Biology subjects

Ebrahim, A.

Publications and source records attributed to Ebrahim, A..

9 recordsLinked to original sources

bioRxiv2026

The first OpenBind release: An open experimental structure-affinity dataset and benchmark for structure-based AI

High-quality experimental datasets that link protein-ligand structures with binding affinity data are essential for developing and evaluating structure-based machine learning methods. To help address this need, we established OpenBind as an open-science initiative to generate large-scale experimental datasets for structure-based AI and molecular discovery. Here, we describe the first public OpenBind release, which, to the best of our knowledge, is the largest public single-target experimental structure-affinity dataset. The dataset focuses on enteroviral 2A protease, comprising 925 crystallographic binding events from 699 compounds and associated affinity measurements for 601 compounds. It combines structures from an initial fragment screen and follow-on molecules, together with affinity data, linking experimentally determined protein-ligand binding modes to biophysical measurements within a coherent antiviral discovery campaign. We used this dataset to evaluate protein-ligand structure prediction, binding-affinity prediction, and virtual screening using representative structure-based methods, including docking and cofolding. This exposed several challenges that are central to practical structure-based modelling: docking performance depends strongly on binding-pocket conformation, poses are difficult to rank, and structure-based affinity prediction remains challenging. Fine-tuning OpenFold3-p2 on the fragment-screen structures substantially improved pose prediction and virtual screening for related follow-on compounds, demonstrating how early-stage experimental structures can support target-specific model adaptation.

bioinformatics↗

bioRxiv2024

Three STEPs forward: A trio of unexpected structures of PTPN5

Protein tyrosine phosphatases (PTPs) play pivotal roles in myriad cellular processes by counteracting protein tyrosine kinases. Striatal-enriched protein tyrosine phosphatase (STEP, PTPN5) regulates synaptic function and neuronal plasticity in the brain and is a therapeutic target for several neurological disorders. Here, we present three new crystal structures of STEP, each with unexpected features. These include high-resolution conformational heterogeneity at multiple sites, and a highly coordinated citrate molecule in the active site, a previously unseen conformational change at an allosteric site, an intramolecular disulfide bond that was characterized biochemically but had never been visualized structurally, and two serendipitous covalent ligand binding events at surface-exposed cysteines that are nearly or entirely unique to STEP among human PTPs. Together, our results offer new views of the conformational landscape of STEP that may inform structure-based design of allosteric small molecules to specifically inhibit this biomedically important enzyme.

biophysics↗

bioRxiv2024

Structures of human PTP1B variants reveal allosteric sites to target for weight loss therapy

Protein Tyrosine Phosphatase 1B (PTP1B) is a negative regulator of leptin signaling whose disruption protects against diet-induced obesity in mice. We investigated whether structural characterization of human PTP1B variant proteins might reveal allosteric sites to target for weight loss therapy. To do so, we selected 12 rare variants for functional characterization from exomes from 997 people with persistent thinness and 200,000 people from UK Biobank. Seven of 12 variants impaired PTP1B function by increasing leptin-stimulated STAT3 phosphorylation in human cells. Focusing on the variants in and near the ordered catalytic domain, we ascribed structural mechanism to their functional effects using in vitro enzyme activity assays, room-temperature X-ray crystallography, and local hydrogen-deuterium exchange mass spectrometry (HDX-MS). By combining these complementary structural biology experiments for multiple variants, we characterize an inherent allosteric network in PTP1B that differs from previously reported allosteric inhibitor-driven mechanisms mediated by catalytic loop motions. The most functionally impactful variant sites map to highly ligandable surface sites, suggesting untapped opportunities for allosteric drug design. Overall, these studies can inform the targeted design of allosteric PTP1B inhibitors for the treatment of obesity. SignificanceObesity is a growing public health concern worldwide. The human enzyme PTP1B is a validated obesity drug target, but to date, effective PTP1B inhibitors have not been developed. Allosteric drugs, which target parts of a protein distant from the active site, offer advantages for specific inhibition -- but finding promising allosteric sites that bind ligands and convey allosteric signals remains challenging. To address this knowledge gap, we used human genetic studies in thin people to identify amino acid variants that diminish PTP1B function. We then used complementary structural biology methods to show that the variants exploit distinct allosteric wiring within PTP1B that includes ligand-enriched binding sites. This work demonstrates how a unique combination of genetics and biophysics can unveil promising allosteric sites in challenging drug target proteins.

biophysics↗

bioRxiv2023

Pushed to extremes: distinct effects of high temperature vs. pressure on the structure of an atypical phosphatase

Protein function hinges on small shifts of three-dimensional structure. Elevating temperature or pressure may provide experimentally accessible insights into such shifts, but the effects of these distinct perturbations on protein structures have not been compared in atomic detail. To quantitatively explore these two axes, we report the first pair of structures at physiological temperature vs. high pressure for the same protein, STEP (PTPN5). We show that these perturbations have distinct and surprising effects on protein volume, patterns of ordered solvent, and local backbone and side-chain conformations. This includes novel interactions between key catalytic loops only at physiological temperature, and a distinct conformational ensemble for another active-site loop only at high pressure. Strikingly, in torsional space, physiological temperature shifts STEP toward previously reported active-like states, while high pressure shifts it toward a previously uncharted region. Together, our work argues that temperature and pressure are complementary, powerful, fundamental macromolecular perturbations.

