Miracle-dz/UniFAR-FTUs huggingface dataset:
Are Deep Learning Structural Models Sufficiently Accurate for Free Energy Calculations? Application of FEP+ to AlphaFold2 Predicted Structures

The availability of AlphaFold2 has led to great excitement in the scientific community - particularly among drug hunters - due to the ability of the algorithm to predict protein structures with high accuracy. However, beyond globally accurate protein structure prediction, it remains to be determined whether ligand binding sites are predicted with sufficient accuracy in these structures to be useful in supporting computationally driven drug discovery programs. We explored this question by performing free energy perturbation (FEP) calculations on a set of well-studied protein-ligand complexes, where AlphaFold2 predictions were performed by removing all templates with >30% identity to the target protein from the training set. We observed that in most cases, the ΔΔG values for ligand transformations calculated with FEP, using these prospective AlphaFold2 structures, were comparable in accuracy to the corresponding calculations previously carried out using X-ray structures. We conclude that under the right circumstances, AlphaFold2 modeled structures are accurate enough to be used by physics-based methods such as FEP, in typical lead optimization stages of a drug discovery program.
The High-quality genome assembly of Lactarius hatsudake

Lactarius hatsudake is a species of Lactarius commonly found in pine forests, is edible with a delicious and nutritious fruiting body and exhibits medicinal properties. It is an ideal natural multi-functional food with bioactive components including fungal polysaccharides, crude fiber, unsaturated fatty acids, nucleic acid derivatives, various amino acids, and vitamins. However, biological and genomic analyses of this mycorrhizal mushroom are sparse, thereby hindering large-scale cultivation. Previously, we isolated and screened L. hatsudake JH5 strains and have applied our garnered knowledge to the large-scale cultivation of mycorrhizal seedlings. In this study, we produced a high-quality genome assembly of L. hatsudake JH5 by combining Illumina paired-end (PE) and PacBio single molecule real-time (SMRT) sequencing, resulting in PacBio SMRT reads of 7.67 Gb and Illumina Pair-End reads of 1560 Mb. Based on the distribution of k-mer frequencies, the genome size of this strain was estimated to be 63.84 Mb (1.14% heterozygosity). Based on de novo genome assembly, the final genome size was determined to be 76.7 Mb, with scaffold N50 of 223.2 kb and N90 of 54.5 kb, and a GC content of 54.38%. BUSCO assessment showed that genome completeness was 89.0%. The N50 length of the JH5 genome was 43.6% longer than that of the previously published L. hatsudake MG20 genome. This high-quality L. hatsudake genome assembly will facilitate research on the functional genome, molecular breeding, yield enhancement and sustainability of L. hatsudake cultivation.
Cryo-electron tomography and deep learning denoising reveal native chromatin landscapes of interphase nuclei

The folding of nucleosome chains influences DNA availability for functional interactions necessary to the regulation of transcription, DNA replication and repair. Despite models based on in vitro studies, the nucleosome chain geometry within the crowded cell nucleus remains elusive. Using cryo-electron tomography and deep learning-based denoising, we directly observed the path of nucleosomal and linker DNA in situ in unstained flash-frozen Drosophila embryos. We quantified linker length and curvature characterizing a disordered zig-zag chromatin folding motif, with a low degree of linker bending. Additionally, nucleosome conformational variability with non-canonical structures and sub-nucleosomal particles were seen as individual objects, without structure averaging, highlighting the high structural heterogeneity of native chromatin. One-Sentence Summary Cryo-ET reveals local zig-zag motifs in interphase chromatin, a range of nucleosome conformations, and sub-nucleosomal particles.
Intrinsic disorder in CENP-ACse4 tail and its chaperone facilitates synergistic association for kinetochore stabilization

