Specialized dynamical properties of promiscuous residues revealed by simulated conformational ensembles

The ability to interact with different partners is one of the most important features in proteins. Proteins that bind a large number of partners (hubs) have been often associated with intrinsic disorder. However, many examples exist of hubs with an ordered structure, and evidence of a general mechanism promoting promiscuity in ordered proteins is still elusive. An intriguing hypothesis is that promiscuous binding sites have specific dynamical properties, distinct from the rest of the interface and pre-existing in the protein isolated state. Here, we present the first comprehensive study of the intrinsic dynamics of promiscuous residues in a large protein data set. Different computational methods, from coarse-grained elastic models to geometry-based sampling methods and to full-atom Molecular Dynamics simulations, were used to generate conformational ensembles for the isolated proteins. The flexibility and dynamic correlations of interface residues with a different degree of binding promiscuity were calculated and compared considering side chain and backbone motions, the latter both on a local and on a global scale. The study revealed that (a) promiscuous residues tend to be more flexible than nonpromiscuous ones, (b) this additional flexibility has a higher degree of organization, and (c) evolutionary conservation and binding promiscuity have opposite effects on intrinsic dynamics. Findings on simulated ensembles were also validated on ensembles of experimental structures extracted from the Protein Data Bank (PDB). Additionally, the low occurrence of single nucleotide polymorphisms observed for promiscuous residues indicated a tendency to preserve binding diversity at these positions. A case study on two ubiquitin-like proteins exemplifies how binding promiscuity in evolutionary related proteins can be modulated by the fine-tuning of the interface dynamics. The interplay between promiscuity and flexibility highlighted here can inspire new directions in protein-protein interaction prediction and design methods. © 2013 American Chemical Society

Predicting Important Residues and Interaction Pathways in Proteins Using Gaussian Network Model: Binding and Stability of HLA Proteins

A statistical thermodynamics approach is proposed to determine structurally and functionally important residues in native proteins that are involved in energy exchange with a ligand and other residues along an interaction pathway. The structure-function relationships, ligand binding and allosteric activities of ten structures of HLA Class I proteins of the immune system are studied by the Gaussian Network Model. Five of these models are associated with inflammatory rheumatic disease and the remaining five are properly functioning. In the Gaussian Network Model, the protein structures are modeled as an elastic network where the inter-residue interactions are harmonic. Important residues and the interaction pathways in the proteins are identified by focusing on the largest eigenvalue of the residue interaction matrix. Predicted important residues match those known from previous experimental and clinical work. Graph perturbation is used to determine the response of the important residues along the interaction pathway. Differences in response patterns of the two sets of proteins are identified and their relations to disease are discussed

Roles of residues in the interface of transient protein-protein complexes before complexation

Transient protein-protein interactions play crucial roles in all facets of cellular physiology. Here, using an analysis on known 3-D structures of transient protein-protein complexes, their corresponding uncomplexed forms and energy calculations we seek to understand the roles of protein-protein interfacial residues in the unbound forms. We show that there are conformationally near invariant and evolutionarily conserved interfacial residues which are rigid and they account for ∼65% of the core interface. Interestingly, some of these residues contribute significantly to the stabilization of the interface structure in the uncomplexed form. Such residues have strong energetic basis to perform dual roles of stabilizing the structure of the uncomplexed form as well as the complex once formed while they maintain their rigid nature throughout. This feature is evolutionarily well conserved at both the structural and sequence levels. We believe this analysis has general bearing in the prediction of interfaces and understanding molecular recognition

Identification of Ligand Binding Sites of Proteins Using the Gaussian Network Model

The nonlocal nature of the protein-ligand binding problem is investigated via the Gaussian Network Model with which the residues lying along interaction pathways in a protein and the residues at the binding site are predicted. The predictions of the binding site residues are verified by using several benchmark systems where the topology of the unbound protein and the bound protein-ligand complex are known. Predictions are made on the unbound protein. Agreement of results with the bound complexes indicates that the information for binding resides in the unbound protein. Cliques that consist of three or more residues that are far apart along the primary structure but are in contact in the folded structure are shown to be important determinants of the binding problem. Comparison with known structures shows that the predictive capability of the method is significant

The United States COVID-19 Forecast Hub dataset

Academic researchers, government agencies, industry groups, and individuals have produced forecasts at an unprecedented scale during the COVID-19 pandemic. To leverage these forecasts, the United States Centers for Disease Control and Prevention (CDC) partnered with an academic research lab at the University of Massachusetts Amherst to create the US COVID-19 Forecast Hub. Launched in April 2020, the Forecast Hub is a dataset with point and probabilistic forecasts of incident cases, incident hospitalizations, incident deaths, and cumulative deaths due to COVID-19 at county, state, and national, levels in the United States. Included forecasts represent a variety of modeling approaches, data sources, and assumptions regarding the spread of COVID-19. The goal of this dataset is to establish a standardized and comparable set of short-term forecasts from modeling teams. These data can be used to develop ensemble models, communicate forecasts to the public, create visualizations, compare models, and inform policies regarding COVID-19 mitigation. These open-source data are available via download from GitHub, through an online API, and through R packages

NPRL2 gene expression in the progression of colon tumors

Genetic and epigenetic factors affecting DNA methylation and gene expression are known to be involved in the development of colon cancer, but the full range of genetic alterations and many key genes involved in the pathogenesis of colon cancer remain to be identified. NPRL2 is a candidate tumor suppressor gene identified in the human chromosome 3p21.3 region. We evaluated the role of this gene in the pathogenesis of colorectal cancer by investigating NPRL2 mRNA expression in 55 matched normal and tumor colon tissue samples using quantitative RT-PCR analysis. There was significantly decreased NPRL2 expression in 45% of the patients. Lower NPRL2 expression was observed significantly more frequently in poorly differentiated tumor samples than in highly or moderately differentiated tumors. We conclude that expression of NPRL2 contributes to progression of colon cancer