Ligand binding to proteins: the binding landscape model

Peptide modulation of class I major histocompatibility complex protein molecular flexibility and the implications for immune recognition

T cells use the αβ T cell receptor (TCR) to recognize antigenic peptides presented by class I major histocompatibility complex proteins (pMHCs) on the surfaces of antigen-presenting cells. Flexibility in both TCRs and peptides plays an important role in antigen recognition and discrimination. Less clear is the role of flexibility in the MHC protein; although recent observations have indicated that mobility in the MHC can impact TCR recognition in a peptide-dependent fashion, the extent of this behavior is unknown. Here, using hydrogen/deuterium exchange, fluorescence anisotropy, and structural analyses, we show that the flexibility of the peptide binding groove of the class I MHC protein HLA-A*0201 varies significantly with different peptides. The variations extend throughout the binding groove, impacting regions contacted by TCRs as well as other activating and inhibitory receptors of the immune system. Our results are consistent with statistical mechanical models of protein structure and dynamics, in which the binding of different peptides alters the populations and exchange kinetics of substates in the MHC conformational ensemble. Altered MHC flexibility will influence receptor engagement, impacting conformational adaptations, entropic penalties associated with receptor recognition, and the populations of binding-competent states. Our results highlight a previously unrecognized aspect of the "altered self" mechanism of immune recognition and have implications for specificity, cross-reactivity, and antigenicity in cellular immunity.

The investigation of the binding of 6-mercaptopurine to site I on human serum albumin

6-Mercaptopurine (6-MP) is one of a large series of purine analogues which has been found active against human leukemias. The equilibrium dialysis, circular dichroism (CD) and molecular docking were employed to study the binding of 6-MP to human serum albumin (HSA). The binding of 6-MP to HSA in the equilibrium dialysis experiment was detected by measuring the displacement of 6-MP by specific markers for site I on HSA, warfarin (RWF), phenylbutazone (PhB) and n-butyl p-aminobenzoate (ABE). It was shown, according to CD data, that binding of 6-MP to HSA leads to alteration of HSA secondary structure. Based on the findings from displacement experiment and molecular docking simulation it was found that 6-MP was located within binding cavity of subdomain IIA and the space occupied by site markers overlapped with that of 6-MP. Displacement of 6-MP by the RWF or PhB was not up the level expected for a competitive mechanism, therefore displacement of 6-MP was rather by non-cooperative than that the direct competition. Instead, in case of the interaction between ABE and 6-MP, when the little enhancement of the binding of ABE by 6-MP was found, the interaction could be via a positively cooperative mechanism.

Structured crowding and its effects on enzyme catalysis

Macromolecular crowding decreases the diffusion rate, shifts the equilibrium of protein-protein and protein-substrate interactions, and changes protein conformational dynamics. Collectively, these effects contribute to enzyme catalysis. Here we describe how crowding may bias the conformational change and dynamics of enzyme populations and in this way affect catalysis. Crowding effects have been studied using artificial crowding agents and in vivo-like environments. These studies revealed a correlation between protein dynamics and function in the crowded environment. We suggest that crowded environments be classified into uniform crowding and structured crowding. Uniform crowding represents random crowding conditions created by synthetic particles with a narrow size distribution. Structured crowding refers to the highly coordinated cellular environment, where proteins and other macromolecules are clustered and organized. In structured crowded environments the perturbation of protein thermal stability may be lower; however, it may still be able to modulate functions effectively and dynamically. Dynamic, allosteric enzymes could be more sensitive to cellular perturbations if their free energy landscape is flatter around the native state; on the other hand, if their free energy landscape is rougher, with high kinetic barriers separating deep minima, they could be more robust. Above all, cells are structured; and this holds both for the cytosol and for the membrane environment. The crowded environment is organized, which limits the search, and the crowders are not necessarily inert. More likely, they too transmit allosteric effects, and as such play important functional roles. Overall, structured cellular crowding may lead to higher enzyme efficiency and specificity.

Modulation of a ligand's energy landscape and kinetics by the chemical environment

Understanding how the chemical environment modulates the predominant conformations and kinetics of flexible molecules is a core interest of biochemistry and a prerequisite for the rational design of synthetic catalysts. This study combines molecular dynamics simulation and Markov state models (MSMs) to a systematic computational strategy for investigating the effect of the chemical environment of a molecule on its conformations and kinetics. MSMs allow quantities to be computed that are otherwise difficult to access, such as the metastable sets, their free energies, and the relaxation time scales related to the rare transitions between metastable states. Additionally, MSMs are useful to identify observables that may act as sensors for the conformational or binding state of the molecule, thus guiding the design of experiments. In the present study, the conformation dynamics of UDP-GlcNAc are studied in vacuum, water, water + Mg(2+), and in the protein UDP-GlcNAc 2-epimerase. It is found that addition of Mg(2+) significantly affects the conformational stability, thermodynamics, and kinetics of UDP-GlcNAc. In particular, the slowest structural process, puckering of the GlcNAc sugar, depends on the overall conformation of UDP-GlcNAc and may thus act as a sensor of whether Mg(2+) is bound or not. Interestingly, transferring the molecule from vacuum to water makes the protein-binding conformations UDP-GlcNAc first accessible, while adding Mg(2+) further stabilizes them by specifically associating to binding-competent conformations. While Mg(2+) is not cocrystallized in the UDP-GlcNAc 2-epimerase complex, the selectively stabilized Mg(2+)/UDP-GlcNAc complex may be a template for the bound state, and Mg(2+) may accompany the binding-competent ligand conformation to the binding pocket. This serves as a possible explanation of the enhanced epimerization rate in the presence of Mg(2+). This role of Mg(2+) has previously not been described and opens the question whether "binding co-factors" may be a concept of general relevance for protein-ligand binding.

