The geometry of the reactive site and of the peptide groups in trypsin, trypsinogen and its complexes with inhibitors

Computational screening of the effects of mutations on protein-protein off-rates and dissociation mechanisms by τRAMD

The dissociation rate, or its reciprocal, the residence time (τ), is a crucial parameter for understanding the duration and biological impact of biomolecular interactions. Accurate prediction of τ is essential for understanding protein-protein interactions (PPIs) and identifying potential drug targets or modulators for tackling diseases. Conventional molecular dynamics simulation techniques are inherently constrained by their limited timescales, making it challenging to estimate residence times, which typically range from minutes to hours. Building upon its successful application in protein-small molecule systems, τ-Random Acceleration Molecular Dynamics (τRAMD) is here investigated for estimating dissociation rates of protein-protein complexes. τRAMD enables the observation of unbinding events on the nanosecond timescale, facilitating rapid and efficient computation of relative residence times. We tested this methodology for three protein-protein complexes and their extensive mutant datasets, achieving good agreement between computed and experimental data. By combining τRAMD with MD-IFP (Interaction Fingerprint) analysis, dissociation mechanisms were characterized and their sensitivity to mutations investigated, enabling the identification of molecular hotspots for selective modulation of dissociation kinetics. In conclusion, our findings underscore the versatility of τRAMD as a simple and computationally efficient approach for computing relative protein-protein dissociation rates and investigating dissociation mechanisms, thereby aiding the design of PPI modulators.

Regiocontrol of the Bulk Polymerization of Lysine Ethyl Ester by the Selection of Suitable Immobilized Enzyme Catalysts

The development of a green and facile method for the controlled synthesis of functional polypeptides is desired for sustainable material applications. In this study, the regioselective synthesis of poly(l-lysine) (polyLys) via enzyme-catalyzed aminolysis was achieved by bulk polymerization of l-lysine ethyl ester (Lys-OEt) using immobilized Candida antarctica lipase Novozym 435 (IM-lipase) or trypsin (IM-trypsin). Structural characterization of the obtained polyLys revealed that IM-lipase resulted solely in ε-linked amide bond formation, whereas IM-trypsin predominantly provided α-linked polyLys. Optimization of the conditions for the bulk polymerization using immobilized enzymes resulted in high monomer conversion and a high degree of polymerization, with excellent regioselectivity. Molecular docking simulations revealed different binding conformations of Lys-OEt to the catalytic pockets of lipase and trypsin, which putatively resulted in different amino moieties being used for amide bond formation. The immobilized enzymes were recovered and recycled for bulk polymerization, and the initial activity was maintained in the case of IM-trypsin. The obtained α- and ε-linked polyLys products exhibited different degradability against proteolysis, demonstrating the possibility of versatile applications as sustainable materials. This enzymatic regioregular control enabled the synthesis of well-defined polypeptide-based materials with a diverging structural variety.

Do All Paths Lead to Rome? How Reliable is Umbrella Sampling Along a Single Path?

Molecular dynamics (MD) simulations are widely applied to estimate absolute binding free energies of protein-ligand and protein-protein complexes. A routinely used method for binding free energy calculations with MD is umbrella sampling (US), which calculates the potential of mean force (PMF) along a single reaction coordinate. Surprisingly, in spite of its widespread use, few validation studies have focused on the convergence of the free energy computed along a single path for specific cases, not addressing the reproducibility of such calculations in general. In this work, we therefore investigate the reproducibility and convergence of US along a standard distance-based reaction coordinate for various protein-protein and protein-ligand complexes, following commonly used guidelines for the setup. We show that repeating the complete US workflow can lead to differences of 2-20 kcal/mol in computed binding free energies. We attribute those discrepancies to small differences in the binding pathways. While these differences are unavoidable in the established US protocol, the popularity of the latter could hint at a lack of awareness of such reproducibility problems. To test if the convergence of PMF profiles can be improved if multiple pathways are sampled simultaneously, we performed additional simulations with an adaptive-biasing method, here the accelerated weight histogram (AWH) approach. Indeed, the PMFs obtained from AHW simulations are consistent and reproducible for the systems tested. To the best of our knowledge, our work is the first to attempt a systematic assessment of the pitfalls in one the most widely used protocols for computing binding affinities. We anticipate therefore that our results will provide an incentive for a critical reassessment of the validity of PMFs computed with US, and make a strong case to further benchmark the performance of adaptive-biasing methods for computing binding affinities.

Prebound State Discovered in the Unbinding Pathway of Fluorinated Variants of the Trypsin-BPTI Complex Using Random Acceleration Molecular Dynamics Simulations

The serine protease trypsin forms a tightly bound inhibitor complex with the bovine pancreatic trypsin inhibitor (BPTI). The complex is stabilized by the P1 residue Lys15, which interacts with negatively charged amino acids at the bottom of the S1 pocket. Truncating the P1 residue of wildtype BPTI to α-aminobutyric acid (Abu) leaves a complex with moderate inhibitor strength, which is held in place by additional hydrogen bonds at the protein-protein interface. Fluorination of the Abu residue partially restores the inhibitor strength. The mechanism with which fluorination can restore the inhibitor strength is unknown, and accurate computational investigation requires knowledge of the binding and unbinding pathways. The preferred unbinding pathway is likely to be complex, as encounter states have been described before, and unrestrained umbrella sampling simulations of these complexes suggest additional energetic minima. Here, we use random acceleration molecular dynamics to find a new metastable state in the unbinding pathway of Abu-BPTI variants and wildtype BPTI from trypsin, which we call the prebound state. The prebound state and the fully bound state differ by a substantial shift in the position, a slight shift in the orientation of the BPTI variants, and changes in the interaction pattern. Particularly important is the breaking of three hydrogen bonds around Arg17. Fluorination of the P1 residue lowers the energy barrier of the transition between the fully bound state and prebound state and also lowers the energy minimum of the prebound state. While the effect of fluorination is in general difficult to quantify, here, it is in part caused by favorable stabilization of a hydrogen bond between Gln194 and Cys14. The interaction pattern of the prebound state offers insights into the inhibitory mechanism of BPTI and might add valuable information for the design of serine protease inhibitors.

Correlation of Experimental and Calculated Inhibition Constants of Protease Inhibitor Complexes

Predicting the potency of inhibitors is key to in silico screening of promising synthetic or natural compounds. Here we describe a predictive workflow that provides calculated inhibitory values, which concord well with empirical data. Calculations of the free interaction energy ΔG with the YASARA plugin FoldX were used to derive inhibition constants Ki from PDB coordinates of protease-inhibitor complexes. At the same time, corresponding KD values were obtained from the PRODIGY server. These results correlated well with the experimental values, particularly for serine proteases. In addition, analyses were performed for inhibitory complexes of cysteine and aspartic proteases, as well as of metalloproteases, whereby the PRODIGY data appeared to be more consistent. Based on our analyses, we calculated theoretical Ki values for trypsin with sunflower trypsin inhibitor (SFTI-1) variants, which yielded the more rigid Pro14 variant, with probably higher potency than the wild-type inhibitor. Moreover, a hirudin variant with an Arg1 and Trp3 is a promising basis for novel thrombin inhibitors with high potency. Further examples from antibody interaction and a cancer-related effector-receptor system demonstrate that our approach is applicable to protein interaction studies beyond the protease field.

