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

PROPERMAB: an integrative framework for in silico prediction of antibody developability using machine learning

Selection of lead therapeutic molecules is often driven predominantly by pharmacological efficacy and safety. Candidate developability, such as biophysical properties that affect the formulation of the molecule into a product, is usually evaluated only toward the end of the drug development pipeline. The ability to evaluate developability properties early in the process of antibody therapeutic development could accelerate the timeline from discovery to clinic and save considerable resources. In silico predictive approaches, such as machine learning models, which map molecular features to predictions of developability properties could offer a cost-effective and high-throughput alternative to experiments for antibody developability assessment. We developed a computational framework, PROPERMAB (PROPERties of Monoclonal AntiBodies), for large-scale and efficient in silico prediction of developability properties for monoclonal antibodies, using custom molecular features and machine learning modeling. We demonstrate the power of PROPERMAB by using it to develop models to predict antibody hydrophobic interaction chromatography retention time and high-concentration viscosity. We further show that structure-derived features can be rapidly and accurately predicted directly from sequences by pre-training simple models for molecular features, thus providing the ability to scale these approaches to repertoire-scale sequence datasets.

PocketDTA: A pocket-based multimodal deep learning model for drug-target affinity prediction

Drug-target affinity prediction is a fundamental task in the field of drug discovery. Extracting and integrating structural information from proteins effectively is crucial to enhance the accuracy and generalization of prediction, which remains a substantial challenge. This paper proposes a pocket-based multimodal deep learning model named PocketDTA for drug-target affinity prediction, based on the principle of "structure determines function". PocketDTA introduces the pocket graph structure that encodes protein residue features pretrained using a biological language model as nodes, while edges represent different protein sequences and spatial distances. This approach overcomes the limitations of lack of spatial information in traditional prediction models with only protein sequence input. Furthermore, PocketDTA employs relational graph convolutional networks at both atomic and residue levels to extract structural features from drugs and proteins. By integrating multimodal information through deep neural networks, PocketDTA combines sequence and structural data to improve affinity prediction accuracy. Experimental results demonstrate that PocketDTA outperforms state-of-the-art prediction models across multiple benchmark datasets by showing strong generalization under more realistic data splits and confirming the effectiveness of pocket-based methods for affinity prediction.

A critical address to advancements and challenges in computational strategies for structural prediction of protein in recent past

Protein structure prediction has undergone significant advancements, driven by the limitations of experimental techniques like X-ray crystallography, NMR, and cryo-EM, which are costly and time-consuming. To bridge the gap between protein sequences and their structures, computational methods have emerged as essential tools. Traditional approaches such as homology modeling, threading, and ab initio folding made progress but often lacked atomic-level precision. The field has been revolutionized by deep learning-based models such as AlphaFold2, RoseTTAFold, and OpenFold, which have demonstrated unprecedented accuracy in predicting protein structures. These AI-driven models leverage vast datasets and neural networks to generate highly reliable structural predictions, sometimes rivaling experimental methods. This review explores the historical evolution of computational protein structure prediction, analyzing the strengths and weaknesses of state-of-the-art models. These models have broad applications in fields such as drug discovery, enzyme engineering, and disease-related protein modeling. However, challenges remain, including the need for extensive training data, computational resource requirements, and difficulties in modeling protein dynamics, intrinsically disordered regions, and protein-protein interactions. Future directions in the field include improving AI models to address current limitations, better integration with experimental techniques, and extending predictions to protein complexes and post-translational modifications. By continuing to refine these methods, computational protein structure prediction will further enhance biomedical research and therapeutic design, reshaping the landscape of structural biology and computational biophysics.

A single amino acid substitution increases both carboxylation turnover number and CO2 affinity of form II Rubisco

Ribulose-1,5-bisphosphate carboxylase/oxygenase (Rubisco), the key CO2-fixing enzyme in photosynthesis, is notorious for its low carboxylation activity. However, the difficulty in rationally engineering a fast Rubisco over the past decades brings a question whether a constraint exists in Rubisco's catalytic mechanism. In this study, we show that altering a single amino acid at position 398 in Form II Rubisco doubles its catalytic efficiency. The T398S and T398A mutations of the Form II Rubisco from the symbiont of Riftia pachyptila increases activity by 61 % and 74 %, respectively. The T398A mutant exhibits a turnover number (kcatC) of 35.84 s-1, twice that of the wild type. Structural simulation analysis indicates that the distance between the amino acid residues at position 398 and 395 influences weak hydrogen bond formation. Remarkably, these enhancements were achieved without compromising CO2 affinity (KMC), challenging the conventional trade-off paradigm. Our findings not only identify residue 398 as a critical determinant of Rubisco's performance but also highlight the untapped potential for engineering more efficient CO2-fixing enzymes.

Systems biology of Haemonchus contortus - Advancing biotechnology for parasitic nematode control

Parasitic nematodes represent a substantial global burden, impacting animal health, agriculture and economies worldwide. Of these worms, Haemonchus contortus - a blood-feeding nematode of ruminants - is a major pathogen and a model for molecular and applied parasitology research. This review synthesises some key advances in understanding the molecular biology, genetic diversity and host-parasite interactions of H. contortus, highlighting its value for comparative studies with the free-living nematode Caenorhabditis elegans. Key themes include recent developments in genomic, transcriptomic and proteomic technologies and resources, which are illuminating critical molecular pathways, including the ubiquitination pathway, protease/protease inhibitor systems and the secretome of H. contortus. Some of these insights are providing a foundation for identifying essential genes and exploring their potential as targets for novel anthelmintics or vaccines, particularly in the face of widespread anthelmintic resistance. Advanced bioinformatic tools, such as machine learning (ML) algorithms and artificial intelligence (AI)-driven protein structure prediction, are enhancing annotation capabilities, facilitating and accelerating analyses of gene functions, and biological pathways and processes. This review also discusses the integration of these tools with cutting-edge single-cell sequencing and spatial transcriptomics to dissect host-parasite interactions at the cellular level. The discussion emphasises the importance of curated databases, improved culture systems and functional genomics platforms to translate molecular discoveries into practical outcomes, such as novel interventions. New research findings and resources not only advance research on H. contortus and related nematodes but may also pave the way for innovative solutions to the global challenges with anthelmintic resistance.

Biomolecular conformational changes and transient druggable binding sites through full-length AMPK molecular dynamics simulations

AMPK (AMP-activated protein kinase) is a crucial signaling protein found in essentially all eukaryotic organisms and acts as an energy sensor. When activated by metabolic stress, AMPK phosphorylates a variety of molecular targets, altering enzyme activity and gene expression to regulate cellular responses. In general, in response to low intracellular ATP levels (high ADP:ATP ratio), AMPK triggers the activation of energy-producing pathways while simultaneously inhibiting energy-consuming processes. Recent studies have established a connection between molecular pathways involved in sensing energy and potential for extending longevity. AMPK indirect activator compounds have shown a potential strategy to obtain an anti-aging biological activity. This study explores the conformational changes and transient druggable binding pockets over the 1 μs trajectory of molecular dynamics simulations to comprehend the behavior of main domains and allosteric drug and metabolite (ADaM) site. The described conformations of the apo-ADaM site suggest an important influence of specific residues on the cavity volume variations. A clustering set of representative AMPK conformations allowed to identify the more favorable binding site volume and shape at the protein apo form, including the carbohydrate-binding module (CBM) region which exhibited a stable movement near the ADaM site of the alpha-subunit. The identification of gamma-subunit transient druggable binding pocket CBS3 during the microscale time trajectory simulations also offers valuable insights into structure-based AMP-mimetic drug design for AMPK activation.

