Biology subjects

Loeffler, J. R.

Publications and source records attributed to Loeffler, J. R..

11 recordsLinked to original sources

bioRxiv2026

Structural basis for conserved and distinct antigen recognition by a lineage of malaria-protective antibodies

Monoclonal antibodies (mAbs) targeting the Plasmodium falciparum circumsporozoite protein (PfCSP) have demonstrated substantial promise in preventing malaria infection and disease. PfCSP is characterized by a central region composed of repetitive NANP motifs, which serve as major targets for protective antibodies. Several potent mAbs targeting this region exhibit homotypic Fab-Fab interactions, which enhance antigen binding and contribute to their neutralization potency. Among these, mAb 399, encoded by the IGHV3-49/IGKV2D-29 (VH3-49/VK2D-29) germline lineages, forms head-to-head inter-Fab contacts mediated primarily by germline-encoded residues. In this study, we determined high-resolution X-ray crystal and cryo-EM structures of two additional Fabs, derived from the same germline lineages, 7160 and 7118, in complex with PfCSP-derived peptides. Both Fabs bound the NANP6 repeats with high affinity (KD 5-10 nM). Fab 7160 formed similar inter-Fab homotypic interactions to Fab 399 upon binding to an extended repeat peptide (NANP), indicating a conserved mode of recognition. In contrast, Fab 7118, which features a longer CDRH3, does not form homotypic contacts. Instead, Fab 7118 induced a bend in the peptide and adopted a distinct binding mode, which prevents inter-Fab interactions. A cryo-EM structure of Fab 7118 in complex with a recombinant shortened PfCSP construct (rsCSP) further revealed potential disruptions at inter-Fab contact sites. These findings highlight the structural versatility of VH3-49/VK2D-29-derived antibodies and demonstrate that CDR loop variations within a shared antibody lineage can modulate antibody conformation, homotypic Fab-Fab interactions, and epitope engagement. Our study provides mechanistic insights into the diverse strategies by which CSP-specific antibodies can achieve high-avidity binding and protective immunity.

microbiology↗

bioRxiv2025

De novo designed protein enables precise epitope-level control of Gremlin-1 antagonism

Growth-factor antagonists regulate key developmental and pathological processes, yet the molecular principles governing their control remain poorly understood. Gremlin-1 (GREM1) is a secreted antagonist of bone morphogenetic proteins (BMPs) whose dysregulation is implicated in fibrosis and cancer. Here, we report a de novo designed protein that binds GREM1 with sub-nanomolar affinity and selectively releases BMPs for downstream signalling. By integrating generative deep-learning-based protein design with molecular-dynamics-derived flexibility descriptors, we identify a predictive relationship between interface rigidity, desolvation energy, and binding success. The resulting binder, RF1-2, reproduces the native BMP-binding epitope on GREM1 at near-atomic precision, as confirmed by cryo-electron microscopy, and competitively blocks BMP-2 and BMP-4 association. These results establish interface rigidity as a key physical determinant of antagonist inhibition and demonstrate how AI-guided protein design can uncover molecular principles underlying extracellular signalling control.

biochemistry↗

bioRxiv2025

Engineering HIV antibodies with enhanced breadth and potency of neutralization through multistate affinity maturation

Broadly neutralizing antibodies (bnAbs) against HIV hold promise as therapeutic and prophylactic agents, but realizing this potential requires antibodies that function across the antigenic heterogeneity of the HIV envelope glycoprotein (Env). Although numerous bnAbs have been isolated from infected individuals, their breadth and potency may not be sufficient to tackle global viral diversity, motivating efforts to further improve their neutralization capacity. Here, we address this challenge using a multistate antibody engineering approach integrating deep mutational scanning with combinatorial library screening across diverse Env variants. This strategy enables identification of mutation patterns that confer improved binding across antigenically distinct targets. Starting from one of the best-in-class CD4-binding site bnAbs, we performed iterative optimization to increase binding affinity across diverse Env variants. The resulting lead candidate exhibited improved breadth and up to 100-fold higher potency against pseudoviruses from large cross-clade historical and contemporary panels while maintaining biophysical and pharmacokinetic profiles conducive to clinical development. Structural and molecular dynamics analyses revealed a unique tri-tyrosine aromatic triad and reinforced electrostatic contacts that stabilized the bnAb/Env interface. These findings demonstrate that systematic in vitro engineering can generate bnAbs with enhanced breadth and potency, providing a generalizable strategy for developing therapeutic antibodies against highly diverse pathogens.

