Biology subjects

Rubenstein, B.

Publications and source records attributed to Rubenstein, B..

4 recordsLinked to original sources

bioRxiv2025

{Phi}X174 bacteriophage viability predicted by protein biophysical modeling

The relationship between genotype and phenotype underlies our ability to understand and predict evolution. Efforts to build genotype-phenotype (GP) maps have revealed several unifying rules: epistasis is pervasive, fitness effects are not normally distributed, and the GP map is non-linear and complicated for high-level phenotypes. A critical step in developing GP maps is evaluating how well predictive models do in explaining observed phenotypes. We utilize the simplicity of a bacteriophage ({Phi}X174) study system to test if an intermediate phenotype (predicted stability of the G capsid protein) explains more complex phenotypes. In doing so, we compare the predictive performance of free energies of folding and binding obtained using numerous molecular modeling methods as well as phylogenetic and basic biochemical/biophysical properties of amino acid substitutions. By creating a large mutational library, we find that {Phi}X174 tolerates about 50% of the amino acid substitutions we inserted into the G protein and that molecular modeling compliments other substitution models for predicting viability. Mutations predicted to have large destabilizing effects are especially informative and are almost universally detrimental. These large-effect substitutions often coincide with the most conserved residues in the G protein. Apart from large-effect mutations, our ability to predict {Phi}X174 phenotypes is fairly poor and we explore various potential confounding factors (e.g., codon bias) that could be considered to improve viability predictions.

molecular biology↗

bioRxiv2025

Lubricin's Mucin Domain Has Strong Polyproline Type-II Helical Character

Lubricin is a glycoprotein that is crucial for maintaining joint health by preventing joint wear by reducing joint friction in the boundary mode. Lubricin was recently observed to hinder the formation of uric acid crystals in the joint and prevent a form of gouty arthritis. However, despite lubricins great physiological importance, our current understanding of the molecular origins of lubricins beneficial properties is limited by a lack of detailed structural information regarding its central mucin domain: lubricins large size (227.5 kDa) and numerous glycosylations pose a substantial obstacle to conventional experimental methods for solving protein structures. In this work, we employ a combination of physics-based replica exchange molecular dynamics (REMD) simulations and circular dichroism (CD) experiments to shed light on the structure of lubricins central mucin domain. Using REMD, we model [KEPAPTTP]2, an amino acid repeat found throughout the mucin domain, and find that the mucin domain is likely to exhibit polyproline type II (PPII) helices, which are further stabilized by O-linked oligosaccharide chains. Motivated by these simulation results, we performed circular dichroism spectroscopy on fragments of the mucin domain that also show clear polyproline-II helical character, corroborating our computational findings. Altogether, this work provides strong evidence of a lubricin mucin domain with significant polyproline type II content. As polyproline helices are often also found in other glycoproteins with antifreeze properties, this work may also explain the atomistic underpinnings of their interfacial functions, including lubrication and competition with crystal formation. SIGNIFICANCELubricin is a mucinous glycoprotein containing a central heavily glycosylated domain that plays a crucial role in the lubrication of joints. However, little is known about the structure of this large central mucin domain, which makes the development of related therapeutics or biomedical devices challenging. In this work, we provide strong evidence that lubricins mucin domain possesses polyproline type II (PPII) character that is enhanced by glycosylation upon the basis of a combination of molecular dynamics simulations and circular dichroism experiments. Lubricins PPII character provides a molecular basis for its lubricating properties, which may provide insights into related antifreeze glycoproteins (AFGP) and the development of new biocompatible lubricants and ryo-preservatives.

biophysics↗

bioRxiv2021

A Topological Data Analytic Approach for Discovering Biophysical Signatures in Protein Dynamics

Identifying structural differences among proteins can be a non-trivial task. When contrasting ensembles of protein structures obtained from molecular dynamics simulations, biologically-relevant features can be easily overshadowed by spurious fluctuations. Here, we present SINATRA Pro, a computational pipeline designed to robustly identify topological differences between two sets of protein structures. Algorithmically, SINATRA Pro works by first taking in the 3D atomic coordinates for each protein snapshot and summarizing them according to their underlying topology. Statistically significant topological features are then projected back onto a user-selected representative protein structure, thus facilitating the visual identification of biophysical signatures of different protein ensembles. We assess the ability of SINATRA Pro to detect minute conformational changes in five independent protein systems of varying complexities. In all test cases, SINATRA Pro identifies known structural features that have been validated by previous experimental and computational studies, as well as novel features that are also likely to be biologically-relevant according to the literature. These results highlight SINATRA Pro as a promising method for facilitating the non-trivial task of pattern recognition in trajectories resulting from molecular dynamics simulations, with substantially increased resolution. Author SummaryStructural features of proteins often serve as signatures of their biological function and molecular binding activity. Elucidating these structural features is essential for a full understanding of underlying biophysical mechanisms. While there are existing methods aimed at identifying structural differences between protein variants, such methods do not have the capability to jointly infer both geometric and dynamic changes, simultaneously. In this paper, we propose SINATRA Pro, a computational framework for extracting key structural features between two sets of proteins. SINATRA Pro robustly outperforms standard techniques in pinpointing the physical locations of both static and dynamic signatures across various types of protein ensembles, and it does so with improved resolution.

biophysics↗

bioRxiv2021

LYRUS: A Machine Learning Model for Predicting the Pathogenicity of Missense Variants

Single amino acid variations (SAVs) are a primary contributor to variations in the human genome. Identifying pathogenic SAVs can aid in the diagnosis and understanding of the genetic architecture of complex diseases, such as cancer. Most approaches for predicting the functional effects or pathogenicity of SAVs rely on either sequence or structural information. Nevertheless, previous analyses have shown that methods that depend on only sequence or structural information may have limited accuracy. Recently, researchers have attempted to increase the accuracy of their predictions by incorporating protein dynamics into pathogenicity predictions. This study presents < Lai Yang Rubenstein Uzun Sarkar > (LYRUS), a machine learning method that uses an XGBoost classifier selected by TPOT to predict the pathogenicity of SAVs. LYRUS incorporates five sequence-based features, six structure-based features, and four dynamics-based features. Uniquely, LYRUS includes a newly-proposed sequence co-evolution feature called variation number. LYRUSs performance was evaluated using a dataset that contains 4,363 protein structures corresponding to 20,307 SAVs based on human genetic variant data from the ClinVar database. Based on our dataset, the LYRUS classifier has a higher accuracy, specificity, F-measure, and Matthews correlation coefficient (MCC) than alternative methods including PolyPhen2, PROVEAN, SIFT, Rhapsody, EVMutation, MutationAssessor, SuSPect, FATHMM, and MVP. Variation numbers used within LYRUS differ greatly between pathogenic and neutral SAVs, and have a high feature weight in the XGBoost classifier employed by this method. Applications of the method to PTEN and TP53 further corroborate LYRUSs strong performance. LYRUS is freely available and the source code can be found at https://github.com/jiaying2508/LYRUS.

bioinformatics↗