Biology subjects

Grishin, N. V.

Publications and source records attributed to Grishin, N. V..

At least 19 recordsLinked to original sources

bioRxiv2026

AI-Based Comparison of UniProt annotations and Literature Across Human Kinases

Curated protein records and their supporting publications organize biological evidence at different levels of detail. Here, we used AI to independently answer a set of biological questions using either UniProt records or the full texts of the literature cited by those records, and then used AI-based evaluation to compare the resulting answers in terms of information coverage and semantic agreement. Fifty human proteins were selected from ten groups in a kinase sequence collection. Target-identity filtering retained 1,749 protein-publication records representing 1,655 distinct publications. For each protein, an AI language model generated one answer set from UniProt and another from the associated literature. A separate AI-based semantic evaluation compared all 2,500 pairwise combinations of the 50 UniProt-derived and 50 literature-derived answer sets, producing 87,500 question-level comparisons. Literature provided answers to 1,211 of 1,750 protein-question combinations, compared with 1,151 for UniProt. After correction for multiple testing across the 35 questions, literature coverage was significantly higher for autoinhibition and inhibitor type. Mean AI-assigned semantic agreement scores were higher for matched protein pairs (0.692) than for unmatched proteins within the same kinase group (0.384) and for proteins from different kinase groups (0.279). The matching literature-derived answer set ranked first for 49 of the 50 UniProt-derived queries, even after questions about gene symbol and kinase family were excluded. The sole exception was GUCY2C, whose UniProt-derived answer set showed slightly greater agreement with the NPR2 literature-derived answer set than with the GUCY2C literature-derived set. TYR proteins had the greatest overall answer coverage, whereas CK1 and RGC proteins had comparatively limited coverage in UniProt and the literature, respectively. Overall, AI analysis showed that UniProt records and their cited publications contain complementary yet strongly protein-specific biological information. Reporting gaps, question scope, and score aggregation influenced apparent agreement, underscoring that the framework evaluates consistency between AI-derived answers, not their biological accuracy.

bioinformatics↗

bioRxiv2026

Genomics Analysis Illuminates Morphology, Ecology, Phenology and Distribution of Two Cryptic Atrytonopsis Skippers (Hesperiidae: Hesperiinae)

The Hesperiine genus Atrytonopsis Godman, 1900, occurs broadly across the American Southwest. Atrytonopsis margarita (Skinner, 1913) and Atrytonopsis python (W. H. Edwards, 1882) have look-alike appearances, concurrent flights, and geographic distributions which converge in New Mexico. Their similar wing markings and intertwined taxonomic history has made it challenging to fully understand the identity and occurrence of each. Burns (2015) revealed differences in genitalia, clarifying that they are distinct species. Genomic DNA analysis of more than 100 specimens now illuminates their genetic uniqueness, phylogenetic relationship, field identification challenges and details of their geographic distributions.

evolutionary biology↗

bioRxiv2025

DrugDomain2.0: comprehensive database of protein domains-ligands/drugs interactions across the whole Protein Data Bank

Proteins carry out essential cellular functions - signaling, metabolism, transport - through the specific interaction of small molecules and drugs within their three-dimensional structural domains. Protein domains are conserved folding units that, when combined, drive evolutionary progress. The Evolutionary Classification Of protein Domains (ECOD) places domains into a hierarchy explicitly built around distant evolutionary relationships, enabling the detection of remote homologs across the proteomes. Yet no single resource has systematically mapped domain-ligand interactions at the structural level. To fill this gap, we introduce DrugDomain v2.0, an updated comprehensive resource, that extends earlier releases by linking evolutionary domain classifications (ECOD) to ligand binding events across the entire Protein Data Bank. We also leverage AI-driven predictions from AlphaFold to extend domain-ligand annotations to human drug targets lacking experimental structures. DrugDomain v2.0 catalogs interactions with over 37,000 PDB ligands and 7,560 DrugBank molecules, integrates more than 6,000 small-molecule-associated post-translational modifications, and provides context for 14,000+ PTM-modified human protein models featuring docked ligands. The database encompasses 43,023 unique UniProt accessions and 174,545 PDB structures. The DrugDomain data is available online: https://drugdomain.cs.ucf.edu/ and https://github.com/kirmedvedev/DrugDomain.

