FMAlign2: a novel fast multiple nucleotide sequence alignment method for ultralong datasets.

MOTIVATION: In bioinformatics, multiple sequence alignment (MSA) is a crucial task. However, conventional methods often struggle with aligning ultralong sequences. To address this issue, researchers have designed MSA methods rooted in a vertical division strategy, which segments sequence data for parallel alignment. A prime example of this approach is FMAlign, which utilizes the FM-index to extract common seeds and segment the sequences accordingly. RESULTS: FMAlign2 leverages the suffix array to identify maximal exact matches, redefining the approach of FMAlign from searching for global chains to partial chains. By using a vertical division strategy, large-scale problem is deconstructed into manageable tasks, enabling parallel execution of subMSA. Furthermore, sequence-profile alignment and refinement are incorporated to concatenate subsets, yielding the final result seamlessly. Compared to FMAlign, FMAlign2 markedly augments the segmentation of sequences and significantly reduces the time while maintaining accuracy, especially on ultralong datasets. Importantly, FMAlign2 enhances existing MSA methods by conferring the capability to handle sequences reaching billions in length within an acceptable time frame. AVAILABILITY AND IMPLEMENTATION: Source code and datasets are available at https://github.com/malabz/FMAlign2 and https://zenodo.org/records/10435770.

TPMA: A two pointers meta-alignment tool to ensemble different multiple nucleic acid sequence alignments.

Accurate multiple sequence alignment (MSA) is imperative for the comprehensive analysis of biological sequences. However, a notable challenge arises as no single MSA tool consistently outperforms its counterparts across diverse datasets. Users often have to try multiple MSA tools to achieve optimal alignment results, which can be time-consuming and memory-intensive. While the overall accuracy of certain MSA results may be lower, there could be local regions with the highest alignment scores, prompting researchers to seek a tool capable of merging these locally optimal results from multiple initial alignments into a globally optimal alignment. In this study, we introduce Two Pointers Meta-Alignment (TPMA), a novel tool designed for the integration of nucleic acid sequence alignments. TPMA employs two pointers to partition the initial alignments into blocks containing identical sequence fragments. It selects blocks with the high sum of pairs (SP) scores to concatenate them into an alignment with an overall SP score superior to that of the initial alignments. Through tests on simulated and real datasets, the experimental results consistently demonstrate that TPMA outperforms M-Coffee in terms of aSP, Q, and total column (TC) scores across most datasets. Even in cases where TPMA's scores are comparable to M-Coffee, TPMA exhibits significantly lower running time and memory consumption. Furthermore, we comprehensively assessed all the MSA tools used in the experiments, considering accuracy, time, and memory consumption. We propose accurate and fast combination strategies for small and large datasets, which streamline the user tool selection process and facilitate large-scale dataset integration. The dataset and source code of TPMA are available on GitHub (https://github.com/malabz/TPMA).

Bioinformatics and Experimental Validation for Identifying Biomarkers Associated with AMG510 (Sotorasib) Resistance in KRASG12C-Mutated Lung Adenocarcinoma.

The Kirsten rat sarcoma viral oncogene homolog (KRAS)G12C mutation is prevalent in lung adenocarcinoma (LUAD), driving tumor progression and indicating a poor prognosis. While the FDA-approved AMG510 (Sotorasib) initially demonstrated efficacy in treating KRASG12C-mutated LUAD, resistance emerged within months. Data from AMG510 treatment-resistant LUAD (GSE204753) and single-cell datasets (GSE149655) were analyzed. Gene set variation analysis (GSVA) and gene set enrichment analysis (GSEA) were used to explore enriched signaling pathways, nomogram models were constructed, and transcription factors predicting resistance biomarkers were predicted. CIBERSORT identified immune cell subpopulations, and their association with resistance biomarkers was assessed through single-cell analysis. AMG510-resistant LUAD cells (H358-AR) were constructed, and proliferative changes were evaluated using a CCK-8 assay. Key molecules for AMG510 resistance, including SLC2A1, TLE1, FAM83A, HMGA2, FBXO44, and MTRNR2L12, were recognized. These molecules impacted multiple signaling pathways and the tumor microenvironment and were co-regulated by various transcription factors. Single-cell analysis revealed a dampening effect on immune cell function, with associations with programmed cell death ligand 1 (PDL1) expression, cytokine factors, and failure factors. The findings indicate that these newly identified biomarkers are linked to the abnormal expression of PDL1 and have the potential to induce resistance through immunosuppression. These results highlight the need for further research and therapeutic intervention to address this issue effectively.