bioengineering↗

bioRxiv2025

CryoEM Structures of Antibodies Elicited by Germline-Targeting HIV MPER Epitope-Scaffolds

Applying cryoEM to small protein complexes is usually challenging due to their lack of features for particle alignment. Here, we characterized antibody responses to 21 kDa HIV membrane-proximal external region germline-targeting (MPER-GT) immunogens through cryoEM by complexing them with 10E8 or Fabs derived from MPER-GT immunized animals. Distinct antibody-antigen interactions were analyzed using atomic models generated from cryoEM maps. Mutagenesis screening revealed off-target mAbs that do not compete with 10E8 bind non-MPER epitopes, and the two most dominant epitopes were verified by cryoEM. The structures of 10E8-class on-target Fabs showed binding patterns that resemble the YxFW motif in the 10E8 HCDR3 loop. Additionally, we demonstrate that high-resolution maps can be generated from heterogeneous samples with pooled competing Fabs. Overall, our findings will facilitate the optimization of MPER GT-antigens and push the size limit for cryoEM-based epitope mapping with smaller antigens and heterogeneous antibody mixes. HighlightsO_LIElucidate the structural basis of increased 10E8 affinity to GT12 over GT10.2 C_LIO_LICryoEM epitope mapping reveals three main binding sites on MPER-GT immunogens C_LIO_LIProof of concept for application of CryoEM epitope mapping to small immunogens C_LIO_LIStructural characterization and MD analysis confirms 10E8-like interactions of the MPER-specific Fabs C_LI

immunology↗

bioRxiv2025

Isolation of genetically diverse influenza antibodies highlights the role of IG germline gene variation and informs the design of population-comprehensive vaccine strategies

The regular emergence of influenza strains with pandemic potential creates a strong incentive to develop vaccines that stimulate protective responses across all human populations. A critical consideration is how variation in the human immunoglobulin (IG) loci influences B cell recognition of viral epitopes and elicitation of neutralizing antibodies. Here, we applied personalized IG germline genotyping and high-throughput sequencing of paired antibody chains from influenza A virus hemagglutinin (HA)-binding B cells to demonstrate that the response to HA is highly individual. We show that a germline-encoded polymorphism in IGHV2-70 alters the functionality of the LPAF-a class of neutralizing antibodies, and we describe HA stem-targeting broadly neutralizing antibodies (bNAbs) that use germline IGHV genes other than the population-restricted IGHV genes used by many previously known stem bNAbs. Our results demonstrate that the approach used here can be used to discover and avert population vulnerabilities arising from IG gene variation when designing HA-based influenza vaccines aimed for the global human population.

immunology↗

bioRxiv2025

Predicting the conformational flexibility of antibody and T-cell receptor CDRs

AbstractMany proteins are highly flexible and their ability to adapt their shape can be fundamental to their functional properties. We can now computationally predict a single, static protein structure with high accuracy. However, we are not yet able to reliably predict structural flexibility. A major factor limiting such predictions is the scarcity of suitable training data. Here, we focus on predicting the structural flexibility of the functionally important antibody and T-cell receptor CDR3 loops. We extracted a dataset of CDR3 like loop motifs from the PDB to create ALL-conformations, a dataset containing 1.2 million structures and more than 100,000 unique sequences. Using this dataset, we develop ITsFlexible a method classifying CDR3 flexibility, which outperforms all alternative approaches on our crystal structure datasets and successfully generalises to MD simulations. We also used ITsFlexible to predict the flexibility of three completely novel CDRH3 loops and experimentally determined their conformations using cryo-EM.

