Improved prediction of protein side-chain conformations with SCWRL4

Integrating immune library probing with structure-based computational design to develop potent neutralizing nanobodies against emerging SARS-CoV-2 variants

To generate antibodies (Abs) against SARS-CoV-2 emerging variants, we integrated multiple tools and engineered molecules with excellent neutralizing breadth and potency. Initially, the screening of an immune library identified a nanobody (Nb), termed Nb4, specific to the receptor-binding domain (RBD) of the Omicron BA.1 variant. A Nb4-derived heavy chain antibody (hcAb4) recognized the spike (S) of the Wuhan, Beta, Delta, Omicron BA.1, and BA.5 SARS-CoV-2 variants. A high-resolution crystal structure of the Nb4 variable (VHH) domain in complex with the SARS-CoV-2 RBD (Wuhan) defined the Nb4 binding mode and interface. The Nb4 VHH domain grasped the RBD and covered most of its outer face, including the core and the receptor-binding motif (RBM), which was consistent with hcAb4 blocking RBD binding to the SARS-CoV-2 receptor. In mouse models, a humanized hcAb4 showed therapeutic potential and prevented the replication of SARS-CoV-2 BA.1 virus in the lungs of the animals. In vitro, hcAb4 neutralized Wuhan, Beta, Delta, Omicron BA.1, and BA.5 viral variants, as well as the BQ.1.1 subvariant, but showed poor neutralization against the Omicron XBB.1.5. Structure-based computation of the RBD-Nb4 interface identified three Nb4 residues with a reduced contribution to the interaction with the XBB.1.5 RBD. Site-saturation mutagenesis of these residues resulted in two hcAb4 mutants with enhanced XBB.1.5 S binding and virus neutralization, further improved by mutant Nb4 trimers. This research highlights an approach that combines library screening, Nb engineering, and structure-based computational predictions for the generation of SARS-CoV-2 Omicron-specific Abs and their adaptation to emerging variants.

Probing binding and allosteric mechanisms of the KRAS interactions with monobodies and affimer proteins: ensemble-based mutational profiling and thermodynamic analysis of binding energetics and allostery reveal diversity of functional hotspots and cryptic pockets linked by conserved communication network

KRAS, a historically "undruggable" oncogenic driver, has eluded targeted therapies due to its lack of accessible binding pockets in its active state. This study investigates the conformational dynamics, binding mechanisms, and allosteric communication networks of KRAS in complexes with monobodies (12D1, 12D5) and affimer proteins (K6, K3, K69) to characterize the binding and allosteric mechanisms and hotspots of KRAS binding. Through molecular dynamics simulations, mutational scanning, binding free energy analysis and network-based analyses, we identified conserved allosteric hotspots that serve as critical nodes for long-range communication in KRAS. Key residues in β-strand 4 (F78, L80, F82), α-helix 3 (I93, H95, Y96), β-strand 5 (V114, N116), and α-helix 5 (Y157, L159, R164) consistently emerged as hotspots across diverse binding partners, forming contiguous networks linking functional regions of KRAS. Notably, β-strand 4 acts as a central hub for propagating conformational changes, while the cryptic allosteric pocket centered around H95/Y96 positions targeted by clinically approved inhibitors was identified as a universal hotspot for both binding and allostery. The study also reveals the interplay between structural rigidity and functional flexibility, where stabilization of one region induces compensatory flexibility in others, reflecting KRAS's adaptability to perturbations. We found that monobodies stabilize the switch II region of KRAS, disrupting coupling between switch I and II regions and leading to enhanced mobility in switch I of KRAS. Similarly, affimer K3 leverages the α3-helix as a hinge point to amplify its effects on KRAS dynamics. Mutational scanning and binding free energy analysis highlighted the energetic drivers of KRAS interactions. Revealing key hotspot residues, including H95 and Y96 in the α3 helix, as major contributors to binding affinity and selectivity. Network analysis identified β-strand 4 as a central hub for propagating conformational changes, linking distant functional sites. The predicted allosteric hotspots strongly aligned with experimental data, validating the robustness of the computational approach. Despite distinct binding interfaces, shared hotspots highlight a conserved allosteric infrastructure, reinforcing their universal importance in KRAS signaling. The results of this study can inform rational design of small-molecule inhibitors that mimic the effects of monobodies and affimer proteins, challenging the "undruggable" reputation of KRAS.

G Protein Selectivity in Dopamine Receptors is Determined before GDP Release

Dopaminergic signaling in neurophysiological processes utilizes multiple G proteins. The dopamine receptor subtypes D1R/D5R selectively couple to Gs/olf proteins, while D2R/D3R/D4R is selective for Gi/o proteins. The molecular mechanisms underlying this selectivity are not clear, so structural models of D1R and D2R were built in complex with their cognate and noncognate G proteins, in either GDP-bound or nucleotide-free states. These eight complexes were relaxed in a membrane environment through 2 μs-long molecular dynamics (MD) simulations. A thermodynamic analysis of these complexes provided free energies of G protein binding to the receptors that was consistent with D1R's preference for Gs protein and D2R's preference for Gi protein, but only for the GDP-bound states of the G proteins, suggesting that Gs vs Gi selectivity happens before GDP release. Biophysical measurements of receptor preassociation with G proteins in cells were also consistent with these preferences. The role of the Gα protein's α5-helix in G protein selectivity was probed by switching the last 18 residues of Gα between Gαs and Gαi to create chimeric Gi18s and Gs18i proteins. Thermodynamic analysis of MD-relaxed chimeric complexes revealed a complete switch in G protein binding selectivity for both D1R and D2R receptors, but again only for the GDP-bound G proteins. Biophysical measurements of receptor preassociation with G proteins in cells also overall supported this selectivity alteration. These studies have shown that G protein selectivity for dopamine receptors is conferred before GDP release; however, additional molecular events may be needed for a productive coupling to enable a successful GDP/GTP exchange.

Analysis of natural compounds identifies potential inhibitors for phosphoglucomutase of Acinetobacter baumannii: a computational approach

Acinetobacter baumannii has become resistant to almost all available antibiotics in the market, emphasizing the need to develop novel antibiotics against this pathogen. The present study aims to identify potential inhibitors for phosphoglucomutase (Pgm) of A. baumannii by screening natural compounds. Pgm, a key enzyme involved in bacterial cell wall biosynthesis, is identified as a promising drug target. The study first employed various computational modeling tools to predict the structure of Pgm protein as its experimental structure was unavailable. After a thorough evaluation, the AlphaFold2 model (Rank 4) was chosen and energy-minimized for molecular docking study with its natural substrates, glucose-1-phosphate (G1P) and glucose-6-phosphate (G6P). Virtual screening of the natural compounds from LOTUS and CMNPD databases against Pgm identified top five compounds DMA, DPD, 2-DPD, HAP, and DTP, which exhibited better docking scores (- 8.287 kcal/mol, - 8.082 kcal/mol, - 8.082 kcal/mol, - 8.081 kcal/mol and - 7.97 kcal/mol) compared to the natural substrates G6P and G1P (- 6.225 kcal/mol, - 5.959 kcal/mol). The drug-likeness assessment of these compounds revealed that DPD had favorable pharmacokinetic profiles and was non-carcinogenic, non-irritating to the eyes, non-corrosive, and free of respiratory toxicity, representing it as a promising drug candidate. Molecular dynamics simulations and binding free energy calculations confirmed the stable interactions of DPD with Pgm, further supporting its potential as an inhibitor. Thus, the present study elucidated a natural compound as potential inhibitor against a vital protein Pgm. Further experimental studies can be carried out to understand its antibacterial properties for developing novel drugs against A. baumannii infections.

Supplementary Information

The online version contains supplementary material available at 10.1007/s40203-025-00360-2.

Computational analysis and protein engineering of artificial PDZ domain/self-binding peptide fusion biomacromolecular system with molecular switch functionality

Self-binding peptides (SBPs) are peptide-dependent intramolecular interactions described by our group, which conceptualize the short peptide segment within a monomeric protein as able to perform biological function by its dynamic and reversible binding to a cognate domain partner in the same monomer. Previously, we offered evidences that the SBPs represent a new biomolecular dynamic phenomenon that works across folding and binding, and also proposed the SBPs as potential drug targets for pharmacological intervention. Here, we attempted to model and design artificial protein systems containing SBP with molecular switch functionality by using computational peptidology strategies and protein engineering methods. In the procedure, the free decapeptide ligands were fused to the C-terminal tail of human CAL PDZ domain through a flexible polypeptide linker, thus resulting in a number of PDZ/SBP fusion biomacromolecular systems. The intramolecular binding event of SBP to PDZ was observed in the fusion systems as well as between the free decapeptide and PDZ. We carefully examined linker effects on the intramolecular binding event and systematically optimized the length and composition of linker to improve binding. Computational analysis suggested dynamics, but not thermodynamics, primarily responsible for the binding of SBP to PDZ. The linker plays an important role, as it restrains the SBP within a small local spatial region nearby the binding site of PDZ. The term effective concentration (EC) was defined to characterize the high local concentration of SBP at a small region due to the linker restriction, which improves the binding probability of SBP to PDZ from a dynamics point of view. The CAL PDZ/SBP(iCAL36-K-6) fusion protein system with poly(G)8 linker can work as designed well, where the SBP(iCAL36-K-6) dynamically binds to/unbinds from PDZ in a regulatable manner by externally controlling its C-terminal deamidation/amidation, thus exhibiting a typical molecular switch functionality.