bioinformatics↗

bioRxiv2025

CASP16 protein monomer structure prediction assessment

The assessment of monomer targets in the Critical Assessment of Structure Prediction Round 16 (CASP16) underscores that the problem of single-domain protein fold prediction is nearly solved--no target folds were incorrectly predicted across all Evaluation Units. However, challenges remain in accurately modeling truncated sequences, irregular secondary structures, and interaction-induced conformational changes. The release of AlphaFold3 (AF3) during CASP16, and its effective integration by many groups, demonstrated its superiority over AlphaFold2 (AF2), particularly in confidence estimation and model selection. Additional improvements in multiple sequence alignments (MSAs) and fragment-based prediction, i.e., selecting the optimal fragment of the full sequence for modeling, also contributed to enhanced prediction accuracy. The top three groups--all from the Yang lab--consistently outperformed others across CASP16 monomer targets, reflecting their robust modeling pipelines and successful adoption of AF3. CASP16 also introduced three new challenges: Phase 0, in which stoichiometry was withheld; Phase 2, which supplied [~]8,000 MassiveFold models per target to test model selection strategies; and Model 6, which limited predictors to using MSAs provided by the organizers. While we evaluated group performance in these additional challenges, the insights gained were limited due to low participation and caveats in the design of experiments. We suggest improvements for the organization of these challenges and encourage broader engagement from the prediction community. The progress in monomer modeling from CASP15 to CASP16 was subtle, but more groups in CASP16 were able to outperform ColabFold, reflecting the communitys improved ability in optimizing AF2 and the growing adoption of AF3. We anticipate that the recent release of the AF3 source code will stimulate future progress through user-driven optimization and innovations in model architecture. Finally, model ranking remains a persistent weakness across most groups, highlighting a critical area for future development.

biophysics↗

bioRxiv2025

Assessment of Protein Complex Predictions in CASP16: Are we making progress?

The assessment of oligomer targets in the Critical Assessment of Structure Prediction Round 16 (CASP16) suggests that complex structure prediction remains an unsolved challenge. More than 30% of targets, particularly antibody-antigen targets, were highly challenging, with each group correctly predicting structures for only about a quarter of such targets. Most CASP16 groups relied on AlphaFold-Multimer (AFM) or AlphaFold3 (AF3) as their core modeling engines. By optimizing input MSAs, refining modeling constructs (using partial rather than full sequences), and employing massive model sampling and selection, top-performing groups were able to significantly outperform the default AFM/AF3 predictions. CASP16 also introduced two additional challenges: Phase 0, which required predictions without stoichiometry information, and Phase 2, which provided participants with thousands of models generated by MassiveFold (MF) to enable large-scale sampling for resource-limited groups. Across all phases, the MULTICOM series and Kiharalab emerged as top performers based on the quality of their best models per target. However, these groups did not have a strong advantage in model ranking, and thus their lead over other teams, such as Yang-Multimer and kozakovvajda, was less pronounced when evaluating only the first submitted models. Compared to CASP15, CASP16 showed moderate overall improvement, likely driven by the release of AF3 and the extensive model sampling employed by top groups. Several notable trends highlight key frontiers for future development. First, the kozakovvajda group significantly outperformed others on antibody-antigen targets, achieving over a 60% success rate without relying on AFM or AF3 as their primary modeling framework, suggesting that alternative approaches may offer promising solutions for these difficult targets. Second, model ranking and selection continue to be major bottlenecks. The PEZYFoldings group demonstrated a notable advantage in selecting their best models as first models, suggesting that their pipeline for model ranking may offer important insights for the field. Finally, the Phase 0 experiment indicated reasonable success in stoichiometry prediction; however, stoichiometry prediction remains challenging for high-order assemblies and targets that differ from available homologous templates. Overall, CASP16 demonstrated steady progress in multimer prediction while emphasizing the urgent need for more effective model ranking strategies, improved stoichiometry prediction, and the development of new modeling methods that extend beyond the current AF-based paradigm.

biophysics↗

bioRxiv2025

Leveraging AI to Explore Structural Contexts of Post-Translational Modifications in Drug Binding

