ABAG-docking benchmark: a non-redundant structure benchmark dataset for antibody-antigen computational docking.

Accurate prediction of antibody-antigen complex structures is pivotal in drug discovery, vaccine design and disease treatment and can facilitate the development of more effective therapies and diagnostics. In this work, we first review the antibody-antigen docking (ABAG-docking) datasets. Then, we present the creation and characterization of a comprehensive benchmark dataset of antibody-antigen complexes. We categorize the dataset based on docking difficulty, interface properties and structural characteristics, to provide a diverse set of cases for rigorous evaluation. Compared with Docking Benchmark 5.5, we have added 112 cases, including 14 single-domain antibody (sdAb) cases and 98 monoclonal antibody (mAb) cases, and also increased the proportion of Difficult cases. Our dataset contains diverse cases, including human/humanized antibodies, sdAbs, rodent antibodies and other types, opening the door to better algorithm development. Furthermore, we provide details on the process of building the benchmark dataset and introduce a pipeline for periodic updates to keep it up to date. We also utilize multiple complex prediction methods including ZDOCK, ClusPro, HDOCK and AlphaFold-Multimer for testing and analyzing this dataset. This benchmark serves as a valuable resource for evaluating and advancing docking computational methods in the analysis of antibody-antigen interaction, enabling researchers to develop more accurate and effective tools for predicting and designing antibody-antigen complexes. The non-redundant ABAG-docking structure benchmark dataset is available at https://github.com/Zhaonan99/Antibody-antigen-complex-structure-benchmark-dataset.

S-Nitrosylation Targets GSNO Reductase for Selective Autophagy during Hypoxia Responses in Plants.

Nitric oxide (NO) regulates diverse cellular signaling through S-nitrosylation of specific Cys residues of target proteins. The intracellular level of S-nitrosoglutathione (GSNO), a major bioactive NO species, is regulated by GSNO reductase (GSNOR), a highly conserved master regulator of NO signaling. However, little is known about how the activity of GSNOR is regulated. Here, we show that S-nitrosylation induces selective autophagy of Arabidopsis GSNOR1 during hypoxia responses. S-nitrosylation of GSNOR1 at Cys-10 induces conformational changes, exposing its AUTOPHAGY-RELATED8 (ATG8)-interacting motif (AIM) accessible by autophagy machinery. Upon binding by ATG8, GSNOR1 is recruited into the autophagosome and degraded in an AIM-dependent manner. Physiologically, the S-nitrosylation-induced selective autophagy of GSNOR1 is relevant to hypoxia responses. Our discovery reveals a unique mechanism by which S-nitrosylation mediates selective autophagy of GSNOR1, thereby establishing a molecular link between NO signaling and autophagy.

Conformational Dynamics of the D53-D3-D14 Complex in Strigolactone Signaling.

Strigolactones (SLs) play fundamental roles in regulating plant architecture, which is a major factor determining crop yield. The perception and signal transduction of SLs require the formation of a complex containing the receptor DWARF14 (D14), an F-box protein D3 and a transcriptional regulator D53 in an SL-dependent manner. Structural and biochemical analyses of D14 and its orthologs DAD2 and AtD14, D3 and the complexes of ASK1-D3-AtD14 and D3CTH-D14 have made great contributions to understanding the mechanisms of SL perception. However, structural analyses of D53 and the D53-D3-D14 holo-complex are challenging, and the biochemical mechanism underlying the complex assembly remains poorly understood. Here, we found that apo-D53 was rather flexible and reconstituted the holo-complex containing D53, S-phase kinase-associated protein 1 (SKP1), D3 and D14 with rac-GR24. The cryo-electron microscopy (cryo-EM) structure of SKP1-D3-D14 in the presence of D53 was analyzed and superimposed on the crystal structure of ASK1-D3-AtD14 without D53. No large conformational rearrangement was observed, but a 9Å rotation appeared between D14 and AtD14. Using hydrogen-deuterium exchange monitored by mass spectrometry, we analyzed dynamic motifs of D14, D3 and D53 in the D53-SKP1-D3-D14 complex assembly process and further identified two potential interfaces in D53 that are located in the N and D2 domains, respectively. Together, our results uncovered the dynamic conformational changes and built a model of the holo-complex D53-SKP1-D3-D14, offering valuable information for the biochemical and genetic mechanisms of SL perception and signal transduction.

