Ligand Recognition Process Research Articles

Neutron crystallographic refinement with REFMAC5 from the CCP4 suite.

Hydrogen (H) atoms are abundant in macromolecules and often play critical roles in enzyme catalysis, ligand-recognition processes and protein-protein interactions. However, their direct visualization by diffraction techniques is challenging. Macromolecular X-ray crystallography affords the localization of only the most ordered H atoms at (sub-)atomic resolution (around 1.2 Å or higher). However, many H atoms of biochemical significance remain undetectable by this method. In contrast, neutron diffraction methods enable the visualization of most H atoms, typically in the form of deuterium (2H) atoms, at much more common resolution values (better than 2.5 Å). Thus, neutron crystallography, although technically demanding, is often the method of choice when direct information on protonation states is sought. REFMAC5 from the Collaborative Computational Project No. 4 (CCP4) is a program for the refinement of macromolecular models against X-ray crystallographic and cryo-EM data. This contribution describes its extension to include the refinement of structural models obtained from neutron crystallographic data. Stereochemical restraints with accurate bond distances between H atoms and their parent atom nuclei are now part of the CCP4 Monomer Library, the source of prior chemical information used in the refinement. One new feature for neutron data analysis in REFMAC5 is refinement of the protium/deuterium (1H/2H) fraction. This parameter describes the relative 1H/2H contribution to neutron scattering for hydrogenisotopes. The newly developed REFMAC5 algorithms were tested by performing the (re-)refinement of several entries available in the PDB and of one novel structure (FutA) using either (i) neutron data only or (ii) neutron data supplemented by external restraints to a reference X-ray crystallographic structure. Re-refinement with REFMAC5 afforded models characterized by R-factor values that are consistent with, and in some cases better than, the originally deposited values. The use of external reference structure restraints during refinement has been observed to be a valuable strategy, especially for structures at medium-low resolution.

Seeing red: a modified RUBY reporter assay for visualizing inplanta protein-protein interactions.

Seeing red: a modified RUBY reporter assay for visualizing inplanta protein-protein interactions.

Resolving Protein Conformational Plasticity and Substrate Binding via Machine Learning.

A long-standing target in elucidating the biomolecular recognition process is the identification of binding-competent conformations of the receptor protein. However, protein conformational plasticity and the stochastic nature of the recognition processes often preclude the assignment of a specific protein conformation to an individual ligand-bound pose. Here, we demonstrate that a computational framework coined as RF-TICA-MD, which integrates an ensemble decision-tree-based Random Forest (RF) machine learning (ML) technique with an unsupervised dimension reduction approach time-structured independent component analysis (TICA), provides an efficient and unambiguous solution toward resolving protein conformational plasticity and the substrate binding process. In particular, we consider multimicrosecond-long molecular dynamics (MD) simulation trajectories of a ligand recognition process in solvent-inaccessible cavities of archetypal proteins T4 lysozyme and cytochrome P450cam. We show that in a scenario in which clear correspondence between protein conformation and binding-competent macrostates could not be obtained via an unsupervised dimension reduction approach, an a priori decision-tree-based supervised classification of the simulated recognition trajectories via RF would help characterize key amino acid residue pairs of the protein that are deemed sensitive for ligand binding. A subsequent unsupervised dimensional reduction of the selected residue pairs via TICA would then delineate a conformational landscape of protein which is able to demarcate ligand-bound poses from unbound ones. The proposed RF-TICA-MD approach is shown to be data agnostic and found to be robust when using other ML-based classification methods such as XGBoost. As a promising spinoff of the protocol, the framework is found to be capable of identifying distal protein locations which would be allosterically important for ligand binding and would characterize their roles in recognition pathways. A Python implementation of a proposed ML workflow is available in GitHub https://github.com/navjeet0211/rf-tica-md.

Structural basis for the molecular recognition of various RGD-containing ligands by integrin αVβ3: The role of the specificity-determining loop (SDL).

