SARS-CoV-2 uses CD4 to infect T helper lymphocytes.

The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is the agent of a major global outbreak of respiratory tract disease known as Coronavirus Disease 2019 (COVID-19). SARS-CoV-2 infects mainly lungs and may cause several immune-related complications, such as lymphocytopenia and cytokine storm, which are associated with the severity of the disease and predict mortality. The mechanism by which SARS-CoV-2 infection may result in immune system dysfunction is still not fully understood. Here, we show that SARS-CoV-2 infects human CD4+ T helper cells, but not CD8+ T cells, and is present in blood and bronchoalveolar lavage T helper cells of severe COVID-19 patients. We demonstrated that SARS-CoV-2 spike glycoprotein (S) directly binds to the CD4 molecule, which in turn mediates the entry of SARS- CoV-2 in T helper cells. This leads to impaired CD4 T cell function and may cause cell death. SARS-CoV-2-infected T helper cells express higher levels of IL-10, which is associated with viral persistence and disease severity. Thus, CD4-mediated SARS-CoV-2 infection of T helper cells may contribute to a poor immune response in COVID-19 patients.

Structural discrimination analysis for constraint selection in protein modeling.

MOTIVATION: Protein structure modeling can be improved by the use of distance constraints between amino acid residues, provided such data reflects-at least partially-the native tertiary structure of the target system. In fact, only a small subset of the native contact map is necessary to successfully drive the model conformational search, so one important goal is to obtain the set of constraints with the highest true-positive rate, lowest redundancy and greatest amount of information. In this work, we introduce a constraint evaluation and selection method based on the point-biserial correlation coefficient, which utilizes structural information from an ensemble of models to indirectly measure the power of each constraint in biasing the conformational search toward consensus structures. RESULTS: Residue contact maps obtained by direct coupling analysis are systematically improved by means of discriminant analysis, reaching in some cases accuracies often seen only in modern deep-learning-based approaches. When combined with an iterative modeling workflow, the proposed constraint classification optimizes the selection of the constraint set and maximizes the probability of obtaining successful models. The use of discriminant analysis for the valorization of the information of constraint datasets is a general concept with possible applications to other constraint types and modeling problems. AVAILABILITY AND IMPLEMENTATION: MSA for the targets in this work is available on https://github.com/m3g/2021_Bottino_Biserial. Modeling data supporting the findings of this study was generated at the Center for Computing in Engineering and Sciences, and is available from the corresponding author LM on request. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Unveiling the interaction between the molecular motor Myosin Vc and the small GTPase Rab3A.

Vertebrates usually have three class V myosin paralogues (MyoV) to control membrane trafficking in the actin-rich cell cortex, but their functional overlapping or differentiation through cargoes selectivity is yet only partially understood. In this work, we reveal that the globular tail domain of MyoVc binds to the active form of small GTPase Rab3A with nanomolar affinity, a feature shared with MyoVa but not with MyoVb. Using molecular docking analyses guided by chemical cross-linking restraints, we propose a model to explain how Rab3A selectively recognizes MyoVa and MyoVc via a distinct binding site from that used by Rab11A. The MyoVa/c binding interface involves multiple residues from both lobules (I and II) and the short helix at the α2-α3 link region, which is conserved between MyoVa and MyoVc, but not in MyoVb. This motif is also responsible for the selective binding of RILPL2 by MyoVa and potentially MyoVc. Together, these findings support the selective recruitment of MyoVa and MyoVc to exocytic pathways via Rab3A and expand our knowledge about the functional evolution of class V myosins. SIGNIFICANCE: Hormone secretion, neurotransmitter release, and cytoplasm membrane recycling are examples of processes that rely on the interaction of molecular motors and Rab GTPases to regulate the intracellular trafficking and tethering of vesicles. Defects in these proteins may cause neurological impairment, immunodeficiency, and other severe disorders, being fatal in some cases. Despite their crucial roles, little is known about how these molecular motors are selectively recruited by specific members of the large family of Rab GTPases. In this study, we unveil the interaction between the actin-based molecular motor Myosin Vc and the small GTPase Rab3A, a key coordinator of vesicle trafficking and exocytosis in mammalian cells. Moreover, we propose a model for their recognition and demonstrate that Rab3A specifically binds to the globular tail of Myosins Va and Vc, but not of Myosin Vb, advancing our knowledge about the molecular basis for the selective recruitment of class V myosins by Rab GTPases.

Statistical force-field for structural modeling using chemical cross-linking/mass spectrometry distance constraints.

