Kinetic characterization of the critical step in HIV-1 protease maturation

Dynamozones are the most obvious sign of the evolution of conformational dynamics in HIV-1 protease

Proteins are not static but are flexible molecules that can adopt many different conformations. The HIV-1 protease is an important target for the development of therapies to treat AIDS, due to its critical role in the viral life cycle. We investigated several dynamics studies on the HIV-1 protease families to illustrate the significance of examining the dynamic behaviors and molecular motions for an entire understanding of their dynamics-structure-function relationships. Using computer simulations and principal component analysis approaches, the dynamics data obtained revealed that: (i) The flap regions are the most obvious sign of the evolution of conformational dynamics in HIV-1 protease; (ii) There are dynamic structural regions in some proteins that contribute to the biological function and allostery of proteins via appropriate flexibility. These regions are a clear sign of the evolution of conformational dynamics of proteins, which we call dynamozones. The flap regions are one of the most important dynamozones members that are critical for HIV-1 protease function. Due to the existence of other members of dynamozones in different proteins, we propose to consider dynamozones as a footprint of the evolution of the conformational dynamics of proteins.

Prediction of HIV-1 protease cleavage site from octapeptide sequence information using selected classifiers and hybrid descriptors

Background

In most parts of the world, especially in underdeveloped countries, acquired immunodeficiency syndrome (AIDS) still remains a major cause of death, disability, and unfavorable economic outcomes. This has necessitated intensive research to develop effective therapeutic agents for the treatment of human immunodeficiency virus (HIV) infection, which is responsible for AIDS. Peptide cleavage by HIV-1 protease is an essential step in the replication of HIV-1. Thus, correct and timely prediction of the cleavage site of HIV-1 protease can significantly speed up and optimize the drug discovery process of novel HIV-1 protease inhibitors. In this work, we built and compared the performance of selected machine learning models for the prediction of HIV-1 protease cleavage site utilizing a hybrid of octapeptide sequence information comprising bond composition, amino acid binary profile (AABP), and physicochemical properties as numerical descriptors serving as input variables for some selected machine learning algorithms. Our work differs from antecedent studies exploring the same subject in the combination of octapeptide descriptors and method used. Instead of using various subsets of the dataset for training and testing the models, we combined the dataset, applied a 3-way data split, and then used a "stratified" 10-fold cross-validation technique alongside the testing set to evaluate the models.

Results

Among the 8 models evaluated in the “stratified” 10-fold CV experiment, logistic regression, multi-layer perceptron classifier, linear discriminant analysis, gradient boosting classifier, Naive Bayes classifier, and decision tree classifier with AUC, F-score, and B. Acc. scores in the ranges of 0.91–0.96, 0.81–0.88, and 80.1–86.4%, respectively, have the closest predictive performance to the state-of-the-art model (AUC 0.96, F-score 0.80 and B. Acc. ~ 80.0%). Whereas, the perceptron classifier and the K-nearest neighbors had statistically lower performance (AUC 0.77–0.82, F-score 0.53–0.69, and B. Acc. 60.0–68.5%) at p < 0.05. On the other hand, logistic regression, and multi-layer perceptron classifier (AUC of 0.97, F-score > 0.89, and B. Acc. > 90.0%) had the best performance on further evaluation on the testing set, though linear discriminant analysis, gradient boosting classifier, and Naive Bayes classifier equally performed well (AUC > 0.94, F-score > 0.87, and B. Acc. > 86.0%).

Conclusions

Logistic regression and multi-layer perceptron classifiers have comparable predictive performances to the state-of-the-art model when octapeptide sequence descriptors consisting of AABP, bond composition and standard physicochemical properties are used as input variables. In our future work, we hope to develop a standalone software for HIV-1 protease cleavage site prediction utilizing the linear regression algorithm and the aforementioned octapeptide sequence descriptors.

GraphVAMPNet, using graph neural networks and variational approach to Markov processes for dynamical modeling of biomolecules

Finding a low dimensional representation of data from long-timescale trajectories of biomolecular processes, such as protein folding or ligand-receptor binding, is of fundamental importance, and kinetic models, such as Markov modeling, have proven useful in describing the kinetics of these systems. Recently, an unsupervised machine learning technique called VAMPNet was introduced to learn the low dimensional representation and the linear dynamical model in an end-to-end manner. VAMPNet is based on the variational approach for Markov processes and relies on neural networks to learn the coarse-grained dynamics. In this paper, we combine VAMPNet and graph neural networks to generate an end-to-end framework to efficiently learn high-level dynamics and metastable states from the long-timescale molecular dynamics trajectories. This method bears the advantages of graph representation learning and uses graph message passing operations to generate an embedding for each datapoint, which is used in the VAMPNet to generate a coarse-grained dynamical model. This type of molecular representation results in a higher resolution and a more interpretable Markov model than the standard VAMPNet, enabling a more detailed kinetic study of the biomolecular processes. Our GraphVAMPNet approach is also enhanced with an attention mechanism to find the important residues for classification into different metastable states.

Nanopore detects γ-radiation inhibited HIV-1 protease activity

Inhibition of HIV-1 protease (PR) activity is realized by exposure to ⁶⁰Co γ-radiation. The radiation effects on enzyme kinetics of HIV-1 PR are subsequently monitored using nanopore sensor. Substantial loss of proteolytic efficiency towards GagPol polypeptide is observed due to the radiation treatment. Results shows ~50% of GagPol polypeptide was not involved in HIV-1 PR proteolysis by exposure to ultra-low intensity of γ-radiation (0.1K Gy), and the values reach to over 90% with high γ-ray treatment. Besides, the inactivation effect is also verified in blood samples which contain the virus protease. Our finding provides the potential benefits of γ-radiation to inactivate viral proteinic function, and might be a complementary to the designation of HIV-1 PR inhibitors.

Multiscale Approach for Computing Gated Ligand Binding from Molecular Dynamics and Brownian Dynamics Simulations

We develop an approach to characterize the effects of gating by a multiconformation protein consisting of macrostate conformations that are either accessible or inaccessible to ligand binding. We first construct a Markov state model of the apo-protein from atomistic molecular dynamics simulations from which we identify macrostates and their conformations, compute their relative macrostate populations and interchange kinetics, and structurally characterize them in terms of ligand accessibility. We insert the calculated first-order rate constants for conformational transitions into a multistate gating theory from which we derive a gating factor γ that quantifies the degree of conformational gating. Applied to HIV-1 protease, our approach yields a kinetic network of three accessible (semi-open, open, and wide-open) and two inaccessible (closed and a newly identified, "parted") macrostate conformations. The parted conformation sterically partitions the active site, suggesting a possible role in product release. We find that the binding kinetics of drugs and drug-like inhibitors to HIV-1 protease falls in the slow gating regime. However, because γ = 0.75, conformational gating only modestly slows ligand binding. Brownian dynamics simulations of the diffusional association of eight inhibitors to the protease─having a wide range of experimental association constants (∼10⁴-10¹⁰ M^-1 s^-1)─yields gated rate constants in the range of ∼0.5-5.7 × 10⁸ M^-1 s^-1. This indicates that, whereas the association rate of some inhibitors could be described by the model, for many inhibitors either subsequent conformational transitions or alternate binding mechanisms may be rate-limiting. For systems known to be modulated by conformational gating, the approach could be scaled computationally efficiently to screen association kinetics for a large number of ligands.

