Epistatic interactions promote persistence of NS3-Q80K in HCV infection by compensating for protein folding instability

The Q80K polymorphism in the NS3-4A protease of the hepatitis C virus is associated with treatment failure of direct-acting antiviral agents. This polymorphism is highly prevalent in genotype 1a infections and stably transmitted between hosts. Here, we investigated the underlying molecular mechanisms of evolutionarily conserved coevolving amino acids in NS3-Q80K and revealed potential implications of epistatic interactions in immune escape and variants persistence. Using purified protein, we characterized the impact of epistatic amino acid substitutions on the physicochemical properties and peptide cleavage kinetics of the NS3-Q80K protease. We found that Q80K destabilized the protease protein fold (p < 0.0001). Although NS3-Q80K showed reduced peptide substrate turnover (p < 0.0002), replicative fitness in an H77S.3 cell culture model of infection was not significantly inferior to the WT virus. Epistatic substitutions at residues 91 and 174 in NS3-Q80K stabilized the protein fold (p < 0.0001) and leveraged the WT protease stability. However, changes in protease stability inversely correlated with enzymatic activity. In infectious cell culture, these secondary substitutions were not associated with a gain of replicative fitness in NS3-Q80K variants. Using molecular dynamics, we observed that the total number of residue contacts in NS3-Q80K mutants correlated with protein folding stability. Changes in the number of contacts reflected the compensatory effect on protein folding instability by epistatic substitutions. In summary, epistatic substitutions in NS3-Q80K contribute to viral fitness by mechanisms not directly related to RNA replication. By compensating for protein-folding instability, epistatic interactions likely protect NS3-Q80K variants from immune cell recognition.

Step-by-step design of proteins for small molecule interaction: A review on recent milestones

Protein design is the field of synthetic biology that aims at developing de novo custom-made proteins and peptides for specific applications. Despite exploring an ambitious goal, recent computational advances in both hardware and software technologies have paved the way to high-throughput screening and detailed design of novel folds and improved functionalities. Modern advances in the field of protein design for small molecule targeting are described in this review, organized in a step-by-step fashion: from the conception of a new or upgraded active binding site, to scaffold design, sequence optimization, and experimental expression of the custom protein. In each step, contemporary examples are described, and state-of-the-art software is briefly explored.

ACES: A co-evolution simulator generates co-varying protein and nucleic acid sequences

Sequence-specific and consequential interactions within or between proteins and/or RNAs can be predicted by identifying co-evolution of residues in these molecules. Different algorithms have been used to detect co-evolution, often using biological data to benchmark a methods ability to discriminate against indirect co-evolution. Such a benchmark is problematic, because not all the interactions and evolutionary constraints underlying real data can be known a priori. Instead, sequences generated in silico to simulate co-evolution would be preferable, and can be obtained using aCES, the software tool presented here. Conservation and co-evolution constraints can be specified for any residue across a number of molecules, allowing the user to capture a complex, realistic set of interactions. Resulting alignments were used to benchmark several co-evolution detection tools for their ability to separate signal from background as well as discriminating direct from indirect signals. This approach can aid in refinement of these algorithms. In addition, systematic tuning of these constraints sheds new light on how they drive co-evolution between residues. Better understanding how to detect co-evolution and the residue interactions they predict can lead to a wide range of insights important for synthetic biologists interested in engineering new, orthogonal interactions between two macromolecules.

Driving Protein Conformational Cycles in Physiology and Disease: "Frustrated" Amino Acid Interaction Networks Define Dynamic Energy Landscapes: Amino Acid Interaction Networks Change Progressively Along Alpha Tryptophan Synthase's Catalytic Cycle

A general framework by which dynamic interactions within a protein will promote the necessary series of structural changes, or "conformational cycle," required for function is proposed. It is suggested that the free-energy landscape of a protein is biased toward this conformational cycle. Fluctuations into higher energy, although thermally accessible, conformations drive the conformational cycle forward. The amino acid interaction network is defined as those intraprotein interactions that contribute most to the free-energy landscape. Some network connections are consistent in every structural state, while others periodically change their interaction strength according to the conformational cycle. It is reviewed here that structural transitions change these periodic network connections, which then predisposes the protein toward the next set of network changes, and hence the next structural change. These concepts are illustrated by recent work on tryptophan synthase. Disruption of these dynamic connections may lead to aberrant protein function and disease states.

Evaluating DCA-based method performances for RNA contact prediction by a well-curated data set

RNA molecules play many pivotal roles in a cell that are still not fully understood. Any detailed understanding of RNA function requires knowledge of its three-dimensional structure, yet experimental RNA structure resolution remains demanding. Recent advances in sequencing provide unprecedented amounts of sequence data that can be statistically analyzed by methods such as direct coupling analysis (DCA) to determine spatial proximity or contacts of specific nucleic acid pairs, which improve the quality of structure prediction. To quantify this structure prediction improvement, we here present a well curated data set of about 70 RNA structures of high resolution and compare different nucleotide-nucleotide contact prediction methods available in the literature. We observe only minor differences between the performances of the different methods. Moreover, we discuss how robust these predictions are for different contact definitions and how strongly they depend on procedures used to curate and align the families of homologous RNA sequences.

Direct coupling analysis improves the identification of beneficial amino acid mutations for the functional thermostabilization of a delicate decarboxylase

The optimization of enzyme properties for specific reaction conditions enables their tailored use in biotechnology. Predictions using established computer-based methods, however, remain challenging, especially regarding physical parameters such as thermostability without concurrent loss of activity. Employing established computational methods such as energy calculations using FoldX can lead to the identification of beneficial single amino acid substitutions for the thermostabilization of enzymes. However, these methods require a three-dimensional (3D)-structure of the enzyme. In contrast, coevolutionary analysis is a computational method, which is solely based on sequence data. To enable a comparison, we employed coevolutionary analysis together with structure-based approaches to identify mutations, which stabilize an enzyme while retaining its activity. As an example, we used the delicate dimeric, thiamine pyrophosphate dependent enzyme ketoisovalerate decarboxylase (Kivd) and experimentally determined its stability represented by a T50 value indicating the temperature where 50% of enzymatic activity remained after incubation for 10 min. Coevolutionary analysis suggested 12 beneficial mutations, which were not identified by previously established methods, out of which four mutations led to a functional Kivd with an increased T50 value of up to 3.9°C.