The kinetochore is a complex multiprotein network that assembles at a specialized DNA locus called the centromere to ensure faithful chromosome segregation. The centromere is epigenetically marked by a histone H3 variant – the CenH3. The budding yeast CenH3, called Cse4, consists of an unusually long and disordered N-terminal tail that has a role in kinetochore assembly. Its disordered chaperone, Scm3 is involved in its centromeric deposition as well as in the maintenance of a segregation-competent kinetochore. The dynamics of the Cse4 N-tail and chaperone interaction have not been studied, leaving a gap in our understanding of their roles at the centromere. Previously, we had shown that Scm3 is an intrinsically disordered protein. Here, using NMR and a variety of biophysical and bioinformatics tools, we show that Cse4 N-tail is also disordered, the two proteins interact with each other at multiple sites, and this interaction reduces the disorder in Scm3; the chain opens up relative to the native state ensemble and the conformational exchange is reduced. Interestingly, this interaction between the two intrinsically disordered protein is fairly specific as seen by positive and negative controls, and is majorly driven by electrostatics as both the proteins have multiple acidic and basic regions. The complex retains a fair amount of disorder, which facilitates a synergistic association with the essential inner kinetochore Ctf19-Mcm21-Okp1-Ame1 complex; a model has been suggested to this effect. Given the abundance of intrinsic disorder in the kinetochore proteins, this type of interaction and adaptation may be prevalent in other proteins as well for mediating kinetochore assembly. Thus, the present study, on one hand, provides significant structural and mechanistic insights into the complex and dynamic process of kinetochore assembly, and on the other hand, illustrates a mechanism that intrinsically disordered proteins would adapt to mediate the formation of complex multiprotein networks, in general. Graphical Abstract
L-type calcium channels link oxidative stress to calcium signaling pathway and membrane excitability: Insights from computational modeling of dopaminergic neurons

Dopamine neurons, which are critical in movement, cognition, and reward learning, are vulnerable to oxidative stress during aging, drugs of abuse, and viral infection and can lead to neurodegeneration. Previous work used computational modeling to study dopamine neuron function based on experimental findings from rodent brain slices containing dopamine neurons. Here we show for the first time the feasibility and utility of applying such computational models of DA neurons to the analysis of experimental findings from in-vitro cultured cells. We used DCFH-DA (and DHE) and time-lapse, Fura-2 ratiometric imaging to measure changes in ROS levels and changes in intracellular calcium (Ca2+) levels, respectively, in two dopaminergic cell models: differentiated SH-SY5Y and differentiated human neural progenitor cells. We investigated how peroxide-dependent changes in the behavior of the L-type channel might alter the excitability of the dopaminergic cell. We found that L-type channels mediated clusters of calcium spikes (or oscillations) and that our model suggested that such increased excitability could be explained by changes in the voltage-dependence of activation of the L-type channels in response to exogenous peroxide. Our findings suggested that L-type channels link oxidative stress responses to modulation of excitability. We conclude that the Ca2+ channel blocker nicardipine may help disrupt this link by reducing oxidative stress and preventing channel activation at more hyperpolarized potentials, thus reducing plasma membrane excitability.
Piezo1 mechanosensing regulates integrin-dependent chemotactic 1 migration in human T cells 2

20 T cells are crucial for efficient antigen-specific immune responses and thus their migration within the 21 body, to inflamed tissues from circulating blood or to secondary lymphoid organs, play a very critical 22 role. T cell extravasation in inflamed tissues depends on chemotactic cues and interaction between 23 endothelial adhesion molecules and cellular integrins. A migrating T cell is expected to sense diverse 24 external and membrane-intrinsic mechano-physical cues, but molecular mechanisms of such 25 mechanosensing in cell migration are not established. We explored if the professional 26 mechanosensor Piezo1 play any role during integrin-dependent chemotaxis of human T cells. We 27 found that deficiency of Piezo1 in human T cells interfered with integrin-dependent cellular motility 28 on ICAM-1-coated surface. Piezo1 recruitment at the leading edge of moving T cells is dependent on 29 and follows focal adhesion formation at the leading edge and local increase in membrane tension on 30 chemokine receptor activation. Piezo1 recruitment and activation, followed by calcium influx and 31 calpain activation, in turn are crucial for the integrin LFA-1 recruitment at the leading edge of the 32 chemotactic human T cells. Thus we find that Piezo1 activation in response to local mechanical cues 33 constitutes a membrane-intrinsic component of the ‘outside-in’ signaling in human T cells, migrating 34 in response to chemokines, that mediates integrin recruitment to the leading edge. 35
A local sequence signature defines a subset of heterochromatin-associated CpGs with minimal loss of methylation in healthy tissues but extensive loss in cancer

