Protein Structure Modeling Research Articles

Insight into the role of TXNRD2 in steroidogenesis through a novel homozygous TXNRD2 splice variant.

Adrenal cortisol production occurs through a biosynthetic pathway which depend on NADH and NADPH for energy supply. The mitochondrial respiratory chain and the reactive oxygen species (ROS) detoxification system are therefore important for steroidogenesis. Mitochondrial dysfunction leading to oxidative stress has been implicated in the pathogenesis of several adrenal conditions. Nonetheless, only very few patients with variants in one gene of the ROS detoxification system, Thioredoxin Reductase 2 (TXNRD2), have been described with variable phenotypes. Clinical, genetic, structural, and functional characterization of a novel, biallelic TXNRD2 splice variant. On human biomaterial, we performed whole exome sequencing to identify and RNA analysis to characterize the specific TXNRD2 splice variant. Amino acid conservation analysis and protein structure modeling were performed in silico. Using patient's fibroblast-derived human induced pluripotent stem cells, we generated adrenal-like cells (iALC) to study the impact of wild-type (WT) and mutant TXNRD2 on adrenal steroidogenesis and ROS production. The patient had a complex phenotype of primary adrenal insufficiency (PAI), combined with genital, ophthalmological, and neurological features. He carried a homozygous splice variant c.1348-1G > T in TXNRD2 which leads to a shorter protein lacking the C-terminus and thereby affecting homodimerization and flavin adenine dinucleotide binding. Patient-derived iALC showed a loss of cortisol production with overall diminished adrenal steroidogenesis, while ROS production was significantly increased. Lack of TXNRD2 activity for mitochondrial ROS detoxification affects adrenal steroidogenesis and predominantly cortisol production.

Unified Aedes aegypti Protein Resource Database (UAAPRD): An Integrated High-Throughput In Silico Platform for Comprehensive Protein Structure Modeling and Functional Target Analysis to Enhance Vector Control Strategies.

A comprehensive examination of Aedes aegypti's proteome to detect key proteins that can be targeted with small molecules can disrupt blood feeding and disease transmission. However, research currently only focuses on finding repellent-like compounds, limiting studies on identifying unexplored proteins in its proteome. High-throughput analysis generates vast amounts of data, raising concerns about accessibility and usability. Establishing a dedicated database is a solution, centralizing information on identified proteins, functions, and modeled structures for easy access and research. This study focuses on scrutinizing key proteins in A. aegypti, modeling their structures using RaptorX standalone tool, identification of druggable binding sites using BiteNet, validating the models via Ramachandran plot studies and refining them via 50-ns molecular dynamic simulations using Schrodinger Maestro. By analyzing ~ 18k proteins in the proteome of A. aegypti in our previous studies, all proteins involved in the light and dark circadian rhythm of the mosquito, inclusive of proteins in blood feeding, metabolism, etc. were chosen for the current study. The outcome is UAAPRD, a unique repository housing information on hundreds of previously unmodeled and un-simulated mosquito proteins. This robust MYSQL database ( https://uaaprd.onrender.com/user ) houses data on 309 modeled & simulated proteins of A. aegypti. It allows users to obtain protein data, view evolutionary analysis data of the protein categories, visualize proteins of interest, and send request to screen against the pharmacophore models present in UAAPRD against ligand of interest. This study offers crucial insights for developing targeted studies, which will ultimately contribute to more effective vector control strategies.

Evaluating the 3D structure prediction tools to identify optimal MEBPVC structure models

Multi-Epitope based Peptide Vaccines Candidates (MEBPVCs) are peptide sequences with the immunogenic epitopes interspersed with linkers, adjuvants and other components. The optimization of one of the components of MEBPVCs, epitope order, resulted in a unique epitope ordered MEBPVC showing improved vaccine potency. In addition to the above combinatorial optimization task, predicting the 3D structure of these MEBPVCs presents a significant challenge owing to the diverse modeling approaches employed by the modeling tools. Here we embarked on evaluating the performance of the threading based ab initio modeling tool: I-TASSER, Large Language Model (LLM) based tool: ESMFold and the MSA based artificial intelligence based tool: AlphaFold2. Our investigation employed RMSD, PEA, SASA, Solubility, Globularity, secondary structure analysis, Docking, MDS and MVP. I-TASSER emerged as the superior performer. The analysis revealed a strong correlation between predicted tertiary structure and secondary structure composition, especially the β-turns and random coils. It appears that accurate secondary structure identification to be a pivotal step, dictating the propagation of accuracy (or inaccuracy). This finding highlights the critical importance of secondary structure prediction/assignment algorithms for enhanced protein structure modeling accuracy especially to integrate predictions where evolutionary information is minimal and or lacking. Small sample size is attributed to the low significance of the parameters though Globularity and SASA have shown good p-values. Overall, the results support the need and utilization of the LLMs in biological research, underscoring their potential to revolutionize protein structure prediction and advance diverse biomolecular applications, accelerating Synthetic Biology research.

