Siberian kale (Brassica napus subsp. pabularia, AACC, 2n = 38) is a distinct subspecies of B. napus, characterized by its deeply lobed leaves and primarily cultivated as a nutritious leafy vegetable. Here, we present a chromosome-level genome of Beta, a Siberian kale variety, integrating Illumina short reads, PacBio HiFi long reads, and Hi-C data. The final assembly size is 1,078.8 Mb, with a scaffold N50 of 57.5 Mb and a genome BUSCO completeness of 99.7%. 954.0 Mb (88.4%) of sequences were successfully anchored to 19 pseudo-chromosomes. The configuration of Beta genome chromosomes is consistent with the distribution of ten A subgenome and nine C subgenome chromosomes in rapeseed. In total, 98,882 protein-coding genes were predicted ab initio in the Beta genome, with an average gene length of 1,997 bp, and 90,415 (91.44%) genes were functionally annotated. Overall, the high-quality genome provides a valuable resource for bridging current knowledge gaps and offers key genetic insights into deeply lobed leaf formation and improvement of Brassica crops.

RNA structures are essential for understanding their biological functions and developing RNA-targeted therapeutics. However, accurate RNA structure prediction from sequence remains a crucial challenge. We introduce DRfold2, a deep learning framework that integrates a novel pre-trained RNA Composite Language Model (RCLM) with a denoising structure module for end-to-end RNA structure prediction. Based solely on single sequence, DRfold2 achieves superior performance in both global topology and secondary structure predictions over other state-of-the-art approaches across multiple benchmark tests from diverse species. Detailed analyses reveal that the improvements primarily stem from the RCLM's ability to capture co-evolutionary pattern and the effective denoising process, with a more than 100% increase in contact prediction precision compared to existing methods. Furthermore, DRfold2 demonstrates high complementarity with AlphaFold3, achieving statistically significant accuracy gains when integrated into our optimization framework. By uniquely combining composite language modeling, denoising-based end-to-end learning, and deep learning-guided post-optimization, DRfold2 establishes a distinct direction for advancing ab initio RNA structure prediction.

Phlebotomus chinensis is the primary vector of visceral leishmaniasis (VL) in China. However, the lack of a high-quality genome assembly for this species has limited research on its biology, vector-pathogen interactions, and evolutionary adaptations. To address this critical gap, the first chromosome-level genome assembly of Ph. chinensis was constructed. Nanopore long-read sequencing served as the primary method, complemented by Illumina short-read sequencing for base-level error correction and Hi-C mapping for chromosomal anchoring and chromosome-level scaffolding. Genome annotation integrated transcriptome data from adult, larvae and pupae, homologous protein predictions from closely related sand fly species, and ab initio gene prediction. Comparative genomic analyses were further performed to explore evolutionary relationships and genomic differences between Ph. chinensis, Ph. papatasi, and Lutzomyia longipalpis. A total of 127.05Gb of Nanopore data, 10.57Gb of Illumina clean data, 52.95Gb of Hi-C clean data, and 14.95Gb of RNA-seq data were obtained. The final assembled genome size was 195.21Mb with a scaffold N50 of 49.30Mb, and 97.24% of the sequences were successfully anchored to 4 chromosomes. Annotation identified 10,909 protein-coding genes (91.48% of which were functionally annotatable), along with 73 rRNAs, 92 small RNAs, 82 regulatory RNAs, 374 tRNAs, 11,870 simple sequence repeats, 6053 tandem repeats, and 478,622 transposable elements. Phylogenetic analysis revealed that Ph. chinensis is phylogenetically closest to Ph. papatasi, with an estimated divergence time of approximately 27.1 million years ago. Gene family evolution was dominated by contraction, with 229 expanded and 575 contracted gene families identified in the Ph. chinensis branch. Additionally, 209 positively selected genes were detected, which are crucial for immune response regulation and metabolic processes related to its vectorial capacity. Furthermore, 95 P450 genes were identified, classified into four subfamilies: CYP2, CYP3, mitochondrial CYP (mito), and CYP4. A high-quality chromosome-level genome assembly of Ph. chinensis is reported here for the first time. This assembly serves as a critical genomic resource to advance research into the vector biology, insecticide resistance mechanisms, and evolutionary history, and lays a solid foundation for the development of precision VL control strategies in China.

