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Holt, G.

Publications and source records attributed to Holt, G..

3 recordsLinked to original sources

PINDER: The protein interaction dataset and evaluation resource

Protein-protein interactions (PPIs) are fundamental to understanding biological processes and play a key role in therapeutic advancements. As deep-learning docking methods for PPIs gain traction, benchmarking protocols and datasets tailored for effective training and evaluation of their generalization capabilities and performance across real-world scenarios become imperative. Aiming to overcome limitations of existing approaches, we introduce PINDER, a comprehensive annotated dataset that uses structural clustering to derive non-redundant interface-based data splits and includes holo (bound), apo (unbound), and computationally predicted structures. PINDER consists of 2,319,564 dimeric PPI systems (and up to 25 million augmented PPIs) and 1,955 high-quality test PPIs with interface data leakage removed. Additionally, PINDER provides a test subset with 180 dimers for comparison to AlphaFold-Multimer without any interface leakage with respect to its training set. Unsurprisingly, the PINDER benchmark reveals that the performance of existing docking models is highly overestimated when evaluated on leaky test sets. Most importantly, by retraining DiffDock-PP on PINDER interface-clustered splits, we show that interface cluster-based sampling of the training split, along with the diverse and less leaky validation split, leads to strong generalization improvements.

bioinformatics↗

Activity of the GHRH Antagonist MIA602 and its Underlying Mechanisms of Action in sarcoidosis

Growth hormone releasing hormone (GHRH) is a potent stimulator of GH secretion from the pituitary gland. Although GHRH is essential for the growth of immune cells, the regulatory effects of its antagonist in granulomatous disease remains unknown. Here, we report expression of GHRH receptor (R) in human tissue with sarcoidosis granuloma and demonstrate the anti-inflammatory effects of MIA602 (a GHRH antagonist) in two in vitro human granuloma models and an in vivo granuloma model. MIA602 decreases levels of IL2, IL12, and IL17A in in vitro granuloma model. We show further that the anti-inflammatory effect of MIA602 appears to be mediated by reduction in CD45++CD68+ cells in granulomatous tissue and upregulation in PD-1 expression in macrophages. In analysis of expression of proteins involved in the mitochondrial stage of apoptosis, we show that MIA602 increases the levels of caspase 3, BCL-xL/BAK dimer, and MCl-1/Bak dimer in granuloma. These findings indicate that MIA602 may not induce apoptosis. The clinical relevance of our findings further suggest that HGRH-R is potentially a target for treatment of granulomatous disease and MIA602 possibly a novel therapeutic agent for sarcoidosis.

immunology↗

Novel, provable algorithms for efficient ensemble-based computational protein design and their application to the redesign of the c-Raf-RBD:KRas protein-protein interface

The K* algorithm provably approximates partition functions for a set of states (e.g., protein, ligand, and protein-ligand complex) to a user-specified accuracy{varepsilon} . Often, reaching an{varepsilon} -approximation for a particular set of partition functions takes a prohibitive amount of time and space. To alleviate some of this cost, we introduce two algorithms into the osprey suite for protein design: O_SCPLOWFRIESC_SCPLOW, a Fast Removal of Inadequately Energied Sequences, and EWAK*, an Energy Window Approximation to K*. In combination, these algorithms provably retain calculational accuracy while limiting the input sequence space and the conformations included in each partition function calculation to only the most energetically favorable. This combined approach leads to significant speed-ups compared to the previous state-of-the-art multi-sequence algorithm, BBK*. As a proof of concept, we used these new algorithms to redesign the protein-protein interface (PPI) of the c-Raf-RBD:KRas complex. The Ras-binding domain of the protein kinase c-Raf (c-Raf-RBD) is the tightest known binder of KRas, a historically \"undruggable\" protein implicated in difficult-to-treat cancers including pancreatic ductal adenocarcinoma (PDAC). O_SCPLOWFRIESC_SCPLOW/EWAK* accurately retrospectively predicted the effect of 38 out of 41 different sets of mutations in the PPI of the c-Raf-RBD:KRas complex. Notably, these mutations include mutations whose effect had previously been incorrectly predicted using other computational methods. Next, we used O_SCPLOWFRIESC_SCPLOW/EWAK* for prospective design and discovered a novel point mutation that improves binding of c-Raf-RBD to KRas in its active, GTP-bound state (KRasGTP). We combined this new mutation with two previously reported mutations (which were also highly-ranked by O_SCPLOWOSPREYC_SCPLOW) to create a new variant of c-Raf-RBD, c-Raf-RBD(RKY). O_SCPLOWFRIESC_SCPLOW/EWAK* in O_SCPLOWOSPREYC_SCPLOW computationally predicted that this new variant would bind even more tightly than the previous best-binding variant, c-Raf-RBD(RK). We measured the binding affinity of c-Raf-RBD(RKY) using a bio-layer interferometry (BLI) assay and found that this new variant exhibits single-digit nanomolar affinity for KRasGTP, confirming the computational predictions made with O_SCPLOWFRIESC_SCPLOW/EWAK*. This study steps through the advancement and development of computational protein design by presenting theory, new algorithms, accurate retrospective designs, new prospective designs, and biochemical validation.\n\nAuthor summaryComputational structure-based protein design is an innovative tool for redesigning proteins to introduce a particular or novel function. One such possible function is improving the binding of one protein to another, which can increase our understanding of biomedically important protein systems toward the improvement or development of novel therapeutics. Herein we introduce two novel, provable algorithms, O_SCPLOWFRIESC_SCPLOW and EWAK*, for more efficient computational structure-based protein design as well as their application to the redesign of the c-Raf-RBD:KRas protein-protein interface. These new algorithms speed up computational structure-based protein design while maintaining accurate calculations, allowing for larger, previously infeasible protein designs. Using O_SCPLOWFRIESC_SCPLOW and EWAK* within the O_SCPLOWOSPREYC_SCPLOW suite, we designed the tightest known binder of KRas, an \"undruggable\" cancer target. This new variant of a KRas-binding domain, c-Raf-RBD, should serve as an important tool to probe the protein-protein interface between KRas and its effectors as work continues toward an effective therapeutic targeting KRas.

biochemistry↗