Accessory mutations balance the marginal stability of the HIV-1 protease in drug resistance

The HIV-1 protease is a major target of inhibitor drugs in AIDS therapies. The therapies are impaired by mutations of the HIV-1 protease that can lead to resistance to protease inhibitors. These mutations are classified into major mutations, which usually occur first and clearly reduce the susceptibility to protease inhibitors, and minor, accessory mutations that occur later and individually do not substantially affect the susceptibility to inhibitors. Major mutations are predominantly located in the active site of the HIV-1 protease and can directly interfere with inhibitor binding. Minor mutations, in contrast, are typically located distal to the active site. A central question is how these distal mutations contribute to resistance development. In this article, we present a systematic computational investigation of stability changes caused by major and minor mutations of the HIV-1 protease. As most small single-domain proteins, the HIV-1 protease is only marginally stable. Mutations that destabilize the folded, active state of the protease therefore can shift the conformational equilibrium towards the unfolded, inactive state. We find that the most frequent major mutations destabilize the HIV-1 protease, whereas roughly half of the frequent minor mutations are stabilizing. An analysis of protease sequences from patients in treatment indicates that the stabilizing minor mutations are frequently correlated with destabilizing major mutations, and that highly resistant HIV-1 proteases exhibit significant fractions of stabilizing mutations. Our results thus indicate a central role of minor mutations in balancing the marginal stability of the protease against the destabilization induced by the most frequent major mutations.     

Comprehensive mutagenesis of HIV-1 protease: a computational geometry approach

A computational geometry technique based on Delaunay tessellation of protein structure, represented by C(alpha) atoms, is used to study effects of single residue mutations on sequence-structure compatibility in HIV-1 protease. Profiles of residue scores derived from the four-body statistical potential are constructed for all 1881 mutants of the HIV-1 protease monomer and compared with the profile of the wild-type protein. The profiles for an isolated monomer of HIV-1 protease and the identical monomer in a dimeric state with an inhibitor are analyzed to elucidate changes to structural stability. Protease residues shown to undergo the greatest impact are those forming the dimer interface and flap region, as well as those known to be involved in inhibitor binding.     

Thoroughly sampling sequence space: large-scale protein design of structural ensembles

Modeling the inherent flexibility of the protein backbone as part of computational protein design is necessary to capture the behavior of real proteins and is a prerequisite for the accurate exploration of protein sequence space. We present the results of a broad exploration of sequence space, with backbone flexibility, through a novel approach: large-scale protein design to structural ensembles. A distributed computing architecture has allowed us to generate hundreds of thousands of diverse sequences for a set of 253 naturally occurring proteins, allowing exciting insights into the nature of protein sequence space. Designing to a structural ensemble produces a much greater diversity of sequences than previous studies have reported, and homology searches using profiles derived from the designed sequences against the Protein Data Bank show that the relevance and quality of the sequences is not diminished. The designed sequences have greater overall diversity than corresponding natural sequence alignments, and no direct correlations are seen between the diversity of natural sequence alignments and the diversity of the corresponding designed sequences. For structures in the same fold, the sequence entropies of the designed sequences cluster together tightly. This tight clustering of sequence entropies within a fold and the separation of sequence entropy distributions for different folds suggest that the diversity of designed sequences is primarily determined by a structure's overall fold, and that the designability principle postulated from studies of simple models holds in real proteins. This has important implications for experimental protein design and engineering, as well as providing insight into protein evolution.     

Computational proteomics analysis of HIV-1 protease interactome

HIV-1 protease is a small homodimeric enzyme that ensures maturation of HIV virions by cleaving the viral precursor Gag and Gag-Pol polyproteins into structural and functional elements. The cleavage sites in the viral polyproteins share neither sequence homology nor binding motif and the specificity of the HIV-1 protease is therefore only partially understood. Using an extensive data set collected from 16 years of HIV proteome research we have here created a general and predictive rule-based model for HIV-1 protease specificity based on rough sets. We demonstrate that HIV-1 protease specificity is much more complex than previously anticipated, which cannot be defined based solely on the amino acids at the substrate's scissile bond or by any other single substrate amino acid position only. Our results show that the combination of at least three particular amino acids is needed in the substrate for a cleavage event to occur. Only by combining and analyzing massive amounts of HIV proteome data it was possible to discover these novel and general patterns of physico-chemical substrate cleavage determinants. Our study is an example how computational biology methods can advance the understanding of the viral interactomes.     