The genus Silene is an important model system for fields as diverse as sex chromosome evolution, speciation, and disease ecology. However, genomic resources remain scarce in the genus. Here, we present a near chromosome-scale genome assembly and high-density linkage map for S. uniflora, a hermaphroditic/gynodioecious species which is an important model for rapid adaptation to anthropogenic disturbance and the role of phenotypic plasticity in adaptive evolution. Using a combination of long-read and Hi-C sequencing technologies, we generated a 1,268 Mb genome assembly with a scaffold N50 of 40.72 Mb and 682 Mb assembled into 12 chromosomes. We annotated the genome using evidence from transcriptome and protein mapping in combination with ab initio gene prediction, resulting in 41,603 protein-coding genes and a BUSCO completeness score of 91%. We also present a linkage map which we used to validate the genome assembly and estimate local recombination rate across the genome. Comparison to the only 2 other Silene species with chromosome-scale genome assemblies reveals widespread genome rearrangements in the genus, suggesting Silene may be a promising study system for the role of genome rearrangement in evolution, particularly in the evolution of sex chromosomes and adaptation.

Ab initio prediction of large thermoelectric effect in distorted Heusler alloy Ti-Fe-Sb compound

BackgroundComputational prediction of protein-protein interactions (PPIs) is crucial for understanding cell biology and drug development, offering an alternative to costly experimental methods. The original GenPPi software advanced ab initio PPI network prediction from bacterial genomes but was limited by its reliance on high sequence similarity. This work introduces GenPPi 1.5 to enhance these predictive capabilities.ResultsGenPPi 1.5 incorporates a Random Forest (RF) algorithm, trained on 60 biophysical features from amino acid propensity indices, to classify protein similarity even in low sequence identity scenarios (targeting >65% identity). To manage computational complexity from the increased interactions generated by the RF model, especially in extensive conserved phylogenetic profiles, we developed and integrated the Reduced Interaction Sampling (RIS) algorithm. RIS stochastically samples interactions within these profiles, optimizing performance for complete genome analysis. Extensive simulations across various configurations validated the methodology. RF integration significantly broadened GenPPi’s predictive power; application to Buchnera aphidicola showed up to 62% overlap with STRING database interactions. Analysis of RIS demonstrated that while introducing some randomness, critical node identification remains robust, particularly for Top_N values ge 100, indicating minimal compromise to network integrity.ConclusionThe combination of Machine Learning (RF) and the RIS algorithm in GenPPi 1.5 represents a significant advancement. It overcomes the high-similarity dependency of the previous version while efficiently handling complex genomes. GenPPi 1.5 provides a robust and scalable alignment-free PPI prediction solution, enabling users to train custom models tailored to specific genomic contexts. GenPPi is freely available on our website https://genppi.facom.ufu.br/, its source code is hosted on GitHub https://github.com/santosardr/genppi, and it can be easily installed via the Python Package Index using the command pip install genppi-py.

Pulsatilla chinensis (Bunge) Regel, a medicinal species in the genus Pulsatilla (Ranunculaceae), exhibits significant anti-inflammatory activity due to its abundant triterpenoid saponins, enhancing its medicinal value. All 48 known Pulsatilla species lack whole genome sequencing data, hindering research on the utilization of genetic resources in the genus and investigations into their biosynthetic mechanisms. Here, we present the first chromosome-level genome assembly of P. chinensis. The assembly spans 6,588.76 Mb with a contig N50 of 4.28 Mb. Using PacBio sequencing and Hi-C scaffolding, 99.32% of sequences were anchored onto eight chromosomes. We annotated 41,786 protein-coding genes through ab initio prediction, homology alignment, and transcriptome evidence, with 98.77% functionally annotated via NR, GO, and KEGG databases. This assembly not only facilitates studies on the molecular mechanisms and biosynthesis of bioactive compounds in P. chinensis, but also supports the further enrichment and utilization of germplasm resources across the Ranunculaceae.

The Black Redstart (Phoenicurus ochruros) is one of the most widely distributed species, occupying diverse habitats and exhibiting altitudinal migration (0–3700 m), making it suitable model for studying altitudinal migration and high-altitude adaptation. In this study, we present the first reference genome of Phoenicurus ochruros, generated using PacBio HiFi long-read sequencing. The nuclear genome is 1.37 Gb in length, assembled into 296 contigs with a contig N50 of 29.9 Mb. Multiple complementary genome validation approaches - including BUSCO analysis, mapping of raw PacBio HiFi reads, and comparative genomics analysis - confirmed the high contiguity and gene-space completeness of the genome. The Black Redstart genome contains one of the highest proportions of transposable elements among passerines (30.58%), second only to Bell’s Sparrow (Artemisiospiza belli), 31.2%. We further integrated RNA-seq data, protein homology evidence, and ab initio gene prediction to build high-quality genome annotation of 18,609 genes in the P. ochruros genome. This genomic resource provides invaluable resources for evolutionary genomics studies of passerines and genetic studies of high-altitude adaptation.

