Biology subjects

Kudlicki, A.

Publications and source records attributed to Kudlicki, A..

5 recordsLinked to original sources

bioRxiv2019

Co-evolutionary analysis accurately predicts details of interactions between the Integrator complex subunits

Solving the structure of large, multi-subunit complexes is difficult despite recent advances in cryoEM, due to remaining challenges to express and purify complex subunits. Computational approaches that predict protein-protein interactions, including Direct Coupling Analysis (DCA), represent an attractive alternative to dissect interactions within protein complexes. However, due to high computational complexity and high false positive rate they are applicable only to small proteins. Here, we present a modified DCA to predict residues and domains involved in interactions of large proteins. To reduce false positive levels and increase accuracy of prediction, we use local Gaussian averaging and predicted secondary structure elements. As a proof-of-concept, we apply our method to two Integrator subunits, INTS9 and INTS11, which form a heterodimeric structure previously solved by crystallography. We accurately predict the domains of INTS9/11 interaction. We then apply this approach to predict the interaction domains of two complexes whose structure is currently unknown: 1) The heterodimer formed by the Cleavage and Polyadenylation Specificity Factor 100-kD (CPSF100) and 73-kD (CPSF73); 2) The heterotrimer formed by INTS4/9/11. Our predictions of interactions within these two complexes are supported by experimental data, demonstrating that our modified DCA is a useful method for predicting interactions and can easily be applied to other complexes.

bioinformatics↗

bioRxiv2019

Protein Co-Evolution Strategies Detect Predicted Functional Interaction Between the Serotonin 5-HT2A and 5-HT2C Receptors

Serotonin is a neurotransmitter that plays a role in regulating activities such as sleep, appetite, mood and substance abuse disorders; serotonin receptors 5-HT2AR and 5-HT2CR are active within pathways associated with substance abuse. It has been suggested that 5-HT2AR and 5-HT2CR may form a dimer that affects behavioral processes. Here we study the coevolution of residues in 5-HT2AR and 5-HT2CR to identify potential interactions between residues in both proteins. Coevolution studies can detect protein interactions, and since the thus uncovered interactions are subject to evolutionary pressure, they are likely functional. We assessed the significance of the 5-HT2AR/5-HT2CR interactions using randomized phylogenetic trees and found the interaction significant (p-value = 0.01). We also discuss how co-expression of the receptors suggests the predicted interaction is functional. Finally, we analyze how several single nucleotide polymorphisms for the 5-HT2AR and 5-HT2CR genes affect their interaction. Our findings are the first to characterize the binding interface of 5-HT2AR/5-HT2CR and indicate a correlation between this interface and location of SNPs in both proteins.

neuroscience↗

bioRxiv2017

Integrated analysis of DSB patterns reveals precisely DSB formation mechanisms following replication fork collapse

DNA double-strand breaks (DSBs) can be detected by label-based sequencing or pulsed-field gel electrophoresis (PFGE). Sequencing yields population-average DSB frequencies genome-wide, while PFGE reveals percentages of broken chromosomes. We constructed a mathematical framework to combine advantages of both: high-resolution DSB locations and their population distribution. We also use sequencing read patterns to identify replication-induced DSBs and active replication origins. We describe changes in spatiotemporal replication program upon hydroxyurea-induced replication stress. We found that one-ended DSBs, resulting from collapsed replication forks, are population-representative, while majority of two-ended DSBs (79-100%) are not. To study replication fork collapse, we used strains lacking the checkpoint protein Mec1 and the endonuclease Mus81 and quantified that 19% and 13% of hydroxyurea-induced one-ended DSBs are Mec1-and Mus81-dependent, respectively. We also clarified that Mus81-induced one-ended DSBs are Mec1-dependent.

genomics↗

bioRxiv2017

Inferring proteome dynamics during yeast cell cycle using gene expression data

Protein levels are most relevant physiologically, but measuring them genome-wide remains a challenge. In contrast, mRNA levels are much easier and less expensive to measure globally. Therefore, RNA levels are typically used to infer the corresponding protein levels. The steady-state condition (assumption that protein levels remain constant) is typically used to calculate protein abundances, as it is mathematically very convenient, even though it is often clear that it is not satisfied for proteins of interest. Here, we propose a simple, yet very effective, method to estimate genome wide protein abundances, which does not require the assumption that protein levels remain constant, and thus allows us to also predict proteome dynamics. Instead, we assume that the system returns to the baseline at the end of experiments; such an assumption is satisfied in many time-course experiments and in all periodic conditions (e.g. cell cycle). The approach only requires availability of gene expression and protein half-life data. As proof-of-concept, we calculated the predicted proteome dynamics for the budding yeast proteome during the cell cycle, which can be conveniently browsed online. The approach was validated experimentally by verifying that the predicted protein concentration changes were consistent with measurements for all proteins tested. Additionally, if proteomic data are also available, our approach can be used to predict how half-lives change in response to posttranslational regulation. We illustrated this application of our method with de novo prediction of changes in the degradation rate of Clb2 in response to post-translational modifications. The predicted changes were consistent with earlier observations in the literature.

bioinformatics↗

bioRxiv2017

The High-resolution Timeline of Expression of Ribosomal Protein Genes in Yeast

The yeast ribosome is a complex molecular machine built from four rRNAs and over 70 r-proteins. Ribosome biogenesis involves ordered incorporation of ribosomal proteins, accompanied by and association and dissociation of other proteins specific to different stages of the process. By model-based analysis of temporal profiles of gene expression in a metabolically regulated system, we obtained an accurate, high-resolution estimation of the time of expression of genes coding for proteins involved in ribosome biogenesis. The ribosomal proteins are expressed in a sequence that spans approximately 25-minutes under metabolically regulated conditions. The genes coding for proteins incorporated into the mature ribosome are expressed significantly later than those that are not incorporated, but are otherwise involved in ribosome biogenesis, localization and assembly, rRNA processing and translational initiation. The relative expression time of proteins localized within specified neighborhood is significantly correlated with the distance to the centroid of the mature ribosome: protein localized closer to the center of mass of the entire complex tend to be expressed earlier than the protein localized further from the center. The timeline of gene expression also agrees with the known dependencies between recruitment of specific proteins into the mature ribosome. These findings are consistent in two independent experiments. We have further identified regulatory elements correlated with the time of regulation, including a possible dependence of expression time on the position of the RAP1 binding site within the 5UTR.

biochemistry↗