A theoretical view of protein dynamics

Development of artificial photosystems based on designed proteins for mechanistic insights into photosynthesis

This review aims to provide an overview of the progress in protein-based artificial photosystem design and their potential to uncover the underlying principles governing light-harvesting in photosynthesis. While significant advances have been made in this area, a gap persists in reviewing these advances. This review provides a perspective of the field, pinpointing knowledge gaps and unresolved challenges that warrant further inquiry. In particular, it delves into the key considerations when designing photosystems based on the chromophore and protein scaffold characteristics, presents the established strategies for artificial photosystems engineering with their advantages and disadvantages, and underscores the recent breakthroughs in understanding the molecular mechanisms governing light-harvesting, charge separation, and the role of the protein motions in the chromophore's excited state relaxation. By disseminating this knowledge, this article provides a foundational resource for defining the field of bio-hybrid photosystems and aims to inspire the continued exploration of artificial photosystems using protein design.

Structural, Spectroscopic, and Computational Insights from Canavanine-Bound and Two Catalytically Compromised Variants of the Ethylene-Forming Enzyme

The ethylene-forming enzyme (EFE) is an Fe(II), 2-oxoglutarate (2OG), and l-arginine (l-Arg)-dependent oxygenase that either forms ethylene and three CO2/bicarbonate from 2OG or couples the decarboxylation of 2OG to C5 hydroxylation of l-Arg. l-Arg binds with C5 toward the metal center, causing 2OG to change from monodentate to chelate metal interaction and OD1 to OD2 switch of D191 metal coordination. We applied anaerobic UV-visible spectroscopy, X-ray crystallography, and computational approaches to three EFE systems with high-resolution structures. The ineffective l-Arg analogue l-canavanine binds to the EFE with O5 pointing away from the metal center while promoting chelate formation by 2OG but fails to switch the D191 metal coordination from OD1 to OD2. Substituting alanine for R171 that interacts with 2OG and l-Arg inactivates the protein, prevents metal chelation by 2OG, and weakens l-Arg binding. The R171A EFE had electron density at the 2OG binding site that was identified by mass spectrometry as benzoic acid. The substitution by alanine of Y306 in the EFE, a residue 12 Å away from the catalytic metal center, generates an interior cavity that leads to multiple local and distal structural changes that reduce l-Arg binding and significantly reduce the enzyme activity. Flexibility analyses revealed correlated and anticorrelated motions in each system, with important distinctions from the wild-type enzyme. In combination, the results are congruent with the currently proposed enzyme mechanism, reinforce the importance of metal coordination by OD2 of D191, and highlight the importance of the second coordination sphere and longer range interactions in promoting EFE activity.

Oliveira AS, Mulholland AJ, Serapian SA, Colombo G. Decrypting Allostery in Membrane-Bound K-Ras4B Using Complementary In Silico Approaches Based on Unbiased Molecular Dynamics Simulations

Protein functions are dynamically regulated by allostery, which enables conformational communication even between faraway residues, and expresses itself in many forms, akin to different "languages": allosteric control pathways predominating in an unperturbed protein are often unintuitively reshaped whenever biochemical perturbations arise (e.g., mutations). To accurately model allostery, unbiased molecular dynamics (MD) simulations require integration with a reliable method able to, e.g., detect incipient allosteric changes or likely perturbation pathways; this is because allostery can operate at longer time scales than those accessible by plain MD. Such methods are typically applied singularly, but we here argue their joint application─as a "multilingual" approach─could work significantly better. We successfully prove this through unbiased MD simulations (∼100 μs) of the widely studied, allosterically active oncotarget K-Ras4B, solvated and embedded in a phospholipid membrane, from which we decrypt allostery using four showcase "languages": Distance Fluctuation analysis and the Shortest Path Map capture allosteric hotspots at equilibrium; Anisotropic Thermal Diffusion and Dynamical Non-Equilibrium MD simulations assess perturbations upon, respectively, either superheating or hydrolyzing the GTP that oncogenically activates K-Ras4B. Chosen "languages" work synergistically, providing an articulate, mutually coherent, experimentally consistent picture of K-Ras4B allostery, whereby distinct traits emerge at equilibrium and upon GTP cleavage. At equilibrium, combined evidence confirms prominent allosteric communication from the membrane-embedded hypervariable region, through a hub comprising helix α5 and sheet β5, and up to the active site, encompassing allosteric "switches" I and II (marginally), and two proposed pockets. Upon GTP cleavage, allosteric perturbations mostly accumulate on the switches and documented interfaces.