Integrins are multifunctional cell surface receptors that play critical roles in normal physiological processes such as proliferation, survival, migration, and cell fate transitions. Integrins are implicated in several pathological conditions, including inflammation, tumor metastasis, lung fibrosis, and autoimmune disorders. Recent studies show that apart from their endogenous ligands, αVβ3 and α5β1 integrins bind directly to the hydroxylated form of cholesterol (25HC and 24HC) to upregulate pro-inflammatory response. Among the integrins, αVβ3 integrin has been considered a viable target for treating many diseases. Despite enormous efforts, currently available RGD-based anti-integrin drugs lack specificity, exhibit partial agonism, and cause serious side effects, including tumor growth and angiogenesis. The development of safe and efficient inhibitors requires a thorough understanding of the structural basis for outside-in activation and inhibition mechanism of integrin αvβ3. In this study, we performed a comprehensive residue-residue contact and network analysis of the αvβ3 integrin headpiece using all available experimental structures. Surprisingly, our analyses show that specific orientation and interactions of residues from the specificity-determining loop (SDL) are critical in the molecular recognition and regulation of integrin αvβ3. In addition, network analysis reveals that residues from the allosteric site (site II) connect to the primary RGD-binding site via SDL, which acts as an interface for the allosteric modulation. The obtained results provide a comprehensive structural perspective, useful insights into the distinct features of active and inactive conformations, and the role of SDL in the ligand recognition process and allostery.

Solvent's Role in Cavity-Ligand Recognition Would Depend on the Mode of Ligand Diffusion.

Solvent is known to play crucial roles in dictating the thermodynamics and kinetics of the biomolecular recognition process. Here, we show that the extent of significance of water in modulating the ligand recognition process is critically contingent on the ligand diffusion and on the constraints introduced on it. Toward the end, we use a well-known prototypical system of spherical ligand diffusing freely toward a hydrophobic concave cavity in explicit water. We analyze a large series of adaptively sampled unbiased molecular dynamics simulation trajectories within the framework of time-structured independent component analysis (TICA). Our quantitative investigations reveal that water would play a significant role in the ligand recognition process, provided that the ligand is constricted to diffuse along a centro-symmetric fashion. On the contrary, water's contribution in the ligand recognition process would diminish to a negligible value if the ligand freely diffuses toward the pocket. A Markov state model (MSM) constructed using the simulated trajectories identifies a set of transiently populated metastable states comprising partially ligand-unbound macro states, alongside ligand-bound and ligand-unbound pose and gives rise to multiple transition paths of ligand in its way to the hydrophobic cavity. Lifting the restriction on ligand movement changes its binding pathway, time scales, and the extent of the role of solvent in modulating the recognition process.

Critical Assessment of a Structure-Based Screening Campaign for IDO1 Inhibitors: Tips and Pitfalls.

Over the last two decades, indoleamine 2,3-dioxygenase 1 (IDO1) has attracted wide interest as a key player in immune regulation, fostering the design and development of small molecule inhibitors to restore immune response in tumor immunity. In this framework, biochemical, structural, and pharmacological studies have unveiled peculiar structural plasticity of IDO1, with different conformations and functional states that are coupled to fine regulation of its catalytic activity and non-enzymic functions. The large plasticity of IDO1 may affect its ligand recognition process, generating bias in structure-based drug design campaigns. In this work, we report a screening campaign of a fragment library of compounds, grounding on the use of three distinct conformations of IDO1 that recapitulate its structural plasticity to some extent. Results are instrumental to discuss tips and pitfalls that, due to the large plasticity of the enzyme, may influence the identification of novel and differentiated chemical scaffolds of IDO1 ligands in structure-based screening campaigns.

Molecular Mechanism of Small-Molecule Inhibitors in Blocking the PD-1/PD-L1 Pathway through PD-L1 Dimerization.

