Protein Structure Prediction Research Articles

DeepFold: enhancing protein structure prediction through optimized loss functions, improved template features, and re-optimized energy function.

Predicting protein structures with high accuracy is a critical challenge for the broad community of life sciences and industry. Despite progress made by deep neural networks like AlphaFold2, there is a need for further improvements in the quality of detailed structures, such as side-chains, along with protein backbone structures. Building upon the successes of AlphaFold2, the modifications we made include changing the losses of side-chain torsion angles and frame aligned point error, adding loss functions for side chain confidence and secondary structure prediction, and replacing template feature generation with a new alignment method based on conditional random fields. We also performed re-optimization by conformational space annealing using a molecular mechanics energy function which integrates the potential energies obtained from distogram and side-chain prediction. In the CASP15 blind test for single protein and domain modeling (109 domains), DeepFold ranked fourth among 132 groups with improvements in the details of the structure in terms of backbone, side-chain, and Molprobity. In terms of protein backbone accuracy, DeepFold achieved a median GDT-TS score of 88.64 compared with 85.88 of AlphaFold2. For TBM-easy/hard targets, DeepFold ranked at the top based on Z-scores for GDT-TS. This shows its practical value to the structural biology community, which demands highly accurate structures. In addition, a thorough analysis of 55 domains from 39 targets with publicly available structures indicates that DeepFold shows superior side-chain accuracy and Molprobity scores among the top-performing groups. DeepFold tools are open-source software available at https://github.com/newtonjoo/deepfold.

Prediction of folding patterns for intrinsic disordered protein

The conformation flexibility of natural protein causes both complexity and difficulty to understand the relationship between structure and function. The prediction of intrinsically disordered protein primarily is focusing on to disclose the regions with structural flexibility involving relevant biological functions and various diseases. The order of amino acids in protein sequence determines possible conformations, folding flexibility and biological function. Although many methods provided the information of intrinsically disordered protein (IDP), but the results are mainly limited to determine the locations of regions without knowledge of possible folding conformations. Here, the developed protein folding fingerprint adopted the protein folding variation matrix (PFVM) to reveal all possible folding patterns for the intrinsically disordered protein along its sequence. The PFVM integrally exhibited the intrinsically disordered protein with disordering regions, degree of disorder as well as folding pattern. The advantage of PFVM will not only provide rich information for IDP, but also may promote the study of protein folding problem.

ATLAS: protein flexibility description from atomistic molecular dynamics simulations.

Dynamical behaviour is one of the most crucial protein characteristics. Despite the advances in the field of protein structure resolution and prediction, analysis and prediction of protein dynamic properties remains a major challenge, mostly due to the low accessibility of data and its diversity and heterogeneity. To address this issue, we present ATLAS, a database of standardised all-atom molecular dynamics simulations, accompanied by their analysis in the form of interactive diagrams and trajectory visualisation. ATLAS offers a large-scale view and valuable insights on protein dynamics for a large and representative set of proteins, by combining data obtained through molecular dynamics simulations with information extracted from experimental structures. Users can easily analyse dynamic properties of functional protein regions, such as domain limits (hinge positions) and residues involved in interaction with other biological molecules. Additionally, the database enables exploration of proteins with uncommon dynamic properties conditioned by their environment such as chameleon subsequences and Dual Personality Fragments. The ATLAS database is freely available at https://www.dsimb.inserm.fr/ATLAS.

Homozygous missense variant in MEIOSIN causes premature ovarian insufficiency.