Global loss of DNA methylation in mammalian genomes occurs during aging and cancer, primarily in heterochromatin-associated Partially Methylated Domains (PMDs). It has previously been shown that local sequence context (100bp) has a strong influence on the rate of demethylation of individual CpG dinucleotides within PMDs. Here, we train a deep learning model to capture this sequence dependence, finding that methylation loss in healthy tissues and cancer can be predicted with high accuracy based on the 150bp surrounding a CpG. We use a published whole-genome map of the re-methylation rate of newly synthesized DNA during mitosis to show that CpGs with a “slow-loss” sequence context are efficiently re-methylated, while CpGs with a “fast-loss” sequence context are inefficiently re-methylated. Intriguingly, we find that the 10% most slow-loss CpGs lose almost no DNA methylation in healthy cell types, but lose significant DNA methylation in many cancer types. This finding suggests that loss of DNA methylation at slow-loss CpGs could underlie some cancer-specific transcriptional deregulation that has been linked to DNA hypomethylation, including the derepression of cancer antigens and transposable elements.
Examining Reproducibility of EEG Schizophrenia Biomarkers Across Explainable Machine Learning Models

Schizophrenia (SZ) is a neuropsychiatric disorder that adversely effects millions of individuals globally. Current diagnostic efforts are symptom based and hampered due to the variability in symptom presentation across individuals and overlap of symptoms with other neuropsychiatric disorders. This spawns the need for (1) biomarkers to aid with empirical SZ diagnosis and (2) the development of automated diagnostic approaches that will eventually serve in a clinical decision support role. In this study, we train random forest (RF) and support vector machine (SVM) models to differentiate between individuals with schizophrenia and healthy controls using spectral features extracted from resting state EEG data. We then perform two explainability analyses to gain insight into key frequency bands and channels. In our explainability analyses, we examine the reproducibility of SZ biomarkers across models with the goal of identifying those that have potential clinical implications. Our model performance results are well above chance level indicating the broader utility of spectral information for SZ diagnosis. Additionally, we find that the RF prioritizes the upper γ-band and is robust to loss of information from individual electrodes, while the SVM prioritizes the α and θ-bands and P4 and T8 electrodes. It is our hope that our findings will inform future efforts towards the empirical diagnosis of SZ and towards the development of clinical decision support systems for SZ diagnosis.
Elucidating the unknown transcriptional responses and PHR1 mediated biotic and abiotic stress tolerance during phosphorus-limitation

Phosphorus (P) limitation in the majority of world soils is a major constraint for plant growth and crop productivity. RNA sequencing was used to discover novel P-responsive gene transcripts (PRGT) in leaves and roots of Arabidopsis. Hisat StringTie and Cufflinks TopHat transcript assembler were used to analyze reads and identify 1,074 PRGTs with a >5-fold altered abundance during P-limitation. Interestingly, 60% of these transcripts were not previously reported. Among the novel PRGT, 106 were from unannotated genes, and some were among the most P-responsive, including At2g36727 which encodes a novel microRNA. Annotated novel PRGTs encode for transcription factors, microRNAs, small signaling peptides, long non-coding RNAs, defense-related proteins, and transporters, along with proteins involved in many biological processes. We identified several genes that undergo alternative splicing during P-limitation, including a novel miR399 resistant splice variant of PHOSPHATE2 (PHO2.2). Several novel P-responsive genes were regulated by PHOSPHATE STARVATION RESPONSE1 (PHR1), PHR1-LIKE 1 (PHL1) and PHO2. We discovered that P-limited plants show increased resistance to pathogens and drought stress mediated by PHR1-PHL1. Identification of novel P-responsive transcripts and the discovery of the influence of P-limitation on biotic and abiotic stress adds a significant component to our understanding of plant P-signaling. Highlight Phosphorus limitation elicits the expression of several novel genes including many previously unannotated genes, noncoding RNAs, small peptides and alternatively spliced RNAs, and leads to enhanced disease and drought tolerance.
FSTest: an efficient tool for cross-population fixation index estimation on variant call format files

