A computational approach identifies two regions of Hepatitis C Virus E1 protein as interacting domains involved in viral fusion process

Sequence-Based Protein Design: A Review of Using Statistical Models to Characterize Coevolutionary Traits for Developing Hybrid Proteins as Genetic Sensors

Statistical analyses of homologous protein sequences can identify amino acid residue positions that co-evolve to generate family members with different properties. Based on the hypothesis that the coevolution of residue positions is necessary for maintaining protein structure, coevolutionary traits revealed by statistical models provide insight into residue-residue interactions that are important for understanding protein mechanisms at the molecular level. With the rapid expansion of genome sequencing databases that facilitate statistical analyses, this sequence-based approach has been used to study a broad range of protein families. An emerging application of this approach is to design hybrid transcriptional regulators as modular genetic sensors for novel wiring between input signals and genetic elements to control outputs. Among many allosterically regulated regulator families, the members contain structurally conserved and functionally independent protein domains, including a DNA-binding module (DBM) for interacting with a specific genetic element and a ligand-binding module (LBM) for sensing an input signal. By hybridizing a DBM and an LBM from two different family members, a hybrid regulator can be created with a new combination of signal-detection and DNA-recognition properties not present in natural systems. In this review, we present recent advances in the development of hybrid regulators and their applications in cellular engineering, especially focusing on the use of statistical analyses for characterizing DBM-LBM interactions and hybrid regulator design. Based on these studies, we then discuss the current limitations and potential directions for enhancing the impact of this sequence-based design approach.

Identification and Characteristics of Fusion Peptides Derived From Enveloped Viruses

Membrane fusion events allow enveloped viruses to enter and infect cells. The study of these processes has led to the identification of a number of proteins that mediate this process. These proteins are classified according to their structure, which vary according to the viral genealogy. To date, three classes of fusion proteins have been defined, but current evidence points to the existence of additional classes. Despite their structural differences, viral fusion processes follow a common mechanism through which they exert their actions. Additional studies of the viral fusion proteins have demonstrated the key role of specific proteinogenic subsequences within these proteins, termed fusion peptides. Such peptides are able to interact and insert into membranes for which they hold interest from a pharmacological or therapeutic viewpoint. Here, the different characteristics of fusion peptides derived from viral fusion proteins are described. These criteria are useful to identify new fusion peptides. Moreover, this review describes the requirements of synthetic fusion peptides derived from fusion proteins to induce fusion by themselves. Several sequences of the viral glycoproteins E1 and E2 of HCV were, for example, identified to be able to induce fusion, which are reviewed here.

VH1-69 antiviral broadly neutralizing antibodies: genetics, structures, and relevance to rational vaccine design

Broadly neutralizing antibodies (bnAbs) are potential therapeutic molecules and valuable tools for studying conserved viral targets for vaccine and drug design. Interestingly, antibody responses to conserved epitopes can be highly convergent at the molecular level. Human antibodies targeting a number of viral antigens have often been found to utilize a restricted set of immunoglobulin germline genes in different individuals. Here we review recent knowledge on V_H1-69-encoded antibodies in antiviral responses to influenza virus, HCV, and HIV-1. These antibodies share common genetic and structural features, and often develop neutralizing activity against a broad spectrum of viral strains. Understanding the genetic and structural characteristics of such antibodies and the target epitopes should help advance novel strategies to elicit bnAbs through vaccination.

The Neutralizing Face of Hepatitis C Virus E2 Envelope Glycoprotein

The high genetic variability of hepatitis C virus, together with the high level of glycosylation on the viral envelope proteins shielding potential neutralizing epitopes, pose a difficult challenge for vaccine development. An effective hepatitis C virus (HCV) vaccine must target conserved epitopes and the HCV E2 glycoprotein is the main target for such neutralizing antibodies (NAbs). Recent structural investigations highlight the presence of a highly conserved and accessible surface on E2 that is devoid of N-linked glycans and known as the E2 neutralizing face. This face is defined as a hydrophobic surface comprising the front layer (FL) and the CD81 binding loop (CD81bl) that overlap with the CD81 receptor binding site on E2. The neutralizing face consists of highly conserved residues for recognition by cross-NAbs, yet it appears to be high conformationally flexible, thereby presenting a moving target for NAbs. Three main overlapping neutralizing sites have been identified in the neutralizing face: antigenic site 412 (AS412), antigenic site 434 (AS434), and antigenic region 3 (AR3). Here, we review the structural analyses of these neutralizing sites, either as recombinant E2 or epitope-derived linear peptides in complex with bNAbs, to understand the functional and preferred conformations for neutralization, and for viral escape. Collectively, these studies provide a foundation and molecular templates to facilitate structure-based approaches for HCV vaccine development.