Cucumber green mottle mosaic virus encodes additional small proteins with specific subcellular localizations and virulence function

The vast majority of known viruses belong to the positive-sense single-stranded RNA (+ssRNA) class. Tobamoviruses are among the most destructive plant viruses and threaten global food security. It is generally accepted that +ssRNA viruses including tobamoviruses encode proteins solely on their positive strand (+RNA). Here, we identified additional open-reading frames (ORFs) in the negative strand of tobamoviruses, named reverse ORFs (rORFs). Using cucumber green mottle mosaic virus (CGMMV) as a model, we detected the corresponding peptides of rORFs by mass spectrometry analysis and confirmed the translation of rORFs by ribosome profiling. Furthermore, we demonstrated that these rORFs may be translated from an internal ribosome entry site. Mutation of rORF1 and rORF2 significantly reduced the virulence of CGMMV, whereas ectopic expression of rORF1 and rORF2 could rescue the pathogenicity of the mutants. While the rORF2 protein localizes at the cell membrane and in the nucleolus, rORF1 colocalizes with peroxisomes, where it interacts with the viral 126-kD replication protein. Additionally, we screened peroxisomal rORF1-interacting proteins using artificial intelligence tools and found that PEX3 mediated rORF1 targeting to peroxisomes. This study reveals that the tobamoviral proteome is larger than previously thought, and sheds light on peroxisomes as novel virulence targets important for virus infectivity.

Structural insights on perlecan and Schwartz-Jampel syndrome

Perlecan is an essential multi-domain, disulfide bond rich basement membrane protein. Mutations in perlecan cause Schwartz-Jampel syndrome and dyssegmental dysplasia. While there has been a large body of experimental work reported on perlecan, there is only minimal structural information available to date. There is no prior structural data for region 3 of perlecan in which some Schwartz-Jampel syndrome causing point mutations have been reported. Here, we produce constructs of the disulfide rich region 3 of perlecan along with five mutations previously reported to cause Schwatz-Jampel syndrome. Four of the mutations resulted in decreased yields and thermal stability compared to the wild-type protein. In contrast, the P1019L mutation was produced in good yields and showed higher thermal stability than the wild-type protein. The crystal structures for both the wild-type and P1019L mutation were solved. As expected, both showed laminin IV-like and laminin-type EGF-like domains, with the P1019L mutation resulting in only a minor conformational change in a loop region and no significant changes in regular secondary or tertiary structure.

Exploring the structural and functional dynamics of trimeric and tetrameric states of influenza encoded PB1-F2 viroporin through molecular dynamics simulations

Influenza Viruses have always been a major health concern due to their highly contagious nature. The PB1-F2 viroporin encoded by the influenza A virus is known to be a pro-apoptotic protein involved in cell death induction of the host immune cells. The structural arrangement and the mode of action of PB1-F2 viroporin have not been fully understood yet. Nonetheless, there is limited information on the oligomeric state of PB1-F2 and its possible role in the pore formation which could act as a channel for ion transport. The probable oligomeric structural existences of the viroporin and their channel-like behavior need to be explored in light of experimental reports cited in the literature. In our study, we report on the structural and dynamical properties of the trimeric and tetrameric state of PB1-F2, investigated by molecular dynamics simulations with improved sampling of conformational states as the initial focus of the study is to establish a rationale for their existence in a lipid environment. The simulation study provides detailed information on the mitochondrial membrane permeation pathway which causes the leakage of mitochondrial contents like cytochrome C and induces apoptosis. By focusing on low-order oligomers, trimer, and tetramer, we have identified key pore-forming characteristics that serve as a foundation for understanding the pro-apoptotic activity of PB1-F2. The structural and dynamical properties of these states were evaluated in the light of experimental reports, which reveal the tetrameric form to be the preferable state in the lipid environment, demonstrating superior structural stability, effective channel symmetry, and ion permeation compared to the higher-order oligomers besides trimer including pentameric and hexameric assemblies. The simulation results also explore the typical ion transportation criteria based on finding a less energetic barrier for ions/water molecules crossing the membrane.

A comprehensive primer and review of PROTACs and their In Silico design

The cutting-edge technique of Proteolysis Targeting Chimeras, or PROTACs, has gained significant attention as a viable approach for specific protein degradation. This innovative technology has vast potential in fields such as cancer therapy and drug development. The development of effective and specific therapies for a range of diseases is within reach with PROTACs, which can target previously "undruggable" proteins while circumventing the off-target effects of conventional small molecule inhibitors. This manuscript aims to discuss the application of in silico techniques to the design of these groundbreaking molecules and develop PROTAC complexes, in order to identify potential PROTAC candidates with favorable drug-like properties. Additionally, this manuscript reviews the strengths and weaknesses of these methods to demonstrate their utility and highlights the challenges and future prospects of in silico PROTAC design. The present review provides a valuable and beginner-friendly resource for researchers and drug developers interested in using in silico methods for PROTAC design, specifically ternary structure prediction.

A Computational Approach to Characterize the Protein S-Mer Tyrosine Kinase (PROS1-MERTK) Protein-Protein Interaction Dynamics

Protein S (PROS1) has recently been identified as a ligand for the TAM receptor MERTK, influencing immune response and cell survival. The PROS1-MERTK interaction plays a role in cancer progression, promoting immune evasion and metastasis in multiple cancers by fostering a tumor-supportive microenvironment. Despite its importance, limited structural insights into this interaction underscore the need for computational studies to explore their binding dynamics, potentially guiding targeted therapies. In this study, we investigated the PROS1-MERTK interaction using advanced computational analyses to support immunotherapy research. High-resolution structural models from ColabFold, an AlphaFold2 adaptation, provided a baseline structure, allowing us to examine the PROS1-MERTK interface with ChimeraX and map residue interactions through Van der Waals criteria. Molecular dynamics (MD) simulations were conducted in GROMACS over 100 ns to assess stability and conformational changes using RMSD, RMSF, and radius of gyration (Rg). The PROS1-MERTK interface was predicted to contain a heterogeneous mix of amino acid contacts, with lysine and leucine as frequent participants. MD simulations demonstrated prominent early structural shifts, stabilizing after approximately 50 ns with small conformational shifts occurring as the simulation completed. In addition, there are various regions in each protein that are predicted to have greater conformational fluctuations as compared to others, which may represent attractive areas to target to halt the progression of the interaction. These insights deepen our understanding of the PROS1-MERTK interaction role in immune modulation and tumor progression, unveiling potential targets for cancer immunotherapy.

Arabidopsis GH3.10 conjugates jasmonates

Jasmonates regulate plant development and defence. In angiosperms, the canonical bioactive jasmonate is jasmonoyl-isoleucine (JA-Ile), which is formed in Arabidopsis thaliana by JAR1 and GH3.10. In contrast to other jasmonate biosynthesis or perception mutants, however, gh3.10 jar1 knockout lines are still fertile. Therefore we investigated whether further jasmonates and GH3 enzymes contribute to regulation of fertility. Jasmonate levels were analysed by liquid chromatography-mass spectrometry. The substrate range of recombinant GH3.10 and related GH3 enzymes was studied using non-targeted ex vivo metabolomics with flower and leaf extracts of A. thaliana and in vitro enzyme assays. Jasmonate application experiments were performed to study their potential bioactivity. In flowers and wounded leaves of gh3.10 jar1 knockout lines JA-Ile was below the detection limit. While 12-hydroxy-JA was identified as the preferred substrate of GH3.10, no other recombinant GH3 enzymes tested were capable of JA-Ile formation. Additional JA conjugates found in wounded leaves (JA-Gln) or formed in flowers upon MeJA treatment in the absence of JA-Ile (JA-Gln, JA-Asn, JA-Glu) were identified. The aos gh3.10 jar1 was introduced as a novel tool to test for the bioactivity of JA-Gln to regulate fertility. This study found JAR1 and GH3.10 are the only contributors to JA-Ile biosynthesis in Arabidopsis and identified a number of JA conjugates as potential bioactive jasmonates acting in the absence of JA-Ile. However, their contribution in regulating fertility is yet to be conclusively determined.