Fixation index ( F _st) statistics provide critical insights into evolutionary processes affecting the structure of genetic variation within and among populations. F _st statistics have been widely applied in population and evolutionary genetics to identify genomic regions targeted by selection pressures. The FSTest 1.3 software was developed to estimate four F _st statistics of Hudson, Weir and Cockerham, Nei, and Wright using high-throughput genotyping or sequencing data. Here, we introduced FSTest 1.3 and compared its performance with two widely used software VCFtools 0.1. 16 and PLINK 2.0. Chromosome 1 of 1000 Genomes Phase III variant data belonging to South Asian ( n = 211) and African ( n = 274) populations were included as an example case in this study. Different F _st estimates were calculated for each single-nucleotide polymorphism (SNP) in a pairwise comparison of South Asian against African populations, and the results of FSTest 1.3 were confirmed by VCFtools 0.1. 16 and PLINK 2.0. Two different sliding window approaches, one based on a fixed number of SNPs and another based on a fixed number of base pair (bp) were conducted using FSTest 1.3 and VCFtools 0.1. 16. Our results showed that regions with low coverage genotypic data could lead to an overestimation of F _st in sliding window analysis using a fixed number of bp. FSTest 1.3 could mitigate this challenge by estimating the average of consecutive SNPs along the chromosome. FSTest 1.3 allows direct analysis of VCF files with a small amount of code and can calculate F _st estimates on a desktop computer for more than a million SNPs in a few minutes. FSTest 1.3 is freely available at https://github.com/similab/FSTest .
PHFinder: assisted detection of point heteroplasmy in Sanger sequencing chromatograms

Heteroplasmy is the presence of two or more organellar genomes (mitochondrial or plastid DNA) in an organism, tissue, cell or organelle. Heteroplasmy can be detected by visual inspection of Sanger sequencing chromatograms, where it appears as multiple peaks of fluorescence at a single nucleotide position. Visual inspection of chromatograms is both consuming and highly subjective, as heteroplasmy is difficult to differentiate from background noise. Few software solutions are available to automate the detection of point heteroplasmies, and those that are available are typically proprietary, lack customization or are unsuitable for automated heteroplasmy assessment in large datasets. Here, we present PHFinder, a Python-based, open source tool to assist in the detection of point heteroplasmies in large numbers of Sanger chromatograms. PHFinder automatically identifies point heteroplasmies directly from the chromatogram trace data. The program was tested with Sanger sequencing data from 100 humpback whale (Megaptera novaeangliae) tissue samples with known heteroplasmies. PHFinder detected most (90%) of the known heteroplasmies thereby greatly reducing the amount of visual inspection required. PHFinder is flexible, enabling explicit specification of key parameters to infer double peaks (i.e., heteroplasmies).
Effectors Enabling Adaptation to Mitochondrial Complex I Loss in Hürthle Cell Carcinoma

Oncocytic (Hürthle cell) carcinoma of the thyroid (HCC) is genetically characterized by complex I mitochondrial DNA mutations and widespread chromosomal losses. Here, we utilize RNA-seq and metabolomics to identify candidate molecular effectors activated by these genetic drivers. We find glutathione biosynthesis, amino acid metabolism, mitochondrial unfolded protein response, and lipid peroxide scavenging, a safeguard against ferroptosis, to be increased in HCC. A CRISPR-Cas9 knockout screen in a new HCC model reveals which pathways are key for fitness, and highlights loss of GPX4, a defense against ferroptosis, as a strong liability. Rescuing complex I redox activity with the yeast NADH dehydrogenase (NDI1) in HCC cells diminishes ferroptosis sensitivity, while inhibiting complex I in normal thyroid cells augments ferroptosis induction. Our work demonstrates unmitigated lipid peroxide stress to be an HCC vulnerability that is mechanistically coupled to the genetic loss of mitochondrial complex I activity. Significance Oncocytic (Hürthle cell) carcinoma of the thyroid (HCC) is a unique tumor with a remarkable accumulation of mitochondria. HCC harbors unique genetic alterations, including mitochondrial DNA (mtDNA) mutations in complex I genes and widespread loss-of-heterozygosity in the nuclear DNA. With less favorable clinical outcomes, new therapies for HCC are needed, especially since these tumors show intrinsic resistance to radioactive iodine, which is one of the main treatments for metastatic well-differentiated thyroid cancer. An absence of authentic HCC cell lines and animal models has hindered the mechanistic understanding of this disease and slowed therapeutic progress. In this study, we describe the transcriptomic and metabolomic landscape of HCC and present new HCC models that recapitulate key mtDNA and nuclear DNA alterations. A targeted CRISPR-Cas9 knockout screen in an HCC cell line highlights the molecular programs nominated by our -omics profiling that are required for cell fitness. This screen suggests that lipid peroxide scavenging, a defense system against an iron-dependent form of cell death known as ferroptosis, is a vulnerability in HCC that is coupled to complex I loss, and that targeting this pathway may help patients with HCC.
Exogenous Sequences in Tumors and Immune Cells (Exotic): A Tool for Estimating the Microbe Abundances in Tumor RNA-seq Data