New variants expand the neurological phenotype of COQ7 deficiency.

The protein encoded by COQ7 is required for CoQ10 synthesis in humans, hydroxylating 3-demethoxyubiquinol (DMQ10) in the second to last steps of the pathway. COQ7 mutations lead to a primary CoQ10 deficiency syndrome associated with a pleiotropic neurological disorder. This study shows the clinical, physiological, and molecular characterization of four new cases of CoQ10 primary deficiency caused by five mutations in COQ7, three of which have not yet been described, inducing mitochondrial dysfunction in all patients. However, the specific combination of the identified variants in each patient generated precise pathophysiological and molecular alterations in fibroblasts, which would explain the differential in vitro response to supplementation therapy. Our results suggest that COQ7 dysfunction could be caused by specific structural changes that affect the interaction with COQ9 required for the DMQ10 presentation to COQ7, the substrate access to the active site, and the maintenance of the active site structure. Remarkably, patients' fibroblasts share transcriptional remodeling, supporting a modification of energy metabolism towards glycolysis, which could be an adaptive mechanism against CoQ10 deficiency. However, transcriptional analysis of mitochondria-associated pathways showed distinct and dramatic differences between patient fibroblasts, which correlated with the extent of pathophysiological and neurological alterations observed in the probands. Overall, this study suggests that the combination of precise genetic diagnostics and the availability of new structural models of human proteins could help explain the origin of phenotypic pleiotropy observed in some genetic diseases and the different responses to available therapies.

De novo atomic protein structure modeling for cryoEM density maps using 3D transformer and HMM

Accurately building 3D atomic structures from cryo-EM density maps is a crucial step in cryo-EM-based protein structure determination. Converting density maps into 3D atomic structures for proteins lacking accurate homologous or predicted structures as templates remains a significant challenge. Here, we introduce Cryo2Struct, a fully automated de novo cryo-EM structure modeling method. Cryo2Struct utilizes a 3D transformer to identify atoms and amino acid types in cryo-EM density maps, followed by an innovative Hidden Markov Model (HMM) to connect predicted atoms and build protein backbone structures. Cryo2Struct produces substantially more accurate and complete protein structural models than the widely used ab initio method Phenix. Additionally, its performance in building atomic structural models is robust against changes in the resolution of density maps and the size of protein structures.

Enhancing generalizability and performance in drug-target interaction identification by integrating pharmacophore and pre-trained models.

In drug discovery, it is crucial to assess the drug-target binding affinity (DTA). Although molecular docking is widely used, computational efficiency limits its application in large-scale virtual screening. Deep learning-based methods learn virtual scoring functions from labeled datasets and can quickly predict affinity. However, there are three limitations. First, existing methods only consider the atom-bond graph or one-dimensional sequence representations of compounds, ignoring the information about functional groups (pharmacophores) with specific biological activities. Second, relying on limited labeled datasets fails to learn comprehensive embedding representations of compounds and proteins, resulting in poor generalization performance in complex scenarios. Third, existing feature fusion methods cannot adequately capture contextual interaction information. Therefore, we propose a novel DTA prediction method named HeteroDTA. Specifically, a multi-view compound feature extraction module is constructed to model the atom-bond graph and pharmacophore graph. The residue concat graph and protein sequence are also utilized to model protein structure and function. Moreover, to enhance the generalization capability and reduce the dependence on task-specific labeled data, pre-trained models are utilized to initialize the atomic features of the compounds and the embedding representations of the protein sequence. A context-aware nonlinear feature fusion method is also proposed to learn interaction patterns between compounds and proteins. Experimental results on public benchmark datasets show that HeteroDTA significantly outperforms existing methods. In addition, HeteroDTA shows excellent generalization performance in cold-start experiments and superiority in the representation learning ability of drug-target pairs. Finally, the effectiveness of HeteroDTA is demonstrated in a real-world drug discovery study. The source code and data are available at https://github.com/daydayupzzl/HeteroDTA.