Accelerating Molecular Dynamics Simulations for Drug Discovery

Accurate prediction of ligand binding thermodynamics and kinetics is crucial in drug design. However, it remains challenging for conventional molecular dynamics (MD) simulations due to sampling issues. Gaussian accelerated MD (GaMD) is an enhanced sampling method that adds a harmonic boost to overcome energy barriers, which has demonstrated significant benefits in exploring protein-ligand interactions. Especially, the ligand GaMD (LiGaMD) applies a selective boost potential to the ligand nonbonded potential energy, significantly improving sampling for ligand binding and dissociation. Furthermore, a selective boost potential is applied to the potential of both ligand and protein residues around binding pocket in LiGaMD2 to further increase the sampling of protein-ligand interaction. LiGaMD and LiGaMD2 simulations could capture repetitive ligand binding and unbinding events within microsecond simulations, allowing to simultaneously characterize ligand binding thermodynamics and kinetics, which is expected to greatly facilitate drug design. In this chapter, we provide a brief review of the status of LiGaMD in drug discovery and outline its usage.

Predicting Protein-Ligand Binding and Unbinding Kinetics with Biased MD Simulations and Coarse-Graining of Dynamics: Current State and Challenges

The prediction of drug-target binding and unbinding kinetics that occur on time scales between milliseconds and several hours is a prime challenge for biased molecular dynamics simulation approaches. This Perspective gives a concise summary of the theory and the current state-of-the-art of such predictions via biased simulations, of insights into the molecular mechanisms defining binding and unbinding kinetics as well as of the extraordinary challenges predictions of ligand kinetics pose in comparison to binding free energy predictions.

Path separation of dissipation-corrected targeted molecular dynamics simulations of protein-ligand unbinding

Protein-ligand (un)binding simulations are a recent focus of biased molecular dynamics simulations. Such binding and unbinding can occur via different pathways in and out of a binding site. Here, we present a theoretical framework on how to compute kinetics along separate paths and on how to combine the path-specific rates into global binding and unbinding rates for comparison with experimental results. Using dissipation-corrected targeted molecular dynamics in combination with temperature-boosted Langevin equation simulations [S. Wolf et al., Nat. Commun. 11, 2918 (2020)] applied to a two-dimensional model and the trypsin-benzamidine complex as test systems, we assess the robustness of the procedure and discuss the aspects of its practical applicability to predict multisecond kinetics of complex biomolecular systems.

Water Network in the Binding Pocket of Fluorinated BPTI-Trypsin Complexes─Insights from Simulation and Experiment

Structural waters in the S1 binding pocket of β-trypsin are critical for the stabilization of the complex of β-trypsin with its inhibitor bovine pancreatic trypsin inhibitor (BPTI). The inhibitor strength of BPTI can be modulated by replacing the critical lysine residue at the P1 position by non-natural amino acids. We study BPTI variants in which the critical Lys15 in BPTI has been replaced by α-aminobutyric acid (Abu) and its fluorinated derivatives monofluoroethylglycine (MfeGly), difluoroethylglycine (DfeGly), and trifluoroethylglycine (TfeGly). We investigate the hypothesis that additional water molecules in the binding pocket can form specific noncovalent interactions with the fluorinated side chains and thereby act as an extension of the inhibitors. We report potentials of mean force (PMF) of the unbinding process for all four complexes and enzyme activity inhibition assays. Additionally, we report the protein crystal structure of the Lys15MfeGly-BPTI-β-trypsin complex (pdb: 7PH1). Both experimental and computational data show a stepwise increase in inhibitor strength with increasing fluorination of the Abu side chain. The PMF additionally shows a minimum for the encounter complex and an intermediate state just before the bound state. In the bound state, the computational analysis of the structure and dynamics of the water molecules in the S1 pocket shows a highly dynamic network of water molecules that does not indicate a rigidification or stabilizing trend in regard to energetic properties that could explain the increase in inhibitor strength. The analysis of the energy and the entropy of the water molecules in the S1 binding pocket using grid inhomogeneous solvation theory confirms this result. Overall, fluorination systematically changes the binding affinity, but the effect cannot be explained by a persistent water network in the binding pocket. Other effects, such as the hydrophobicity of fluorinated amino acids and the stability of the encounter complex as well as the additional minimum in the potential of mean force in the bound state, likely influence the affinity more directly.

Computational Assessment of Protein-Protein Binding Specificity within a Family of Synaptic Surface Receptors

Atomic-level information is essential to explain the formation of specific protein complexes in terms of structure and dynamics. The set of Dpr and DIP proteins, which play a key role in the neuromorphogenesis in the nervous system of Drosophila melanogaster, offer a rich paradigm to learn about protein-protein recognition. Many members of the DIP subfamily cross-react with several members of the Dpr family and vice versa. While there exists a total of 231 possible Dpr-DIP heterodimer complexes from the 21 Dpr and 11 DIP proteins, only 57 "cognate" pairs have been detected by surface plasmon resonance (SPR) experiments, suggesting that the remaining 174 pairs have low or unreliable binding affinity. Our goal is to assess the performance of computational approaches to characterize the global set of interactions between Dpr and DIP proteins and identify the specificity of binding between each DIP with their corresponding Dpr binding partners. In addition, we aim to characterize how mutations influence the specificity of the binding interaction. In this work, a wide range of knowledge-based and physics-based approaches are utilized, including mutual information, linear discriminant analysis, homology modeling, molecular dynamics simulations, Poisson-Boltzmann continuum electrostatics calculations, and alchemical free energy perturbation to decipher the origin of binding specificity of the Dpr-DIP complexes examined. Ultimately, the results show that those two broad strategies are complementary, with different strengths and limitations. Biological inter-relations are more clearly revealed through knowledge-based approaches combining evolutionary and structural features, the molecular determinants controlling binding specificity can be predicted accurately with physics-based approaches based on atomic models.

Mechanism of trypsin activation by pulsed electric field treatment revealed based on chemical experiments and molecular dynamics simulations

A pulsed electric field (PEF) treatment exhibits different effects on trypsin; however, the mechanism of enzyme activation remains unclear. Herein, chemical experiments combined with molecular dynamics simulations revealed the mechanism of trypsin activation by PEF treatment at the molecular level. The results indicated that compared with the values at 0 kV/cm, the enzyme activity, Vmax, and Kcat at 20 kV/cm increased by 9.30%, 4.74%, and 4.30%, respectively, and Km decreased by 11.14%, indicating an improved interaction between the enzyme and substrate. The simulation results revealed that PEF treatment increased the number of molecular hydrogen bonds and the solvent-accessible surface area, while decreasing the rotation radius and random coil content by 5.00% and 3.37%, respectively. Molecular docking indicated that PEF treatment altered the active center and increased the affinity between the enzyme and substrate. The simulation results were consistent with those of the spectroscopic experiments conducted on trypsin after PEF treatment.

AIM: A Mapping Program for Infrared Spectroscopy of Proteins

Here, we present a new analysis program, AIM, that allows extracting the vibrational amide-I Hamiltonian using molecular dynamics trajectories for protein infrared spectroscopy modeling. The constructed Hamiltonians can be used as input for spectral calculations allowing the calculation of infrared absorption spectra, vibrational circular dichroism, and two-dimensional infrared spectra. These spectroscopies allow the study of the structure and dynamics of proteins. We will explain the essence of how AIM works and give examples of the information and spectra that can be obtained with the program using the Trypsin Inhibitor as an example. AIM is freely available from GitHub, and the package contains a demonstration allowing easy introduction to the use of the program.