Programmed cell death-1 (PD-1), which is a molecule involved in the inhibitory signal in the immune system and is important due to blocking of the interactions between PD-1 and programmed cell death ligand-1 (PD-L1), has emerged as a promising immunotherapy for treating cancer. In this work, molecular dynamics simulations were performed on complex systems consisting of the PD-L1 dimer with (S)-BMS-200, (R)-BMS-200 and (MOD)-BMS-200 (i.e., S, R and MOD systems) to systematically evaluate the inhibitory mechanism of BMS-200-related small-molecule inhibitors in detail. Among them, (MOD)-BMS-200 was modified from the original (S)-BMS-200 by replacing the hydroxyl group with a carbonyl to remove its chirality. Binding free energy analysis indicates that BMS-200-related inhibitors can promote the dimerization of PD-L1. Meanwhile, no significant differences were observed between the S and MOD systems, though the R system exhibited a slightly higher energy. Residue energy decomposition, nonbonded interaction, and contact number analyses show that the inhibitors mainly bind with the C, F and G regions of the PD-L1 dimer, while nonpolar interactions of key residues Ile54, Tyr56, Met115, Ala121 and Tyr123 on both PD-L1 monomers are the dominant binding-related stability factors. Furthermore, compared with (S)-BMS-200, (R)-BMS-200 is more likely to form hydrogen bonds with charged residues. Finally, free energy landscape and protein–protein interaction analyses show that the key residues of the PD-L1 dimer undergo remarkable conformational changes induced by (S)-BMS-200, which boosts its intimate interactions. This systematic investigation provides a comprehensive molecular insight into the ligand recognition process, which will benefit the design of new small-molecule inhibitors targeting PD-L1 for use in anticancer therapy.

New machine learning and physics-based scoring functions for drug discovery

Scoring functions are essential for modern in silico drug discovery. However, the accurate prediction of binding affinity by scoring functions remains a challenging task. The performance of scoring functions is very heterogeneous across different target classes. Scoring functions based on precise physics-based descriptors better representing protein–ligand recognition process are strongly needed. We developed a set of new empirical scoring functions, named DockTScore, by explicitly accounting for physics-based terms combined with machine learning. Target-specific scoring functions were developed for two important drug targets, proteases and protein–protein interactions, representing an original class of molecules for drug discovery. Multiple linear regression (MLR), support vector machine and random forest algorithms were employed to derive general and target-specific scoring functions involving optimized MMFF94S force-field terms, solvation and lipophilic interactions terms, and an improved term accounting for ligand torsional entropy contribution to ligand binding. DockTScore scoring functions demonstrated to be competitive with the current best-evaluated scoring functions in terms of binding energy prediction and ranking on four DUD-E datasets and will be useful for in silico drug design for diverse proteins as well as for specific targets such as proteases and protein–protein interactions. Currently, the MLR DockTScore is available at www.dockthor.lncc.br.

An Appraisal of Computer Simulation Approaches in Elucidating Biomolecular Recognition Pathways.

Computer simulation approaches in biomolecular recognition processes have come a long way. In this Perspective, we highlight a series of recent success stories in which computer simulations have played a remarkable role in elucidating the atomic resolution mechanism of kinetic processes of protein-ligand binding in a quantitative fashion. In particular, we show that a robust combination of unbiased simulation, harnessed by a high-fidelity computing environment, and Markov state modeling approaches has been instrumental in revealing novel protein-ligand recognition pathways in multiple systems. We also elucidate the role of recent developments in enhanced sampling approaches in providing the much-needed impetus in accelerating simulation of the ligand recognition process. We identify multiple key issues, including force fields and the sampling bottleneck, which are currently preventing the field from achieving quantitative reconstruction of experimental measurements. Finally, we suggest a possible way forward via adoption of multiscale approaches and coarse-grained simulations as next steps toward efficient elucidation of ligand binding kinetics.

On the role of solvent in hydrophobic cavity-ligand recognition kinetics.

