Peptides from human BNIP5 and PXT1 and non-native binders of pro-apoptotic BAK can directly activate or inhibit BAK-mediated membrane permeabilization.

Apoptosis is important for development and tissue homeostasis, and its dysregulation can lead to diseases, including cancer. As an apoptotic effector, BAK undergoes conformational changes that promote mitochondrial outer membrane disruption, leading to cell death. This is termed "activation" and can be induced by peptides from the human proteins BID, BIM, and PUMA. To identify additional peptides that can regulate BAK, we used computational protein design, yeast surface display screening, and structure-based energy scoring to identify 10 diverse new binders. We discovered peptides from the human proteins BNIP5 and PXT1 and three non-native peptides that activate BAK in liposome assays and induce cytochrome c release from mitochondria. Crystal structures and binding studies reveal a high degree of similarity among peptide activators and inhibitors, ruling out a simple function-determining property. Our results shed light on the vast peptide sequence space that can regulate BAK function and will guide the design of BAK-modulating tools and therapeutics.

High-throughput computational discovery of inhibitory protein fragments with AlphaFold.

Peptides can bind to specific sites on larger proteins and thereby function as inhibitors and regulatory elements. Peptide fragments of larger proteins are particularly attractive for achieving these functions due to their inherent potential to form native-like binding interactions. Recently developed experimental approaches allow for high-throughput measurement of protein fragment inhibitory activity in living cells. However, it has thus far not been possible to predict de novo which of the many possible protein fragments bind their protein targets, let alone act as inhibitors. We have developed a computational method, FragFold, that employs AlphaFold to predict protein fragment binding to full-length protein targets in a high-throughput manner. Applying FragFold to thousands of fragments tiling across diverse proteins revealed peaks of predicted binding along each protein sequence. These predictions were compared with experimentally measured peaks of inhibitory activity in E. coli. We establish that our approach is a sensitive predictor of protein fragment function: Evaluating inhibitory fragments derived from known protein-protein interaction interfaces, we found 87% were predicted by FragFold to bind in a native-like mode. Across full protein sequences, 68% of FragFold-predicted binding peaks match experimentally measured inhibitory peaks. This is true even when the underlying inhibitory mechanism is unclear from existing structural data, and we find FragFold is able to predict novel binding modes for inhibitory fragments of unknown structure, explaining previous genetic and biochemical data for these fragments. The success rate of FragFold demonstrates that this computational approach should be broadly applicable for discovering inhibitory protein fragments across proteomes.

Tertiary motifs as building blocks for the design of protein-binding peptides.

Despite advances in protein engineering, the de novo design of small proteins or peptides that bind to a desired target remains a difficult task. Most computational methods search for binder structures in a library of candidate scaffolds, which can lead to designs with poor target complementarity and low success rates. Instead of choosing from pre-defined scaffolds, we propose that custom peptide structures can be constructed to complement a target surface. Our method mines tertiary motifs (TERMs) from known structures to identify surface-complementing fragments or "seeds." We combine seeds that satisfy geometric overlap criteria to generate peptide backbones and score the backbones to identify the most likely binding structures. We found that TERM-based seeds can describe known binding structures with high resolution: the vast majority of peptide binders from 486 peptide-protein complexes can be covered by seeds generated from single-chain structures. Furthermore, we demonstrate that known peptide structures can be reconstructed with high accuracy from peptide-covering seeds. As a proof of concept, we used our method to design 100 peptide binders of TRAF6, seven of which were predicted by Rosetta to form higher-quality interfaces than a native binder. The designed peptides interact with distinct sites on TRAF6, including the native peptide-binding site. These results demonstrate that known peptide-binding structures can be constructed from TERMs in single-chain structures and suggest that TERM information can be applied to efficiently design novel target-complementing binders.

Low-cost, rapidly-developed, 3D printed in vitro corpus callosum model for mucopolysaccharidosis type I.

The rising prevalence of high throughput screening and the general inability of (1) two dimensional (2D) cell culture and (2) in vitro release studies to predict in vivo neurobiological and pharmacokinetic responses in humans has led to greater interest in more realistic three dimensional (3D) benchtop platforms. Advantages of 3D human cell culture over its 2D analogue, or even animal models, include taking the effects of microgeometry and long-range topological features into consideration. In the era of personalized medicine, it has become increasingly valuable to screen candidate molecules and synergistic therapeutics at a patient-specific level, in particular for diseases that manifest in highly variable ways. The lack of established standards and the relatively arbitrary choice of probing conditions has limited in vitro drug release to a largely qualitative assessment as opposed to a predictive, quantitative measure of pharmacokinetics and pharmacodynamics in tissue. Here we report the methods used in the rapid, low-cost development of a 3D model of a mucopolysaccharidosis type I patient's corpus callosum, which may be used for cell culture and drug release. The CAD model is developed from in vivo brain MRI tracing of the corpus callosum using open-source software, printed with poly (lactic-acid) on a Makerbot Replicator 5X, UV-sterilized, and coated with poly (lysine) for cellular adhesion. Adaptations of material and 3D printer for expanded applications are also discussed.