Biology subjects

Loeffler, J. R.

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

3 recordsLinked to original sources

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↗