A solvent often manifests itself as the key determinant of the kinetic aspect of the molecular recognition process. While the solvent is often depicted as a source of barrier in the ligand recognition process by the polar cavity, the nature of solvent's role in the recognition process involving hydrophobic cavity and hydrophobic ligand remains to be addressed. In this work, we quantitatively assess the role of solvent in dictating the kinetic process of recognition in a popular system involving the hydrophobic cavity and ligand. In this prototypical system, the hydrophobic cavity undergoes dewetting transition as the ligand approaches the cavity, which influences the cavity-ligand recognition kinetics. Here, we build a Markov state model (MSM) using adaptively sampled unrestrained molecular dynamics simulation trajectories to map the kinetic recognition process. The MSM-reconstructed free energy surface recovers a broad water distribution at an intermediate cavity-ligand separation, consistent with a previous report of dewetting transition in this system. Time-structured independent component analysis of the simulated trajectories quantitatively shows that cavity-solvent density contributes considerably in an optimized reaction coordinate involving cavity-ligand separation and water occupancy. Our approach quantifies two solvent-mediated macrostates at an intermediate separation of the cavity-ligand recognition pathways, apart from the fully ligand-bound and fully ligand-unbound macrostates. Interestingly, we find that these water-mediated intermediates, while transient in populations, can undergo slow mutual interconversion and create possibilities of multiple pathways of cavity recognition by the ligand. Overall, the work provides a quantitative assessment of the role that the solvent plays in facilitating the recognition process involving the hydrophobic cavity.

Receptor-Receptor Interactions of G Protein-Coupled Receptors in the Carotid Body: A Working Hypothesis.

In the carotid body (CB), a wide series of neurotransmitters and neuromodulators have been identified. They are mainly produced and released by type I cells and act on many different ionotropic and metabotropic receptors located in afferent nerve fibers, type I and II cells. Most metabotropic receptors are G protein-coupled receptors (GPCRs). In other transfected or native cells, GPCRs have been demonstrated to establish physical receptor–receptor interactions (RRIs) with formation of homo/hetero-complexes (dimers or receptor mosaics) in a dynamic monomer/oligomer equilibrium. RRIs modulate ligand binding, signaling, and internalization of GPCR protomers and they are considered of relevance for physiology, pharmacology, and pathology of the nervous system. We hypothesize that RRI may also occur in the different structural elements of the CB (type I cells, type II cells, and afferent fibers), with potential implications in chemoreception, neuromodulation, and tissue plasticity. This ‘working hypothesis’ is supported by literature data reporting the contemporary expression, in type I cells, type II cells, or afferent terminals, of GPCRs which are able to physically interact with each other to form homo/hetero-complexes. Functional data about cross-talks in the CB between different neurotransmitters/neuromodulators also support the hypothesis. On the basis of the above findings, the most significant homo/hetero-complexes which could be postulated in the CB include receptors for dopamine, adenosine, ATP, opioids, histamine, serotonin, endothelin, galanin, GABA, cannabinoids, angiotensin, neurotensin, and melatonin. From a methodological point of view, future studies should demonstrate the colocalization in close proximity (less than 10 nm) of the above receptors, through biophysical (i.e., bioluminescence/fluorescence resonance energy transfer, protein-fragment complementation assay, total internal reflection fluorescence microscopy, fluorescence correlation spectroscopy and photoactivated localization microscopy, X-ray crystallography) or biochemical (co-immunoprecipitation, in situ proximity ligation assay) methods. Moreover, functional approaches will be able to show if ligand binding to one receptor produces changes in the biochemical characteristics (ligand recognition, decoding, and trafficking processes) of the other(s). Plasticity aspects would be also of interest, as development and environmental stimuli (chronic continuous or intermittent hypoxia) produce changes in the expression of certain receptors which could potentially invest the dynamic monomer/oligomer equilibrium of homo/hetero-complexes and the correlated functional implications.

Atomic resolution mechanism of ligand binding to a solvent inaccessible cavity in T4 lysozyme.