An all-atom protein generative model

Proteins mediate their functions through chemical interactions; modeling these interactions, which are typically through sidechains, is an important need in protein design. However, constructing an all-atom generative model requires an appropriate scheme for managing the jointly continuous and discrete nature of proteins encoded in the structure and sequence. We describe an all-atom diffusion model of protein structure, Protpardelle, which represents all sidechain states at once as a "superposition" state; superpositions defining a protein are collapsed into individual residue types and conformations during sample generation. When combined with sequence design methods, our model is able to codesign all-atom protein structure and sequence. Generated proteins are of good quality under the typical quality, diversity, and novelty metrics, and sidechains reproduce the chemical features and behavior of natural proteins. Finally, we explore the potential of our model to conduct all-atom protein design and scaffold functional motifs in a backbone- and rotamer-free way.

Ascorbate peroxidase catalyses synthesis of protocatechualdehyde from p-hydroxybenzaldehyde in Lycoris aurea

Protocatechualdehyde is a plant natural phenolic aldehyde and an active ingredient with important bioactivities in traditional Chinese medicine. Protocatechualdehyde is also a key intermediate in the synthesis of Amaryllidaceae alkaloids for supplying the C6-C1 skeleton. However, the biosynthesis of protocatechualdehyde in plants remains obscure. In this study, we measured the protocatechualdehyde contents in the root, bulb, scape and flower of the Amaryllidaceae plant Lycoris aurea (L’Hér.) Herb., and performed the correlation analysis between the protocatechualdehyde contents and the transcriptional levels of the phenolic oxidization candidate protein encoding genes. We found that a novel ascorbate peroxidase encoded by the contig_24999 in the L. aurea transcriptome database had potential role in the biosynthesis of protocatechualdehyde. The LauAPX_24999 gene was then cloned from the cDNA of the scape of L. aurea. The transient expression of LauAPX_24999 protein in Arabidopsis protoplasts demonstrated that LauAPX_24999 protein was localized in the cytoplasm, thus belonging to Class II L-ascorbate peroxidase. Subsequently, LauAPX_24999 protein was heterogenously expressed in Escherichia coli, and identified that LauAPX_24999 biosynthesized protocatechualdehyde from p-hydroxybenzaldehyde using L-ascorbic acid as the electron donor. The protein structure modelling and molecular docking indicated that p-hydroxybenzaldehyde could access to the active pocket of LauAPX_24999 protein, and reside at the δ-edge of the heme group while L-ascorbic acid binds at the γ-heme edge. To our knowledge, LauAPX_24999 is the first enzyme discovered in plants able to biosynthesize protocatechualdehyde from p-hydroxybenzaldehyde, and offers a competent enzyme resource for the biosynthesis of Amaryllidaceae alkaloids via synthetic biology.

GATSol, an enhanced predictor of protein solubility through the synergy of 3D structure graph and large language modeling

BackgroundProtein solubility is a critically important physicochemical property closely related to protein expression. For example, it is one of the main factors to be considered in the design and production of antibody drugs and a prerequisite for realizing various protein functions. Although several solubility prediction models have emerged in recent years, many of these models are limited to capturing information embedded in one-dimensional amino acid sequences, resulting in unsatisfactory predictive performance.ResultsIn this study, we introduce a novel Graph Attention network-based protein Solubility model, GATSol, which represents the 3D structure of proteins as a protein graph. In addition to the node features of amino acids extracted by the state-of-the-art protein large language model, GATSol utilizes amino acid distance maps generated using the latest AlphaFold technology. Rigorous testing on independent eSOL and the Saccharomyces cerevisiae test datasets has shown that GATSol outperforms most recently introduced models, especially with respect to the coefficient of determination R2, which reaches 0.517 and 0.424, respectively. It outperforms the current state-of-the-art GraphSol by 18.4% on the S. cerevisiae_test set.ConclusionsGATSol captures 3D dimensional features of proteins by building protein graphs, which significantly improves the accuracy of protein solubility prediction. Recent advances in protein structure modeling allow our method to incorporate spatial structure features extracted from predicted structures into the model by relying only on the input of protein sequences, which simplifies the entire graph neural network prediction process, making it more user-friendly and efficient. As a result, GATSol may help prioritize highly soluble proteins, ultimately reducing the cost and effort of experimental work. The source code and data of the GATSol model are freely available at https://github.com/binbinbinv/GATSol.

