Relating the Structure of HIV-1 Reverse Transcriptase to Its Processing Step

Sampling of Protein Conformational Space Using Hybrid Simulations: A Critical Assessment of Recent Methods

Recent years have seen several hybrid simulation methods for exploring the conformational space of proteins and their complexes or assemblies. These methods often combine fast analytical approaches with computationally expensive full atomic molecular dynamics (MD) simulations with the goal of rapidly sampling large and cooperative conformational changes at full atomic resolution. We present here a systematic comparison of the utility and limits of four such hybrid methods that have been introduced in recent years: MD with excited normal modes (MDeNM), collective modes-driven MD (CoMD), and elastic network model (ENM)-based generation, clustering, and relaxation of conformations (ClustENM) as well as its updated version integrated with MD simulations (ClustENMD). We analyzed the predicted conformational spaces using each of these four hybrid methods, applied to four well-studied proteins, triosephosphate isomerase (TIM), 3-phosphoglycerate kinase (PGK), HIV-1 protease (PR) and HIV-1 reverse transcriptase (RT), which provide extensive ensembles of experimental structures for benchmarking and comparing the methods. We show that a rigorous multi-faceted comparison and multiple metrics are necessary to properly assess the differences between conformational ensembles and provide an optimal protocol for achieving good agreement with experimental data. While all four hybrid methods perform well in general, being especially useful as computationally efficient methods that retain atomic resolution, the systematic analysis of the same systems by these four hybrid methods highlights the strengths and limitations of the methods and provides guidance for parameters and protocols to be adopted in future studies.

Computational Ways to Enhance Protein Inhibitor Design

Two new computational approaches are described to aid in the design of new peptide-based drugs by evaluating ensembles of protein structures from their dynamics and through the assessing of structures using empirical contact potential. These approaches build on the concept that conformational variability can aid in the binding process and, for disordered proteins, can even facilitate the binding of more diverse ligands. This latter consideration indicates that such a design process should be less restrictive so that multiple inhibitors might be effective. The example chosen here focuses on proteins/peptides that bind to hemagglutinin (HA) to block the large-scale conformational change for activation. Variability in the conformations is considered from sets of experimental structures, or as an alternative, from their simple computed dynamics; the set of designe peptides/small proteins from the David Baker lab designed to bind to hemagglutinin, is the large set considered and is assessed with the new empirical contact potentials.

Nonequilibrium Relaxation of Conformational Dynamics Facilitates Catalytic Reaction in an Elastic Network Model of T7 DNA Polymerase

Nucleotide-induced conformational closing of the finger domain of DNA polymerase is crucial for its catalytic action during DNA replication. Such large-amplitude molecular motion is often not fully accessible to either direct experimental monitoring or molecular dynamics simulations. However, a coarse-grained model can offer an informative alternative, especially for probing the relationship between conformational dynamics and catalysis. Here we investigate the dynamics of T7 DNA polymerase catalysis using a Langevin-type elastic network model incorporating detailed structural information on the open conformation without the substrate bound. Such a single-parameter model remarkably captures the induced conformational dynamics of DNA polymerase upon dNTP binding, and reveals its close coupling to the advancement toward transition state along the coordinate of the target reaction, which contributes to significant lowering of the activation energy barrier. Furthermore, analysis of stochastic catalytic rates suggests that when the activation energy barrier has already been significantly lowered and nonequilibrium relaxation toward the closed form dominates the catalytic rate, one must appeal to a picture of two-dimensional free energy surface in order to account for the full spectrum of catalytic modes. Our semiquantitative study illustrates the general role of conformational dynamics in achieving transition-state stabilization, and suggests that such an elastic network model, albeit simplified, possesses the potential to furnish significant mechanistic insights into the functioning of a variety of enzymatic systems.

Effects of protein subunits removal on the computed motions of partial 30S structures of the ribosome

The Anisotropic Network Model (ANM) is used to study motions of the 30S small ribosomal subunit. The effect of the absence of certain subunits on the motions of the remaining partial structures was investigated by removing one protein, pairs of proteins and selected sets of proteins at a time. Our results show that the removal of some proteins doesn't change the large-scale dynamics of the partial structures, but the removal of certain subunits does cause significant changes in motion of the remaining structure, and these changes can be reverted by the removal of other subunits, which indicates interdependence between motions of various parts of the 30S ribosomal structure. We further found that the subunits showing such interdependence have strong positive correlation of their motions, which indicates that these subunits function as a unit block in the 30S small ribosomal subunit. Dynamically interdependent subunit pairs identified in this paper are consistent with previous experimental observations that suggested dimerization of those subunits.

Packing regularities in biological structures relate to their dynamics

The high packing density inside proteins leads to certain geometric regularities and also is one of the most important contributors to the high extent of cooperativity manifested by proteins in their cohesive domain motions. The orientations between neighboring nonbonded residues in proteins substantially follow the similar geometric regularities, regardless of whether the residues are on the surface or buried, a direct result of hydrophobicity forces. These orientations are relatively fixed and correspond closely to small deformations from those of the face-centered cubic lattice, which is the way in which identical spheres pack at the highest density. Packing density also is related to the extent of conservation of residues, and we show this relationship for residue packing densities by averaging over a large sample or residue packings. There are three regimes: (1) over a broad range of packing densities the relationship between sequence entropy and inverse packing density is nearly linear, (2) over a limited range of low packing densities the sequence entropy is nearly constant, and (3) at extremely low packing densities the sequence entropy is highly variable. These packing results provide important justification for the simple elastic network models that have been shown for a large number of proteins to represent protein dynamics so successfully, even when the models are extremely coarse grained. Elastic network models for polymeric chains are simple and could be combined with these protein elastic networks to represent partially denatured parts of proteins. Finally, we show results of applications of the elastic network model to study the functional motions of the ribosome, based on its known structure. These results indicate expected correlations among its components for the step-wise processing steps in protein synthesis, and suggest ways to use these elastic network models to develop more detailed mechanisms, an important possibility because most experiments yield only static structures.