Identifying and quantifying membrane interactions of the protein human cis-prenyltransferase

Prenyl chains come in multiple sizes, fulfilling unique and essential functions across all domains of life. Prenyl chains are synthesized by prenyltransferase proteins. Despite their structural similarity, prenyltransferases exhibit substantial functional diversity to create lipophilic products of varying lengths. Human cis-prenyltransferase (h-cisPT) is a tetrameric enzyme responsible for the synthesis of long prenyl chains, consisting of 20-prenyl-unit products that are essential to specific posttranslational modifications such as N-glycosylation upon downstream processing. These long products are hypothesized to transfer from h-cisPT to the ER membrane, but the mechanism of this transfer is not known. We use molecular dynamics simulations to identify a consistent membrane binding pose for h-cisPT. By quantifying protein-membrane contacts, we identify the aromatic amino acid residues in the conserved catalytic domain as critical to membrane binding. Determining relative protein-membrane binding free energies through free energy perturbation highlights the importance of these residues for membrane association, as mutations lower membrane affinity by as much as 27 kcal/mol. These results are validated using FRET to demonstrate decreased catalytic activity and membrane binding in response to mutation. Together, our results suggest a possible mechanism for prenyl substrate transfer, where key aromatic residues facilitate h-cisPT binding to the ER membrane in an orientation that holds the substrate-containing active site near the membrane surface. Molecular dynamics simulations of the mutant exhibiting lower FRET show greater orientational variability relative to wild type. This evidence for a specific orientation of h-cisPT provides a structural basis for isoprenoid association to the membrane during synthesis and prior to its release.

An easy-to-use three-dimensional protein-structure-prediction online platform "DPL3D" based on deep learning algorithms

The change in the three-dimensional (3D) structure of a protein can affect its own function or interaction with other protein(s), which may lead to disease(s). Gene mutations, especially missense mutations, are the main cause of changes in protein structure. Due to the lack of protein crystal structure data, about three-quarters of human mutant proteins cannot be predicted or accurately predicted, and the pathogenicity of missense mutations can only be indirectly evaluated by evolutionary conservation. Recently, many computational methods have been developed to predict protein 3D structures with accuracy comparable to experiments. This progress enables the information of structural biology to be further utilized by clinicians. Thus, we developed a user-friendly platform named DPL3D (http://nsbio.tech:3000) which can predict and visualize the 3D structure of mutant proteins. The crystal structure and other information of proteins were downloaded together with the software including AlphaFold 2, RoseTTAFold, RoseTTAFold All-Atom, and trRosettaX-Single. We implemented a query module for 210,180 molecular structures, including 52,248 human proteins. Visualization of protein two-dimensional (2D) and 3D structure prediction can be generated via LiteMol automatically or manually and interactively. This platform will allow users to easily and quickly retrieve large-scale structural information for biological discovery.

The structural basis of the G protein-coupled receptor and ion channel axis

Sensory neurons play an essential role in recognizing and responding to detrimental, irritating, and inflammatory stimuli from our surroundings, such as pain, itch, cough, and neurogenic inflammation. The transduction of these physiological signals is chiefly mediated by G protein-coupled receptors (GPCRs) and ion channels. The binding of ligands to GPCRs triggers a signaling cascade, recruiting G proteins or β-arrestins, which subsequently interact with ion channels (e.g., GIRK and TRP channels). This interaction leads to the sensitization and activation of these channels, initiating the neuron's protective mechanisms. This review delves into the complex interplay between GPCRs and ion channels that underpin these physiological processes, with a particular focus on the role of structural biology in enhancing our comprehension. Through unraveling the intricacies of the GPCR-ion channel axis, we aim to shed light on the sophisticated intermolecular dynamics within these pivotal membrane protein families, ultimately guiding the development of precise therapeutic interventions.

High-Throughput Evaluation of Natural Diversity of F-Type ATP Synthase Rotor Ring Stoichiometries

Adenosine triphosphate (ATP) synthases are large enzymes present in every living cell. They consist of a transmembrane and a soluble domain, each comprising multiple subunits. The transmembrane part contains an oligomeric rotor ring (c-ring), whose stoichiometry defines the ratio between the number of synthesized ATP molecules and the number of ions transported through the membrane. Currently, c-rings of F-Type ATP synthases consisting of 8-17 (except 16) subunits have been experimentally demonstrated, but it is not known whether other stoichiometries are present in natural organisms. Here, we present an easy-to-use high-throughput computational approach based on AlphaFold that allows us to estimate the stoichiometry of all homo-oligomeric c-rings, whose sequences are present in genomic databases. We validate the approach on the available experimental data, obtaining the correlation as high as 0.94 for the reference dataset and use it to predict the existence of c-rings with stoichiometry varying at least from 8 to 27. We then conduct molecular dynamics simulations of two c-rings with stoichiometry above 17 to corroborate the machine learning-based predictions. Our work strongly suggests existence of rotor rings with previously undescribed high stoichiometry in natural organisms and highlights the utility of AlphaFold-based approaches for studying homo-oligomeric proteins.

Discovery of Bioactive Peptides Through Peptide Scanning

Therapeutic peptides targeted at various diseases are becoming increasingly relevant for the pharmaceutical industry. Several of these drugs were originally designed by mimicking a segment of a protein of interest. As such, protein mimicry represents a promising strategy both in immunology, for the identification of B- and T-cell epitopes, as well as for the modulation of protein activity, including the disruption of protein-protein interactions (PPIs) and the interference with biological or pathological cellular functions. Several methods have been developed to pinpoint the (binding) epitopes of a protein or the regions responsible for biological activity. One of such strategies is the scanning of the protein or selected domains with synthetic overlapping peptides. As the mechanism of action of a mimetic peptide can be similar to that of the whole protein, this method offers a powerful tool for the investigation of protein function, along with providing a solid basis for the development of therapeutic candidates. This review gives a general overview of different applications of the peptide scanning methodology, describing a comparison of the preparation and use of solid-phase libraries (peptide arrays) with isolated peptide libraries and highlighting their strengths and most common applications.