Sustained transmission over two decades of a previously unrecognised MPT64 negative Mycobacterium tuberculosis strain in Queensland, Australia: a whole genome sequencing study

MPT64 is a key protein used for Mycobacterium tuberculosis (MTB) complex strain identification. We describe protracted transmission of an MPT64 negative MTB strain in Queensland, Australia, and explore genomic factors related to its successful spread. All MPT64 negative strains identified between 2002 and 2022 by the Queensland Mycobacteria Reference Laboratory, and an additional 2 isolates from New South Wales (NSW), were whole genome sequenced. Bayesian modelling and phylogeographical analyses were used to assess their evolutionary history and transmission dynamics. Protein structural modelling to understand the putative functional effects of the mutated gene coding for MPT64 protein was performed. Forty-three MPT64 negative isolates were sequenced, belonging to a single MTB cluster of Lineage 4.1.1.1 strains. Combined with a UK dataset of the same lineage, molecular dating estimated 1990 (95% HPD 1987-1993) as the likely time of strain introduction into Australia. Although the strain has spread over a wide geographic area and new cases linked to the cluster continue to arise, phylodynamic analysis suggest the outbreak peaked around 2003. All MPT64 negative strains had a frame shift mutation (delAT, p.Val216fs) within the MPT64 gene, which confers two major structural rearrangements at the C-terminus of the protein. This study uncovered the origins of an MPT64 negative MTB outbreak in Australia, providing a richer understanding of its biology and transmission dynamics, as well as guidance for clinical diagnosis and public health action. The potential spread of MPT64 negative strains undermines the diagnostic utility of the MPT64 immunochromatographic test. This study was funded from an operational budget provided to the Queensland Mycobacterium Reference Laboratory by Pathology Queensland, Queensland Department of Health.

DAB2IP associates with hereditary angioedema: Insights into the role of VEGF signaling in HAE pathophysiology

BackgroundIn the recent years, there was an important improvement in the understanding of the pathogenesis of hereditary angioedema (HAE). Notwithstanding, in a large portion of patients with unknown mutation (HAE-UNK) the genetic cause remains to be identified. ObjectiveTo identify new genetic targets associated with HAE, a large Argentine family with HAE- UNK spanning three generations was studied. MethodsWhole exome sequencing was performed on affected family members to identify potential genetic variants associated with HAE-UNK. In silico analyses and experimental studies were applied to assess the role of the identified gene variant. ResultsA missense variant (p.D239N) in the DAB2IP gene was identified. The variant occurred in the C2-domain, the region interacting with VEGFR2. It was found to be rare, and predicted to have a detrimental effect on the functionality of the DAB2IP protein. Protein structure modeling predicted changes in the mutant p.D239N protein structure, impacting protein stability. The p.D239N variant affected the subcellular localization of VEGFR2. Cells transfected with the DAB2IP-239N transcript exhibited an intracellular distribution, and VEGFR2 remained associated with the cell membrane. The altered localization pattern indicated reduced co-localization of the mutant protein with VEGFR2, suggesting a diminished ability of VEGFR2 binding. ConclusionThe study identified a novel missense variant (p.D239N) in the DAB2IP gene in a family with HAE-UNK and highlighted the role of dysregulated VEGF-mediated signaling in altered endothelial permeability. DAB2IP loss-of-function pathogenic variants lead to the impairment of the endothelial VEGF/VEGFR2 ligand system and represent a new pathophysiologic cause of HAE-UNK.