Markovian Weighted Ensemble Milestoning (M-WEM): Long-Time Kinetics from Short Trajectories

We introduce a rare-event sampling scheme, named Markovian Weighted Ensemble Milestoning (M-WEM), which inlays a weighted ensemble framework within a Markovian milestoning theory to efficiently calculate thermodynamic and kinetic properties of long-time-scale biomolecular processes from short atomistic molecular dynamics simulations. M-WEM is tested on the Müller-Brown potential model, the conformational switching in alanine dipeptide, and the millisecond time-scale protein-ligand unbinding in a trypsin-benzamidine complex. Not only can M-WEM predict the kinetics of these processes with quantitative accuracy but it also allows for a scheme to reconstruct a multidimensional free-energy landscape along additional degrees of freedom, which are not part of the milestoning progress coordinate. For the ligand-receptor system, the experimental residence time, association and dissociation kinetics, and binding free energy could be reproduced using M-WEM within a simulation time of a few hundreds of nanoseconds, which is a fraction of the computational cost of other currently available methods, and close to 4 orders of magnitude less than the experimental residence time. Due to the high accuracy and low computational cost, the M-WEM approach can find potential applications in kinetics and free-energy-based computational drug design.

Conformational dynamics in peptide toxins: Implications for receptor interactions and molecular design

Peptide toxins are potent and often exquisitely selective probes of the structure and function of ion channels and receptors, and are therefore of significant interest to the pharmaceutical and biotech industries as both pharmacological tools and therapeutic leads. The three-dimensional structures of peptide toxins are essential as a basis for understanding their structure-activity relationships and their binding to target receptors, as well as in guiding the design of analogues with modified potency and/or selectivity for key targets. NMR spectroscopy has played a key role in elucidating the structures of peptide toxins and probing their structure-function relationships. In this article, we highlight the additional important contribution of NMR to characterising the dynamics of peptide toxins. We also compare the information available from NMR measurements with that afforded by molecular dynamics simulations. We describe several examples of the importance of dynamics measurements over a range of timescales for understanding the structure-function relationships of peptide toxins and their receptor engagement. Peptide toxins that inhibit the voltage-gated potassium channel K_V1.3 with pM affinities display different degrees of conformational flexibility, even though they contain multiple disulfide bonds, and this flexibility can affect the relative orientation of residues that have been shown to be critical for channel binding. Information on the dynamic properties of peptide toxins is important in the design of analogues or mimetics where receptor-bound structures are not available.

Microscopic insights into dynamic disorder in the isomerization dynamics of the protein BPTI

Understanding the dynamic disorder behind a process, i.e., the dynamic effect of fluctuations that occur on a timescale slower or comparable with the timescale of the process, is essential for elucidating the dynamics and kinetics of complicated molecular processes in biomolecules and liquids. Despite numerous theoretical studies of single-molecule kinetics, our microscopic understanding of dynamic disorder remains limited. In the present study, we investigate the microscopic aspects of dynamic disorder in the isomerization dynamics of the Cys14-Cys38 disulfide bond in the protein bovine pancreatic trypsin inhibitor, which has been observed by nuclear magnetic resonance. We use a theoretical model with a stochastic transition rate coefficient, which is calculated from the 1-ms-long time molecular dynamics trajectory obtained by Shaw et al. [Science 330, 341-346 (2010)]. The isomerization dynamics are expressed by the transitions between coarse-grained states consisting of internal states, i.e., conformational sub-states. In this description, the rate for the transition from the coarse-grained states is stochastically modulated due to fluctuations between internal states. We examine the survival probability for the conformational transitions from a coarse-grained state using a theoretical model, which is a good approximation to the directly calculated survival probability. The dynamic disorder changes from a slow modulation limit to a fast modulation limit depending on the aspects of the coarse-grained states. Our analysis of the rate modulations behind the survival probability, in relation to the fluctuations between internal states, reveals the microscopic origin of dynamic disorder.

Inhibitory effect of lignin from Canna edulis Ker residues on trypsin: kinetics and molecular docking studies

BACKGROUND

Lignin extracted from Canna edulis Ker residues shows a strong inhibitory effect on α-glucosidase and a promoting effect on α-amylase. Protease activity inhibition may play a key role in disease processes, such as metastasis, tumor invasion and bacterial colonization. Hence, in the present study, the inhibitory mechanism of lignin on trypsin was examined, including the interaction type, thermodynamic parameters, structure, reaction site and molecular docking.

RESULTS

The isolated lignin presented an inhibitory effect on trypsin activity with an IC₅₀ value of 1.35 μmol L^-1 . This inhibition was a mixed linear type with a constant K_i of 3.92 μmol L^-1 . The lignin could bind with the key amino acid residue Ser195 on the active site of the trypsin molecule to inhibit its activity, and the phenolic hydroxyl group and -OH on the β-O-4 structure of the lignin molecule were the major groups bound with trypsin.

CONCLUSION

These results illustrate the inhibitory effects of Canna edulis residue lignin on protease, which helps with respect to understanding the possible application of lignin in the food industry in functional foods. © 2020 Society of Chemical Industry.

On the vibrational free energy of hydrated proteins

When the hydration shell of a protein is filled with at least 0.6 gram of water per gram of protein, a significant anti-correlation between the vibrational free energy and the potential energy of energy-minimized conformers is observed. This means that low potential energy, well-hydrated, protein conformers tend to be more rigid than high-energy ones. On the other hand, in the case of CASP target 624, when its hydration shell is filled, a significant energy gap is observed between the crystal structure and the best conformers proposed during the prediction experiment, strongly suggesting that including explicit water molecules may help identifying unlikely conformers among good-looking ones.

Computing Protein pKas Using the TABI Poisson–Boltzmann Solver

A common approach to computing protein pKas uses a continuum dielectric model in which the protein is a low dielectric medium with embedded atomic point charges, the solvent is a high dielectric medium with a Boltzmann distribution of ionic charges, and the pKa is related to the electrostatic free energy which is obtained by solving the Poisson–Boltzmann equation. Starting from the model pKa for a titrating residue, the method obtains the intrinsic pKa and then computes the protonation probability for a given pH including site–site interactions. This approach assumes that acid dissociation does not affect protein conformation aside from adding or deleting charges at titratable sites. In this work, we demonstrate our treecode-accelerated boundary integral (TABI) solver for the relevant electrostatic calculations. The pKa computing procedure is enclosed in a convenient Python wrapper which is publicly available at the corresponding author’s website. Predicted results are compared with experimental pKas for several proteins. Among ongoing efforts to improve protein pKa calculations, the advantage of TABI is that it reduces the numerical errors in the electrostatic calculations so that attention can be focused on modeling assumptions.

Mechanisms Applied by Protein Inhibitors to Inhibit Cysteine Proteases

Protein inhibitors of proteases are an important tool of nature to regulate and control proteolysis in living organisms under physiological and pathological conditions. In this review, we analyzed the mechanisms of inhibition of cysteine proteases on the basis of structural information and compiled kinetic data. The gathered structural data indicate that the protein fold is not a major obstacle for the evolution of a protease inhibitor. It appears that nature can convert almost any starting fold into an inhibitor of a protease. In addition, there appears to be no general rule governing the inhibitory mechanism. The structural data make it clear that the "lock and key" mechanism is a historical concept with limited validity. However, the analysis suggests that the shape of the active site cleft of proteases imposes some restraints. When the S1 binding site is shaped as a pocket buried in the structure of protease, inhibitors can apply substrate-like binding mechanisms. In contrast, when the S1 binding site is in part exposed to solvent, the substrate-like inhibition cannot be employed. It appears that all proteases, with the exception of papain-like proteases, belong to the first group of proteases. Finally, we show a number of examples and provide hints on how to engineer protein inhibitors.