Combining geometric pocket detection and desolvation properties to detect putative ligand binding sites on proteins

The accurate identification of cavities that can bind ligands on the surface of proteins is of major importance for the characterization of the function of proteins based on its structure. In addition it can be helpful for rational structure-based drug design on target proteins of medical relevance and for evaluating the tendency of proteins to aggregate or oligomerize. A new approach termed dPredGB to detect and evaluate putative binding cavities on protein surfaces has been developed. In contrast to existing prediction methods that are based on purely geometric features of binding sites or on possible direct interactions with a putative binding partner the dPredGB approach combines rapid geometric detection with an evaluation of the desolvation properties of the putative binding pocket. It has been tested on a variety of proteins known to bind ligands in bound and unbound conformations. The approach outperforms most available methods and offers also the spatial characterization of the desolvation properties of a binding region. On a test set of proteins the method identifies in 69% of the unbound cases and 85% of the bound cases the known ligand binding cavity as the top ranking prediction. Possibilities to improve the prediction performance even further are also discussed.

Kinetic Analysis of Guanidine Hydrochloride Inactivation of β-Galactosidase in the Presence of Galactose

Inactivation of purified β-Galactosidase was done with GdnHCl in the absence and presence of varying [galactose] at 50°C and at pH 4.5. Lineweaver-Burk plots of initial velocity data, in the presence and absence of guanidine hydrochloride (GdnHCl) and galactose, were used to determine the relevant K_m and V_max values, with p-nitrophenyl β-D-galactopyranoside (pNPG) as substrate, S. Plots of ln([P]_∞ − [P]_t) against time in the presence of GdnHCl yielded the inactivation rate constant, A. Plots of A versus [S] at different galactose concentrations were straight lines that became increasingly less steep as the [galactose] increased, showing that A was dependent on [S]. Slopes and intercepts of the 1/[P]_∞ versus 1/[S] yielded k₊₀ and k'₊₀, the microscopic rate constants for the free enzyme and the enzyme-substrate complex, respectively. Plots of k₊₀ and k'₊₀ versus [galactose] showed that galactose protected the free enzyme as well as the enzyme-substrate complex (only at the lowest and highest [galactose]) against GdnHCl inactivation. In the absence of galactose, GdnHCl exhibited some degree of non-competitive inhibition. In the presence of GdnHCl, galactose exhibited competitive inhibition at the lower [galactose] of 5 mM which changed to non-competitive as the [galactose] increased. The implications of our findings are further discussed.

Macrostate identification from biomolecular simulations through time series analysis

This paper builds upon the need for a more descriptive and accurate understanding of the landscape of intermolecular interactions, particularly those involving macromolecules such as proteins. For this, we need methods that move away from the single conformation description of binding events, toward a descriptive free energy landscape where different macrostates can coexist. Molecular dynamics simulations and molecular mechanics Poisson-Boltzmann surface area (MM-PBSA) methods provide an excellent approach for such a dynamic description of the binding events. An alternative to the standard method of the statistical reporting of such results is proposed.

Selective molecular recognition in amyloid growth and transmission and cross-species barriers

Mutual conformational selection and population shift followed by minor induced-fit optimization is the key mechanism in biomolecular recognition, and monomers and small oligomers binding to amyloid seeds in fibril growth is a molecular recognition event. Here, we describe amyloid aggregation, preferred species, cross-species barriers and transmission within the broad framework of molecular recognition. Cross-seeding of amyloid species is governed by conformational selection of compatible (complementary) states. If the dominant conformations of two species are similar, they can cross-seed each other; on the other hand, if they are sufficiently different, they will grow into different fibrils, reflecting species barriers. Such a scenario has recently been observed for the tau protein, which has four repeats. While a construct consisting of repeats 1, 3 and 4 can serve as a seed for the entire four-repeat tau segment, the inverse does not hold. On the other hand, the tau protein repeats with the characteristic U-turn shape can cross-seed Alzheimer's amyloid β and, similarly, the islet amyloid polypeptide. Within this framework, we suggest that the so-called "central dogma" of amyloid formation, where aggregation takes place through nonspecific backbone hydrogen bonding interactions, which are common to all peptides and proteins, is a simple reflection of the heterogeneous, polymorphic free-energy landscape of amyloid species. Here, we review available data and make some propositions addressing this key problem. In particular, we argue that recent theoretical and experimental observations support the key role of selective molecular recognition in amyloidosis and in determining cross-species barriers and transmission.

Super folds, networks, and barriers

Exhaustive enumeration of sequences and folds is conducted for a simple lattice model of conformations, sequences, and energies. Examination of all foldable sequences and their nearest connected neighbors (sequences that differ by no more than a point mutation) illustrates the following: (i) There exist unusually large number of sequences that fold into a few structures (super-folds). The same observation was made experimentally and computationally using stochastic sampling and exhaustive enumeration of related models. (ii) There exist only a few large networks of connected sequences that are not restricted to one fold. These networks cover a significant fraction of fold spaces (super-networks). (iii) There exist barriers in sequence space that prevent foldable sequences of the same structure to "connect" through a series of single point mutations (super-barrier), even in the presence of the sequence connection between folds. While there is ample experimental evidence for the existence of super-folds, evidence for a super-network is just starting to emerge. The prediction of a sequence barrier is an intriguing characteristic of sequence space, suggesting that the overall sequence space may be disconnected. The implications and limitations of these observations for evolution of protein structures are discussed.

Revealing conformational substates of lipidated N-Ras protein by pressure modulation

Regulation of protein function is often linked to a conformational switch triggered by chemical or physical signals. To evaluate such conformational changes and to elucidate the underlying molecular mechanisms of subsequent protein function, experimental identification of conformational substates and characterization of conformational equilibria are mandatory. We apply pressure modulation in combination with FTIR spectroscopy to reveal equilibria between spectroscopically resolved substates of the lipidated signaling protein N-Ras. Pressure has the advantage that its thermodynamic conjugate is volume, a parameter that is directly related to structure. The conformational dynamics of N-Ras in its different nucleotide binding states in the absence and presence of a model biomembrane was probed by pressure perturbation. We show that not only nucleotide binding but also the presence of the membrane has a drastic effect on the conformational dynamics and selection of conformational substates of the protein, and a new substate appearing upon membrane binding could be uncovered. Population of this new substate is accompanied by structural reorientations of the G domain, as also indicated by complementary ATR-FTIR and IRRAS measurements. These findings thus illustrate that the membrane controls signaling conformations by acting as an effective interaction partner, which has consequences for the G-domain orientation of membrane-associated N-Ras, which in turn is known to be critical for its effector and modulator interactions. Finally, these results provide insights into the influence of pressure on Ras-controlled signaling events in organisms living under extreme environmental conditions as they are encountered in the deep sea where pressures reach the kbar range.