Ligand binding sites in proteins are often localized to deeply buried cavities, inaccessible to bulk solvent. Yet, in many cases binding of cognate ligands occurs rapidly. An intriguing system is presented by the L99A cavity mutant of T4 Lysozyme (T4L L99A) that rapidly binds benzene (~106 M-1s-1). Although the protein has long served as a model system for protein thermodynamics and crystal structures of both free and benzene-bound T4L L99A are available, the kinetic pathways by which benzene reaches its solvent-inaccessible binding cavity remain elusive. The current work, using extensive molecular dynamics simulation, achieves this by capturing the complete process of spontaneous recognition of benzene by T4L L99A at atomistic resolution. A series of multi-microsecond unbiased molecular dynamics simulation trajectories unequivocally reveal how benzene, starting in bulk solvent, diffuses to the protein and spontaneously reaches the solvent inaccessible cavity of T4L L99A. The simulated and high-resolution X-ray derived bound structures are in excellent agreement. A robust four-state Markov model, developed using cumulative 60 μs trajectories, identifies and quantifies multiple ligand binding pathways with low activation barriers. Interestingly, none of these identified binding pathways required large conformational changes for ligand access to the buried cavity. Rather, these involve transient but crucial opening of a channel to the cavity via subtle displacements in the positions of key helices (helix4/helix6, helix7/helix9) leading to rapid binding. Free energy simulations further elucidate that these channel-opening events would have been unfavorable in wild type T4L. Taken together and via integrating with results from experiments, these simulations provide unprecedented mechanistic insights into the complete ligand recognition process in a buried cavity. By illustrating the power of subtle helix movements in opening up multiple pathways for ligand access, this work offers an alternate view of ligand recognition in a solvent-inaccessible cavity, contrary to the common perception of a single dominant pathway for ligand binding.

LibME-automatic extraction of 3D ligand-binding motifs for mechanistic analysis of protein-ligand recognition.

Identifying conserved binding motifs is an efficient way to study protein–ligand recognition. Most 3D binding motifs only contain information from the protein side, and so motifs that combine information from both protein and ligand sides are desired. Here, we propose an algorithm called LibME (Ligand‐binding Motif Extractor), which automatically extracts 3D binding motifs composed of the target ligand and surrounding conserved residues. We show that the motifs extracted by LibME for ATP and its analogs are highly similar to well‐known motifs reported by previous studies. The superiority of our method to handle flexible ligands was also demonstrated using isocitric acid as an example. Finally, we show that these motifs, together with their visual exhibition, permit better investigating and understanding of protein–ligand recognition process.

Computational Approaches to Toll-Like Receptor 4 Modulation.

Toll-like receptor 4 (TLR4), along with its accessory protein myeloid differentiation factor 2 (MD-2), builds a heterodimeric complex that specifically recognizes lipopolysaccharides (LPS), which are present on the cell wall of Gram-negative bacteria, activating the innate immune response. Some TLR4 modulators are undergoing preclinical and clinical evaluation for the treatment of sepsis, inflammatory diseases, cancer and rheumatoid arthritis. Since the relatively recent elucidation of the X-ray crystallographic structure of the extracellular domain of TLR4, research around this fascinating receptor has risen to a new level, and thus, new perspectives have been opened. In particular, diverse computational techniques have been applied to decipher some of the basis at the atomic level regarding the mechanism of functioning and the ligand recognition processes involving the TLR4/MD-2 system at the atomic level. This review summarizes the reported molecular modeling and computational studies that have recently provided insights into the mechanism regulating the activation/inactivation of the TLR4/MD-2 system receptor and the key interactions modulating the molecular recognition process by agonist and antagonist ligands. These studies have contributed to the design and the discovery of novel small molecules with promising activity as TLR4 modulators.

Using crystallographic water properties for the analysis and prediction of lectin-carbohydrate complex structures.

Understanding protein-ligand interactions is a fundamental question in basic biochemistry, and the role played by the solvent along this process is not yet fully understood. This fact is particularly relevant in lectins, proteins that mediate a large variety of biological processes through the recognition of specific carbohydrates. In the present work, we have thoroughly analyzed a nonredundant and well-curated set of lectin structures looking for a potential relationship between the structural water properties in the apo-structures and the corresponding protein-ligand complex structures. Our results show that solvent structure adjacent to the binding sites mimics the ligand oxygen structural framework in the resulting protein-ligand complex, allowing us to develop a predictive method using a Naive Bayes classifier. We also show how these properties can be used to improve docking predictions of lectin-carbohydrate complex structures in terms of both accuracy and precision, thus developing a solid strategy for the rational design of glycomimetic drugs. Overall our results not only contribute to the understanding of protein-ligand complexes, but also underscore the role of the water solvent in the ligand recognition process. Finally, we discuss our findings in the context of lectin specificity and ligand recognition properties.