Cideb controls sterol-regulated ER export of SREBP/SCAP by promoting cargo loading at ER exit sites.

SREBPs are master regulators of lipid homeostasis and undergo sterol-regulated export from ER to Golgi apparatus for processing and activation via COPII-coated vesicles. While COPII recognizes SREBP through its escort protein SCAP, factor(s) specifically promoting SREBP/SCAP loading to the COPII machinery remains unknown. Here, we show that the ER/lipid droplet-associated protein Cideb selectively promotes the loading of SREBP/SCAP into COPII vesicles. Sterol deprivation releases SCAP from Insig and enhances ER export of SREBP/SCAP by inducing SCAP-Cideb interaction, thereby modulating sterol sensitivity. Moreover, Cideb binds to the guanine nucleotide exchange factor Sec12 to enrich SCAP/SREBP at ER exit sites, where assembling of COPII complex initiates. Loss of Cideb inhibits the cargo loading of SREBP/SCAP, reduces SREBP activation, and alleviates diet-induced hepatic steatosis. Our data point to a linchpin role of Cideb in regulated ER export of SREBP and lipid homeostasis.

GdX/UBL4A specifically stabilizes the TC45/STAT3 association and promotes dephosphorylation of STAT3 to repress tumorigenesis.

Impaired phosphatase activity contributes to the persistent activation of STAT3 in tumors. Given that STAT family members with various or even opposite functions are often phosphorylated or dephosphorylated by the same enzymes, the mechanism for STAT3-specific dephosphorylation in cells remains largely unknown. Here, we report that GdX (UBL4A) promotes STAT3 dephosphorylation via mediating the interaction between TC45 (the nuclear isoform of TC-PTP) and STAT3 specifically. GdX stabilizes the TC45-STAT3 complex to bestow upon STAT3 an efficient dephosphorylation by TC45. Inasmuch, GdX suppresses tumorigenesis and tumor development by reducing the level of phospho-STAT3 (p-STAT3), whereas deletion of GdX results in a high level of p-STAT3 and accelerated colorectal tumorigenesis induced by AOM/DSS. Thus, GdX converts TC45, a nonspecific phosphatase, into a STAT3-specific phosphatase by bridging an association between TC45 and STAT3.

Mechanistic insights into CED-4-mediated activation of CED-3.

Programmed cell death in Caenorhabditis elegans requires activation of the caspase CED-3, which strictly depends on CED-4. CED-4 forms an octameric apoptosome, which binds the CED-3 zymogen and facilitates its autocatalytic maturation. Despite recent advances, major questions remain unanswered. Importantly, how CED-4 recognizes CED-3 and how such binding facilitates CED-3 activation remain completely unknown. Here we demonstrate that the L2' loop of CED-3 directly binds CED-4 and plays a major role in the formation of an active CED-4-CED-3 holoenzyme. The crystal structure of the CED-4 apoptosome bound to the L2' loop fragment of CED-3, determined at 3.2 Å resolution, reveals specific interactions between a stretch of five hydrophobic amino acids from CED-3 and a shallow surface pocket within the hutch of the funnel-shaped CED-4 apoptosome. Structure-guided biochemical analysis confirms the functional importance of the observed CED-4-CED-3 interface. Structural analysis together with published evidence strongly suggest a working model in which two molecules of CED-3 zymogen, through specific recognition, are forced into the hutch of the CED-4 apoptosome, consequently undergoing dimerization and autocatalytic maturation. The mechanism of CED-3 activation represents a major revision of the prevailing model for initiator caspase activation.

Heterogeneous multiple kernel learning for breast cancer outcome evaluation.