Protein promiscuity: drug resistance and native functions--HIV-1 case

The association of a drug with its target protein has the effect of blocking the protein activity and is termed a promiscuous function to distinguish from the protein's native function (Tawfik and associates, Nat. Genet. 37, 73-6, 2005). Obviously, a protein has not evolved naturally for drug association or drug resistance. Promiscuous protein functions exhibit unique traits of evolutionary adaptability, or evolvability, which is dependent on the induction of novel phenotypic traits by a small number of mutations. These mutations might have small effects on native functions, but large effects on promiscuous function; for example, an evolving protein could become increasingly drug resistant while maintaining its original function. Ariel Fernandez, in his opinion piece, notes that drug-binding "promiscuity" can hardly be dissociated from native functions; a dominant approach to drug discovery is the protein-native-substrate transition-state mimetic strategy. Thus, man-made ligands (e.g. drugs) have been successfully crafted to restrain enzymatic activity by focusing on the very same structural features that determine the native function. Using the successful inhibition of HIV-1 protease as an example, Fernandez illustrates how drug designers have employed naturally evolved features of the protein to suppress its activity. Based on these arguments, he dismisses the notion that drug binding is quintessentially promiscuous, even though in principle, proteins did not evolve to associate with man made ligands. In short, Fernandez argues that there may not be separate protein domains that one could term promiscuous domains. While acknowledging that drugs may bind promiscuously or in a native-like manner a la Fernandez, Tawfik maintains the role of evolutionary adaptation, even when a drug binds native-like. In the case of HIV-1 protease, drugs bind natively, and the initial onset of mutations results in drug resistance in addition to a dramatic decline in enzymatic activity and fitness of the virus. A chain of compensatory mutations follows this, and then the virus becomes fully fit and drug resistant. Ben Berkhout and Rogier Sanders subscribe to the evolution of new protein functions through gene duplication. With two identical protein domains, one domain can be released from a constraint imposed by the original function and it is thus free to move in sequence space toward a new function without loss of the original function. They emphasize that the forced evolution of drug-resistance differs significantly from the spontaneous evolution of an additional protein function. For instance, the latter process could proceed gradually on an evolutionary time scale, whereas the acquisition of drug-resistance is an all or nothing process for a virus, leading to the failure or success of therapy. They find no evidence to the thesis that resistance-mutations appear more rapidly in promiscuous domains than native domains. Berkhout and Sanders illustrate the genetic plasticity of HIV-1 by citing examples in which well-conserved amino acid residues of catalytic domains are forced to mutate under drug-pressure. HIV drug resistance biology is very complex. Instead of a viral protein, a drug can be targeted at a cellular protein. For example, Berkhout and Sanders claim, a drug targeted at the cellular protein CCR5 inhibits the binding of the viral envelope glycoprotein (Env) to CCR5. However, Env mutates so that it binds to the CCR5-drug complex and develops drug resistance. Interestingly, CCR5 has not evolved to bind to Env, but to a series of chemokines. Andrzej Kloczkowski, Taner Sen, and Bob Jernigan point out the importance of protein motions for binding. They believe it is likely that different ligands can bind to the diverse protein conformations sampled in the course of normal protein conformational fluctuations. They have been applying simple elastic network models to extract the motions as normal modes, which yield relatively small numbers of conformations that are useful for developing protein mechanisms; while these are typically small motions, for some proteins they can be quite large in scale. One of the major advantages of the approach is that only relatively small numbers of modes are important contributors to the overall motion -- so the approach provides a way to systematically map out a protein's motions. These models successfully represent the conformational fluctuations manifested in the crystallographic B-factors, and often suggest motions related to protein functional behaviors, such as those observed for reverse transcriptase, where two dominant hinges clearly relate to the processing steps -- one showing anti-correlation between the polymerase and ribonuclease H sites related to the translation and positioning of the nucleic acid chain, and another for opening and closing the polymerase site. Disordered proteins represent a more extreme case where the set of accessible conformations is much larger; thus they could offer up a broader range of possible binding forms. Whether evolution controls the functional motions for proteins remains little studied. Intriguingly, buried in the existing databases of protein-protein interactions may be information that can shed light on the extent of promiscuous binding among proteins themselves. Within these data there are cases where large numbers of diverse proteins have been shown to interact with a single protein; some of these could represent promiscuous protein-protein binding. Uncovering these promiscuous behaviors could be important for comprehending the details of how proteins can bind promiscuously to one another, and can exhibit even greater promiscuity in their binding to small molecules. The evolutionary routes, the dynamics of the target protein, and the many other aspects that need to be addressed while designing a drug that may dodge drug resistance, indicate the complexity and multi-disciplinary nature of the issue of drug resistance.