Natural history of eukaryotic DNA viruses with double jelly-roll major capsid proteins

The phylum Preplasmiviricota (kingdom Bamfordvirae, realm Varidnaviria) is a broad assemblage of diverse viruses with comparatively short double-stranded DNA genomes (<50 kbp) that produce icosahedral capsids built from double jelly-roll major capsid proteins. Preplasmiviricots infect hosts from all cellular domains, testifying to their ancient origin, and, in particular, are associated with six of the seven supergroups of eukaryotes. Preplasmiviricots comprise four major groups of viruses, namely, polintons, polinton-like viruses (PLVs), virophages, and adenovirids. We used protein structure modeling and analysis to show that protein-primed DNA polymerases (pPolBs) of polintons, virophages, and cytoplasmic linear plasmids encompass an N-terminal domain homologous to the terminal proteins (TPs) of prokaryotic PRD1-like tectivirids and eukaryotic adenovirids that are involved in protein-primed replication initiation, followed by a viral ovarian tumor-like cysteine deubiquitinylase (vOTU) domain. The vOTU domain is likely responsible for the cleavage of the TP from the large pPolB polypeptide and is inactivated in adenovirids, in which TP is a separate protein. Many PLVs and transpovirons encode a distinct derivative of polinton-like pPolB that retains the TP, vOTU, and pPolB polymerization palm domains but lacks the exonuclease domain and instead contains a superfamily 1 helicase domain. Analysis of the presence/absence and inactivation of the vOTU domains and replacement of pPolB with other DNA polymerases in eukaryotic preplasmiviricots enabled us to outline a complete scenario for their origin and evolution.

Respiratory Syncytial Virus Vaccine Design Using Structure-Based Machine-Learning Models.

When designing live-attenuated respiratory syncytial virus (RSV) vaccine candidates, attenuating mutations can be developed through biologic selection or reverse-genetic manipulation and may include point mutations, codon and gene deletions, and genome rearrangements. Attenuation typically involves the reduction in virus replication, due to direct effects on viral structural and replicative machinery or viral factors that antagonize host defense or cause disease. However, attenuation must balance reduced replication and immunogenic antigen expression. In the present study, we explored a new approach in order to discover attenuating mutations. Specifically, we used protein structure modeling and computational methods to identify amino acid substitutions in the RSV nonstructural protein 1 (NS1) predicted to cause various levels of structural perturbation. Twelve different mutations predicted to alter the NS1 protein structure were introduced into infectious virus and analyzed in cell culture for effects on viral mRNA and protein expression, interferon and cytokine expression, and caspase activation. We found the use of structure-based machine learning to predict amino acid substitutions that reduce the thermodynamic stability of NS1 resulted in various levels of loss of NS1 function, exemplified by effects including reduced multi-cycle viral replication in cells competent for type I interferon, reduced expression of viral mRNAs and proteins, and increased interferon and apoptosis responses.

Insights into the Mechanism of Catalytic Activity of Plasmodium Parasite Malate-Quinone Oxidoreductase.

Plasmodium malate-quinone oxidoreductase (MQO) is a membrane flavoprotein catalyzing the oxidation of malate to oxaloacetate and the reduction of quinone to quinol. Recently, using a yeast expression system, we demonstrated that MQO, expressed in place of mitochondrial malate dehydrogenase (MDH), contributes to the TCA cycle and the electron transport chain in mitochondria, making MQO attractive as a promising drug target in Plasmodium malaria parasites, which lack mitochondrial MDH. However, there is little information on the structure of MQO and its catalytic mechanism, information that will be required to develop novel drugs. Here, we investigated the catalytic site of P. falciparum MQO (PfMQO) using our yeast expression system. We generated a model structure for PfMQO with the AI tool AlphaFold and used protein footprinting by acetylation with acetic anhydride to analyze the surface topology of the model, confirming the computational prediction to be reasonably accurate. Moreover, a putative catalytic site, which includes a possible flavin-binding site, was identified by this combination of protein footprinting and structural prediction model. This active site was analyzed by site-directed mutagenesis. By measuring enzyme activity and protein expression levels in the PfMQO mutants, we showed that several residues at the active site are essential for enzyme function. In addition, a single substitution mutation near the catalytic site resulted in enhanced sensitivity to ferulenol, an inhibitor of PfMQO that competes with malate for binding to the enzyme. This strongly supports the notion that the substrate binds to the proposed catalytic site. Then, the location of the catalytic site was demonstrated by structural comparison with a homologous enzyme. Finally, we used our results to propose a mechanism for the catalytic activity of MQO by reference to the mechanism of action of structurally or functionally homologous enzymes.