BACKGROUND: Breast cancer is one of the common kinds of cancer among women, and it ranks second among all cancers in terms of incidence, after lung cancer. Therefore, it is of great necessity to study the detection methods of breast cancer. Recent research has focused on using gene expression data to predict outcomes, and kernel methods have received a lot of attention regarding the cancer outcome evaluation. However, selecting the appropriate kernels and their parameters still needs further investigation. RESULTS: We utilized heterogeneous kernels from a specific kernel set including the Hadamard, RBF and linear kernels. The mixed coefficients of the heterogeneous kernel were computed by solving the standard convex quadratic programming problem of the quadratic constraints. The algorithm is named the heterogeneous multiple kernel learning (HMKL). Using the particle swarm optimization (PSO) in HMKL, we selected the kernel parameters, then we employed HMKL to perform the breast cancer outcome evaluation. By testing real-world microarray datasets, the HMKL method outperforms the methods of the random forest, decision tree, GA with Rotation Forest, BFA + RF, SVM and MKL. CONCLUSIONS: On one hand, HMKL is effective for the breast cancer evaluation and can be utilized by physicians to better understand the patient's condition. On the other hand, HMKL can choose the function and parameters of the kernel. At the same time, this study proves that the Hadamard kernel is effective in HMKL. We hope that HMKL could be applied as a new method to more actual problems.

A Two-Layer SVM Ensemble-Classifier to Predict Interface Residue Pairs of Protein Trimers.

Study of interface residue pairs is important for understanding the interactions between monomers inside a trimer protein-protein complex. We developed a two-layer support vector machine (SVM) ensemble-classifier that considers physicochemical and geometric properties of amino acids and the influence of surrounding amino acids. Different descriptors and different combinations may give different prediction results. We propose feature combination engineering based on correlation coefficients and F-values. The accuracy of our method is 65.38% in independent test set, indicating biological significance. Our predictions are consistent with the experimental results. It shows the effectiveness and reliability of our method to predict interface residue pairs of protein trimers.

Attention mechanism enhanced LSTM with residual architecture and its application for protein-protein interaction residue pairs prediction.

BACKGROUND: Recurrent neural network(RNN) is a good way to process sequential data, but the capability of RNN to compute long sequence data is inefficient. As a variant of RNN, long short term memory(LSTM) solved the problem in some extent. Here we improved LSTM for big data application in protein-protein interaction interface residue pairs prediction based on the following two reasons. On the one hand, there are some deficiencies in LSTM, such as shallow layers, gradient explosion or vanishing, etc. With a dramatic data increasing, the imbalance between algorithm innovation and big data processing has been more serious and urgent. On the other hand, protein-protein interaction interface residue pairs prediction is an important problem in biology, but the low prediction accuracy compels us to propose new computational methods. RESULTS: In order to surmount aforementioned problems of LSTM, we adopt the residual architecture and add attention mechanism to LSTM. In detail, we redefine the block, and add a connection from front to back in every two layers and attention mechanism to strengthen the capability of mining information. Then we use it to predict protein-protein interaction interface residue pairs, and acquire a quite good accuracy over 72%. What's more, we compare our method with random experiments, PPiPP, standard LSTM, and some other machine learning methods. Our method shows better performance than the methods mentioned above. CONCLUSION: We present an attention mechanism enhanced LSTM with residual architecture, and make deeper network without gradient vanishing or explosion to a certain extent. Then we apply it to a significant problem- protein-protein interaction interface residue pairs prediction and obtain a better accuracy than other methods. Our method provides a new approach for protein-protein interaction computation, which will be helpful for related biomedical researches.

Structure of a presenilin family intramembrane aspartate protease.

Presenilin and signal peptide peptidase (SPP) are intramembrane aspartyl proteases that regulate important biological functions in eukaryotes. Mechanistic understanding of presenilin and SPP has been hampered by lack of relevant structural information. Here we report the crystal structure of a presenilin/SPP homologue (PSH) from the archaeon Methanoculleus marisnigri JR1. The protease, comprising nine transmembrane segments (TMs), adopts a previously unreported protein fold. The amino-terminal domain, consisting of TM1-6, forms a horseshoe-shaped structure, surrounding TM7-9 of the carboxy-terminal domain. The two catalytic aspartate residues are located on the cytoplasmic side of TM6 and TM7, spatially close to each other and approximately 8 Å into the lipid membrane surface. Water molecules gain constant access to the catalytic aspartates through a large cavity between the amino- and carboxy-terminal domains. Structural analysis reveals insights into the presenilin/SPP family of intramembrane proteases.

Structure of a bacterial energy-coupling factor transporter.