Switches of hydrogen bonds during ligand–protein association processes determine binding kinetics

Revealing the processes of ligand–protein associations deepens our understanding of molecular recognition and binding kinetics. Hydrogen bonds (H‐bonds) play a crucial role in optimizing ligand–protein interactions and ligand specificity. In addition to the formation of stable H‐bonds in the final bound state, the formation of transient H‐bonds during binding processes contributes binding kinetics that define a ligand as a fast or slow binder, which also affects drug action. However, the effect of forming the transient H‐bonds on the kinetic properties is little understood. Guided by results from coarse‐grained Brownian dynamics simulations, we used classical molecular dynamics simulations in an implicit solvent model and accelerated molecular dynamics simulations in explicit waters to show that the position and distribution of the H‐bond donor or acceptor of a drug result in switching intermolecular and intramolecular H‐bond pairs during ligand recognition processes. We studied two major types of HIV‐1 protease ligands: a fast binder, xk263, and a slow binder, ritonavir. The slow association rate in ritonavir can be attributed to increased flexibility of ritonavir, which yields multistep transitions and stepwise entering patterns and the formation and breaking of complex H‐bond pairs during the binding process. This model suggests the importance of conversions of spatiotemporal H‐bonds during the association of ligands and proteins, which helps in designing inhibitors with preferred binding kinetics. Copyright © 2014 John Wiley & Sons, Ltd.

Molecular interactions between fenoterol stereoisomers and derivatives and the β2-adrenergic receptor binding site studied by docking and molecular dynamics simulations

The β2 adrenergic receptor (β2-AR) has become a model system for studying the ligand recognition process and mechanism of the G protein coupled receptors activation. In the present study stereoisomers of fenoterol and some of its derivatives (N = 94 molecules) were used as molecular probes to identify differences in stereo-recognition interactions between β2-AR and structurally similar agonists. The present study aimed at determining the 3D molecular models of the fenoterol derivative-β2-AR complexes. Molecular models of β2-AR have been developed by using the crystal structure of the human β2-AR T4 lysozyme fusion protein with bound (S)-carazolol (PDB ID: 2RH1) and more recently reported structure of a nanobody-stabilized active state of the β2-AR with the bound full agonist BI-167107 (PDB ID: 3P0G). The docking procedure allowed us to study the similarities and differences in the recognition binding site(s) for tested ligands. The agonist molecules occupied the same binding region, between TM III, TM V, TM VI and TM VII. The residues identified by us during docking procedure (Ser203, Ser207, Asp113, Lys305, Asn312, Tyr308, Asp192) were experimentally indicated in functional and biophysical studies as being very important for the agonist-receptor interactions. Moreover, the additional space, an extension of the orthosteric pocket, was identified and described. Furthermore, the molecular dynamics simulations were used to study the molecular mechanism of interaction between ligands ((R,R’)- and (S,S’)-fenoterol) and β2-AR. Our research offers new insights into the ligand stereoselective interaction with one of the most important GPCR member. This study may also facilitate the design of improved selective medications, which can be used to treat, prevent and control heart failure symptoms.Electronic supplementary materialThe online version of this article (doi:10.1007/s00894-013-1981-y) contains supplementary material, which is available to authorized users.

Folding and ligand recognition of the TPP riboswitch aptamer at single-molecule resolution

