Biology subjects

Gainza, P.

Publications and source records attributed to Gainza, P..

11 recordsLinked to original sources

bioRxiv2025

Humanized Caffeine-Inducible Systems for Controlling Cellular Functions

Current cell therapies are limited by the lack of tools for controlling gene expression using humanized systems responsive to non-toxic stimuli. Starting from nanobodies that homodimerize in response to caffeine, we computationally designed inducible heterodimers and humanized the best-performing pairs. We used the resulting caffeine-inducible domains in engineered cytokine receptors for caffeine-inducible STAT3 signaling and in split transcription factors (caff-TFs) containing human-derived zinc-finger proteins. Heterodimerization of split transcription factors drastically enhanced their performance compared to homodimerization. We demonstrate that caff-TFs are compatible with lentiviral and retroviral delivery to Jurkat T-cells and enable inducible expression of therapeutic genes such as chimeric antigen receptors (CARs) in response to caffeine concentrations consistent with normal coffee consumption. By using the common, non-toxic molecule caffeine, and exclusively humanized protein components, these systems promise to be a safer alternative to existing systems and may be used in synthetic biology applications and for safer, more effective cell therapies.

synthetic biology↗

bioRxiv2025

Molecular surface mimicry enables CRBN to target G3BP2 for degradation

Molecular glue degraders (MGDs) are small molecule compounds that repurpose the ubiquitin-proteasome system to induce degradation of challenging therapeutic targets. Clinically effective MGDs bind cereblon (CRBN), a substrate receptor of the Cullin-4/RING E3 ubiquitin ligase (CRL4CRBN), and impose a gain-of-function activity to recruit, ubiquitinate and degrade so-called neosubstrate proteins. Known neosubstrates bind the CRBN/MGD neosurface on the CRBN CULT domain through a structural G-loop recognition motif that mimics contacts of a natural CRBN degron. Here we report the binding mode of G3BP2, a CRBN neosubstrate that bypasses the G-loop requirement by engaging an unconventional binding site on the CRBN LON domain. The MGD-induced ternary complex interface does not resemble known protein-protein interactions (PPI) with CRBN. Instead, CRBN mimics an endogenous binding partner of G3BP2 and repurposes a preexisting PPI hotspot on the target protein. Our findings provide a novel generalizable concept for the rationalization of unconventional neosubstrate binding modes on CRBN, demonstrate unprecedented potential for the reprogrammability of this substrate receptor by MGDs, and offer opportunities for rational expansion of the target repertoire accessible to this modality.

molecular biology↗

bioRxiv2024

Mining the CRBN Target Space Redefines Rules for Molecular Glue-induced Neosubstrate Recognition

The CRL4CRBN ubiquitin ligase is leveraged by molecular glue degraders, small molecules that reprogram ligase specificity to induce degradation of clinically relevant neosubstrate proteins. Known CRBN neosubstrates share a generalizable {beta}-hairpin G-loop recognition motif, yet systematic exploration of the CRBN target landscape is still pending. Through computational mining of the human proteome using structure-based approaches, we predict over 1,400 CRBN-compatible {beta}-hairpin G-loop proteins across diverse target classes, identify novel mechanisms of neosubstrate recognition through structurally differentiated helical motifs and molecular surface mimicry, and validate 22 representative neosubstrates with clinical implications. This work broadens the CRBN target space, redefines rules for neosubstrate recognition and establishes a platform for the elimination of challenging drug targets by repurposing CRL4CRBN through next-generation molecular glue degraders.

biochemistry↗

bioRxiv2024

Targeting protein-ligand neosurfaces using a generalizable deep learning approach

Molecular recognition events between proteins drive biological processes in living systems. However, higher levels of mechanistic regulation have emerged, where protein-protein interactions are conditioned to small molecules. Here, we present a computational strategy for the design of proteins that target neosurfaces, i.e. surfaces arising from protein-ligand complexes. To do so, we leveraged a deep learning approach based on learned molecular surface representations and experimentally validated binders against three drug-bound protein complexes. Remarkably, surface fingerprints trained only on proteins can be applied to neosurfaces emerging from small molecules, serving as a powerful demonstration of generalizability that is uncommon in deep learning approaches. The designed chemically-induced protein interactions hold the potential to expand the sensing repertoire and the assembly of new synthetic pathways in engineered cells.