Unsupervised Learning Methods for Molecular Simulation Data

Unsupervised learning is becoming an essential tool to analyze the increasingly large amounts of data produced by atomistic and molecular simulations, in material science, solid state physics, biophysics, and biochemistry. In this Review, we provide a comprehensive overview of the methods of unsupervised learning that have been most commonly used to investigate simulation data and indicate likely directions for further developments in the field. In particular, we discuss feature representation of molecular systems and present state-of-the-art algorithms of dimensionality reduction, density estimation, and clustering, and kinetic models. We divide our discussion into self-contained sections, each discussing a specific method. In each section, we briefly touch upon the mathematical and algorithmic foundations of the method, highlight its strengths and limitations, and describe the specific ways in which it has been used-or can be used-to analyze molecular simulation data.

Human Transbodies to Reverse Transcriptase Connection Subdomain of HIV-1 Gag-Pol Polyprotein Reduce Infectiousness of the Virus Progeny

HIV-1 progeny are released from infected cells as immature particles that are unable to infect new cells. Gag-Pol polyprotein dimerization via the reverse transcriptase connection domain (RTCDs) is pivotal for proper activation of the virus protease (PR protein) in an early event of the progeny virus maturation process. Thus, the RTCD is a potential therapeutic target for a broadly effective anti-HIV agent through impediment of virus maturation. In this study, human single-chain antibodies (HuscFvs) that bound to HIV-1 RTCD were generated using phage display technology. Computerized simulation guided the selection of the transformed Escherichia coli-derived HuscFvs that bound to the RTCD dimer interface. The selected HuscFvs were linked molecularly to human-derived-cell-penetrating peptide (CPP) to make them cell-penetrable (i.e., become transbodies). The CPP-HuscFvs/transbodies produced by a selected transformed E. coli clone were tested for anti-HIV-1 activity. CPP-HuscFvs of transformed E. coli clone 11 (CPP-HuscFv11) that presumptively bound at the RTCD dimer interface effectively reduced reverse transcriptase activity in the newly released virus progeny. Infectiousness of the progeny viruses obtained from CPP-HuscFv11-treated cells were reduced by a similar magnitude to those obtained from protease/reverse transcriptase inhibitor-treated cells, indicating anti-HIV-1 activity of the transbodies. The CPP-HuscFv11/transbodies to HIV-1 RTCD could be an alternative, anti-retroviral agent for long-term HIV-1 treatment.

Conformational Analysis of Heparin-Analogue Pentasaccharides by Nuclear Magnetic Resonance Spectroscopy and Molecular Dynamics Simulations

Elucidation and improvement of the blood coagulant properties of heparin are the focus of intense research. In this study, we performed conformational analysis using nuclear magnetic resonance (NMR) spectroscopy and molecular dynamics (MD) simulations on the heparin pentasaccharide analogue idraparinux, its disulfonatomethyl analogue, which features a slightly improved blood coagulation property, and a trisulfonatomethyl analogue, in which the activity has been totally abolished. As the ring conformation of the G subunit has been suggested as a major determinant of the biological properties, we analyzed the sugar ring conformations and dynamics of the interglycosidic linkages. We found that the conformation of the G ring is dominated by the ²S_O skewed boat next to the ¹C₄ chair in all three derivatives. Both the thermodynamics and the kinetics of the conformational states were found to be highly similar in the three derivatives. Molecular kinetic analysis showed that the ²S_O skewed boat state of the G ring is equally favorable in the three analogues, resulting in similar ²S_O populations. Also, the transition kinetics from the ¹C₄ chair to the ²S_O skewed boat was found to be comparable in the derivatives, which indicates a similar energy barrier between the two states of the G subunit. We also identified a slower conformational transition between the dominant ⁴C₁ chair and the boat conformations on the E subunit. Both G and E ring flips are also accompanied by changes along the interglycosidic linkages, which take place highly synchronously with the ring flips. These findings indicate that conformational plasticity of the G ring and the dominance of the ²S_O skewed boat populations do not necessarily warrant the biological activity of the derivatives and hence the impact of other factors also needs to be considered.

"Dividing and Conquering" and "Caching" in Molecular Modeling

Molecular modeling is widely utilized in subjects including but not limited to physics, chemistry, biology, materials science and engineering. Impressive progress has been made in development of theories, algorithms and software packages. To divide and conquer, and to cache intermediate results have been long standing principles in development of algorithms. Not surprisingly, most important methodological advancements in more than half century of molecular modeling are various implementations of these two fundamental principles. In the mainstream classical computational molecular science, tremendous efforts have been invested on two lines of algorithm development. The first is coarse graining, which is to represent multiple basic particles in higher resolution modeling as a single larger and softer particle in lower resolution counterpart, with resulting force fields of partial transferability at the expense of some information loss. The second is enhanced sampling, which realizes "dividing and conquering" and/or "caching" in configurational space with focus either on reaction coordinates and collective variables as in metadynamics and related algorithms, or on the transition matrix and state discretization as in Markov state models. For this line of algorithms, spatial resolution is maintained but results are not transferable. Deep learning has been utilized to realize more efficient and accurate ways of "dividing and conquering" and "caching" along these two lines of algorithmic research. We proposed and demonstrated the local free energy landscape approach, a new framework for classical computational molecular science. This framework is based on a third class of algorithm that facilitates molecular modeling through partially transferable in resolution "caching" of distributions for local clusters of molecular degrees of freedom. Differences, connections and potential interactions among these three algorithmic directions are discussed, with the hope to stimulate development of more elegant, efficient and reliable formulations and algorithms for "dividing and conquering" and "caching" in complex molecular systems.

Predicting HIV-1 Protease Cleavage Sites With Positive-Unlabeled Learning

Understanding the substrate specificity of HIV-1 protease plays an essential role in the prevention of HIV infection. A variety of computational models have thus been developed to predict substrate sites that are cleaved by HIV-1 protease, but most of them normally follow a supervised learning scheme to build classifiers by considering experimentally verified cleavable sites as positive samples and unknown sites as negative samples. However, certain noisy can be contained in the negative set, as false negative samples are possibly existed. Hence, the performance of the classifiers is not as accurate as they could be due to the biased prediction results. In this work, unknown substrate sites are regarded as unlabeled samples instead of negative ones. We propose a novel positive-unlabeled learning algorithm, namely PU-HIV, for an effective prediction of HIV-1 protease cleavage sites. Features used by PU-HIV are encoded from different perspectives of substrate sequences, including amino acid identities, coevolutionary patterns and chemical properties. By adjusting the weights of errors generated by positive and unlabeled samples, a biased support vector machine classifier can be built to complete the prediction task. In comparison with state-of-the-art prediction models, benchmarking experiments using cross-validation and independent tests demonstrated the superior performance of PU-HIV in terms of AUC, PR-AUC, and F-measure. Thus, with PU-HIV, it is possible to identify previously unknown, but physiologically existed substrate sites that are able to be cleaved by HIV-1 protease, thus providing valuable insights into designing novel HIV-1 protease inhibitors for HIV treatment.