The energy-coupling factor (ECF) transporters constitute a novel family of conserved membrane transporters in prokaryotes that have a similar domain organization to the ATP-binding cassette transporters. Each ECF transporter comprises a pair of cytosolic ATPases (the A and A' components, or EcfA and EcfA'), a membrane-embedded substrate-binding protein (the S component, or EcfS) and a transmembrane energy-coupling component (the T component, or EcfT) that links the EcfA-EcfA' subcomplex to EcfS. The structure and transport mechanism of the quaternary ECF transporter remain largely unknown. Here we report the crystal structure of a nucleotide-free ECF transporter from Lactobacillus brevis at a resolution of 3.5 Å. The T component has a horseshoe-shaped open architecture, with five α-helices as transmembrane segments and two cytoplasmic α-helices as coupling modules connecting to the A and A' components. Strikingly, the S component, thought to be specific for hydroxymethyl pyrimidine, lies horizontally along the lipid membrane and is bound exclusively by the five transmembrane segments and the two cytoplasmic helices of the T component. These structural features suggest a plausible working model for the transport cycle of the ECF transporters.

The E3 ubiquitin ligase SCFFBXL14 complex stimulates neuronal differentiation by targeting the Notch signaling factor HES1 for proteolysis.

Molecular oscillators are important cellular regulators of, for example, circadian clocks, oscillations of immune regulators, and short-period (ultradian) rhythms during embryonic development. The Notch signaling factor HES1 (hairy and enhancer of split 1) is a well-known repressor of proneural genes, and HES1 ultradian oscillation is essential for keeping cells in an efficiently proliferating progenitor state. HES1 oscillation is driven by both transcriptional self-repression and ubiquitin-dependent proteolysis. However, the E3 ubiquitin ligase targeting HES1 for proteolysis remains unclear. Based on siRNA-mediated gene silencing screening, co-immunoprecipitation, and ubiquitination assays, we discovered that the E3 ubiquitin ligase SCFFBXL14 complex regulates HES1 ubiquitination and proteolysis. siRNA-mediated knockdown of the Cullin-RING E3 ubiquitin ligases RBX1 or CUL1 increased HES1 protein levels, prolonged its half-life, and dampened its oscillation. FBXL14, an F-box protein for SCF ubiquitin ligase, associates with HES1. FBXL14 silencing stabilized HES1, whereas FBXL14 overexpression decreased HES1 protein levels. Of note, the SCFFBXL14 complex promoted the ubiquitination of HES1 in vivo, and a conserved WRPW motif in HES1 was essential for HES1 binding to FBXL14 and for ubiquitin-dependent HES1 degradation. HES1 knockdown promoted neuronal differentiation, but FBXL14 silencing inhibited neuronal differentiation induced by HES1 ablation in mES and F9 cells. Our results suggest that SCFFBXL14 promotes neuronal differentiation by targeting HES1 for ubiquitin-dependent proteolysis and that the C-terminal WRPW motif in HES1 is required for this process.

A new probability method to understand protein-protein interface formation mechanism at amino acid level.

Protein-protein interactions and recognitions are essential for organisms to accomplish many cellular processes. The interface compositions of protein-protein complexes are the key to understand the molecular mechanisms of these processes. There are many studies on near native interface recognition for protein-protein interactions, but the formation mechanism of protein-protein interface is still ambiguous. Here, we propose a new probability method to understand protein-protein interface formation mechanism at amino acid level. The probability of two surface residues from two monomers as a true interface residue pair is estimated by their properties in the structures of protein monomers. Estimated residue pairs with interface possibilities combine together to form a protein-protein interface. The possibility values are integrated to discriminate the near native interface from non-near native ones. In this paper, five simple probability based methods are constructed for near native interface recognition based on several geometric and physicochemical descriptors. The performances are comparable to the best results reported previously, which suggests this is a promising way. The idea proposed here will have a certain significance to the future research on protein-protein interactions.

Crystal structure of a bacterial homologue of glucose transporters GLUT1-4.

Glucose transporters are essential for metabolism of glucose in cells of diverse organisms from microbes to humans, exemplified by the disease-related human proteins GLUT1, 2, 3 and 4. Despite rigorous efforts, the structural information for GLUT1-4 or their homologues remains largely unknown. Here we report three related crystal structures of XylE, an Escherichia coli homologue of GLUT1-4, in complex with d-xylose, d-glucose and 6-bromo-6-deoxy-D-glucose, at resolutions of 2.8, 2.9 and 2.6 Å, respectively. The structure consists of a typical major facilitator superfamily fold of 12 transmembrane segments and a unique intracellular four-helix domain. XylE was captured in an outward-facing, partly occluded conformation. Most of the important amino acids responsible for recognition of D-xylose or d-glucose are invariant in GLUT1-4, suggesting functional and mechanistic conservations. Structure-based modelling of GLUT1-4 allows mapping and interpretation of disease-related mutations. The structural and biochemical information reported here constitutes an important framework for mechanistic understanding of glucose transporters and sugar porters in general.