Discrete Molecular Dynamics Can Predict Helical Prestructured Motifs in Disordered Proteins

Intrinsically disordered proteins (IDPs) lack a stable tertiary structure, but their short binding regions termed Pre-Structured Motifs (PreSMo) can form transient secondary structure elements in solution. Although disordered proteins are crucial in many biological processes and designing strategies to modulate their function is highly important, both experimental and computational tools to describe their conformational ensembles and the initial steps of folding are sparse. Here we report that discrete molecular dynamics (DMD) simulations combined with replica exchange (RX) method efficiently samples the conformational space and detects regions populating α-helical conformational states in disordered protein regions. While the available computational methods predict secondary structural propensities in IDPs based on the observation of protein-protein interactions, our ab initio method rests on physical principles of protein folding and dynamics. We show that RX-DMD predicts α-PreSMos with high confidence confirmed by comparison to experimental NMR data. Moreover, the method also can dissect α-PreSMos in close vicinity to each other and indicate helix stability. Importantly, simulations with disordered regions forming helices in X-ray structures of complexes indicate that a preformed helix is frequently the binding element itself, while in other cases it may have a role in initiating the binding process. Our results indicate that RX-DMD provides a breakthrough in the structural and dynamical characterization of disordered proteins by generating the structural ensembles of IDPs even when experimental data are not available.     

Synthetic HIV-2 protease cleaves the GAG precursor of HIV-1 with the same specificity as HIV-1 protease

A 99-amino acid protein having the deduced sequence of the protease from human immunodeficiency virus type 2 (HIV-2) was synthesized by the solid phase method and tested for specificity. The folded peptide catalyzes specific processing of a recombinant 43-kDa GAG precursor protein (F-16) of HIV-1. Although the protease of HIV-2 shares only 48% amino acid identity with that of HIV-1, the HIV-2 enzyme exhibits the same specificity toward the HIV-1 GAG precursor. Fragments of 34, 32, 24, 10, and 9 kDa were generated from F-16 GAG incubated with the protease. N-terminal amino acid sequence analysis of proteolytic fragments indicate that cleavage sites recognized by HIV-2 protease are identical to those of HIV-1 protease. The verified cleavage sites in F-16 GAG appear to be processed independently, as indicated by the formation of the intermediate fragments P32 and P34 in nearly equal ratios. The site nearest the amino terminus is quite conserved between the two viral GAG proteins (...VSQNY-PIVQN...in HIV-1,...KGGNY-PVQHV...in HIV-2). In contrast, the putative second site (...IPFAA-AQQKG...) of HIV-2 GAG shares minimal sequence identity with site 2 of HIV-1 GAG (...SATIM-MQRGN...). These sequence variations in the substrates suggest higher order structural features that may influence recognition by the proteases. Pepstatin A inhibits HIV-2 protease, whereas 1,10-phenanthroline and phenylmethylsulfonylfluoride do not; these results are in agreement with the finding that proteases of HIV and other retroviruses are aspartyl proteases.     

Relation between sequence and structure of HIV-1 protease inhibitor complexes: a model system for the analysis of protein flexibility.