Promiscuity in protein-RNA interactions: conformational ensembles facilitate molecular recognition in the spliceosome: conformational diversity in U2AF⁶⁵ facilitates binding to diverse RNA sequences

Here I discuss findings that suggest a universal mechanism for proteins (and RNA) to recognize and interact with various binding partners by selectively binding to different conformations that pre-exist in the free protein's conformational ensemble. The tandem RNA recognition motif domains of splicing factor U2AF⁶⁵ fluctuate in solution between a predominately closed conformation in which the RNA binding site of one of the domains is blocked, and a lowly populated open conformation in which both RNA binding pockets are accessible. RNA binding to U2AF⁶⁵ may thus occur through the weakly populated open conformation, and the binding interaction stabilizes the open conformation. The conformational diversity observed in U2AF⁶⁵ might also facilitate binding to diverse RNA sequences as found in the polypyrimidine tracts that help define 3' splice sites. Similar binding pathways in other systems have important consequences in biological regulation, molecular evolution, and information storage.

Accurate prediction of the binding free energy and analysis of the mechanism of the interaction of replication protein A (RPA) with ssDNA

The eukaryotic replication protein A (RPA) has several pivotal functions in the cell metabolism, such as chromosomal replication, prevention of hairpin formation, DNA repair and recombination, and signaling after DNA damage. Moreover, RPA seems to have a crucial role in organizing the sequential assembly of DNA processing proteins along single stranded DNA (ssDNA). The strong RPA affinity for ssDNA, K(A) between 10(-9)-10(-10) M, is characterized by a low cooperativity with minor variation for changes on the nucleotide sequence. Recently, new data on RPA interactions was reported, including the binding free energy of the complex RPA70AB with dC(8) and dC(5), which has been estimated to be -10 ± 0.4 kcal mol(-1) and -7 ± 1 kcal mol(-1), respectively. In view of these results we performed a study based on molecular dynamics aimed to reproduce the absolute binding free energy of RPA70AB with the dC(5) and dC(8) oligonucleotides. We used several tools to analyze the binding free energy, rigidity, and time evolution of the complex. The results obtained by MM-PBSA method, with the use of ligand free geometry as a reference for the receptor in the separate trajectory approach, are in excellent agreement with the experimental data, with ±4 kcal mol(-1) error. This result shows that the MM-PB(GB)SA methods can provide accurate quantitative estimates of the binding free energy for interacting complexes when appropriate geometries are used for the receptor, ligand and complex. The decomposition of the MM-GBSA energy for each residue in the receptor allowed us to correlate the change of the affinity of the mutated protein with the ΔG(gas+sol) contribution of the residue considered in the mutation. The agreement with experiment is optimal and a strong change in the binding free energy can be considered as the dominant factor in the loss for the binding affinity resulting from mutation.

Induced fit or conformational selection? The role of the semi-closed state in the maltose binding protein

A full characterization of the thermodynamic forces underlying ligand-associated conformational changes in proteins is essential for understanding and manipulating diverse biological processes, including transport, signaling, and enzymatic activity. Recent experiments on the maltose binding protein (MBP) have provided valuable data about the different conformational states implicated in the ligand recognition process; however, a complete picture of the accessible pathways and the associated changes in free energy remains elusive. Here we describe results from advanced accelerated molecular dynamics (aMD) simulations, coupled with adaptively biased force (ABF) and thermodynamic integration (TI) free energy methods. The combination of approaches allows us to track the ligand recognition process on the microsecond time scale and provides a detailed characterization of the protein’s dynamic and the relative energy of stable states. We find that an induced-fit (IF) mechanism is most likely and that a mechanism involving both a conformational selection (CS) step and an IF step is also possible. The complete recognition process is best viewed as a “Pac Man” type action where the ligand is initially localized to one domain and naturally occurring hinge-bending vibrations in the protein are able to assist the recognition process by increasing the chances of a favorable encounter with side chains on the other domain, leading to a population shift. This interpretation is consistent with experiments and provides new insight into the complex recognition mechanism. The methods employed here are able to describe IF and CS effects and provide formally rigorous means of computing free energy changes. As such, they are superior to conventional MD and flexible docking alone and hold great promise for future development and applications to drug discovery.

Blind docking method combining search of low-resolution binding sites with ligand pose refinement by molecular dynamics-based global optimization

This study describes the development of a new blind hierarchical docking method, bhDock, its implementation, and accuracy assessment. The bhDock method uses two-step algorithm. First, a comprehensive set of low-resolution binding sites is determined by analyzing entire protein surface and ranked by a simple score function. Second, ligand position is determined via a molecular dynamics-based method of global optimization starting from a small set of high ranked low-resolution binding sites. The refinement of the ligand binding pose starts from uniformly distributed multiple initial ligand orientations and uses simulated annealing molecular dynamics coupled with guided force-field deformation of protein-ligand interactions to find the global minimum. Assessment of the bhDock method on the set of 37 protein-ligand complexes has shown the success rate of predictions of 78%, which is better than the rate reported for the most cited docking methods, such as AutoDock, DOCK, GOLD, and FlexX, on the same set of complexes.