Spontaneous binding of potential COVID-19 drugs (Camostat and Nafamostat) to human serine protease TMPRSS2

Effective treatment or vaccine is not yet available for combating SARS coronavirus 2 (SARS-CoV-2) that caused the COVID-19 pandemic. Recent studies showed that two drugs, Camostat and Nafamostat, might be repurposed to treat COVID-19 by inhibiting human TMPRSS2 required for proteolytic activation of viral spike (S) glycoprotein. However, their molecular mechanisms of pharmacological action remain unclear. Here, we perform molecular dynamics simulations to investigate their native binding sites on TMPRSS2. We revealed that both drugs could spontaneously and stably bind to the TMPRSS2 catalytic center, and thereby inhibit its proteolytic processing of the S protein. Also, we found that Nafamostat is more specific than Camostat for binding to the catalytic center, consistent with reported observation that Nafamostat blocks the SARS-CoV-2 infection at a lower concentration. Thus, this study provides mechanistic insights into the Camostat and Nafamostat inhibition of the SARS-CoV-2 infection, and offers useful information for COVID-19 drug development.

Dissection of the key steps of amyloid-β peptide 1-40 fibrillogenesis

The aggregation kinetics of Aβ1-40 peptide was characterized using a synergistic approach by a combination of nuclear magnetic resonance, thioflavin-T fluorescence, transmission electron microscopy and dynamic light scattering. A major finding is the experimental detection of high molecular weight oligomers (HMWO) that converts into fibrils nuclei. Our observations are consistent with a mechanism of Aβ1-40 fibrillogenesis that includes the following key steps: i) slow formation of HMWO (Rh ~ 20 nm); ii) conversion of the HMWO into more compact Rh ~ 10 nm fibrils nuclei; iii) fast formation of additional fibrils nuclei through fibril surface catalysed processes; and iv) growth of fibrils by addition of soluble Aβ species. Moreover, NMR diffusion experiments show that at 37 °C soluble Aβ1-40 remains intrinsically disordered and mostly in monomeric form despite evidences of the presence of dimers and/or other small oligomers. A mathematical model is proposed to simulate the aggregation kinetics of Aβ1-40.

Ligand Gaussian Accelerated Molecular Dynamics (LiGaMD): Characterization of Ligand Binding Thermodynamics and Kinetics

Calculations of ligand binding free energies and kinetic rates are important for drug design. However, such tasks have proven challenging in computational chemistry and biophysics. To address this challenge, we have developed a new computational method, ligand Gaussian accelerated molecular dynamics (LiGaMD), which selectively boosts the ligand nonbonded interaction potential energy based on the Gaussian accelerated molecular dynamics (GaMD) enhanced sampling technique. Another boost potential could be applied to the remaining potential energy of the entire system in a dual-boost algorithm (LiGaMD_Dual) to facilitate ligand binding. LiGaMD has been demonstrated on host-guest and protein-ligand binding model systems. Repetitive guest binding and unbinding in the β-cyclodextrin host were observed in hundreds-of-nanosecond LiGaMD_Dual simulations. The calculated guest binding free energies agreed excellently with experimental data with <1.0 kcal/mol errors. Compared with converged microsecond-time scale conventional molecular dynamics simulations, the sampling errors of LiGaMD_Dual simulations were also <1.0 kcal/mol. Accelerations of ligand kinetic rate constants in LiGaMD simulations were properly estimated using Kramers' rate theory. Furthermore, LiGaMD allowed us to capture repetitive dissociation and binding of the benzamidine inhibitor in trypsin within 1 μs simulations. The calculated ligand binding free energy and kinetic rate constants compared well with the experimental data. In summary, LiGaMD provides a powerful enhanced sampling approach for characterizing ligand binding thermodynamics and kinetics simultaneously, which is expected to facilitate computer-aided drug design.

Protein-Protein Binding as a Two-Step Mechanism: Preselection of Encounter Poses during the Binding of BPTI and Trypsin

Biomolecular recognition between proteins follows complex mechanisms, the understanding of which can substantially advance drug discovery efforts. Here, we track each step of the binding process in atomistic detail with molecular dynamics simulations using trypsin and its inhibitor bovine pancreatic trypsin inhibitor (BPTI) as a model system. We use umbrella sampling to cover a range of unbinding pathways. Starting from these simulations, we subsequently seed classical simulations at different stages of the process and combine them to a Markov state model. We clearly identify three kinetically separated states (an unbound state, an encounter state, and the final complex) and describe the mechanisms that dominate the binding process. From our model, we propose the following sequence of events. The initial formation of the encounter complex is driven by long-range interactions because opposite charges in trypsin and BPTI draw them together. The encounter complex features the prealigned binding partners with binding sites still partially surrounded by solvation shells. Further approaching leads to desolvation and increases the importance of van der Waals interactions. The native binding pose is adopted by maximizing short-range interactions. Thereby side-chain rearrangements ensure optimal shape complementarity. In particular, BPTI’s P1 residue adapts to the S1 pocket and prime site residues reorient to optimize interactions. After the paradigm of conformation selection, binding-competent conformations of BPTI and trypsin are already present in the apo ensembles and their probabilities increase during this proposed two-step association process. This detailed characterization of the molecular forces driving the binding process includes numerous aspects that have been discussed as central to the binding of trypsin and BPTI and protein complex formation in general. In this study, we combine all these aspects into one comprehensive model of protein recognition. We thereby contribute to enhance our general understanding of this fundamental mechanism, which is particularly critical as the development of biopharmaceuticals continuously gains significance.

Multisecond ligand dissociation dynamics from atomistic simulations

Coarse-graining of fully atomistic molecular dynamics simulations is a long-standing goal in order to allow the description of processes occurring on biologically relevant timescales. For example, the prediction of pathways, rates and rate-limiting steps in protein-ligand unbinding is crucial for modern drug discovery. To achieve the enhanced sampling, we perform dissipation-corrected targeted molecular dynamics simulations, which yield free energy and friction profiles of molecular processes under consideration. Subsequently, we use these fields to perform temperature-boosted Langevin simulations which account for the desired kinetics occurring on multisecond timescales and beyond. Adopting the dissociation of solvated sodium chloride, trypsin-benzamidine and Hsp90-inhibitor protein-ligand complexes as test problems, we reproduce rates from molecular dynamics simulation and experiments within a factor of 2–20, and dissociation constants within a factor of 1–4. Analysis of friction profiles reveals that binding and unbinding dynamics are mediated by changes of the surrounding hydration shells in all investigated systems.

Protein-ligand unbinding processes are out of reach for atomistic simulations due to time-scale involved. Here the authors demonstrate an approach relying on dissipation-corrected targeted molecular dynamics that enables to provide binding and unbinding rates with a speed-up of several orders of magnitude.

