Molecular chaperone RAP interacts with LRP1 in a dynamic bivalent mode and enhances folding of ligand-binding regions of other LDLR family receptors.

The low-density lipoprotein receptor (LDLR) family of receptors are cell-surface receptors that internalize numerous ligands and play crucial role in various processes, such as lipoprotein metabolism, hemostasis, fetal development, etc. Previously, receptor-associated protein (RAP) was described as a molecular chaperone for LDLR-related protein 1 (LRP1), a prominent member of the LDLR family. We aimed to verify this role of RAP for LRP1 and two other LDLR family receptors, LDLR and vLDLR, and to investigate the mechanisms of respective interactions using a cell culture model system, purified system, and in silico modelling. Upon coexpression of RAP with clusters of the ligand-binding complement repeats (CRs) of the receptors in secreted form in insect cells culture, the isolated proteins had increased yield, enhanced folding, and improved binding properties compared with proteins expressed without RAP, as determined by circular dichroism and surface plasmon resonance. Within LRP1 CR-clusters II and IV, we identified multiple sites comprised of adjacent CR doublets, which provide alternative bivalent binding combinations with specific pairs of lysines on RAP. Mutational analysis of these lysines within each of isolated RAP D1/D2 and D3 domains having high affinity to LRP1 and of conserved tryptophans on selected CR-doublets of LRP1, as well as in silico docking of a model LRP1 CR-triplet with RAP, indicated a universal role for these residues in interaction of RAP and LRP1. Consequently, we propose a new model of RAP interaction with LDLR family receptors based on switching of the bivalent contacts between molecules over time in a dynamic mode.

Engineering and evaluation of FXa bypassing agents that restore hemostasis following Apixaban associated bleeding.

Direct oral anticoagulants (DOACs) targeting activated factor Xa (FXa) are used to prevent or treat thromboembolic disorders. DOACs reversibly bind to FXa and inhibit its enzymatic activity. However, DOAC treatment carries the risk of anticoagulant-associated bleeding. Currently, only one specific agent, andexanet alfa, is approved to reverse the anticoagulant effects of FXa-targeting DOACs (FXaDOACs) and control life-threatening bleeding. However, because of its mechanism of action, andexanet alfa requires a cumbersome dosing schedule, and its use is associated with the risk of thrombosis. Here, we present the computational design, engineering, and evaluation of FXa-variants that exhibit anticoagulation reversal activity in the presence of FXaDOACs. Our designs demonstrate low DOAC binding affinity, retain FXa-enzymatic activity and reduce the DOAC-associated bleeding by restoring hemostasis in mice treated with apixaban. Importantly, the FXaDOACs reversal agents we designed, unlike andexanet alfa, do not inhibit TFPI, and consequently, may have a safer thrombogenic profile.

Computational design of nanomolar-binding antibodies specific to multiple SARS-CoV-2 variants by engineering a specificity switch of antibody 80R using RosettaAntibodyDesign (RAbD) results in potential generalizable therapeutic antibodies for novel SARS-CoV-2 virus.

The human infectious disease COVID-19 caused by the SARS-CoV-2 virus has become a major threat to global public health. Developing a vaccine is the preferred prophylactic response to epidemics and pandemics. However, for individuals who have contracted the disease, the rapid design of antibodies that can target the SARS-CoV-2 virus fulfils a critical need. Further, discovering antibodies that bind multiple variants of SARS-CoV-2 can aid in the development of rapid antigen tests (RATs) which are critical for the identification and isolation of individuals currently carrying COVID-19. Here we provide a proof-of-concept study for the computational design of high-affinity antibodies that bind to multiple variants of the SARS-CoV-2 spike protein using RosettaAntibodyDesign (RAbD). Well characterized antibodies that bind with high affinity to the SARS-CoV-1 (but not SARS-CoV-2) spike protein were used as templates and re-designed to bind the SARS-CoV-2 spike protein with high affinity, resulting in a specificity switch. A panel of designed antibodies were experimentally validated. One design bound to a broad range of variants of concern including the Omicron, Delta, Wuhan, and South African spike protein variants.

Corrigendum to "Computational design of nanomolar-binding antibodies specific to multiple SARS-CoV-2 variants by engineering a specificity switch of antibody 80R using RosettaAntibodyDesign (RAbD) results in potential generalizable therapeutic antibodies for novel SARS-CoV-2 virus" [Heliyon 9(4) (April 2023) e15032].

