Stabilization of Synthetic Collagen Triple Helices: Charge Pairs and Covalent Capture.

Collagen mimetic peptides are composed of triple helices. Triple helical formation frequently utilizes charge pair interactions to direct protein assembly. The design of synthetic triple helices is challenging due to the large number of competing species and the overall fragile nature of collagen mimetics. A successfully designed triple helix incorporates both positive and negative criteria to achieve maximum specificity of the supramolecular assembly. Intrahelical charge pair interactions, particularly those involved in lysine-aspartate and lysine-glutamate pairs, have been especially successful both in driving helix specificity and for subsequent stabilization by covalent capture. Despite this progress, the important sequential and geometric relationships of charged residues in a triple helical context have not been fully explored for either supramolecular assembly or covalent capture stabilization. In this study, we compare the eight canonical axial and lateral charge pairs of lysine and arginine with glutamate and aspartate to their noncanonical, reversed charge pairs. These findings are put into the context of collagen triple helical design and synthesis.

DLPacker: Deep learning for prediction of amino acid side chain conformations in proteins.

Prediction of side chain conformations of amino acids in proteins (also termed "packing") is an important and challenging part of protein structure prediction with many interesting applications in protein design. A variety of methods for packing have been developed but more accurate ones are still needed. Machine learning (ML) methods have recently become a powerful tool for solving various problems in diverse areas of science, including structural biology. In this study, we evaluate the potential of deep neural networks (DNNs) for prediction of amino acid side chain conformations. We formulate the problem as image-to-image transformation and train a U-net style DNN to solve the problem. We show that our method outperforms other physics-based methods by a significant margin: reconstruction RMSDs for most amino acids are about 20% smaller compared to SCWRL4 and Rosetta Packer with RMSDs for bulky hydrophobic amino acids Phe, Tyr, and Trp being up to 50% smaller.

Cation-π Interactions and Their Role in Assembling Collagen Triple Helices.

Cation-π interactions play a significant role in the stabilization of globular proteins. However, their role in collagen triple helices is less well understood and they have rarely been used in de novo designed collagen mimetic systems. In this study, we analyze the stabilizing and destabilizing effects in pairwise amino acid interactions between cationic and aromatic residues in both axial and lateral sequential relationships. Thermal unfolding experiments demonstrated that only axial pairs are stabilizing, while the lateral pairs are uniformly destabilizing. Molecular dynamics simulations show that pairs with an axial relationship can achieve a near-ideal interaction distance, but pairs in a lateral relationship do not. Arginine-π systems were found to be more stabilizing than lysine-π and histidine-π. Arginine-π interactions were then studied in more chemically diverse ABC-type heterotrimeric helices, where arginine-tyrosine pairs were found to form the best helix. This work helps elucidate the role of cation-π interactions in triple helices and illustrates their utility in designing collagen mimetic peptides.

Surface-facilitated trapping by active sites: From catalysts to viruses.

Trapping by active sites on surfaces plays important roles in various chemical and biological processes, including catalysis, enzymatic reactions, and viral entry into host cells. However, the mechanisms of these processes remain not well understood, mostly because the existing theoretical descriptions are not fully accounting for the role of the surfaces. Here, we present a theoretical investigation on the dynamics of surface-assisted trapping by specific active sites. In our model, a diffusing particle can occasionally reversibly bind to the surface and diffuse on it before reaching the final target site. An approximate theoretical framework is developed, and its predictions are tested by Brownian dynamics computer simulations. It is found that the surface diffusion can be crucial in mediating trapping by active sites. Our theoretical predictions work reasonably well as long as the area of the active site is much smaller than the overall surface area. Potential applications of our approach are discussed.

Kinetic network model to explain gain-of-function mutations in ERK2 enzyme.

ERK2 is a kinase protein that belongs to a Ras/Raf/MEK/ERK signaling pathway, which is activated in response to a range of extracellular signals. Malfunctioning of this cascade leads to a variety of serious diseases, including cancers. This is often caused by mutations in proteins belonging to the cascade, frequently leading to abnormally high activity of the cascade even in the absence of an external signal. One such "gain-of-function" mutation in the ERK2 protein, called a "sevenmaker" mutation (D319N), was discovered in 1994 in Drosophila. The mutation leads to disruption of interactions of other proteins with the D-site of ERK2 and results, contrary to expectations, in an increase of its activity in vivo. However, no molecular mechanism to explain this effect has been presented so far. The difficulty is that this mutation should equally negatively affect interactions of ERK2 with all substrates, activators, and deactivators. In this paper, we present a semiquantitative kinetic network model that gives a possible explanation of the increased activity of mutant ERK2 species. A simplified biochemical network for ERK2, viewed as a system of coupled Michaelis-Menten processes, is presented. Its dynamic properties are calculated explicitly using the method of first-passage processes. The effect of mutation is associated with changes in the strength of interaction energy between the enzyme and the substrates. It is found that the dependence of kinetic properties of the protein on the interaction energy is nonmonotonic, suggesting that some mutations might lead to more efficient catalytic properties, despite weakening intermolecular interactions. Our theoretical predictions agree with experimental observations for the sevenmaker mutation in ERK2. It is also argued that the effect of mutations might depend on the concentrations of substrates.

Heterotrimeric collagen helix with high specificity of assembly results in a rapid rate of folding.