PackPPI: An integrated framework for protein-protein complex side-chain packing and ΔΔG prediction based on diffusion model

Deep learning methods have played an increasingly pivotal role in advancing side-chain packing and mutation effect prediction (ΔΔG) for protein complexes. Although these two tasks are inherently closely related, they are typically treated separately in practice. Furthermore, the lack of effective post-processing in most approaches results in sub-optimal refinement of generated conformations, limiting the plausibility of the predicted conformations. In this study, we introduce an integrated framework, PackPPI, which employs a diffusion model and a proximal optimization algorithm to improve side-chain prediction for protein complexes while using learned representations to predict ΔΔG. The results demonstrate that PackPPI achieved the lowest atom RMSD (0.9822) on the CASP15 dataset. The proximal optimization algorithm effectively reduces spatial clashes between side-chain atoms while maintaining a low-energy landscape. Furthermore, PackPPI achieves state-of-the-art performance in predicting binding affinity changes induced by multi-point mutations on the SKEMPI v2.0 dataset. These findings underscore the potential of PackPPI as a robust and versatile computational tool for protein design and engineering. The implementation of PackPPI is available at https://github.com/Jackz915/PackPPI.

Exploring Diverse Binding Mechanisms of Broadly Neutralizing Antibodies S309, S304, CYFN-1006 and VIR-7229 Targeting SARS-CoV-2 Spike Omicron Variants: Integrative Computational Modeling Reveals Balance of Evolutionary and Dynamic Adaptability in Shaping Molecular Determinants of Immune Escape

Evolution of SARS-CoV-2 has led to the emergence of variants with increased immune evasion capabilities, posing significant challenges to antibody-based therapeutics and vaccines. The cross-neutralization activity of antibodies against Omicron variants is governed by a complex and delicate interplay of multiple energetic factors and interaction contributions. In this study, we conducted a comprehensive analysis of the interactions between the receptor-binding domain (RBD) of the SARS-CoV-2 spike protein and four neutralizing antibodies S309, S304, CYFN1006, and VIR-7229. Using integrative computational modeling that combined all-atom molecular dynamics (MD) simulations, mutational scanning, and MM-GBSA binding free energy calculations, we elucidated the structural, energetic, and dynamic determinants of antibody binding. Our findings reveal distinct dynamic binding mechanisms and evolutionary adaptation driving broad neutralization effect of these antibodies. We show that S309 targets conserved residues near the ACE2 interface, leveraging synergistic van der Waals and electrostatic interactions, while S304 focuses on fewer but sensitive residues, making it more susceptible to escape mutations. The analysis of CYFN-1006.1 and CYFN-1006.2 antibody binding highlights broad epitope coverage with critical anchors at T345, K440, and T346, enhancing its efficacy against variants carrying the K356T mutation which caused escape from S309 binding. Our analysis of broadly potent VIR-7229 antibody binding to XBB.1.5 and EG.5 Omicron variants emphasized a large and structurally complex epitope, demonstrating certain adaptability and compensatory effects to F456L and L455S mutations. Mutational profiling identified key residues crucial for antibody binding, including T345, P337, and R346 for S309, and T385 and K386 for S304, underscoring their roles as evolutionary "weak spots" that balance viral fitness and immune evasion. The results of this energetic analysis demonstrate a good agreement between the predicted binding hotspots and critical mutations with respect to the latest experiments on average antibody escape scores. The results of this study dissect distinct energetic mechanisms of binding and importance of targeting conserved residues and diverse epitopes to counteract viral resistance. Broad-spectrum antibodies CYFN1006 and VIR-7229 maintain efficacy across multiple variants and achieve neutralization by targeting convergent evolution hotspots while enabling tolerance to mutations in these positions through structural adaptability and compensatory interactions at the binding interface. The results of this study underscore the diversity of binding mechanisms employed by different antibodies and molecular basis for high affinity and excellent neutralization activity of the latest generation of antibodies.

The Photophysical Path to the Triplet State in Light-Oxygen-Voltage (LOV) Domains

Upon blue-light absorption, LOV domains efficiently undergo intersystem crossing (ISC) to the triplet state. Several factors potentially contribute to this efficiency. One often proposed in the literature is the heavy atom effect of the nearby (and eventually adduct-forming) cysteine. However, some LOV domain derivatives that lack the cysteine residue also undergo ISC efficiently. Using hybrid multireference quantum mechanical/molecular mechanical (QM / MM) models, we investigated the effect of the electrostatic environment in a prototypal LOV domain, Arabidopsis thaliana Phototropin 1 LOV2 (AtLOV2), compared to the effect of the dielectric of an aqueous solution. We find that the electrostatic environment of AtLOV2 is especially well tuned to stabilize a triplet( n N , π * ) ${(n_{\rm{N}}, \pi ^{\ast} )}$ state, which we posit is the state involved in the ISC step. Other low-lying triplet states that have( π , π * ) ${(\pi, \pi ^{\ast} )}$ and( n O , π * ) ${(n_{\rm{O}}, \pi ^{\ast} )}$ character are ruled out on the basis of energetics and/or their orbital character. The mechanistic picture that emerges from the calculations is one that involves the ISC of photoexcited flavin to a triplet( n N , π * ) ${(n_{\rm{N}}, \pi ^{\ast} )}$ state followed by rapid internal conversion to a triplet( π , π * ) ${(\pi, \pi ^{\ast} )}$ state, which is the state detected spectroscopically. This insight into the ISC mechanism can provide guidelines for tuning flavin's photophysics through mutations that alter the protein electrostatic environment and potentially helps to explain why ISC (and subsequent flavin photochemistry) does not occur readily in many classes of flavin-binding enzymes.

AI-augmented physics-based docking for antibody-antigen complex prediction

MOTIVATION

Predicting the structure of antibody-antigen complexes is a challenging task with significant implications for the design of better antibody therapeutics. However, the levels of success have remained dauntingly low, particularly when high standards for model quality are required, a necessity for efficient antibody design. Artificial intelligence (AI) has significantly impacted the landscape of structure prediction for antibodies, both alone and in complex with their antigens.

METHODS

We utilized AI-guided antibody modeling tools to generate ensembles displaying diversity in the complementarity-determining region (CDR) and integrated those into our previously published AlphaFold2-rescored docking pipeline, a strategy called AI-augmented physics-based docking. In this study, we also compare docking performance with AlphaFold and Boltz-1, the new state-of-the-art. We distinguish between two types of success tailored to specific downstream applications: (i) criteria sufficient for epitope mapping, where gross quality is adequate and can complement experimental techniques, and (ii) criteria for producing higher-quality models suitable for engineering purposes.

RESULTS

We highlight that the quality of the ensemble is crucial for docking performance, that including too many models can be detrimental, and that prioritization of models is essential for achieving good performance. In a scenario analogous to docking using a crystallized antigen, our results robustly demonstrate the advantages of AI-augmented docking over AlphaFold2, further accentuated when higher standards in quality are imposed. Docking also shows improvements over Boltz-1, but those are less pronounced. Docking performance is still noticeably lower than AlphaFold3 in both epitope mapping and antibody design use cases. We observe a strong dependence on CDR-H3 loop length for physics-based tools on their ability to successfully predict. This helps define an applicability range where physics-based docking can be competitive to the newer generation of AI tools.

AVAILABILITY AND IMPLEMENTATION

The AF2 rescoring scripts are available at github.com/gaudreaultfnrc/AF2-Rescoring.

Multiphasic condensates formed with mono-component of tetrapeptides via phase separation

Biomolecular condensates, formed by liquid-liquid phase separation of biomacromolecules, play crucial roles in regulating physiological events in biological systems. While multiphasic condensates have been extensively studied, those derived from a single component of short peptides have not yet been reported. Here, we report the symmetrical core-shell structural biomolecular condensates formed with a programmable tetrapeptide library via phase separation. Our findings reveal that tryptophan is essential for core-shell structure formation due to its strongest homotypical π-π interaction, enabling us to modulate the structure of condensates from core-shell to homogeneous by altering the amino acid composition. Molecular dynamics simulation combined with cryogenic focused ion beam scanning electron microscopy and cryogenic electron microscopy show that the inner core of multiphasic tetrapeptide condensates is solid-like, consisting of ordered structures. The core is enveloped by a liquid-like shell, stabilizing the core structure. Furthermore, we demonstrate control over multiphasic condensate formation through intrinsic redox reactions or post-translational modifications, facilitating the rational design of synthetic multiphasic condensates for various applications on demand.

Sifting through the noise: A survey of diffusion probabilistic models and their applications to biomolecules

Diffusion probabilistic models have made their way into a number of high-profile applications since their inception. In particular, there has been a wave of research into using diffusion models in the prediction and design of biomolecular structures and sequences. Their growing ubiquity makes it imperative for researchers in these fields to understand them. This paper serves as a general overview for the theory behind these models and the current state of research. We first introduce diffusion models and discuss common motifs used when applying them to biomolecules. We then present the significant outcomes achieved through the application of these models in generative and predictive tasks. This survey aims to provide readers with a comprehensive understanding of the increasingly critical role of diffusion models.

Atomistic Profiling of KRAS Interactions with Monobodies and Affimer Proteins Through Ensemble-Based Mutational Scanning Unveils Conserved Residue Networks Linking Cryptic Pockets and Regulating Mechanisms of Binding, Specificity and Allostery

KRAS, a historically "undruggable" oncogenic driver, has eluded targeted therapies due to its lack of accessible binding pockets in its active state. This study investigates the conformational dynamics, binding mechanisms, and allosteric communication networks of KRAS in complexes with monobodies (12D1, 12D5) and affimer proteins (K6, K3, K69) to characterize the binding and allosteric mechanisms and hotspots of KRAS binding. Through molecular dynamics simulations, mutational scanning, binding free energy analysis and network-based analyses, we identified conserved allosteric hotspots that serve as critical nodes for long-range communication in KRAS. Key residues in β-strand 4 (F78, L80, F82), α-helix 3 (I93, H95, Y96), β-strand 5 (V114, N116), and α-helix 5 (Y157, L159, R164) consistently emerged as hotspots across diverse binding partners, forming contiguous networks linking functional regions of KRAS. Notably, β-strand 4 acts as a central hub for propagating conformational changes, while the cryptic allosteric pocket centered around H95/Y96 positions targeted by clinically approved inhibitors was identified as a universal hotspot for both binding and allostery. The study also reveals the interplay between structural rigidity and functional flexibility, where stabilization of one region induces compensatory flexibility in others, reflecting KRAS's adaptability to perturbations. We found that monobodies stabilize the switch II region of KRAS, disrupting coupling between switch I and II regions and leading to enhanced mobility in switch I of KRAS. Similarly, affimer K3 leverages the α3-helix as a hinge point to amplify its effects on KRAS dynamics. Mutational scanning and binding free energy analysis highlighted the energetic drivers of KRAS interactions. revealing key hotspot residues, including H95 and Y96 in the α3 helix, as major contributors to binding affinity and selectivity. Network analysis identified β-strand 4 as a central hub for propagating conformational changes, linking distant functional sites. The predicted allosteric hotspots strongly aligned with experimental data, validating the robustness of the computational approach. Despite distinct binding interfaces, shared hotspots highlight a conserved allosteric infrastructure, reinforcing their universal importance in KRAS signaling. The results of this study can inform rational design of small-molecule inhibitors that mimic the effects of monobodies and affimer proteins, challenging the "undruggable" reputation of KRAS.