A Catalytic Asymmetric Hydrolactonization.

Despite recent advancements in the development of catalytic asymmetric electrophile induced lactonization reactions of olefinic carboxylic acids, the archetypical hydrolactonization has long remained an unsolved and well-recognized challenge. Here, we report the realization of a catalytic asymmetric hydrolactonization using a confined imidodiphosphorimidate (IDPi) Brønsted acid catalyst. The method is operationally simple, scalable, and compatible with a wide variety of substrates. Its potential is showcased with concise syntheses of the sesquiterpenes (-)-boivinianin A and (+)-gossonorol. Through in-depth physicochemical and DFT analyses, we derive a nuanced picture of the mechanism and enantioselectivity of this reaction.

Silver(I)-Catalyzed Diastereoselective Hydroborylation of Cyclopropenes.

An effective (NHC)AgCl catalysis was developed in the hydroborylation of cyclopropenes with B2pin2, delivering a variety of cyclopylboronates in a stereoselective manner, which could be easily transformed for the construction of versatile cyclopropanes. This protocol works effectively under mild reaction conditions in an open-air atmosphere, and it was easy to apply on a gram scale. This novel method in detail was also explored by control experiments, providing a number of key insights. The kinetic process followed by 1H NMR indicated that the reaction was finished in 15 min. Furthermore, the mechanism of silver(I)-catalyzed hydroborylation of cyclopropenes was proposed, with the protonation by methanol as the rate-determining step.

Transition-metal-free and additive-free intermolecular hydroarylation of alkenes with indoles in hexafluoroisopropanol.

Hydroarylation of alkenes is one of the most straightforward and atom-economical strategy for the construction of multi-aryl-substituted alkanes, but systematic studies have been limited to transition metal catalysis. Here we report a hexafluoroisopropanol (HFIP)-promoted hydroarylation of alkenes with indoles without the presence of transition metal catalysts or any additive. HFIP was the only reagent used in this work, and could be easily removed via evaporation, and recovered via distillation in industry settings. This reaction was shown to provide an efficient, clean and operationally simple procedure with a remarkable substrate scope and versatile transformations, delivering a variety of multi-aryl alkanes incorporating the indole motif. In preliminary studies, several of these products showed biologically activity against cells from an array of human cancer cell lines. A mechanistic study was also carried out and suggested that the quinone methide might be the key intermediate. And in contrast to the conclusions of a previous report, the current work suggested that protonation by HFIP might not be the rate-determining step.

Strong and Confined Acids Catalyze Asymmetric Intramolecular Hydroarylations of Unactivated Olefins with Indoles.

In recent years, several organocatalytic asymmetric hydroarylations of activated, electron-poor olefins with activated, electron-rich arenes have been described. In contrast, only a few approaches that can handle unactivated, electronically neutral olefins have been reported and invariably require transition metal catalysts. Here we show how an efficient and highly enantioselective catalytic asymmetric intramolecular hydroarylation of aliphatic and aromatic olefins with indoles can be realized using strong and confined IDPi Brønsted acid catalysts. This unprecedented transformation is enabled by tertiary carbocation formation and establishes quaternary stereogenic centers in excellent enantioselectivity and with a broad substrate scope that includes an aliphatic iodide, an azide, and an alkyl boronate, which can be further elaborated into bioactive molecules.