bioinformatics↗

bioRxiv2024

De novo designed pMHC binders facilitate T cell induced killing of cancer cells

The recognition of intracellular antigens by CD8+ T cells through T-cell receptors (TCRs) is central to adaptive immunity, enabling responses against infections and cancer. The recent approval of TCR-gene-edited T cells for cancer therapy demonstrates the therapeutic advantage of using pMHC recognition to eliminate cancer. However, identification and selection of TCRs from patient material is complex and influenced by the TCR repertoire of the donors used. To overcome these limitations, we here present a rapid and robust de novo binder design platform leveraging state-of-the-art generative models, including RFdiffusion, ProteinMPNN, and AlphaFold2, to engineer minibinders (miBds) targeting the cancer-associated pMHC complex, NY-ESO-1(157-165)/HLA-A*02:01. By incorporating in silico cross-panning and molecular dynamics simulations, we enhanced specificity screening to minimise off-target interactions. We identified a miBd that exhibited high specificity for the NY-ESO-1-derived peptide SLLMWITQC in complex with HLA-A*02:01 and minimal cross-reactivity in mammalian display assays. We further demonstrate the therapeutic potential of this miBd by integrating it into a chimeric antigen receptor, as de novo Binders for Immune-mediated Killing Engagers (BIKEs). BIKE-transduced T cells selectively and effectively killed NY-ESO-1+ melanoma cells compared to non-transduced controls, demonstrating the promise of this approach in precision cancer immunotherapy. Our findings underscore the transformative potential of generative protein design for accelerating the discovery of high-specificity pMHC-targeting therapeutics. Beyond CAR-T applications, our workflow establishes a foundation for developing miBds as versatile tools, heralding a new era of precision immunotherapy.

immunology↗

bioRxiv2024

Assessing AF2's ability to predict structural ensembles of proteins

Recent breakthroughs in protein structure prediction have enhanced the precision and speed at which protein configurations can be determined, setting new benchmarks for accuracy and efficiency in the field. However, the fundamental mechanisms of biological processes at a molecular level are often connected to conformational changes of proteins. Molecular dynamics (MD) simulations serve as a crucial tool for capturing the conformational space of proteins, providing valuable insights into their structural fluctuations. However, the scope of MD simulations is often limited by the accessible timescales and the computational resources available, posing challenges to comprehensively exploring protein behaviors. Recently emerging approaches have focused on expanding the capability of AlphaFold2 (AF2) to predict conformational substates of protein structures by manipulating the input multiple sequence alignment (MSA). These approaches operate under the assumption that the MSA also contains information about the heterogeneity of protein structures. Here, we benchmark the performance of various workflows that have adapted AF2 for ensemble prediction focusing on the subsampling of the MSA as implemented in ColabFold and compare the obtained structures with ensembles obtained from MD simulations and NMR. As test cases, we chose four proteins namely the bovine pancreatic inhibitor protein (BPTI), thrombin and two antigen binding fragments (antibody Fv and nanobody), for which reliable experimentally validated structural information (X-ray and/or NMR) was available. Thus, we provide an overview of the levels of performance and accessible timescales that can currently be achieved with machine learning (ML) based ensemble generation. In three out of the four test cases, we find structural variations fall within the predicted ensembles. Nevertheless, significant minima of the free energy surfaces remain undetected. This study highlights the possibilities and pitfalls when generating ensembles with AF2 and thus may guide the development of future tools while informing upon the results of currently available applications.

