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Register a new userSubstrate inhibition imposes fitness penalty at high protein stability
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Proteins are only moderately stable. It has long been debated whether this narrow range of stabilities is solely a result of neutral drift towards lower stability or purifying selection against excess stability is also at work - for which no experimental evidence was found so far. Here we show that mutations outside the active site in the essential E. coli enzyme adenylate kinase result in stability-dependent increase in substrate inhibition by AMP, thereby impairing overall enzyme activity at high stability. Such inhibition caused substantial fitness defects not only in the presence of excess substrate but also under physiological conditions. In the latter case, substrate inhibition caused differential accumulation of AMP in the stationary phase for the inhibition prone mutants. Further, we show that changes in flux through Adk could accurately describe the variation in fitness effects. Taken together, these data suggest that selection against substrate inhibition and hence excess stability may have resulted in a narrow range of optimal stability observed for modern proteins.
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The common understanding of protein evolution has been that neutral or slightly deleterious mutations are fixed by random drift, and evolutionary rate is determined primarily by the proportion of neutral mutations. However, recent studies have revealed that highly expressed genes evolve slowly because of fitness costs due to misfolded proteins. Here we study selection maintaining protein stability. Protein fitness is taken to be $s = kappa exp(betaDelta G) (1 - exp(betaDeltaDelta G))$, where $s$ and $DeltaDelta G$ are selective advantage and stability change of a mutant protein, $Delta G$ is the folding free energy of the wild-type protein, and $kappa$ represents protein abundance and indispensability. The distribution of $DeltaDelta G$ is approximated to be a bi-Gaussian function, which represents structurally slightly- or highly-constrained sites. Also, the mean of the distribution is negatively proportional to $Delta G$. The evolution of this gene has an equilibrium ($Delta G_e$) of protein stability, the range of which is consistent with experimental values. The probability distribution of $K_a/K_s$, the ratio of nonsynonymous to synonymous substitution rate per site, over fixed mutants in the vicinity of the equilibrium shows that nearly neutral selection is predominant only in low-abundant, non-essential proteins of $Delta G_e > -2.5$ kcal/mol. In the other proteins, positive selection on stabilizing mutations is significant to maintain protein stability at equilibrium as well as random drift on slightly negative mutations, although the average $langle K_a/K_s rangle$ is less than 1. Slow evolutionary rates can be caused by high protein abundance/indispensability, which produces positive shifts of $DeltaDelta G$ through decreasing $Delta G_e$, and by strong structural constraints, which directly make $DeltaDelta G$ more positive.
Background: Typically, proteins perform key biological functions by interacting with each other. As a consequence, predicting which protein pairs interact is a fundamental problem. Experimental methods are slow, expensive, and may be error prone. Many computational methods have been proposed to identify candidate interacting pairs. When accurate, they can serve as an inexpensive, preliminary filtering stage, to be followed by downstream experimental validation. Among such methods, sequence-based ones are very promising. Results: We present MPS(T&B) (Maximum Protein Similarity Topological and Biological), a new algorithm that leverages both topological and biological information to predict protein-protein interactions. We comprehensively compare MPS(T) and MPS(T&B) with state-of-the-art approaches on reliable PPIs datasets, showing that they have competitive or higher accuracy on biologically validated test sets. Conclusion: MPS(T) and MPS(T&B) are topological only and topological plus sequence-based computational methods that can effectively predict the entire human interactome.
The zinc finger structure where a Zn2+ ion binds to 4 cysteine or histidine amino acids in a tetrahedral structure is very common motif of nucleic acid binding proteins. The corresponding interaction model is present in 3% of the genes of human genome. As a result, zinc finger has been shown to be extremely useful in various therapeutic and research capacities, as well as in biotechnology. In stable configuration, the cysteine amino acids are deprotonated and become negatively charged. This means the Zn2+ ion is overscreened by 4 cysteine charges (overcharged). It is question of whether this overcharged configuration is also stable when such negatively charged zinc finger binds to negatively charged DNA molecule. Using all atom molecular dynamics simulation up to microsecond range of an androgen receptor protein dimer, we investigate how the deprotonated state of cysteine influences its structure, dynamics, and function in binding o DNA molecules. Our results show that the deprotonated state of cysteine residues are essential for mechanical stabilization of the functional, folded conformation. Not only this state stabilizes the protein structure, it also stabilizes the protein-DNA binding complex. The differences in structural and energetic properties of the two (sequence-identical) monomers are also investigated showing the strong influence of DNA on the structure of zinc fingers upon complexation. Our result has potential impact on better molecular understanding of one of the most common classes of zinc fingers
We use a coarse-grained model to study the conformational changes in two barley proteins, LTP1 and its ligand adduct isoform LTP1b, that result from their adsorption to the air-water interface. The model introduces the interface through hydropathy indices. We justify the model by all-atom simulations. The choice of the proteins is motivated by making attempts to understand formation and stability of foam in beer. We demonstrate that both proteins flatten out at the interface and can make a continuous stabilizing and denser film. We show that the degree of the flattening depends on the protein -- the layers of LTP1b should be denser than those of LTP1 -- and on the presence of glycation. It also depends on the number ($le 4$) of the disulfide bonds in the proteins. The geometry of the proteins is sensitive to the specificity of the absent bonds. We provide estimates of the volume of cavities of the proteins when away from the interface.
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