Miracle-dz/UniFAR-FTUs huggingface dataset:
De novo protein fold families expand the designable ligand binding site space

A major challenge in designing proteins de novo to bind user-defined ligands with high specificity and affinity is finding backbones structures that can accommodate a desired binding site geometry with high precision. Recent advances in methods to generate protein fold families de novo have expanded the space of accessible protein structures, but it is not clear to what extend de novo proteins with diverse geometries also expand the space of designable ligand binding functions. We constructed a library of 25,806 high-quality ligand binding sites and developed a fast protocol to place (“match”) these binding sites into both naturally occurring and de novo protein families with two fold topologies: Rossman and NTF2. 5,896 and 7,475 binding sites could be matched to the Rossmann and NTF2 fold families, respectively. De novo designed Rossman and NTF2 protein families can support 1,791 and 678 binding sites that cannot be matched to naturally existing structures with the same topologies, respectively. While the number of protein residues in ligand binding sites is the major determinant of matching success, ligand size and primary sequence separation of binding site residues also play important roles. The number of matched binding sites are power law functions of the number of members in a fold family. Our results suggest that de novo sampling of geometric variations on diverse fold topologies can significantly expand the space of designable ligand binding sites for a wealth of possible new protein functions. Author summary De novo design of proteins that can bind to novel and highly diverse user-defined small molecule ligands could have broad biomedical and synthetic biology applications. Because ligand binding site geometries need to be accommodated by protein backbone scaffolds at high accuracy, the diversity of scaffolds is a major limitation for designing new ligand binding functions. Advances in computational protein structure design methods have significantly increased the number of accessible stable scaffold structures. Understanding how many new ligand binding sites can be accommodated by the de novo scaffolds is important for designing novel ligand binding proteins. To answer this question, we constructed a large library of ligand binding sites from the Protein Data Bank (PDB). We tested the number of ligand binding sites that can be accommodated by de novo scaffolds and naturally existing scaffolds with same fold topologies. The results showed that de novo scaffolds significantly expanded the ligand binding space of their respective fold topologies. We also identified factors that affect difficulties of binding site accommodation, as well as the relationship between the number of scaffolds and the accessible ligand binding site space. We believe our findings will benefit future method development and applications of ligand binding protein design.
A convergent structure-function substrate of cognitive imbalances in autism

Autism is a common neurodevelopmental condition characterized by substantial phenotypic heterogeneity, which hinders diagnosis, research, and intervention. A leading example can be found in marked imbalances in language and perceptual skills, where deficits in one domain often co-exist with normal or even superior performance in the other domain. The current work capitalized on multiple data analytics including data-driven subtyping and dimensional approaches to quantify cognitive imbalances in multi-site datasets of individuals diagnosed with autism spectrum disorder (ASD) and neurotypical controls, and assessed structural and functional brain network substrates. Studying cognitive dimensions as well as multimodal neuroimaging signatures in 155 ASD and 151 neurotypical individuals, we observed robust evidence for a structure-function substrate of cognitive imbalances in ASD. Specifically, ASD presented with marked imbalances in cognitive profiles relative to neurotypical controls, characterized by verbal to non-verbal intelligence discrepancy. Different analytical approaches including subtyping and dimensional regression methods converged in showing that these imbalances were reflected in atypical cortical thickening and functional integration of language networks, alongside with sensory and higher cognitive networks. Phenotypic findings could be replicated in an independent sample of 325 ASD and 569 neurotypical controls. Although verbal and non-verbal intelligence are currently considered as specifiers unrelated to the categorical diagnosis of autism, our results show that intelligence disparities are accentuated in ASD and relate to a consistent structure-function substrate affecting multiple brain networks. Our findings motivate the incorporation of cognitive imbalances in future autism research, which may help to parse the phenotypic heterogeneity of autism and potentially inform intervention-oriented subtyping.
MiDAS—Meaningful Immunogenetic Data at Scale