AcrDB update: Predicted 3D structures of anti-CRISPRs in human gut viromes

Anti-CRISPR (Acr) proteins play a key role in phage-host interactions and hold great promise for advancing genome-editing technologies. However, finding new Acrs has been challenging due to their low sequence similarity. Recent advances in protein structure prediction have opened new pathways for Acr discovery by using 3D structure similarity. This study presents an updated AcrDB, with the following new features not available in other databases: (1) predicted Acrs from human gut virome databases, (2) Acr structures predicted by AlphaFold2, (3) a structural similarity search function to allow users to submit new sequences and structures to search against 3D structures of experimentally known Acrs. The updated AcrDB contains predicted 3D structures of 795 candidate Acrs with structural similarity (TM-score ≥0.7) to known Acrs supported by at least two of the three non-sequence similarity-based tools (TM-Vec, Foldseek, AcrPred). Among these candidate Acrs, 121 are supported by all three tools. AcrDB also includes 3D structures of 122 experimentally characterized Acr proteins. The 121 most confident candidate Acrs were combined with the 122 known Acrs and clustered into 163 sequence similarity-based Acr families. The 163 families were further subject to a structure similarity-based hierarchical clustering, revealing structural similarity between 44 candidate Acr (cAcr) families and 119 known Acr families. The bacterial hosts of these 163 Acr families are mainly from Bacillota, Pseudomonadota, and Bacteroidota, which are all dominant gut bacterial phyla. Many of these 163 Acr families are also co-localized in Acr operons. All the data and visualization are provided on our website: https://pro.unl.edu/AcrDB.

Molecular mechanism of bacteriophage contraction structure of an S-layer-penetrating bacteriophage

The molecular details of phage tail contraction and bacterial cell envelope penetration remain poorly understood and are completely unknown for phages infecting bacteria enveloped by proteinaceous S-layers. Here, we reveal the extended and contracted atomic structures of an intact contractile-tailed phage (φCD508) that binds to and penetrates the protective S-layer of the Gram-positive human pathogen Clostridioides difficile The tail is unusually long (225 nm), and it is also notable that the tail contracts less than those studied in related contractile injection systems such as the model phage T4 (∼20% compared with ∼50%). Surprisingly, we find no evidence of auxiliary enzymatic domains that other phages exploit in cell wall penetration, suggesting that sufficient energy is released upon tail contraction to penetrate the S-layer and the thick cell wall without enzymatic activity. Instead, the unusually long tail length, which becomes more flexible upon contraction, likely contributes toward the required free energy release for envelope penetration.

DeepUSPS: Deep Learning-Empowered Unconstrained-Structural Protein Sequence Design

Currently, the unconstrained-structural protein sequence design models suffer from low optimization efficiency, and their generated proteins exhibit significant similarities to natural proteins and low thermal stability. To address these challenges, we propose the Deep Learning-Empowered Unconstrained-Structural Protein Sequence Design (DeepUSPS) model. To effectively address the inadequate thermal stability problem, we employ the innovative Inverted Dense Residual Network (IDRNet). To mitigate the designed proteins similarity issue, the Sequence-Pairwise Features Extraction Synthetic Network (SPFESN) is constructed. Furthermore, we introduce the Warm Restart AngularGrad (WRA) optimizer to optimize the 3D Position-Specific Scoring Matrix (3Dpssm) for unconstrained-structural protein sequence, only involving 2100 iterations (140.36 min) updates to generate idealization (IDE) protein sequences. We obtained a total of 1000 IDE protein sequences. Then we utilized in silico experiments to evaluate them, including similarity, clarity and iterations, thermal stability, spatial distribution of similarity, and predicted local-distance difference test (pLDDT) confidence assessment. Notably, the mean lg(E-value) for IDE protein sequences reached -0.051, the mean TM-score for IDE protein structures reached 0.594, the iterations only need 2100, and the mean Tm (melting point) for thermal stability reached 74.78°C. The average pLDDT value for 3D structures reached 76. Additionally, the IDE proteins' 3D structures exhibit diverse types. These in silico results conclusively demonstrate the superior performance of DeepUSPS compared with Hallucinate.

BindUP-Alpha: A Webserver for Predicting DNA- and RNA-Binding Proteins based on Experimental and Computational Structural Models

Structural data provides important information on the proteins' function. Recent development of advanced machine learning and artificial intelligence tools, such as AlphaFold, have led to an explosion of predicted protein structures. However, many of the computed protein models contain unstructured and disordered regions, posing challenges in protein function characterization. Here we present BindUP-Alpha, an upgraded webserver for predicting nucleic acid binding proteins. Our structure-based algorithm utilizes the electrostatic features of the protein surface and other physiochemical and structural properties extracted from the protein sequence. Using a Support Vector Machine (SVM) learning approach, BindUP-Alpha successfully predicts DNA- and RNA-binding proteins from both experimentally solved structures and predicted models. In addition, BindUP-Alpha identifies electrostatic patches on the protein's surface that represent potential nucleic-acid binding interfaces. BindUP-Alpha is freely accessible at https://bindup.technion.ac.il, providing interactive three-dimensional visualizations and downloadable text-based results.

NMR Structural Characterization of SARS-CoV-2 ORF6 Reveals an N-Terminal Membrane Anchor

SARS-CoV-2, the virus responsible for the COVID-19 pandemic, encodes several accessory proteins, among which ORF6, a potent interferon inhibitor, is recognized as one of the most cytotoxic. Here, we investigated the structure, oligomeric state, and membrane interactions of ORF6 using NMR spectroscopy and molecular dynamics simulations. Using chemical-shift-ROSETTA, we show that ORF6 in proteoliposomes adopts a straight α-helical structure with an extended, rigid N-terminal part and flexible C-terminal residues. Cross-linking experiments indicate that ORF6 forms oligomers within lipid bilayers, and paramagnetic spin labeling suggests an antiparallel arrangement in its multimers. The amphipathic ORF6 helix establishes multiple contacts with the membrane surface with its N-terminal residues acting as membrane anchors. Our work demonstrates that ORF6 is an integral monotopic membrane protein and provides key insights into its conformation and the importance of the N-terminal region for the interaction with the membrane.

Machine learning-assisted design of the molecular structure of p-phenylenediamine antioxidants

This study employed machine learning to predict the solubility parameter (δ) and bond dissociation energy (BDE) of antioxidant molecules, focusing on p-phenylenediamine derivatives with varying carbon chain lengths, side group positions, and functional groups (-CH3, -OH, and -NH2). The multilayer perceptron (MLP) model, enhanced by data augmentation and genetic algorithms, was developed to correlate the "molecular structure-descriptor-target parameter" relationship. The model achieved high prediction accuracy (coefficient of determination >0.86, relative percent difference >2.62). SHapley Additive exPlanations analysis revealed molecular polarity as the key factor influencing antioxidant performance. Molecules with -NH2 side groups exhibited lower BDE values. A p-phenylenediamine derivative with 'CH3[CH2]13CH(NH2)-' connected to an aniline group showed optimal properties (Δδ = 0.02 (J cm-3)0.5, BDE = 289.46 kJ mol-1). Molecular simulations confirmed that the proposed antioxidant has excellent compatibility, anti-migration, and antioxidant activity in triglyceride oil. This study demonstrates the utility of MLP models for designing high-efficiency antioxidants for edible oils.

Computer-Aided Drug Discovery for Undruggable Targets

Undruggable targets are those of therapeutical significance but challenging for conventional drug design approaches. Such targets often exhibit unique features, including highly dynamic structures, a lack of well-defined ligand-binding pockets, the presence of highly conserved active sites, and functional modulation by protein-protein interactions. Recent advances in computational simulations and artificial intelligence have revolutionized the drug design landscape, giving rise to innovative strategies for overcoming these obstacles. In this review, we highlight the latest progress in computational approaches for drug design against undruggable targets, present several successful case studies, and discuss remaining challenges and future directions. Special emphasis is placed on four primary target categories: intrinsically disordered proteins, protein allosteric regulation, protein-protein interactions, and protein degradation, along with discussion of emerging target types. We also examine how AI-driven methodologies have transformed the field, from applications in protein-ligand complex structure prediction and virtual screening to de novo ligand generation for undruggable targets. Integration of computational methods with experimental techniques is expected to bring further breakthroughs to overcome the hurdles of undruggable targets. As the field continues to evolve, these advancements hold great promise to expand the druggable space, offering new therapeutic opportunities for previously untreatable diseases.

