Biology subjects

Sali, A.

Publications and source records attributed to Sali, A..

At least 19 recordsLinked to original sources

bioRxiv2026

Extending structural surfaceomics to identify aberrant conformations of tumor surface proteins as potential immunotherapy targets

The complement of tumor cell surface proteins, or "surfaceome", is a rich source of potential immunotherapy targets. To move beyond expression-based target discovery, we previously described "structural surfaceomics," combining crosslinking mass spectrometry (XL-MS) with surface protein biotinylation to identify conformation-selective targets. In our prior work, we applied this method to a single model of acute myeloid leukemia (AML), identifying active integrin beta-2 as a promising target. Here, we expand structural surfaceomics to identify additional immunotherapy targets and surface protein biology across additional models of AML, multiple myeloma, and prostate cancer, as well as donor peripheral blood mononuclear cells. Utilizing these models and different chemical crosslinkers, we compile an extensive database of 5,209 crosslinks. We characterize both shared and unique crosslink-based features, identifying 1,612 disease model-specific crosslinks, including 212 potentially defining tumor-specific conformations based on distance constraint violations relative to AlphaFold predictions. We further implement a suite of emerging modeling tools to predict tumor-specific protein structures. We probe crosslinking patterns suggesting multiple myeloma-specific CD48 and AML-specific integrin 1/{beta}4 heterodimer conformations. This work establishes a resource for cancer structural biology by implementation of structural surfaceomics. Our findings also point toward more realistic protein design models, potentially enabling systematic detection of targetable cancer-specific epitopes for next-generation immunotherapies.

cancer biology↗

bioRxiv2025

In silico analysis of the Chikungunya virus and SARS-CoV-2 Macrodomain

The macrodomain of the Chikungunya virus NSP3 protein (ChikV Mac1) hydrolyzes mono-ADP ribose post-translational modifications on the human host proteins. Mutations in Mac1 reduce ChikV virulence. Thus, ChikV Mac1 is a viral drug target. While no potent ChikV Mac1 inhibitors are available, high-affinity inhibitors were developed for Mac1 in Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2). Here, we rationalize this difference in the ligand binding affinity in terms of three differences in the binding site structure and dynamics. First, the apo ChikV Mac1 binding site exhibits substantially more conformational heterogeneity than that of SARS-CoV-2, based on microsecond-scale molecular dynamics simulations; it also has a less druggable binding site, based on program SiteMap. Second, water binding sites overlapping with the ligand binding site in ChikV Mac1 are predicted by program WaterMap to have a stronger affinity for water molecules than the corresponding sites in SARS-CoV-2 Mac1, thus decreasing ligand binding affinity. Finally, a smaller number of Mac1 residues interacts persistently with ADP-ribose in holo ChikV Mac1 than in SARS-CoV-2. With these rationalizations in hand, we designed ligands of ChikV Mac1 using a fragment growth strategy; subsequent molecular dynamics simulations of a representative ligand in complex with ChikV Mac1 substantiated our design.

bioinformatics↗

bioRxiv2025

Bayesian multi-state multi-condition modeling of a protein structure based on X-ray crystallography data

1An atomic structure model of a protein can be computed from a diffraction pattern of its crystal. While most crystallographic studies produce a single set of atomic coordinates, the billions of protein molecules in a crystal sample many conformational modes during data collection. As a result, a "multi-state" model that depicts these conformations could reproduce the X-ray data better than a single conformation, and thus likely be more accurate. Computing such a multistate model is challenging due to a lower data-to-parameter ratio than that for single-state modeling. To address this challenge, additional information could be considered, such as X-ray datasets collected for the same system under distinct experimental conditions (eg, temperature, ligands, mutations, and pressure). Here, we develop, benchmark, and illustrate MultiXray: Bayesian multi-state multi-condition modeling for X-ray crystallography. The input information is several X-ray datasets collected under distinct conditions and a molecular mechanics force field. The model consists of an independent coordinate set for each of several states and the weight of each state under each condition. A Bayesian posterior model density quantifies the match of the model with all X-ray datasets and the force field. A sample of models is drawn from the posterior model density using biased molecular dynamics (MD) simulations. We benchmark MultiXray on simulated CypA X-ray data. Using a second X-ray dataset improves the Rfree from 0.105 to 0.089. We then demonstrate MultiXray on experimental temperature-dependent data for SARS-CoV-2 Mpro. Using multiple X-ray datasets improves Rfree of the PDB-deposited structure from 0.253 to 0.237. MultiXray is implemented in our open-source Integrative Modeling Platform (IMP) software, relying on integration with Phenix, thus making it easily applicable to many studies.