The flexibility of different regions of HIV-1 protease was examined by using a database consisting of 73 X-ray structures that differ in terms of sequence, ligands or both. The root-mean-square differences of the backbone for the set of structures were shown to have the same variation with residue number as those obtained from molecular dynamics simulations, normal mode analyses and X-ray B-factors. This supports the idea that observed structural changes provide a measure of the inherent flexibility of the protein, although specific interactions between the protease and the ligand play a secondary role. The results suggest that the potential energy surface of the HIV-1 protease is characterized by many local minima with small energetic differences, some of which are sampled by the different X-ray structures of the HIV-1 protease complexes. Interdomain correlated motions were calculated from the structural fluctuations and the results were also in agreement with molecular dynamics simulations and normal mode analyses. Implications of the results for the drug-resistance engendered by mutations are discussed briefly.     

Studies on adaptability of binding residues and flap region of TMC-114 resistance HIV-1 protease mutants

Drug resistant mutations have severely restricted the success of HIV therapy. These mutations frequently involve the aspartic protease encoded by the virus. Knowledge of the molecular mechanisms underlying the conformational changes of HIV-1 protease mutants may be useful in developing more effective and longer lasting treatment regimes. The flap regions of the protease are the target of a particular type of mutations occurring far from the active site, which are able to produce significant resistance against the anti-HIV drug TMC-114. We provide insight into the molecular basis of TMC-114 resistance major flap mutations (I50V and I54M) in HIV-1 protease. It reports the shape complementarity and receptor-ligand interaction analysis supported by unrestrained all-atom molecular dynamics simulations of wild and major flap mutants of HIV-1 protease that sample large conformational changes of the flaps and active site binding residues. Both resistant flap mutants showed less atomic interaction toward TMC-114 and more structural deviation compared to wild HIV-protease. It is due to increasing flexibility at TMC-114 binding cavity and deviation of binding residues in 3-D space. Distortion in binding cavity and deviation in binding residues are the result of alteration in hydrogen bonding. Flap region also exhibited similar behaviour due to changes in number of hydrogen bonds during simulations.     

Recapitulation of protein family divergence using flexible backbone protein design

We use flexible backbone protein design to explore the sequence and structure neighborhoods of naturally occurring proteins. The method samples sequence and structure space in the vicinity of a known sequence and structure by alternately optimizing the sequence for a fixed protein backbone using rotamer based sequence search, and optimizing the backbone for a fixed amino acid sequence using atomic-resolution structure prediction. We find that such a flexible backbone design method better recapitulates protein family sequence variation than sequence optimization on fixed backbones or randomly perturbed backbone ensembles for ten diverse protein structures. For the SH3 domain, the backbone structure variation in the family is also better recapitulated than in randomly perturbed backbones. The potential application of this method as a model of protein family evolution is highlighted by a concerted transition to the amino acid sequence in the structural core of one SH3 domain starting from the backbone coordinates of an homologous structure.     

Evolution and disorder

The evolution of disordered proteins or regions of proteins differs from that of ordered proteins because of the differences in their sequence composition, intramolecular contacts, and function. Recent assessments of disordered protein evolution at the sequence, structural, and functional levels support this hypothesis. Disordered proteins have a different pattern of accepted point mutations, exhibit higher rates of insertions and deletions, and generally, but not always, evolve more rapidly than ordered proteins. Even with these high rates of sequence evolution, a few examples have shown that disordered proteins maintain their flexibility under physiological conditions, and it is hypothesized that they maintain specific structural ensembles.     

Dynamics of Preferential Substrate Recognition in HIV-1 Protease: Redefining the Substrate Envelope

Human immunodeficiency virus type 1 (HIV-1) protease (PR) permits viral maturation by processing the gag and gag-pro-pol polyproteins. HIV-1 PR inhibitors (PIs) are used in combination antiviral therapy but the emergence of drug resistance has limited their efficacy. The rapid evolution of HIV-1 necessitates consideration of drug resistance in novel drug design. Drug-resistant HIV-1 PR variants no longer inhibited efficiently, continue to hydrolyze the natural viral substrates. Though highly diverse in sequence, the HIV-1 PR substrates bind in a conserved three-dimensional shape we termed the substrate envelope. Earlier, we showed that resistance mutations arise where PIs protrude beyond the substrate envelope, because these regions are crucial for drug binding but not for substrate recognition. We extend this model by considering the role of protein dynamics in the interaction of HIV-1 PR with its substrates. We simulated the molecular dynamics of seven PR-substrate complexes to estimate the conformational flexibility of the bound substrates. Interdependence of substrate-protease interactions might compensate for variations in cleavage-site sequences and explain how a diverse set of sequences are recognized as substrates by the same enzyme. This diversity might be essential for regulating sequential processing of substrates. We define a dynamic substrate envelope as a more accurate representation of PR-substrate interactions. This dynamic substrate envelope, described by a probability distribution function, is a powerful tool for drug design efforts targeting ensembles of resistant HIV-1 PR variants with the aim of developing drugs that are less susceptible to resistance.     