The accurate identification of genes is vital for understanding biological function, yet this remains challenging across many newly sequenced or less-studied species. Here we present Helixer, an artificial intelligence-based tool for ab initio gene prediction that delivers highly accurate gene models across fungal, plant, vertebrate and invertebrate genomes. Unlike traditional methods, Helixer operates without requiring additional experimental data such as RNA sequencing, making it broadly applicable to diverse species. We show that Helixer's pretrained models achieve accuracy on par with or exceeding current tools, producing gene annotations that closely match expert-curated references across multiple evaluation metrics. Its design enables immediate use on genomes without retraining, providing an efficient, accessible solution for genome annotation in both research and applied settings. The tool is available as an open-source software for local installation via GitHub. An online web interface is also available as well as through the Galaxy ToolShed.

Abstract Prior to 1970, no successful ab initio electronic structure predictions were made that challenged experiment for polyatomic molecules. For diatomics, the work of Ernest Davidson stands out, in 1960 explaining that two spectroscopically known but misunderstood electronic states of H2 were in fact part of the very same potential energy curve. Another diatomic example was the startling 1968 overturn by Kolos and Wolniewicz of the “known” experimental dissociation energy of H2. Moving to polyatomics, the research in 1970 concerning the structure of triplet methylene captured the imagination of many chemists, and the 1982 success of theory for the singlet-triplet separation of methylene confirmed for many the great usefulness of ab initio theory. In the second half of this paper, the utility of methods based on single Slater determinant reference wavefunctions for both singlet and triplet methylene is demonstrated. In particular, we examine how the optimized geometries and harmonic vibrational frequencies of triplet and singlet CH2 evolve with systematic improvements in basis set size and valence electron correlation treatment. To accurately pinpoint the geometric structures and singlet-triplet splitting of methylene, we perform comprehensive focal point analyses (FPA) that push the level of theory to new heights, leveraging core-valence basis sets up to sextuple-zeta quality and all-electron coupled-cluster methods through CCSDTQ(P) appended with relativistic (MVD1) and non-Born-Oppenheimer (DBOC) corrections. Our final FPA prediction for the singlet-triplet splitting is 9.01 kcal mol−1, in complete agreement with the best empirical estimate of 9.00 ± 0.01 kcal mol−1. The corresponding optimized FPA geometries are [r e(H–C), θ e(H–C–H)] = (1.1063 Å, 102.35°) for ã1A1 CH2 and (1.0756 Å, 133.94°) for X̃3B1 CH2, in close agreement with the best existing experimental and theoretical structures but with a little finer precision. Our outcomes not only affirm the validity of the contemporary single-reference coupled-cluster theory pushed to high order but also provide definitive resolutions for a paradigmatic molecule that has long been emblematic of the challenges and triumphs that have shaped a century of quantum chemistry.

Through ab initio evolutionary crystal structure prediction, we have discovered a novel monoclinic phase of manganese hexaboride (MnB6) with the space group P2/m. This new polymorph is energetically favorable over the previously known hexagonal Pm2 structure at pressures below 58 GPa. Although metastable with respect to decomposition into MnB4 and B, the P2/m phase is demonstrated to be dynamically and mechanically stable at ambient pressure, as confirmed by the absence of imaginary phonon modes and the satisfaction of the Born stability criteria. Calculations of the elastic properties reveal that MnB6-P2/m is a hard, brittle material with a high Vickers hardness of 33 GPa, which surpasses that of common industrial ceramics like tungsten carbide and silicon carbide. Its bulk modulus (273 GPa), shear modulus (220 GPa), and Young modulus (519 GPa) indicate superior resistance to elastic deformation. The compound exhibits significant elastic anisotropy, as visualized by the directional dependence of its moduli and hardness. These properties establish the new MnB6-P2/m polymorph as a promising candidate for experimental synthesis and potential application as a hard material.