A Fischer-Type Ruthenium Carbene Complex as a Metathesis Catalyst for the Synthesis of Enol Ethers.

The Grubbs G-I or G-II catalyst gives the ruthenium ethoxy carbene complex, which catalyzes ring-opening cross metathesis (ROCM) of a strained cyclic alkene to give a diene where one of the two alkene moieties in the product contains an ethoxy substituent. No polymeric products are detected. Hydrocarbons such as parent norbornene or substituted cyclopropenes can proceed with the reaction smoothly. Tertiary amines, N-alkylimides, esters, and aryl or alkyl bromides remain intact under the reaction conditions. In addition to vinyl ethers, vinylic esters can also be used. The time required to reach a 50% yield of the ROCM product t50 varies from 0.01 to 140 h depending on the strain and nucleophilicity of the double bond. Anchimeric participation of an electron-rich group would result in significant enhancement of the reactivity, and the t50 could be as short as several minutes. A similar substrate without such a neighboring group shows a much slower rate. An exo-norborne derivative reacts much faster than the corresponding endo-isomer. Alkenes with poor nucleophilicity are less favored for the ROCM process, so is less strained cyclooctene.

Capturing the Monomeric (L)CuH in NHC-Capped Cyclodextrin: Cavity-Controlled Chemoselective Hydrosilylation of α,ß-Unsaturated Ketones.

The encapsulation of copper inside a cyclodextrin capped with an N-heterocyclic carbene (ICyD) allowed both to catch the elusive monomeric (L)CuH and a cavity-controlled chemoselective copper-catalyzed hydrosilylation of α,ß-unsaturated ketones. Remarkably, (α-ICyD)CuCl promoted the 1,2-addition exclusively, while (ß-ICyD)CuCl produced the fully reduced product. The chemoselectivity is controlled by the size of the cavity and weak interactions between the substrate and internal C-H bonds of the cyclodextrin.

Cyclodextrin Cavity-Induced Mechanistic Switch in Copper-Catalyzed Hydroboration.

N-heterocyclic carbene-capped cyclodextrin (ICyD) ligands, α-ICyD and ß-ICyD derived from α- and ß-cyclodextrin, respectively give opposite regioselectivities in a copper-catalyzed hydroboration. The site-selectivity results from two different mechanisms: the conventional parallel one and a new orthogonal mechanism. The shape of the cavity was shown not only to induce a regioselectivity switch but also a mechanistic switch. The scope of interest of the encapsulation of a reactive center is therefore broadened by this study.

Application and Comparison of Machine Learning and Database-Based Methods in Taxonomic Classification of High-Throughput Sequencing Data.

The advent of high-throughput sequencing technologies has not only revolutionized the field of bioinformatics but has also heightened the demand for efficient taxonomic classification. Despite technological advancements, efficiently processing and analyzing the deluge of sequencing data for precise taxonomic classification remains a formidable challenge. Existing classification approaches primarily fall into two categories, database-based methods and machine learning methods, each presenting its own set of challenges and advantages. On this basis, the aim of our study was to conduct a comparative analysis between these two methods while also investigating the merits of integrating multiple database-based methods. Through an in-depth comparative study, we evaluated the performance of both methodological categories in taxonomic classification by utilizing simulated data sets. Our analysis revealed that database-based methods excel in classification accuracy when backed by a rich and comprehensive reference database. Conversely, while machine learning methods show superior performance in scenarios where reference sequences are sparse or lacking, they generally show inferior performance compared with database methods under most conditions. Moreover, our study confirms that integrating multiple database-based methods does, in fact, enhance classification accuracy. These findings shed new light on the taxonomic classification of high-throughput sequencing data and bear substantial implications for the future development of computational biology. For those interested in further exploring our methods, the source code of this study is publicly available on https://github.com/LoadStar822/Genome-Classifier-Performance-Evaluator. Additionally, a dedicated webpage showcasing our collected database, data sets, and various classification software can be found at http://lab.malab.cn/~tqz/project/taxonomic/.