Post-translational modifications (PTMs) play a crucial role in allowing cells to expand the functionality of their proteins and adaptively regulate their signaling pathways. Defects in PTMs have been linked to numerous developmental disorders and human diseases, including cancer, diabetes, heart, neurodegenerative and metabolic diseases. PTMs are important targets in drug discovery, as they can significantly influence various aspects of drug interactions including binding affinity. The structural consequences of PTMs, such as phosphorylation-induced conformational changes or their effects on ligand binding affinity, have historically been challenging to study on a large scale, primarily due to reliance on experimental methods. Recent advancements in computational power and artificial intelligence, particularly in deep learning algorithms and protein structure prediction tools like AlphaFold3, have opened new possibilities for exploring the structural context of interactions between PTMs and drugs. These AI-driven methods enable accurate modeling of protein structures including prediction of PTM-modified regions and simulation of ligand-binding dynamics on a large scale. In this work, we identified small molecule binding-associated PTMs that can influence drug binding across all human proteins listed as small molecule targets in the DrugDomain database, which we developed recently. 6,131 identified PTMs were mapped to structural domains from Evolutionary Classification of Protein Domains (ECOD) database. Scientific contribution. Using recent AI-based approaches for protein structure prediction (AlphaFold3, RoseTTAFold All-Atom, Chai-1), we generated 14,178 models of PTM-modified human proteins with docked ligands. Our results demonstrate that these methods can predict PTM effects on small molecule binding, but precise evaluation of their accuracy requires a much larger benchmarking set. We also found that phosphorylation of NADPH-Cytochrome P450 Reductase, observed in cervical and lung cancer, causes significant structural disruption in the binding pocket, potentially impairing protein function. All data and generated models are available from DrugDomain database v1.1 (http://prodata.swmed.edu/DrugDomain/) and GitHub (https://github.com/kirmedvedev/DrugDomain). This resource is the first to our knowledge in offering structural context for small molecule binding-associated PTMs on a large scale.

bioinformatics↗

bioRxiv2024

Ancient inversion polymorphisms are locally adaptive in a widespread butterfly species

Wide-ranging species are subject to varying biotic and abiotic selection pressures across their distribution. While local adaptation does not manifest in obvious morphological changes, population genomic studies can reveal cryptic diversity and provide insights into local adaptive processes. In this study, we investigated the biogeographic history and genomic diversity across the range of the zebra longwing butterfly Heliconius charithonia, a species with a widespread distribution in the Neotropics, but which is phenotypically homogenous across its range. We examined whole genome sequence data from 55 individuals from the eight described subspecies. We infer that there were at least two distinct colonization events of the Caribbean islands from the mainland. The second colonization wave occurred relatively recently, accounting for the genetic homogeneity observed across the species range. Despite low divergence across most of the genome, two large non-recombining genomic regions showed deeply divergent haplotypes that correspond to chromosomal inversions. Phylogenetic analyses indicate ancient origins of these inversion polymorphisms, and there is no evidence that they were introgressed from another extant lineage of Heliconius. These ancient polymorphisms are likely maintained by heterogeneous selection across the landscape, with the inversion on chromosome 19 likely playing a role in local adaptation to cold and desiccation. Our findings underscore the importance of genomic analysis in uncovering hidden diversity and adaptation in phenotypically homogenous species and highlight the significant role of chromosomal inversions in driving local adaptation.

evolutionary biology↗

bioRxiv2024

DrugDomain: the evolutionary context of drugs and small molecules bound to domains

Interactions between proteins and small organic compounds play a crucial role in regulating protein functions. These interactions can modulate various aspects of protein behavior, including enzymatic activity, signaling cascades, and structural stability. By binding to specific sites on proteins, small organic compounds can induce conformational changes, alter protein-protein interactions, or directly affect catalytic activity. Therefore, many drugs available on the market today are small molecules (72% of all approved drugs in the last five years). Proteins are composed of one or more domains: evolutionary units that convey function or fitness either singly or in concert with others. Understanding which domain(s) of the target protein binds to a drug can lead to additional opportunities for discovering novel targets. The Evolutionary Classification Of protein Domains (ECOD) classifies domains into an evolutionary hierarchy that focuses on distant homology. Previously, no structure-based protein domain classification existed that included information about both the interaction between small molecules or drugs and the structural domains of a target protein. This data is especially important for multidomain proteins and large complexes. Here, we present the DrugDomain database that reports the interaction between ECOD domains of human target proteins and DrugBank molecules and drugs. The pilot version of DrugDomain describes the interaction of 5,160 DrugBank molecules associated with 2,573 human proteins. It describes domains for all experimentally determined structures of these proteins and incorporates AlphaFold models when such structures are unavailable. The DrugDomain database is available online: http://prodata.swmed.edu/DrugDomain/

