Biology subjects

Hackel, B. J.

Publications and source records attributed to Hackel, B. J..

8 recordsLinked to original sources

bioRxiv2026

StrucNS reveals interaction-weighted network topology as the driving predictor of absolute stability of natural and de novo proteins

MotivationFolded protein function requires stability, yet mapping structure and sequence to a fitness landscape remains difficult. The protein fold is the physical realization of complex, spatially-sensitive physicochemical interactions among residues; quantitatively elucidating how these subtle relationships dictate thermodynamic stability remains challenging. We present StrucNS, a mathematical framework that identifies principles governing protein fitness by employing network science to learn physicochemical relationships between residues and their stability contributions directly from the protein fold. Representing the fold as a network topology, we utilize an inverse approach: while the fold is traditionally viewed as the phenotypic consequence of the underlying chemical forces, we use the topology to decode the very physicochemical dependencies that govern protein stability. Unlike protein language models reliant on high-dimensional evolutionary embeddings, StrucNS extracts these signals directly from the interaction-weighted network topology. Independence from evolutionary history uniquely suits StrucNS for de novo design prediction. ResultsDespite reduced dimensionality and training depth, StrucNS outperforms ESM-2 and ProteinMPNN on predicting mutational stability. StrucNS outperforms supervised UniRep in predicting absolute stability of de novo designs. Feature analysis reveals network topology as the key driver of predictive power, contributing 59% of model importance. SHAP analysis reveals two highly influential features as high degree and low modularity of polar/hydrophobic mixed subnetworks, which highlights the importance of connectivity between the hydrophobic core and protein surface to drive stability contrary to the conventional focus on the hydrophobic core. Revelation of predictive topological features underscores the utility of an interpretable model. AvailabilitySource codes are available: https://github.com/Hackel-Group-CEMS/StrucNS.

bioinformatics↗

bioRxiv2024

Sequence-function mapping of proline-rich antimicrobial peptides

Antimicrobial peptides (AMPs) are essential elements of natural cellular combat and candidates as antibiotic therapy. Elevated function may be needed for robust physiological performance. Yet, both pure protein design and combinatorial library discovery are hindered by the complexity of antimicrobial activity. We applied a recently developed high-throughput technique, sequence-activity mapping of AMPs via depletion (SAMP-Dep), to proline-rich AMPs. Robust self-inhibition was achieved for metalnikowin 1 (Met) and apidaecin 1b (Api). SAMP-Dep exhibited high reproducibility with correlation coefficients 0.90 and 0.92, for Met and Api, respectively, between replicates and 0.99 and 0.96 for synonymous genetic variants. Sequence-activity maps were obtained via characterization of 26,000 and 34,000 mutants of Met and Api, respectively. Both AMPs exhibit similar mutational profiles including beneficial mutations at one terminus, the C-terminus for Met and N-terminus for Api, which is consistent with their opposite binding orientations in the ribosome. While Met and Api reside with the family of proline-rich AMPs, different proline sites exhibit substantially different mutational tolerance. Within the PRP motif, proline mutation eliminates activity, whereas non-PRP prolines readily tolerate mutation. Homologous mutations are more tolerated, particularly at alternating sites on one face of the peptide. Important and consistent epistasis was observed following the PRP domain within the segment that extends into the ribosomal exit tunnel for both peptides. Variants identified from the SAMP-Dep platform were produced and exposed toward Gram-negative species exogenously, showing either increased potency or specificity for strains tested. In addition to mapping sequence-activity space for fundamental insight and therapeutic engineering, the results advance the robustness of the SAMP-Dep platform for activity characterization.

