RESUMEN
Here, we present the second generation of our bicyclic peptide library (NTB), featuring a stereodiversified structure and a simplified construction strategy. We utilized a tandem ring-opening metathesis and ring-closing metathesis reaction (ROM-RCM) to cyclize the linear peptide library in a single step, representing the first reported instance of this reaction being applied to the preparation of macrocyclic peptides. Moreover, the resulting bicyclic peptide can be easily linearized for MS/MS sequencing with a one-step deallylation process. We employed this library to screen against the E363-R378 epitope of MYC and identified several MYC-targeting bicyclic peptides. Subsequent in vitro cell studies demonstrated that one candidate, NT-B2R, effectively suppressed MYC transcription activities and cell proliferation.
Asunto(s)
Biblioteca de Péptidos , Espectrometría de Masas en Tándem , Péptidos/farmacología , Péptidos/químicaRESUMEN
PROteolysis TArgeting Chimeras (PROTACs) are small molecules that induce target protein degradation via the ubiquitin proteasome system. PROTACs recruit the target protein and E3 ligase; a critical first step is forming a ternary complex. However, while the formation a ternary complex is crucial, it may not always guarantee successful protein degradation. The dynamics of the PROTAC induced degradation complex play a key role in ubiquitination and subsequent degradation. In this study, we computationally modelled protein complex structures and dynamics associated with a series of PROTACs featuring different linkers to investigate why these PROTACs, all of which formed ternary complexes with Cereblon (CRBN) E3 ligase and the target protein bromodomain containing protein 4 (BRD4BD1), exhibited varying degrees of degradation potency. We constructed the degradation machinery complexes with Culling Ring Ligase 4A (CRL4A) E3 ligase scaffolds. Through atomistic molecular dynamics simulations, we illustrated how PROTAC dependent protein dynamics facilitate the arrangement of surface lysine residues of BRD4BD1 into the catalytic pocket of E2/ubiquitin for ubiquitination. Despite featuring identical warheads in this PROTAC series, the linkers were found to affect the residue interaction networks, and thus governing the essential motions of the entire degradation machine for ubiquitination. These findings offer a dynamic perspective on ligand induced protein degradation, providing insights to guide future PROTAC design endeavors.
RESUMEN
Proteins are inherently dynamic, and their conformational ensembles are functionally important in biology. Large-scale motions may govern protein structure-function relationship, and numerous transient but stable conformations of intrinsically disordered proteins (IDPs) can play a crucial role in biological function. Investigating conformational ensembles to understand regulations and disease-related aggregations of IDPs is challenging both experimentally and computationally. In this paper we first introduced an unsupervised deep learning-based model, termed Internal Coordinate Net (ICoN), which learns the physical principles of conformational changes from molecular dynamics (MD) simulation data. Second, we selected interpolating data points in the learned latent space that rapidly identify novel synthetic conformations with sophisticated and large-scale sidechains and backbone arrangements. Third, with the highly dynamic amyloid-ß 1-42 (Aß42) monomer, our deep learning model provided a comprehensive sampling of Aß42's conformational landscape. Analysis of these synthetic conformations revealed conformational clusters that can be used to rationalize experimental findings. Additionally, the method can identify novel conformations with important interactions in atomistic details that are not included in the training data. New synthetic conformations showed distinct sidechain rearrangements that are probed by our EPR and amino acid substitution studies. This approach is highly transferable and can be used for any available data for training. The work also demonstrated the ability for deep learning to utilize learned natural atomistic motions in protein conformation sampling.