bioinformatics↗

bioRxiv2024

Structure classification of the proteins from Salmonella enterica pangenome revealed novel potential pathogenicity islands

Salmonella enterica is a pathogenic bacterium known for causing severe typhoid fever in humans, making it important to study due to its potential health risks and significant impact on public health. This study provides evolutionary classification of proteins from Salmonella enterica pangenome. We classified 17,238 domains from 13,147 proteins from 79,758 Salmonella enterica strains and studied in detail domains of 272 proteins from fourteen characterized Salmonella pathogenicity islands (SPIs). Among SPIs-related proteins, 90 proteins function in the secretion machinery. 41% domains of SPI proteins have no previous sequence annotation. By comparing clinical and environmental isolates, we identified 3682 proteins that are overrepresented in clinical group that we consider as potentially pathogenic. 69 of them overlap with SPI proteins. Among domains of potentially pathogenic proteins only 50% domains were annotated by sequence methods previously. Moreover, 36% (1330 out of 3682) of potentially pathogenic proteins cannot be classified into Evolutionary Classification of Protein Domains database (ECOD). Among classified domains of potentially pathogenic proteins the most populated homology groups include helix-turn-helix (HTH), Immunoglobulin-related, and P-loop domains-related. Functional analysis revealed overrepresentation of these protein in biological processes related to viral entry into host cell, antibiotic biosynthesis, DNA metabolism and conformation change, and underrepresentation in translational processes. Analysis of the potentially pathogenic proteins indicates that they form 119 clusters (islands) within the Salmonella genome, suggesting their potential contribution to the bacteriums virulence. Overall, our analysis revealed that identified potentially pathogenic proteins are poorly studied. ECOD hierarchy of classified Salmonella enterica domains is available online: http://prodata.swmed.edu/ecod/index_salm.php Author SummarySalmonella enterica is a dangerous bacterium known for causing severe typhoid fever in humans, posing significant health risks. Our study focuses on understanding the proteins of this bacteriums genetic composition, unraveling their evolutionary classification. Analyzing a vast collection of strains, we identified and classified over 17,000 protein domains, with special focus on 272 proteins within Salmonella pathogenicity islands (SPIs). By comparing strains from clinical and environmental sources, we pinpointed 3,682 proteins overrepresented in clinical samples, signifying potential pathogenicity. Surprisingly, half of these proteins domains were not identified previously using sequence-based approaches. Our analysis identified these proteins forming 119 clusters within the Salmonella genome, suggesting their involvement in its virulence. Our study underscores the insufficient understanding of these potentially pathogenic proteins, highlighting the need for further investigation into their roles and implications in Salmonella-related illnesses.

bioinformatics↗

bioRxiv2023

ECOD domain classification of 48 whole proteomes from AlphaFold Structure Database using DPAM