Identification of novel single nucleotide variants in the drug resistance mechanism of Mycobacterium tuberculosis isolates by whole-genome analysis

BackgroundTuberculosis (TB) represents a major global health challenge. Drug resistance in Mycobacterium tuberculosis (MTB) poses a substantial obstacle to effective TB treatment. Identifying genomic mutations in MTB isolates holds promise for unraveling the underlying mechanisms of drug resistance in this bacterium.MethodsIn this study, we investigated the roles of single nucleotide variants (SNVs) in MTB isolates resistant to four antibiotics (moxifloxacin, ofloxacin, amikacin, and capreomycin) through whole-genome analysis. We identified the drug-resistance-associated SNVs by comparing the genomes of MTB isolates with reference genomes using the MuMmer4 tool.ResultsWe observed a strikingly high proportion (94.2%) of MTB isolates resistant to ofloxacin, underscoring the current prevalence of drug resistance in MTB. An average of 3529 SNVs were detected in a single ofloxacin-resistant isolate, indicating a mutation rate of approximately 0.08% under the selective pressure of ofloxacin exposure. We identified a set of 60 SNVs associated with extensively drug-resistant tuberculosis (XDR-TB), among which 42 SNVs were non-synonymous mutations located in the coding regions of nine key genes (ctpI, desA3, mce1R, moeB1, ndhA, PE_PGRS4, PPE18, rpsA, secF). Protein structure modeling revealed that SNVs of three genes (PE_PGRS4, desA3, secF) are close to the critical catalytic active sites in the three-dimensional structure of the coding proteins.ConclusionThis comprehensive study elucidates novel resistance mechanisms in MTB against antibiotics, paving the way for future design and development of anti-tuberculosis drugs.

Bacterial cysteate dissimilatory pathway involves a racemase and d-cysteate sulfo-lyase

The sulfite-reducing bacterium Bilophila wadsworthia, a common human intestinal pathobiont, is unique in its ability to metabolize a wide variety of sulfonates to generate sulfite as a terminal electron acceptor (TEA). The resulting formation of H2S is implicated in inflammation and colon cancer. l-cysteate, an oxidation product of l-cysteine, is among the sulfonates metabolized by B. wadsworthia, although the enzymes involved remain unknown. Here we report a pathway for l-cysteate dissimilation in B. wadsworthia RZATAU, involving isomerization of l-cysteate to d-cysteate by a cysteate racemase (BwCuyB), followed by cleavage into pyruvate, ammonia and sulfite by a d-cysteate sulfo-lyase (BwCuyA). The strong selectivity of BwCuyA for d-cysteate over l-cysteate was rationalized by protein structural modeling. A homolog of BwCuyA in the marine bacterium Silicibacter pomeroyi (SpCuyA) was previously reported to be a l-cysteate sulfo-lyase, but our experiments confirm that SpCuyA too displays a strong selectivity for d-cysteate. Growth of B. wadsworthia with cysteate as the electron acceptor is accompanied by production of H2S and induction of BwCuyA. Close homologs of BwCuyA and BwCuyB are present in diverse bacteria, including many sulfate- and sulfite-reducing bacteria, suggesting their involvement in cysteate degradation in different biological environments.

The Development and Opportunities of Predictive Biotechnology.

Recent advances in bioeconomy allow a holistic view of existing and new process chains and enable novel production routines continuously advanced by academia and industry. All this progress benefits from a growing number of prediction tools that have found their way into the field. For example, automated genome annotations, tools for building model structures of proteins, and structural protein prediction methods such as AlphaFold2TM or RoseTTAFold have gained popularity in recent years. Recently, it has become apparent that more and more AI-based tools are being developed and used for biocatalysis and biotechnology. This is an excellent opportunity for academia and industry to accelerate advancements in the field further. Biotechnology, as a rapidly growing interdisciplinary field, stands to benefit greatly from these developments.