Structure and mechanism of a eukaryotic transmembrane ascorbate-dependent oxidoreductase.

Vitamin C, also known as ascorbate, is required in numerous essential metabolic reactions in eukaryotes. The eukaryotic ascorbate-dependent oxidoreductase cytochrome b561 (Cyt b561), a family of highly conserved transmembrane enzymes, plays an important role in ascorbate recycling and iron absorption. Although Cyt b561 was identified four decades ago, its atomic structure and functional mechanism remain largely unknown. Here, we report the high-resolution crystal structures of cytochrome b561 from Arabidopsis thaliana in both substrate-free and substrate-bound states. Cyt b561 forms a homodimer, with each protomer consisting of six transmembrane helices and two heme groups. The negatively charged substrate ascorbate, or monodehydroascorbate, is enclosed in a positively charged pocket on either side of the membrane. Two highly conserved amino acids, Lys(81) and His(106), play an essential role in substrate recognition and catalysis. Our structural and biochemical analyses allow the proposition of a general electron transfer mechanism for members of the Cyt b561 family.

A small-molecule screen identifies L-kynurenine as a competitive inhibitor of TAA1/TAR activity in ethylene-directed auxin biosynthesis and root growth in Arabidopsis.

The interactions between phytohormones are crucial for plants to adapt to complex environmental changes. One example is the ethylene-regulated local auxin biosynthesis in roots, which partly contributes to ethylene-directed root development and gravitropism. Using a chemical biology approach, we identified a small molecule, l-kynurenine (Kyn), which effectively inhibited ethylene responses in Arabidopsis thaliana root tissues. Kyn application repressed nuclear accumulation of the ETHYLENE INSENSITIVE3 (EIN3) transcription factor. Moreover, Kyn application decreased ethylene-induced auxin biosynthesis in roots, and TRYPTOPHAN AMINOTRANSFERASE OF ARABIDOPSIS1/TRYPTOPHAN AMINOTRANSFERASE RELATEDs (TAA1/TARs), the key enzymes in the indole-3-pyruvic acid pathway of auxin biosynthesis, were identified as the molecular targets of Kyn. Further biochemical and phenotypic analyses revealed that Kyn, being an alternate substrate, competitively inhibits TAA1/TAR activity, and Kyn treatment mimicked the loss of TAA1/TAR functions. Molecular modeling and sequence alignments suggested that Kyn effectively and selectively binds to the substrate pocket of TAA1/TAR proteins but not those of other families of aminotransferases. To elucidate the destabilizing effect of Kyn on EIN3, we further found that auxin enhanced EIN3 nuclear accumulation in an EIN3 BINDING F-BOX PROTEIN1 (EBF1)/EBF2-dependent manner, suggesting the existence of a positive feedback loop between auxin biosynthesis and ethylene signaling. Thus, our study not only reveals a new level of interactions between ethylene and auxin pathways but also offers an efficient method to explore and exploit TAA1/TAR-dependent auxin biosynthesis.

Review and Comparative Analysis of Methods and Advancements in Predicting Protein Complex Structure.

Protein complexes perform diverse biological functions, and obtaining their three-dimensional structure is critical to understanding and grasping their functions. In many cases, it's not just two proteins interacting to form a dimer; instead, multiple proteins interact to form a multimer. Experimentally resolving protein complex structures can be quite challenging. Recently, there have been efforts and methods that build upon prior predictions of dimer structures to attempt to predict multimer structures. However, in comparison to monomeric protein structure prediction, the accuracy of protein complex structure prediction remains relatively low. This paper provides an overview of recent advancements in efficient computational models for predicting protein complex structures. We introduce protein-protein docking methods in detail and summarize their main ideas, applicable modes, and related information. To enhance prediction accuracy, other critical protein-related information is also integrated, such as predicting interchain residue contact, utilizing experimental data like cryo-EM experiments, and considering protein interactions and non-interactions. In addition, we comprehensively review computational approaches for end-to-end prediction of protein complex structures based on artificial intelligence (AI) technology and describe commonly used datasets and representative evaluation metrics in protein complexes. Finally, we analyze the formidable challenges faced in current protein complex structure prediction tasks, including the structure prediction of heteromeric complex, disordered regions in complex, antibody-antigen complex, and RNA-related complex, as well as the evaluation metrics for complex assessment. We hope that this work will provide comprehensive knowledge of complex structure predictions to contribute to future advanced predictions.

