229 research outputs found
Most Monogenic Disorders Are Caused by Mutations Altering Protein Folding Free Energy
No full textRevealing the molecular effect that pathogenic missense mutations have on the corresponding protein is crucial for developing therapeutic solutions. This is especially important for monogenic diseases since, for most of them, there is no treatment available, while typically, the treatment should be provided in the early development stages. This requires fast targeted drug development at a low cost. Here, we report an updated database of monogenic disorders (MOGEDO), which includes 768 proteins and the corresponding 2559 pathogenic and 1763 benign mutations, along with the functional classification of the corresponding proteins. Using the database and various computational tools that predict folding free energy change (ΔΔG), we demonstrate that, on average, 70% of pathogenic cases result in decreased protein stability. Such a large fraction indicates that one should aim at in silico screening for small molecules stabilizing the structure of the mutant protein. We emphasize that knowledge of ΔΔG is essential because one wants to develop stabilizers that compensate for ΔΔG, but do not make protein over-stable, since over-stable protein may be dysfunctional. We demonstrate that, by using ΔΔG and predicted solvent exposure of the mutation site, one can develop a predictive method that distinguishes pathogenic from benign mutations with a success rate even better than some of the leading pathogenicity predictors. Furthermore, hydrophobic–hydrophobic mutations have stronger correlations between folding free energy change and pathogenicity compared with others. Also, mutations involving Cys, Gly, Arg, Trp, and Tyr amino acids being replaced by any other amino acid are more likely to be pathogenic. To facilitate further detection of pathogenic mutations, the wild type of amino acids in the 768 proteins mentioned above was mutated to other 19 residues (14,847,817 mutations), the ΔΔG was calculated with SAAFEC-SEQ, and 5,506,051 mutations were predicted to be pathogenic
On the pH-optimum of Activity and Stability of Proteins
No full textBiological macromolecules evolved to perform their function in specific cellular environment (subcellular compartments or tissues); therefore, they should be adapted to the biophysical characteristics of the corresponding environment, one of them being the characteristic pH. Many macromolecular properties are pH dependent, such as activity and stability. However, only activity is biologically important, while stability may not be crucial for the corresponding reaction. Here, we show that the pH-optimum of activity (the pH of maximal activity) is correlated with the pH-optimum of stability (the pH of maximal stability) on a set of 310 proteins with available experimental data. We speculate that such a correlation is needed to allow the corresponding macromolecules to tolerate small pH fluctuations that are inevitable with cellular function. Our findings rationalize the efforts of correlating the pH of maximal stability and the characteristic pH of subcellular compartments, as only pH of activity is subject of evolutionary pressure. In addition, our analysis confirmed the previous observation that pH-optimum of activity and stability are not correlated with the isoelectric point, pI, or with the optimal temperature
Processivity vs. Beating: Comparing Cytoplasmic and Axonemal Dynein Microtubule Binding Domain Association with Microtubule
No full textThis study compares the role of electrostatics in the binding process between microtubules and two dynein microtubule-binding domains (MTBDs): cytoplasmic and axonemal. These two dyneins are distinctively different in terms of their functionalities: cytoplasmic dynein is processive, while axonemal dynein is involved in beating. In both cases, the binding requires frequent association/disassociation between the microtubule and MTBD, and involves highly negatively charged microtubules, including non-structured C-terminal domains (E-hooks), and an MTBD interface that is positively charged. This indicates that electrostatics play an important role in the association process. Here, we show that the cytoplasmic MTBD binds electrostatically tighter to microtubules than to the axonemal MTBD, but the axonemal MTBD experiences interactions with microtubule E-hooks at longer distances compared with the cytoplasmic MTBD. This allows the axonemal MTBD to be weakly bound to the microtubule, while at the same time acting onto the microtubule via the flexible E-hooks, even at MTBD–microtubule distances of 45 Å. In part, this is due to the charge distribution of MTBDs: in the cytoplasmic MTBD, the positive charges are concentrated at the binding interface with the microtubule, while in the axonemal MTBD, they are more distributed over the entire structure, allowing E-hooks to interact at longer distances. The dissimilarities of electrostatics in the cases of axonemal and cytoplasmic MTBDs were found not to result in a difference in conformational dynamics on MTBDs, while causing differences in the conformational states of E-hooks. The E-hooks’ conformations in the presence of the axonemal MTBD were less restricted than in the presence of the cytoplasmic MTBD. In parallel with the differences, the common effect was found that the structural fluctuations of MTBDs decrease as either the number of contacts with E-hooks increases or the distance to the microtubule decreases
PROTCOM: Searchable Database of Protein Complexes Enhanced with Domain-Domain Structures
No full textThe database of protein complexes (PROTCOM) is a compilation of known 3D structures of protein–protein complexes enriched with artificially created domain–domain structures using the available entries in the Protein Data Bank. The domain–domain structures are generated by parsing single chain structures into loosely connected domains and are important features of the database. The database (http://www.ces.clemson.edu/compbio/protcom) could be used for benchmarking purposes of the docking and other algorithms for predicting 3D structures of protein–protein complexes. The database can be utilized as a template database in the homology or threading methods for modeling the 3D structures of unknown protein–protein complexes. PROTCOM provides the scientific community with an integrated set of tools for browsing, searching, visualizing and downloading a pool of protein complexes. The user is given the option to select a subset of entries using a combination of up to 10 different criteria. As on July 2006 the database contains 1770 entries, each of which consists of the known 3D structures and additional relevant information that can be displayed either in text-only or in visual mode
Computational Investigation of Proton Transfer, pKa Shifts and pH-Optimum of Protein-Dna and Protein-RNA Complexes
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