variations cause SCZ and ASD. This methodology could provide the bioinformatics protocol to investigate the effects of target mutations on multiple AlphaFold structures.

Evolution of Protein Functional Annotation: Text Mining Study.

Within the Human Proteome Project initiative framework for creating functional annotations of uPE1 proteins, the neXt-CP50 Challenge was launched in 2018. In analogy with the missing-protein challenge, each command deciphers the functional features of the proteins in the chromosome-centric mode. However, the neXt-CP50 Challenge is more complicated than the missing-protein challenge: the approaches and methods for solving the problem are clear, but neither the concept of protein function nor specific experimental and/or bioinformatics protocols have been standardized to address it. We proposed using a retrospective analysis of the key HPP repository, the neXtProt database, to identify the most frequently used experimental and bioinformatic methods for analyzing protein functions, and the dynamics of accumulation of functional annotations. It has been shown that the dynamics of the increase in the number of proteins with known functions are greater than the progress made in the experimental confirmation of the existence of questionable proteins in the framework of the missing-protein challenge. At the same time, the functional annotation is based on the guilty-by-association postulate, according to which, based on large-scale experiments on API-MS and Y2H, proteins with unknown functions are most likely mapped through “handshakes” to biochemical processes.