Are variants of genes involved in meiosis initiation responsible for premature ovarian insufficiency (POI)? A MEIOSIN variant participates in the pathogenesis of human POI by impairing meiosis due to insufficient transcriptional activation of essential meiotic genes. Meiosis is the key event for the establishment of the ovarian reserve, and several gene defects impairing meiotic homologous recombination have been found to contribute to the pathogenesis of POI. Although STRA8 and MEIOISN variants have been found to associate with POI in a recent study, the condition of other meiosis initiation genes is unknown and direct evidence of variants participating in the pathogenesis of POI is still lacking. This was a retrospective genetic study. An in-house whole exome sequencing (WES) database of 1030 idiopathic POI patients was screened for variations of meiosis initiation genes. Homozygous or compound heterozygous variations of genes involved in meiosis initiation were screened in the in-house WES database. The pathogenicity of the variation was verified by in vitro experiments, including protein structure prediction and dual-luciferase reporter assay. The effect of the variant on ovarian function and meiosis was demonstrated through histological analyses in a point mutation mouse model. One homozygous variant in MEIOSIN (c.1735C>T, p.R579W) and one in STRA8 (c.258 + 1G>A), which initiates meiosis via the retinoic acid-dependent pathway, were identified in a patient with idiopathic POI respectively. The STRA8 variation has been reported in the recently published work. For the MEIOSIN variation, the dual-luciferase reporter assay revealed that the variant adversely affected the transcriptional function of MEIOSIN in upregulating meiotic genes. Furthermore, knock-in mice with the homologous mutation confirmed that the variation impacted the meiotic prophase I program and accelerated oocyte depletion. Moreover, the variant p.R579W localizing in the high-mobility group (HMG) box domain disrupted the nuclear localization of the MEIOSIN protein but was dispensable for the cell-cycle switch of oocytes, suggesting a unique role of the MEIOSIN HMG box domain in meiosis initiation. Further studies are needed to explore the role of other meiosis initiation genes in the pathogenesis of POI. The MEIOSIN variant was verified to cause POI by impaired transcriptional regulation of meiotic genes and was inherited by a recessive mode. The function of HMG box domain in MEIOSIN protein was also expanded by this study. Although causative variations in meiotic initiation genes are rare in POI, our study confirmed the pathogenicity of a MEIOSIN variant and elucidated another mechanism of human infertility. This work was supported by the National Key Research & Developmental Program of China (2022YFC2703800, 2022YFC2703000), National Natural Science Foundation for Distinguished Young Scholars (82125014), National Natural Science Foundation of China (32070847, 32170867, 82071609), Basic Science Center Program of NSFC (31988101), Natural Science Foundation of Shandong Province for Grand Basic Projects (ZR2021ZD33), Natural Science Foundation of Shandong Province for Excellent Young Scholars (ZR2022YQ69), Taishan Scholars Program for Young Experts of Shandong Province (tsqn202211371), and Qilu Young Scholars Program of Shandong University. The authors declare no conflict of interest. N/A.

A sequence-based evolutionary distance method for Phylogenetic analysis of highly divergent proteins

Because of the limited effectiveness of prevailing phylogenetic methods when applied to highly divergent protein sequences, the phylogenetic analysis problem remains challenging. Here, we propose a sequence-based evolutionary distance algorithm termed sequence distance (SD), which innovatively incorporates site-to-site correlation within protein sequences into the distance estimation. In protein superfamilies, SD can effectively distinguish evolutionary relationships both within and between protein families, producing phylogenetic trees that closely align with those based on structural information, even with sequence identity less than 20%. SD is highly correlated with the similarity of the protein structure, and can calculate evolutionary distances for thousands of protein pairs within seconds using a single CPU, which is significantly faster than most protein structure prediction methods that demand high computational resources and long run times. The development of SD will significantly advance phylogenetics, providing researchers with a more accurate and reliable tool for exploring evolutionary relationships.

What AlphaFold tells us about cohesin's retention on and release from chromosomes.

Cohesin is a trimeric complex containing a pair of SMC proteins (Smc1 and Smc3) whose ATPase domains at the end of long coiled coils (CC) are interconnected by Scc1. During interphase, it organizes chromosomal DNA topology by extruding loops in a manner dependent on Scc1's association with two large hook-shaped proteins called SA (yeast: Scc3) and Nipbl (Scc2). The latter's replacement by Pds5 recruits Wapl, which induces release from chromatin via a process requiring dissociation of Scc1's N-terminal domain (NTD) from Smc3. If blocked by Esco (Eco)-mediated Smc3 acetylation, cohesin containing Pds5 merely maintains pre-existing loops, but a third fate occurs during DNA replication, when Pds5-containing cohesin associates with Sororin and forms structures that hold sister DNAs together. How Wapl induces and Sororin blocks release has hitherto remained mysterious. In the 20 years since their discovery, not a single testable hypothesis has been proposed as to their role. Here, AlphaFold 2 (AF) three-dimensional protein structure predictions lead us to propose formation of a quarternary complex between Wapl, SA, Pds5, and Scc1's NTD, in which the latter is juxtaposed with (and subsequently sequestered by) a highly conserved cleft within Wapl's C-terminal domain. AF also reveals how Scc1's dissociation from Smc3 arises from a distortion of Smc3's CC induced by engagement of SMC ATPase domains, how Esco acetyl transferases are recruited to Smc3 by Pds5, and how Sororin prevents release by binding to the Smc3/Scc1 interface. Our hypotheses explain the phenotypes of numerous existing mutations and are highly testable.