Structure-based design and disulfide stapling of interfacial cyclic peptidic inhibitors from thymic stromal lymphopoietin (TSLP) receptor to competitively target TSLP

Human thymic stromal lymphopoietin (TSLP) is a pro-inflammatory cytokine located at the top of inflammatory cascade that makes it a promising therapeutic target in allergic asthma. The cell surface receptor of TSLP is a heterodimer consisting of a TSLP receptor (TSLPR) and an interleukin-17 receptor α (IL-7Rα). The TSLPR subunit should be first added to the free TSLP to form a TSLPR/TSLP pre-complex, which further recruits the IL-7Rα subunit to obtain the final TSLPR/IL-7Rα/TSLP complex. Previous works have been focused on targeting the IL-7Rα-binding site of TSLP. Instead, we herein reported an attempt for rational design of cyclic peptidic inhibitors to competitively disrupt the TSLPR-TSLP interaction based on their complex crystal structure by integrating dynamics simulation and energetics analysis as well as experimental assays at molecular level. An interfacial peptide segment derived from the hotspots of TSLPR that cover a specific TSLP-binding site on the TSLPR interface, which is expected to natively form a U-shaped conformation recognized by TSLP and thus compete with the cognate TSLPR for TSLP. The eS4P peptide was further stapled by a disulfide bridge between different residue pairs across its two arms, thus separately resulting in its two stapled cyclic counterparts, i.e. eS4P[189-198] and eS4P[188-200] peptides. Circular dichroism characterized that the stapling can effectively constrain the peptide into a native-like U-shpared conformation in free state, thus largely minimizing the entropy penalty upon its binding to TSLP. Affinity assays revealed that the stapling can considerably improve the peptide binding potency to TSLP by 2.9-fold and 8.3-fold at molecular level. In addition, we further demonstrated that the potent eS4P[188-200] peptide has a good selectivity for its cognate TSLP over other four noncognate cytokines IL-2, IL-7, IL-13 and IL-22 that are relevant with the TSLP. In this respect, it is considered that the disulfide-stapled cyclic peptide-mediated blockade of TLSP inflammatory cascade may be a new and promising therapeutic strategy against allergic asthma.

To pack or not to pack: revisiting protein side-chain packing in the post-AlphaFold era

Motivation

Protein side-chain packing (PSCP), the problem of predicting side-chain conformation given a fixed backbone structure, has important implications in modeling of structures and interactions. However, despite the groundbreaking progress in protein structure prediction pioneered by AlphaFold, the existing PSCP methods still rely on experimental inputs, and do not leverage AlphaFold-predicted backbone coordinates to enable PSCP at scale.

Results

Here, we perform a large-scale benchmarking of the predictive performance of various PSCP methods on public datasets from multiple rounds of the Critical Assessment of Structure Prediction (CASP) challenges using a diverse set of evaluation metrics. Empirical results demonstrate that the PSCP methods perform well in packing the side-chains with experimental inputs, but they fail to generalize in repacking AlphaFold-generated structures. We additionally explore the effectiveness of leveraging the self-assessment confidence scores from AlphaFold by implementing a backbone confidence-aware integrative approach. While such a protocol often leads to performance improvement by attaining modest yet statistically significant accuracy gains over the AlphaFold baseline, it does not yield consistent and pronounced improvements. Our study highlights the recent advances and remaining challenges in PSCP in the post-AlphaFold era.

Availability

The code and raw data are freely available at https://github.com/Bhattacharya-Lab/PackBench.

Mutational Scanning and Binding Free Energy Computations of the SARS-CoV-2 Spike Complexes with Distinct Groups of Neutralizing Antibodies: Energetic Drivers of Convergent Evolution of Binding Affinity and Immune Escape Hotspots

The rapid evolution of SARS-CoV-2 has led to the emergence of variants with increased immune evasion capabilities, posing significant challenges to antibody-based therapeutics and vaccines. In this study, we conducted a comprehensive structural and energetic analysis of SARS-CoV-2 spike receptor-binding domain (RBD) complexes with neutralizing antibodies from four distinct groups (A-D), including group A LY-CoV016, group B AZD8895 and REGN10933, group C LY-CoV555, and group D antibodies AZD1061, REGN10987, and LY-CoV1404. Using coarse-grained simplified simulation models, rapid energy-based mutational scanning, and rigorous MM-GBSA binding free energy calculations, we elucidated the molecular mechanisms of antibody binding and escape mechanisms, identified key binding hotspots, and explored the evolutionary strategies employed by the virus to evade neutralization. The residue-based decomposition analysis revealed energetic mechanisms and thermodynamic factors underlying the effect of mutations on antibody binding. The results demonstrate excellent qualitative agreement between the predicted binding hotspots and the latest experiments on antibody escape. These findings provide valuable insights into the molecular determinants of antibody binding and viral escape, highlighting the importance of targeting conserved epitopes and leveraging combination therapies to mitigate the risk of immune evasion.

Quantitative Characterization and Prediction of the Binding Determinants and Immune Escape Hotspots for Groups of Broadly Neutralizing Antibodies Against Omicron Variants: Atomistic Modeling of the SARS-CoV-2 Spike Complexes with Antibodies

A growing body of experimental and computational studies suggests that the cross-neutralization antibody activity against Omicron variants may be driven by the balance and tradeoff between multiple energetic factors and interaction contributions of the evolving escape hotspots involved in antigenic drift and convergent evolution. However, the dynamic and energetic details quantifying the balance and contribution of these factors, particularly the balancing nature of specific interactions formed by antibodies with epitope residues, remain largely uncharacterized. In this study, we performed molecular dynamics simulations, an ensemble-based deep mutational scanning of SARS-CoV-2 spike residues, and binding free energy computations for two distinct groups of broadly neutralizing antibodies: the E1 group (BD55-3152, BD55-3546, and BD5-5840) and the F3 group (BD55-3372, BD55-4637, and BD55-5514). Using these approaches, we examined the energetic determinants by which broadly potent antibodies can largely evade immune resistance. Our analysis revealed the emergence of a small number of immune escape positions for E1 group antibodies that correspond to the R346 and K444 positions in which the strong van der Waals and interactions act synchronously, leading to the large binding contribution. According to our results, the E1 and F3 groups of Abs effectively exploit binding hotspot clusters of hydrophobic sites that are critical for spike functions along with the selective complementary targeting of positively charged sites that are important for ACE2 binding. Together with targeting conserved epitopes, these groups of antibodies can lead expand the breadth and resilience of neutralization to the antigenic shifts associated with viral evolution. The results of this study and the energetic analysis demonstrate excellent qualitative agreement between the predicted binding hotspots and critical mutations with respect to the latest experiments on average antibody escape scores. We argue that the E1 and F3 groups of antibodies targeting binding epitopes may leverage strong hydrophobic interactions with the binding epitope hotspots that are critical for the spike stability and ACE2 binding, while escape mutations tend to emerge in sites associated with synergistically strong hydrophobic and electrostatic interactions.

CryoEM-enabled visual proteomics reveals de novo structures of oligomeric protein complexes from Azotobacter vinelandii

Single particle cryoelectron microscopy (cryoEM) and cryoelectron tomography (cryoET) are powerful methods for unveiling unique and functionally relevant structural states. Aided by mass spectrometry and machine learning, they promise to facilitate the visual exploration of proteomes. Leveraging visual proteomics, we interrogate structures isolated from a complex cellular milieu by cryoEM to identify and classify molecular structures and complexes de novo . That approach determines the identity of six distinct oligomeric protein complexes from partially purified extracts of Azotobacter vinelandii using both anaerobic and aerobic cryoEM. Identification of the first unknown species, phosphoglucoisomerase (Pgi1), is achieved by comparing three automated model building programs: CryoID, DeepTracer, and ModelAngelo with or without a priori proteomics data. All three programs identify the Pgi1 protein, revealed to be in a new decameric state, as well as additional globular structures identified as glutamine synthetase (GlnA) and bacterioferritin (Bfr). Large filamentous assemblies are observed in tomograms reconstructed from cryoFIB milled lamellae of nitrogen-fixing A. vinelandii . Enrichment of these species from the cells by centrifugation allows for structure determination of three distinct filament types by helical reconstruction methods: the Type 6 Secretion System non-contractile sheath tube (TssC), a novel filamentous form of the soluble pyridine transhydrogenase (SthA), and the flagellar filament (FliC). The multimeric states of Pgi1 and SthA stand out in contrast to known crystallographic structures and offer a new structural framework from which to evaluate their activities. Overall, by allowing the study of near-native oligomeric protein states, cryoEM-enabled visual proteomics reveals novel structures that correspond to relevant species observed in situ .