Human immunogenetic variation in the form of HLA and KIR types has been shown to be strongly associated with a multitude of immune-related phenotypes. We present MiDAS, an R package enabling statistical association analysis and using immunogenetic data transformation functions for HLA amino acid fine mapping, analysis of HLA evolutionary divergence as well as HLA-KIR interactions. MiDAS closes the gap between inference of immunogenetic variation and its efficient utilization to make meaningful discoveries.
Idiosyncratic Purifying Selection on Metabolic Enzymes in the Long-Term Evolution Experiment with Escherichia coli

Bacteria, Archaea, and Eukarya all share a common set of metabolic reactions. This implies that the function and topology of central metabolism has been evolving under purifying selection over deep time. Central metabolism may similarly evolve under purifying selection during longterm evolution experiments, although it is unclear how long such experiments would have to run (decades, centuries, millennia) before signs of purifying selection on metabolism appear. I hypothesized that central and superessential metabolic enzymes would show evidence of purifying selection in the long-term evolution experiment with Escherichia coli (LTEE). I also hypothesized that enzymes that specialize on single substrates would show stronger evidence of purifying selection in the LTEE than generalist enzymes that catalyze multiple reactions. I tested these hypotheses by analyzing metagenomic time series covering 62,750 generations of the LTEE. I find mixed support for these hypotheses, because the observed patterns of purifying selection are idiosyncratic and population-specific. To explain this finding, I propose the Jenga hypothesis, named after a children’s game in which blocks are removed from a tower until it falls. The Jenga hypothesis postulates that loss-of-function mutations degrade costly, redundant, and nonessential metabolic functions. Replicate populations can therefore follow idiosyncratic trajectories of lost redundancies, despite purifying selection on overall function. I tested the Jenga hypothesis by simulating the evolution of 1,000 minimal genomes under strong purifying selection. As predicted, the minimal genomes converge to different metabolic networks. Strikingly, the core genes common to all 1,000 minimal genomes show consistent signatures of purifying selection in the LTEE. Significance Statement Purifying selection conserves organismal function over evolutionary time. However, few studies have examined the role of purifying selection during adaptation to novel environments. I tested metabolic enzymes for purifying selection in an ongoing long-term evolution experiment with Escherichia coli. While some populations show signs of purifying selection, the overall pattern is inconsistent. To explain these findings, I propose the Jenga hypothesis, in which loss-of-function mutations first degrade costly, redundant, and nonessential metabolic functions, after which purifying selection begins to dominate. I then tested several predictions of the Jenga hypothesis using computational simulations. On balance, the simulations confirm that we should find evidence of purifying selection on the metabolic pathways that sustain growth in a novel environment.
Individual differences in representational similarity of first and second languages in the bilingual brain

Current theories of bilingualism disagree on the extent to which separate brain regions are used to maintain or process one’s first and second language. The present study took a novel multivariate approach to address this question. We examined whether bilinguals maintain distinct neural representations of two languages; specifically, we tested whether brain areas that are involved in processing word meaning in either language are reliably representing each language differently, and whether language representation is influenced by individual differences in proficiency level and age of acquisition of L2. Thirty-one English-Mandarin bilingual adults performed a picture-word matching task in both languages. We then used representational similarity analysis to examine which brain regions reliably showed different patterns of activity for each language. As a group, there were no regions that reliably represented languages distinctly. However, both proficiency and age of acquisition predicted dissimilarity between language representations in several brain areas within the language network as well as several regions of the ventral visual pathway, demonstrating that top-down language knowledge and individual language experience shapes concept representation in the processing stream. The results support the model of an integrated language system in bilinguals, along with a novel description of how representations for each language change with proficiency level and L2 age of acquisition.
The influence of positive emotion and negative emotion on false memory based on EEG signal analysis