MGACL: Prediction Drug-Protein Interaction Based on Meta-Graph Association-Aware Contrastive Learning.

The identification of drug-target interaction (DTI) is crucial for drug discovery. However, how to reduce the graph neural network's false positives due to its bias and negative transfer in the original bipartite graph remains to be clarified. Considering that the impact of heterogeneous auxiliary information on DTI varies depending on the drug and target, we established an adaptive enhanced personalized meta-knowledge transfer network named Meta Graph Association-Aware Contrastive Learning (MGACL), which can transfer personalized heterogeneous auxiliary information from different nodes and reduce data bias. Meanwhile, we propose a novel DTI association-aware contrastive learning strategy that aligns high-frequency drug representations with learned auxiliary graph representations to prevent negative transfer. Our study improves the DTI prediction performance by about 3%, evaluated by analyzing the area under the curve (AUC) and area under the precision-recall curve (AUPRC) compared with existing methods, which is more conducive to accurately identifying drug targets for the development of new drugs.

Rapid Access to Chiral Spiro[2.3] Lactams: Stereoselective Hydroborylation and Hydrosilylation and Remote Control of Axial Chirality by Copper-Catalyzed Desymmetrization of Spirocyclopropenes.

Chiral spirocyclopropyl ß-lactams are common motifs in bioactive compounds and pharmaceuticals. Here we disclose a diastereoselective and enantioselective hydroborylation and hydrosilylation of spirocyclopropenes, via a Cu-catalyzed desymmetrization strategy, for the rapid preparation of enantio-enriched spirocyclopropyl ß-lactams. The efficient desymmetrization strategy allows the remote control of axial chirality, offering the borylated and silylated products bearing central, spiro, and axial chirality. The combination of multichiral elements would provide a novel motif for biological evaluation in potential drug discovery.

Sequential olefination-dimerisation of benzylic dithioacetals by the nickel-catalysed reaction with methyl Grignard or zinc reagent.

Products of main group elements from cross-coupling reactions have been shown to serve as Lewis acids, mediating further reactions of organic coupling products. Thus, the nickel-catalysed olefination of benzylic dithioacetal with MeMgI in benzene in a sealed Schlenk tube at 130 °C generates magnesium mercaptide which regioselectively converts 2-arylpropene into a dimer in good yield. Aryl iodide reacts with 2-propenylmagnesium bromide in the presence of 1,2-ethanedithiol and NiCl2(PPh3)2 to yield the same dimer. Replacement of the Grignard reagent by an organozinc reagent gives the dimers in a better yield.

Serum biomarker panels for the diagnosis of gastric cancer.

Gastric cancer is a leading cause of mortality due to neoplastic disease. Although early detection of gastric cancers can decrease the mortality rate, it remains a diagnostic challenge because of the lack of effective biomarkers. In this study, fifteen gastric cancer patients and ten healthy subjects were recruited to assess novel serum biomarkers for gastric cancer using antibody microarray technology. ELISA was utilized to validate the antibody array results. As a result, compared to the controls, eleven cytokines were found to be significantly increased in gastric cancer, including interferon gamma receptor 1 (IFNGR1), neurogenic locus notch homolog protein 3 (Notch-3), tumor necrosis factor receptor superfamily member 19L (TNFRSF19L), growth hormone receptor (GHR), signaling lymphocytic activation molecule family 8 (SLAMF8), folate receptor beta (FR-beta), integrin alpha 5, galectin-8, erythropoietin-producing hepatocellular A1 (EphA1), epiregulin, and fibroblast growth factor 12 (FGF-12) with P < 0.05. ELISA validation supported the results of the antibody array. More importantly, most of these eleven cytokines, including IFNGR1, TNFRSF19L, GHR, SLAMF8, FR-beta, and integrin alpha 5 were discovered to be elevated in gastric cancer serum samples for the first time in this study, suggesting that these proteins may serve as novel biomarkers for the early diagnosis and prognosis determination of gastric cancer.