Protein structure prediction has now been deployed widely across several different large protein sets. Large-scale domain annotation of these predictions can aid in the development of biological insights. Using our Evolutionary Classification of Protein Domains (ECOD) from experimental structures as a basis for classification, we describe the detection and cataloging of domains from 48 whole proteomes deposited in the AlphaFold Database. On average, we can provide positive classification (either of domains or other identifiable non-domain regions) for 90% of residues in all proteomes. We classified 746,349 domains from 536,808 proteins comprised of over 226,424,000 amino acid residues. We examine the varying populations of homologous groups in both eukaryotes and bacteria. In addition to containing a higher fraction of disordered regions and unassigned domains, eukaryotes show a higher proportion of repeated proteins, both globular and small repeats. We enumerate those highly populated domains that are shared in both eukaryotes and bacteria, such as the Rossmann domains, TIM barrels, and P-loop domains. Additionally, we compare the sampling of homologous groups from this whole proteome set against our stable ECOD reference and discuss groups that have been enriched by structure predictions. Finally, we discuss the implication of these results for protein target selection for future classification strategies for very large protein sets. Author SummaryProteins can contain one or more domains, regions that are evolutionary independent and convey fiction and function. Here we present our classification of proteins within 48 proteomes provided by the AlphaFold Structural Database. These proteomes span multiple model organisms used in research as a common ground for studying biological principles as well as organisms involved in prevalent human infectious diseases. We classify these domains by our AlphaFold-specific Domain Parser for AlphaFold Models (DPAM), which was previously tested on the human proteome. We find that eukaryotic and bacterial proteomes can be classified to different degrees, with significantly more disordered and low-confident regions in eukaryotic proteins.

bioinformatics↗

bioRxiv2023

Association of an estrogen-sensitive Pax1-Col11a1-Mmp3 signaling axis with adolescent idiopathic scoliosis

Adolescent idiopathic scoliosis (AIS) is a common and progressive spinal deformity in children that exhibits striking sexual dimorphism, with girls at more than five-fold greater risk of severe disease compared to boys. Despite its medical impact, the molecular mechanisms that drive AIS are largely unknown. We previously defined a female-specific AIS genetic risk locus in an enhancer near the PAX1 gene. Here we sought to define the roles of PAX1 and newly-identified AIS-associated genes in the developmental mechanism of AIS. In a genetic study of 10,519 individuals with AIS and 93,238 unaffected controls, significant association was identified with a variant in COL11A1 encoding collagen (1) XI (rs3753841; NM_080629.2_c.4004C>T; p.(Pro1335Leu); P=7.07e-11, OR=1.118). Using CRISPR mutagenesis we generated Pax1 knockout mice (Pax1-/-). In postnatal spines we found that PAX1 and collagen (1) XI protein both localize within the intervertebral disc (IVD)-vertebral junction region encompassing the growth plate, with less collagen (1) XI detected in Pax1-/- spines compared to wildtype. By genetic targeting we found that wildtype Col11a1 expression in costal chondrocytes suppresses expression of Pax1 and of Mmp3, encoding the matrix metalloproteinase 3 enzyme implicated in matrix remodeling. However, this suppression was abrogated in the presence of the AIS-associated COL11A1P1335L mutant. Further, we found that either knockdown of the estrogen receptor gene Esr2, or tamoxifen treatment, significantly altered Col11a1 and Mmp3 expression in chondrocytes. We propose a new molecular model of AIS pathogenesis wherein genetic variation and estrogen signaling increase disease susceptibility by altering a Pax1-Col11a1-Mmp3 signaling axis in spinal chondrocytes.

genetics↗

bioRxiv2022

DPAM: A Domain Parser for AlphaFold Models

The recent breakthroughs in structure prediction, where methods such as AlphaFold demonstrated near atomic accuracy, herald a paradigm shift in structure biology. The 200 million high-accuracy models released in the AlphaFold Database are expected to guide protein science in the coming decades. Partitioning these AlphaFold models into domains and subsequently assigning them to our evolutionary hierarchy provides an efficient way to gain functional insights of proteins. However, classifying such a large number of predicted structures challenges the infrastructure of current structure classifications, including our Evolutionary Classification of protein Domains (ECOD). Better computational tools are urgently needed to automatically parse and classify domains from AlphaFold models. Here we present a Domain Parser for AlphaFold Models (DPAM) that can automatically recognize globular domains from these models based on predicted aligned errors, inter-residue distances in 3D structures, and ECOD domains found by sequence (HHsuite) and structural (DALI) similarity searches. Based on a benchmark of 18,759 AlphaFold models, we demonstrated that DPAM could recognize 99.5% domains and assign correct boundaries for 85.2% of them, significantly outperforming structure-based domain parsers and homology-based domain assignment using ECOD domains found by HHsuite or DALI. Application of DPAM to the massive set of AlphaFold models will allow for more efficient classification of domains, providing evolutionary contexts and facilitating functional studies.