Abstract Figure

Presynaptic hyperexcitability reversed by positive allosteric modulation of a GABABR epilepsy variant

GABABRs are key membrane proteins that continually adapt the excitability of the nervous system. These G-protein coupled receptors are activated by the brain's premier inhibitory neurotransmitter GABA. They are obligate heterodimers composed of GABA-binding GABABR1 and G-protein-coupling GABABR2 subunits. Recently, three variants (G693W, S695I, I705N) have been identified in the gene (GABBR2) encoding for GABABR2. Individuals that harbour any of these variants exhibit severe developmental epileptic encephalopathy and intellectual disability, but the underlying pathogenesis that is triggered in neurons remains unresolved. Using a range of confocal imaging, flow cytometry, structural modelling, biochemistry, live cell Ca2+ imaging of presynaptic terminals, whole-cell electrophysiology of human embryonic kidney (HEK)-293 T cells and neurons and two-electrode voltage clamping of Xenopus oocytes, we have probed the biophysical and molecular trafficking and functional profiles of G693W, S695I and I705N variants. We report that all three point mutations impair neuronal cell surface expression of GABABRs, reducing signalling efficacy. However, a negative effect evident for one variant perturbed neurotransmission by elevating presynaptic Ca2+ signalling. This is reversed by enhancing GABABR signalling via positive allosteric modulation. Our results highlight the importance of studying neuronal receptors expressed in nervous system tissue and provide new mechanistic insights into how GABABR variants can initiate neurodevelopmental disease whilst highlighting the translational suitability and therapeutic potential of allosteric modulation for correcting these deficits.

A computationally designed antigen eliciting broad humoral responses against SARS-CoV-2 and related sarbecoviruses

The threat of spillovers of coronaviruses associated with the severe acute respiratory syndrome (SARS) from animals to humans necessitates vaccines that offer broader protection from sarbecoviruses. By leveraging a viral-genome-informed computational method for selecting immune-optimized and structurally engineered antigens, here we show that a single antigen based on the receptor binding domain of the spike protein of sarbecoviruses elicits broad humoral responses against SARS-CoV-1, SARS-CoV-2, WIV16 and RaTG13 in mice, rabbits and guinea pigs. When administered as a DNA immunogen or by a vector based on a modified vaccinia virus Ankara, the optimized antigen induced vaccine protection from the Delta variant of SARS-CoV-2 in mice genetically engineered to express angiotensin-converting enzyme 2 and primed by a viral-vector vaccine (AZD1222) against SARS-CoV-2. A vaccine formulation incorporating mRNA coding for the optimized antigen further validated its broad immunogenicity. Vaccines that elicit broad immune responses across subgroups of coronaviruses may counteract the threat of zoonotic spillovers of betacoronaviruses.

Accurate Characterization of the Allosteric Energy Landscapes, Binding Hotspots and Long-Range Communications for KRAS Complexes with Effector Proteins : Integrative Approach Using Microsecond Molecular Dynamics, Deep Mutational Scanning of Binding Energetics and Allosteric Network Modeling

KRAS is a pivotal oncoprotein that regulates cell proliferation and survival through interactions with downstream effectors such as RAF1. Oncogenic mutations in KRAS, including G12V, G13D, and Q61R, drive constitutive activation and hyperactivation of signaling pathways, contributing to cancer progression. Despite significant advances in understanding KRAS biology, the structural and dynamic mechanisms of KRAS binding and allostery by which oncogenic mutations enhance KRAS-RAF1 binding and signaling remain incompletely understood. In this study, we employ microsecond molecular dynamics simulations, Markov State Modeling, mutational scanning and binding free energy calculations together with dynamic network modeling to elucidate the effect of KRAS mutations and characterize the thermodynamic and allosteric drivers and hotspots of KRAS binding and oncogenic activation. Our simulations revealed that oncogenic mutations stabilize the open active conformation of KRAS by differentially modulating the flexibility of the switch I and switch II regions, thereby enhancing RAF1 binding affinity. The G12V mutation rigidifies both switch I and switch II, locking KRAS in a stable, active state. In contrast, the G13D mutation moderately reduces switch I flexibility while increasing switch II dynamics, restoring a balance between stability and flexibility. The Q61R mutation induces a more complex conformational landscape, characterized by the increased switch II flexibility and expansion of functional macrostates, which promotes prolonged RAF1 binding and signaling. Mutational scanning of KRAS-RAF1 complexes identified key binding affinity hotspots, including Y40, E37, D38, and D33, and together with the MM-GBSA analysis revealed the hotspots leverage synergistic electrostatic and hydrophobic binding interactions in stabilizing the KRAS-RAF1 complexes. Network-based analysis of allosteric communication identifies critical KRAS residues (e.g., L6, E37, D57, R97) that mediate long-range interactions between the KRAS core and the RAF1 binding interface. The central β-sheet of KRAS emerges as a hub for transmitting conformational changes, linking distant functional sites and facilitating allosteric regulation. Strikingly, the predicted allosteric hotspots align with experimentally identified allosteric binding hotspots that define the energy landscape of KRAS allostery. This study highlights the power of integrating computational modeling with experimental data to unravel the complex dynamics of KRAS and its mutants. The identification of binding hotspots and allosteric communication routes offers new opportunities for developing targeted therapies to disrupt KRAS-RAF1 interactions and inhibit oncogenic signaling. Our results underscore the potential of computational approaches to guide the design of allosteric inhibitors and mutant-specific therapies for KRAS-driven cancers.

FlowBack: A Generalized Flow-Matching Approach for Biomolecular Backmapping

Coarse-grained models have become ubiquitous in biomolecular modeling tasks aimed at studying slow dynamical processes such as protein folding and DNA hybridization. These models can considerably accelerate sampling but it remains challenging to accurately and efficiently restore all-atom detail to the coarse-grained trajectory, which can be vital for detailed understanding of molecular mechanisms and calculation of observables contingent on all-atom coordinates. In this work, we introduce FlowBack as a deep generative model employing a flow-matching objective to map samples from a coarse-grained prior distribution to an all-atom data distribution. We construct our prior distribution to be agnostic to the coarse-grained map and molecular type. A protein-specific model trained on ∼65k structures from the Protein Data Bank achieves state-of-the-art performance on structural metrics compared to previous generative and rules-based approaches in applications to static PDB structures, all-atom simulations of fast-folding proteins, and coarse-grained trajectories generated by a machine-learned force field. A DNA-protein model trained on ∼1.5k DNA-protein complexes achieves excellent reconstruction and generative capabilities on static DNA-protein complexes from the Protein Data Bank as well as on out-of-distribution coarse-grained dynamical simulations of DNA-protein complexation. FlowBack offers an accurate, efficient, and easy-to-use tool to recover all-atom structures from coarse-grained molecular simulations with higher robustness and fewer steric clashes than previous approaches. We make FlowBack freely available to the community as an open source Python package.

Phyre2.2: A Community Resource for Template-based Protein Structure Prediction

Template-based modelling, also known as homology modelling, is a powerful approach to predict the structure of a protein from its amino acid sequence. The approach requires one to identify a sequence similarity between the query sequence and that of a known structure as they will adopt a similar conformation, and the known structure can be used as the template for modelling the query sequence. Recently several approaches, most notably AlphaFold, have employed enhanced machine learning and have yielded accurate models irrespective of whether there is an identifiable template. Here we report Phyre2.2 which incorporates several enhancements to our widely-used template modelling portal Phyre2. The main development is facilitating a user to submit their sequence and then Phyre2.2 identifies the most suitable AlphaFold model to be used as a template. In Phyre2.2 the user searches a template library of known structures. We have now included in our library a representative structure for every protein sequence in the protein databank (PDB). In addition, there are representatives for an apo and a holo structure if they are in the PDB. The ranking of hits has been modified to highlight to the user if there are different domains spanning the sequence. Phyre2.2 continues to support batch processing where a user can submit up to 100 sequences facilitating processing of proteomes. Phyre2.2 is freely available to all users, including commercial users, at https://www.sbg.bio.ic.ac.uk/phyre2/.

Further Development of SAMPDI-3D: A Machine Learning Method for Predicting Binding Free Energy Changes Caused by Mutations in Either Protein or DNA

BACKGROUND/OBJECTIVES

Predicting the effects of protein and DNA mutations on the binding free energy of protein-DNA complexes is crucial for understanding how DNA variants impact wild-type cellular function. As many cellular interactions involve protein-DNA binding, accurately predicting changes in binding free energy (ΔΔG) is valuable for distinguishing pathogenic mutations from benign ones.

METHODS

This study describes the development and optimization of the SAMPDI-3Dv2 machine learning method, which is trained on an expanded database of experimentally measured ΔΔGs. This enhanced model incorporates new features, including the 3D structure of the mutant protein, features of the mutant structure, and a position-specific scoring matrix (PSSM). Benchmarking was conducted using 5-fold cross-validation.

RESULTS

The updated SAMPDI-3D model (SAMPDI-3Dv2) achieved Pearson correlation coefficients (PCCs) of 0.68 for protein and 0.80 for DNA mutations. These results represent significant improvements over existing tools. Additionally, the method's rapid execution time enables genome-scale predictions.

CONCLUSIONS

The improved SAMPDI-3Dv2 shows enhanced predictive performance for analyzing mutations in protein-DNA complexes. By leveraging structural information and an expanded training dataset, SAMPDI-3Dv2 provides researchers with a more accurate and efficient tool for mutation analysis, contributing to identifying pathogenic variants and improving our understanding of cellular function.