Seven transmembrane receptors as shapeshifting proteins: the impact of allosteric modulation and functional selectivity on new drug discovery

It is useful to consider seven transmembrane receptors (7TMRs) as disordered proteins able to allosterically respond to a number of binding partners. Considering 7TMRs as allosteric systems, affinity and efficacy can be thought of in terms of energy flow between a modulator, conduit (the receptor protein), and a number of guests. These guests can be other molecules, receptors, membrane-bound proteins, or signaling proteins in the cytosol. These vectorial flows of energy can yield standard canonical guest allostery (allosteric modification of drug effect), effects along the plane of the cell membrane (receptor oligomerization), or effects directed into the cytosol (differential signaling as functional selectivity). This review discusses these apparently diverse pharmacological effects in terms of molecular dynamics and protein ensemble theory, which tends to unify 7TMR behavior toward cells. Special consideration will be given to functional selectivity (biased agonism and biased antagonism) in terms of mechanism of action and potential therapeutic application. The explosion of technology that has enabled observation of diverse 7TMR behavior has also shown how drugs can have multiple (pluridimensional) efficacies and how this can cause paradoxical drug classification and nomenclatures.

Binding selectivity of RecA to a single stranded DNA, a computational approach

Homologous recombination (HR) is the major DNA double strand break repair pathway which maintains the genomic integrity. It is fundamental for the survivability and functionality of all organisms. One of the initial steps in HR is the formation of the nucleoprotein filament composed by a single stranded DNA chain surrounded by the recombinases protein. The filament orchestrates the search for an undamaged homologue, as a template for the repair process. Our theoretical study was aimed at elucidating the selectivity of the interaction between a monomer of the recombinases enzyme in the Escherichia coli, EcRecA, the bacterial homologue of human Rad51, with a series of oligonucleotides of nine bases length. The complex, equilibrated for 20 ns with Langevian dynamics, was inserted in a periodic box with a 8 Å buffer of water molecules explicitly described by the TIP3P model. The absolute binding free energies are calculated in an implicit solvent using the Poisson-Boltzmann (PB) and the generalized Born (GB) solvent accessible surface area, using the MM-PB(GB)SA model. The solute entropic contribution is also calculated by normal mode analysis. The results underline how a significant contribution of the binding free energy is due to the interaction with the Arg196, a critical amino acid for the activity of the enzyme. The study revealed how the binding affinity of EcRecA is significantly higher toward dT₉ rather than dA₉, as expected from the experimental results.

Chemical frustration in the protein folding landscape: grand canonical ensemble simulations of cytochrome c

A grand canonical formalism is developed to combine discrete simulations for chemically distinct species in equilibrium. Each simulation is based on a perturbed funneled landscape. The formalism is illustrated using the alkaline-induced transitions of cytochrome c as observed by FTIR spectroscopy and with various other experimental approaches. The grand canonical simulation method accounts for the acid/base chemistry of deprotonation, the inorganic chemistry of heme ligation and misligation, and the minimally frustrated folding energy landscape, thus elucidating the physics of protein folding involved with an acid/base titration of a protein. The formalism combines simulations for each of the relevant chemical species, varying by protonation and ligation states. In contrast to models based on perfectly funneled energy landscapes that contain only contacts found in the native structure, this study introduces "chemical frustration" from deprotonation and misligation that gives rise to many intermediates at alkaline pH. While the nature of these intermediates cannot be easily inferred from available experimental data, this study provides specific structural details of these intermediates, thus extending our understanding of how cytochrome c changes with an increase in pH. The results demonstrate the importance of chemical frustration for understanding biomolecular energy landscapes.

Why does binding of proteins to DNA or proteins to proteins not necessarily spell function?

Studies of binding are often question: first, is the observed binding functional, and second, if it is, which function? Is it activation or repression? The first question relates to binding at different sites; the second relates to binding at similar sites. These questions apply to transcription factors binding to genomic DNA and to protein interaction domains binding to their partners. Here, we explain that both can be understood in terms of allostery and the cellular (or in vitro) environment. The idea is simple yet powerful; it emphasizes the role of allostery in defining whether binding between transcription factors and (cognate or noncognate) DNA sequences will lead to function and to the type of function. Allosteric effects are the outcome of dynamically shifting populations; thus binding to even slightly different DNA sequences will lead to different transcription factor conformations that can be reflected in the binding sites to their co-regulators. Currently, allostery is not considered when trying to understand how binding phenomena determine the functional outcome. Allosteric effects can enhance the binding specificity in a function-oriented manner. Here we provide a biological rationale that considers cellular crowding effects.

T cell receptor cross-reactivity directed by antigen-dependent tuning of peptide-MHC molecular flexibility

T cell-mediated immunity requires T cell receptor (TCR) cross-reactivity, the mechanisms behind which remain incompletely elucidated. The alphabeta TCR A6 recognizes both the Tax (LLFGYPVYV) and Tel1p (MLWGYLQYV) peptides presented by the human class I MHC molecule HLA-A2. Here we found that although the two ligands are ideal structural mimics, they form substantially different interfaces with A6, with conformational differences in the peptide, the TCR, and unexpectedly, the MHC molecule. The differences between the Tax and Tel1p ternary complexes could not be predicted from the free peptide-MHC structures and are inconsistent with a traditional induced-fit mechanism. Instead, the differences were attributable to peptide and MHC molecular motion present in Tel1p-HLA-A2 but absent in Tax-HLA-A2. Differential "tuning" of the dynamic properties of HLA-A2 by the Tax and Tel1p peptides thus facilitates cross-recognition and impacts how structural diversity can be presented to and accommodated by receptors of the immune system.