Analyzing the influence of emotion on false memory through electroencephalogram is helpful to further explore the cognition function of brain. In this study, we improved the Deese-Roediger-McDermott paradigm experiment to study the false memory. The memory materials are combined with mixed emotions, which are closer to real life. Twenty-eight participants were randomly divided into positive group and negative group. We used music to induce the participants in the positive group and the negative group to generate corresponding emotions. Finally, we analyzed the difference between the positive group and the negative group from the behavior data, source location and cortex functional network of event related potential. The results of behavioral data analysis show that the false memory rate of positive group (85%) is significantly higher than that of negative group (72%). The results of source localization show that the brain of the positive group is more active than that of the negative group, and the difference of brain activation location between the two groups is mainly manifested in the BA24 and BA45 brain regions. The results of cortex functional network show that the node degree, clustering coefficient, global efficiency and small-world property of the positive group are significantly higher than those of the negative group. It can be found from the three aspects that participants’ emotional state and brain’s understanding of semantic are the main reasons for the difference in the incidence of false memory between the two groups.
Using image-based haplotype alignments to map global adaptation of SARS-CoV-2

Quantifying evolutionary change among viral genomes is an important clinical device to track critical adaptations geographically and temporally. We built image-based haplotype-guided evolutionary inference (ImHapE) to quantify adaptations in expanding populations of non-recombining SARS-CoV-2 genomes. By combining classic population genetic summaries with image-based deep learning methods, we show that different rates of positive selection are driving evolutionary fitness and dispersal of SARS-CoV-2 globally. A 1.35-fold increase in evolutionary fitness is observed within the UK, associated with expansion of both the B.1. 177 and B.1. 1.7 SARS-CoV-2 lineages.
Metagenomic methylation patterns resolve complex microbial genomes

The plasticity of bacterial and archaeal genomes makes examining their ecological and evolutionary dynamics both exciting and challenging. The same mechanisms that enable rapid genomic change and adaptation confound current approaches for recovering complete genomes from metagenomes. Here, we use strain-specific patterns of DNA methylation to resolve complex bacterial genomes from the long-read metagenome of a marine microbial consortia, the “pink berries” of the Sippewissett Marsh. Unique combinations of restriction-modification (RM) systems encoded by the bacteria produced distinctive methylation profiles that accurately binned and classified metagenomic sequences. We linked the methylation patterns of each metagenome-assembled genome with encoded DNA methyltransferases and discovered new restriction modification (RM) defense systems, including novel associations of RM systems with RNase toxins. Using this approach, we finished the largest and most complex circularized bacterial genome ever recovered from a metagenome (7.9 Mb with >600 IS elements), the finished genome of Thiohalocapsa sp. PB-PSB1 the dominant bacteria in the consortia. From these methylation-binned genomes, we identified instances of lateral gene transfer between sulfur-cycling symbionts (Thiohalocapsa sp. PB-PSB1 and Desulfofustis sp. PB-SRB1), phage infection, and strain-level structural variation.
Enhanced CICR activity reduces ER Ca2+ level in cells expressing CPVT-linked mutant RyR2