Capitalizing on knowledge of hepatitis C virus neutralizing epitopes for rational vaccine design

Hepatitis C virus infects nearly 3% of the world's population and is often referred as a silent epidemic. It is a leading cause of liver cirrhosis and hepatocellular carcinoma in endemic countries. Although antiviral drugs are now available, they are not readily accessible to marginalized social groups and developing nations that are disproportionally impacted by HCV. To stop the HCV pandemic, a vaccine is needed. Recent advances in HCV research have provided new opportunities for studying HCV neutralizing antibodies and their subsequent use for rational vaccine design. It is now recognized that neutralizing antibodies to conserved antigenic sites of the virus can cross-neutralize diverse HCV genotypes and protect against infection in vivo. Structural characterization of the neutralizing epitopes has provided valuable information for design of candidate immunogens.

An experimental and computational evolution-based method to study a mode of co-evolution of overlapping open reading frames in the AAV2 viral genome

Overlapping open reading frames (ORFs) in viral genomes undergo co-evolution; however, how individual amino acids coded by overlapping ORFs are structurally, functionally, and co-evolutionarily constrained remains difficult to address by conventional homologous sequence alignment approaches. We report here a new experimental and computational evolution-based methodology to address this question and report its preliminary application to elucidating a mode of co-evolution of the frame-shifted overlapping ORFs in the adeno-associated virus (AAV) serotype 2 viral genome. These ORFs encode both capsid VP protein and non-structural assembly-activating protein (AAP). To show proof of principle of the new method, we focused on the evolutionarily conserved QVKEVTQ and KSKRSRR motifs, a pair of overlapping heptapeptides in VP and AAP, respectively. In the new method, we first identified a large number of capsid-forming VP3 mutants and functionally competent AAP mutants of these motifs from mutant libraries by experimental directed evolution under no co-evolutionary constraints. We used Illumina sequencing to obtain a large dataset and then statistically assessed the viability of VP and AAP heptapeptide mutants. The obtained heptapeptide information was then integrated into an evolutionary algorithm, with which VP and AAP were co-evolved from random or native nucleotide sequences in silico. As a result, we demonstrate that these two heptapeptide motifs could exhibit high degeneracy if coded by separate nucleotide sequences, and elucidate how overlap-evoked co-evolutionary constraints play a role in making the VP and AAP heptapeptide sequences into the present shape. Specifically, we demonstrate that two valine (V) residues and β-strand propensity in QVKEVTQ are structurally important, the strongly negative and hydrophilic nature of KSKRSRR is functionally important, and overlap-evoked co-evolution imposes strong constraints on serine (S) residues in KSKRSRR, despite high degeneracy of the motifs in the absence of co-evolutionary constraints.

Looking for a sequence based allostery definition: a statistical journey at different resolution scales

The aim of this work was to detect allosteric hotspots signatures characterizing protein regions acting as the 'key drivers' of global allosteric conformational change. We computationally estimated the relative strength of intra-molecular interaction in allosteric proteins between two putative allostery-susceptible sites using a co-evolution model based upon the optimization of the cross-correlation in terms of free-energy-transfer hydrophobicity scale (Tanford scale) distribution along the chain. Cross-Recurrence Quantification Analysis (Cross-RQA) applied on the sequences of allostery susceptible sites showed evidence of strong interaction amongst allosteric susceptible sites. This could be due to transient weak molecular bonds between allostery susceptible patches enabling regions far-apart to come together. Further, using a large protein dataset, by comparing allosteric protein set with a randomly generated sequence population as well as a generic protein set, we reconfirmed our earlier findings that hydrophobicity patterning (as formalized by Recurrence Quantification Analysis (RQA) descriptors) may serve as determinant of allostery and its relevance in the transmission of allosteric conformational change. We applied RQA to free-energy-transfer hydrophobicity-transformed amino acid sequences of the allostery dataset to extract allostery specific global sequence features. These free-energy-transfer hydrophobicity-based RQA markers proved to be representative of allosteric signatures and not related to the differences between randomly generated and real proteins. These free-energy-transfer hydrophobicity-based RQA markers when evaluated by pattern recognition tools could distinguish allosteric proteins with 92% accuracy.

Sequence signatures of allosteric proteins towards rational design

Allostery is the phenomenon of changes in the structure and activity of proteins that appear as a consequence of ligand binding at sites other than the active site. Studying mechanistic basis of allostery leading to protein design with predetermined functional endpoints is an important unmet need of synthetic biology. Here, we screened the amino acid sequence landscape in search of sequence-signatures of allostery using Recurrence Quantitative Analysis (RQA) method. A characteristic vector, comprised of 10 features extracted from RQA was defined for amino acid sequences. Using Principal Component Analysis, four factors were found to be important determinants of allosteric behavior. Our sequence-based predictor method shows 82.6% accuracy, 85.7% sensitivity and 77.9% specificity with the current dataset. Further, we show that Laminarity-Mean-hydrophobicity representing repeated hydrophobic patches is the most crucial indicator of allostery. To our best knowledge this is the first report that describes sequence determinants of allostery based on hydrophobicity. As an outcome of these findings, we plan to explore possibility of inducing allostery in proteins.