The role of dynamic conformational ensembles in biomolecular recognition

Molecular recognition is central to all biological processes. For the past 50 years, Koshland's 'induced fit' hypothesis has been the textbook explanation for molecular recognition events. However, recent experimental evidence supports an alternative mechanism. 'Conformational selection' postulates that all protein conformations pre-exist, and the ligand selects the most favored conformation. Following binding the ensemble undergoes a population shift, redistributing the conformational states. Both conformational selection and induced fit appear to play roles. Following binding by a primary conformational selection event, optimization of side chain and backbone interactions is likely to proceed by an induced fit mechanism. Conformational selection has been observed for protein-ligand, protein-protein, protein-DNA, protein-RNA and RNA-ligand interactions. These data support a new molecular recognition paradigm for processes as diverse as signaling, catalysis, gene regulation and protein aggregation in disease, which has the potential to significantly impact our views and strategies in drug design, biomolecular engineering and molecular evolution.

Binding of small-molecule ligands to proteins: "what you see" is not always "what you get"

We review insights from computational studies of affinities of ligands binding to proteins. The power of structural biology is in translating knowledge of protein structures into insights about their forces, binding, and mechanisms. However, the complementary power of computer modeling is in showing "the rest of the story" (i.e., how motions and ensembles and alternative conformers and the entropies and forces that cannot be seen in single molecular structures also contribute to binding affinities). Upon binding to a protein, a ligand can bind in multiple orientations; the protein or ligand can be deformed by the binding event; waters, ions, or cofactors can have unexpected involvement; and conformational or solvation entropies can sometimes play large and otherwise unpredictable roles. Computer modeling is helping to elucidate these factors.

The ruggedness of protein-protein energy landscape and the cutoff for 1/r(n) potentials

MOTIVATION

Computational studies of the energetics of protein association are important for revealing the underlying fundamental principles and for designing better tools to model protein complexes. The interaction cutoff contribution to the ruggedness of protein-protein energy landscape is studied in terms of relative energy fluctuations for 1/r(n) potentials based on a simplistic model of a protein complex. This artificial ruggedness exists for short cutoffs and gradually disappears with the cutoff increase.

RESULTS

The critical values of the cutoff were calculated for each of 11 popular power-type potentials with n=0/9, 12 and for two thresholds of 5% and 10%. The artificial ruggedness decreases to tolerable thresholds for cutoffs larger than the critical ones. The results showed that for both thresholds the critical cutoff is a non-monotonic function of the potential power n. The functions reach the maximum at n=3/4 and then decrease with the increase of the potential power. The difference between two cutoffs for 5% and 10% artificial ruggedness becomes negligible for potentials decreasing faster than 1/r(12). The analytical results obtained for the simple model of protein complexes agree with the analysis of artificial ruggedness in a dataset of 62 protein-protein complexes, with different parameterizations of soft Lennard-Jones potential and two types of protein representations: all-atom and coarse-grained. The results suggest that cutoffs larger than the critical ones can be recommended for protein-protein potentials.

Conformational changes and flexibility in T-cell receptor recognition of peptide-MHC complexes

A necessary feature of the immune system, TCR (T-cell receptor) cross-reactivity has been implicated in numerous autoimmune pathologies and is an underlying cause of transplant rejection. Early studies of the interactions of alphabeta TCRs (T-cell receptors) with their peptide-MHC ligands suggested that conformational plasticity in the TCR CDR (complementarity determining region) loops is a dominant contributor to T-cell cross-reactivity. Since these initial studies, the database of TCRs whose structures have been solved both bound and free is now large enough to permit general conclusions to be drawn about the extent of TCR plasticity and the types and locations of motion that occur. In the present paper, we review the conformational differences between free and bound TCRs, quantifying the structural changes that occur and discussing their possible roles in specificity and cross-reactivity. We show that, rather than undergoing major structural alterations or 'folding' upon binding, the majority of TCR CDR loops shift by relatively small amounts. The structural changes that do occur are dominated by hinge-bending motions, with loop remodelling usually occurring near loop apexes. As predicted from previous studies, the largest changes are in the hypervariable CDR3alpha and CDR3beta loops, although in some cases the germline-encoded CDR1alpha and CDR2alpha loops shift in magnitudes that approximate those of the CDR3 loops. Intriguingly, the smallest shifts are in the germline-encoded loops of the beta-chain, consistent with recent suggestions that the TCR beta domain may drive ligand recognition.

Overcoming the inadequacies or limitations of experimental structures as drug targets by using computational modeling tools and molecular dynamics simulations

X-ray crystallography, NMR spectroscopy, and cryoelectron microscopy stand out as powerful tools that enable us to obtain atomic detail about biomolecules that can be potentially targeted by drugs. This knowledge is essential if virtual screening or structure-based ligand-design methods are going to be used in drug discovery. However, the macromolecule of interest is not always amenable to these types of experiment or, as is often the case, the conformation found experimentally cannot be used directly for docking studies because of significant changes between apo and bound forms. Furthermore, sometimes the desired insight into the binding mechanism cannot be gained because the structure of the ligand-receptor complex, not having been time-resolved, represents the endpoint of the binding process and therefore retains little or no information about the intermediate stages that led to its creation. Molecular dynamics (MD) simulations are routinely applied these days to the study of biomolecular systems with the aims of sampling configuration space more efficiently and getting a better understanding of the factors that determine structural stability and relevant biophysical and biochemical processes such as protein folding, ligand binding, and enzymatic reactions. This field has matured significantly in recent years, and strategies have been devised (for example activated, steered, or targeted MD) that allow the calculated trajectories to be biased in attempts to properly shape a ligand binding pocket or simulate large-scale motions involving one or more protein domains. On the other hand, low-frequency motions can be simulated quite inexpensively by calculation of normal modes which allow the investigation of alternative receptor conformations. Selected examples in which these methods have been applied to several medicinal chemistry and in silico pharmacology endeavors are presented.