Predicting drug resistance of the HIV-1 protease using molecular interaction energy components

Drug resistance significantly impairs the efficacy of AIDS therapy. Therefore, precise prediction of resistant viral mutants is particularly useful for developing effective drugs and designing therapeutic regimen. In this study, we applied a structure-based computational approach to predict mutants of the HIV-1 protease resistant to the seven FDA approved drugs. We analyzed the energetic pattern of the protease-drug interaction by calculating the molecular interaction energy components (MIECs) between the drug and the protease residues. Support vector machines (SVMs) were trained on MIECs to classify protease mutants into resistant and nonresistant categories. The high prediction accuracies for the test sets of cross-validations suggested that the MIECs successfully characterized the interaction interface between drugs and the HIV-1 protease. We conducted a proof-of-concept study on a newly approved drug, darunavir (TMC114), on which no drug resistance data were available in the public domain. Compared with amprenavir, our analysis suggested that darunavir might be more potent to combat drug resistance. To quantitatively estimate binding affinities of drugs and study the contributions of protease residues to causing resistance, linear regression models were trained on MIECs using partial least squares (PLS). The MIEC-PLS models also achieved satisfactory prediction accuracy. Analysis of the fitting coefficients of MIECs in the regression model revealed the important resistance mutations and shed light into understanding the mechanisms of these mutations to cause resistance. Our study demonstrated the advantages of characterizing the protease-drug interaction using MIECs. We believe that MIEC-SVM and MIEC-PLS can help design new agents or combination of therapeutic regimens to counter HIV-1 protease resistant strains.     

Flap opening and dimer-interface flexibility in the free and inhibitor-bound HIV protease, and their implications for function.

BACKGROUND: (1)H and (15)N transverse relaxation measurements on perdeuterated proteins are ideally suited for detecting backbone conformational fluctuations on the millisecond-microsecond timescale. The identification of conformational exchange on this timescale by measuring the relaxation of both (1)H and (15)N holds great promise for the elucidation of functionally relevant conformational changes in proteins. RESULTS: We measured the transverse (1)H and (15)N relaxation rates of backbone amides of HIV-1 protease in its free and inhibitor-bound forms. An analysis of these rates, obtained as a function of the effective rotating frame field, provided information about the timescale of structural fluctuations in several regions of the protein. The flaps that cover the active site of the inhibitor-bound protein undergo significant changes of backbone (φ,psi) angles, on the 100 micros timescale, in the free protein. In addition, the intermonomer beta-sheet interface of the bound form, which from protease structure studies appears to be rigid, was found to fluctuate on the millisecond timescale. CONCLUSIONS: We present a working model of the flap-opening mechanism in free HIV-1 protease which involves a transition from a semi-open to an open conformation that is facilitated by interaction of the Phe53 ring with the substrate. We also identify a surprising fluctuation of the beta-sheet intermonomer interface that suggests a structural requirement for maturation of the protease. Thus, slow conformational fluctuations identified by (1)H and (15)N transverse relaxation measurements can be related to the biological functions of proteins.     