Type 2 ryanodine receptor (RyR2) is a cardiac Ca2+ release channel in the endoplasmic reticulum (ER). Mutations in RyR2 are linked to catecholaminergic polymorphic ventricular tachycardia (CPVT), which is considered to be associated with enhanced spontaneous Ca2+ release. This spontaneous Ca2+ release tends to occur when ER Ca2+ ([Ca2+]ER) reaches a certain threshold level, and CPVT mutations are reported to lower this threshold. There are two explanations for this lowered threshold: the mutations increase sensitivity to luminal Ca2+ or they enhance cytosolic [Ca2+] ([Ca2+]cyt)-induced Ca2+ release (CICR) activity. However, no quantitative analysis of this issue has been performed so far. Here, we quantitatively explored how the change in CICR activity of RyR2 affects the threshold [Ca2+]ER experimentally and by model-based simulation. Wild-type (WT) and CPVT-linked mutant RyR2s were expressed in HEK293 cells. [Ca2+]cyt and [Ca2+]ER measurements with Ca2+ indicators revealed that CPVT RyR2 cells showed higher oscillation frequency and lower threshold [Ca2+]ER in a mutation-specific manner compared with WT cells. The CICR activity of mutant RyR2s was assessed by Ca2+-dependent [3H]ryanodine binding and parameter analysis. CICR activity at resting [Ca2+]cyt, A7.0, was higher in CPVT mutants than in WT and a strong inverse correlation was found between threshold [Ca2+]ER and A7.0. Interestingly, lowering RyR2 expression increased threshold [Ca2+]ER, suggesting that the threshold [Ca2+]ER depends on net Ca2+ release rate via RyR2, a product of A7.0 for each mutant and the density of RyR2 molecules. A model-based simulation successfully reproduced the [Ca2+]cyt and [Ca2+]ER changes. Interestingly, the CICR activity associated with specific mutations correlated well with the age of onset of the disease in CPVT patients carrying the mutations. Our data suggest that the reduction in threshold [Ca2+]ER for spontaneous Ca2+ release by CPVT mutation is explained by enhanced CICR activity without considering a change in the [Ca2+]ER sensitivity of RyR2. Summary CPVT-linked RyR2 mutations are prone to induce spontaneous Ca2+ release from ER, which is strongly associated with arrhythmias. Kurebayashi et al. quantitatively explore how the changes in CICR activity by RyR2 mutations affect spontaneous Ca2+ experimentally and by model simulation.
Proton-conductive membranes based on vanadium substituted heteropoly acids with Keggin structure and polymers

In this article, novel proton-conducting composite membranes SPEEK/PW11V and PVA/SiW11V were synthesized from vanadium substituted heteropoly acids (H4PW11VO40·8H2O and H5SiW11VO40·15H2O, abbreviated as PW11V and SiW11V) and polymers (SPEEK or PVA) at the weight ratio 70 : 30. The membranes were characterized by the infrared spectroscopy, X-ray powder diffraction, and scanning electron microscopy, which confirmed the maintenance of the Keggin framework and dispersion homogeneously in the polymer matrix without long-range ordering. Their proton-conducting properties were investigated with electrochemical impedance spectroscopy. The results show that the respective proton conductivities of SPEEK/PW11V and PVA/SiW11V membranes were in the order of 10−2 and 10−4 S cm−1 at ambient temperature. The temperature dependence of the two composite membrane electrolytes exhibit Arrhenius behavior, and the observed activation energies to be 15.82 kJ mol−1 for SPEEK/PW11V and 14.40 kJ mol−1 for PVA/SiW11V, which indicates that the proton conduction complies with the Grotthuss mechanism. © 2015 Wiley Periodicals, Inc. J. Appl. Polym. Sci. 2015, 132, 42204.
Robust RNA editing via recruitment of endogenous ADARs using circular guide RNAs

Akin to short-hairpin RNAs and antisense oligonucleotides which efficaciously recruit endogenous cellular machinery such as Argonaute and RNase H to enable targeted RNA knockdown, simple long antisense guide RNAs (1) can recruit endogenous adenosine deaminases acting on RNA (ADARs) to enable programmable A-to-I RNA editing, without requiring co-delivery of any exogenous proteins. This approach is highly specific, however the efficiency is typically lower than observed with enzyme overexpression. Conjecturing this was due in part to the short half-life and residence times of guide RNAs, here we engineer highly stable circular ADAR recruiting guide RNAs (cadRNAs), which can be delivered not only by genetically encoding on DNA vectors, but also via transfection of RNA molecules transcribed in vitro. Using these cadRNAs, we observed robust RNA editing across multiple sites and cell lines, in both untranslated and coding regions of RNAs, vastly improved efficiency and durability of RNA editing, and high transcriptome-wide specificity. High transcript-level specificity was achieved by further engineering to reduce bystander editing. Additionally, in vivo delivery of cadRNAs via adeno-associated viruses (AAVs) enabled robust 38% RNA editing of the mPCSK9 transcript in C57BL/6J mice livers, and 12% UAG-to-UGG RNA correction of the amber nonsense mutation in the IDUA-W392X mouse model of mucopolysaccharidosis type I-Hurler (MPS I-H) syndrome. Taken together, cadRNAs enable efficacious programmable RNA editing with application across diverse protein modulation and gene therapeutic settings.
Anterior insula-associated social novelty recognition: orchestrated regulation by a local retinoic acid cascade and oxytocin signaling