Modeling ensembles of transmembrane beta-barrel proteins

Transmembrane beta-barrel (TMB) proteins are embedded in the outer membrane of gram-negative bacteria, mitochondria, and chloroplasts. Despite their importance, very few nonhomologous TMB structures have been determined by X-ray diffraction because of the experimental difficulty encountered in crystallizing transmembrane proteins. We introduce the program partiFold to investigate the folding landscape of TMBs. By computing the Boltzmann partition function, partiFold estimates inter-beta-strand residue interaction probabilities, predicts contacts and per-residue X-ray crystal structure B-values, and samples conformations from the Boltzmann low energy ensemble. This broad range of predictive capabilities is achieved using a single, parameterizable grammatical model to describe potential beta-barrel supersecondary structures, combined with a novel energy function of stacked amino acid pair statistical potentials. PartiFold outperforms existing programs for inter-beta-strand residue contact prediction on TMB proteins, offering both higher average predictive accuracy as well as more consistent results. Moreover, the integration of these contact probabilities inside a stochastic contact map can be used to infer a more meaningful picture of the TMB folding landscape, which cannot be achieved with other methods. Partifold's predictions of B-values are competitive with recent methods specifically designed for this problem. Finally, we show that sampling TMBs from the Boltzmann ensemble matches the X-ray crystal structure better than single structure prediction methods. A webserver running partiFold is available at http://partiFold.csail.mit.edu/.

The size of the intermolecular energy funnel in protein-protein interactions

Revealing the fundamental principles of protein interactions is essential for the basic knowledge of molecular processes and designing better predictive tools. Protein docking procedures allow systematic sampling of intermolecular energy landscapes, revealing the distribution of energy basins and their characteristics. A systematic search docking procedure GRAMM-X was applied to a comprehensive nonredundant database of nonobligate protein-protein complexes to determine the size of the intermolecular energy funnel. The unbound structures were simulated using rotamer library. The procedure generated grid-based matches, based on a smoothed Lennard-Jones potential, and minimized them off the grid with the same potential. The minimization generated a distribution of distances, based on a variety of metrics, between the grid-based and the minimized matches. The metric selected for the analysis, ligand interface RMSD, provided three independent estimates of the funnel size: based on the distribution amplitude for the near-native matches, deviation from random, and correlation with the energy values. The three methods converge to similar estimates of approximately 6-8 A ligand interface RMSD. The results indicated dependence of the funnel size on the type of the complex (smaller for antigen-antibody, medium for enzyme-inhibitor, and larger for the rest of the complexes) and the funnel size correlation with the size of the interface. Guidelines for the optimal sampling of docking coordinates, based on the funnel size estimates, were explored.

Chasing funnels on protein-protein energy landscapes at different resolutions

Studies of intermolecular energy landscapes are important for understanding protein association and adequate modeling of protein interactions. Landscape representation at different resolutions can be used for the refinement of docking predictions and detection of macro characteristics, like the binding funnel. A representative set of protein-protein complexes was used to systematically map the intermolecular landscape by grid-based docking. The change of the resolution was achieved by varying the range of the potential, according to the variable resolution GRAMM methodology. A formalism was developed to consistently parameterize the potential and describe essential characteristics of the landscape. The results of gradual landscape smoothing, from high to low resolution, indicate that i), the number of energy basins, the landscape ruggedness, and the slope decrease accordingly; ii), the number of near-native matches, defined as those inside the funnel, increases until the trend breaks down at critical resolution; the rate of the increase and the critical resolution are specific to the type of a complex (enzyme inhibitor, antigen-antibody, and other), reflect known underlying recognition factors, and correlate with earlier determined estimates of the funnel size; iii), the native/nonnative energy gap, a major characteristic of the energy minima hierarchy, remains constant; and iv), the putative funnel (defined as the deepest basin) has the largest average depth-related ruggedness and slope, at all resolutions. The results facilitate better understanding of the binding landscapes and suggest directions for implementation in practical docking protocols.

Understanding ligand-based modulation of the Hsp90 molecular chaperone dynamics at atomic resolution

Molecular switching and ligand-based modulation of the 90-kDa heat-shock protein (Hsp90) chaperone activity may ultimately facilitate conformational coupling to the ATPase cycle along with activation and recruitment of the broad range of client proteins. We present an atomic resolution analysis of the Hsp90 N-terminal domain (NTD) binding energy landscape by simulating protein dynamics with a range of binding partners. We show that the activity of the molecular chaperone may be linked to (i) local folding-unfolding transitions and conformational switching of the "active site lid" upon binding and (ii) differences in the underlying protein dynamics as a function of the binding partner. This study suggests that structural plasticity of the Hsp90 NTD can be exploited by the molecular chaperone machinery to modulate enhanced structural rigidity during ATP binding and increased protein flexibility as a consequence of the inhibitor binding. The present study agrees with the experimental structural data and provides a plausible molecular model for understanding mechanisms of modulation of molecular chaperone activities by binding partners.

Global fitting without a global model: regularization based on the continuity of the evolution of parameter distributions

We introduce a new approach to global data fitting based on a regularization condition that invokes continuity in the global data coordinate. Stabilization of the data fitting procedure comes from probabilistic constraint of the global solution to physically reasonable behavior rather than to specific models of the system behavior. This method is applicable to the fitting of many types of spectroscopic data including dynamic light scattering, time-correlated single-photon counting (TCSPC), and circular dichroism. We compare our method to traditional approaches to fitting an inverse Laplace transform by examining the evolution of multiple lifetime components in synthetic TCSPC data. The global regularizer recovers features in the data that are not apparent from traditional fitting. We show how our approach allows one to start from an essentially model-free fit and progress to a specific model by moving from probabilistic to deterministic constraints in both Laplace transformed and nontransformed coordinates.

Interaction cutoff effect on ruggedness of protein-protein energy landscape

The concept of the energy landscape is important for better understanding of protein-protein interactions and for designing adequate docking procedures. The intermolecular landscape has a rugged terrain that impedes search procedures. Its inherent ruggedness is related to the conformational characteristics of the molecules and to the form of the potential function--more rugged for short-range potentials and less rugged for "soft," typically long-range potentials. Our study determined that the landscape ruggedness is further substantially exacerbated by truncation of the potentials. This additional ruggedness appears below certain critical interaction ranges that depend on the form of the potential. The theoretical model describing the cutoff effect on the landscape ruggedness is confirmed by the energy calculation on a dataset of protein-protein complexes. The negative effect of the potentials cutoff is well known. However, revealing its physical basis in terms of the energy landscape is important for better understanding of intermolecular interactions.