Low-throughput model design of protein folding inhibitors

The stabilization energy of proteins in their native conformation is not distributed uniformly among all the amino acids, but is concentrated in few (short) fragments, fragments which play a key role in the folding process and in the stability of the protein. Peptides displaying the same sequence as these key fragments can compete with the formation of the most important native contacts, destabilizing the protein and thus inhibiting its biological activity. We present an essentially automatic method to individuate such peptidic inhibitors based on a low-throughput screening of the fragments which build the target protein. The efficiency and generality of the method is tested on proteins Src-SH3, G, CI2, and HIV-1-PR with the help of a simplified computational model. In each of the cases studied, we find few peptides displaying strong inhibitory properties, properties which are quite robust with respect to point mutations. The possibility of implementing the method through low-throughput experimental screening of the target protein is discussed.     

A peptide inhibitor of HIV-1 protease using alpha, beta- dehydro residues: a structure based computer model

HIV-1 encodes an aspartic protease, an enzyme crucial to viral maturation and infectivity. It is responsible for the cleavage of various protein precursors into viral proteins. Inhibition of this enzyme prevents the formation of mature, infective viral particles and therefore, it is a potential target for therapeutic intervention following infection. Several drugs that inhibit the action of this enzyme have been discovered. These include peptidomimetic inhibitors such as ABT-538 and saquinavir, and structure based inhibitors such as indinavir and nelfinavir. Several of these have been tested in human clinical trials and have demonstrated significant reduction in viral load. However, most of them have been found to be of limited clinical utility because of their poor pharmacological properties and also because the viral protease becomes rapidly resistant to these drugs on account of mutations in the enzyme. One way to overcome these limitations is to design an inhibitor that interacts mainly with the conserved residues of HIV-1 protease. By a rational drug design approach based on the high resolution X-ray crystal structure of the HIV-1 protease with--MVT 101 (a substrate based inhibitor) and the specific design principles of peptides containing dehydro-Alanine (delta Ala) derived from our earlier studies, we have designed a tetrapeptide with the sequence: NH2-Thr-delta Ala-delta Ala-Gln-COOH. Energy minimization and molecular modelling of the interaction of the designed tetrapeptide with the inhibitor binding site indicate that the inhibitor is in an extended conformation and makes excessive contacts with the viral enzyme at the interface between the protein subunits. The designed inhibitor has 33% of its interaction with the conserved region of HIV-1 protease which is of the same order as that of MVT 101 with the enzyme.     

An unbiased adaptive sampling algorithm for the exploration of RNA mutational landscapes under evolutionary pressure

The analysis of the relationship between sequences and structures (i.e., how mutations affect structures and reciprocally how structures influence mutations) is essential to decipher the principles driving molecular evolution, to infer the origins of genetic diseases, and to develop bioengineering applications such as the design of artificial molecules. Because their structures can be predicted from the sequence data only, RNA molecules provide a good framework to study this sequence-structure relationship. We recently introduced a suite of algorithms called RNAmutants which allows a complete exploration of RNA sequence-structure maps in polynomial time and space. Formally, RNAmutants takes an input sequence (or seed) to compute the Boltzmann-weighted ensembles of mutants with exactly k mutations, and sample mutations from these ensembles. However, this approach suffers from major limitations. Indeed, since the Boltzmann probabilities of the mutations depend of the free energy of the structures, RNAmutants has difficulties to sample mutant sequences with low G+C-contents. In this article, we introduce an unbiased adaptive sampling algorithm that enables RNAmutants to sample regions of the mutational landscape poorly covered by classical algorithms. We applied these methods to sample mutations with low G+C-contents. These adaptive sampling techniques can be easily adapted to explore other regions of the sequence and structural landscapes which are difficult to sample. Importantly, these algorithms come at a minimal computational cost. We demonstrate the insights offered by these techniques on studies of complete RNA sequence structures maps of sizes up to 40 nucleotides. Our results indicate that the G+C-content has a strong influence on the size and shape of the evolutionary accessible sequence and structural spaces. In particular, we show that low G+C-contents favor the apparition of internal loops and thus possibly the synthesis of tertiary structure motifs. On the other hand, high G+C-contents significantly reduce the size of the evolutionary accessible mutational landscapes.     