MOTIVATION: Chemical cross-linking/mass spectrometry (XLMS) is an experimental method to obtain distance constraints between amino acid residues which can be applied to structural modeling of tertiary and quaternary biomolecular structures. These constraints provide, in principle, only upper limits to the distance between amino acid residues along the surface of the biomolecule. In practice, attempts to use of XLMS constraints for tertiary protein structure determination have not been widely successful. This indicates the need of specifically designed strategies for the representation of these constraints within modeling algorithms. RESULTS: A force-field designed to represent XLMS-derived constraints is proposed. The potential energy functions are obtained by computing, in the database of known protein structures, the probability of satisfaction of a topological cross-linking distance as a function of the Euclidean distance between amino acid residues. First, the strategy suggests that XL constraints should be set to shorter distances than usually assumed. Second, the complete statistical force-field improves the models obtained and can be easily incorporated into current modeling methods and software. The force-field was implemented and is distributed to be used within the Rosetta ab initio relax protocol. AVAILABILITY AND IMPLEMENTATION: Force-field parameters and usage instructions are freely available online (http://m3g.iqm.unicamp.br/topolink/xlff). SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

TopoLink: evaluation of structural models using chemical crosslinking distance constraints.

SUMMARY: A software was developed to evaluate structural models using chemical crosslinking experiments. The user provides the types of linkers used and their reactivity, and the observed crosslinks and dead-ends. The software computes the minimum length of a physically inspired linker that connects the reactive atoms of interest, and reports the consistency of each distance with the experimental observation. Statistics on model consistency with the links are provided. Tools to evaluate the correlation of crosslinks in ensembles of models were developed. TopoLink was used to evaluate the potential crosslinks of all structures of the CATH database. The number of crosslinks expected as a function of protein size and linker length can be used as guide for experimental design. AVAILABILITY AND IMPLEMENTATION: TopoLink is available as free software at http://m3g.iqm.unicamp.br/topolink, and distributed as source code with a user-friendly graphical interface for Windows. A web server is also provided. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Enhancing protein fold determination by exploring the complementary information of chemical cross-linking and coevolutionary signals.

Motivation: Elucidation of protein native states from amino acid sequences is a primary computational challenge. Modern computational and experimental methodologies, such as molecular coevolution and chemical cross-linking mass-spectrometry allowed protein structural characterization to previously intangible systems. Despite several independent successful examples, data from these distinct methodologies have not been systematically studied in conjunction. One challenge of structural inference using coevolution is that it is limited to sequence fragments within a conserved and unique domain for which sufficient sequence datasets are available. Therefore, coupling coevolutionary data with complimentary distance constraints from orthogonal sources can provide additional precision to structure prediction methodologies. Results: In this work, we present a methodology to combine residue interaction data obtained from coevolutionary information and cross-linking/mass spectrometry distance constraints in order to identify functional states of proteins. Using a combination of structure-based models (SBMs) with optimized Gaussian-like potentials, secondary structure estimation and simulated annealing molecular dynamics, we provide an automated methodology to integrate constraint data from diverse sources in order to elucidate the native conformation of full protein systems with distinct complexity and structural topologies. We show that cross-linking mass spectrometry constraints improve the structure predictions obtained from SBMs and coevolution signals, and that the constraints obtained by each method have a useful degree of complementarity that promotes enhanced fold estimates. Availability and implementation: Scripts and procedures to implement the methodology presented herein are available at https://github.com/mcubeg/DCAXL. Supplementary information: Supplementary data are available at Bioinformatics online.

Crystal structure of the human Tip41 orthologue, TIPRL, reveals a novel fold and a binding site for the PP2Ac C-terminus.

TOR signaling pathway regulator-like (TIPRL) is a regulatory protein which inhibits the catalytic subunits of Type 2A phosphatases. Several cellular contexts have been proposed for TIPRL, such as regulation of mTOR signaling, inhibition of apoptosis and biogenesis and recycling of PP2A, however, the underlying molecular mechanism is still poorly understood. We have solved the crystal structure of human TIPRL at 2.15 Å resolution. The structure is a novel fold organized around a central core of antiparallel beta-sheet, showing an N-terminal α/ß region at one of its surfaces and a conserved cleft at the opposite surface. Inside this cleft, we found a peptide derived from TEV-mediated cleavage of the affinity tag. We show by mutagenesis, pulldown and hydrogen/deuterium exchange mass spectrometry that this peptide is a mimic for the conserved C-terminal tail of PP2A, an important region of the phosphatase which regulates holoenzyme assembly, and TIPRL preferentially binds the unmodified version of the PP2A-tail mimetic peptide DYFL compared to its tyrosine-phosphorylated version. A docking model of the TIPRL-PP2Ac complex suggests that TIPRL blocks the phosphatase's active site, providing a structural framework for the function of TIPRL in PP2A inhibition.