biochemistry↗

bioRxiv2022

Rational design of chemically controlled antibodies and protein therapeutics

Protein-based therapeutics such as monoclonal antibodies and cytokines are important therapies in various pathophysiological conditions such as oncology, auto-immune disorders, and viral infections. However, the wide application of such protein therapeutics is often hindered by dose-limiting toxicities and adverse effects, namely cytokine storm syndrome, organ failure and others. Therefore, spatiotemporal control of the activities of these proteins is crucial to further expand their application. Here, we report the design and application of small molecule-controlled switchable protein therapeutics by taking advantage of a previously engineered OFF-switch system. We used Rosetta modeling suite to computationally optimize the affinity between B-cell lymphoma 2 (Bcl-2) protein and a previously developed computationally designed protein partner (LD3) to obtain a fast and efficient heterodimer disruption upon addition of a competing drug (Venetoclax). The incorporation of the engineered OFF-switch system into CTLA4, anti-HER2 antibodies or an Fc-fused IL-15 cytokine demonstrated an efficient disruption in vitro, as well as fast clearance in vivo upon addition of the competing drug Venetoclax. These results provide a proof-of-concept for the rational design of controllable biologics by introducing a drug-induced OFF-switch into existing protein-based therapeutics.

bioengineering↗

bioRxiv2022

De novo design of site-specific protein interactions with learned surface fingerprints

Physical interactions between proteins are essential for most biological processes governing life. However, the molecular determinants of such interactions have been challenging to understand, even as genomic, proteomic, and structural data grows. This knowledge gap has been a major obstacle for the comprehensive understanding of cellular protein-protein interaction (PPI) networks and for the de novo design of protein binders that are crucial for synthetic biology and translational applications. We exploit a geometric deep learning framework operating on protein surfaces that generates fingerprints to describe geometric and chemical features critical to drive PPIs. We hypothesized these fingerprints capture the key aspects of molecular recognition that represent a new paradigm in the computational design of novel protein interactions. As a proof-of-principle, we computationally designed several de novo protein binders to engage four protein targets: SARS-CoV-2 spike, PD-1, PD-L1, and CTLA-4. Several designs were experimentally optimized while others were purely generated in silico, reaching nanomolar affinity with structural and mutational characterization showing highly accurate predictions. Overall, our surface-centric approach captures the physical and chemical determinants of molecular recognition, enabling a novel approach for the de novo design of protein interactions and, more broadly, of artificial proteins with function.

bioengineering↗

bioRxiv2021

A rational blueprint for the design of chemically-controlled protein switches

Small-molecule responsive protein switches are crucial components to control synthetic cellular activities. However, the repertoire of small-molecule protein switches is insufficient for many applications, including those in the translational spaces, where properties such as safety, immunogenicity, drug half-life, and drug side-effects are critical. Here, we present a computational protein design strategy to repurpose drug-inhibited protein-protein interactions as OFF- and ON-switches. The designed binders and drug-receptors form chemically-disruptable heterodimers (CDH) which dissociate in the presence of small molecules. To design ON-switches, we converted the CDHs into a multi-domain architecture which we refer to as activation by inhibitor release switches (AIR) that incorporate a rationally designed drug-insensitive receptor protein. CDHs and AIRs showed excellent performance as drug responsive switches to control combinations of synthetic circuits in mammalian cells. This approach effectively expands the chemical space and logic responses in living cells and provides a blueprint to develop new ON- and OFF-switches for basic and translational applications.

biochemistry↗

bioRxiv2020

Chiral evasion and stereospecific antifolate resistance in Staphylococcus aureus

Antimicrobial resistance is a health care crisis. The resistance-conferring mutation F98Y in Staphylococcus aureus dihydrofolate reductase (SaDHFR) reduces effectiveness of antifolates, e.g., trimethoprim (TMP). Although propargyl-linked antifolates (PLAs) are much more resilient than TMP towards F98Y, this substitution still vitiates their inhibition potency. Surprisingly, differences in the enantiomeric configuration at the stereogenic center of PLAs influence the isomeric state of NADPH cofactor. Is resistance correlated with chiral evasion? A mechanism of action underpinning this coupling is unknown. To understand the molecular basis of F98Y-mediated resistance and how PLAs inhibition drives NADPH isomeric states, we used OSPREY to analyze a comprehensive suite of structural, biophysical, biochemical, and computational data. We present a model showing how F98Y SaDHFR exploits a different anomeric configuration of NADPH to evade certain PLAs inhibition, while other PLAs remain resilient to resistance. Our model should enable general design of inhibitors that are resilient to chiral evasion.