Reliable protein-protein docking with AlphaFold, Rosetta, and replica exchange

Despite the recent breakthrough of AlphaFold (AF) in the field of protein sequence-to-structure prediction, modeling protein interfaces and predicting protein complex structures remains challenging, especially when there is a significant conformational change in one or both binding partners. Prior studies have demonstrated that AF-multimer (AFm) can predict accurate protein complexes in only up to 43% of cases (Yin et al., 2022). In this work, we combine AF as a structural template generator with a physics-based replica exchange docking algorithm to better sample conformational changes. Using a curated collection of 254 available protein targets with both unbound and bound structures, we first demonstrate that AF confidence measures (pLDDT) can be repurposed for estimating protein flexibility and docking accuracy for multimers. We incorporate these metrics within our ReplicaDock 2.0 protocol to complete a robust in silico pipeline for accurate protein complex structure prediction. AlphaRED (AlphaFold-initiated Replica Exchange Docking) successfully docks failed AF predictions, including 97 failure cases in Docking Benchmark Set 5.5. AlphaRED generates CAPRI acceptable-quality or better predictions for 63% of benchmark targets. Further, on a subset of antigen-antibody targets, which is challenging for AFm (20% success rate), AlphaRED demonstrates a success rate of 43%. This new strategy demonstrates the success possible by integrating deep learning-based architectures trained on evolutionary information with physics-based enhanced sampling. The pipeline is available at https://github.com/Graylab/AlphaRED.

Global Optimization of Large Molecular Systems Using Rigid-Body Chain Stochastic Surface Walking

The global potential energy surface (PES) search of large molecular systems remains a significant challenge in chemistry due to "the curse of dimensionality". To address this, here we develop a rigid-body chain method in the framework of a stochastic surface walking (SSW) global optimization method, termed rigid-body chain SSW (RC-SSW). Based on the angle-axis representation for a single rigid body, our algorithm realizes the cooperative motion of connected rigid bodies and achieves the coupling between rigid-body chain movement and lattice variation in the generalized coordinate. By exploiting the numerical energy second derivative information on rigid bodies, RC-SSW can optimize the global PES of large molecular systems with an unprecedentedly high efficiency. We show that RC-SSW is more than 10 times faster in locating the model protein global minimum while revealing many more low energy conformations than molecular dynamics and can identify low energy phases of molecular crystals up to 172 atoms missed in the sixth CCDC blind test.

The GRAS protein RAM1 interacts with WRI transcription factors to regulate plant genes required for arbuscule development and function

During arbuscular mycorrhiza (AM) symbiosis AM fungi form tree-shaped structures called arbuscules in root cortex cells of host plants. Arbuscules and their host cells are central for reciprocal nutrient exchange between the symbionts. REQUIRED FOR ARBUSCULAR MYCORRHIZATION1 (RAM1) encodes a GRAS protein crucial for transcriptionally regulating plant genes needed for arbuscule development and nutrient exchange. Similar to other GRAS proteins, RAM1 likely does not bind to DNA and how RAM1 activates its target promoters remained elusive. Here, we demonstrate that RAM1 interacts with five AM-induced APETALA 2 (AP2) transcription factors of the WRINKLED1-like family called CTTC MOTIF-BINDING TRANSCRIPTION FACTOR1 (CBX1), WRI3, WRI5a, WRI5b, and WRI5c via a C-terminal domain containing the M2/M2a motif. This motif is conserved and enriched in WRI proteins encoded by genomes of AM-competent plants. RAM1 together with any of these WRI proteins activates the promoters of genes required for symbiotic nutrient exchange, namely RAM2, STUNTED ARBUSCULES (STR), and PHOSPHATE TRANSPORTER 4 (PT4), in Nicotiana benthamiana leaves. This activation as well as target promoter induction in Lotus japonicus hairy roots depends on MYCS (MYCORRHIZA SEQUENCE)-elements and AW-boxes, previously identified as WRI-binding sites. The WRI genes are activated in two waves: Transcription of RAM1, CBX1, and WRI3 is coregulated by calcium- and calmodulin-dependent protein kinase-activated CYCLOPS, through the AMCYC-RE in their promoter, and DELLA, while WRI5a, b, and c promoters contain MYCS-elements and AW-boxes and can be activated by RAM1 heterocomplexes with CBX1 or WRI3. We propose that RAM1 provides an activation domain to DNA-binding WRI proteins to activate genes with central roles in AM development and function.

Targeting TetR-family transcription regulators for combating tetracycline resistance in resilient Acinetobacter baumannii: in silico identification of potent inhibitors

Acinetobacter baumannii stands out as a potent pathogenic microbe responsible for healthcare-associated infections characterized by elevated morbidity and mortality. This bacterium has acquired a range of mechanisms for resisting antibiotics, resulting in the emergence of strains that can withstand antibiotics from multiple classes. Effectively addressing this urgent concern requires finding ways to overcome these resistance mechanisms. In this context, our study focuses on TetR Transcriptional Factor Regulators (TetR-FTRs). It coordinates functions of tetracycline efflux pump proteins (TetA and TetR) and exert influence over metabolic pathways, quorum sensing, and biofilm formation. The primary objective is to identify potent inhibitors targeting TetR-FTRs through scaffold-based shape screening across thirteen distinct databases. A wide array of in silico techniques was employed, including molecular docking, molecular dynamics simulations, Swiss Similarity search, Virtual Screening, MM/GBSA analysis, ADMET assessment, PAINS assay, SIFT analysis, and MM/PBSA calculations. The initial Swiss similarity search yielded 2178 compounds for subsequent virtual screening, with the application of PAINS analysis rigorously pruning the list, eliminating 14 false positive hits. Further refinement through SIFT approach discriminated closely related interacting compounds into three distinct clusters - ChemBridge5963254, BDH33906706, and ZINC000013607604, which fulfilled all SIFT criteria. Comparative evaluation against reference compounds revealed favorable glide scores, lower binding free energies, and interactions with crucial active site residue Hsd128-Mg2+. Molecular dynamics simulations consistently exhibited stable binding for these clusters in contrast to reference compounds. Our analysis underscores three specific compounds, namely ChemBridge5963254, BDH33906706, and ZINC000013607604, as promising candidates for addressing tetracycline resistance and combating A. baumannii infections.

Advancements in Ligand-Based Virtual Screening through the Synergistic Integration of Graph Neural Networks and Expert-Crafted Descriptors

The fusion of traditional chemical descriptors with graph neural networks (GNNs) offers a compelling strategy for enhancing ligand-based virtual screening methodologies. A comprehensive evaluation revealed that the benefits derived from this integrative strategy vary significantly among different GNNs. Specifically, while GCN and SchNet demonstrate pronounced improvements by incorporating descriptors, SphereNet exhibits only marginal enhancement. Intriguingly, despite SphereNet's modest gain, all three models-GCN, SchNet, and SphereNet-achieve comparable performance levels when leveraging this combination strategy. This observation underscores a pivotal insight: sophisticated GNN architectures may be substituted with simpler counterparts without sacrificing efficacy, provided that they are augmented with descriptors. Furthermore, our analysis reveals a set of expert-crafted descriptors' robustness in scaffold-split scenarios, frequently outperforming the combined GNN-descriptor models. Given the critical importance of scaffold splitting in accurately mimicking real-world drug discovery contexts, this finding accentuates an imperative for GNN researchers to innovate models that can adeptly navigate and predict within such frameworks. Our work not only validates the potential of integrating descriptors with GNNs in advancing ligand-based virtual screening but also illuminates pathways for future enhancements in model development and application. Our implementation can be found at https://github.com/meilerlab/gnn-descriptor.