Conformational dynamics and multi-modal interaction of Paxillin with the Focal Adhesion Targeting Domain

Paxillin (PXN) and focal adhesion kinase (FAK) are two major components of the focal adhesion complex, a multiprotein structure linking the intracellular cytoskeleton to the cell exterior. PXN interacts directly with the C-terminal targeting domain of FAK (FAT) via its intrinsically disordered N-terminal domain. This interaction is necessary and sufficient for localizing FAK to focal adhesions. Furthermore, PXN serves as a platform for recruiting other proteins that together control the dynamic changes needed for cell migration and survival. Here, we show that the PXN disordered region undergoes large-scale conformational restriction upon binding to FAT, forming a 48-kDa multi-modal complex consisting of four major interconverting states. Although the complex is flexible, each state has unique sets of contacts involving disordered regions that are both highly represented in ensembles and conserved. Moreover, conserved intramolecular contacts from glutamine-rich regions in PXN contribute to high entropy and thus stability of the FAT bound complex. As PXN is a hub protein, the results provide a structural basis for understanding how perturbations that lead to cellular network rewiring, such as ligand binding and phosphorylation, may lead to shifts in the multi-state equilibrium and phenotypic switching.

Structural Basis for Antagonist Binding to Vasopressin V1b Receptor Revealed by the Molecular Dynamics Simulations

The human V1b receptor (V1bR) is primarily expressed in the corticotropic cells of the anterior pituitary where it is involved in the regulation of the hypothalamic-pituitary-adrenal (HPA) axis. The activation of V1bR induces the secretion of adrenocorticotropin hormone (ACTH) from the anterior pituitary cells which, in turn, stimulates the production of cortisol via the adrenal cortex. Clinical studies have demonstrated the chronic dysfunction of the HPA axis in patients with several psychiatric disorders. Thus, the inhibition of the V1b receptor and normalizing the HPA axis hyperactivity is a promising approach to the treatment of many stress-related disorders such as anxiety and depression. Nelivaptan is a selective V1bR antagonist that can be used for this purpose and an excellent molecule to study how antagonists interact with V1bR, especially since in recent years the experimental structures of vasopressin V2 and oxytocin receptors were solved, providing high-similarity templates for homology modeling of V1bR. Therefore, in this work, six independent molecular dynamics simulations of a V1bR-nelivaptan complex in a fully hydrated lipid bilayer, yielding a total simulation time of 6.0 μs, have been conducted. In the lowest-energy complexes obtained in this work and proposed to be the most probable structure of the V1bR-nelivaptan complex, the location of the ligand inside the receptor pocket is very similar to that of the other ligands observed in the experimental structures of the vasopressin/oxytocin receptor family. The receptor-ligand interaction has been analyzed and described, revealing the details of the molecular mechanism of this antagonist binding to V1bR and a probable contribution of L2005×40 and T2035×43 to binding selectivity.

Approaching Pharmacological Space: Events and Components

The pharmacological space comprises all the dynamic events that determine the bioactivity (and/or the metabolism and toxicity) of a given ligand. The pharmacological space accounts for the structural flexibility and property variability of the two interacting molecules as well as for the mutual adaptability characterizing their molecular recognition process. The dynamic behavior of all these events can be described by a set of possible states (e.g., conformations, binding modes, isomeric forms) that the simulated systems can assume. For each monitored state, a set of state-dependent ligand- and structure-based descriptors can be calculated. Instead of considering only the most probable state (as routinely done), the pharmacological space proposes to consider all the monitored states. For each state-dependent descriptor, the corresponding space can be evaluated by calculating various dynamic parameters such as mean and range values.The reviewed examples emphasize that the pharmacological space can find fruitful applications in structure-based virtual screening as well as in toxicity prediction. In detail, in all reported examples, the inclusion of the pharmacological space parameters enhances the resulting performances. Beneficial effects are obtained by combining both different binding modes to account for ligand mobility and different target structures to account for protein flexibility/adaptability.The proposed computational workflow that combines docking simulations and rescoring analyses to enrich the arsenal of docking-based descriptors revealed a general applicability regardless of the considered target and utilized docking engine. Finally, the EFO approach that generates consensus models by linearly combining various descriptors yielded highly performing models in all discussed virtual screening campaigns.

Thermodynamic role of receptor phosphorylation barcode in cannabinoid receptor desensitization

The endocannabinoid signaling system is comprised of CB1 and CB2 G protein-coupled receptors (GPCRs). CB2 receptor subtype is predominantly expressed in the immune cells and signals through its transducer proteins (Gi protein and β-arrestin-2). Arrestins are signaling proteins that bind to many GPCRs after receptor phosphorylation to terminate G protein signaling (desensitization) and to initiate specific G protein-independent arrestin-mediated signaling pathways via a "phosphorylation barcode", that captures sequence patterns of phosphorylated Ser/Thr residues in the receptor's intracellular domains and can lead to different signaling effects. The structural basis for how arrestins and G proteins compete with the receptor for biased signaling and how different barcodes lead to different signaling profiles is not well understood as there is a lack of phosphorylated receptor structures in complex with arrestins. In this work, structural models of β-arrestin-2 were built in complex with the phosphorylated and unphosphorylated forms of the CB2 receptor. The complex structures were relaxed in the lipid bilayer environment with molecular dynamics (MD) simulations and analyzed structurally and thermodynamically. The β-arrestin-2 complex with the phosphorylated receptor was more stable than the non-phosphorylated one, highlighting the thermodynamic role of the receptor phosphorylation. It was also more stable than any of the G protein complexes with CB2 suggesting that phosphorylation signals receptor desensitization (end of G protein signaling) and arrest of the receptor by arrestins. These models are beginning to provide the thermodynamic landscape of CB2 signaling, which can help bias signaling towards therapeutically beneficial pathways in drug discovery applications.

Quantitative Characterization and Prediction of the Binding Determinants and Immune Escape Hotspots for Groups of Broadly Neutralizing Antibodies Against Omicron Variants: Atomistic Modeling of the SARS-CoV-2 Spike Complexes with Antibodies

The growing body of experimental and computational studies suggested that the cross-neutralization antibody activity against Omicron variants may be driven by balance and tradeoff of multiple energetic factors and interaction contributions of the evolving escape hotspots involved in antigenic drift and convergent evolution. However, the dynamic and energetic details quantifying the balance and contribution of these factors, particularly the balancing nature of specific interactions formed by antibodies with the epitope residues remain scarcely characterized. In this study, we performed molecular dynamics simulations, ensemble-based deep mutational scanning of SARS-CoV-2 spike residues and binding free energy computations for two distinct groups of broadly neutralizing antibodies : E1 group (BD55-3152, BD55-3546 and BD5-5840) and F3 group (BD55-3372, BD55-4637 and BD55-5514). Using these approaches, we examine the energetic determinants by which broadly potent antibodies can largely evade immune resistance. Our analysis revealed the emergence of a small number of immune escape positions for E1 group antibodies that correspond to R346 and K444 positions in which the strong van der Waals and interactions act synchronously leading to the large binding contribution. According to our results, E1 and F3 groups of Abs effectively exploit binding hotspot clusters of hydrophobic sites critical for spike functions along with selective complementary targeting of positively charged sites that are important for ACE2 binding. Together with targeting conserved epitopes, these groups of antibodies can lead to the expanded neutralization breadth and resilience to antigenic shift associated with viral evolution. The results of this study and the energetic analysis demonstrate excellent qualitative agreement between the predicted binding hotspots and critical mutations with respect to the latest experiments on average antibody escape scores. We argue that E1 and F3 groups of antibodies targeting binding epitopes may leverage strong hydrophobic interactions with the binding epitope hotspots critical for the spike stability and ACE2 binding, while escape mutations tend to emerge in sites associated with synergistically strong hydrophobic and electrostatic interactions.

Strengths and limitations of web servers for the modeling of TCRpMHC complexes

Cellular immunity relies on the ability of a T-cell receptor (TCR) to recognize a peptide (p) presented by a class I major histocompatibility complex (MHC) receptor on the surface of a cell. The TCR-peptide-MHC (TCRpMHC) interaction is a crucial step in activating T-cells, and the structural characteristics of these molecules play a significant role in determining the specificity and affinity of this interaction. Hence, obtaining 3D structures of TCRpMHC complexes offers valuable insights into various aspects of cellular immunity and can facilitate the development of T-cell-based immunotherapies. Here, we aimed to compare three popular web servers for modeling the structures of TCRpMHC complexes, namely ImmuneScape (IS), TCRpMHCmodels, and TCRmodel2, to examine their strengths and limitations. Each method employs a different modeling strategy, including docking, homology modeling, and deep learning. The accuracy of each method was evaluated by reproducing the 3D structures of a dataset of 87 TCRpMHC complexes with experimentally determined crystal structures available on the Protein Data Bank (PDB). All selected structures were limited to human MHC alleles, presenting a diverse set of peptide ligands. A detailed analysis of produced models was conducted using multiple metrics, including Root Mean Square Deviation (RMSD) and standardized assessments from CAPRI and DockQ. Special attention was given to the complementarity-determining region (CDR) loops of the TCRs and to the peptide ligands, which define most of the unique features and specificity of a given TCRpMHC interaction. Our study provides an optimistic view of the current state-of-the-art for TCRpMHC modeling but highlights some remaining challenges that must be addressed in order to support the future application of these tools for TCR engineering and computer-aided design of TCR-based immunotherapies.

Interfacial rheology of lanthanide binding peptide surfactants at the air-water interface

Peptide surfactants (PEPS) are studied to capture and retain rare earth elements (REEs) at air-water interfaces to enable REE separations. Peptide sequences, designed to selectively bind REEs, depend crucially on the position of ligands within their binding loop domain. These ligands form a coordination sphere that wraps and retains the cation. We study variants of lanthanide binding tags (LBTs) designed to complex strongly with Tb3+. The peptide LBT5- (with net charge -5) is known to bind Tb3+ and adsorb with more REE cations than peptide molecules, suggesting that undesired non-specific coulombic interactions occur. Rheological characterization of interfaces of LBT5- and Tb3+ solutions reveal the formation of an interfacial gel. To probe whether this gelation reflects chelation among intact adsorbed LBT5-:Tb3+ complexes or destruction of the binding loop, we study a variant, LBT3-, designed to form net neutral LBT3-:Tb3+ complexes. Solutions of LBT3- and Tb3+ form purely viscous layers in the presence of excess Tb3+, indicating that each peptide binds a single REE in an intact coordination sphere. We introduce the variant RR-LBT3- with net charge -3 and anionic ligands outside of the coordination sphere. We find that such exposed ligands promote interfacial gelation. Thus, a nuanced requirement for interfacial selectivity of PEPS is proposed: that anionic ligands outside of the coordination sphere must be avoided to prevent the non-selective recruitment of REE cations. This view is supported by simulation, including interfacial molecular dynamics simulations, and interfacial metadynamics simulations of the free energy landscape of the binding loop conformational space.