Studies of Thioamide Effects on Serine Protease Activity Enable Two-Site Stabilization of Cancer Imaging Peptides

Thioamide substitutions in peptides can be used as fluorescence quenchers in protease sensors and as stabilizing modifications of hormone analogs. To guide these applications in the context of serine proteases, we here examine the cleavage of several model substrates, scanning a thioamide between the P3 and P3' positions, and identify perturbing positions for thioamide substitution. While all serine proteases tested were affected by P1 thioamidation, certain proteases were also significantly affected by other thioamide positions. We demonstrate how these findings can be applied by harnessing the combined P3/P1 effect of a single thioamide on kallikrein proteolysis to protect two key positions in a neuropeptide Y-based imaging probe, increasing its serum half-life to >24 h while maintaining potency for binding to Y1 receptor expressing cells. Such stabilized peptide probes could find application in imaging cell populations in animal models or even in clinical applications such as fluorescence-guided surgery.

Development of a Force Field for the Simulation of Single-Chain Proteins and Protein-Protein Complexes

The accuracy of atomistic physics-based force fields for the simulation of biological macromolecules has typically been benchmarked experimentally using biophysical data from simple, often single-chain systems. In the case of proteins, the careful refinement of force field parameters associated with torsion-angle potentials and the use of improved water models have enabled a great deal of progress toward the highly accurate simulation of such monomeric systems in both folded and, more recently, disordered states. In living organisms, however, proteins constantly interact with other macromolecules, such as proteins and nucleic acids, and these interactions are often essential for proper biological function. Here, we show that state-of-the-art force fields tuned to provide an accurate description of both ordered and disordered proteins can be limited in their ability to accurately describe protein-protein complexes. This observation prompted us to perform an extensive reparameterization of one variant of the Amber protein force field. Our objective involved refitting not only the parameters associated with torsion-angle potentials but also the parameters used to model nonbonded interactions, the specification of which is expected to be central to the accurate description of multicomponent systems. The resulting force field, which we call DES-Amber, allows for more accurate simulations of protein-protein complexes, while still providing a state-of-the-art description of both ordered and disordered single-chain proteins. Despite the improvements, calculated protein-protein association free energies still appear to deviate substantially from experiment, a result suggesting that more fundamental changes to the force field, such as the explicit treatment of polarization effects, may simultaneously further improve the modeling of single-chain proteins and protein-protein complexes.

Exploring the Ligand Binding/Unbinding Pathway by Selectively Enhanced Sampling of Ligand in a Protein-Ligand Complex

Understanding the protein-ligand binding is of fundamental biological interest and is essential for structure-based drug design. The difficulty in capturing the dynamic process, however, poses a great challenge for current experimental and theoretical simulation techniques. A selective integrated-tempering-sampling molecular dynamics (SITSMD) method offering an option for selectively enhanced sampling of the ligand in a protein-ligand complex was utilized to quantitatively illuminate the binding of benzamidine to the wild-type trypsin protease and its two mutants (S214E and S214K). The SITSMD simulations could produce consistent results as the extensive conventional MD simulation and gave additional insights into the binding pathway for the test protein-ligand complex system using significantly saved computational resource and time, indicating the potential of such a method in investigating protein-ligand binding. Additionally, the simulations identified the different roles of trypsin-benzamidine van der Waals (vdW) and electrostatic interactions in the binding: the former interaction works as the driving force for dragging the benzamidine close to the native binding pocket, and the latter interaction mainly contributes to stabilizing the benzamidine inside the pocket. The S214E mutation introduces more favorable electrostatic interactions, and as a result, both vdW and electrostatic interactions drive the benzamidine binding, lowering the binding and unbinding free energy barrier. In contrast, the S214K mutation prohibits the binding of the benzamidine to the native ligand binding pocket by introducing disliked charge-charge interactions. In summary, these findings suggest that the change in specific residues could modify the protein druggability, including the binding kinetics and thermodynamics.

Interaction vs Preorganization in Enzyme Catalysis. A Dispute That Calls for Resolution

This essay focuses on the debate between Warshel et al. (proponents of preorganization) and Menger and Nome (proponents of spatiotemporal effects) over the source of fast enzyme catalysis. The Warshel model proposes that the main function of enzymes is to push the solvent coordinate toward the transition state. Other physical-organic factors (e.g., desolvation, entropic effects, ground state destabilization, etc.) do not, ostensibly, contribute substantially to the rate. Indeed, physical organic chemistry in its entirety was claimed to be "irrelevant to an enzyme's active site". Preorganization had been applied by Warshel to his "flagship" enzyme, ketosteroid isomerase, but we discuss troubling issues with their ensuing analysis. For example, the concepts of "general acid" and "general base", known to play a role in this enzyme's mechanism, are ignored in the text. In contrast, the spatiotemporal theory postulates that enzyme-like rates (i.e., accelerations >10⁸) occur when two functionalities are held rigidly at contact distances less than ca. 3 Å. Numerous diverse organic systems are shown to bear this out experimentally. Many of these are intramolecular systems where distances between functionalities are known. Among them are fast intramolecular systems where strain is actually generated during the reaction, thereby excluding steric compression as a source of the observed enzyme-like rates. Finally, the account ends with structural data from four active sites of enzymes, obtained by others, all showing contact distances between substrate analogues and enzyme. To our knowledge, contact distances less than the diameter of water are found universally among enzymes, and it is to this fact that we attribute their extremely fast rates given the assumption that enzymes, whatever their particular mechanism, obey elementary chemical principles.

Learning protein constitutive motifs from sequence data

Statistical analysis of evolutionary-related protein sequences provides information about their structure, function, and history. We show that Restricted Boltzmann Machines (RBM), designed to learn complex high-dimensional data and their statistical features, can efficiently model protein families from sequence information. We here apply RBM to 20 protein families, and present detailed results for two short protein domains (Kunitz and WW), one long chaperone protein (Hsp70), and synthetic lattice proteins for benchmarking. The features inferred by the RBM are biologically interpretable: they are related to structure (residue-residue tertiary contacts, extended secondary motifs (α-helixes and β-sheets) and intrinsically disordered regions), to function (activity and ligand specificity), or to phylogenetic identity. In addition, we use RBM to design new protein sequences with putative properties by composing and 'turning up' or 'turning down' the different modes at will. Our work therefore shows that RBM are versatile and practical tools that can be used to unveil and exploit the genotype-phenotype relationship for protein families.

Interaction landscape of a 'CαNN' motif with arsenate and arsenite: a potential peptide-based scavenger of arsenic

Arsenic (As) is a toxic metalloid that has drawn immense attention from the scientific community recently due to its fatal effects through its unwanted occurrence in ground water around the globe. The presence of an excess amount of water soluble arsenate and/or arsenite salt (permissible limit 10 μg L-1 as recommended by the WHO) in water has been correlated with several human diseases. Although arsenate (HAsO4 2-) is a molecular analogue of phosphate (HPO4 2-), phosphate is indispensable for life, while arsenic and its salts are toxic. Therefore, it is worthwhile to focus on the removal of arsenic from water. Towards this end, the design of peptide-based scaffolds for the recognition of arsenate and arsenite would add a new dimension. Utilizing the stereochemical similarity between arsenate (HAsO4 2-) and phosphate (HPO4 2-), we successfully investigated the recognition of arsenate and arsenite with a naturally occurring novel phosphate binding 'CαNN' motif and its related designed analogues. Using computational as well as biophysical approaches, for the first time, we report here that a designed peptide-based scaffold based on the 'CαNN' motif can recognize anions of arsenic in a thermodynamically favorable manner in a context-free system. This peptide-based arsenic binding agent has the potential for future development as a scavenger of arsenic anions to obtain arsenic free water.