RESUMEN
Proteins are inherently dynamic, and their conformational ensembles are functionally important in biology. Large-scale motions may govern protein structure-function relationship, and numerous transient but stable conformations of intrinsically disordered proteins (IDPs) can play a crucial role in biological function. Investigating conformational ensembles to understand regulations and disease-related aggregations of IDPs is challenging both experimentally and computationally. In this paper first an unsupervised deep learning-based model, termed Internal Coordinate Net (ICoN), is developed that learns the physical principles of conformational changes from molecular dynamics (MD) simulation data. Second, interpolating data points in the learned latent space are selected that rapidly identify novel synthetic conformations with sophisticated and large-scale sidechains and backbone arrangements. Third, with the highly dynamic amyloid-ß1-42 (Aß42) monomer, our deep learning model provided a comprehensive sampling of Aß42's conformational landscape. Analysis of these synthetic conformations revealed conformational clusters that can be used to rationalize experimental findings. Additionally, the method can identify novel conformations with important interactions in atomistic details that are not included in the training data. New synthetic conformations showed distinct sidechain rearrangements that are probed by our EPR and amino acid substitution studies. The proposed approach is highly transferable and can be used for any available data for training. The work also demonstrated the ability for deep learning to utilize learned natural atomistic motions in protein conformation sampling.
RESUMEN
Identifying critical residues in protein-protein binding and efficiently designing stable and specific protein binders to target another protein is challenging. In addition to direct contacts in a protein-protein binding interface, our study employs computation modeling to reveal the essential network of residue interaction and dihedral angle correlation critical in protein-protein recognition. We propose that mutating residues regions exhibited highly correlated motions within the interaction network can efficiently optimize protein-protein interactions to create tight and selective protein binders. We validated our strategy using ubiquitin (Ub) and MERS coronaviral papain-like protease (PLpro) complexes, where Ub is one central player in many cellular functions and PLpro is an antiviral drug target. Molecular dynamics simulations and experimental assays were used to predict and verify our designed Ub variant (UbV) binders. Our designed UbV with 3 mutated residues resulted in a ~3,500-fold increase in functional inhibition, compared with the wild-type Ub. Further optimization by incorporating 2 more residues within the network, the 5-point mutant achieved a KD of 1.5 nM and IC50 of 9.7 nM. The modification led to a 27,500-fold and 5,500-fold enhancements in affinity and potency, respectively, as well as improved selectivity, without destabilizing the UbV structure. Our study illustrates the importance of residue correlation and interaction networks in protein-protein interaction and introduces a new approach that can effectively design high affinity protein binder for cell biology studies and future therapeutic solution.
RESUMEN
Identifying residues critical to protein-protein binding and efficient design of stable and specific protein binders are challenging tasks. Extending beyond the direct contacts in a protein-protein binding interface, our study employs computational modeling to reveal the essential network of residue interactions and dihedral angle correlations critical in protein-protein recognition. We hypothesized that mutating residues exhibiting highly correlated dynamic motion within the interaction network could efficiently optimize protein-protein interactions to create tight and selective protein binders. We tested this hypothesis using the ubiquitin (Ub) and MERS coronaviral papain-like protease (PLpro) complex, since Ub is a central player in multiple cellular functions and PLpro is an antiviral drug target. Our designed ubiquitin variant (UbV) hosting three mutated residues displayed a â¼3,500-fold increase in functional inhibition relative to wild-type Ub. Further optimization of two C-terminal residues within the Ub network resulted in a KD of 1.5 nM and IC50 of 9.7 nM for the five-point Ub mutant, eliciting 27,500-fold and 5,500-fold enhancements in affinity and potency, respectively, as well as improved selectivity, without destabilizing the UbV structure. Our study highlights residue correlation and interaction networks in protein-protein interactions, and introduces an effective approach to design high-affinity protein binders for cell biology research and future therapeutics.