BDM: An Assessment Metric for Protein Complex Structure Models Based on Distance Difference Matrix.

Protein complex structure prediction is an important problem in computational biology. While significant progress has been made for protein monomers, accurate evaluation of protein complexes remains challenging. Existing assessment methods in CASP, lack dedicated metrics for evaluating complexes. DockQ, a widely used metric, has some limitations. In this study, we propose a novel metric called BDM (Based on Distance difference Matrix) for assessing protein complex prediction structures. Our approach utilizes a distance difference matrix derived from comparing real and predicted protein structures, establishing a linear correlation with Root Mean Square Deviation (RMSD). BDM overcomes limitations associated with receptor-ligand differentiation and eliminates the requirement for structure alignment, making it a more effective and efficient metric. Evaluation of BDM using CASP14 and CASP15 test sets demonstrates superior performance compared to the official CASP scoring. BDM provides accurate and reasonable assessments of predicted protein complexes, wide adoption of BDM has the potential to advance protein complex structure prediction and facilitate related researches across scientific domains. Code is available at http://mialab.ruc.edu.cn/BDMServer/ .

Interferon-induced transmembrane protein-1 competitively blocks Ephrin receptor A2-mediated Epstein-Barr virus entry into epithelial cells.

Epstein-Barr virus (EBV) can infect both B cells and epithelial cells (ECs), causing diseases such as mononucleosis and cancer. It enters ECs via Ephrin receptor A2 (EphA2). The function of interferon-induced transmembrane protein-1 (IFITM1) in EBV infection of ECs remains elusive. Here we report that IFITM1 inhibits EphA2-mediated EBV entry into ECs. RNA-sequencing and clinical sample analysis show reduced IFITM1 in EBV-positive ECs and a negative correlation between IFITM1 level and EBV copy number. IFITM1 depletion increases EBV infection and vice versa. Exogenous soluble IFITM1 effectively prevents EBV infection in vitro and in vivo. Furthermore, three-dimensional structure prediction and site-directed mutagenesis demonstrate that IFITM1 interacts with EphA2 via its two specific residues, competitively blocking EphA2 binding to EBV glycoproteins. Finally, YTHDF3, an m6A reader, suppresses IFITM1 via degradation-related DEAD-box protein 5 (DDX5). Thus, this study underscores IFITM1's crucial role in blocking EphA2-mediated EBV entry into ECs, indicating its potential in preventing EBV infection.

A single residue within the V5 region of HIV-1 envelope facilitates viral escape from the broadly neutralizing monoclonal antibody VRC01.

VRC01, a broadly neutralizing monoclonal antibody, is capable of neutralizing a diverse array of HIV-1 isolates by mimicking CD4 binding with the envelope glycoprotein gp120. Nonetheless, resistant strains have been identified. Here, we examined two genetically related and two unrelated envelope clones, derived from CRF08_BC-infected patients, with distinct VRC01 neutralization profiles. A total of 22 chimeric envelope clones was generated by interchanging the loop D and/or V5 regions between the original envelopes or by single alanine substitutions within each region. Analysis of pseudoviruses built from these mutant envelopes showed that interchanging the V5 region between the genetically related or unrelated clones completely swapped their VRC01 sensitivity profiles. Mutagenesis analysis revealed that the asparagine residue at position 460 (Asn-460), a potential N-linked glycosylation site in the V5 region, is a key factor for observed resistance in these strains, which is further supported by our structural modeling. Moreover, changes in resistance were found to positively correlate with deviations in VRC01 binding affinity. Overall, our study indicates that Asn-460 in the V5 region is a critical determinant of sensitivity to VRC01 specifically in these viral strains. The long side chain of Asn-460, and potential glycosylation, may create steric hindrance that lowers binding affinity, thereby increasing resistance to VRC01 neutralization.