biophysics↗

bioRxiv2025

Hydrophobic interactions of FG-nucleoporins are required for dilating nuclear membrane pores into selective transport channels after mitosis

Nuclear envelope (NE) reformation after mitosis is essential for daughter cell viability and requires tightly coordinated nuclear pore complex (NPC) assembly and nuclear membrane reformation. To reveal how these processes are mechanistically linked, we combined acute molecule perturbations in live cells with correlative 3D electron tomography or MINFLUX super-resolution microscopy. We show that degrading Nup62 during mitosis arrests NPC assembly at an intermediate step with smaller membrane pores and removes the whole central transport channel. Molecular dynamics simulations predicted that 32 copies of the central channel subcomplex, recruited into the previously unoccupied pore center, can self-associate via hydrophobic interactions to occupy the volume required for full pore size and exert an outward pushing force; indeed, disrupting these interactions during NPC assembly blocked pore dilation. Later in mitotic exit, perturbed cells exhibited impaired nuclear import, smaller nuclei, and looser NE spacing. Acute inhibition of nuclear import recapitulated these NE defects without affecting NPC assembly. Together, our findings reveal a new, two-step molecular mechanism linking NPC assembly and NE reformation. First, hydrophobic FG-nucleoporins dilate the assembling nuclear pore to its full width by forming the central transport channel, which then allows nuclear import-driven nuclear expansion leading to tight, regular NE membrane spacing.

cell biology↗

bioRxiv2025

Evolutionary Sequence and Structural Basis for the Epistatic Origins of Drug Resistance in HIV

The emergence of drug resistance in the human immunodeficiency virus (HIV) remains a formidable challenge to the long-term efficacy of antiretroviral therapy (ART). A growing body of evidence highlights the critical role of epistasis, the dependence of mutational effects on the sequence context, in shaping the fitness landscape of HIV under ART-induced selection pressure. However, the biophysical origins of the epistatic interactions involved in engendering drug-resistance mutations (DRMs) remain unclear. Are the mutational correlations "intrinsic" to the properties of the protein, or do they arise because of drug binding? We use a Potts sequence-covariation statistical energy model built on patient-derived HIV-1 protein sequences to construct computational double mutant cycles that probe pairwise epistasis for all observed mutations across the three major HIV drug-target enzymes. We find that the strongest epistatic effects occur between mutations at residue positions that frequently mutate during the course of ART, termed resistance-associated positions. To investigate the structural origins of the strongest epistatic interactions, we perform [~]100 free energy perturbation molecular dynamics simulations, revealing that the primary contribution to the pairwise epistasis between DRMs arises from cooperative effects on protein stability and folding as an intrinsic consequence of the protein mutational landscape. The results collectively reinforce a mechanism of resistance evolution whereby viruses escape drug pressure by selectively engendering mutations at "intrinsically" coupled sites, allowing them to cooperatively ameliorate fitness detriments incurred by individual DRMs. SignificanceEpistasis refers to the phenomenon where the effect of a mutation on protein structure and function is dependent on the genetic sequence background of the mutation, resulting in the combined effect of mutations being non-additive. Epistasis plays a significant role in the evolution of drug resistance in viruses such as HIV under therapeutic selection pressure. We combine a protein sequence coevolutionary model and molecular dynamics free energy simulations to identify and probe the mechanistic origins of the strongest epistatic interactions connecting HIV drug-resistance mutations. The work establishes a foundation to probe the molecular bases of epistasis and predict the evolution of resistance predicated on the knowledge of epistatic interaction networks.

biophysics↗

bioRxiv2025

AlphaCross-XL: a seamless tool for automated and proteome-scale map-ping of crosslinked peptides onto three-dimensional protein structures