What AlphaFold tells us about cohesin’s retention on and release from chromosomes

Cohesin is a trimeric complex containing a pair of SMC proteins (Smc1 and Smc3) whose ATPase domains at the end of long coiled coils (CC) are interconnected by Scc1. During interphase, it organizes chromosomal DNA topology by extruding loops in a manner dependent on Scc1’s association with two large hook-shaped proteins called SA (yeast: Scc3) and Nipbl (Scc2). The latter’s replacement by Pds5 recruits Wapl, which induces release from chromatin via a process requiring dissociation of Scc1’s N-terminal domain (NTD) from Smc3. If blocked by Esco (Eco)-mediated Smc3 acetylation, cohesin containing Pds5 merely maintains pre-existing loops, but a third fate occurs during DNA replication, when Pds5-containing cohesin associates with Sororin and forms structures that hold sister DNAs together. How Wapl induces and Sororin blocks release has hitherto remained mysterious. In the 20 years since their discovery, not a single testable hypothesis has been proposed as to their role. Here, AlphaFold 2 (AF) three-dimensional protein structure predictions lead us to propose formation of a quarternary complex between Wapl, SA, Pds5, and Scc1’s NTD, in which the latter is juxtaposed with (and subsequently sequestered by) a highly conserved cleft within Wapl’s C-terminal domain. AF also reveals how Scc1’s dissociation from Smc3 arises from a distortion of Smc3’s CC induced by engagement of SMC ATPase domains, how Esco acetyl transferases are recruited to Smc3 by Pds5, and how Sororin prevents release by binding to the Smc3/Scc1 interface. Our hypotheses explain the phenotypes of numerous existing mutations and are highly testable.

Protein language models can capture protein quaternary state

BackgroundDetermining a protein’s quaternary state, i.e. the number of monomers in a functional unit, is a critical step in protein characterization. Many proteins form multimers for their activity, and over 50% are estimated to naturally form homomultimers. Experimental quaternary state determination can be challenging and require extensive work. To complement these efforts, a number of computational tools have been developed for quaternary state prediction, often utilizing experimentally validated structural information. Recently, dramatic advances have been made in the field of deep learning for predicting protein structure and other characteristics. Protein language models, such as ESM-2, that apply computational natural-language models to proteins successfully capture secondary structure, protein cell localization and other characteristics, from a single sequence. Here we hypothesize that information about the protein quaternary state may be contained within protein sequences as well, allowing us to benefit from these novel approaches in the context of quaternary state prediction.ResultsWe generated ESM-2 embeddings for a large dataset of proteins with quaternary state labels from the curated QSbio dataset. We trained a model for quaternary state classification and assessed it on a non-overlapping set of distinct folds (ECOD family level). Our model, named QUEEN (QUaternary state prediction using dEEp learNing), performs worse than approaches that include information from solved crystal structures. However, it successfully learned to distinguish multimers from monomers, and predicts the specific quaternary state with moderate success, better than simple sequence similarity-based annotation transfer. Our results demonstrate that complex, quaternary state related information is included in such embeddings.ConclusionsQUEEN is the first to investigate the power of embeddings for the prediction of the quaternary state of proteins. As such, it lays out strengths as well as limitations of a sequence-based protein language model approach, compared to structure-based approaches. Since it does not require any structural information and is fast, we anticipate that it will be of wide use both for in-depth investigation of specific systems, as well as for studies of large sets of protein sequences. A simple colab implementation is available at: https://colab.research.google.com/github/Furman-Lab/QUEEN/blob/main/QUEEN_prediction_notebook.ipynb.

