Biology subjects

Burgin, T. E.

Publications and source records attributed to Burgin, T. E..

3 recordsLinked to original sources

bioRxiv2025

Regulation of the formin INF2 by actin monomers and calcium-calmodulin

In response to increased intracellular calcium, the formin INF2 polymerizes 20-30% of the total cellular actin pool within 30 sec, suggesting robust regulation. INF2 regulation requires an auto-inhibitory interaction between the N-terminal Diaphanous Inhibitory Domain (DID) and the C-terminal Diaphanous Auto-regulatory Domain (DAD). DID mutations are dominantly linked to two human diseases, and constitutively activate INF2. However, DAD binding to actin monomers competes with DID binding, disrupting regulation. Here, we use a novel cell-free assay for detailed investigation of INF2 regulation. Contrary to our previous findings, INF2 inhibition does not require CAP proteins but does require actin buffering by monomer-binding proteins such as profilin or thymosin. INF2 is activated by calcium-bound calmodulin (CALM) through CALM binding to the N-terminus. In addition, the N-terminus plays an important role in INF2 regulation beyond CALM binding. These findings support a role for actin monomer binding proteins in not only regulating overall actin dynamics but also in specific regulation of an actin polymerization factor. SummaryIn this work, a concerted regulatory mechanism for INF2 is described, in which INF2 is activated by a combination of calcium-calmodulin and free actin monomers. In other words, INF2 senses actin monomers, making monomer binding proteins like profilin and thymosin important for INF2 regulation.

cell biology↗

bioRxiv2025

Colorectal cancer-associated PCBP1 mutations disrupt protein stability in a dominant negative manner

Mutations in RNA-binding proteins are increasingly identified in cancers through tumor sequencing and are correlated with disease progression, therapy response, and overall patient outcomes, underscoring the need to study them. Here, we focus on the RNA-binding protein Poly-C binding protein 1 (PCBP1), which binds target RNAs through K-homology (KH) domains to regulate RNA fate. PCBP1 is a tumor suppressor gene and hotspot missense mutations at leucine residues 100 and 102 are observed in colorectal cancer (CRC). PCBP1 mutations have been recurrently reported in CRC genome-wide mutation studies and are associated with poor clinical outcomes; however, their effects on PCBP1 expression and function remain largely unexplored. We show that cancer-associated mutations substituting leucine 100 and 102 with glutamine, proline, or arginine destabilize PCBP1, leading to increased protein turnover. The L100/L102 residues occur at the interface of the RNA-binding KH1 and KH2 domains, and our molecular dynamics simulations show that mutations at these residues disrupt the secondary structure of PCBP1. Additionally, these mutants display increased cytoplasmic localization. Importantly, mutant PCBP1 physically interacts with wild type PCBP1 and suppresses its expression through a dominant-negative mechanism. Together, our data demonstrate that CRC-associated PCBP1 mutations destabilize the protein and act as dominant-negative variants, revealing a novel mechanism of tumor suppressor inactivation in colorectal cancer.

cancer biology↗

bioRxiv2025

Quantified Dynamics-Property Relationships: Data-Efficient Protein Engineering with Machine Learning of Protein Dynamics

Machine learning has proven to be very powerful for predicting mutation effects in proteins, but the simplest approaches require a substantial amount of training data. Because experiments to collect training data are often expensive, time-consuming, and/or otherwise limited, alternatives that make good use of small amounts of data to guide protein engineering are of high potential value. One potential alternative to large-scale benchtop experiments for collecting training data is high-throughput molecular dynamics simulation; however, to date this source of data has been largely absent from the literature. Here, I introduce a new method for selecting desirable protein variants based on quantified relationships between a small number of experimentally determined labels and descriptors of their dynamic properties. These descriptors are provided by deep neural networks trained on data from molecular dynamics simulations of variants of the protein of interest. I demonstrate that this approach can obtain very highly optimized variants based on small amounts of experimental data, outperforming alternative supervised approaches to machine learning-guided directed evolution with the same amount of experimental data. Furthermore, I show that quantified dynamics-property relationships based on only a handful of experimentally labeled example sequences can be used to accurately predict the key residues that are most relevant to determining the property in question, even when that information could not have been known or predicted based on either the molecular dynamics simulations or the experimental data alone. This work establishes a new and practical framework for incorporating general protein dynamics information from simulations of mutants to guide protein engineering. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=111 SRC="FIGDIR/small/650227v3_ufig1.gif" ALT="Figure 1"> View larger version (25K): org.highwire.dtl.DTLVardef@302943org.highwire.dtl.DTLVardef@1e4f709org.highwire.dtl.DTLVardef@1167c20org.highwire.dtl.DTLVardef@12f3627_HPS_FORMAT_FIGEXP M_FIG O_FLOATNOToc GraphicC_FLOATNO C_FIG

biochemistry↗