PTMdyna: exploring the influence of post-translation modifications on protein conformational dynamics

Protein post-translational modifications (PTM) play vital roles in cellular regulation, modulating functions by driving changes in protein structure and dynamics. Exploring comprehensively the influence of PTM on conformational dynamics can facilitate the understanding of the related biological function and molecular mechanism. Currently, a series of excellent computation tools have been designed to analyze the time-dependent structural properties of proteins. However, the protocol aimed to explore conformational dynamics of post-translational modified protein is still a blank. To fill this gap, we present PTMdyna to visually predict the conformational dynamics differences between unmodified and modified proteins, thus indicating the influence of specific PTM. PTMdyna exhibits an AUC of 0.884 tested on 220 protein-protein complex structures. The case of heterochromatin protein 1α complexed with lysine 9-methylated histone H3, which is critical for genomic stability and cell differentiation, was used to demonstrate its applicability. PTMdyna provides a reliable platform to predict the influence of PTM on protein dynamics, making it easier to interpret PTM functionality at the structure level. The web server is freely available at http://ccbportal.com/PTMdyna.

SARS-CoV-2 Proteins Bind to Hemoglobin and Its Metabolites

(1) Background: coronavirus disease 2019 (COVID-19), caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has been linked to hematological dysfunctions, but there are little experimental data that explain this. Spike (S) and Nucleoprotein (N) proteins have been putatively associated with these dysfunctions. In this work, we analyzed the recruitment of hemoglobin (Hb) and other metabolites (hemin and protoporphyrin IX-PpIX) by SARS-Cov2 proteins using different approaches. (2) Methods: shotgun proteomics (LC-MS/MS) after affinity column adsorption identified hemin-binding SARS-CoV-2 proteins. The parallel synthesis of the peptides technique was used to study the interaction of the receptor bind domain (RBD) and N-terminal domain (NTD) of the S protein with Hb and in silico analysis to identify the binding motifs of the N protein. The plaque assay was used to investigate the inhibitory effect of Hb and the metabolites hemin and PpIX on virus adsorption and replication in Vero cells. (3) Results: the proteomic analysis by LC-MS/MS identified the S, N, M, Nsp3, and Nsp7 as putative hemin-binding proteins. Six short sequences in the RBD and 11 in the NTD of the spike were identified by microarray of peptides to interact with Hb and tree motifs in the N protein by in silico analysis to bind with heme. An inhibitory effect in vitro of Hb, hemin, and PpIX at different levels was observed. Strikingly, free Hb at 1mM suppressed viral replication (99%), and its interaction with SARS-CoV-2 was localized into the RBD region of the spike protein. (4) Conclusions: in this study, we identified that (at least) five proteins (S, N, M, Nsp3, and Nsp7) of SARS-CoV-2 recruit Hb/metabolites. The motifs of the RDB of SARS-CoV-2 spike, which binds Hb, and the sites of the heme bind-N protein were disclosed. In addition, these compounds and PpIX block the virus's adsorption and replication. Furthermore, we also identified heme-binding motifs and interaction with hemin in N protein and other structural (S and M) and non-structural (Nsp3 and Nsp7) proteins.

Protein-Protein Docking with Large-Scale Backbone Flexibility Using Coarse-Grained Monte-Carlo Simulations

Most of the protein–protein docking methods treat proteins as almost rigid objects. Only the side-chains flexibility is usually taken into account. The few approaches enabling docking with a flexible backbone typically work in two steps, in which the search for protein–protein orientations and structure flexibility are simulated separately. In this work, we propose a new straightforward approach for docking sampling. It consists of a single simulation step during which a protein undergoes large-scale backbone rearrangements, rotations, and translations. Simultaneously, the other protein exhibits small backbone fluctuations. Such extensive sampling was possible using the CABS coarse-grained protein model and Replica Exchange Monte Carlo dynamics at a reasonable computational cost. In our proof-of-concept simulations of 62 protein–protein complexes, we obtained acceptable quality models for a significant number of cases.

SAAMBE-3D: Predicting Effect of Mutations on Protein-Protein Interactions

Maintaining wild type protein-protein interactions is essential for the normal function of cell and any mutation that alter their characteristics can cause disease. Therefore, the ability to correctly and quickly predict the effect of amino acid mutations is crucial for understanding disease effects and to be able to carry out genome-wide studies. Here, we report a new development of the SAAMBE method, SAAMBE-3D, which is a machine learning-based approach, resulting in accurate predictions and is extremely fast. It achieves the Pearson correlation coefficient ranging from 0.78 to 0.82 depending on the training protocol in benchmarking five-fold validation test against the SKEMPI v2.0 database and outperforms currently existing algorithms on various blind-tests. Furthermore, optimized and tested via five-fold cross-validation on the Cornell University dataset, the SAAMBE-3D achieves AUC of 1.0 and 0.96 on a homo and hereto-dimer test datasets. Another important feature of SAAMBE-3D is that it is very fast, it takes less than a fraction of a second to complete a prediction. SAAMBE-3D is available as a web server and as well as a stand-alone code, the last one being another important feature allowing other researchers to directly download the code and run it on their local computer. Combined all together, SAAMBE-3D is an accurate and fast software applicable for genome-wide studies to assess the effect of amino acid mutations on protein-protein interactions. The webserver and the stand-alone codes (SAAMBE-3D for predicting the change of binding free energy and SAAMBE-3D-DN for predicting if the mutation is disruptive or non-disruptive) are available.

Analysis of mitogen-activated protein kinase activation and interactions with regulators and substrates

Mitogen-activated protein kinase (MAPK) cascades are ubiquitous signal transduction modules in eukaryotes that are of great interest and importance. Here, we summarize some useful methods for the analysis of MAPK signaling, including methods to (1) detect MAPK activation in cells, with an emphasis on using phosphorylation-state-specific antibodies raised against mammalian phosphopeptide sequences to detect the activation of MAPKs in other species; (2) estimate the cellular concentrations of MAPKs and other proteins of interest; (3) detect and quantify the stable physical association of MAPKs with their substrates and regulators, and estimate the relevant dissociation constants; (4) delineate the MAPK-binding regions or domains of MAPK-interacting proteins, with particular emphasis on the identification and verification of MAPK-docking sites. These procedures are broadly applicable to many organisms, including both yeast and mammalian cells.