Thiamine pyrophosphate (TPP)-sensitive mRNA domains are the most prevalent riboswitches known. Despite intensive investigation, the complex ligand recognition and concomitant folding processes in the TPP riboswitch that culminate in the regulation of gene expression remain elusive. Here, we used single-molecule fluorescence resonance energy transfer imaging to probe the folding landscape of the TPP aptamer domain in the absence and presence of magnesium and TPP. To do so, distinct labeling patterns were used to sense the dynamics of the switch helix (P1) and the two sensor arms (P2/P3 and P4/P5) of the aptamer domain. The latter structural elements make interdomain tertiary contacts (L5/P3) that span a region immediately adjacent to the ligand-binding site. In each instance, conformational dynamics of the TPP riboswitch were influenced by ligand binding. The P1 switch helix, formed by the 5' and 3' ends of the aptamer domain, adopts a predominantly folded structure in the presence of Mg(2+) alone. However, even at saturating concentrations of Mg(2+) and TPP, the P1 helix, as well as distal regions surrounding the TPP-binding site, exhibit an unexpected degree of residual dynamics and disperse kinetic behaviors. Such plasticity results in a persistent exchange of the P3/P5 forearms between open and closed configurations that is likely to facilitate entry and exit of the TPP ligand. Correspondingly, we posit that such features of the TPP aptamer domain contribute directly to the mechanism of riboswitch-mediated translational regulation.

How maltose influences structural changes to bind to maltose‐binding protein: Results from umbrella sampling simulation

A well-studied periplasmic-binding protein involved in the abstraction of maltose is maltose-binding protein (MBP), which undergoes a ligand-induced conformational transition from an open (ligand-free) to a closed (ligand-bound) state. Umbrella sampling simulations have been us to estimate the free energy of binding of maltose to MBP and to trace the potential of mean force of the unbinding event using the center-of-mass distance between the protein and ligand as the reaction coordinate. The free energy thus obtained compares nicely with the experimentally measured value justifying our theoretical basis. Measurement of the domain angle (N-terminal-domain - hinge - C-terminal-domain) along the unbinding pathway established the existence of three different states. Starting from a closed state, the protein shifts to an open conformation during the initial unbinding event of the ligand then resides in a semi-open conformation and later resides predominantly in an open-state. These transitions along the ligand unbinding pathway have been captured in greater depth using principal component analysis. It is proposed that in mixed-model, both conformational selection and an induced-fit mechanism combine to the ligand recognition process in MBP.

Femtosecond to Second Studies of a Water-Soluble Porphyrin Derivative in Chemical and Biological Nanocavities

The interactions of 5,10,15,20-tetrakis(4-sulfonatophenyl)-porphyrin (TSPP) with a quaternary ammonium modified β-cyclodextrin (QA-β-CD) and human serum albumin (HSA) protein in aqueous solutions at pH 7 were studied using steady-state, stopped-flow, and femtosecond to millisecond spectroscopy. TSPP forms 1:1 and 1:2 complexes with QA-β-CD (K(1) = 1.9 × 10(5) M(-1) and K(2) = 7 × 10(3) M(-1)) at 293 K, whereas with the HSA protein only 1:1 complex (K(1) = 1.7 × 10(6) M(-1)) has been found. The chemical and biological nanocavities have notable effects on the fluorescence lifetimes of the Q(x) state (from 9.3 to 11.1 ns in QA-β-CD and 11.6 ns in HSA). Furthermore, the rotational times (400 ps for the free TSPP, 1.6 and 19 ns for QA-β-CD and HSA protein complexes, respectively) clearly indicate the robustness of the formed entities. The confined environment does not affect much the fs dynamics (0.1-0.2 ps) of the encapsulated molecule. However, it clearly affect the ps one (1-2 ps (H(2)O) and 5-10 ps (QA-β-CD and HSA)). The effect of O(2) on the relaxation of the triplet state of the free and encapsulated TSPP is also studied and the obtained results are discussed in light of the shielding effect provided by the chemical and biological cavities. The observed difference, longer triplet lifetime upon encapsulation, might be relevant to the efficiency of this porphyrin in photodynamic therapy. The presteady-state kinetics of the TSPP:HSA has been studied by the stopped-flow spectrometer, and a two-step model was proposed for the complexation processes. The results show the importance of the initial association step for the overall ligand recognition process. This first step occurs with rate constant of ~4 × 10(5) M(-1) s(-1), which is about 5 orders of magnitude larger than the rate constant of the consecutive relaxation processes. We believe that our observations of molecular interaction between TSPP, QA-β-CD, and HSA protein from femtosecond to second at both ground and electronically first excited state give detailed information to improve our understanding of this kind of system and thus for a better design of drug delivery nanocarriers.