biophysics↗

bioRxiv2022

Room-temperature serial synchrotron crystallography of apo PTP1B

Room-temperature X-ray crystallography provides unique insights into protein conformational heterogeneity, but a common hurdle is obtaining sufficiently large protein crystals. Serial synchrotron crystallography (SSX) helps address this hurdle by allowing the use of many medium- to small-sized crystals. We have used a recently introduced serial sample support chip system to obtain the first SSX structure of a human phosphatase, specifically Protein Tyrosine Phosphatase 1B (PTP1B) in the unliganded (apo) state. In previous apo room-temperature structures, the active site and allosteric sites adopted alternate conformations, including open and closed conformations for the active-site WPD loop and for a distal allosteric site. By contrast, in our SSX structure, the active site is best fit with a single conformation, but the distal allosteric site is best fit with alternate conformations. This observation argues for additional nuance in interpreting the nature of allosteric coupling in this protein. Overall, our results illustrate the promise of serial methods for room-temperature crystallography, as well as future avant-garde crystallography experiments, for PTP1B and other proteins.

biophysics↗

bioRxiv2021

The temperature-dependent conformational ensemble of SARS-CoV-2 main protease (Mpro)

The COVID-19 pandemic, instigated by the SARS-CoV-2 coronavirus, continues to plague the globe. The SARS-CoV-2 main protease, or Mpro, is a promising target for development of novel antiviral therapeutics. Previous X-ray crystal structures of Mpro were obtained at cryogenic temperature or room temperature only. Here we report a series of high-resolution crystal structures of unliganded Mpro across multiple temperatures from cryogenic to physiological, and another at high humidity. We interrogate these datasets with parsimonious multiconformer models, multi-copy ensemble models, and isomorphous difference density maps. Our analysis reveals a temperature-dependent conformational landscape for Mpro, including mobile solvent interleaved between the catalytic dyad, mercurial conformational heterogeneity in a key substrate-binding loop, and a far-reaching intramolecular network bridging the active site and dimer interface. Our results may inspire new strategies for antiviral drug development to counter-punch COVID-19 and combat future coronavirus pandemics. SynopsisX-ray crystallography at variable temperature for SARS-CoV-2 Mpro reveals a complex conformational landscape, including mobile solvent at the catalytic dyad, mercurial conformational heterogeneity in a key substrate-binding loop, and an intramolecular network bridging the active site and dimer interface.

biophysics↗

bioRxiv2020

Computation of condition-dependent proteome allocation reveals variability in the macro and micro nutrient requirements for growth

Sustaining a robust metabolic network requires a balanced and fully functioning proteome. In addition to amino acids, many enzymes require cofactors (coenzymes and engrafted prosthetic groups) to function properly. Extensively validated genome-scale models of metabolism and gene expression (ME-models) have the unique ability to compute an optimal proteome composition underlying a metabolic phenotype, including the provision of all required cofactors. Here we use the ME-model for Escherichia coli K-12 MG1655 to computationally examine how environmental conditions change the proteome and its accompanying cofactor usage. We found that: (1) The cofactor requirements computed by the ME model mostly agree with the standard biomass objective function used in models of metabolism alone (M models); (2) ME-model computations reveal non-intuitive variability in cofactor use under different growth conditions; (3) An analysis of ME-model predicted protein use in aerobic and anaerobic conditions suggests an enrichment in the use of prebiotic amino acids in the proteins used to sustain anaerobic growth (4) The ME-model could describe how limitation in key protein components affect the metabolic state of E. coli. Genome-scale models have thus reached a level of sophistication where they reveal intricate properties of functional proteomes and how they support different E. coli lifestyles.

systems biology↗

bioRxiv2018

DynamicME: Dynamic simulation and refinement of integrated models of metabolism and protein expression

Genome-scale models of metabolism and macromolecular expression (ME models) enable systems-level computation of proteome allocation coupled to metabolic phenotype. We develop dynamicME, an algorithm enabling time-course simulation of cell metabolism and protein expression. Our dynamicME correctly predicted the substrate utilization hierarchy on mixed carbon substrate medium. We also found good agreement between predicted and measured time-course expression profiles. ME models involve considerably more parameters than metabolic models (M models). We thus present two methods to calibrate ME models, specifically using time-course measurements such as from a (fed-) batch culture. Overall, dynamicME and the methods presented provide novel methods for understanding proteome allocation and metabolism under complex and transient environments, and to utilize time-course cell culture data for model-based interpretation or model refinement.

systems biology↗

bioRxiv2017

COBRAme: A Computational Framework for Building and Manipulating Models of Metabolism and Gene Expression

Genome-scale models of metabolism and macromolecular expression (ME-models) explicitly compute the optimal proteome composition of a growing cell. ME-models expand upon the well-established genome-scale models of metabolism (M-models), and they enable new and exciting insights that are fundamental to understanding the basis of cellular growth. ME-models have increased predictive capabilities and accuracy due to their inclusion of the biosynthetic costs for the machinery of life, but they come with a significant increase in model size and complexity. This challenge results in models which are both difficult to compute and challenging to understand conceptually. As a result, ME-models exist for only two organisms (Escherichia coli and Thermotoga maritima) and are still used by relatively few researchers. To address these challenges, we have developed a new software framework called COBRAme for building and simulating ME-models. It is coded in Python and built on COBRApy, a popular platform for using M-models. COBRAme streamlines computation and analysis of ME-models. It provides tools to simplify constructing and editing ME-models to enable ME-model reconstructions for new organisms. We used COBRAme to reconstruct a condensed E. coli ME-model called iJL1678b-ME. This reformulated model gives virtually identical solutions to previous E. coli ME-models while using [1/4] the number of free variables and solving in less than 10 minutes, a marked improvement over the 6 hour solve time of previous ME-model formulations. This manuscript outlines the architecture of COBRAme and demonstrates how ME-models can be reconstructed and edited most efficiently using the software.

systems biology↗