Role of environmental specificity in CASP results

BackgroundRecently, significant progress has been made in the field of protein structure prediction by the application of artificial intelligence techniques, as shown by the results of the CASP13 and CASP14 (Critical Assessment of Structure Prediction) competition. However, the question of the mechanism behind the protein folding process itself remains unanswered. Correctly predicting the structure also does not solve the problem of, for example, amyloid proteins, where a polypeptide chain with an unaltered sequence adopts a different 3D structure.ResultsThis work was an attempt at explaining the structural variation by considering the contribution of the environment to protein structuring. The application of the fuzzy oil drop (FOD) model to assess the validity of the selected models provided in the CASP13, CASP14 and CASP15 projects reveals the need for an environmental factor to determine the 3D structure of proteins. Consideration of the external force field in the form of polar water (Fuzzy Oil Drop) and a version modified by the presence of the hydrophobic compounds, FOD-M (FOD-Modified) reveals that the protein folding process is environmentally dependent. An analysis of selected models from the CASP competitions indicates the need for structure prediction as dependent on the consideration of the protein folding environment.ConclusionsThe conditions governed by the environment direct the protein folding process occurring in a certain environment. Therefore, the variation of the external force field should be taken into account in the models used in protein structure prediction.

Unsupervisedly Prompting AlphaFold2 for Accurate Few-Shot Protein Structure Prediction.

Data-driven predictive methods that can efficiently and accurately transform protein sequences into biologically active structures are highly valuable for scientific research and medical development. Determining an accurate folding landscape using coevolutionary information is fundamental to the success of modern protein structure prediction methods. As the state of the art, AlphaFold2 has dramatically raised the accuracy without performing explicit coevolutionary analysis. Nevertheless, its performance still shows strong dependence on available sequence homologues. Based on the interrogation on the cause of such dependence, we presented EvoGen, a meta generative model, to remedy the underperformance of AlphaFold2 for poor MSA targets. By prompting the model with calibrated or virtually generated homologue sequences, EvoGen helps AlphaFold2 fold accurately in the low-data regime and even achieve encouraging performance with single-sequence predictions. Being able to make accurate predictions with few-shot MSA not only generalizes AlphaFold2 better for orphan sequences but also democratizes its use for high-throughput applications. Besides, EvoGen combined with AlphaFold2 yields a probabilistic structure generation method that could explore alternative conformations of protein sequences, and the task-aware differentiable algorithm for sequence generation will benefit other related tasks including protein design.

CNSC-33. MOLECULAR AND FUNCTIONAL DRIVERS OF ACTIVITY DEPENDENT GLIOBLASTOMA PROLIFERATION

Abstract Glioblastoma, the most common primary brain tumor in adults, is a major cause of neurological morbidity and mortality with no effective therapies. Its proliferation and invasion are regulated by direct and paracrine-mediated neuronal activity. In this study, we uncover molecular targets and protein-protein signaling driving activity dependent proliferation. To identify candidate molecular pathways, we used high-density electrode arrays in vivo to record human local field potentials. Single-cell RNA sequencing identified Netrin-G1 (NTNG1) and thrombospondin-1 (TSP1) elevation in glioblastoma clonal population that proliferates in response to the presence of neurons. The amino acid sequence of NTNG1 and the TSR1 domain of TSP1 were utilized to predict the 3D interaction surface of NTNG1 and TSP1. Compared to structure predictions of NTNG1 and its known binding partner NGL1, NTNG1 and TSP1 complex predictions showed agreement between machine learning predicted models and lower predicted aligned error in interdomain regions of the complex, suggesting higher confidence in the relative position of NTNG1-TSP1 as compared to NTNG1-NGL1. We performed 45 protein structure predictions and two molecular dynamics simulations of mutant and WT TSP1 in complex with NTNG1. Each simulation box consisted of >263,000 atoms, each run for 50 million 2 femtosecond timesteps for a total simulation time of 200 ns. These calculations confirmed a stable complex in the WT condition and significant conformational change in the R46A condition, emphasizing the importance of this residue in mediating NTNG1-TSP1 binding. Computational affinity models between NTNG1 and TSP1 were validated by pull-down assays and immunofluorescence labelling using cerebral organoid and human glioma-neuronal coculture models. Finally, we design small molecule and fusion-protein inhibitors disrupting this interaction. Here we advance the development of targeted precision-medicine therapies to treat glioblastoma proliferation through activity-dependent mechanisms.