Activation of G protein-coupled receptors: beyond two-state models and tertiary conformational changes

Transformation of G protein-coupled receptors (GPCRs) from a quiescent to an active state initiates signal transduction. All GPCRs share a common architecture comprising seven transmembrane-spanning alpha-helices, which accommodates signal propagation from a diverse repertoire of external stimuli across biological membranes to a heterotrimeric G protein. Signal propagation through the transmembrane helices likely involves mechanistic features common to all GPCRs. The structure of the light receptor rhodopsin may serve as a prototype for the transmembrane architecture of GPCRs. Early biochemical, biophysical, and pharmacological studies led to the conceptualization of receptor activation based on the context of two-state equilibrium models and conformational changes in protein structure. More recent studies indicate a need to move beyond these classical paradigms and to consider additional aspects of the molecular character of GPCRs, such as the oligomerization and dynamics of the receptor.

Dynamical regulations of protein-ligand bindings at single molecular level

We present new quantitative regulations of the binding-affinity using dynamical single-molecule detection system with X-rays. In the study of antigen-antibody interactions, we found that structural fluctuations of single-molecules were negatively regulated by antigen-binding. Although strategies to produce ligand-induced stability have been well studied from the macro aspect both theoretically and experimentally, our dynamical single-molecular experimental results are first observations with angstrom accuracy in the real-time and space. It is considered that those negative regulations of protein structural fluctuations with binding event are related to biological functions. In addition, we clarified that ratio between antigen-binding condition and no-binding one in observed structural fluctuations are extremely relative to the binding-affinity. These results indicate that the phenomena of protein-ligand interactions considered as stable states can be defined as results of dynamical processes at the single-molecule level. Such new quantifications from angstrom-level structural fluctuations can be applied to various biological science and biotechnologies.

SODOCK: swarm optimization for highly flexible protein-ligand docking

Protein-ligand docking can be formulated as a parameter optimization problem associated with an accurate scoring function, which aims to identify the translation, orientation, and conformation of a docked ligand with the lowest energy. The parameter optimization problem for highly flexible ligands with many rotatable bonds is more difficult than that for less flexible ligands using genetic algorithm (GA)-based approaches, due to the large numbers of parameters and high correlations among these parameters. This investigation presents a novel optimization algorithm SODOCK based on particle swarm optimization (PSO) for solving flexible protein-ligand docking problems. To improve efficiency and robustness of PSO, an efficient local search strategy is incorporated into SODOCK. The implementation of SODOCK adopts the environment and energy function of AutoDock 3.05. Computer simulation results reveal that SODOCK is superior to the Lamarckian genetic algorithm (LGA) of AutoDock, in terms of convergence performance, robustness, and obtained energy, especially for highly flexible ligands. The results also reveal that PSO is more suitable than the conventional GA in dealing with flexible docking problems with high correlations among parameters. This investigation also compared SODOCK with four state-of-the-art docking methods, namely GOLD 1.2, DOCK 4.0, FlexX 1.8, and LGA of AutoDock 3.05. SODOCK obtained the smallest RMSD in 19 of 37 cases. The average 2.29 A of the 37 RMSD values of SODOCK was better than those of other docking programs, which were all above 3.0 A.

Exploring protein's optimal HP configurations by self-organizing mapping

Self-organizing map (SOM) has been used in protein folding prediction when the HP model is employed. The existing work uses a square-like shape lattice with l = m x n points to represent the optimal compact structure of a sequence of l amino acids. In this paper, a general l'-size sequence of amino acids is self-organized in a two dimensional lattice with l (> l') points. The obtained minimum configuration then has a flexible shape, in contrast to the compact structure limited in the lattice. To fulfil this extension, a new self-organizing map (SOM) technique is proposed to deal with the difficulty of the unsymmetric input and output spaces. New competition rules in the training phase are introduced and a local search method is applied to overcome the multi-mapping phenomena. Several HP benchmark examples with up to 36 amino acids are tested to verify the effectiveness of the proposed approach in this paper.

Ligand-induced thermostability in proteins: thermodynamic analysis of ANS-albumin interaction

A comparative thermodynamic study of the interaction of anilinonaphthalene sulfonate (ANS) derivatives with bovine serum albumin (BSA) was performed by using differential scanning calorimetry (DSC) and isothermal titration calorimetry (ITC). The chemically related ligands, 1,8-ANS and 2,6-ANS, present a similar affinity for BSA with different binding energetics. The analysis of the binding driving forces suggests that not only hydrophobic effect but also electrostatic interactions are relevant, even though they have been extensively used as probes for non-polar domains in proteins. Ligand association leads to an increase in protein thermostability, indicating that both dyes interact mainly with native BSA. ITC data show that 1,8-ANS and 2,6-ANS have a moderate affinity for BSA, with an association constant of around 1-9x10(5) M(-1) for the high-affinity site. Ligand binding is disfavoured by conformational entropy. The theoretical model used to simulate DSC data satisfactorily reproduces experimental thermograms, validating this approach as one which provides new insights into the interaction between one or more ligands with a protein. By comparison with 1,8-ANS, 2,6-ANS appears as a more "inert" probe to assess processes which involve conformational changes in proteins.

Thermodynamic and dynamic factors involved in the stability of native protein structure in amorphous solids in relation to levels of hydration

The internal, dynamical fluctuations of protein molecules exhibit many of the features typical of polymeric and bulk small molecule glass forming systems. The response of a protein's internal molecular mobility to temperature changes is similar to that of other amorphous systems, in that different types of motions freeze out at different temperatures, suggesting they exhibit the alpha-beta-modes of motion typical of polymeric glass formers. These modes of motion are attributed to the dynamic regimes that afford proteins the flexibility for function but that also develop into the large-scale collective motions that lead to unfolding. The protein dynamical transition, T(d), which has the same meaning as the T(g) value of other amorphous systems, is attributed to the temperature where protein activity is lost and the unfolding process is inhibited. This review describes how modulation of T(d) by hydration and lyoprotectants can determine the stability of protein molecules that have been processed as bulk, amorphous materials. It also examines the thermodynamic, dynamic, and molecular factors involved in stabilizing folded proteins, and the effects typical pharmaceutical processes can have on native protein structure in going from the solution state to the solid state.