Combining the polarizable Drude force field with a continuum electrostatic Poisson-Boltzmann implicit solvation model

In this work, we have combined the polarizable force field based on the classical Drude oscillator with a continuum Poisson-Boltzmann/solvent-accessible surface area (PB/SASA) model. In practice, the positions of the Drude particles experiencing the solvent reaction field arising from the fixed charges and induced polarization of the solute must be optimized in a self-consistent manner. Here, we parameterized the model to reproduce experimental solvation free energies of a set of small molecules. The model reproduces well-experimental solvation free energies of 70 molecules, yielding a root mean square difference of 0.8 kcal/mol versus 2.5 kcal/mol for the CHARMM36 additive force field. The polarization work associated with the solute transfer from the gas-phase to the polar solvent, a term neglected in the framework of additive force fields, was found to make a large contribution to the total solvation free energy, comparable to the polar solute-solvent solvation contribution. The Drude PB/SASA also reproduces well the electronic polarization from the explicit solvent simulations of a small protein, BPTI. Model validation was based on comparisons with the experimental relative binding free energies of 371 single alanine mutations. With the Drude PB/SASA model the root mean square deviation between the predicted and experimental relative binding free energies is 3.35 kcal/mol, lower than 5.11 kcal/mol computed with the CHARMM36 additive force field. Overall, the results indicate that the main limitation of the Drude PB/SASA model is the inability of the SASA term to accurately capture non-polar solvation effects. © 2018 Wiley Periodicals, Inc.

Structures and mechanism of dipeptidyl peptidases 8 and 9, important players in cellular homeostasis and cancer

Dipeptidyl peptidases 8 and 9 are intracellular N-terminal dipeptidyl peptidases (preferentially postproline) associated with pathophysiological roles in immune response and cancer biology. While the DPP family member DPP4 is extensively characterized in molecular terms as a validated therapeutic target of type II diabetes, experimental 3D structures and ligand-/substrate-binding modes of DPP8 and DPP9 have not been reported. In this study we describe crystal and molecular structures of human DPP8 (2.5 Å) and DPP9 (3.0 Å) unliganded and complexed with a noncanonical substrate and a small molecule inhibitor, respectively. Similar to DPP4, DPP8 and DPP9 molecules consist of one β-propeller and α/β hydrolase domain, forming a functional homodimer. However, they differ extensively in the ligand binding site structure. In intriguing contrast to DPP4, where liganded and unliganded forms are closely similar, ligand binding to DPP8/9 induces an extensive rearrangement at the active site through a disorder-order transition of a 26-residue loop segment, which partially folds into an α-helix (R-helix), including R160/133, a key residue for substrate binding. As vestiges of this helix are also seen in one of the copies of the unliganded form, conformational selection may contributes to ligand binding. Molecular dynamics simulations support increased flexibility of the R-helix in the unliganded state. Consistently, enzyme kinetics assays reveal a cooperative allosteric mechanism. DPP8 and DPP9 are closely similar and display few opportunities for targeted ligand design. However, extensive differences from DPP4 provide multiple cues for specific inhibitor design and development of the DPP family members as therapeutic targets or antitargets.

Synthesis meets theory: Past, present and future of rational chemistry

Chemical synthesis has its roots in the empirical approach of alchemy. Nonetheless, the birth of the scientific method, the technical and technological advances (exploiting revolutionary discoveries in physics) and the improved management and sharing of growing databases greatly contributed to the evolution of chemistry from an esoteric ground into a mature scientific discipline during these last 400 years. Furthermore, thanks to the evolution of computational resources, platforms and media in the last 40 years, theoretical chemistry has added to the puzzle the final missing tile in the process of “rationalizing” chemistry. The use of mathematical models of chemical properties, behaviors and reactivities is nowadays ubiquitous in literature. Theoretical chemistry has been successful in the difficult task of complementing and explaining synthetic results and providing rigorous insights when these are otherwise unattainable by experiment. The first part of this review walks the reader through a concise historical overview on the evolution of the “model” in chemistry. Salient milestones have been highlighted and briefly discussed. The second part focuses more on the general description of recent state-of-the-art computational techniques currently used worldwide by chemists to produce synergistic models between theory and experiment. Each section is complemented by key-examples taken from the literature that illustrate the application of the technique discussed therein.

A cleft type receptor which combines an oxyanion hole with electrostatic interactions

A receptor for carboxylic acids which combines an oxyanion-hole structure with electrostatic forces has been prepared. X-ray diffraction studies have been carried out to evaluate the geometry of both the free receptor and its associated species with several carboxylic acids and many different arrangements have been discovered for the H-bond pattern in the associated species.

Do Fragments and Crystallization Additives Bind Similarly to Drug-like Ligands?

The success of fragment-based drug design (FBDD) hinges upon the optimization of low-molecular-weight compounds (MW < 300 Da) with weak binding affinities to lead compounds with high affinity and selectivity. Usually, structural information from fragment-protein complexes is used to develop ideas about the binding mode of similar but drug-like molecules. In this regard, crystallization additives such as cryoprotectants or buffer components, which are highly abundant in crystal structures, are frequently ignored. Thus, the aim of this study was to investigate the information present in protein complexes with fragments as well as those with additives and how they relate to the binding modes of their drug-like counterparts. We present a thorough analysis of the binding modes of crystallographic additives, fragments, and drug-like ligands bound to four diverse targets of wide interest in drug discovery and highly represented in the Protein Data Bank: cyclin-dependent kinase 2, β-secretase 1, carbonic anhydrase 2, and trypsin. We identified a total of 630 unique molecules bound to the catalytic binding sites, among them 31 additives, 222 fragments, and 377 drug-like ligands. In general, we observed that, independent of the target, protein-fragment interaction patterns are highly similar to those of drug-like ligands and mostly cover the residues crucial for binding. Crystallographic additives are also able to show conserved binding modes and recover the residues important for binding in some of the cases. Moreover, we show evidence that the information from fragments and drug-like ligands can be applied to rescore docking poses in order to improve the prediction of binding modes.

SEEKR: Simulation Enabled Estimation of Kinetic Rates, A Computational Tool to Estimate Molecular Kinetics and Its Application to Trypsin-Benzamidine Binding

We present the Simulation Enabled Estimation of Kinetic Rates (SEEKR) package, a suite of open-source scripts and tools designed to enable researchers to perform multiscale computation of the kinetics of molecular binding, unbinding, and transport using a combination of molecular dynamics, Brownian dynamics, and milestoning theory. To demonstrate its utility, we compute the kon, koff, and ΔGbind for the protein trypsin with its noncovalent binder, benzamidine, and examine the kinetics and other results generated in the context of the new software, and compare our findings to previous studies performed on the same system. We compute a kon estimate of (2.1 ± 0.3) × 107 M-1 s-1, a koff estimate of 83 ± 14 s-1, and a ΔGbind of -7.4 ± 0.1 kcal·mol-1, all of which compare closely to the experimentally measured values of 2.9 × 107 M-1 s-1, 600 ± 300 s-1, and -6.71 ± 0.05 kcal·mol-1, respectively.

Solvent effects on ligand binding to a serine protease

ITC experiments and MD simulations reveal the mechanism behind enthalpy/entropy compensation upon trypsin-benzamidine binding at different solvation conditions.