Background Deficits in social cognition consistently underlie functional disabilities in a wide range of psychiatric disorders. Neuroimaging studies have suggested that the anterior insula is a ‘common core’ brain region that is impaired across neurological and psychiatric disorders, which include social cognition deficits. Nevertheless, neurobiological mechanisms of the anterior insula for social cognition remain elusive. Methods To determine the role of anterior insula in social cognition, we manipulated expression of Cyp26B1, an anterior insula-enriched molecule that is crucial for retinoic acid degradation and involved in the pathology of neuropsychiatric conditions. Social cognition was mainly assayed using the three-chamber social interaction test. We conducted multimodal analyses at the molecular, cellular, circuitry, and behavioral levels. Results At the molecular/cellular level, anterior insula-mediated social novelty recognition is maintained by proper activity of the layer 5 pyramidal neurons, for which retinoic acid-mediated gene transcription can play a role. We also demonstrate that oxytocin influences the anterior insula-mediated social novelty recognition, not by direct projection of oxytocin neurons, nor by direct diffusion of oxytocin to the anterior insula, which contrasts the modes of oxytocin regulation onto the posterior insula. Instead, oxytocin affects oxytocin receptor-expressing neurons in the dorsal raphe nucleus where serotonergic neurons are projected to the anterior insula. Furthermore, we show that serotonin 5HT2C receptor expressed in the anterior insula influences social novelty recognition. Conclusions Anterior insula plays a pivotal role in social novelty recognition that is partly regulated by a local retinoic acid cascade, but also remotely regulated by oxytocin via a non-classic mechanism.
High-resolution model of Arabidopsis Photosystem II reveals the consequences of digitonin-extraction

In higher plants, the photosynthetic process is performed and regulated by Photosystem II (PSII). Arabidopsis thaliana was the first higher plant with a fully sequenced genome, conferring it the status of a model organism; nonetheless, a high-resolution structure of its Photosystem II is missing. We present the first Cryo-EM high-resolution structure of Arabidopsis PSII supercomplex with average resolution of 2.79 Å, an important model for future PSII studies. The digitonin extracted PSII complexes demonstrate the importance of: the LHG2630-lipid-headgroup in the trimerization of the light-harvesting complex II; the stabilization of the PsbJ subunit and the CP43-loop E by DGD520-lipid; the choice of detergent for the integrity of membrane protein complexes. Furthermore, our data shows at the anticipated Mn4CaO5-site a single metal ion density as a reminiscent early stage of Photosystem II photoactivation.
Applying cis-regulatory codes to predict conserved and variable heat and cold stress response in maize

Changes in gene expression are important for response to abiotic stress. Transcriptome profiling performed on maize inbred and hybrid genotypes subjected to heat or cold stress identifies many transcript abundance changes in response to these environmental conditions. Motifs that are enriched near differentially expressed genes were used to develop machine learning models to predict gene expression responses to heat or cold. The best performing models utilize the sequences both upstream and downstream of the transcription start site. Prediction accuracies could be improved using models developed for specific co-expression clusters compared to using all up- or down-regulated genes or by only using motifs within unmethylated regions. Comparisons of expression responses in multiple genotypes were used to identify genes with variable response and to identify cis- or trans-regulatory variation. Models trained on B73 data have lower performance when applied to Mo17 or W22, this could be improved by using models trained on data from all genotypes. However, the models have low accuracy for correctly predicting genes with variable responses to abiotic stress. This study provides insights into cis-regulatory motifs for heat- and cold-responsive gene expression and provides a framework for developing models to predict expression response to abiotic stress across multiple genotypes. One sentence summary Transcriptome profiling of maize inbred and hybrid seedlings subjected to heat or cold stress was used to identify key cis-regulatory elements and develop models to predict gene expression responses.