Crosslinking mass spectrometry (XL-MS) is an exciting proteomics technology to capture native protein conformations in real time within biological systems. Historically, however, implementation of this technology has typically been limited to single purified recombinant proteins or in vitro assembled protein complexes. These limitations are associated with inherent challenges in XL-MS analysis, including extremely low abundance of crosslinked (XL) peptides and complex deconvolution of XL peptide-derived spectral data. However, impressive recent developments in computation and instrumentation have now made it feasible to address biological questions using proteome-wide XL-MS analysis. Although some XL mapping software tools exist, these require manual input of specific Protein Data Bank (PDB) structures at the single protein level, and do not function at the high throughput scale required to analyze datasets derived from thousands of proteins. To address this need, we therefore sought to develop a strategy enabling automated mapping of XL peptides onto the three-dimensional (3D) structures of proteins, at a proteome-wide scale. Herein we describe AlphaCross-XL, a first-in-class seamless computational tool for automated mapping of XL peptides onto the protein structures for intra-protein crosslinks and loop-links. The AlphaCross-XL software first retrieves protein structures from the AlphaFold Protein Structure Database and maps all the identified crosslinks onto the 3D structure. It also calculates the Euclidian distance between the crosslinked residues and reports the violated and satisfied crosslink distances based on a user-defined distance threshold, which is visually discriminated by color in PyMOL. Lastly, the tool also supports further validation of user-submitted protein structures, that can include any computer-predicted protein structure and experimentally derived protein structures (i.e., from PDB). AlphaCross-XL is available at https://github.com/sanjyotshenoy/alphacross-xl.

bioinformatics↗

bioRxiv2024

Cellular Function of a Biomolecular Condensate Is Determined by Its Ultrastructure

Biomolecular condensates play key roles in the spatiotemporal regulation of cellular processes. Yet, the relationship between atomic features and condensate function remains poorly understood. We studied this relationship using the polar organizing protein Z (PopZ) as a model system, revealing how its material properties and cellular function depend on its ultrastructure. We revealed PopZs hierarchical assembly into a filamentous condensate by integrating cryo-electron tomography, biochemistry, single-molecule techniques, and molecular dynamics simulations. The helical domain drives filamentation and condensation, while the disordered domain inhibits them. Phase-dependent conformational changes prevent interfilament contacts in the dilute phase and expose client binding sites in the dense phase. These findings establish a multiscale framework that links molecular interactions and condensate ultrastructure to macroscopic material properties that drive cellular function.

cell biology↗

bioRxiv2024

Recognizing amino acid sidechains in a medium resolution cryo-electron density map

Building an accurate atomic structure model of a protein into a cryo-electron microscopy (cryo-EM) map at worse than 3 [A] resolution is difficult. To facilitate this task, we devised a method for assigning the amino acid residue sequence to the backbone fragments traced in an input cryo-EM map (EMSequenceFinder). EMSequenceFinder relies on a Bayesian scoring function for ranking 20 standard amino acid residue types at a given backbone position, based on the fit to a density map, map resolution, and secondary structure propensity. The fit to a density is quantified by a convolutional neural network that was trained on [~]5.56 million amino acid residue densities extracted from cryo-EM maps at 3-10 [A] resolution and corresponding atomic structure models deposited in the Electron Microscopy Data Bank (EMDB). We benchmarked EMSequenceFinder by predicting the sequences of 58,044 distinct L-helix and {beta}-strand fragments, given the fragment backbone coordinates fitted in their density maps. EMSequenceFinder identifies the correct sequence as the best-scoring sequence in 77.8% of these cases. We also assessed EMSequenceFinder on separate datasets of cryo-EM maps at resolutions from 4 to 6 L. The accuracy of EMSequenceFinder (63.5%) was better than that of three tested state-of-the-art methods, including findMysequence (45%), ModelAngelo (27%), and sequence_from_map in Phenix (12.9%). We further illustrate EMSequenceFinder by threading the SARS-CoV-2 NSP2 sequence into eight cryo-EM maps at resolutions from 3.7 to 7.0 [A]. EMSequenceFinder is implemented in our open-source Integrative Modeling Platform (IMP) program. Thus, it is expected to be helpful for integrative structure modeling based on a cryo-EM map and other information, such as models of protein complex components and chemical crosslinks between them. EMSequenceFinder is available as part of our open source IMP distribution at https://integrativemodeling.org/.