Theoretical Insights into Sub-Terahertz Acoustic Vibrations of Proteins Measured in Single-Molecule Experiments

Proteins are an important class of nanobioparticles with acoustical modes in the sub-THz frequency range. There is considerable interest to measure and establish the role of these acoustical vibrations for biological function. So far, the technique providing the most detailed information about the acoustical modes of proteins is the very recent Extraordinary Acoustic Raman (EAR) spectroscopy. In this technique, proteins are trapped in nanoholes and excited by two optical lasers of slightly different wavelengths producing an electric field at low frequency (<100 GHz). We demonstrate that the acoustical modes of proteins studied by EAR spectroscopy are both infrared- and Raman-active modes, and we provided interpretation of the spectroscopic fingerprints measured at the single-molecule level. A combination of the present calculations with techniques based on the excitation of a single nanobioparticle by an electric field, such as EAR spectroscopy, should provide a wealth of information on the role of molecular dynamics for biological function.

S2'-subsite variations between human and mouse enzymes (plasmin, factor XIa, kallikrein) elucidate inhibition differences by tissue factor pathway inhibitor -2 domain1-wild-type, Leu17Arg-mutant and aprotinin

Essentials Current antifibrinolytics - aminocaproic acid and tranexamic acid-can cause seizures or renal injury. KD1L17R -KT , aprotinin and tranexamic acid were tested in a modified mouse tail-amputation model. S2'-subsite variations between human and mouse factor XIa result in vastly different inhibition profiles. KD1L17R -KT reduces blood loss and D-dimer levels in mouse with unobserved seizures or renal injury.

SUMMARY

Background Using tissue factor pathway inhibitor (TFPI)-2 Kunitz domain1 (KD1), we obtained a bifunctional antifibrinolytic molecule (KD1L17R -KT ) with C-terminal lysine (kringle domain binding) and P2'-residue arginine (improved specificity towards plasmin). KD1L17R -KT strongly inhibited human plasmin (hPm), with no inhibition of human kallikrein (hKLK) or factor XIa (hXIa). Furthermore, KD1L17R -KT reduced blood loss comparable to aprotinin in a mouse liver-laceration model of organ hemorrhage. However, effectiveness of these antifibrinolytic agents in a model of hemorrhage mimicking extremity trauma and their inhibition efficiencies for mouse enzymes (mPm, mKLK or mXIa) remain to be determined. Objective To determine potential differences in inhibition constants of various antifibrinolytic agents against mouse and human enzymes and test their effectiveness in a modified mouse tail-amputation hemorrhage model. Methods/Results Unexpectedly, mXIa was inhibited with ~ 17-fold increased affinity by aprotinin (Ki ~ 20 nm) and with measurable affinity for KD1L17R -KT (Ki ~ 3 μm); in contrast, KD1WT -VT inhibited hXIa or mXIa with similar affinity. Compared with hPm, mPm had ~ 3-fold reduced affinity, whereas species specificity for hKLK and mKLK was comparable for each inhibitor. S2'-subsite variations largely accounted for the observed differences. KD1L17R -KT and aprotinin were more effective than KD1WT -VT or tranexamic acid in inhibiting tPA-induced mouse plasma clot lysis. Further, KD1L17R -KT was more effective than KD1WT -VT and was comparable to aprotinin and tranexamic acid in reducing blood loss and D-dimer levels in the mouse tail-amputation model. Conclusions Inhibitor potencies differ between antifibrinolytic agents against human and mouse enzymes. KD1L17R -KT is effective in reducing blood loss in a tail-amputation model that mimics extremity injury.

Use of multiple picosecond high-mass molecular dynamics simulations to predict crystallographic B-factors of folded globular proteins

Predicting crystallographic B-factors of a protein from a conventional molecular dynamics simulation is challenging, in part because the B-factors calculated through sampling the atomic positional fluctuations in a picosecond molecular dynamics simulation are unreliable, and the sampling of a longer simulation yields overly large root mean square deviations between calculated and experimental B-factors. This article reports improved B-factor prediction achieved by sampling the atomic positional fluctuations in multiple picosecond molecular dynamics simulations that use uniformly increased atomic masses by 100-fold to increase time resolution. Using the third immunoglobulin-binding domain of protein G, bovine pancreatic trypsin inhibitor, ubiquitin, and lysozyme as model systems, the B-factor root mean square deviations (mean ± standard error) of these proteins were 3.1 ± 0.2–9 ± 1 Ų for Cα and 7.3 ± 0.9–9.6 ± 0.2 Ų for Cγ, when the sampling was done for each of these proteins over 20 distinct, independent, and 50-picosecond high-mass molecular dynamics simulations with AMBER forcefield FF12MC or FF14SB. These results suggest that sampling the atomic positional fluctuations in multiple picosecond high-mass molecular dynamics simulations may be conducive to a priori prediction of crystallographic B-factors of a folded globular protein.

A colostrum trypsin inhibitor gene expressed in the Cape fur seal mammary gland during lactation

The colostrum trypsin inhibitor (CTI) gene and transcript were cloned from the Cape fur seal mammary gland and CTI identified by in silico analysis of the Pacific walrus and polar bear genomes (Order Carnivora), and in marine and terrestrial mammals of the Orders Cetartiodactyla (yak, whales, camel) and Perissodactyla (white rhinoceros). Unexpectedly, Weddell seal CTI was predicted to be a pseudogene. Cape fur seal CTI was expressed in the mammary gland of a pregnant multiparous seal, but not in a seal in its first pregnancy. While bovine CTI is expressed for 24-48 h postpartum (pp) and secreted in colostrum only, Cape fur seal CTI was detected for at least 2-3 months pp while the mother was suckling its young on-shore. Furthermore, CTI was expressed in the mammary gland of only one of the lactating seals that was foraging at-sea. The expression of β-casein (CSN2) and β-lactoglobulin II (LGB2), but not CTI in the second lactating seal foraging at-sea suggested that CTI may be intermittently expressed during lactation. Cape fur seal and walrus CTI encode putative small, secreted, N-glycosylated proteins with a single Kunitz/bovine pancreatic trypsin inhibitor (BPTI) domain indicative of serine protease inhibition. Mature Cape fur seal CTI shares 92% sequence identity with Pacific walrus CTI, but only 35% identity with BPTI. Structural homology modelling of Cape fur seal CTI and Pacific walrus trypsin based on the model of the second Kunitz domain of human tissue factor pathway inhibitor (TFPI) and porcine trypsin (Protein Data Bank: 1TFX) confirmed that CTI inhibits trypsin in a canonical fashion. Therefore, pinniped CTI may be critical for preventing the proteolytic degradation of immunoglobulins that are passively transferred from mother to young via colostrum and milk.

A highly selective receptor for zwitterionic proline

Enantioselective extraction of zwitterionic proline from water to chloroform has been achieved with a chiral apolar chromane receptor.

A molecular receptor for zwitterionic phenylalanine

Extraction of zwitterionic phenylalanine from water to chloroform has been achieved with a chiral apolar benzofuran receptor.

