Biology subjects

Lynch, M. L.

Publications and source records attributed to Lynch, M. L..

7 recordsLinked to original sources

bioRxiv2025

Burkholderia pseudomallei rubrerythrin promiscuously binds metals in a structurally pre-formed bimetallic binding site

Rubrerythrins are a group of proteins within the Ferritin-like superfamily that display a defining four-helix bundle domain. They also show multiple structural features that are crucial to their functionality as iron storage proteins and in detoxification and oxidative stress response. Here we investigate rubrerythrin (Rbr) in multiple metalated states, from the pathogen Burkholderia pseudomallei (Bp). We use X-ray crystallography for structure determination of Rbr to probe the capacity and specificity of metal binding. BpRbr lacks the rubredoxin moiety found in canonical Rbrs from anaerobic lineages, and we demonstrate that BpRbr also possesses a domain-swapped dimer, which has functional implications for its putative role in oxidative stress response. We also carry out in crystallo spectroscopic assessment of BpRbr with various metals, using energy dispersive X-ray (EDX) spectroscopy. We observe that samples can contain metals other than those supplied in crystallization conditions, and developed a strategy of utilizing EDX spectroscopy to select those samples with single metal incorporation for downstream diffraction data collection. Our work underscores the importance of spectroscopic probing for definitive metal identification and characterization. HighlightsO_LIPresent structures of apo-, Fe-, Mn-, and Co-bound rubrerythrin from B. pseudomallei C_LIO_LIBpRbr lacks rubredoxin domain and possesses unique domain swap fold in the dimer C_LIO_LIBpRbr features include promiscuous metal binding and pre-formed metal binding sites C_LIO_LIHighlights importance of in crystallo spectroscopy in investigating metalloproteins C_LIO_LIMutant Rbr with abrogated metal binding supports premise of pre-formed binding sites C_LI

biochemistry↗

bioRxiv2023

ATOMDANCE: interpretable kernel-based learning for denoising functional and evolutionary comparisons of protein dynamics

Comparative methods in molecular biology and molecular evolution rely exclusively upon the analysis of DNA sequence and protein structure, both static forms of information. However, it is widely accepted that protein function results from changes in dynamic machine-like motions induced by molecular interactions, a type of data for which comparative methods of analysis are challenged by the large fraction of protein motion created by random thermal noise in the surrounding solvent. Here, we introduce ATOMDANCE, a suite of statistical and kernel-based machine learning tools designed for comparing and denoising functional states of protein motion captured in time-series from molecular dynamics simulations. ATOMDANCE employs interpretable Gaussian kernel functions to compute site-wise maximum mean discrepancy (MMD) between learned features of motion representing two functional, malfunctional or evolutionary protein states (e.g. bound vs. unbound, wild-type vs. mutant). ATOMDANCE derives empirical p-values identifying functional similarity/difference in dynamics at each amino acid site on the protein. ATOMDANCE also employs MMD to contextually analyze potential random amino-acid replacements thus allowing for a site-wise test of neutral vs. non-neutral evolution in the divergence of dynamic function in protein homologs. Lastly, ATOMDANCE also employs mixed-model ANOVA combined with graph network community detection to identify functional shifts in protein regions that exhibit time-coordinated dynamics or resonance of motion across sites. Here, we demonstrate the utility of the software for identifying key sites involved in dynamic responses during functional binding interactions involving DNA, small molecule drugs, and virus-host recognition. We also demonstrate its utility in understanding dynamic resonance changes occurring during the allosteric activation of a pathogenic protease. ATOMDANCE offers a user-friendly interface and only requires an input structure, topology and trajectory files for each of the two proteins being compared (i.e .pdb, .prmtop, and .nc). A separate interface for generating molecular dynamics simulations via open-source tools is also offered. Lay Audience Summary - ATOMDANCE is a suite of software pipelines controlled by a single user interface and designed to comprehensively simulate, calculate and compare protein motions between two functional or evolutionary states while controlling for random noise. It is useful for finding amino acid sites on a given protein that are important in binding other proteins, DNA, or drugs/toxins. It can also be used to assess the effect of genetic mutation on protein motion, and identifies regions of sites on proteins that tend to move together or resonate as a whole unit or community.

biophysics↗

bioRxiv2022

Evolution of drug resistance drives progressive destabilizations in functionally conserved molecular dynamics of the flap region of the HIV-1 protease

The HIV-1 protease is one of several common key targets of combination drug therapies for human immunodeficiency virus infection and acquired immunodeficiency syndrome (HIV/AIDS). During the progression of the disease, some individual patients acquire -drug resistance due to mutational hotspots on the viral proteins targeted by combination drug therapies. It has recently been discovered that drug-resistant mutations accumulate on the flap region of the HIV-1 protease, which is a critical dynamic region involved in non-specific polypeptide binding during invasion and infection of the host cell. In this study, we utilize machine learning assisted comparative molecular dynamics, conducted at single amino acid site resolution, to investigate the dynamic changes that occur during functional dimerization and polypeptide binding of the main protease. We use a multi-agent machine learning model to identify conserved dynamics of the HIV-1 main protease that are preserved across simian and feline protease orthologs (SIV and FIV). We also investigate changes in dynamics due to common drug-resistant mutations in many patients. We find that a key functional site in the flap region, a solvent-exposed isoleucine (ILE50) and surrounding sites that control flap dynamics is often targeted by drug-resistance mutations, likely leading to malfunctional molecular dynamics affecting the overall flexibility of the flap region. We conclude that better long term patient outcomes may be achieved by designing drugs that target protease regions which are less dependent upon single sites with large functional binding effects.

bioinformatics↗

bioRxiv2021

DDK inhibition disrupts replication leading to mitotic catastrophe in Ewing sarcoma