biophysics↗

bioRxiv2024

Broadly inhibitory antibodies against severe malaria virulence proteins

Plasmodium falciparum pathology is driven by the accumulation of parasite-infected erythrocytes in microvessels. This process is mediated by the parasites polymorphic erythrocyte membrane protein 1 (PfEMP1) adhesion proteins. A subset of PfEMP1 variants that bind human endothelial protein C receptor (EPCR) through their CIDR1 domains is responsible for severe malaria pathogenesis. A longstanding question is whether individual antibodies can recognize the large repertoire of circulating PfEMP1 variants. Here, we describe two broadly reactive and binding-inhibitory human monoclonal antibodies against CIDR1. The antibodies isolated from two different individuals exhibited a similar and consistent EPCR-binding inhibition of 34 CIDR1 domains, representing five of the six subclasses of CIDR1. Both antibodies inhibited EPCR binding of both recombinant full-length and native PfEMP1 proteins as well as parasite sequestration in bioengineered 3D brain microvessels under physiologically relevant flow conditions. Structural analyses of the two antibodies in complex with two different CIDR1 antigen variants reveal similar binding mechanisms that depend on interactions with three highly conserved amino acid residues of the EPCR-binding site in CIDR1. These broadly reactive antibodies likely represent a common mechanism of acquired immunity to severe malaria and offer novel insights for the design of a vaccine or treatment targeting severe malaria.

immunology↗

bioRxiv2023

Learning high-dimensional reaction coordinates of fast-folding proteins using State Predictive Information Bottleneck and Bias Exchange Metadynamics

Biological events occurring on long timescales, such as protein folding, remain hard to capture with conventional molecular dynamics (MD) simulation. To overcome these limitations, enhanced sampling techniques can be used to sample regions of the free energy landscape separated by high energy barriers, thereby allowing to observe these rare events. However, many of these techniques require a priori knowledge of the appropriate reaction coordinates (RCs) that describe the process of interest. In recent years, Artificial Intelligence (AI) models have emerged as promising approaches to accelerate rare event sampling. However, integration of these AI methods with MD for automated learning of improved RCs is not trivial, particularly when working with undersampled trajectories and highly complex systems. In this study, we employed the State Predictive Information Bottleneck (SPIB) neural network, coupled with bias exchange metadynamics simulations (BE-metaD), to investigate the unfolding process of two proteins, chignolin and villin. By utilizing the high-dimensional RCs learned from SPIB even with poor training data, BE-metaD simulations dramatically accelerate the sampling of the unfolding process for both proteins. In addition, we compare different RCs and find that the careful selection of RCs is crucial to substantially speed up the sampling of rare events. Thus, this approach, leveraging the power of AI and enhanced sampling techniques, holds great promise for advancing our understanding of complex biological processes occurring on long timescales. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=139 SRC="FIGDIR/small/550401v1_ufig1.gif" ALT="Figure 1"> View larger version (29K): org.highwire.dtl.DTLVardef@18a9296org.highwire.dtl.DTLVardef@9dc2f3org.highwire.dtl.DTLVardef@16a0cf8org.highwire.dtl.DTLVardef@1799db1_HPS_FORMAT_FIGEXP M_FIG TABLE OF CONTENT GRAPHIC C_FIG

biophysics↗

bioRxiv2023

PEP-Patch: Electrostatics in Protein-Protein Recognition, Specificity and Antibody Developability

The electrostatic properties of proteins arise from the number and distribution of polar and charged residues. Due to their long-ranged nature, electrostatic interactions in proteins play a critical role in numerous processes, such as molecular recognition, protein solubility, viscosity, and antibody developability. Thus, characterizing and quantifying electrostatic properties of a protein is a pre-requisite for understanding these processes. Here, we present PEP-Patch, a tool to visualize and quantify the electrostatic potential on the protein surface and showcase its applicability to elucidate protease substrate specificity, antibody-antigen recognition and predict heparin column retention times of antibodies as an indicator of pharmacokinetics.

biophysics↗