Enhancing alphafold-multimer-based protein complex structure prediction with MULTICOM in CASP15

To enhance the AlphaFold-Multimer-based protein complex structure prediction, we developed a quaternary structure prediction system (MULTICOM) to improve the input fed to AlphaFold-Multimer and evaluate and refine its outputs. MULTICOM samples diverse multiple sequence alignments (MSAs) and templates for AlphaFold-Multimer to generate structural predictions by using both traditional sequence alignments and Foldseek-based structure alignments, ranks structural predictions through multiple complementary metrics, and refines the structural predictions via a Foldseek structure alignment-based refinement method. The MULTICOM system with different implementations was blindly tested in the assembly structure prediction in the 15th Critical Assessment of Techniques for Protein Structure Prediction (CASP15) in 2022 as both server and human predictors. MULTICOM_qa ranked 3rd among 26 CASP15 server predictors and MULTICOM_human ranked 7th among 87 CASP15 server and human predictors. The average TM-score of the first predictions submitted by MULTICOM_qa for CASP15 assembly targets is ~0.76, 5.3% higher than ~0.72 of the standard AlphaFold-Multimer. The average TM-score of the best of top 5 predictions submitted by MULTICOM_qa is ~0.80, about 8% higher than ~0.74 of the standard AlphaFold-Multimer. Moreover, the Foldseek Structure Alignment-based Multimer structure Generation (FSAMG) method outperforms the widely used sequence alignment-based multimer structure generation.

TrRosettaRNA: automated prediction of RNA 3D structure with transformer network

RNA 3D structure prediction is a long-standing challenge. Inspired by the recent breakthrough in protein structure prediction, we developed trRosettaRNA, an automated deep learning-based approach to RNA 3D structure prediction. The trRosettaRNA pipeline comprises two major steps: 1D and 2D geometries prediction by a transformer network; and 3D structure folding by energy minimization. Benchmark tests suggest that trRosettaRNA outperforms traditional automated methods. In the blind tests of the 15th Critical Assessment of Structure Prediction (CASP15) and the RNA-Puzzles experiments, the automated trRosettaRNA predictions for the natural RNAs are competitive with the top human predictions. trRosettaRNA also outperforms other deep learning-based methods in CASP15 when measured by the Z-score of the Root-Mean-Square Deviation. Nevertheless, it remains challenging to predict accurate structures for synthetic RNAs with an automated approach. We hope this work could be a good start toward solving the hard problem of RNA structure prediction with deep learning.

AlphaFold Blindness to Topological Barriers Affects Its Ability to Correctly Predict Proteins' Topology.

AlphaFold is a groundbreaking deep learning tool for protein structure prediction. It achieved remarkable accuracy in modeling many 3D structures while taking as the user input only the known amino acid sequence of proteins in question. Intriguingly though, in the early steps of each individual structure prediction procedure, AlphaFold does not respect topological barriers that, in real proteins, result from the reciprocal impermeability of polypeptide chains. This study aims to investigate how this failure to respect topological barriers affects AlphaFold predictions with respect to the topology of protein chains. We focus on such classes of proteins that, during their natural folding, reproducibly form the same knot type on their linear polypeptide chain, as revealed by their crystallographic analysis. We use partially artificial test constructs in which the mutual non-permeability of polypeptide chains should not permit the formation of complex composite knots during natural protein folding. We find that despite the formal impossibility that the protein folding process could produce such knots, AlphaFold predicts these proteins to form complex composite knots. Our study underscores the necessity for cautious interpretation and further validation of topological features in protein structures predicted by AlphaFold.