Protein storytelling through physics

Every protein has a story-how it folds, what it binds, its biological actions, and how it misbehaves in aging or disease. Stories are often inferred from a protein's shape (i.e., its structure). But increasingly, stories are told using computational molecular physics (CMP). CMP is rooted in the principled physics of driving forces and reveals granular detail of conformational populations in space and time. Recent advances are accessing longer time scales, larger actions, and blind testing, enabling more of biology's stories to be told in the language of atomistic physics.

Conditional activation of an HIV-1 protease attenuated mutant by a leucine zipper dimerization motif

Mature HIV-1 protease (PR) functions as a dimer. Changes in HIV-1 PR activation can block virus assembly via premature or enhanced Gag cleavage. HIV-1 PR precursor contains N terminal-linked p6*, a possible modulating factor in PR activation. We found that p6* replacement with a leucine zipper (LZ) dimerization motif (creating a DWzPR construct) or an LZ insertion at the PR C-terminus significantly reduced virus yields due to enhanced Gag cleavage, suggesting that an LZ insertion promotes PR activation by facilitating PR dimer formation. However, introducing T26S (a PR activity-attenuated mutation) into DWzPR strongly impaired Gag cleavage, except when the native C-terminal p6* tetrapeptide remained at the LZ/PR junction. LZ insertion at the PR C-terminus still strongly enhanced PR T26S Gag cleavage. Our data suggest that in addition to p6* mutations, a single amino acid substitution within PR can impair PR activation, likely due to conformational changes triggered by the PR precursor.

Incorporating the Coevolving Information of Substrates in Predicting HIV-1 Protease Cleavage Sites

Human immunodeficiency virus 1 (HIV-1) protease (PR) plays a crucial role in the maturation of the virus. The study of substrate specificity of HIV-1 PR as a new endeavor strives to increase our ability to understand how HIV-1 PR recognizes its various cleavage sites. To predict HIV-1 PR cleavage sites, most of the existing approaches have been developed solely based on the homogeneity of substrate sequence information with supervised classification techniques. Although efficient, these approaches are found to be restricted to the ability of explaining their results and probably provide few insights into the mechanisms by which HIV-1 PR cleaves the substrates in a site-specific manner. In this work, a coevolutionary pattern-based prediction model for HIV-1 PR cleavage sites, namely EvoCleave, is proposed by integrating the coevolving information obtained from substrate sequences with a linear SVM classifier. The experiment results showed that EvoCleave yielded a very promising performance in terms of ROC analysis and f-measure. We also prospectively assessed the biological significance of coevolutionary patterns by applying them to study three fundamental issues of HIV-1 PR cleavage site. The analysis results demonstrated that the coevolutionary patterns offered valuable insights into the understanding of substrate specificity of HIV-1 PR.

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.

Fine-Tuning of Sequence Specificity by Near Attack Conformations in Enzyme-Catalyzed Peptide Hydrolysis

The catalytic role of near attack conformations (NACs), molecular states that lie on the pathway between the ground state (GS) and transition state (TS) of a chemical reaction, is not understood completely. Using a computational approach that combines Bürgi–Dunitz theory with all-atom molecular dynamics simulations, the role of NACs in catalyzing the first stages of HIV-1 protease peptide hydrolysis was previously investigated using a substrate that represents the recognized SP1-NC cleavage site of the HIV-1 Gag polyprotein. NACs were found to confer no catalytic effect over the uncatalyzed reaction there ( Δ Δ G N ‡ ∼ 0 kcal/mol). Here, using the same approach, the role of NACs across multiple substrates that each represent a further recognized cleavage site is investigated. Overall rate enhancement varies by | Δ Δ G ‡ | ∼ 12–15 kcal/mol across this set, and although NACs contribute a small and approximately constant barrier to the uncatalyzed reaction (< Δ G N ‡ u > = 4.3 ± 0.3 kcal/mol), they are found to contribute little significant catalytic effect ( | Δ Δ G N ‡ | ∼ 0–2 kcal/mol). Furthermore, no correlation is exhibited between NAC contributions and the overall energy barrier ( R 2 = 0.01). However, these small differences in catalyzed NAC contributions enable rates to match those required for the kinetic order of processing. Therefore, NACs may offer an alternative and subtle mode compared to non-NAC contributions for fine-tuning reaction rates during complex evolutionary sequence selection processes—in this case across cleavable polyproteins whose constituents exhibit multiple functions during the virus life-cycle.

Inhibition of the activity of HIV-1 protease through antibody binding and mutations probed by molecular dynamics simulations

HIV-1 protease is an essential enzyme in the life cycle of the HIV-1 virus. The conformational dynamics of the flap region of the protease is critical for the ligand binding mechanism, as well as for the catalytic activity. The monoclonal antibody F11.2.32 raised against HIV-1 protease inhibits its activity on binding. We have studied the conformational dynamics of protease in its free, inhibitor ritonavir and antibody bound forms using molecular dynamics simulations. We find that upon Ab binding to the epitope region (residues 36-46) of protease, the overall flexibility of the protease is decreased including the flap region and the active site, which is similar to the decrease in flexibility observed by inhibitor binding to the protease. This suggests an allosteric mechanism to inhibit protease activity. Further, the protease mutants G40E and G40R are known to have decreased activity and were also subjected to MD simulations. We find that the loss of flexibility in the mutants is similar to that observed in the protease bound to the Ab/inhibitor. These insights highlight the role played by dynamics in the function of the protease and how control of flexibility through Ab binding and site specific mutations can inhibit protease activity.

Sodium-induced population shift drives activation of thrombin

The equilibrium between active E and inactive E* forms of thrombin is assumed to be governed by the allosteric binding of a Na⁺ ion. Here we use molecular dynamics simulations and Markov state models to sample transitions between active and inactive states. With these calculations we are able to compare thermodynamic and kinetic properties depending on the presence of Na⁺. For the first time, we directly observe sodium-induced conformational changes in long-timescale computer simulations. Thereby, we are able to explain the resulting change in activity. We observe a stabilization of the active form in presence of Na⁺ and a shift towards the inactive form in Na⁺-free simulations. We identify key structural features to quantify and monitor this conformational shift. These include the accessibility of the S1 pocket and the reorientation of W215, of R221a and of the Na⁺ loop. The structural characteristics exhibit dynamics at various timescales: Conformational changes in the Na⁺ binding loop constitute the slowest observed movement. Depending on its orientation, it induces conformational shifts in the nearby substrate binding site. Only after this shift, residue W215 is able to move freely, allowing thrombin to adopt a binding-competent conformation.