Machine learning to predict de novo protein-protein interactions

Advances in machine learning for structural biology have dramatically enhanced our capacity to predict protein-protein interactions (PPIs). Here, we review recent developments in the computational prediction of PPIs, particularly focusing on innovations that enable interaction predictions that have no precedence in nature, termed de novo. We discuss novel machine learning algorithms for PPI prediction, including approaches based on co-folding and atomic graphs. We further highlight methods that learn from molecular surfaces, which can predict PPIs not found in nature including interactions induced by small molecules. Finally, we explore the emerging biotechnological applications enabled by these predictive capabilities, including the prediction of antibody-antigen complexes and molecular glue-induced PPIs, and discuss their potential to empower drug discovery and protein engineering.

Recent advances and future trends for protein-small molecule interaction predictions with protein language models

In recent years, the application of natural language models to protein amino acid sequences, referred to as protein language models (PLMs), has demonstrated a significant potential for uncovering hidden patterns related to protein structure, function, and stability. The critical functions of proteins in biological processes often arise through interactions with small molecules; central examples are enzymes, receptors, and transporters. Understanding these interactions is particularly important for drug design, for bioengineering, and for understanding cellular metabolism. In this review, we present state-of-the-art PLMs and explore how they can be integrated with small molecule information to predict protein-small molecule interactions. We present several such prediction tasks and discuss current limitations and potential areas for improvement.

Epigallocatechin-3-gallate binds tandem RNA recognition motifs of TDP-43 and inhibits its aggregation

Transactive response DNA-binding Protein 43 (TDP-43) aggregation is a key pathological feature in Amyotrophic Lateral Sclerosis and related neurodegenerative diseases. This study investigates the inhibitory effects of Epigallocatechin-3-gallate (EGCG), a polyphenol found in green tea, on TDP-43 aggregation. Using a combination of fluorescence assays, NMR spectroscopy, and computational modeling, we demonstrate that Epigallocatechin-3-gallate significantly delays the nucleation phase of TDP-43 aggregation process, thus inhibiting the formation of TDP-43 aggregates in vitro. Additionally, we proved a direct interaction of the compound with the RNA recognition motifs of TDP-43 and modeled the mechanism of interaction. Our findings reveal that EGCG stabilizes the RRM domains, counteracting aggregation by interfering with the early stages of the amyloidogenic pathway. Furthermore, EGCG's stability under experimental conditions was ensured using reducing agents, highlighting the importance of maintaining its reduced form for reproducible results. These insights underscore the therapeutic potential of EGCG in TDP-43 proteinopathies and provide a foundation for developing targeted treatments for ALS and related disorders.

The VanS sensor histidine kinase from type-B vancomycin-resistant enterococci recognizes vancomycin directly

Vancomycin-resistant enterococci (VRE) are high-priority targets for new therapeutic development. In VRE, expression of the resistance phenotype is controlled by the VanRS two-component system, which senses the presence of the antibiotic and responds by initiating transcription of resistance genes. VanS is a transmembrane sensor histidine kinase that is known to detect the antibiotic and then transduce this signal to the VanR transcription factor; however, fundamental questions remain about how exactly VanS senses vancomycin. Here, we focus on a purified VanRS system from one of the most clinically prevalent forms of VRE, type B. We show that in a native-like membrane environment, vancomycin strongly stimulates the autokinase activity of type-B VanS. We additionally demonstrate that this effect is mediated by a direct physical interaction between the antibiotic and the VanS periplasmic domain. This represents the first time that a direct sensing mechanism has been confirmed for any VanS protein from a human pathogen.

Structural elucidation of full-length Pfs48/45 in complex with potent monoclonal antibodies isolated from a naturally exposed individual

Biomedical interventions that block the transmission of Plasmodium falciparum (Pf) from humans to mosquitoes may be critical for malaria elimination. Pfs48/45, a gamete-surface protein essential for Pf development in the mosquito midgut, is a target of clinical-stage transmission-blocking vaccines and monoclonal antibodies (mAbs) that disrupt Pf transmission to mosquitoes. Antibodies directed to domain 3 of Pfs48/45 have been structurally and functionally described; however, in-depth information about other inhibitory epitopes on Pfs48/45 is currently limited. Here, we present a cryo-electron microscopy structure of full-length Pfs48/45 in complex with potent human mAbs targeting all three domains. Our data indicate that although Pfs48/45 domains 1 and 2 are rigidly coupled, there is substantial conformational flexibility between domains 2 and 3. Characterization of mAbs against domain 1 revealed the presence of a conformational epitope class that is largely conserved across Pf field isolates and is associated with recognition by potent antibodies. Our study provides insights into epitopes across full-length Pfs48/45 and has implications for the design of next-generation malaria interventions.

Molecular insights fast-tracked: AI in biosynthetic pathway research

Covering: 2000 to 2025This review explores the potential of artificial intelligence (AI) in addressing challenges and accelerating molecular insights in biosynthetic pathway research, which is crucial for developing bioactive natural products with applications in pharmacology, agriculture, and biotechnology. It provides an overview of various AI techniques relevant to this research field, including machine learning (ML), deep learning (DL), natural language processing, network analysis, and data mining. AI-powered applications across three main areas, namely, pathway discovery and mining, pathway design, and pathway optimization, are discussed, and the benefits and challenges of integrating omics data and AI for enhanced pathway research are also elucidated. This review also addresses the current limitations, future directions, and the importance of synergy between AI and experimental approaches in unlocking rapid advancements in biosynthetic pathway research. The review concludes with an evaluation of AI's current capabilities and future outlook, emphasizing the transformative impact of AI on biosynthetic pathway research and the potential for new opportunities in the discovery and optimization of bioactive natural products.

Interplay Between tRNA Modifications and Processing

Transfer RNAs play a key role during protein synthesis by decoding genetic information at the translating ribosome. During their biosynthesis, tRNA molecules undergo numerous processing steps. Moreover, tRNAs represent the RNA class that carries the largest variety and highest relative number of chemical modifications. While our functional and mechanistic understanding of these processes is primarily based on studies in yeast, the findings on dynamic tRNA maturation can be translated to higher eukaryotes including humans, particularly regarding the biochemical characterization of the multitude of enzymes involved. In this review, we summarize current knowledge on the sequential hierarchy and interplay of various processing and modification steps for mitochondrial and cytoplasmic tRNA, as well as tRNA-like structures in eukaryotic cells. We also highlight recent structural advances that shed light on the function of enzyme-tRNA complexes.

Cyclic peptide structure prediction and design using AlphaFold2

Small cyclic peptides have gained significant traction as a therapeutic modality; however, the development of deep learning methods for accurately designing such peptides has been slow, mostly due to the lack of sufficiently large training sets. Here, we introduce AfCycDesign, a deep learning approach for accurate structure prediction, sequence redesign, and de novo hallucination of cyclic peptides. Using AfCycDesign, we identified over 10,000 structurally-diverse designs predicted to fold into the designed structures with high confidence. X-ray crystal structures for eight tested de novo designed sequences match very closely with the design models (RMSD < 1.0 Å), highlighting the atomic level accuracy in our approach. Further, we used the set of hallucinated peptides as starting scaffolds to design binders with nanomolar IC50 against MDM2 and Keap1. The computational methods and scaffolds developed here provide the basis for the custom design of peptides for diverse protein targets and therapeutic applications.