Large language models (LLMs) have demonstrated transformative potential for materials discovery in condensed matter systems, but their full utility requires both broader application scenarios and integration with ab initio crystal structure prediction (CSP), density functional theory (DFT) methods and domain knowledge to benefit future inverse material design. Here, we develop an integrated computational framework combining language model-guided materials screening with genetic algorithm (GA) and graph neural network (GNN)-based CSP methods to predict new photovoltaic material. This LLM + CSP + DFT approach successfully identifies a previously overlooked oxide material with unexpected photovoltaic potential. Through transformer-based vector similarity analysis coupled with unsupervised clustering and first-principles calculations, we demonstrate that this material exhibits a direct band gap and high theoretical efficiencies that are suitable for photovoltaic application. Our work highlights a hierarchical computational inverse design pipeline that can efficiently navigate the material space to identify nonintuitive functional materials with tailored optoelectronic properties.

Understanding the three-dimensional (3D) structure and stability of DNA is essential for elucidating its biological functions and advancing structure-based drug design. Here, we present an improved coarse-grained (CG) model for ab initio prediction of DNA folding, integrating a refined electrostatic potential, replica-exchange Monte Carlo simulations, and weighted histogram analysis. The model accurately predicts the 3D structures of DNA with multi-way junctions (e.g., achieving a mean RMSD of ~8.8 Å for top-ranked structures across four DNAs with three- or four-way junctions) from sequence, outperforming existing fragment-assembly and AI-based approaches. The model also reproduces the thermal stability of junctions across diverse sequences and lengths, with predicted melting temperatures deviating by less than 5 °C from experimental values, under both monovalent (Na⁺) and divalent (Mg2⁺) ionic conditions. Furthermore, analysis of the thermal unfolding pathways reveals that the overall stability of multi-way junctions is primarily determined by the relative free energies of key intermediate states. These results provide a robust framework for predicting complex DNA architectures and offer mechanistic insights into DNA folding and function.

Both computational and experimental methods are used to identify and describe genes within DNA sequences. Experimental methods such as cDNA cloning, RNA-Seq, and CRISPR/Cas9 evaluate gene expression and function directly, whereas computational approaches such as ab initio prediction, homology-based methods, and machine learning predict gene locations using DNA sequence features and comparisons. By measuring sequence similarity or divergence, distance measures are essential to these procedures and support gene grouping, phylogenetic analysis, comparative genomics, and sequence alignment. This paper aims to explore some distance measures (DMs) such as Hamming distance measure, Zhang distance measure, Normalized Hamming distance measure, and Zeeshan distance measure under the environment of complex fuzzy sets (CFSs). We studied some basic properties of complex fuzzy distance measures (CFDMs). Moreover, we employed CFDMs to extract pertinent features from gene that provides uncertainty and ambiguous data. We proposed an innovative digital signal processing method for gene identification using CFDMs. We developed an algorithm utilizing CFDMs to identify a healthy gene out of several affected genes. To demonstrate the effectiveness and advancements of the proposed work, a comparison with various current methodologies was also conducted.

RNA three-dimensional structures are critical for their roles in gene expression and regulation. However, predicting RNA structures remains challenging due to complex tertiary interactions, ion dependency, molecular flexibility, and the limited availability of known 3D structures. To address these challenges, our team (GuangzhouRNA-human) employed a hybrid strategy combining computational tools with expert refinement in the CASP16 RNA structure prediction challenge, achieving second place based on the sum Z-score. Our approach integrates multiple techniques through modular workflows, including template-based modeling for targets with homologous templates and abinitio prediction using deep learning tools (e.g., AlphaFold3 and DeepFoldRNA) for novel sequences. Additionally, we incorporate experimental constraints and iterative optimization to enhance prediction accuracy. For targets shorter than 200 nucleotides (nt) with homologous templates, our method demonstrated exceptional performance, achieving 75% of predictions with root-mean-square deviations (RMSD) below 5 Å, and all predictions falling under 10 Å. Furthermore, our strategy demonstrated promising results for targets without homologous templates, such as R1209, through comprehensive literature reviews and structural selection. Despite these advances, RNA structure prediction continues to face challenges, particularly in predicting complex topologies like pseudoknots and coaxial stacking. Future improvements in integrating computational tools with expert knowledge are essential to enhance the accuracy and applicability of RNA tertiary structure prediction.