The most abundant natural collagens form heterotrimeric triple helices. Synthetic mimics of collagen heterotrimers have been found to fold slowly, even compared to the already slow rates of homotrimeric helices. These prolonged folding rates are not understood. Here we compare the stabilities, specificities and folding rates of three heterotrimeric collagen mimics designed through a computationally assisted approach. The crystal structure of one ABC-type heterotrimer verified a well-controlled composition and register and elucidated the geometry of pairwise cation-π and axial and lateral salt bridges in the assembly. This collagen heterotrimer folds much faster (hours versus days) than comparable, well-designed systems. Circular dichroism and NMR data suggest the folding is frustrated by unproductive, competing heterotrimer species and these species must unwind before refolding into the thermodynamically favoured assembly. The heterotrimeric collagen folding rate is inhibited by the introduction of preformed competing triple-helical assemblies, which suggests that slow heterotrimer folding kinetics are dominated by the frustration of the energy landscape caused by competing triple helices.

Light-activated molecular machines are fast-acting broad-spectrum antibacterials that target the membrane.

The increasing occurrence of antibiotic-resistant bacteria and the dwindling antibiotic research and development pipeline have created a pressing global health crisis. Here, we report the discovery of a distinctive antibacterial therapy that uses visible (405 nanometers) light-activated synthetic molecular machines (MMs) to kill Gram-negative and Gram-positive bacteria, including methicillin-resistant Staphylococcus aureus, in minutes, vastly outpacing conventional antibiotics. MMs also rapidly eliminate persister cells and established bacterial biofilms. The antibacterial mode of action of MMs involves physical disruption of the membrane. In addition, by permeabilizing the membrane, MMs at sublethal doses potentiate the action of conventional antibiotics. Repeated exposure to antibacterial MMs is not accompanied by resistance development. Finally, therapeutic doses of MMs mitigate mortality associated with bacterial infection in an in vivo model of burn wound infection. Visible light-activated MMs represent an unconventional antibacterial mode of action by mechanical disruption at the molecular scale, not existent in nature and to which resistance development is unlikely.

Role of Intrinsically Disordered Regions in Acceleration of Protein-Protein Association.

Although intrinsically disordered proteins and intrinsically disordered regions (IDRs) in folded proteins are not able to form stable structures, it is known that they play critically important roles in various biological processes. However, despite multiple studies, the molecular mechanisms of their functions remain not fully understood. In this work, we theoretically investigate the role of IDRs in acceleration of protein-protein association processes. Our hypothesis is that, in protein pairs with several independent binding sites, the association process goes faster if some of these binding sites are located on IDRs or connected by IDRs. To test this idea, we employed analytical modeling, numerical calculations, and Brownian dynamics computer simulations to calculate protein-protein association reaction rates for the ERK2-EtsΔ138 system, belonging to the RAS-RAF-MEK-ERK signaling pathway in living cells. It is found that putting a binding site on IDR accelerates the association process by a factor of 3 to 4. Possible molecular explanations for these observations are presented, and other systems that might use this mechanism are also mentioned.

Theoretical Investigations of the Role of Mutations in Dynamics of Kinesin Motor Proteins.

Motor proteins are active enzymatic molecules that are critically important for a variety of biological phenomena. It is known that some neurodegenerative diseases are caused by specific mutations in motor proteins that lead to their malfunctioning. Hereditary spastic paraplegia is one of such diseases, and it is associated with the mutations in the neuronal conventional kinesin gene, producing the decreased speed and processivity of this motor protein. Despite the importance of this problem, there is no clear understanding on the role of mutations in modifying dynamic properties of motor proteins. In this work, we investigate theoretically the molecular basis for negative effects of two specific mutations, N256S and R280S, on the dynamics of kinesin motor proteins. We hypothesize that these mutations might accelerate the adenosine triphosphate (ATP) release by increasing the probability of open conformations for the ATP-binding pocket. Our approach is based on the use of coarse-grained structure-based molecular dynamics simulations to analyze the conformational changes and chemical transitions in the kinesin molecule, which is also supplemented by investigation of a mesoscopic discrete-state stochastic model. Computer simulations suggest that mutations N256S and R280S can decrease the free energy difference between open and closed biochemical states, making the open conformation more stable and the ATP release faster, which is in agreement with our hypothesis. Furthermore, we show that in the case of N256S mutation, this effect is caused by disruption of interactions between α helix and switch I and loop L11 structural elements. Our computational results are qualitatively supported by the explicit analysis of the discrete-state stochastic model.

Theoretical Investigation of the Mechanisms of ERK2 Enzymatic Catalysis.

ERK2 are protein kinases that during the enzymatic catalysis, in contrast to traditional enzymes, utilize additional interactions with substrates outside of the active sites. It is widely believed that these docking interactions outside of the enzymatic pockets enhance the specificity of these proteins. However, the molecular mechanisms of how the docking interactions affect the catalysis remain not well understood. Here, we develop a simple theoretical approach to analyze the enzymatic catalysis in ERK2 proteins. Our method is based on first-passage process analysis, and it provides explicit expressions for all dynamic properties of the system. It is found that there are specific binding energies for substrates in docking and catalytic domains that lead to maximal enzymatic reaction rates. Thus, we propose that the role of the docking interactions is not only to increase the enzymatic specificity but also to optimize the dynamics of the catalytic process. Our theoretical results are utilized to describe experimental observations on ERK2 enzymatic activities.