Accurate prediction of virulence factors using pre-train protein language model and ensemble learning

BACKGROUND

As bacterial pathogens develop increasing resistance to antibiotics, strategies targeting virulence factors (VFs) have emerged as a promising and effective approach for treating bacterial infections. Existing methods mainly relied on sequence similarity, and remote homology relationships cannot be discovered by sequence analysis alone.

RESULTS

To address this limitation, we developed a protein language model and ensemble learning approach for VF identification (PLMVF). Specifically, we extracted features from protein sequences using ESM-2 and their three-dimensional (3D) structures using ESMFold. We calculated the true TM-score of the proteins based on their 3D structures and trained a TM-predictor model to predict structural similarity, thereby capturing hidden remote homology information within the sequences. Subsequently, we concatenated the sequence-level features extracted by ESM-2 with the predicted TM-score features to form a comprehensive feature set for prediction. Extensive experimental validation demonstrated that PLMVF achieved an accuracy (ACC) of 86.1%, significantly outperforming existing models across multiple evaluation metrics. This study provided an ideal tool for identifying novel targets in the development of anti-virulence therapies, offering promise for the effective prevention and control of pathogenic bacterial infections.

CONCLUSIONS

The proposed PLMVF model offers an efficient computational approach for VF identification.

Crystal structure and catalytic mechanism of PL35 family glycosaminoglycan lyases with an ultrabroad substrate spectrum

Recently, a new class of glycosaminoglycan (GAG) lyases (GAGases) belonging to PL35 family has been discovered with an ultrabroad substrate spectrum that can degrade three types of uronic acid-containing GAGs (hyaluronic acid, chondroitin sulfate and heparan sulfate) or even alginate. In this study, the structures of GAGase II from Spirosoma fluviale and GAGase VII from Bacteroides intestinalis DSM 17393 were determined at 1.9 and 2.4 Å resolution, respectively, and their catalytic mechanism was investigated by the site-directed mutant of their crucial residues and molecular docking assay. Structural analysis showed that GAGase II and GAGase VII consist of an N-terminal (α/α)6 toroid multidomain and a C-terminal two-layered β-sheet domain with Mn2+. Notably, although GAGases share similar folds and catalytic mechanisms with some GAG lyases and alginate lyases, they exhibit higher structural similarity with alginate lyases than GAG lyases, which may present a crucial structural evidence for the speculation that GAG lyases with (α/α)n toroid and antiparallel β-sheet structures arrived by a divergent evolution from alginate lyases with the same folds. Overall, this study not only solved the structure of PL35 GAG lyases for the first time and investigated their catalytic mechanism, especially the reason why GAGase III can additionally degrade alginate, but also provided a key clue in the divergent evolution of GAG lyases that originated from alginate lyases.

Machine Learning for Toxicity Prediction Using Chemical Structures: Pillars for Success in the Real World

Machine learning (ML) is increasingly valuable for predicting molecular properties and toxicity in drug discovery. However, toxicity-related end points have always been challenging to evaluate experimentally with respect to in vivo translation due to the required resources for human and animal studies; this has impacted data availability in the field. ML can augment or even potentially replace traditional experimental processes depending on the project phase and specific goals of the prediction. For instance, models can be used to select promising compounds for on-target effects or to deselect those with undesirable characteristics (e.g., off-target or ineffective due to unfavorable pharmacokinetics). However, reliance on ML is not without risks, due to biases stemming from nonrepresentative training data, incompatible choice of algorithm to represent the underlying data, or poor model building and validation approaches. This might lead to inaccurate predictions, misinterpretation of the confidence in ML predictions, and ultimately suboptimal decision-making. Hence, understanding the predictive validity of ML models is of utmost importance to enable faster drug development timelines while improving the quality of decisions. This perspective emphasizes the need to enhance the understanding and application of machine learning models in drug discovery, focusing on well-defined data sets for toxicity prediction based on small molecule structures. We focus on five crucial pillars for success with ML-driven molecular property and toxicity prediction: (1) data set selection, (2) structural representations, (3) model algorithm, (4) model validation, and (5) translation of predictions to decision-making. Understanding these key pillars will foster collaboration and coordination between ML researchers and toxicologists, which will help to advance drug discovery and development.

Predicting Protein Function in the AI and Big Data Era

It is an exciting time for researchers working to link proteins to their functions. Most techniques for extracting functional information from genomic sequences were developed several years ago, with major progress driven by the availability of big data. Now, groundbreaking advances in deep-learning and AI-based methods have enriched protein databases with three-dimensional information and offer the potential to predict biochemical properties and biomolecular interactions, providing key functional insights. This progress is expected to increase the proportion of functionally bright proteins in databases and deepen our understanding of life at the molecular level.

Surfactant-like peptide gels are based on cross-β amyloid fibrils

Surfactant-like peptides, in which hydrophilic and hydrophobic residues are encoded within different domains in the peptide sequence, undergo facile self-assembly in aqueous solution to form supramolecular hydrogels. These peptides have been explored extensively as substrates for the creation of functional materials since a wide variety of amphipathic sequences can be prepared from commonly available amino acid precursors. The self-assembly behavior of surfactant-like peptides has been compared to that observed for small molecule amphiphiles in which nanoscale phase separation of the hydrophobic domains drives the self-assembly of supramolecular structures. Here, we investigate the relationship between sequence and supramolecular structure for a pair of bola-amphiphilic peptides, Ac-KLIIIK-NH2 (L2) and Ac-KIIILK-NH2 (L5). Despite similar length, composition, and polar sequence pattern, L2 and L5 form morphologically distinct assemblies, nanosheets and nanotubes, respectively. Cryo-EM helical reconstruction was employed to determine the structure of the L5 nanotube at near-atomic resolution. Rather than displaying self-assembly behavior analogous to conventional amphiphiles, the packing arrangement of peptides in the L5 nanotube displayed steric zipper interfaces that resembled those observed in the structures of β-amyloid fibrils. Like amyloids, the supramolecular structures of the L2 and L5 assemblies were sensitive to conservative amino acid substitutions within an otherwise identical amphipathic sequence pattern. This study highlights the need to better understand the relationship between sequence and supramolecular structure to facilitate the development of functional peptide-based materials for biomaterials applications.

High-purity AAV vector production utilizing recombination-dependent minicircle formation and genetic coupling

Triple transfection of HEK293 cells is the most widely used method for producing recombinant adeno-associated virus (rAAV), a leading gene delivery vector for human gene therapy. Despite its tremendous success, this approach generates several vector-related impurities that could potentially compromise the safety and potency of rAAV. In this study, we introduce a method for high-purity AAV vector production utilizing recombination-dependent minicircle formation and genetic coupling (AAVPureMfg). Compared with traditional triple transfection, AAVPureMfg substantially improves vector purity by reducing prokaryotic DNA contaminants by 10- to 50-fold and increasing the full capsid ratio up to threefold. Mechanistically, Bxb1-mediated excision of the transgene cassette generates a minicircle cis construct devoid of bacterial sequences and ensures synchronized colocalization of trans and cis constructs in productive cells. Furthermore, we developed iterations that enhance vector genome homogeneity and streamline the production of rAAV with various transgenes, serotypes, and ITR configurations. Overall, our findings demonstrate that AAVPureMfg overcomes the inherent limitations associated with triple transfection, offering a broadly applicable and easy-to-implement method for producing high-purity rAAV with reduced plasmid costs.