Ab initio protein structure prediction: the necessary presence of external force field as it is delivered by Hsp40 chaperone

BackgroundThe aqueous environment directs the protein folding process towards the generation of micelle-type structures, which results in the exposure of hydrophilic residues on the surface (polarity) and the concentration of hydrophobic residues in the center (hydrophobic core). Obtaining a structure without a hydrophobic core requires a different type of external force field than those generated by a water. The examples are membrane proteins, where the distribution of hydrophobicity is opposite to that of water-soluble proteins. Apart from these two extreme examples, the process of protein folding can be directed by chaperones, resulting in a structure devoid of a hydrophobic core.ResultsThe current work presents such example: DnaJ Hsp40 in complex with alkaline phosphatase PhoA-U (PDB ID—6PSI)—the client molecule. The availability of WT form of the folding protein—alkaline phosphatase (PDB ID—1EW8) enables a comparative analysis of the structures: at the stage of interaction with the chaperone and the final, folded structure of this biologically active protein. The fuzzy oil drop model in its modified FOD-M version was used in this analysis, taking into account the influence of an external force field, in this case coming from a chaperone.ConclusionsThe FOD-M model identifies the external force field introduced by chaperon influencing the folding proces. The identified specific external force field can be applied in Ab Initio protein structure prediction as the environmental conditioning the folding proces.

Predicting the effects of mutations on protein solubility using graph convolution network and protein language model representation.

Solubility is one of the most important properties of protein. Protein solubility can be greatly changed by single amino acid mutations and the reduced protein solubility could lead to diseases. Since experimental methods to determine solubility are time-consuming and expensive, in-silico methods have been developed to predict the protein solubility changes caused by mutations mostly through protein evolution information. However, these methods are slow since it takes long time to obtain evolution information through multiple sequence alignment. In addition, these methods are of low performance because they do not fully utilize protein 3D structures due to a lack of experimental structures for most proteins. Here, we proposed a sequence-based method DeepMutSol to predict solubility change from residual mutations based on the Graph Convolutional Neural Network (GCN), where the protein graph was initiated according to predicted protein structure from Alphafold2, and the nodes (residues) were represented by protein language embeddings. To circumvent the small data of solubility changes, we further pretrained the model over absolute protein solubility. DeepMutSol was shown to outperform state-of-the-art methods in benchmark tests. In addition, we applied the method to clinically relevant genes from the ClinVar database and the predicted solubility changes were shown able to separate pathogenic mutations. All of the data sets and the source code are available at https://github.com/biomed-AI/DeepMutSol.

Protein Secondary Structure Prediction and Perceptions of Complexities using Deep Neural Network

The Protein molecule is known as the large biological molecule in a living organism. The protein performs several works like transporting molecules, catalysing metabolic reaction, responding to stimuli etc in a human body. Protein Structure analysis and prediction is very much essential to make any research about the same protein molecule. The basic intention of protein structure prediction (PSP) is to predict the three dimensional structure that generate by the amino acid sequence. The very peculiar matter is only twenty amino acid found in a living body where as approximately one lakh protein molecules can be framed from the same amino acid compositions in different percentages. The three dimensional structure framed by the amino acid compositions generally changes its shape and size due to the effect of external agents or medicines that comes in contact with these protein molecules. The basic intention behind the prediction of structure of the protein is to design new drugs or medicines. From the structures the medicine researchers working for the development of medicines may easily detect the changes in the living body or the requirement of drugs or medicines. The detection of the structure and the prediction of perfect structure is always a challenging task. The protein structure is basically a three dimensional structure in its secondary transformation. The structure may be in the form of ? Helix, ? sheets or loop etc. In this paper the identification of the secondary structures and the percentages of ? Helix, ? sheets or loop structures are being predicted and the probable complexities that may occur during the prediction is discussed. Deep neural network is a deep structured learning process is an application of the broader family machine learning. Deep learning architectures has a number application in various fields like medical science, bioinformatics, medical image analysis etc. A novel method is being proposed in this research article for the detection, correction and removal of various complexities during prediction using deep neural network. This technique will be helpful for different researchers working in the field for drug design and medicine research.

Selective Activation of a TRPC6 Ion Channel Over TRPC3 by Metalated Type-B Polycyclic Polyprenylated Acylphloroglucinols.