Asunto(s)
Proteasas Similares a la Papaína de Coronavirus , Coronavirus del Síndrome Respiratorio de Oriente Medio , Ubiquitina , Coronavirus del Síndrome Respiratorio de Oriente Medio/enzimología , Unión Proteica , Ubiquitina/química , Ubiquitina/metabolismo , Proteasas Similares a la Papaína de Coronavirus/química , Proteasas Similares a la Papaína de Coronavirus/metabolismoRESUMEN
Identifying critical residues in protein-protein binding and efficiently designing stable and specific protein binders is challenging. In addition to direct contacts in a protein-protein binding interface, our study employs computation modeling to reveal the essential network of residue interaction and dihedral angle correlation critical in protein-protein recognition. We propose that mutating residues regions exhibited highly correlated motions within the interaction network can efficiently optimize protein-protein interactions to create tight and selective protein binders. We validated our strategy using ubiquitin (Ub) and MERS coronaviral papain-like protease (PLpro) complexes, where Ub is one central player in many cellular functions and PLpro is an antiviral drug target. Our designed UbV with 3 mutated residues resulted in a ~3,500-fold increase in functional inhibition, compared with the wild-type Ub. Further optimization by incorporating 2 more residues within the network, the 5-point mutant achieved a KD of 1.5 nM and IC50 of 9.7 nM. The modification led to a 27,500-fold and 5,500-fold enhancements in affinity and potency, respectively, as well as improved selectivity, without destabilizing the UbV structure. Our study highlights residue correlation and interaction networks in protein-protein interaction, introduces an effective approach to design high affinity protein binders for cell biology and future therapeutics solutions.
RESUMEN
For past two decades, p53 has been claimed as the primary sensor initiating apoptosis. Under severe cellular stress, p53 transcriptional activity activates BH3-only proteins such as Bim, Puma, or Noxa to nullify the inhibitory effects of anti-apoptotic proteins on pro-apoptotic proteins for mitochondrial outer membrane permeabilization. Cellular stress determines the expression level of p53, and the amount of p53 corresponds to the magnitude of apoptosis. However, our studies indicated that Bim and Puma are not the target genes of p53 in three cancer models, prostate cancer, glioblastoma, and osteosarcoma. Bim counteracted with Bcl-xl to activate apoptosis independently of p53 in response to doxorubicin-induced severe DNA damage in prostate cancer. Moreover, the transcriptional activity of p53 was more related to cell cycle arrest other than apoptosis for responding to DNA damage stress generated by doxorubicin in prostate cancer and glioblastoma. A proteasome inhibitor that causes protein turnover dysfunction, bortezomib, produced apoptosis in a p53-independent manner in glioblastoma and osteosarcoma. p53 in terms of both protein level and nuclear localization in combining doxorubicin with bortezomib treatment was obviously lower than when using DOX alone, inversely correlated with the magnitude of apoptosis in glioblastoma. Using a BH3-mimetic, ABT-263, to treat doxorubicin-sensitive p53-wild type and doxorubicin-resistant p53-null osteosarcoma cells demonstrated only limited apoptotic response. The combination of doxorubicin or bortezomib with ABT-263 generated a synergistic outcome of apoptosis in both p53-wild type and p53-null osteosarcoma cells. Together, this suggested that p53 might have no role in doxorubicin-induced apoptosis in prostate cancer, glioblastoma and osteosarcoma. The effects of ABT-263 in single and combination treatment of osteosarcoma or prostate cancer indicated a dual control to regulate apoptosis in response to severe cellular stress. Whether our findings only apply in these three types of cancers or extend to other cancer types remains to be explored.
RESUMEN
We previously reported that p53-mediated apoptosis is determined by severity of DNA damage, not by the level of p53, in doxorubicin-treated prostate cancer cells. In addition to doxorubicin, our results here indicated that camptothecin and bortezomib, which are a topoisomerase 1 poison and a 26 S proteasome inhibitor, respectively, could also induce apoptosis in a p53-dependent manner in prostate cancer. Then, we examined whether p53-mediated apoptosis induced by genotoxic and non-genotoxic stress occur in the same or a different way. By using dominant negative p53 to compete with wild-type p53 in transcription activity, we demonstrated that p53-mediated apoptosis in response to doxorubicin- or camptothecin-induced genotoxic stress is transcription-independent. In contrast, p53-mediated apoptosis from bortezomib-induced stress is transcription-dependent. Interestingly, we also found that doxorubicin-induced p21 expression is activated by p53 in transcription-dependent manner, while camptothecin-induced p21 expression is p53-independent. We then investigated the p53 ratio of nucleus to cytosol corresponding to low and high dose doxorubicin, camptothecin, or bortezomib treatment. The results suggested that p53 translocation from cytoplasm to nucleus actively drives cells toward apoptosis in either transcription-dependent or -independent manner for responding to non-genotoxic or genotoxic stress, respectively.