Rhabdosargus sarba, the goldlined seabream, is a euryhaline marine fish of great aquaculture potential. Genome sequencing and assembly of R. sarba was carried utilizing a multi-platform sequencing strategy that included long-read sequencing (PacBio HiFi), short-read sequencing (Illumina), and chromatin interaction mapping (Hi-C). The final genome assembly size after scaffolding was 764.59 Mb in 31 scaffolds with an N50 length of 33.98 Mb. Repeat profiling of primary assembly showed that 28.71% of the genome comprises of repeat elements. Gene prediction utilising the evidence from ab initio prediction and transcriptome data revealed 26,913 protein encoding genes and functional annotation and pathway analysis showed their participation in 332 pathways. This genome is an excellent resource for future research on genetic improvement and molecular breeding programmes for R. sarba.

Chanodichthys mongolicus, a carnivorous fish belonging to the Cyprinidae family (Erythroculter), is widely distributed in reservoirs and lakes across China. However, the lack of research on whole genome assembly has impeded advancements in genetic studies for this species. In this study, we employed PacBio sequencing and Hi-C technology to assemble high-quality genomes for both female and male Chanodichthys mongolicus at the chromosome level. The assembly results revealed a male genome size of 1.10 GB with a scaffold N50 of 43 Mb, while the female genome was 1.09 GB with a scaffold N50 of 42 Mb. Both assemblies consist of 24 chromosomes and demonstrate an average genome integrity of 98.5%, as assessed by BUSCO. We annotated the male genome using a combination of ab initio predictions, protein homology comparisons, and RNAseq data, resulting in the identification of 33,581 genes, of which 88.15% were predicted to have functional roles. These findings provide a valuable resource for future research on the genetic breeding and genome evolution of Chanodichthys mongolicus.

Abstract The isotope effect on zonal flows (ZFs) and turbulence remains a key issue that is not completely solved in fusion plasmas. This Letter presents the first experimental results of the ab initio prediction of causal relation between geodesic acoustic mode (GAM) and ambient turbulence within different isotopes in the edge of HL-2A tokamak, where transfer entropy method based on information-theoretical approach is utilized as a quantified indicator of causality. Analysis shows that GAM is more pronounced in deuterium plasmas than in hydrogen, leading to a lower heat transport as well as more peaked profiles in the former situation. The causal impact of GAM on conductive heat flux component is stronger than the convective component, which is resulted from a larger causal influence of zonal flow on temperature fluctuation. While a stronger GAM in deuterium plasmas has larger influence on all flux components, the relative change in temperature fluctuation and coefficient is more obvious when the ion mass varies. These findings not only offer an in-depth understanding of the real causality between zonal flow and turbulence in the present isotope experiments, but also provide useful ways for the physical understandings of transport and zonal flow dynamics in future deuterium-tritium fusion plasmas.

Theoretical ab initio predictions of L12-Al3Zr structure stabilization by vacancy incorporation on the Zr sublattice

Accurate prediction of protein-peptide complex structures plays a critical role in structure-based drug design, including antibody design. Most peptide-docking benchmark studies were conducted using crystal structures of protein-peptide complexes; as such, the performance of the current peptide docking tools in the practical setting is unknown. Here, the practical setting implies there are no crystal or other experimental structures for the complex, nor for the receptor and peptide. In this work, we have developed a practical docking protocol that incorporated two famous machine learning models, AlphaFold 2 for structural prediction and ANI-2x for abinitio potential prediction, to achieve a high success rate in modeling protein-peptide complex structures. The docking protocol consists of three major stages. In the first stage, the 3D structure of the receptor is predicted by AlphaFold 2 using the monomer mode, and that of the peptide is predicted by AlphaFold 2 using the multimer mode. We found that it is essential to include the receptor information to generate a high-quality 3D structure of the peptide. In the second stage, rigid protein-peptide docking is performed using ZDOCK software. In the last stage, the top 10 docking poses are relaxed and refined by ANI-2x in conjunction with our in-house geometry optimization algorithm-conjugate gradient with backtracking line search (CG-BS). CG-BS was developed by us to more efficiently perform geometry optimization, which takes the potential and force directly from ANI-2x machine learning models. The docking protocol achieved a very encouraging performance for a set of 62 very challenging protein-peptide systems which had an overall success rate of 34% if only the top 1 docking poses were considered. This success rate increased to 45% if the top 3 docking poses were considered. It is emphasized that this encouraging protein-peptide docking performance was achieved without using any crystal or experimental structures.