A major role for a set of non-active site mutations in the development of HIV-1 protease drug resistance

A major problem in the chemotherapy of HIV-1 infection is the appearance of drug resistance. In the case of HIV-1 protease inhibitors, resistance originates from mutations in the protease molecule that lower the affinity of inhibitors while still maintaining a viable enzymatic profile. Drug resistance mutations can be classified as active site or non-active site mutations depending on their location within the protease molecule. Active site mutations directly affect drug/target interactions, and their action can be readily understood in structural terms. Non-active site mutations influence binding from distal locations, and their mechanism of action is not immediately apparent. In this paper, we have characterized a mutant form of the HIV-1 protease, ANAM-11, identified in clinical isolates from HIV-1 infected patients treated with protease inhibitors. This mutant protease contains 11 mutations, 10 of which are located outside the active site (L10I/M36I/S37D/M46I/R57K/L63P/A71V/G73S/L90M/I93L) and 1 within the active site (I84V). ANAM-11 lowers the binding affinity of indinavir, nelfinavir, saquinavir, and ritonavir by factors of 4000, 3300, 5800, and 80000, respectively. Surprisingly, most of the loss in inhibitor affinity is due to the non-active site mutations as demonstrated by additional experiments performed with a protease containing only the 10 non-active site mutations (NAM-10) and another containing only the active site mutation (A-1). Kinetic analysis with two different substrates yielded comparable catalytic efficiencies for A-1, ANAM-11, NAM-10, and the wild-type protease. These studies demonstrate that non-active site mutations can be the primary source of resistance and that their role is not necessarily limited to compensate deleterious effects of active site mutations. Analysis of the structural stability of the proteases by differential scanning calorimetry reveals that ANAM-11 and NAM-10 are structurally more stable than the wild-type protease while A-1 is less stable. Together, the binding and structural thermodynamic results suggest that the non-active site mutants affect inhibitor binding by altering the geometry of the binding site cavity through the accumulation of mutations within the core of the protease molecule.     

Adaptability and flexibility of HIV-1 protease.

Even though more than 200 three-dimensional structures of HIV-1 protease complexed to a variety of inhibitors are available in the Protein Data Bank; very few structures of unliganded protein have been determined. We have recently solved structures of unliganded HIV-1 protease tethered dimer mutants to resolutions of 1.9 A and 2.1 A, and have found that the flaps assume closed-flap conformation even in the absence of any bound ligand. We report comparison of the unliganded closed-flap structure with structures of HIV-1 protease inhibitor complexes with a view to accurately identifying structural changes that the ligand can induce on binding to HIV-1 protease in the crystal. These studies reveal that the least flexible region present in the active site of HIV-1 protease need not also be the least adaptable to external stress, thus highlighting the conceptual difference between flexibility and adaptability of proteins in general.     

Identification of protein-protein interaction sites from docking energy landscapes

Protein recognition is one of the most challenging and intriguing problems in structural biology. Despite all the available structural, sequence and biophysical information about protein-protein complexes, the physico-chemical patterns, if any, that make a protein surface likely to be involved in protein-protein interactions, remain elusive. Here, we apply protein docking simulations and analysis of the interaction energy landscapes to identify protein-protein interaction sites. The new protocol for global docking based on multi-start global energy optimization of an all-atom model of the ligand, with detailed receptor potentials and atomic solvation parameters optimized in a training set of 24 complexes, explores the conformational space around the whole receptor without restrictions. The ensembles of the rigid-body docking solutions generated by the simulations were subsequently used to project the docking energy landscapes onto the protein surfaces. We found that highly populated low-energy regions consistently corresponded to actual binding sites. The procedure was validated on a test set of 21 known protein-protein complexes not used in the training set. As much as 81% of the predicted high-propensity patch residues were located correctly in the native interfaces. This approach can guide the design of mutations on the surfaces of proteins, provide geometrical details of a possible interaction, and help to annotate protein surfaces in structural proteomics.