biophysics↗

bioRxiv2024

Structure and inhibition mechanisms of Mycobacterium tuberculosis essential transporterefflux protein A

A broad chemical genetics screen in Mycobacterium tuberculosis (Mtb) to identify inhibitors of established or previously untapped targets for therapeutic development yielded compounds (BRD-8000.3 and BRD-9327) that inhibit the essential efflux pump EfpA. To understand the mechanisms of inhibition by these compounds, we determined the structures of EfpA with inhibitors bound at 2.7 -3.4 [A] resolution. Our structures reveal different mechanisms of inhibition for the two inhibitors. BRD-8000.3 binds in a tunnel making contact with the lipid bilayer and extending toward the central cavity to displace the fatty acid chain of a lipid molecule bound in the apo structure, suggesting its blocking of an access route for a natural lipidic substrate, in contrast to its uncompetitive mechanism for the small molecule substrate ethidium bromide which likely enters through an alternative tunnel. Meanwhile, BRD-9327 binds in the outer vestibule without complete blockade of the substrate path to the outside, suggesting its possible inhibition of the dynamical motion necessary for "alternate access" to the two different sides of the membrane, as is characteristic of major facilitator superfamily (MFS) transporters. Both inhibitors may have a role in inhibiting the "alternate access" mechanism that could account for the uncompetitive nature of their efflux of some substrates. Our results explain the basis of the synergy of these inhibitors and their potential for combination in a multi drug strategy for anti-tuberculosis therapy. They also potentially point to a possible function for this essential efflux pump as a lipid transporter. The structures provide a foundation for rational modification of these inhibitors to increase potency.

biochemistry↗

bioRxiv2024

Integrative spatiotemporal modeling of biomolecular processes: application to the assembly of the Nuclear Pore Complex

Dynamic processes involving biomolecules are essential for the function of the cell. Here, we introduce an integrative method for computing models of these processes based on multiple heterogeneous sources of information, including time-resolved experimental data and physical models of dynamic processes. We first compute integrative structure models at fixed time points and then optimally select and connect these snapshots into a series of trajectories that optimize the likelihood of both the snapshots and transitions between them. The method is demonstrated by application to the assembly process of the human Nuclear Pore Complex in the context of the reforming nuclear envelope during mitotic cell division, based on live-cell correlated electron tomography, bulk fluorescence correlation spectroscopy-calibrated quantitative live imaging, and a structural model of the fully-assembled Nuclear Pore Complex. Modeling of the assembly process improves the model precision over static integrative structure modeling alone. The method is applicable to a wide range of time-dependent systems in cell biology, and is available to the broader scientific community through an implementation in the open source Integrative Modeling Platform software.

biophysics↗

bioRxiv2024

Cell Maps for Artificial Intelligence: AI-Ready Maps of Human Cell Architecture from Disease-Relevant Cell Lines

This article describes the Cell Maps for Artificial Intelligence (CM4AI) project and its goals, methods, standards, current datasets, software tools, status, and future directions. CM4AI is the Functional Genomics Data Generation Project in the U.S. National Institute of Healths (NIH) Bridge2AI program. Its overarching mission is to produce ethical, AI-ready datasets of cell architecture, inferred from multimodal data collected for human cell lines, to enable transformative biomedical AI research.

systems biology↗

bioRxiv2024

The Molecular Architecture of the Nuclear Basket

The nuclear pore complex (NPC) is the sole mediator of nucle-ocytoplasmic transport. Despite great advances in understanding its conserved core architecture, the peripheral regions can exhibit considerable variation within and between species. One such structure is the cage-like nuclear basket. Despite its crucial roles in mRNA surveillance and chromatin organization, an architectural understanding has remained elusive. Using in-cell cryo-electron tomography and subtomogram analysis, we explored the NPCs structural variations and the nuclear basket across fungi (yeast; S. cerevisiae), mammals (mouse; M. musculus), and protozoa (T. gondii). Using integrative structural modeling, we computed a model of the basket in yeast and mammals that revealed how a hub of Nups in the nuclear ring binds to basket-forming Mlp/Tpr proteins: the coiled-coil domains of Mlp/Tpr form the struts of the basket, while their unstructured termini constitute the basket distal densities, which potentially serve as a docking site for mRNA preprocessing before nucleocytoplasmic transport