Asymmetric fluctuation of overlapping dinucleosome studied by cryoelectron microscopy and small-angle X-ray scattering

Nucleosome remodelers modify the local structure of chromatin to release the region from nucleosome-mediated transcriptional suppression. Overlapping dinucleosomes (OLDNs) are nucleoprotein complexes formed around transcription start sites as a result of remodeling, and they consist of two nucleosome moieties: a histone octamer wrapped by DNA (octasome) and a histone hexamer wrapped by DNA (hexasome). While OLDN formation alters chromatin accessibility to proteins, the structural mechanism behind this process is poorly understood. Thus, this study investigated the characteristics of structural fluctuations in OLDNs. First, multiple structures of the OLDN were visualized through cryoelectron microscopy (cryoEM), providing an overview of the tilting motion of the hexasome relative to the octasome at the near-atomistic resolution. Second, small-angle X-ray scattering (SAXS) revealed the presence of OLDN conformations with a larger radius of gyration than cryoEM structures. A more complete description of OLDN fluctuation was proposed by SAXS-based ensemble modeling, which included possible transient structures. The ensemble model supported the tilting motion of the OLDN outlined by the cryoEM models, further suggesting the presence of more diverse conformations. The amplitude of the relative tilting motion of the hexasome was larger, and the nanoscale fluctuation in distance between the octasome and hexasome was also proposed. The cryoEM models were found to be mapped in the energetically stable region of the conformational distribution of the ensemble. Exhaustive complex modeling using all conformations that appeared in the structural ensemble suggested that conformational and motional asymmetries of the OLDN result in asymmetries in the accessibility of OLDN-binding proteins.

Allosteric competition and inhibition in AMPA receptors

Excitatory neurotransmission is principally mediated by α-amino-3-hydroxy-5-methyl-4-isoxazolepropionic acid (AMPA)-subtype ionotropic glutamate receptors (AMPARs). Negative allosteric modulators are therapeutic candidates that inhibit AMPAR activation and can compete with positive modulators to control AMPAR function through unresolved mechanisms. Here we show that allosteric inhibition pushes AMPARs into a distinct state that prevents both activation and positive allosteric modulation. We used cryo-electron microscopy to capture AMPARs bound to glutamate, while a negative allosteric modulator, GYKI-52466, and positive allosteric modulator, cyclothiazide, compete for control of the AMPARs. GYKI-52466 binds in the ion channel collar and inhibits AMPARs by decoupling the ligand-binding domains from the ion channel. The rearrangement of the ligand-binding domains ruptures the cyclothiazide site, preventing positive modulation. Our data provide a framework for understanding allostery of AMPARs and for rational design of therapeutics targeting AMPARs in neurological diseases.

Invariant point message passing for protein side chain packing

Protein side chain packing (PSCP) is a fundamental problem in the field of protein engineering, as high-confidence and low-energy conformations of amino acid side chains are crucial for understanding (and designing) protein folding, protein-protein interactions, and protein-ligand interactions. Traditional PSCP methods (such as the Rosetta Packer) often rely on a library of discrete side chain conformations, or rotamers, and a forcefield to guide the structure to low-energy conformations. Recently, deep learning (DL) based methods (such as DLPacker, AttnPacker, and DiffPack) have demonstrated state-of-the-art predictions and speed in the PSCP task. Building off the success of geometric graph neural networks for protein modeling, we present the Protein Invariant Point Packer (PIPPack) which effectively processes local structural and sequence information to produce realistic, idealized side chain coordinates using χ -angle distribution predictions and geometry-aware invariant point message passing (IPMP). On a test set of ∼1400 high-quality protein chains, PIPPack is highly competitive with other state-of-the-art PSCP methods in rotamer recovery and per-residue RMSD but is significantly faster.

All-Atom Protein Sequence Design Based on Geometric Deep Learning

Designing sequences for specific protein backbones is a key step in creating new functional proteins. Here, we introduce GeoSeqBuilder, a deep learning framework that integrates protein sequence generation with side chain conformation prediction to produce the complete all-atom structures for designed sequences. GeoSeqBuilder uses spatial geometric features from protein backbones and explicitly includes three-body interactions of neighboring residues. GeoSeqBuilder achieves native residue type recovery rate of 51.6 %, comparable to ProteinMPNN and other leading methods, while accurately predicting side chain conformations. We first used GeoSeqBuilder to design sequences for thioredoxin and a hallucinated three-helical bundle protein. All the 15 tested sequences expressed as soluble monomeric proteins with high thermal stability, and the 2 high-resolution crystal structures solved closely match the designed models. The generated protein sequences exhibit low similarity (minimum 23 %) to the original sequences, with significantly altered hydrophobic cores. We further redesigned the hydrophobic core of glutathione peroxidase 4, and 3 of the 5 designs showed improved enzyme activity. Although further testing is needed, the high experimental success rate in our testing demonstrates that GeoSeqBuilder is a powerful tool for designing novel sequences for predefined protein structures with atomic details. GeoSeqBuilder is available at https://github.com/PKUliujl/GeoSeqBuilder.

Deep generative models of protein structure uncover distant relationships across a continuous fold space

Our views of fold space implicitly rest upon many assumptions that impact how we analyze, interpret and understand protein structure, function and evolution. For instance, is there an optimal granularity in viewing protein structural similarities (e.g., architecture, topology or some other level)? Similarly, the discrete/continuous dichotomy of fold space is central, but remains unresolved. Discrete views of fold space bin similar folds into distinct, non-overlapping groups; unfortunately, such binning can miss remote relationships. While hierarchical systems like CATH are indispensable resources, less heuristic and more conceptually flexible approaches could enable more nuanced explorations of fold space. Building upon an Urfold model of protein structure, here we present a deep generative modeling framework, termed DeepUrfold, for analyzing protein relationships at scale. DeepUrfold's learned embeddings occupy high-dimensional latent spaces that can be distilled for a given protein in terms of an amalgamated representation uniting sequence, structure and biophysical properties. This approach is structure-guided, versus being purely structure-based, and DeepUrfold learns representations that, in a sense, define superfamilies. Deploying DeepUrfold with CATH reveals evolutionarily-remote relationships that evade existing methodologies, and suggests a mostly-continuous view of fold space-a view that extends beyond simple geometric similarity, towards the realm of integrated sequence ↔ structure ↔ function properties.

AlphaFold2 Modeling and Molecular Dynamics Simulations of the Conformational Ensembles for the SARS-CoV-2 Spike Omicron JN.1, KP.2 and KP.3 Variants: Mutational Profiling of Binding Energetics Reveals Epistatic Drivers of the ACE2 Affinity and Escape Hotspots of Antibody Resistance

The most recent wave of SARS-CoV-2 Omicron variants descending from BA.2 and BA.2.86 exhibited improved viral growth and fitness due to convergent evolution of functional hotspots. These hotspots operate in tandem to optimize both receptor binding for effective infection and immune evasion efficiency, thereby maintaining overall viral fitness. The lack of molecular details on structure, dynamics and binding energetics of the latest FLiRT and FLuQE variants with the ACE2 receptor and antibodies provides a considerable challenge that is explored in this study. We combined AlphaFold2-based atomistic predictions of structures and conformational ensembles of the SARS-CoV-2 spike complexes with the host receptor ACE2 for the most dominant Omicron variants JN.1, KP.1, KP.2 and KP.3 to examine the mechanisms underlying the role of convergent evolution hotspots in balancing ACE2 binding and antibody evasion. Using the ensemble-based mutational scanning of the spike protein residues and computations of binding affinities, we identified binding energy hotspots and characterized the molecular basis underlying epistatic couplings between convergent mutational hotspots. The results suggested the existence of epistatic interactions between convergent mutational sites at L455, F456, Q493 positions that protect and restore ACE2-binding affinity while conferring beneficial immune escape. To examine immune escape mechanisms, we performed structure-based mutational profiling of the spike protein binding with several classes of antibodies that displayed impaired neutralization against BA.2.86, JN.1, KP.2 and KP.3. The results confirmed the experimental data that JN.1, KP.2 and KP.3 harboring the L455S and F456L mutations can significantly impair the neutralizing activity of class 1 monoclonal antibodies, while the epistatic effects mediated by F456L can facilitate the subsequent convergence of Q493E changes to rescue ACE2 binding. Structural and energetic analysis provided a rationale to the experimental results showing that BD55-5840 and BD55-5514 antibodies that bind to different binding epitopes can retain neutralizing efficacy against all examined variants BA.2.86, JN.1, KP.2 and KP.3. The results support the notion that evolution of Omicron variants may favor emergence of lineages with beneficial combinations of mutations involving mediators of epistatic couplings that control balance of high ACE2 affinity and immune evasion.

Statistical accuracy of molecular dynamics-based methods for sampling conformational ensembles of disordered proteins

The characterization of the statistical ensemble of conformations of intrinsically disordered regions (IDRs) is a great challenge both from experimental and computational points of view. In this respect, a number of protocols have been developed using molecular dynamics (MD) simulations to sample the huge conformational space of the molecule. In this work, we consider one of the best methods available, replica exchange solute tempering (REST), as a reference to compare the results obtained using this method with the results obtained using other methods, in terms of experimentally measurable quantities. Along with the methods assessed, we propose here a novel protocol called probabilistic MD chain growth (PMD-CG), which combines the flexible-meccano and hierarchical chain growth methods with the statistical data obtained from tripeptide MD trajectories as the starting point. The system chosen for testing is a 20-residue region from the C-terminal domain of the p53 tumor suppressor protein (p53-CTD). Our results show that PMD-CG provides an ensemble of conformations extremely quickly, after suitable computation of the conformational pool for all peptide triplets of the IDR sequence. The measurable quantities computed on the ensemble of conformations agree well with those based on the REST conformational ensemble.