Energetics of Flap Opening in HIV-1 Protease: String Method Calculations

HIV-1 protease (PR) is the viral protein responsible for virion maturation, and its mechanisms of action remain incompletely understood. PR is dimeric and contains two flexible, symmetry-related flaps, which act as a gate to inhibit access to the binding pocket and hold the polypeptide substrate in the binding pocket once bound. Wide flap opening, a conformational change assumed to be necessary for substrate binding, is a rare event in the closed and bound form. In this study, we use molecular dynamics (MD) simulations and advanced MD techniques including temperature acceleration and string method in collective variables to study the conformational changes associated with substrate unbinding of both wild-type and F99Y mutant PR. The F99Y mutation is shown via MD to decouple the closing of previously unrecognized distal pockets from substrate unbinding. To determine whether or not the F99Y mutation affects the energetic cost of wide flap opening, we use string method in collective variables to determine the minimum free-energy mechanism for wide flap opening in concert with distal pocket closing. The results indicate that the major energetic cost in flap opening is disengagement of the two flap-tip Ile50 residues from each other and is not affected by the F99Y mutation.

Schistosoma mansoni cathepsin D1: Biochemical and biophysical characterization of the recombinant enzyme expressed in HEK293T cells

Schistosomes express a variety of aspartyl proteases (APs) with distinct roles in the helminth pathophysiology, among which degradation of host haemoglobin is key, since it is the main amino acid source for these parasites. A cathepsin D-like AP from Schistosoma mansoni (SmCD1) has been used as a model enzyme for vaccine and drug development studies in schistosomes and yet a reliable expression system for readily producing the recombinant enzyme in high yield has not been reported. To contribute to further advancing the knowledge about this valuable antischistosomal target, we developed a transient expression system in HEK 293T mammalian cells and performed a biochemical and biophysical characterization of the recombinant enzyme (rSmCD1). It was possible to express a recombinant C-terminal truncated form of SmCD1 (rSmCD1ΔCT) and purify it with high yield (16 mg/L) from the culture supernatant. When analysed by Size-Exclusion Chromatography and multi-angle laser light scattering, rSmCD1ΔCT behaved as a dimer at neutral pH, which is unusual for cathepsins D, turning into a monomer after acidification of the medium. Through analytical ultrancentrifugation, the dimer was confirmed for free rSmCD1ΔCT in solution as well as stabilization of the monomer during interaction with pepstatin. The mammalian cell expression system used here was able to produce rSmCD1ΔCT with high yields allowing for the first time the characterization of important kinetic parameters as well as initial description of its biophysical properties.

A subset of calcium-binding S100 proteins show preferential heterodimerization

The assembly of proteins into dimers and oligomers is a necessary step for the proper function of transcription factors, muscle proteins, and proteases. In uncontrolled states, oligomerization can also contribute to illnesses such as Alzheimer's disease. The S100 protein family is a group of dimeric proteins that have important roles in enzyme regulation, cell membrane repair, and cell growth. Most S100 proteins have been examined in their homodimeric state, yet some of these important proteins are found in similar tissues implying that heterodimeric molecules can also be formed from the combination of two different S100 members. In this work, we have established co-expression methods in order to identify and quantify the distribution of homo- and heterodimers for four specific pairs of S100 proteins in their calcium-free states. The split GFP trap methodology was used in combination with other GFP variants to simultaneously quantify homo- and heterodimeric S100 proteins in vitro and in living cells. For the specific S100 proteins examined, NMR, mass spectrometry, and GFP trap experiments consistently show that S100A1:S100B, S100A1:S100P, and S100A11:S100B heterodimers are the predominant species formed compared to their corresponding homodimers. We expect the tools developed here will help establish the roles of S100 heterodimeric proteins and identify how heterodimerization might alter the specificity for S100 protein action in cells.

Proteases: Pivot Points in Functional Proteomics

Proteases drive the life cycle of all proteins, ensuring the transportation and activation of newly minted, would-be proteins into their functional form while recycling spent or unneeded proteins. Far from their image as engines of protein digestion, proteases play fundamental roles in basic physiology and regulation at multiple levels of systems biology. Proteases are intimately associated with disease and modulation of proteolytic activity is the presumed target for successful therapeutics. "Proteases: Pivot Points in Functional Proteomics" examines the crucial roles of proteolysis across a wide range of physiological processes and diseases. The existing and potential impacts of proteolysis-related activity on drug and biomarker development are presented in detail. All told the decisive roles of proteases in four major categories comprising 23 separate subcategories are addressed. Within this construct, 15 sets of subject-specific, tabulated data are presented that include identification of proteases, protease inhibitors, substrates, and their actions. Said data are derived from and confirmed by over 300 references. Cross comparison of datasets indicates that proteases, their inhibitors/promoters and substrates intersect over a range of physiological processes and diseases, both chronic and pathogenic. Indeed, "Proteases: Pivot Points …" closes by dramatizing this very point through association of (pro)Thrombin and Fibrin(ogen) with: hemostasis, innate immunity, cardiovascular and metabolic disease, cancer, neurodegeneration, and bacterial self-defense.

All-atom virus simulations

The constant threat of viral disease can be combated by the development of novel vaccines and therapeutics designed to disrupt key features of virus structure or infection cycle processes. Such development relies on high-resolution characterization of viruses and their dynamical behaviors, which are often challenging to obtain solely by experiment. In response, all-atom molecular dynamics simulations are widely leveraged to study the structural components of viruses, leading to some of the largest simulation endeavors undertaken to date. The present work reviews exemplary all-atom simulation work on viruses, as well as progress toward simulating entire virions.

Time-lagged autoencoders: Deep learning of slow collective variables for molecular kinetics

Inspired by the success of deep learning techniques in the physical and chemical sciences, we apply a modification of an autoencoder type deep neural network to the task of dimension reduction of molecular dynamics data. We can show that our time-lagged autoencoder reliably finds low-dimensional embeddings for high-dimensional feature spaces which capture the slow dynamics of the underlying stochastic processes-beyond the capabilities of linear dimension reduction techniques.