Perspectives on the Current and Future State of Artificial Intelligence in Medical Genetics

Artificial intelligence (AI) is rapidly transforming numerous aspects of daily life, including clinical practice and biomedical research. In light of this rapid transformation, and in the context of medical genetics, we assembled a group of leaders in the field to respond to the question about how AI is affecting, and especially how AI will affect, medical genetics. The authors who contributed to this collection of essays intentionally represent different areas of expertise, career stages, and geographies, and include diverse types of clinicians, computer scientists, and researchers. The individual pieces cover a wide range of areas related to medical genetics; we expect that these pieces may provide helpful windows into the ways in which AI is being actively studied, used, and considered in medical genetics.

Bridging Dimensionality Reduction and Stochastic Sampling: The DA2-MC Algorithm for Protein Dynamics

Elucidating protein dynamics and conformational changes is crucial for understanding their biological functions. This work introduces a data-driven accelerated conformational searching algorithm incorporating a Monte Carlo strategy, termed the DA2-MC method, which integrates dimensionality reduction techniques with Monte Carlo strategies to efficiently explore unknown protein conformations. The DA2-MC method was applied to investigate the folding mechanisms of two miniproteins, chignolin and WW domain, revealing their dynamic behavior in different conformational states at a reasonable computational cost. A Markov state model-based analysis of chignolin's folding pathway corroborated the dynamic insights obtained from the DA2-MC method. Moreover, free energy calculations initiated with the intermediate structures identified by DA2-MC yielded results consistent with published literature, affirming the method's reliability in accelerating conformational searches and reconstructing equilibrium properties. Collectively, the DA2-MC method emerges as an effective tool for efficiently exploring protein conformations, facilitating the identification of potential functional conformations on complex energy landscapes.

Molecular machineries shaping the mitochondrial inner membrane

Mitochondria display intricately shaped deep invaginations of the mitochondrial inner membrane (MIM) termed cristae. This peculiar membrane architecture is essential for diverse mitochondrial functions, such as oxidative phosphorylation or the biosynthesis of cellular building blocks. Conserved protein nano-machineries such as F1Fo-ATP synthase oligomers and the mitochondrial contact site and cristae organizing system (MICOS) act as adaptable protein-lipid scaffolds controlling MIM biogenesis and its dynamic remodelling. Signal-dependent rearrangements of cristae architecture and MIM fusion events are governed by the dynamin-like GTPase optic atrophy 1 (OPA1). Recent groundbreaking structural insights into these nano-machineries have considerably advanced our understanding of the functional architecture of mitochondria. In this Review, we discuss how the MIM-shaping machineries cooperate to control cristae and crista junction dynamics, including MIM fusion, in response to cellular signalling pathways. We also explore how mutations affecting MIM-shaping machineries compromise mitochondrial functions.

Next generation technologies for protein structure determination: challenges and breakthroughs in plant biology applications

Advancements in structural biology have significantly deepened our understanding of plant proteins, which are central to critical biological functions such as photosynthesis, metabolism, signal transduction, and structural architechture. Gaining insights into their structures is crucial for unraveling their functions and mechanisms, which in turn has profound implications for agriculture, biotechnology, and environmental sustainability. Traditional methods in protein structural biology often fall short in addressing large protein assemblies and membrane proteins, and, in particular the dynamics and structural features of proteins in the native cellular context. This paper explores how next-generation technologies are transforming the field of plant protein structural biology, offering powerful tools to overcome longstanding obstacles and enabling remarkable scientific breakthroughs. Key technologies discussed include advanced X-ray crystallography, Cryo-Electron microscopy, Nuclear Magnetic Resonance spectroscopy, Cross-linking mass spectrometry, and Artificial Intelligence-driven approaches. These technologies are examined in terms of their challenges, innovations, and application with particular emphasis on their relevance to plant systems. Future directions in plant protein structural biology are also discussed. Although technical details are not covered in depth, readers are referred to the primary literature for more comprehensive information.

HDXRank: A Deep Learning Framework for Ranking Protein Complex Predictions with Hydrogen-Deuterium Exchange Data

Accurate modeling of protein-protein complex structures is essential for understanding biological mechanisms. Hydrogen-deuterium exchange (HDX) experiments provide valuable insights into binding interfaces. Incorporating HDX data into protein complex modeling workflows offers a promising approach to improve prediction accuracy. Here, we developed HDXRank, a graph neural network (GNN)-based framework for candidate structure ranking utilizing alignment with HDX experimental data. Trained on a newly curated HDX data set, HDXRank captures nuanced local structural features critical for accurate HDX profile prediction. This versatile framework can be integrated with a variety of protein complex modeling tools, transforming the HDX profile alignment into a model quality metric. HDXRank demonstrates effectiveness at ranking models generated by rigid docking or AlphaFold, successfully prioritizing functionally relevant models and improving prediction quality across all tested protein targets. These findings underscore HDXRank's potential to become a pivotal tool for understanding molecular recognition in complex biological systems.

NCPepFold: Accurate Prediction of Noncanonical Cyclic Peptide Structures via Cyclization Optimization with Multigranular Representation

Artificial intelligence-based peptide structure prediction methods have revolutionized biomolecular science. However, restricting predictions to peptides composed solely of 20 natural amino acids significantly limits their practical application; as such, peptides often demonstrate poor stability under physiological conditions. Here, we present NCPepFold, a computational approach that can utilize a specific cyclic position matrix to directly predict the structure of cyclic peptides with noncanonical amino acids. By integrating multigranularity information at the residual and atomic level, along with fine-tuning techniques, NCPepFold significantly improves prediction accuracy, with the average peptide root-mean-square deviation (RMSD) for cyclic peptides being 1.640 Å. In summary, this is a novel deep learning model designed specifically for cyclic peptides with noncanonical amino acids, offering great potential for peptide drug design and advancing biomedical research.

Comprehensive immunoinformatics and bioinformatics strategies for designing a multi-epitope based vaccine targeting structural proteins of Nipah virus

Background

Nipah virus (NiV) is characterized by recurring outbreaks and causes severe neurological impact, leading to increased mortality rates. Despite the severity of the disease, there is no proven post-exposure treatment available, emphasizing the critical need for the development of an effective vaccine.

Objective

This study was aimed at designing a multi-epitope based vaccine candidate based on an in-silico approach.

Methods

NiV's Structural proteins were screened for B and T-cell epitopes, assessing characteristics like antigenicity, immunogenicity, allergenicity, and toxicity. Two vaccine constructs (NiV_1 & 2) were designed using different adjuvants (Cholera toxin and Beta-defensin 3) and linkers and their predicted 3D structures were evaluated for interaction with Toll-Like Receptor TLR-3 using docking and molecular dynamics (MD) simulation studies. Finally, The potential expression of the vaccine construct in Escherichia coli (E. coli.) was verified by cloning it into the PET28a (+) vector and immune simulations were undertaken.

Results

The study identified 30 conserved, antigenic, immunogenic, non-allergenic, and non-toxic epitopes with a broad population coverage. Based on the stability of vaccine construct in MD simulations results, NiV_1 was considered for further analysis. In-silico immune simulations of NiV_1 indicated a substantial immunogenic response. Moreover, codon optimization and in-silico cloning validated the expressions of designed vaccine construct NiV_1 in E. coli.

Conclusion

The findings indicate that the NiV_1 vaccine construct has the potential to elicit both cellular and humoral immune responses. Additional in vitro and in vivo investigations are required to validate the computational observations.