Selective modulation of TRPC6 ion channels is a promising therapeutic approach for neurodegenerative diseases and depression. A significant advancement showcases the selective activation of TRPC6 through metalated type-B PPAP, termed PPAP53. This success stems from PPAP53's 1,3-diketone motif facilitating metal coordination. PPAP53 is water-soluble and as potent as hyperforin, the gold standard in this field. In contrast to type-A, type-B PPAPs offer advantages such as gram-scale synthesis, easy derivatization, and long-term stability. Our investigations reveal PPAP53 selectively binding to the C-terminus of TRPC6. Although cryoelectron microscopy has resolved the majority of the TRPC6 structure, the binding site in the C-terminus remained unresolved. To address this issue, we employed state-of-the-art artificial-intelligence-based protein structure prediction algorithms to predict the missing region. Our computational results, validated against experimental data, indicate that PPAP53 binds to the 777LLKL780-region of the C-terminus, thus providing critical insights into the binding mechanism of PPAP53.

AlphaFold Protein Structure Database in 2024: providing structure coverage for over 214 million protein sequences.

The AlphaFold Database Protein Structure Database (AlphaFold DB, https://alphafold.ebi.ac.uk) has significantly impacted structural biology by amassing over 214 million predicted protein structures, expanding from the initial 300k structures released in 2021. Enabled by the groundbreaking AlphaFold2 artificial intelligence (AI) system, the predictions archived in AlphaFold DB have been integrated into primary data resources such as PDB, UniProt, Ensembl, InterProand MobiDB. Our manuscript details subsequent enhancements in data archiving, covering successive releases encompassing model organisms, global health proteomes, Swiss-Prot integration, and a host of curated protein datasets. We detail the data access mechanisms of AlphaFold DB, from direct file access via FTP to advanced queries using Google Cloud Public Datasets and the programmatic access endpoints of the database. We also discuss the improvements and services added since its initial release, including enhancements to the Predicted Aligned Error viewer, customisation options for the 3D viewer, and improvements in the search engine of AlphaFold DB.

Transcriptional deciphering of the metabolic pathways associated with the bioactive ingredients of wolfberry species with different quality characteristics

BackgroundWolfberry is rich in carotenoids, flavonoids, vitamins, alkaloids, betaines and other bioactive ingredients. For over 2,000 years, wolfberry has been used in China as a medicinal and edible plant resource. Nevertheless, the content of bioactive ingredients varies by cultivars, resulting in uneven quality across wolfberry cultivars and species. To date, research has revealed little about the underlying molecular mechanism of the metabolism of flavonoids, carotenoids, and other bioactive ingredients in wolfberry.ResultsIn this context, the transcriptomes of the Lycium barbarum L. cultivar ‘Ningqi No. 1’ and Lycium chinense Miller were compared during the fruit maturity stage using the Illumina NovaSeq 6000 sequencing platform, and subsequently, the changes of the gene expression profiles in two types of wolfberries were analysed. In total, 256,228,924 clean reads were obtained, and 8817 differentially expressed genes (DEGs) were identified, then assembled by Basic Local Alignment Search Tool (BLAST) similarity searches and annotated using Gene Ontology (GO), Clusters of Orthologous Groups of proteins (KOG), and the Kyoto Encyclopedia of Genes and Genomes (KEGG). By combining these transcriptome data with data from the PubMed database, 36 DEGs related to the metabolism of bioactive ingredients and implicated in the metabolic pathway of carotenoids, flavonoids, terpenoids, alkaloids, vitamins, etc., were identified. In addition, among the 9 differentially expressed transcription factors, LbAPL, LbPHL11 and LbKAN4 have raised concerns. The protein physicochemical properties, structure prediction and phylogenetic analysis indicated that LbAPL and LbPHL11 may be good candidate genes involved in regulating the flavonoid metabolism pathway in wolfberry.ConclusionsThis study provides preliminary evidence for the differences in bioactive ingredient content at the transcription level among different wolfberry species, as well as a research and theoretical basis for the screening, cloning and functional analysis of key genes involved in the metabolism of bioactive ingredients in wolfberry.