Cryo2StructData: A Large Labeled Cryo-EM Density Map Dataset for AI-based Modeling of Protein Structures

The advent of single-particle cryo-electron microscopy (cryo-EM) has brought forth a new era of structural biology, enabling the routine determination of large biological molecules and their complexes at atomic resolution. The high-resolution structures of biological macromolecules and their complexes significantly expedite biomedical research and drug discovery. However, automatically and accurately building atomic models from high-resolution cryo-EM density maps is still time-consuming and challenging when template-based models are unavailable. Artificial intelligence (AI) methods such as deep learning trained on limited amount of labeled cryo-EM density maps generate inaccurate atomic models. To address this issue, we created a dataset called Cryo2StructData consisting of 7,600 preprocessed cryo-EM density maps whose voxels are labelled according to their corresponding known atomic structures for training and testing AI methods to build atomic models from cryo-EM density maps. Cryo2StructData is larger than existing, publicly available datasets for training AI methods to build atomic protein structures from cryo-EM density maps. We trained and tested deep learning models on Cryo2StructData to validate its quality showing that it is ready for being used to train and test AI methods for building atomic models.

Computational analysis of missense mutations in dystrophin protein: Insights into domain-specific effects and functional implications

BackgroundDystrophin is a structural protein primarily found in skeletal muscles that connects trans-membrane component of dystrophin-glycoprotein complex to the intracellular cytoskeleton network. N-terminal domain of dystrophin binds to F-actin and its C-terminal domain attaches to dystrophin-associated complex (DAG) in the membrane. It serves as the bridge connecting the extracellular matrix to the actin based cytoskeleton of muscle cells across the plasma membrane. This complex works together to strengthen muscle fibers and shield them from damage as they contract and relax. ObjectiveOur Objective is to investigate the different pathogenic mutations in four distinct domains of dystrophin protein and to decipher how these mutations impact protein structure and function. In this study, we investigated the impact of disease causing missense mutations in isoforms of dystrophin – P.11532–1 (K18N.A165V, D3187G, F3228L); P11532–4 (Y223N, D3179G) and P-11532-11 (Y227N,D3183G).Occurrence of pathogenic mutations in dystrophin might compromise structural stability, interfere with protein-protein interactions or alter cellular signaling pathways collectively. All these can contribute to the progressive muscle degeneration observed in Duchene or Becker muscular dystrophy(DMD or BMD) and X-lined dilated cardiomyopathy(CMD3B) affected individuals. MethodsEvolutionary conservation analysis using multiple sequence alignment followed by pathogenicity prediction using web based server was performed. Homology modeling of mutants was performed bySWISS MODEL a fully automated homology-modeling server, accessible through the Expasy web server. These models were then analyzed using various servers such as Dynamut. Pymol was used for visualization and structural analysis. ResultsAnalyzed mutations cause structural instability by either gaining or losing specific interactions involved in the folding of protein. Loss of pi-pi stacking interactions has the potential to significant impact the overall stability of the protein. It is important to note that the majority of the mutations lead to stability defects rather than functional defects, ultimately contributing to different forms of muscular dystrophy. ConclusiónThe mutations in different domains of Dystrophin protein significantly alters the protein structure. This may in turn impact its ability to interact with other proteins. Understanding the specific impacts of these mutations can pave the way for development of targeted therapies that aims at restoring functionality of dystrophin and ameliorating the debilitating effects of muscular dystrophy.

Unraveling the key step in the aroma puzzle: Insights into alcohol acyltransferases in strawberries

Alcohol acyltransferases (AATs) play a crucial role in catalyzing the transfer of acyl groups, contributing to the diverse aroma of fruits, including strawberries. In this research we identified nine AAT genes in strawberries through a comprehensive analysis involving phylogenetics, gene structure, conserved motifs, and structural protein model examinations. The study used the ‘Camarosa’ strawberry genome database, and experiments were conducted with fruits harvested at different developmental and ripening stages. The transcriptional analysis revealed differential expression patterns among the AAT genes during fruit ripening, with only four genes (SAAT, FaAAT2, FaAAT7, and FaAAT9) showing increased transcript accumulation correlated with total AAT enzyme activity. Additionally, the study employed in silico methods, including sequence alignment, phylogenetic analysis, and structural modeling, to gain insights into the AAT protein model structures with increase expression pattern during fruit ripening. The four modeled AAT proteins exhibited structural similarities, including conserved catalytic sites and solvent channels. Furthermore, the research investigated the interaction of AAT proteins with different substrates, highlighting the enzymes' promiscuity in substrate preferences. The study contributes with valuable information to unveil AAT gene family members in strawberries, providing scientific background for further exploration of their biological characteristics and their role in aroma biosynthesis during fruit ripening.