Computational design and experimental confirmation of a disulfide-stapled YAP helixα1-trap derived from TEAD4 helical hairpin to selectively capture YAP α1-helix with potent antitumor activity

Human Hippo signaling pathway is an evolutionarily conserved regulator network that controls organ development and has been implicated in various cancers. Transcriptional enhanced associate domain-4 (TEAD4) is the final nuclear effector of Hippo pathway, which is activated by Yes-associated protein (YAP) through binding to two separated YAP regions of α1-helix and Ω-loop. Previous efforts have all been addressed on deriving peptide inhibitors from the YAP to target TEAD4. Instead, we herein attempted to rationally design a so-called 'YAP helixα1-trap' based on the TEAD4 to target YAP by using dynamics simulation and energetics analysis as well as experimental assays at molecular and cellular levels. The trap represents a native double-stranded helical hairpin covering a specific YAP-binding site on TEAD4 surface, which is expected to form a three-helix bundle with the α1-helical region of YAP, thus competitively disrupting TEAD4-YAP interaction. The hairpin was further stapled by a disulfide bridge across its two helical arms. Circular dichroism characterized that the stapling can effectively constrain the trap into a native-like structured conformation in free state, thus largely minimizing the entropy penalty upon its binding to YAP. Affinity assays revealed that the stapling can considerably improve the trap binding potency to YAP α1-helix by up to 8.5-fold at molecular level, which also exhibited a good tumor-suppressing effect at cellular level if fused with TAT cell permeation sequence. In this respect, it is considered that the YAP helixα1-trap-mediated blockade of Hippo pathway may be a new and promising therapeutic strategy against cancers.

Biosynthesis of a clickable pyoverdine via in vivo enzyme engineering of an adenylation domain

The engineering of non ribosomal peptide synthetases (NRPS) for new substrate specificity is a potent strategy to incorporate non-canonical amino acids into peptide sequences, thereby creating peptide diversity and broadening applications. The non-ribosomal peptide pyoverdine is the primary siderophore produced by Pseudomonas aeruginosa and holds biomedical promise in diagnosis, bio-imaging and antibiotic vectorization. We engineered the adenylation domain of PvdD, the terminal NRPS in pyoverdine biosynthesis, to accept a functionalized amino acid. Guided by molecular modeling, we rationally designed mutants of P. aeruginosa with mutations at two positions in the active site. A single amino acid change results in the successful incorporation of an azido-L-homoalanine leading to the synthesis of a new pyoverdine analog, functionalized with an azide function. We further demonstrated that copper free click chemistry is efficient on the functionalized pyoverdine and that the conjugated siderophore retains the iron chelation properties and its capacity to be recognized and transported by P. aeruginosa. The production of clickable pyoverdine holds substantial biotechnological significance, paving the way for numerous downstream applications.

OPUS-Rota5: A highly accurate protein side-chain modeling method with 3D-Unet and RotaFormer

Accurate protein side-chain modeling is crucial for protein folding and design. This is particularly true for molecular docking as ligands primarily interact with side chains. In this study, we introduce a two-stage side-chain modeling approach called OPUS-Rota5. It leverages a modified 3D-Unet to capture the local environmental features, including ligand information of each residue, and then employs the RotaFormer module to aggregate various types of features. Evaluation on three test sets, including recently released targets from CAMEO and CASP15, shows that OPUS-Rota5 significantly outperforms some other leading side-chain modeling methods. We also employ OPUS-Rota5 to refine the side chains of 25 G protein-coupled receptor targets predicted by AlphaFold2 and achieve a significantly improved success rate in a subsequent "back" docking of their natural ligands. Therefore, OPUS-Rota5 is a useful and effective tool for molecular docking, particularly for targets with relatively accurate predicted backbones but not side chains such as high-homology targets.

Atomistic Prediction of Structures, Conformational Ensembles and Binding Energetics for the SARS-CoV-2 Spike JN.1, KP.2 and KP.3 Variants Using AlphaFold2 and Molecular Dynamics Simulations: Mutational Profiling and Binding Free Energy Analysis Reveal Epistatic Hotspots of the ACE2 Affinity and Immune Escape

The most recent wave of SARS-CoV-2 Omicron variants descending from BA.2 and BA.2.86 exhibited improved viral growth and fitness due to convergent evolution of functional hotspots. These hotspots operate in tandem to optimize both receptor binding for effective infection and immune evasion efficiency, thereby maintaining overall viral fitness. The lack of molecular details on structure, dynamics and binding energetics of the latest FLiRT and FLuQE variants with the ACE2 receptor and antibodies provides a considerable challenge that is explored in this study. We combined AlphaFold2-based atomistic predictions of structures and conformational ensembles of the SARS-CoV-2 Spike complexes with the host receptor ACE2 for the most dominant Omicron variants JN.1, KP.1, KP.2 and KP.3 to examine the mechanisms underlying the role of convergent evolution hotspots in balancing ACE2 binding and antibody evasion. Using the ensemble-based mutational scanning of the spike protein residues and computations of binding affinities, we identified binding energy hotspots and characterized molecular basis underlying epistatic couplings between convergent mutational hotspots. The results suggested that the existence of epistatic interactions between convergent mutational sites at L455, F456, Q493 positions that enable to protect and restore ACE2 binding affinity while conferring beneficial immune escape. To examine immune escape mechanisms, we performed structure-based mutational profiling of the spike protein binding with several classes of antibodies that displayed impaired neutralization against BA.2.86, JN.1, KP.2 and KP.3. The results confirmed the experimental data that JN.1, KP.2 and KP.3 harboring the L455S and F456L mutations can significantly impair the neutralizing activity of class-1 monoclonal antibodies, while the epistatic effects mediated by F456L can facilitate the subsequent convergence of Q493E changes to rescue ACE2 binding. Structural and energetic analysis provided a rationale to the experimental results showing that BD55-5840 and BD55-5514 antibodies that bind to different binding epitopes can retain neutralizing efficacy against all examined variants BA.2.86, JN.1, KP.2 and KP.3. The results support the notion that evolution of Omicron variants may favor emergence of lineages with beneficial combinations of mutations involving mediators of epistatic couplings that control balance of high ACE2 affinity and immune evasion.

TransfIGN: A Structure-Based Deep Learning Method for Modeling the Interaction between HLA-A*02:01 and Antigen Peptides

The intricate interaction between major histocompatibility complexes (MHCs) and antigen peptides with diverse amino acid sequences plays a pivotal role in immune responses and T cell activity. In recent years, deep learning (DL)-based models have emerged as promising tools for accelerating antigen peptide screening. However, most of these models solely rely on one-dimensional amino acid sequences, overlooking crucial information required for the three-dimensional (3-D) space binding process. In this study, we propose TransfIGN, a structure-based DL model that is inspired by our previously developed framework, Interaction Graph Network (IGN), and incorporates sequence information from transformers to predict the interactions between HLA-A*02:01 and antigen peptides. Our model, trained on a comprehensive data set containing 61,816 sequences with 9051 binding affinity labels and 56,848 eluted ligand labels, achieves an area under the curve (AUC) of 0.893 on the binary data set, better than state-of-the-art sequence-based models trained on larger data sets such as NetMHCpan4.1, ANN, and TransPHLA. Furthermore, when evaluated on the IEDB weekly benchmark data sets, our predictions (AUC = 0.816) are better than those of the recommended methods like the IEDB consensus (AUC = 0.795). Notably, the interaction weight matrices generated by our method highlight the strong interactions at specific positions within peptides, emphasizing the model's ability to provide physical interpretability. This capability to unveil binding mechanisms through intricate structural features holds promise for new immunotherapeutic avenues.

Amino-Acid Characteristics in Protein Native State Structures

The molecular machines of life, proteins, are made up of twenty kinds of amino acids, each with distinctive side chains. We present a geometrical analysis of the protrusion statistics of side chains in more than 4000 high-resolution protein structures. We employ a coarse-grained representation of the protein backbone viewed as a linear chain of Cα atoms and consider just the heavy atoms of the side chains. We study the large variety of behaviors of the amino acids based on both rudimentary structural chemistry as well as geometry. Our geometrical analysis uses a backbone Frenet coordinate system for the common study of all amino acids. Our analysis underscores the richness of the repertoire of amino acids that is available to nature to design protein sequences that fit within the putative native state folds.

An engineered enzyme embedded into PLA to make self-biodegradable plastic

Plastic production reached 400 million tons in 2022 (ref. 1), with packaging and single-use plastics accounting for a substantial amount of this2. The resulting waste ends up in landfills, incineration or the environment, contributing to environmental pollution3. Shifting to biodegradable and compostable plastics is increasingly being considered as an efficient waste-management alternative4. Although polylactide (PLA) is the most widely used biosourced polymer5, its biodegradation rate under home-compost and soil conditions remains low6-8. Here we present a PLA-based plastic in which an optimized enzyme is embedded to ensure rapid biodegradation and compostability at room temperature, using a scalable industrial process. First, an 80-fold activity enhancement was achieved through structure-based rational engineering of a new hyperthermostable PLA hydrolase. Second, the enzyme was uniformly dispersed within the PLA matrix by means of a masterbatch-based melt extrusion process. The liquid enzyme formulation was incorporated in polycaprolactone, a low-melting-temperature polymer, through melt extrusion at 70 °C, forming an 'enzymated' polycaprolactone masterbatch. Masterbatch pellets were integrated into PLA by melt extrusion at 160 °C, producing an enzymated PLA film (0.02% w/w enzyme) that fully disintegrated under home-compost conditions within 20-24 weeks, meeting home-composting standards. The mechanical and degradation properties of the enzymated film were compatible with industrial packaging applications, and they remained intact during long-term storage. This innovative material not only opens new avenues for composters and biomethane production but also provides a feasible industrial solution for PLA degradation.