Systems protobiology: origin of life in lipid catalytic networks

Life is that which replicates and evolves, but there is no consensus on how life emerged. We advocate a systems protobiology view, whereby the first replicators were assemblies of spontaneously accreting, heterogeneous and mostly non-canonical amphiphiles. This view is substantiated by rigorous chemical kinetics simulations of the graded autocatalysis replication domain (GARD) model, based on the notion that the replication or reproduction of compositional information predated that of sequence information. GARD reveals the emergence of privileged non-equilibrium assemblies (composomes), which portray catalysis-based homeostatic (concentration-preserving) growth. Such a process, along with occasional assembly fission, embodies cell-like reproduction. GARD pre-RNA evolution is evidenced in the selection of different composomes within a sparse fitness landscape, in response to environmental chemical changes. These observations refute claims that GARD assemblies (or other mutually catalytic networks in the metabolism first scenario) cannot evolve. Composomes represent both a genotype and a selectable phenotype, anteceding present-day biology in which the two are mostly separated. Detailed GARD analyses show attractor-like transitions from random assemblies to self-organized composomes, with negative entropy change, thus establishing composomes as dissipative systems-hallmarks of life. We show a preliminary new version of our model, metabolic GARD (M-GARD), in which lipid covalent modifications are orchestrated by non-enzymatic lipid catalysts, themselves compositionally reproduced. M-GARD fills the gap of the lack of true metabolism in basic GARD, and is rewardingly supported by a published experimental instance of a lipid-based mutually catalytic network. Anticipating near-future far-reaching progress of molecular dynamics, M-GARD is slated to quantitatively depict elaborate protocells, with orchestrated reproduction of both lipid bilayer and lumenal content. Finally, a GARD analysis in a whole-planet context offers the potential for estimating the probability of life's emergence. The invigorated GARD scrutiny presented in this review enhances the validity of autocatalytic sets as a bona fide early evolution scenario and provides essential infrastructure for a paradigm shift towards a systems protobiology view of life's origin.

RNA Structural Dynamics As Captured by Molecular Simulations: A Comprehensive Overview

With both catalytic and genetic functions, ribonucleic acid (RNA) is perhaps the most pluripotent chemical species in molecular biology, and its functions are intimately linked to its structure and dynamics. Computer simulations, and in particular atomistic molecular dynamics (MD), allow structural dynamics of biomolecular systems to be investigated with unprecedented temporal and spatial resolution. We here provide a comprehensive overview of the fast-developing field of MD simulations of RNA molecules. We begin with an in-depth, evaluatory coverage of the most fundamental methodological challenges that set the basis for the future development of the field, in particular, the current developments and inherent physical limitations of the atomistic force fields and the recent advances in a broad spectrum of enhanced sampling methods. We also survey the closely related field of coarse-grained modeling of RNA systems. After dealing with the methodological aspects, we provide an exhaustive overview of the available RNA simulation literature, ranging from studies of the smallest RNA oligonucleotides to investigations of the entire ribosome. Our review encompasses tetranucleotides, tetraloops, a number of small RNA motifs, A-helix RNA, kissing-loop complexes, the TAR RNA element, the decoding center and other important regions of the ribosome, as well as assorted others systems. Extended sections are devoted to RNA-ion interactions, ribozymes, riboswitches, and protein/RNA complexes. Our overview is written for as broad of an audience as possible, aiming to provide a much-needed interdisciplinary bridge between computation and experiment, together with a perspective on the future of the field.

HIV-1 protease with leucine zipper fused at N-terminus exhibits enhanced linker amino acid-dependent activity

Background

HIV-1 protease (PR) activation is triggered by Gag-Pol dimerization. Premature PR activation results in reduced virion yields due to enhanced Gag cleavage. A p6* transframe peptide located directly upstream of protease is believed to play a modulating role in PR activation. Previous reports indicate that the C-terminal p6* tetra-peptide prevents premature PR activation triggered by a leucine zipper (LZ) dimerization motif inserted in the deleted p6* region. To clarify the involvement of C-terminal p6* residues in mitigating enhanced LZ-incurred Gag processing, we engineered constructs containing C-terminal p6* residue substitutions with and without a mutation blocking the p6*/PR cleavage site, and created other Gag or p6* domain-removing constructs. The capabilities of these constructs to mediate virus maturation were assessed by Western blotting and single-cycle infection assays.

Results

p6*-PR cleavage blocking did not significantly reduce the LZ enhancement effect on Gag cleavage when only four amino acid residues were present between the p6* and PR. This suggests that the potent LZ dimerization motif may enhance PR activation by facilitating PR dimer formation, and that PR precursors may trigger sufficient enzymatic activity without breaking off from the PR N-terminus. Enhanced LZ-induced activation of PR embedded in Gag-Pol was found to be independent of the Gag assembly domain. In contrast, the LZ enhancement effect was markedly reduced when six amino acids were present at the p6*-PR junction, in part due to impaired PR maturation by substitution mutations. We also observed that a proline substitution at the P3 position eliminated the ability of p6*-deleted Gag-Pol to mediate virus maturation, thus emphasizing the importance of C-terminal p6* residues to modulating PR activation.

Conclusions

The ability of HIV-1 C-terminal p6* amino acid residues to modulate PR activation contributes, at least in part, to their ability to counteract enhanced Gag cleavage induced by a leucine zipper substituted for a deleted p6*. Changes in C-terminal p6* residues between LZ and PR may affect PR-mediated virus maturation, thus providing a possible method for assessing HIV-1 protease precursor activation in the context of virus assembly.

Identifying Conformational-Selection and Induced-Fit Aspects in the Binding-Induced Folding of PMI from Markov State Modeling of Atomistic Simulations

Unstructured proteins and peptides typically fold during binding to ligand proteins. A challenging problem is to identify the mechanism and kinetics of these binding-induced folding processes in experiments and atomistic simulations. In this Article, we present a detailed picture for the folding of the inhibitor peptide PMI into a helix during binding to the oncoprotein fragment ^25-109Mdm2 obtained from atomistic, explicit-water simulations and Markov state modeling. We find that binding-induced folding of PMI is highly parallel and can occur along a multitude of pathways. Some pathways are induced-fit-like with binding occurring prior to PMI helix formation, while other pathways are conformational-selection-like with binding after helix formation. On the majority of pathways, however, binding is intricately coupled to folding, without clear temporal ordering. A central feature of these pathways is PMI motion on the Mdm2 surface, along the binding groove of Mdm2 or over the rim of this groove. The native binding groove of Mdm2 thus appears as an asymmetric funnel for PMI binding. Overall, binding-induced folding of PMI does not fit into the classical picture of induced fit or conformational selection that implies a clear temporal ordering of binding and folding events. We argue that this holds in general for binding-induced folding processes because binding and folding events in these processes likely occur on similar time scales and do exhibit the time-scale separation required for temporal ordering.

Computational studies on horseshoe shape pocket of human orexin receptor type 2 and boat conformation of suvorexant by molecular dynamics simulations

The FDA approved drug suvorexant binds to the horseshoe shape pocket of OX₂ R with the boat conformation. The horseshoe shape pocket plays an important role on the biological activity of OX₂ R in the cell membrane. To study the binding mechanism between the horseshoe shape pocket of OX₂ R and boat conformation of suvorexant, the crystal structures of wild type and N324A mutant of OX₂ R in complex with antagonist suvorexant are chosen to perform molecular dynamics (MD) simulations, QM/MM, and MMGBSA calculations. By comparison with the wild type of OX₂ R, the results show the 1,2,3-triazole and p-toluamide groups of suvorexant are changed in the N324A mutant of OX₂ R during 200 ns MD simulations. The QM/MM and weak interaction analysis are employed to calculate the non-covalent bonds interaction between suvorexant and key residues in the wild type and N324A mutant of OX₂ R. The MMGBSA calculations indicate the entropy energy is an important influence factor for suvorexant affinity in the distorted horseshoe shape pocket of OX₂ R. Our results not only show the horseshoe shape pocket of OX₂ R is the necessary conformation for the binding of antagonist suvorexant, but also give the important sites and structural features for antagonist design of OX₂ R.