[This corrects the article DOI: 10.1016/j.heliyon.2023.e15032.].

Mathematical Models of Protease-Based Enzymatic Biosensors.

An important goal of synthetic biology is to build biosensors and circuits with well-defined input-output relationships that operate at speeds found in natural biological systems. However, for molecular computation, most commonly used genetic circuit elements typically involve several steps from input detection to output signal production: transcription, translation, and post-translational modifications. These multiple steps together require up to several hours to respond to a single stimulus, and this limits the overall speed and complexity of genetic circuits. To address this gap, molecular frameworks that rely exclusively on post-translational steps to realize reaction networks that can process inputs at a time scale of seconds to minutes have been proposed. Here, we build mathematical models of fast biosensors capable of producing Boolean logic functionality. We employ protease-based chemical and light-induced switches, investigate their operation, and provide selection guidelines for their use as on-off switches. As a proof of concept, we implement a rapamycin-induced switch in vitro and demonstrate that its response qualitatively agrees with the predictions from our models. We then use these switches as elementary blocks, developing models for biosensors that can perform OR and XOR Boolean logic computation while using reaction conditions as tuning parameters. We use sensitivity analysis to determine the time-dependent sensitivity of the output to proteolytic and protein-protein binding reaction parameters. These fast protease-based biosensors can be used to implement complex molecular circuits with a capability of processing multiple inputs controllably and algorithmically. Our framework for evaluating and optimizing circuit performance can be applied to other molecular logic circuits.

In silico features of ADAMTS13 contributing to plasmatic ADAMTS13 levels in neonates with congenital heart disease.

INTRODUCTION: Risk factors contributing to heightened thrombosis in pediatric congenital heart disease (CHD) patients are not fully understood. Among the neonatal CHD population, those presenting with single ventricular physiology are at the highest risk for perioperative thrombosis. The von Willebrand factor and ADAMTS13 interactions have emerged as causative risk factors for pediatric stroke and could contribute to heightened thrombosis in CHD neonates. METHODS: This study investigates a cohort of children with single ventricle physiology and undergoing cardiac surgery, during which some patients developed thrombosis. In this cohort, we analyzed the relationship of several molecular features of ADAMTS13 with the plasma and activity levels in patients at risk of thrombosis. Additionally, in light of the natural antithrombotic activity of ADAMTS13, we have sequenced the ADAMTS13 gene for each patient and evaluated the role of genetic variants in determining the plasma ADAMTS13 levels using a series of in silico tools including Hidden Markov Models, EVmutation, and Rosetta. RESULTS: Lower ADAMTS13 levels were found in patients that developed thrombosis. A novel in silico analysis to assess haplotype effect of co-occurring variants identified alterations in relative surface area and solvation energy as important contributors. Our analysis suggested that beneficial or deleterious effect of a variant can be reasonably predicted by comprehensive analysis of in silico assessment and in vitro and/or in vivo data. CONCLUSION: Findings from this study add to our understanding the role of genetic features of ADAMTS13 in patients at high risk of thrombosis related to an imbalanced relation between VWF and ADAMTS13.

Stimulus-responsive self-assembly of protein-based fractals by computational design.

Fractal topologies, which are statistically self-similar over multiple length scales, are pervasive in nature. The recurrence of patterns in fractal-shaped branched objects, such as trees, lungs and sponges, results in a high surface area to volume ratio, which provides key functional advantages including molecular trapping and exchange. Mimicking these topologies in designed protein-based assemblies could provide access to functional biomaterials. Here we describe a computational design approach for the reversible self-assembly of proteins into tunable supramolecular fractal-like topologies in response to phosphorylation. Guided by atomic-resolution models, we develop fusions of Src homology 2 (SH2) domain or a phosphorylatable SH2-binding peptide, respectively, to two symmetric, homo-oligomeric proteins. Mixing the two designed components resulted in a variety of dendritic, hyperbranched and sponge-like topologies that are phosphorylation-dependent and self-similar over three decades (~10 nm-10 µm) of length scale, in agreement with models from multiscale computational simulations. Designed assemblies perform efficient phosphorylation-dependent capture and release of cargo proteins.