The microbiome affects cancer, from carcinogenesis to response to treatments. New evidence suggests that microbes are also present in many tumors, though the scope of how they affect tumor biology and clinical outcomes is unclear. A broad survey of tumor microbiome samples across several independent datasets is needed to identify robust correlations for follow-up testing. We created a tool to carefully identify the tumor microbiome within RNAseq datasets and then applied it to samples collected through the Oncology Research Information Exchange Network (ORIEN) and The Cancer Genome Atlas (TCGA). We showed how the processing removes contaminants and batch effects to yield microbe abundances consistent with non-high-throughput sequencing-based approaches. We sought to establish clinical relevance by correlating the microbe abundances with various clinical and tumor measurements, such as age and tumor hypoxia. This process leveraged the two datasets and raised up only the concordant (significant and in the same direction) associations. We identify associations with survival and clinical variables that are highly cancer-specific and relatively few associations with immune composition. Finally, we explore potential mechanisms by which microbes and tumors may interact using a network approach. Alistipes, a common gut commensal, showed the highest network degree centrality and was associated with genes related to metabolism and inflammation. The exotic tool can support the discovery of microbes in tumors in a way that leverages the many existing and growing RNAseq datasets. Statement of Significance The intrinsic tumor microbiome holds great potential for its ability to predict various aspects of cancer biology and as a target for rational manipulation. Here, we describe a tool to quantify microbes from within tumor RNAseq and apply it to two independent datasets. We show new associations with clinical variables that justify biomarker uses and more experimentation into the mechanisms by which tumor microbiomes affect cancer outcomes.
Resistance screening and in silico characterization of cloned novel RGA from multi-race resistant lentil germplasm against Fusarium wilt (Fusarium oxysporum f. sp. lentis)

Fusarium wilt caused by Fusarium oxysporum f. sp. lentis (Fol) is the most devastating disease of lentil present worldwide and in India. Identification of multi-race fusarium wilt resistance genes and incorporation into existing cultivar will help to reduce yield loss. In the present study, a hundred lentil germplasm were screened against seven prevalent races of Fol and accession IC201561, EC714243 and EC718238 were identified resistant. The typical R gene codes for the nucleotide-binding site and leucine-rich repeats (NBS-LRR) at the C terminal linked to either Toll/interleukin 1-like receptor (TIR) or coiled-coil (CC) at the N terminal. In the present study degenerate primers designed from the NBS region amplifying P-loop to GLPLA motif isolated forty-five resistance gene analogues (RGA) from identified resistant accessions. The sequence alignment identified both classes of RGA, TIR and non-TIR based on the presence of Aspartate (D) and Tryptophan (W) at the end of kinase motif respectively. The phylogenetic analysis grouped RGA into six classes, LRGA1 to LRGA6 determining the diversity of RGA present in the host. Grouping of RGA identified from Lens nigricans, LnRGA 2, 9, 13 with I2 reveals a probable role in Fusarium resistance. The similarity index of 27.85% to 86.98% was found among RGA and 26.83% to 49.41% between known R genes, I2, Gpa2, M and L6. Active binding sites present along the conserved motifs have grouped the RGA into 13 groups. ADP/ATP being the potential ligand determines ATP binding and ATP hydrolysis activity of RGA. The isolated RGA can be used in developing marker linked to the functional R gene. Further, expression analysis and full-length gene isolation further pave path to identifying the molecular mechanism involved in resistance.