Simulations meet machine learning in structural biology

Classical molecular dynamics (MD) simulations will be able to reach sampling in the second timescale within five years, producing petabytes of simulation data at current force field accuracy. Notwithstanding this, MD will still be in the regime of low-throughput, high-latency predictions with average accuracy. We envisage that machine learning (ML) will be able to solve both the accuracy and time-to-prediction problem by learning predictive models using expensive simulation data. The synergies between classical, quantum simulations and ML methods, such as artificial neural networks, have the potential to drastically reshape the way we make predictions in computational structural biology and drug discovery.

C-Terminal HIV-1 Transframe p6* Tetrapeptide Blocks Enhanced Gag Cleavage Incurred by Leucine Zipper Replacement of a Deleted p6* Domain

HIV-1 protease (PR) functions as a homodimer mediating virus maturation following virus budding. Gag-Pol dimerization is believed to trigger embedded PR activation by promoting PR dimer formation. Early PR activation can lead to markedly reduced virus yields due to premature Gag cleavage. The p6* peptide, located between Gag and PR, is believed to ensure virus production by preventing early PR maturation. Studies aimed at finding supporting evidence for this proposal are limited due to a reading frame overlap between p6* and the p6gag budding domain. To determine if p6* affects virus production via the modulation of PR activation, we engineered multiple constructs derived from Dp6*PR (an assembly- and processing-competent construct with Pol fused at the inactivated PR C terminus). The data indicated that a p6* deletion adjacent to active PR significantly impaired virus processing. We also observed that the insertion of a leucine zipper (LZ) dimerization motif in the deleted region eliminated virus production in a PR activity-dependent manner, suggesting that the LZ insertion triggered premature PR activation by facilitating PR dimer formation. As few as four C-terminal p6* residues remaining at the p6*/PR junction were sufficient to restore virus yields, with a Gag processing profile similar to that of the wild type. Our study provides supporting evidence in a virus assembly context that the C-terminal p6* tetrapeptide plays a role in preventing premature PR maturation.IMPORTANCE Supporting evidence for the assumption that p6* retards PR maturation in the context of virus assembly is lacking. We found that replacing p6* with a leucine zipper peptide abolished virus assembly due to the significant enhancement of Gag cleavage. However, as few as four C-terminal p6* residues remaining in the deleted region were sufficient for significant PR release, as well as for counteracting leucine zipper-incurred premature Gag cleavage. Our data provide evidence that (i) p6* ensures virus assembly by preventing early PR activation and (ii) four C-terminal p6* residues are critical for modulating PR activation. Current PR inhibitor development efforts are aimed largely at mature PR, but there is a tendency for HIV-1 variants that are resistant to multiple protease inhibitors to emerge. Our data support the idea of modulating PR activation by targeting PR precursors as an alternative approach to controlling HIV-1/AIDS.

A Network of Conformational Transitions in the Apo Form of NDM-1 Enzyme Revealed by MD Simulation and a Markov State Model

New Delhi metallo-β-lactamase-1 (NDM-1) is a novel β-lactamase enzyme that confers enteric bacteria with nearly complete resistance to all β-lactam antibiotics, so it raises a formidable and global threat to human health. However, the binding mechanism between apo-NDM-1 and antibiotics as well as related conformational changes remains poorly understood, which largely hinders the overcoming of its antibiotic resistance. In our study, long-time conventional molecular dynamics simulation and Markov state models were applied to reveal both the dynamical and conformational landscape of apo-NDM-1: the MD simulation demonstrates that loop L3, which is responsible for antibiotic binding, is the most flexible and undergoes dramatic conformational changes; moreover, the Markov state model built from the simulation maps four metastable states including open, semiopen, and closed conformations of loop L3 as well as frequent transitions between the states. Our findings propose a possible conformational selection model for the binding mechanism between apo-NDM-1 and antibiotics, which facilitates the design of novel inhibitors and antibiotics.

A perspective on conformational control of electron transfer in nitric oxide synthases

This perspective reviews single molecule and ensemble fluorescence spectroscopy studies of the three tissue specific nitric oxide synthase (NOS) isoenzymes and the related diflavin oxidoreductase cytochrome P450 reductase. The focus is on the role of protein dynamics and the protein conformational landscape and we discuss how recent fluorescence-based studies have helped in illustrating how the nature of the NOS conformational landscape relates to enzyme turnover and catalysis.

Optimized parameter selection reveals trends in Markov state models for protein folding

As molecular dynamics simulations access increasingly longer time scales, complementary advances in the analysis of biomolecular time-series data are necessary. Markov state models offer a powerful framework for this analysis by describing a system's states and the transitions between them. A recently established variational theorem for Markov state models now enables modelers to systematically determine the best way to describe a system's dynamics. In the context of the variational theorem, we analyze ultra-long folding simulations for a canonical set of twelve proteins [K. Lindorff-Larsen et al., Science 334, 517 (2011)] by creating and evaluating many types of Markov state models. We present a set of guidelines for constructing Markov state models of protein folding; namely, we recommend the use of cross-validation and a kinetically motivated dimensionality reduction step for improved descriptions of folding dynamics. We also warn that precise kinetics predictions rely on the features chosen to describe the system and pose the description of kinetic uncertainty across ensembles of models as an open issue.

Mechanism of the Association Pathways for a Pair of Fast and Slow Binding Ligands of HIV-1 Protease

Equilibrium constants, together with kinetic rate constants of binding, are key factors in the efficacy and safety of drug compounds, informing drug design. However, the association pathways of protein-ligand binding, which contribute to their kinetic behaviors, are little understood. In this work, we used unbiased all-atom molecular dynamics (MD) simulations with an explicit solvent model to study the association processes of protein-ligand binding. Using the HIV protease (HIVp)-xk263 and HIVp-ritonavir protein-ligand systems as cases, we observed that ligand association is a multistep process involving diffusion, localization, and conformational rearrangements of the protein, ligand, and water molecules. Moreover, these two ligands preferred different routes of binding, which reflect two well-known binding mechanisms: induced-fit and conformation selection models. Our study shows that xk263 has a stronger capacity for desolvating surrounding water molecules, thereby inducing a semiopen conformation of the HIVp flaps (induced-fit model). In contrast, the slow dehydration characteristic of ritonavir allows for gradual association with the binding pocket of HIVp when the protein's flap conformation is fully open (conformation selection model). By studying the mechanism of ligand association and understanding the role of solvent molecules during the binding event, we can obtain a different perspective on the mechanism of macromolecule recognition, providing insights into drug discovery.

