Biology subjects

Zarin, T.

Publications and source records attributed to Zarin, T..

7 recordsLinked to original sources

bioRxiv2025

Allosteric and Energetic Remodeling by Protein Domain Extensions

Many functions of proteins are performed by independently folding structural units called domains. The structures of domains are conserved during evolution but they are not identical. For example, the >270 human PDZ domains vary in the number of secondary structure elements and in the length of loops. An important but largely unexplored question is the impact of these extensions on protein energy landscapes: beyond any immediate functional effects, do extensions also alter the consequences of perturbations elsewhere in the domain, altering the potential for regulation and evolvability? Here we perform massively parallel energetic measurements on a model human PDZ domain to directly and comprehensively answer this question. In total we quantify the binding to a ligand and abundance of [~]190,000 protein variants to quantify free energy changes for mutations throughout the canonical domain fold and [~]7,000 energetic couplings between these mutations and the two domain extensions, both alone and in combination. We find that both extensions--one structured and one more dynamic--substantially and specifically re-shape the energy landscape of the domain, with the removal of an [a]-helix altering the energetic consequences of 424 mutations in 54 sites on fold stability and 420 mutations in 56 sites on binding to a ligand. These changes to the energy landscape alter the effects of 330 allosteric mutations, including at solvent-accessible surface sites. Extending or pruning the domain therefore reshapes its energetic and allosteric landscape, adding and removing opportunities for the allosteric control of protein function.

biophysics↗

bioRxiv2024

A complete map of specificity encoding for a partially fuzzy protein interaction

The human genome encodes the affinities and specificities of >half a million molecular interactions. Changes in specificity drive evolution, disease and therapeutic efficacy, but how specificity is encoded and reprogrammed is not well understood. Here we present a complete map of the encoding of specificity in a protein sequence and how mutations quantitatively interact to reprogram binding. By measuring >200,000 energetic interactions we identify 17 major sites that directly and allosterically encode the specificity for six sites in a ligand. Combining mutations allows specificity for all six sites to be simultaneously re-programmed. However, binding reprogramming is not fully independent, with energetic couplings within both the protein and ligand required to accurately predict binding. This approach of measuring the specificities of thousands of proteins in a single experiment will allow many different types of molecular interaction to be understood and interpretably reprogrammed. HighlightsO_LIMassively parallel quantification of >200,000 energetic couplings between mutations in a protein and its ligand. C_LIO_LISpecificity is directly and allosterically encoded in 17 sites for six positions in the ligand. C_LIO_LISpecificity for all six sites can be simultaneously re-programmed with accurate binding scores for >150,000 intermediately reprogrammed variants. C_LIO_LIAccurate binding prediction requires energetic interactions between the protein and ligand and within both the protein and ligand. C_LI

biophysics↗

bioRxiv2024

A Functional Map of the Human Intrinsically Disordered Proteome

Intrinsically disordered regions (IDRs) represent at least one-third of the human proteome and defy the established structure-function paradigm. Because IDRs often have limited positional sequence conservation, the functional classification of IDRs using standard bioinformatics is generally not possible. Here, we show that evolutionarily conserved molecular features of the intrinsically disordered human proteome (IDR-ome), termed evolutionary signatures, enable classification and prediction of IDR functions. Hierarchical clustering of the human IDR-ome based on evolutionary signatures reveals strong enrichments for frequently studied functions of IDRs in transcription and RNA processing, as well as diverse, rarely studied functions, ranging from sub-cellular localization and biomolecular condensates to cellular signaling, transmembrane transport, and the constitution of the cytoskeleton. We exploit the information that is encoded within evolutionary conservation of molecular features to propose functional annotations for every IDR in the human proteome, inspect the conserved molecular features that correlate with different functions, and discover frequently co-occurring IDR functions on the proteome scale. Further, we identify patterns of evolutionary conserved molecular features of IDRs within proteins of unknown function and disease-risk genes for conditions such as cancer and developmental disorders. Our map of the human IDR-ome should be a valuable resource that aids in the discovery of new IDR biology.

bioinformatics↗

bioRxiv2021

Discovering molecular features of intrinsically disordered regions by using evolution for contrastive learning