biochemistry↗

bioRxiv2019

Deciphering interaction fingerprints from protein molecular surfaces

Predicting interactions between proteins and other biomolecules purely based on structure is an unsolved problem in biology. A high-level description of protein structure, the molecular surface, displays patterns of chemical and geometric features that fingerprint a proteins modes of interactions with other biomolecules. We hypothesize that proteins performing similar interactions may share common fingerprints, independent of their evolutionary history. Fingerprints may be difficult to grasp by visual analysis but could be learned from large-scale datasets. We present MaSIF, a conceptual framework based on a new geometric deep learning method to capture fingerprints that are important for specific biomolecular interactions. We showcase MaSIF with three prediction challenges: protein pocket-ligand prediction, protein-protein interaction site prediction, and ultrafast scanning of protein surfaces for prediction of protein-protein complexes. We anticipate that our conceptual framework will lead to improvements in our understanding of protein function and design.

bioinformatics↗

bioRxiv2018

Boosting subdominant neutralizing antibody responses with a computationally designed epitope-focused immunogen

Throughout the last decades, vaccination has been key to prevent and eradicate infectious diseases. However, many pathogens (e.g. respiratory syncytial virus (RSV), influenza, dengue and others) have resisted vaccine development efforts, largely due to the failure to induce potent antibody responses targeting conserved epitopes. Deep profiling of human B-cells often reveals potent neutralizing antibodies that emerge from natural infection, but these specificities are generally subdominant (i.e., are present in low titers). A major challenge for next-generation vaccines is to overcome established immunodominance hierarchies and focus antibody responses on crucial neutralization epitopes. Here, we show that a computationally designed epitope-focused immunogen presenting a single RSV neutralization epitope elicits superior epitope-specific responses compared to the viral fusion protein. In addition, the epitope-focused immunogen efficiently boosts antibodies targeting the Palivizumab epitope, resulting in enhanced neutralization. Overall, we show that epitope-focused immunogens can boost subdominant neutralizing antibody responses in vivo and reshape established antibody hierarchies.

immunology↗

bioRxiv2018

OSPREY 3.0: Open-Source Protein Redesign for You, with Powerful New Features

We present O_SCPCAPOSPREYC_SCPCAP 3.0, a new and greatly improved release of the O_SCPLOWOSPREYC_SCPLOW protein design software. O_SCPLOWOSPREYC_SCPLOW 3.0 features a convenient new Python interface, which greatly improves its ease of use. It is over two orders of magnitude faster than previous versions of O_SCPLOWOSPREYC_SCPLOW when running the same algorithms on the same hardware. Moreover, O_SCPLOWOSPREYC_SCPLOW 3.0 includes several new algorithms, which introduce substantial speedups as well as improved biophysical modeling. It also includes GPU support, which provides an additional speedup of over an order of magnitude. Like previous versions of O_SCPLOWOSPREYC_SCPLOWO_SCPCAP, C_SCPCAPO_SCPLOWOSPREYC_SCPLOW 3.0 offers a unique package of advantages over other design software, including provable design algorithms that account for continuous flexibility during design and model conformational entropy. Finally, we show here empirically that O_SCPLOWOSPREYC_SCPLOW 3.0 accurately predicts the effect of mutations on protein-protein binding. O_SCPLOWOSPREYC_SCPLOW 3.0 is available at http://www.cs.duke.edu/donaldlab/osprey.php as free and open-source software.\n\nO_TEXTBOX\n\nWe present the third major release of the OSPREY protein design software, along with comparisons to experimental data that confirm its ability to optimize protein mutants for desired functions. osprey 3.0 has significant efficiency, ease-of-use, and algorithmic improvements over previous versions, including GPU acceleration and a new Python interface.\n\nC_TEXTBOX\n\nO_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=162 SRC=\"FIGDIR/small/306324v1_ufig1.gif\" ALT=\"Figure 1\">\nView larger version (54K):\norg.highwire.dtl.DTLVardef@f08b0dorg.highwire.dtl.DTLVardef@393b61org.highwire.dtl.DTLVardef@16997ddorg.highwire.dtl.DTLVardef@1713405_HPS_FORMAT_FIGEXP M_FIG C_FIG

bioinformatics↗