HIV Genome-Wide Protein Associations: a Review of 30 Years of Research

The HIV genome encodes a small number of viral proteins (i.e., 16), invariably establishing cooperative associations among HIV proteins and between HIV and host proteins, to invade host cells and hijack their internal machineries. As a known example, the HIV envelope glycoprotein GP120 is closely associated with GP41 for viral entry. From a genome-wide perspective, a hypothesis can be worked out to determine whether 16 HIV proteins could develop 120 possible pairwise associations either by physical interactions or by functional associations mediated via HIV or host molecules. Here, we present the first systematic review of experimental evidence on HIV genome-wide protein associations using a large body of publications accumulated over the past 3 decades. Of 120 possible pairwise associations between 16 HIV proteins, at least 34 physical interactions and 17 functional associations have been identified. To achieve efficient viral replication and infection, HIV protein associations play essential roles (e.g., cleavage, inhibition, and activation) during the HIV life cycle. In either a dispensable or an indispensable manner, each HIV protein collaborates with another viral protein to accomplish specific activities that precisely take place at the proper stages of the HIV life cycle. In addition, HIV genome-wide protein associations have an impact on anti-HIV inhibitors due to the extensive cross talk between drug-inhibited proteins and other HIV proteins. Overall, this study presents for the first time a comprehensive overview of HIV genome-wide protein associations, highlighting meticulous collaborations between all viral proteins during the HIV life cycle.

Exploration of gated ligand binding recognizes an allosteric site for blocking FABP4-protein interaction

Fatty acid binding protein 4 (FABP4), reversibly binding to fatty acids and other lipids with high affinities, is a potential target for treatment of cancers. The binding site of FABP4 is buried in an interior cavity and thereby ligand binding/unbinding is coupled with opening/closing of FABP4. It is a difficult task both experimentally and computationally to illuminate the entry or exit pathway, especially with the conformational gating. In this report we combine extensive computer simulations, clustering analysis, and the Markov state model to investigate the binding mechanism of FABP4 and troglitazone. Our simulations capture spontaneous binding and unbinding events as well as the conformational transition of FABP4 between the open and closed states. An allosteric binding site on the protein surface is recognized for the development of novel FABP4 inhibitors. The binding affinity is calculated and compared with the experimental value. The kinetic analysis suggests that ligand residence on the protein surface may delay the binding process. Overall, our results provide a comprehensive picture of ligand diffusion on the protein surface, ligand migration into the buried cavity, and the conformational change of FABP4 at an atomic level.

Multibody cofactor and substrate molecular recognition in the myo-inositol monophosphatase enzyme

Molecular recognition is rarely a two-body protein-ligand problem, as it often involves the dynamic interplay of multiple molecules that together control the binding process. Myo-inositol monophosphatase (IMPase), a drug target for bipolar disorder, depends on 3 Mg(2+) ions as cofactor for its catalytic activity. Although the crystallographic pose of the pre-catalytic complex is well characterized, the binding process by which substrate, cofactor and protein cooperate is essentially unknown. Here, we have characterized cofactor and substrate cooperative binding by means of large-scale molecular dynamics. Our study showed the first and second Mg(2+) ions identify the binding pocket with fast kinetics whereas the third ion presents a much higher energy barrier. Substrate binding can occur in cooperation with cofactor, or alone to a binary or ternary cofactor-IMPase complex, although the last scenario occurs several orders of magnitude faster. Our atomic description of the three-body mechanism offers a particularly challenging example of pathway reconstruction, and may prove particularly useful in realistic contexts where water, ions, cofactors or other entities cooperate and modulate the binding process.

HIV Genome-Wide Protein Associations: a Review of 30 Years of Research

The HIV genome encodes a small number of viral proteins (i.e., 16), invariably establishing cooperative associations among HIV proteins and between HIV and host proteins, to invade host cells and hijack their internal machineries. As a known example, the HIV envelope glycoprotein GP120 is closely associated with GP41 for viral entry. From a genome-wide perspective, a hypothesis can be worked out to determine whether 16 HIV proteins could develop 120 possible pairwise associations either by physical interactions or by functional associations mediated via HIV or host molecules. Here, we present the first systematic review of experimental evidence on HIV genome-wide protein associations using a large body of publications accumulated over the past 3 decades. Of 120 possible pairwise associations between 16 HIV proteins, at least 34 physical interactions and 17 functional associations have been identified. To achieve efficient viral replication and infection, HIV protein associations play essential roles (e.g., cleavage, inhibition, and activation) during the HIV life cycle. In either a dispensable or an indispensable manner, each HIV protein collaborates with another viral protein to accomplish specific activities that precisely take place at the proper stages of the HIV life cycle. In addition, HIV genome-wide protein associations have an impact on anti-HIV inhibitors due to the extensive cross talk between drug-inhibited proteins and other HIV proteins. Overall, this study presents for the first time a comprehensive overview of HIV genome-wide protein associations, highlighting meticulous collaborations between all viral proteins during the HIV life cycle.

Conserve Water: A Method for the Analysis of Solvent in Molecular Dynamics

Molecular dynamics with explicit solvent is favored for its ability to more correctly simulate aqueous biological processes and has become routine thanks to increasingly powerful computational resources. However, analysis techniques including Markov state models (MSMs) ignore solvent atoms and focus solely on solute coordinates despite solvent being implicated in myriad biological phenomena. We present a unified framework called "solvent-shells featurization" for including solvent degrees of freedom in analysis and show that this method produces better models. We apply this method to simulations of dewetting in the two-domain protein BphC to generate a predictive MSM and identify functional water molecules. Furthermore, the proposed methodology could be easily extended for building MSMs of any systems with indistinguishable components.

Protein Ensembles: How Does Nature Harness Thermodynamic Fluctuations for Life? The Diverse Functional Roles of Conformational Ensembles in the Cell

All soluble proteins populate conformational ensembles that together constitute the native state. Their fluctuations in water are intrinsic thermodynamic phenomena, and the distributions of the states on the energy landscape are determined by statistical thermodynamics; however, they are optimized to perform their biological functions. In this review we briefly describe advances in free energy landscape studies of protein conformational ensembles. Experimental (nuclear magnetic resonance, small-angle X-ray scattering, single-molecule spectroscopy, and cryo-electron microscopy) and computational (replica-exchange molecular dynamics, metadynamics, and Markov state models) approaches have made great progress in recent years. These address the challenging characterization of the highly flexible and heterogeneous protein ensembles. We focus on structural aspects of protein conformational distributions, from collective motions of single- and multi-domain proteins, intrinsically disordered proteins, to multiprotein complexes. Importantly, we highlight recent studies that illustrate functional adjustment of protein conformational ensembles in the crowded cellular environment. We center on the role of the ensemble in recognition of small- and macro-molecules (protein and RNA/DNA) and emphasize emerging concepts of protein dynamics in enzyme catalysis. Overall, protein ensembles link fundamental physicochemical principles and protein behavior and the cellular network and its regulation.