Monte Carlo simulation of protein folding in the presence of residue-specific binding sites

Ensemble growth Monte Carlo (EGMC) and dynamic Monte Carlo (DMC) simulations are used to study sequential folding and thermodynamic stability of hydrophobic-polar (HP) chains that fold to a compact structure. Molecularly imprinted cavities are modeled as hard walls having sites that are attractive to specific polar residues on the chain. Using EGMC simulation, we find that the folded conformation can be stabilized using a small number of carefully selected residue-specific sites while a random selection of surface-bound residues may only slightly contribute toward stabilizing the folded conformation, and in some cases may hinder the folding of the chain. DMC simulations of the surface-bound chain confirm increased stability of the folded conformation over a free chain. However, a different trend of the equilibrium population of folded chains as a function of residue-external site interactions is predicted with the two simulation methods.

Gibbs adsorption isotherm combined with Monte Carlo sampling to see action of cosolutes on protein folding

Driven by conditions set by smaller solutes, proteins fold and unfold. Experimentally, these conditions are stated as intensive variables--pH and other chemical potentials--as though small solutes were infinite resources that come at an externally varied free energy cost. Computationally, the finite spaces of simulation allow only fixed numbers of these solutes. By combining the analytic Gibbs adsorption isotherm with the computational Monte Carlo sampling of polymer configurations, we have been able to overcome an inherent limitation of computer simulation. The idea is to compute analytically the free energy changes wrought by solutes on each particular configuration. Then numerical computation is needed only to sample the set of configurations as efficiently as when no bathing solute is present. For illustration, the procedure is applied to an idealized two-dimensional heteropolymer to yield lessons about the effect of cosolutes on protein stability.

Allosteric inhibition through core disruption

Although inhibitors typically bind pre-formed sites on proteins, it is theoretically possible to inhibit by disrupting the folded structure of a protein or, in the limit, to bind preferentially to the unfolded state. Equilibria defining how such molecules act are well understood, but structural models for such binding are unknown. Two novel inhibitors of beta-lactamase were found to destabilize the enzyme at high temperatures, but at lower temperatures showed no preference for destabilized mutant enzymes versus stabilized mutants. X-ray crystal structures showed that both inhibitors bound to a cryptic site in beta-lactamase, which the inhibitors themselves created by forcing apart helixes 11 and 12. This opened up a portion of the hydrophobic core of the protein, into which these two inhibitors bind. Although this binding site is 16 A from the center of the active site, the conformational changes were transmitted through a sequence of linked motions to a key catalytic residue, Arg244, which in the complex adopts conformations very different from those in catalytically competent enzyme conformations. These structures offer a detailed view of what has heretofore been a theoretical construct, and suggest the possibility for further design against this novel site.

Stability and the evolvability of function in a model protein

Functional proteins must fold with some minimal stability to a structure that can perform a biochemical task. Here we use a simple model to investigate the relationship between the stability requirement and the capacity of a protein to evolve the function of binding to a ligand. Although our model contains no built-in tradeoff between stability and function, proteins evolved function more efficiently when the stability requirement was relaxed. Proteins with both high stability and high function evolved more efficiently when the stability requirement was gradually increased than when there was constant selection for high stability. These results show that in our model, the evolution of function is enhanced by allowing proteins to explore sequences corresponding to marginally stable structures, and that it is easier to improve stability while maintaining high function than to improve function while maintaining high stability. Our model also demonstrates that even in the absence of a fundamental biophysical tradeoff between stability and function, the speed with which function can evolve is limited by the stability requirement imposed on the protein.

GEMDOCK: A generic evolutionary method for molecular docking

We have developed an evolutionary approach for flexible ligand docking. This approval, GEMDOCK, uses a Generic Evolutionary Method for molecular DOCKing and an empirical scoring function. The former combines both discrete and continuous global search strategies with local search strategies to speed up convergence, whereas the latter results in rapid recognition of potential ligands. GEMDOCK was tested on a diverse data set of 100 protein-ligand complexes from the Protein Data Bank. In 79% of these complexes, the docked lowest energy ligand structures had root-mean-square derivations (RMSDs) below 2.0 A with respect to the corresponding crystal structures. The success rate increased to 85% if the structure water molecules were retained. We evaluated GEMDOCK on two cross-docking experiments in which each ligand of a protein ensemble was docked into each protein of the ensemble. Seventy-six percent of the docked structures had RMSDs below 2.0 A when the ligands were docked into foreign structures. We analyzed and validated GEMDOCK with respect to various search spaces and scoring functions, and found that if the scoring function was perfect, then the predicted accuracy was also essentially perfect. This study suggests that GEMDOCK is a useful tool for molecular recognition and may be used to systematically evaluate and thus improve scoring functions.

Simulation of the effect of reorientation of hydrogen bonds in proteins on long-range electron transfer

We present Monte Carlo simulations illustrating the influence of reorientation of hydrogen bonds in proteins on long-range interprotein electron transfer. The lattice protein model employed mimics the electron donor (or acceptor) interacting with an antiparallel β sheet. In addition, we take into account harmonic vibrations of the medium and also the dependence of the coupling matrix element on orientation of hydrogen bonds near the donor and/or acceptor. The results obtained show that the interaction between the tunneling electron and amino-acid residues, which are responsible for the formation of hydrogen bonds, may result in broadening the parabolic dependence of the electron-transfer rate constant on the reaction exothermicity and also in deviations from this dependence especially in the cases when the β sheet is linked with the electron donor.