biophysics↗

bioRxiv2024

Integrative spatiotemporal map of nucleocytoplasmic transport

Nuclear Pore Complexes (NPCs) enable rapid, selective, and robust nucleocytoplasmic transport. To explain how transport emerges from the system components and their interactions, we used experimental data and theoretical information to construct an integrative Brownian dynamics model of transport through an NPC, coupled to a kinetic model of transport in the cell. The model recapitulates key aspects of transport for a wide range of molecular cargos, including pre-ribosomes and viral capsids. It quantifies how flexible phenylalanine-glycine (FG) repeat proteins raise an entropy barrier to passive diffusion and how this barrier is selectively lowered in facilitated diffusion by the many transient interactions of nuclear transport receptors with the FG repeats. Selective transport is enhanced by "fuzzy" multivalent interactions, redundant FG repeats, coupling to the energy-dependent RanGTP concentration gradient, and exponential dependence of transport kinetics on the transport barrier. Our model will facilitate rational modulation of the NPC and its artificial mimics.

cell biology↗

bioRxiv2023

Multi-scale photocatalytic proximity labeling reveals cell surface neighbors on and between cells

The cell membrane proteome is the primary biohub for cell communication, yet we are only beginning to understand the dynamic protein neighborhoods that form on the cell surface and between cells. Proximity labeling proteomics (PLP) strategies using chemically reactive probes are powerful approaches to yield snapshots of protein neighborhoods but are currently limited to one single resolution based on the probe labeling radius. Here, we describe a multi-scale PLP method with tunable resolution using a commercially available histological dye, Eosin Y, which upon visible light illumination, activates three different photo-probes with labeling radii ranging from [~]100 to 3000 [A]. We applied this platform to profile neighborhoods of the oncogenic epidermal growth factor receptor (EGFR) and orthogonally validated >20 neighbors using immuno-assays and AlphaFold-Multimer prediction that generated plausible binary interaction models. We further profiled the protein neighborhoods of cell-cell synapses induced by bi-specific T-cell engagers (BiTEs) and chimeric antigen receptor (CAR)T cells at longer length scales. This integrated multi-scale PLP platform maps local and distal protein networks on cell surfaces and between cells. We believe this information will aid in the systematic construction of the cell surface interactome and reveal new opportunities for immunotherapeutics.

biochemistry↗

bioRxiv2023

Bayesian Fluorescence Framework for integrative modeling of biomolecules

Fluorescence spectroscopic and imaging techniques, such as fluorescence-correlation spectroscopy, image correlation spectroscopy, time-resolved fluorescence spectroscopy, and intensity-based spectroscopy, can provide sparse time-dependent positional and inter-fluorophore distance information for macromolecules and their complexes in vitro and in living cells. Here, we formulated a Bayesian framework for processing and using the fluorescence data for interpreting by static and dynamic models of biomolecules. We introduce Bayesian Fluorescence Framework (BFF) as part of the open-source Integrative Modeling Platform (IMP) software environment, facilitating the development of modeling protocols based in part on fluorescence data. BFF improves the accuracy, precision, and completeness of the resulting models by formulating the modeling problem as a sampling problem dependent on general and flexible libraries of (i) atomic and coarse-grained molecular representations of single-state models, multi-state models, and dynamic processes, (ii) Bayesian data likelihoods and priors, as well as (iii) sampling schemes. To illustrate the framework, we apply it to a sample synthetic single-molecule FRET dataset of the human transglutaminase 2. We show how to integrate time-resolved fluorescence intensities, fluorescence correlation spectroscopy curves, and fluorescence anisotropies to simultaneously resolve dynamic structures, state populations, and molecular kinetics. As BFF is part of IMP, fluorescence data can be easily integrated with other data types to solve challenging modeling problems. Statement of SignificanceBayesian Framework for Fluorescence (BFF) is software that implements a probabilistic framework for processing experimental fluorescence data to provide input information for Bayesian integrative structure modeling. BFF facilitates constructing integrative modeling protocols based in part on fluorescence data by reducing the required fluorescence spectroscopy and microscopy domain knowledge. In addition, it improves the precision and accuracy of the resulting models.