Visualizing the Ensemble Structures of Protein Complexes Using Chemical Cross-Linking Coupled with Mass Spectrometry

Graphical Abstract

Abstract

Chemical cross-linking coupled with mass spectrometry (CXMS) identifies protein residues that are close in space, and has been increasingly used for modeling the structures of protein complexes. Here we show that a single structure is usually sufficient to account for the intermolecular cross-links identified for a stable complex with sub-µmol/L binding affinity. In contrast, we show that the distance between two cross-linked residues in the different subunits of a transient or fleeting complex may exceed the maximum length of the cross-linker used, and the cross-links cannot be fully accounted for with a unique complex structure. We further show that the seemingly incompatible cross-links identified with high confidence arise from alternative modes of protein-protein interactions. By converting the intermolecular cross-links to ambiguous distance restraints, we established a rigid-body simulated annealing refinement protocol to seek the minimum set of conformers collectively satisfying the CXMS data. Hence we demonstrate that CXMS allows the depiction of the ensemble structures of protein complexes and elucidates the interaction dynamics for transient and fleeting complexes.

Electronic supplementary material

The online version of this article (doi:10.1007/s41048-015-0015-y) contains supplementary material, which is available to authorized users.

Restricting the ψ Torsion Angle Has Stereoelectronic Consequences on a Scissile Bond: An Electronic Structure Analysis

Protein motion is intimately linked to enzymatic catalysis, yet the stereoelectronic changes that accompany different conformational states of a substrate are poorly defined. Here we investigate the relationship between conformation and stereoelectronic effects of a scissile amide bond. Structural studies have revealed that the C-terminal glycine of ubiquitin and ubiquitin-like proteins adopts a syn (ψ ∼ 0°) or gauche (ψ ∼ ±60°) conformation upon interacting with deubiquitinases/ubiquitin-like proteases. We used hybrid density functional theory and natural bond orbital analysis to understand how the stereoelectronic effects of the scissile bond change as a function of φ and ψ torsion angles. This led to the discovery that when ψ is between 30° and -30° the scissile bond becomes geometrically and electronically deformed. Geometric distortion occurs through pyramidalization of the carbonyl carbon and amide nitrogen. Electronic distortion is manifested by a decrease in the strength of the donor-acceptor interaction between the amide nitrogen and antibonding orbital (π*) of the carbonyl. Concomitant with the reduction in nN → π* delocalization energy, the sp(2) hybrid orbital of the carbonyl carbon becomes richer in p-character, suggesting the syn configuration causes the carbonyl carbon hybrid orbitals to adopt a geometry reminiscent of a tetrahedral-like intermediate. Our work reveals important insights into the role of substrate conformation in activating the reactive carbonyl of a scissile bond. These findings have implications for designing potent active site inhibitors based on the concept of transition state analogues.

pK(A) in proteins solving the Poisson-Boltzmann equation with finite elements

Knowledge on pK(A) values is an eminent factor to understand the function of proteins in living systems. We present a novel approach demonstrating that the finite element (FE) method of solving the linearized Poisson-Boltzmann equation (lPBE) can successfully be used to compute pK(A) values in proteins with high accuracy as a possible replacement to finite difference (FD) method. For this purpose, we implemented the software molecular Finite Element Solver (mFES) in the framework of the Karlsberg+ program to compute pK(A) values. This work focuses on a comparison between pK(A) computations obtained with the well-established FD method and with the new developed FE method mFES, solving the lPBE using protein crystal structures without conformational changes. Accurate and coarse model systems are set up with mFES using a similar number of unknowns compared with the FD method. Our FE method delivers results for computations of pK(A) values and interaction energies of titratable groups, which are comparable in accuracy. We introduce different thermodynamic cycles to evaluate pK(A) values and we show for the FE method how different parameters influence the accuracy of computed pK(A) values.

On interpretation of protein X-ray structures: Planarity of the peptide unit

Pauling's mastery of peptide stereochemistry-based on small molecule crystal structures and the theory of chemical bonding-led to his realization that the peptide unit is planar and then to the Pauling-Corey-Branson model of the α-helix. Similarly, contemporary protein structure refinement is based on experimentally determined diffraction data together with stereochemical restraints. However, even an X-ray structure at ultra-high resolution is still an under-determined model in which the linkage among refinement parameters is complex. Consequently, restrictions imposed on any given parameter can affect the entire structure. Here, we examine recent studies of high resolution protein X-ray structures, where substantial distortions of the peptide plane are found to be commonplace. Planarity is assessed by the ω-angle, a dihedral angle determined by the peptide bond (C-N) and its flanking covalent neighbors; for an ideally planar trans peptide, ω = 180°. By using a freely available refinement package, Phenix [Afonine et al. (2012) Acta Cryst. D, 68:352-367], we demonstrate that tightening default restrictions on the ω-angle can significantly reduce apparent deviations from peptide unit planarity without consequent reduction in reported evaluation metrics (e.g., R-factors). To be clear, our result does not show that substantial non-planarity is absent, only that an equivalent alternative model is possible. Resolving this disparity will ultimately require improved understanding of the deformation energy. Meanwhile, we urge inclusion of ω-angle statistics in new structure reports in order to focus critical attention on the usual practice of assigning default values to ω-angle constraints during structure refinement.

Engineering trypsin for inhibitor resistance

The development of effective protease therapeutics requires that the proteases be more resistant to naturally occurring inhibitors while maintaining catalytic activity. A key step in developing inhibitor resistance is the identification of key residues in protease-inhibitor interaction. Given that majority of the protease therapeutics currently in use are trypsin-fold, trypsin itself serves as an ideal model for studying protease-inhibitor interaction. To test the importance of several trypsin-inhibitor interactions on the prime-side binding interface, we created four trypsin single variants Y39A, Y39F, K60A, and K60V and report biochemical sensitivity against bovine pancreatic trypsin inhibitor (BPTI) and M84R ecotin. All variants retained catalytic activity against small, commercially available peptide substrates [kcat /KM  = (1.2 ± 0.3) × 10(7) M(-1 ) s(-1) . Compared with wild-type, the K60A and K60V variants showed increased sensitivity to BPTI but less sensitivity to ecotin. The Y39A variant was less sensitive to BPTI and ecotin while the Y39F variant was more sensitive to both. The relative binding free energies between BPTI complexes with WT, Y39F, and Y39A were calculated based on 3.5 µs combined explicit solvent molecular dynamics simulations. The BPTI:Y39F complex resulted in the lowest binding energy, while BPTI:Y39A resulted in the highest. Simulations of Y39F revealed increased conformational rearrangement of F39, which allowed formation of a new hydrogen bond between BPTI R17 and H40 of the variant. All together, these data suggest that positions 39 and 60 are key for inhibitor binding to trypsin, and likely more trypsin-fold proteases.

Protein conformational plasticity and complex ligand-binding kinetics explored by atomistic simulations and Markov models

Understanding the structural mechanisms of protein–ligand binding and their dependence on protein sequence and conformation is of fundamental importance for biomedical research. Here we investigate the interplay of conformational change and ligand-binding kinetics for the serine protease Trypsin and its competitive inhibitor Benzamidine with an extensive set of 150 μs molecular dynamics simulation data, analysed using a Markov state model. Seven metastable conformations with different binding pocket structures are found that interconvert at timescales of tens of microseconds. These conformations differ in their substrate-binding affinities and binding/dissociation rates. For each metastable state, corresponding solved structures of Trypsin mutants or similar serine proteases are contained in the protein data bank. Thus, our wild-type simulations explore a space of conformations that can be individually stabilized by adding ligands or making suitable changes in protein sequence. These findings provide direct evidence of conformational plasticity in receptors.

Conformational plasticity influences several aspects of protein function. Here the authors combine extensive MD simulations with Markov state models—using trypsin as model—to reveal new mechanistic details of how conformational plasticity influence ligand-receptors interactions.