synthetic biology↗

bioRxiv2023

Systemic evaluation of protein-small molecule hybrids on the yeast surface

Protein-small molecule hybrids are structures that have the potential to combine the inhibitory properties of small molecules and the specificities of binding proteins. However, achieving such synergies is a substantial engineering challenge, with fundamental principles yet to be elucidated. Recent work has demonstrated the power of yeast display-based discovery of hybrids using a combination of fibronectin binding domains and thiol-mediated conjugations to introduce small molecule warheads. Here, we systematically study the effects of expanding the chemical diversity of these hybrids on the yeast surface, investigating a combinatorial set of fibronectins, noncanonical amino acid (ncAA) substitutions, and small molecule pharmacophores. Our results show that previously discovered thiol-fibronectin hybrids are generally tolerant of a range of ncAA substitutions and retain binding to carbonic anhydrases following click chemistry-mediated assembly of hybrids with diverse linker structures. Most surprisingly, we identified several cases where replacement of a potent acetazolamide warhead with a substantially weaker benzenesulfonamide warhead still resulted in the assembly of functional hybrids. In addition to these unexpected findings, we expanded the throughput of our system by validating a 96-well plate-based format to produce yeast-displayed hybrid conjugates in parallel. These efficient explorations of hybrid chemical diversity demonstrate that there are abundant opportunities to expand the functions of protein-small molecule hybrids and elucidate principles that dictate their efficient discovery and design.

bioengineering↗

bioRxiv2022

Predicting and Interpreting Protein Developability via Transfer of Convolutional Sequence Representation

Engineered proteins have emerged as novel diagnostics, therapeutics, and catalysts. Often, poor protein developability - quantified by expression, solubility, and stability - hinders utility. The ability to predict protein developability from amino acid sequence would reduce the experimental burden when selecting candidates. Recent advances in screening technologies enabled a high-throughput developability dataset for 105 of 1020 possible variants of protein ligand scaffold Gp2. In this work, we evaluate the ability of neural networks to learn a developability representation from a high-throughput dataset and transfer this knowledge to predict recombinant expression beyond observed sequences. The model convolves learned amino acid properties to predict expression levels 44% closer to the experimental variance compared to a non-embedded control. Analysis of learned amino acid embeddings highlights the uniqueness of cysteine, the importance of hydrophobicity and charge, and the unimportance of aromaticity, when aiming to improve the developability of small proteins. We identify clusters of similar sequences with increased developability through nonlinear dimensionality reduction and we explore the inferred developability landscape via nested sampling. The analysis enables the first direct visualization of the fitness landscape and highlights the existence of evolutionary bottlenecks in sequence space giving rise to competing subpopulations of sequences with different developability. The work advances applied protein engineering efforts by predicting and interpreting protein scaffold developability from a limited dataset. Furthermore, our statistical mechanical treatment of the problem advances foundational efforts to characterize the structure of the protein fitness landscape and the amino acid characteristics that influence protein developability. Significance statementProtein developability prediction and understanding constitutes a critical limiting step in biologic discovery and engineering due to limited experimental throughput. We demonstrate the ability of a machine learning model to learn sequence-developability relationships first through the use of high-throughput assay data, followed by the transfer of the learned developability representation to predict the true metric of interest, recombinant yield in bacterial production. Model performance is 44% better than a model not pre-trained using the high-throughput assays. Analysis of model behavior reveals the importance of cysteine, charge, and hydrophobicity to developability, as well as of an evolutionary bottleneck that greatly limited sequence diversity above 1.3 mg/L yield. Experimental characterization of model predicted candidates confirms the benefit of this transfer learning and in-silico evolution approach.

bioinformatics↗

bioRxiv2021

A platform for deep sequence-activity mapping and engineering antimicrobial peptides