biophysics↗

bioRxiv2023

Illuminating the Function of the Orphan Transporter, SLC22A10 in Humans and Other Primates

SLC22A10 is classified as an orphan transporter with unknown substrates and function. Here we describe the discovery of the substrate specificity and functional characteristics of SLC22A10. The human SLC22A10 tagged with green fluorescent protein was found to be absent from the plasma membrane, in contrast to the SLC22A10 orthologs found in great apes. Estradiol-17{beta}-glucuronide accumulated in cells expressing great ape SLC22A10 orthologs (over 4-fold, p<0.001). In contrast, human SLC22A10 displayed no uptake function. Sequence alignments revealed two amino acid differences including a proline at position 220 of the human SLC22A10 and a leucine at the same position of great ape orthologs. Site-directed mutagenesis yielding the human SLC22A10-P220L produced a protein with excellent plasma membrane localization and associated uptake function. Neanderthal and Denisovan genomes show human-like sequences at proline 220 position, corroborating that SLC22A10 were rendered nonfunctional during hominin evolution after the divergence from the pan lineage (chimpanzees and bonobos). These findings demonstrate that human SLC22A10 is a unitary pseudogene and was inactivated by a missense mutation that is fixed in humans, whereas orthologs in great apes transport sex steroid conjugates.

pharmacology and toxicology↗

bioRxiv2023

Nanobody repertoire generated against the spike protein of ancestral SARS-CoV-2 remains efficacious against the rapidly evolving virus

To date, all major modes of monoclonal antibody therapy targeting SARS-CoV-2 have lost significant efficacy against the latest circulating variants. As SARS-CoV-2 omicron sublineages account for over 90% of COVID-19 infections, evasion of immune responses generated by vaccination or exposure to previous variants poses a significant challenge. A compelling new therapeutic strategy against SARS-CoV-2 is that of single domain antibodies, termed nanobodies, which address certain limitations of monoclonal antibodies. Here we demonstrate that our high-affinity nanobody repertoire, generated against wild-type SARS-CoV-2 spike protein (Mast, Fridy et al. 2021), remains effective against variants of concern, including omicron BA.4/BA.5; a subset is predicted to counter resistance in emerging XBB and BQ.1.1 sublineages. Furthermore, we reveal the synergistic potential of nanobody cocktails in neutralizing emerging variants. Our study highlights the power of nanobody technology as a versatile therapeutic and diagnostic tool to combat rapidly evolving infectious diseases such as SARS-CoV-2.

biochemistry↗

bioRxiv2023

Dynamic molecular mechanism of the nuclear pore complex permeability barrier

Nuclear pore complexes (NPCs) mediate nucleocytoplasmic transport of specific macromolecules while impeding the exchange of unsolicited material. However, key aspects of this gating mechanism remain controversial. To address this issue, we determined the nanoscopic behavior of the permeability barrier directly within yeast S. cerevisiae NPCs at transport-relevant timescales. We show that the large intrinsically disordered domains of phenylalanine-glycine repeat nucleoporins (FG Nups) exhibit highly dynamic fluctuations to create transient voids in the permeability barrier that continuously shape-shift and reseal, resembling a radial polymer brush. Together with cargo-carrying transport factors the FG domains form a feature called the central plug, which is also highly dynamic. Remarkably, NPC mutants with longer FG domains show interweaving meshwork-like behavior that attenuates nucleocytoplasmic transport in vivo. Importantly, the bona fide nanoscale NPC behaviors and morphologies are not recapitulated by in vitro FG domain hydrogels. NPCs also exclude self-assembling FG domain condensates in vivo, thereby indicating that the permeability barrier is not generated by a self-assembling phase condensate, but rather is largely a polymer brush, organized by the NPC scaffold, whose dynamic gating selectivity is strongly enhanced by the presence of transport factors.

biophysics↗