Atomistic picture of conformational exchange in a T4 lysozyme cavity mutant: an experiment-guided molecular dynamics study

Despite the importance of dynamics to protein function there is little information about the states that are formed as the protein explores its conformational landscape or about the mechanism by which transitions between the different states occur. Here we used a combined NMR spin relaxation and unbiased molecular dynamics (MD) approach to investigate the exchange process by which a cavity in an L99A mutant of T4 lysozyme (T4L 99A) interconverts between an empty and occupied form that involves repositioning of an aromatic residue, Phe114. Although structures of the end-states of the exchange process are available, insight into the mechanism by which the transition takes place cannot be obtained from experiment and the timescales involved are too slow to address using brute force MD. Using spin relaxation NMR methods, we have identified a triple-mutant of T4L that undergoes the same exchange process as T4L L99A but where the minor state lifetime has decreased significantly so that the spontaneous conformational transition to the major state can be studied using all-atom MD simulations. The simulation trajectories were analyzed using Markov state models and the energy landscape so obtained is in good agreement with expectations based on NMR studies. Notably there is no large-scale perturbation of the structure during the transition, multiple intermediates are formed between the two similar exchanging conformations and the free energy barrier between these two well-folded, compact forms is small (6kBT), only slightly larger than for processes considered to be barrierless.

Multiensemble Markov models of molecular thermodynamics and kinetics

We introduce the general transition-based reweighting analysis method (TRAM), a statistically optimal approach to integrate both unbiased and biased molecular dynamics simulations, such as umbrella sampling or replica exchange. TRAM estimates a multiensemble Markov model (MEMM) with full thermodynamic and kinetic information at all ensembles. The approach combines the benefits of Markov state models-clustering of high-dimensional spaces and modeling of complex many-state systems-with those of the multistate Bennett acceptance ratio of exploiting biased or high-temperature ensembles to accelerate rare-event sampling. TRAM does not depend on any rate model in addition to the widely used Markov state model approximation, but uses only fundamental relations such as detailed balance and binless reweighting of configurations between ensembles. Previous methods, including the multistate Bennett acceptance ratio, discrete TRAM, and Markov state models are special cases and can be derived from the TRAM equations. TRAM is demonstrated by efficiently computing MEMMs in cases where other estimators break down, including the full thermodynamics and rare-event kinetics from high-dimensional simulation data of an all-atom protein-ligand binding model.

Binding of Clinical Inhibitors to a Model Precursor of a Rationally Selected Multidrug Resistant HIV-1 Protease Is Significantly Weaker Than That to the Released Mature Enzyme

We have systematically validated the activity and inhibition of a HIV-1 protease (PR) variant bearing 17 mutations (PR(S17)), selected to represent high resistance by machine learning on genotype-phenotype data. Three of five mutations in PR(S17) correlating with major drug resistance, M46L, G48V, and V82S, and five of 11 natural variations differ from the mutations in two clinically derived extreme mutants, PR20 and PR22 bearing 19 and 22 mutations, respectively. PR(S17), which forms a stable dimer (<10 nM), is ∼10- and 2-fold less efficient in processing the Gag polyprotein than the wild type and PR20, respectively, but maintains the same cleavage order. Isolation of a model precursor of PR(S17) flanked by the 56-amino acid transframe region (TFP-p6pol) at its N-terminus, which is impossible upon expression of an analogous PR20 precursor, allowed systematic comparison of inhibition of TFP-p6pol-PR(S17) and mature PR(S17). Resistance of PR(S17) to eight protease inhibitors (PIs) relative to PR (Ki) increases by 1.5-5 orders of magnitude from 0.01 to 8.4 μM. Amprenavir, darunavir, atazanavir, and lopinavir, the most effective of the eight PIs, inhibit precursor autoprocessing at the p6pol/PR site with IC50 values ranging from ∼7.5 to 60 μM. Thus, this process, crucial for stable dimer formation, shows inhibition ∼200-800-fold weaker than that of the mature PR(S17). TFP/p6pol cleavage, which occurs faster, is inhibited even more weakly by all PIs except darunavir (IC50 = 15 μM); amprenavir shows a 2-fold increase in IC50 (∼15 μM), and atazanavir and lopinavir show increased IC50 values of >42 and >70 μM, respectively.

In silico target fishing and pharmacological profiling for the isoquinoline alkaloids of Macleaya cordata (Bo Luo Hui)

Background

Some isoquinoline alkaloids from Macleaya cordata (Willd). R. Br. (Bo Luo Hui) exhibited antibacterial, antiparasitic, antitumor, and analgesic effects. The targets of these isoquinoline alkaloids are undefined. This study aims to investigate the compound–target interaction network and potential pharmacological actions of isoquinoline alkaloids of M. cordata by reverse pharmacophore database screening.

Methods

The targets of 26 isoquinoline alkaloids identified from M. cordata were predicted by a pharmacophore-based target fishing approach. Discovery Studio 3.5 and two pharmacophore databases (PharmaDB and HypoDB) were employed for the target profiling. A compound–target interaction network of M. cordata was constructed and analyzed by Cytoscape 3.0.

Results

Thirteen of the 65 predicted targets identified by PharmaDB were confirmed as targets by HypoDB screening. The targets in the interaction network of M. cordata were involved in cancer (31 targets), microorganisms (12 targets), neurodegeneration (10 targets), inflammation and autoimmunity (8 targets), parasitosis (5 targets), injury (4 targets), and pain (3 targets). Dihydrochelerythrine (C6) was found to hit 23 fitting targets. Macrophage migration inhibitory factor (MIF) hits 15 alkaloids (C1–2, C11–16, C19–25) was the most promising target related to cancer.

Conclusion

Through in silico target fishing, the anticancer, anti-inflammatory, and analgesic effects of M. cordata were the most significant among many possible activities. The possible anticancer effects were mainly contributed by the isoquinoline alkaloids as active components.

PyEMMA 2: A Software Package for Estimation, Validation, and Analysis of Markov Models

Markov (state) models (MSMs) and related models of molecular kinetics have recently received a surge of interest as they can systematically reconcile simulation data from either a few long or many short simulations and allow us to analyze the essential metastable structures, thermodynamics, and kinetics of the molecular system under investigation. However, the estimation, validation, and analysis of such models is far from trivial and involves sophisticated and often numerically sensitive methods. In this work we present the open-source Python package PyEMMA ( http://pyemma.org ) that provides accurate and efficient algorithms for kinetic model construction. PyEMMA can read all common molecular dynamics data formats, helps in the selection of input features, provides easy access to dimension reduction algorithms such as principal component analysis (PCA) and time-lagged independent component analysis (TICA) and clustering algorithms such as k-means, and contains estimators for MSMs, hidden Markov models, and several other models. Systematic model validation and error calculation methods are provided. PyEMMA offers a wealth of analysis functions such that the user can conveniently compute molecular observables of interest. We have derived a systematic and accurate way to coarse-grain MSMs to few states and to illustrate the structures of the metastable states of the system. Plotting functions to produce a manuscript-ready presentation of the results are available. In this work, we demonstrate the features of the software and show new methodological concepts and results produced by PyEMMA.

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.