A major challenge to the characterization of intrinsically disordered regions (IDRs), which are widespread in the proteome, but relatively poorly understood, is the identification of molecular features that mediate functions of these regions, such as short motifs, amino acid repeats and physicochemical properties. Here, we introduce a proteome-scale feature discovery approach for IDRs. Our approach, which we call "reverse homology", exploits the principle that important functional features are conserved over evolution. We use this as a contrastive learning signal for deep learning: given a set of homologous IDRs, the neural network has to correctly choose a held-out homologue from another set of IDRs sampled randomly from the proteome. We pair reverse homology with a simple architecture and standard interpretation techniques, and show that the network learns conserved features of IDRs that can be interpreted as motifs, repeats, or bulk features like charge or amino acid propensities. We also show that our model can be used to produce visualizations of what residues and regions are most important to IDR function, generating hypotheses for uncharacterized IDRs. Our results suggest that feature discovery using unsupervised neural networks is a promising avenue to gain systematic insight into poorly understood protein sequences.

bioinformatics↗

bioRxiv2020

Identifying molecular features that are associated with biological function of intrinsically disordered protein regions

Previously, we showed that intrinsically disordered regions in proteins (IDRs) contain multiple sequence-distributed molecular features that are conserved over evolution, despite little sequence similarity that can be detected in alignments (Zarin et al. 2019). Here, we aim to use these molecular features to make specific functional predictions for individual IDRs and identify the molecular features within them that are responsible for specific functions. We find that the predictable functions are diverse, identifying previously known associated molecular features, as well as features that were previously not known to be associated with these functions. We experimentally confirm that elevated isoelectric point and hydrophobicity, features that are positively associated with mitochondrial localization, are necessary for mitochondrial targeting function. Remarkably, increasing isoelectric point in a synthetic IDR restores weak mitochondrial targeting. We believe feature analysis represents a new systematic approach to understand how biological functions of IDRs are specified by their protein sequences.

cell biology↗

bioRxiv2019

Proteome-wide signatures of function in highly diverged intrinsically disordered regions

Intrinsically disordered regions make up a large part of the proteome, but the sequence-to-function relationship in these regions is poorly understood, in part because the primary amino acid sequences of these regions are poorly conserved in alignments. Here we use an evolutionary approach to detect molecular features that are preserved in the amino acid sequences of orthologous intrinsically disordered regions. We find that most disordered regions contain multiple molecular features that are preserved, and we define these as \"evolutionary signatures\" of disordered regions. We demonstrate that intrinsically disordered regions with similar evolutionary signatures can rescue function in vivo, and that groups of intrinsically disordered regions with similar evolutionary signatures are strongly enriched for functional annotations and phenotypes. We propose that evolutionary signatures can be used to predict function for many disordered regions from their amino acid sequences.

genomics↗

bioRxiv2017

Short linear motifs in intrinsically disordered regions modulate HOG signaling capacity

The effort to characterize intrinsically disordered regions of signaling proteins is rapidly expanding. An important class of disordered interaction modules are ubiquitous and functionally diverse elements known as short linear motifs (SLiMs). To further examine the role of SLiMs in signal transduction, we used a previously devised bioinformatics method to predict evolutionarily conserved SLiMs within a well-characterized pathway in S. cerevisiae. Using a single cell, reporter-based flow cytometry assay in conjunction with a fluorescent reporter driven by a pathway-specific promoter, we quantitatively assessed pathway output via systematic deletions of individual motifs. We found that, when deleted, 34% (10/29) of predicted SLiMs displayed a significant decrease in pathway output, providing evidence that these motifs play a role in signal transduction. In addition, we show that perturbations of parameters in a previously published stochastic model of HOG signaling could reproduce the quantitative effects of 4 out of 7 mutations in previously unknown SLiMs. Our study suggests that, even in well-characterized pathways, large numbers of functional elements remain undiscovered, and that challenges remain for application of systems biology models to interpret the effects of mutations in signalling pathways.\n\nOne-sentence SummaryMutations of short conserved elements in disordered regions have quantitative effects on a model signaling pathway.

systems biology↗