Ewing sarcoma is the second most common bone malignancy in children and adolescents. Patients with upfront metastatic or recurrent disease have poor outcomes with 5-year survival rates of <30%. CDC7, also known as DDK (DBF4-dependent kinase), is a serine-threonine kinase that, in coordination with its activation subunit ASK (or DBF4), is involved in a diverse array of cellular functions including the regulation of DNA replication initiation and activation of the replication stress response. Due to DDKs diverse roles during replication, coupled with an increased level of genomic instability and R-loop-mediated replication stress within Ewing sarcoma cells, we hypothesized that Ewing sarcoma cells would be particularly vulnerable to DDK inhibitors. Here, we show that treatment with two selective DDK inhibitors, TAK-931 and XL413, results in apoptosis and a significant reduction in cell viability in EWS-FLI1-harboring Ewing sarcoma cell lines. We show that low dose DDK inhibition in Ewing sarcoma cells causes an accumulation of cells in late-S phase with a reduced replication capacity. There is also evidence of premature mitotic entry indicating an inability to properly complete DNA replication in a timely manner upon DDK inhibition. Also, there is a significant increase in the formation of micronuclei and other aberrant mitotic structures upon DDK inhibition in Ewing sarcoma cells indicating a failure to properly progress through S-phase followed by improper mitotic entry/progression, resulting in mitotic catastrophe. Interestingly, we observed minimal signs of mitotic accumulation, despite clear evidence of replication and mitotic stress, suggesting a failure to properly enforce the mitotic checkpoint. Together, these results suggest that Ewing sarcoma cells rely on the activity of DDK to maintain cell viability and suggest that DDK inhibition may prove to be a viable therapeutic strategy for patients with Ewing sarcoma.

cancer biology↗

bioRxiv2021

Evolutionarily conserved clusters of colon with lung cancer susceptibility loci, linked with most DUSP phosphatase genes, may help to dissect mechanisms of cancer susceptibility

We show evolutionarily conserved pairwise genetic linkage and clustering of majority of colon and lung cancer susceptibility QTLs in mice, rats and humans. The patterns of susceptibility or resistance to these two cancers in recombinant congenic mouse strains were concordant and the responsible susceptibility loci closely linked, in spite of completely different carcinogens and protocols used for induction of the two tumors. Most DUSP (Dual specificity phosphatase) genes are linked to these clusters. These data suggest that an important part of colon and lung cancer susceptibility is controlled by related and evolutionarily conserved processes.

genetics↗

bioRxiv2020

A comparative survey of betacoronavirus strain molecular dynamics identifies key ACE2 binding sites

Comparative functional analysis of the dynamic interactions between various Betacoronavirus mutant strains and broadly utilized target proteins such as ACE2 and CD26, is crucial for a more complete understanding of zoonotic spillovers of viruses that cause diseases such as COVID-19. Here, we employ machine learning to replicated sets of nanosecond scale GPU accelerated molecular dynamics simulations to statistically compare and classify atom motions of these target proteins in both the presence and absence of different endemic and emergent strains of the viral receptor binding domain (RBD) of the S spike glycoprotein. Machine learning was used to identify functional binding dynamics that are evolutionarily conserved from bat CoV-HKU4 to human endemic/emergent strains. Conserved dynamics regions of ACE2 involve both the N-terminal helices, as well as a region of more transient dynamics encompassing K353, Q325 and a novel motif AAQPFLL 386-92 that appears to coordinate their dynamic interactions with the viral RBD at N501. We also demonstrate that the functional evolution of Betacoronavirus zoonotic spillovers involving ACE2 interaction dynamics are likely pre-adapted from two precise and stable binding sites involving the viral bat progenitor strains interaction with CD26 at SAMLI 291-5 and SS 333-334. Our analyses further indicate that the human endemic strains hCoV-HKU1 and hCoV-OC43 have evolved more stable N-terminal helix interactions through enhancement of an interfacing loop region on the viral RBD, whereas the highly transmissible SARS-CoV-2 variants (B.1.1.7, B.1.351 and P.1) have evolved more stable viral binding via more focused interactions between the viral N501 and ACE2 K353 alone.

biophysics↗

bioRxiv2020

Function and evolution of B-Raf loop dynamics relevant to cancer recurrence under drug inhibition

Oncogenic mutations in the kinase domain of the B-Raf protein have long been associated with cancers involving the MAPK pathway. One constitutive MAPK activating mutation in B-Raf, the V600E (valine to glutamate) replacement occurring adjacent to a site of threonine phosphorylation (T599) occurs in many types of cancer, and in a large percentage of certain cancers, such as melanoma. Because ATP binding activity and the V600E mutation are both known to alter the physical behavior of the activation loop in the B-Raf ATP binding domain, this system is especially amenable to comparative analyses of molecular dynamics simulations modeling various genetic and drug class variants. Here, we employ machine learning enabled identification of functionally conserved protein dynamics to compare how the binding interactions of four B-Raf inhibitors impact the functional loop dynamics controlling ATP activation. We demonstrate that drug development targeting B-Raf has progressively moved towards ATP competitive inhibitors that demonstrate less tendency to mimic the functionally conserved dynamic changes associated with ATP activation and leading to the side effect of hyperactivation (i.e. inducing MAPK activation in non-tumorous cells in the absence of secondary mutation). We compare the functional dynamic impacts of V600E and other sensitizing and drug resistance causing mutations in the regulatory loops of B-Raf, confirming sites of low mutational tolerance in these regions. Lastly, we investigate V600E sensitivity of B-Raf loop dynamics in an evolutionary context, demonstrating that while sensitivity has an ancient origin with primitive eukaryotes, it was also secondarily increased during early jawed vertebrate evolution.

biophysics↗