Developing potent antimicrobials, and platforms for their study and engineering, is critical as antibiotic resistance grows. A high-throughput method to quantify antimicrobial peptide and protein (AMP) activity across a broad continuum can elucidate sequence-activity landscapes and identify potent mutants. We developed a platform to perform sequence-activity mapping of AMPs via depletion (SAMP-Dep): a bacterial host culture is transformed with an AMP mutant library, induced to express AMPs, grown, and deep sequenced to quantify mutant frequency. The slope of mutant growth rate versus induction level indicates potency. Using SAMP-Dep, we screened 170,000 mutants of oncocin, a proline-rich AMP, for intracellular activity against Escherichia coli. Clonal validation of 36 mutants supported SAMP-Dep sensitivity and accuracy. The efficiency and accuracy of SAMP-Dep enabled mapping the oncocin sequence-activity space with remarkable detail and scale and guided focused, successful synthetic peptide library design, yielding a mutant with two-fold enhancement in both intracellular and extracellular activity.

bioengineering↗

bioRxiv2021

Engineered protein-small molecule conjugates empower selective enzyme inhibition

SO_SCPLOWUMMARYC_SCPLOWSpecific, potent ligands drive precision medicine and fundamental biology. Proteins, peptides, and small molecules constitute effective ligand classes. Yet greater molecular diversity would aid the pursuit of ligands to elicit precise biological activity against challenging targets. We demonstrate a platform to discover protein-small molecule (PriSM) hybrids to combine unique pharmacophore activities and shapes with constrained, efficiently engineerable proteins. A fibronectin protein library was yeast displayed with a single cysteine coupled to acetazolamide via a maleimide-poly(ethylene glycol) linker. Magnetic and flow cytometric sorts enriched specific binders to carbonic anhydrase isoforms. Isolated PriSMs exhibited potent, specific inhibition of carbonic anhydrase isoforms with superior efficacy to acetazolamide or protein alone including an 80-fold specificity increase and 9-fold potency gain. PriSMs were engineered with multiple linker lengths, protein conjugation sites, and sequences against two different isoforms, which reveals platform flexibility and impacts of molecular designs. PriSMs advance the molecular diversity of efficiently engineerable ligands.

bioengineering↗

bioRxiv2020

High-Throughput Developability Assays Enable Library-Scale Identification of Producible Protein Scaffold Variants

Proteins require high developability - quantified by expression, solubility, and stability - for robust utility as therapeutics, diagnostics, and in other biotechnological applications. Measuring traditional developability metrics is low-throughput in nature, often slowing the developmental pipeline. We evaluated the ability of three high-throughput developability assays to predict the bacterial recombinant expression of paratope variants of the protein scaffold Gp2. Enabled by a phenotype/genotype linkage, assay performance for 105 variants was calculated via deep sequencing of populations sorted by proxied developability. We trained a random forest model that predicts expression from assay performance that is 35% closer to the experimental variance and trains 80% more efficiently than a model predicting from sequence information alone. Utilizing the predicted expression, we performed a sitewise analysis and predicted mutations consistent with enhanced developability. The validated assays offer the ability to identify developable proteins at unprecedented scales, reducing the bottleneck of protein commercialization.

bioengineering↗

bioRxiv2020

CD19-targeting CAR T cells potently redirected to kill solid tumor cells

Successful CAR T cell therapy for the treatment of solid tumors requires exemplary CAR T cell expansion, persistence and fitness, and the ability to target tumor antigens safely. Here we address this constellation of critical attributes for successful cellular therapy by using integrated technologies that simplify development and derisk clinical translation. We have developed a CAR-CD19 T cell that secretes a CD19-anti-Her2 bridging protein. This cell therapy strategy exploits the ability of CD19-targeting CAR T cells to interact with CD19 on normal B cells to drive expansion, persistence and fitness. The secreted bridging protein potently binds to Her2-positive tumor cells, mediating CAR-CD19 T cell cytotoxicity in vitro and in vivo. Because of its short half-life, the secreted bridging protein will selectively accumulate at the site of highest antigen expression, ie. at the tumor. Bridging proteins that bind to multiple different tumor antigens have been created. Therefore, antigen-bridging CAR-CD19 T cells incorporate critical attributes for successful solid tumor cell therapy. This platform can be exploited to attack tumor antigens on any cancer.

cancer biology↗