Exploring conformational landscapes and binding mechanisms of convergent evolution for the SARS-CoV-2 spike Omicron variant complexes with the ACE2 receptor using AlphaFold2-based structural ensembles and molecular dynamics simulations

In this study, we combined AlphaFold-based approaches for atomistic modeling of multiple protein states and microsecond molecular simulations to accurately characterize conformational ensembles evolution and binding mechanisms of convergent evolution for the SARS-CoV-2 spike Omicron variants BA.1, BA.2, BA.2.75, BA.3, BA.4/BA.5 and BQ.1.1. We employed and validated several different adaptations of the AlphaFold methodology for modeling of conformational ensembles including the introduced randomized full sequence scanning for manipulation of sequence variations to systematically explore conformational dynamics of Omicron spike protein complexes with the ACE2 receptor. Microsecond atomistic molecular dynamics (MD) simulations provide a detailed characterization of the conformational landscapes and thermodynamic stability of the Omicron variant complexes. By integrating the predictions of conformational ensembles from different AlphaFold adaptations and applying statistical confidence metrics we can expand characterization of the conformational ensembles and identify functional protein conformations that determine the equilibrium dynamics for the Omicron spike complexes with the ACE2. Conformational ensembles of the Omicron RBD-ACE2 complexes obtained using AlphaFold-based approaches for modeling protein states and MD simulations are employed for accurate comparative prediction of the binding energetics revealing an excellent agreement with the experimental data. In particular, the results demonstrated that AlphaFold-generated extended conformational ensembles can produce accurate binding energies for the Omicron RBD-ACE2 complexes. The results of this study suggested complementarities and potential synergies between AlphaFold predictions of protein conformational ensembles and MD simulations showing that integrating information from both methods can potentially yield a more adequate characterization of the conformational landscapes for the Omicron RBD-ACE2 complexes. This study provides insights in the interplay between conformational dynamics and binding, showing that evolution of Omicron variants through acquisition of convergent mutational sites may leverage conformational adaptability and dynamic couplings between key binding energy hotspots to optimize ACE2 binding affinity and enable immune evasion.

Three-Dimensional Interaction Homology: Deconstructing Residue-Residue and Residue-Lipid Interactions in Membrane Proteins

A method is described to deconstruct the network of hydropathic interactions within and between a protein's sidechain and its environment into residue-based three-dimensional maps. These maps encode favorable and unfavorable hydrophobic and polar interactions, in terms of spatial positions for optimal interactions, relative interaction strength, as well as character. In addition, these maps are backbone angle-dependent. After map calculation and clustering, a finite number of unique residue sidechain interaction maps exist for each backbone conformation, with the number related to the residue's size and interaction complexity. Structures for soluble proteins (~749,000 residues) and membrane proteins (~387,000 residues) were analyzed, with the latter group being subdivided into three subsets related to the residue's position in the membrane protein: soluble domain, core-facing transmembrane domain, and lipid-facing transmembrane domain. This work suggests that maps representing residue types and their backbone conformation can be reassembled to optimize the medium-to-high resolution details of a protein structure. In particular, the information encoded in maps constructed from the lipid-facing transmembrane residues appears to paint a clear picture of the protein-lipid interactions that are difficult to obtain experimentally.

Gaseous inhibition of the transsulfuration pathway by cystathionine β-synthase

The transsulfuration pathway plays a key role in mammals for maintaining the balance between cysteine and homocysteine, whose concentrations are critical in several biochemical processes. Human cystathionine β-synthase is a heme-containing, pyridoxal 5'-phosphate (PLP)-dependent enzyme found in this pathway. The heme group does not participate directly in catalysis, but has a regulatory function, whereby CO or NO binding inhibits the PLP-dependent reactions. In this study, we explore the detailed structural changes responsible for inhibition using quantum chemical calculations to validate the experimentally observed bonding patterns associated with heme CO and NO binding and molecular dynamics simulations to explore the medium-range structural changes triggered by gas binding and propagating to the PLP active site, which is more than 20 Å distant from the heme group. Our results support a previously proposed mechanical signaling model, whereby the cysteine decoordination associated with gas ligand binding leads to breaking of a hydrogen bond with an arginine residue on a neighbouring helix. In turn, this leads to a shift in position of the helix, and hence also of the PLP cofactor, ultimately disrupting a key hydrogen bond that stabilizes the PLP in its catalytically active form.

AlphaFold2 Predictions of Conformational Ensembles and Atomistic Simulations of the SARS-CoV-2 Spike XBB Lineages Reveal Epistatic Couplings between Convergent Mutational Hotspots that Control ACE2 Affinity

In this study, we combined AlphaFold-based atomistic structural modeling, microsecond molecular simulations, mutational profiling, and network analysis to characterize binding mechanisms of the SARS-CoV-2 spike protein with the host receptor ACE2 for a series of Omicron XBB variants including XBB.1.5, XBB.1.5+L455F, XBB.1.5+F456L, and XBB.1.5+L455F+F456L. AlphaFold-based structural and dynamic modeling of SARS-CoV-2 Spike XBB lineages can accurately predict the experimental structures and characterize conformational ensembles of the spike protein complexes with the ACE2. Microsecond molecular dynamics simulations identified important differences in the conformational landscapes and equilibrium ensembles of the XBB variants, suggesting that combining AlphaFold predictions of multiple conformations with molecular dynamics simulations can provide a complementary approach for the characterization of functional protein states and binding mechanisms. Using the ensemble-based mutational profiling of protein residues and physics-based rigorous calculations of binding affinities, we identified binding energy hotspots and characterized the molecular basis underlying epistatic couplings between convergent mutational hotspots. Consistent with the experiments, the results revealed the mediating role of the Q493 hotspot in the synchronization of epistatic couplings between L455F and F456L mutations, providing a quantitative insight into the energetic determinants underlying binding differences between XBB lineages. We also proposed a network-based perturbation approach for mutational profiling of allosteric communications and uncovered the important relationships between allosteric centers mediating long-range communication and binding hotspots of epistatic couplings. The results of this study support a mechanism in which the binding mechanisms of the XBB variants may be determined by epistatic effects between convergent evolutionary hotspots that control ACE2 binding.

Herpes simplex encephalitis due to a mutation in an E3 ubiquitin ligase

Encephalitis is a rare and potentially fatal manifestation of herpes simplex type 1 infection. Following genome-wide genetic analyses, we identified a previously uncharacterized and very rare heterozygous variant in the E3 ubiquitin ligase WWP2, in a 14-month-old girl with herpes simplex encephalitis. The p.R841H variant (NM_007014.4:c.2522G > A) impaired TLR3 mediated signaling in inducible pluripotent stem cells-derived neural precursor cells and neurons; cells bearing this mutation were also more susceptible to HSV-1 infection compared to control cells. The p.R841H variant increased TRIF ubiquitination in vitro. Antiviral immunity was rescued following the correction of p.R841H by CRISPR-Cas9 technology. Moreover, the introduction of p.R841H in wild type cells reduced such immunity, suggesting that this mutation is linked to the observed phenotypes.

L-serine treatment in patients with GRIN-related encephalopathy: a phase 2A, non-randomized study

GRIN-related disorders are rare developmental encephalopathies with variable manifestations and limited therapeutic options. Here, we present the first non-randomized, open-label, single-arm trial (NCT04646447) designed to evaluate the tolerability and efficacy of L-serine in children with GRIN genetic variants leading to loss-of-function. In this phase 2A trial, patients aged 2-18 years with GRIN loss-of-function pathogenic variants received L-serine for 52 weeks. Primary end points included safety and efficacy by measuring changes in the Vineland Adaptive Behavior Scales, Bayley Scales, age-appropriate Wechsler Scales, Gross Motor Function-88, Sleep Disturbance Scale for Children, Pediatric Quality of Life Inventory, Child Behavior Checklist and the Caregiver-Teacher Report Form following 12 months of treatment. Secondary outcomes included seizure frequency and intensity reduction and EEG improvement. Assessments were performed 3 months and 1 day before starting treatment and 1, 3, 6 and 12 months after beginning the supplement. Twenty-four participants were enrolled (13 males/11 females, mean age 9.8 years, SD 4.8), 23 of whom completed the study. Patients had GRIN2B, GRIN1 and GRIN2A variants (12, 6 and 5 cases, respectively). Their clinical phenotypes showed 91% had intellectual disability (61% severe), 83% had behavioural problems, 78% had movement disorders and 58% had epilepsy. Based on the Vineland Adaptive Behavior Composite standard scores, nine children were classified as mildly impaired (cut-off score > 55), whereas 14 were assigned to the clinically severe group. An improvement was detected in the Daily Living Skills domain (P = 0035) from the Vineland Scales within the mild group. Expressive (P = 0.005), Personal (P = 0.003), Community (P = 0.009), Interpersonal (P = 0.005) and Fine Motor (P = 0.031) subdomains improved for the whole cohort, although improvement was mostly found in the mild group. The Growth Scale Values in the Cognitive subdomain of the Bayley-III Scale showed a significant improvement in the severe group (P = 0.016), with a mean increase of 21.6 points. L-serine treatment was associated with significant improvement in the median Gross Motor Function-88 total score (P = 0.002) and the mean Pediatric Quality of Life total score (P = 0.00068), regardless of severity. L-serine normalized the EEG pattern in five children and the frequency of seizures in one clinically affected child. One patient discontinued treatment due to irritability and insomnia. The trial provides evidence that L-serine is a safe treatment for children with GRIN loss-of-function variants, having the potential to improve adaptive behaviour, motor function and quality of life, with a better response to the treatment in mild phenotypes.