bioinformatics↗

bioRxiv2022

Assessing risk for butterflies in the context of climate change, demographic uncertainty, and heterogenous data sources

AbstractOngoing declines in insect populations have led to substantial concern and calls for conservation action. However, even for relatively well-studied groups, like butterflies, information relevant to species-specific status and risk is scattered across field guides, the scientific literature, and agency reports. Consequently, attention and resources have been spent on a miniscule fraction of insect diversity, including a few well-studied butterflies. Here we bring together heterogenous sources of information for 396 butterfly species to provide the first regional assessment of butterflies for the 11 western US states. For 184 species, we use monitoring data to characterize historical and projected trends in population abundance. For another 212 species (for which monitoring data are not available, but other types of information can be collected), we use exposure to climate change, development, geographic range, number of host plants, and other factors to rank species for conservation concern. A phylogenetic signal is apparent, with concentrations of declining and at-risk species in the families Lycaenidae and Hesperiidae. A geographic bias exists in that many species that lack monitoring data occur in more southern states where we expect that impacts of warming and drying trends will be most severe. Legal protection is rare among the taxa with the highest risk values: of the top 100 species, one is listed as threatened under the US Endangered Species Act and one is a candidate for listing. Among the many taxa not currently protected, we highlight a short list of species in decline, including Vanessa annabella, Thorybes mexicanus, Euchloe ausonides, and Pholisora catullus. Notably, many of these species have broad geographic ranges, which perhaps highlights a new era of insect conservation in which small or fragmented ranges will not be the only red flags that attract conservation attention.

ecology↗

bioRxiv2022

Integrated molecular analysis reveals two distinct subtypes of pure seminoma of the testis

Testicular germ cell tumors (TGCT) are the most common solid malignancy in adolescent and young men, with a rising incidence over the past 20 years. Overall, TGCTs are second in terms of the average life years lost per person dying of cancer, and clinical therapeutics without adverse long-term side effects are lacking. Platinum-based regimens for TGCTs have heterogeneous outcomes even within the same histotype that frequently leads to under- and over-treatment. Understanding of molecular differences that lead to diverse outcomes of TGCT patients may improve current treatment approaches. Seminoma is the most common subtype of TGCTs, which can either be pure or present in combination with other histotypes. Here we conducted a computational study of 64 pure seminoma samples from The Cancer Genome Atlas, applied consensus clustering approach to their transcriptomic data and revealed two clinically relevant seminoma subtypes: seminoma subtype 1 and 2. Our analysis identified significant differences in pluripotency stage, activity of double stranded DNA breaks repair mechanisms, rates of loss of heterozygosity, DNA methylation, and expression of lncRNA responsible for cisplatin resistance between the subtypes. Seminoma subtype 1 is characterized by higher pluripotency state, while subtype 2 showed attributes of reprogramming into non-seminomatous TGCT. The seminoma subtypes we identified may provide a molecular underpinning for variable responses to chemotherapy and radiation. Translating these findings into clinical care may help improve risk stratification of seminoma, decrease overtreatment rates, and increase long-term quality of life for TGCT survivors.

cancer biology↗

bioRxiv2021

Evolution of a chordate-specific mechanism for myoblast fusion

The size of an animal is determined by the size of its musculoskeletal system. Myoblast fusion is an innovative mechanism that allows for multinucleated muscle fibers to compound the size and strength of individual mononucleated cells. However, the evolutionary history of the control mechanism underlying this important process is currently unknown. The phylum Chordata hosts closely related groups that span distinct myoblast fusion states: no fusion in cephalochordates, restricted fusion and multinucleation in tunicates, and extensive, obligatory fusion in vertebrates. To elucidate how these differences may have evolved, we studied the evolutionary origins and function of membrane-coalescing agents Myomaker and Myomixer in various groups of chordates. Here we report that Myomaker likely arose through gene duplication in the last common ancestor of tunicates and vertebrates, while Myomixer appears to have evolved de novo in early vertebrates. Functional tests revealed an unexpectedly complex evolutionary history of myoblast fusion in chordates. A pre-vertebrate phase of muscle multinucleation driven by Myomaker was followed by the later emergence of Myomixer that enables the highly efficient fusion system of vertebrates. Thus, our findings reveal the evolutionary origins of chordate-specific fusogens and illustrate how new genes can shape the emergence of novel morphogenetic traits and mechanisms.

developmental biology↗

bioRxiv2021

Pseudomonas syringae effector AvrE associates with plant membrane nanodomains and binds phosphatidylinositides in vitro

Bacterial phytopathogens deliver effector proteins into host cells as key virulence weapons to cause disease. Extensive studies revealed diverse functions and biochemical properties of different effector proteins from pathogens. In this study, we show that the Pseudomonas syringae effector AvrE, the founding member of a broadly conserved and pathologically important bacterial effector family, binds to phosphatidylinositides (PIPs) in vitro and shares some properties with eukaryotic PROPPINs ({beta}-propellers that bind polyphosphoinositides). In planta pull down experiments with transgenic Arabidopsis plants expressing AvrE revealed that AvrE is associated with several plant proteins including plasma membrane lipid-raft proteins. These results shed new light on the properties of a bacterial effector that is crucial for bacterial virulence in plants.

pathology↗

bioRxiv2021

Accurate prediction of protein structures and interactions using a 3-track network

DeepMind presented remarkably accurate protein structure predictions at the CASP14 conference. We explored network architectures incorporating related ideas and obtained the best performance with a 3-track network in which information at the 1D sequence level, the 2D distance map level, and the 3D coordinate level is successively transformed and integrated. The 3-track network produces structure predictions with accuracies approaching those of DeepMind in CASP14, enables rapid solution of challenging X-ray crystallography and cryo-EM structure modeling problems, and provides insights into the functions of proteins of currently unknown structure. The network also enables rapid generation of accurate models of protein-protein complexes from sequence information alone, short circuiting traditional approaches which require modeling of individual subunits followed by docking. We make the method available to the scientific community to speed biological research. One-Sentence SummaryAccurate protein structure modeling enables rapid solution of structure determination problems and provides insights into biological function.

bioinformatics↗

bioRxiv2020

Role of two metacaspases in development and pathogenicity of the Rice Blast fungus, Magnaporthe oryzae

Rice blast disease caused by Magnaporthe oryzae is a devastating disease of cultivated rice worldwide. Infections by this fungus lead to a significant reduction in rice yields and threats to food security. To gain better insight into growth and cell death in M. oryzae during infection, we characterized two predicted M. oryzae metacaspase proteins, MoMca1 and MoMca2. These proteins appear to be functionally redundant and are able to complement the yeast Yca1 homologue. Biochemical analysis revealed that M. oryzae metacaspases exhibited Ca2+ dependent caspase activity in vitro. Deletion of both MoMca1 and MoMca2 in M. oryzae resulted in reduced sporulation, delay in conidial germination and attenuation of disease severity. In addition, the double {Delta}Momca1mca2 mutant strain showed increased radial growth in the presence of oxidative stress. Interestingly, the {Delta}Momca1mca2 strain showed an increase accumulation of insoluble aggregates compared to the wild-type strain during vegetative growth. Our findings suggest that MoMca1 and MoMca2 promote the clearance of insoluble aggregates in M. oryzae, demonstrating the important role these metacaspases have in fungal protein homeostasis. Furthermore, these metacaspase proteins may play additional roles, like in regulating stress responses, that would help maintain the fitness of fungal cells required for host infection. IMPORTANCEMagnaporthe oryzae causes rice blast disease that threatens global food security by resulting in the severe loss of rice production every year. A tightly regulated life cycle allows M. oryzae to disarm the host plant immune system during its biotrophic stage before triggering plant cell death in its necrotrophic stage. The ways M. oryzae navigates its complex life cycle remains unclear. This work characterizes two metacaspase proteins with peptidase activity in M. oryzae that are shown to be involved in the regulation of fungal growth and development prior to infection by potentially helping maintain fungal fitness. This study provides new insight into the role of metacaspase proteins in filamentous fungi by illustrating the delays in M. oryzae morphogenesis in the absence of these proteins. Understanding the mechanisms by which M. oryzae morphology and development promote its devastating pathogenicity may lead to the emergence of proper methods for disease control.

microbiology↗