Single-Molecule X-ray Scattering Used to Visualize the Conformation Distribution of Biological Molecules via Single-Object Scattering Sampling

Biological macromolecules, the fundamental building blocks of life, exhibit dynamic structures in their natural environment. Traditional structure determination techniques often oversimplify these multifarious conformational spectra by capturing only ensemble- and time-averaged molecular structures. Addressing this gap, in this work, we extend the application of the single-object scattering sampling (SOSS) method to diverse biological molecules, including RNAs and proteins. Our approach, referred to as "Bio-SOSS", leverages ultrashort X-ray pulses to capture instantaneous structures. In Bio-SOSS, we employ two gold nanoparticles (AuNPs) as labels, which provide strong contrast in the X-ray scattering signal, to ensure precise distance determinations between labeled sites. We generated hypothetical Bio-SOSS images for RNAs, proteins, and an RNA-protein complex, each labeled with two AuNPs at specified positions. Subsequently, to validate the accuracy of Bio-SOSS, we extracted distances between these nanoparticle labels from the images and compared them with the actual values used to generate the Bio-SOSS images. Specifically, for a representative RNA (1KXK), the standard deviation in distance discrepancies between molecular dynamics snapshots and Bio-SOSS retrievals was found to be optimally around 0.2 Å, typically within 1 Å under practical experimental conditions at state-of-the-art X-ray free-electron laser facilities. Furthermore, we conducted an in-depth analysis of how various experimental factors, such as AuNP size, X-ray properties, and detector geometry, influence the accuracy of Bio-SOSS. This comprehensive investigation highlights the practicality and potential of Bio-SOSS in accurately capturing the diverse conformation spectrum of biological macromolecules, paving the way for deeper insights into their dynamic natures.

Scipion-EM-ProDy: A Graphical Interface for the ProDy Python Package within the Scipion Workflow Engine Enabling Integration of Databases, Simulations and Cryo-Electron Microscopy Image Processing

Macromolecular assemblies, such as protein complexes, undergo continuous structural dynamics, including global reconfigurations critical for their function. Two fast analytical methods are widely used to study these global dynamics, namely elastic network model normal mode analysis and principal component analysis of ensembles of structures. These approaches have found wide use in various computational studies, driving the development of complex pipelines in several software packages. One common theme has been conformational sampling through hybrid simulations incorporating all-atom molecular dynamics and global modes of motion. However, wide functionality is only available for experienced programmers with limited capabilities for other users. We have, therefore, integrated one popular and extensively developed software for such analyses, the ProDy Python application programming interface, into the Scipion workflow engine. This enables a wider range of users to access a complete range of macromolecular dynamics pipelines beyond the core functionalities available in its command-line applications and the normal mode wizard in VMD. The new protocols and pipelines can be further expanded and integrated into larger workflows, together with other software packages for cryo-electron microscopy image analysis and molecular simulations. We present the resulting plugin, Scipion-EM-ProDy, in detail, highlighting the rich functionality made available by its development.

Internal Normal Mode Analysis Applied to RNA Flexibility and Conformational Changes

We investigated the capability of internal normal modes to reproduce RNA flexibility and predict observed RNA conformational changes and, notably, those induced by the formation of RNA-protein and RNA-ligand complexes. Here, we extended our iNMA approach developed for proteins to study RNA molecules using a simplified representation of the RNA structure and its potential energy. Three data sets were also created to investigate different aspects. Despite all the approximations, our study shows that iNMA is a suitable method to take into account RNA flexibility and describe its conformational changes opening the route to its applicability in any integrative approach where these properties are crucial.

Statistical Learning of Protein Elastic Network from Positional Covariance Matrix

Positional fluctuation and covariance during protein dynamics are key observables for understanding the molecular origin of biological functions. A frequently employed potential energy function for describing protein structural variation at the coarse-gained level is elastic network model (ENM). A long-standing issue in biomolecular simulation is thus the parametrization of ENM spring constants from the components of positional covariance matrix (PCM). Based on sensitivity analysis of PCM, the direct-coupling statistics of each spring, which is a specific combination of position fluctuation and covariance, is found to exhibit prominent signal of parameter dependence. This finding provides the basis for devising the objective function and the scheme of running through the effective one-dimensional optimization of every spring by self-consistent iteration. Formal derivation of the positional covariance statistical learning (PCSL) method also motivates the necessary data regularization for stable calculations. Robust convergence of PCSL is achieved in taking an all-atom molecular dynamics trajectory or an ensemble of homologous structures as input data. The PCSL framework can also be generalized with mixed objective functions to capture specific property such as the residue flexibility profile. Such physical chemistry-based statistical learning thus provides a useful platform for integrating the mechanical information encoded in various experimental or computational data.

Evolutionary conservation of protein dynamics: insights from all-atom molecular dynamics simulations of 'peptidase' domain of Spt16

Protein function is encoded in its sequence, manifested in its three-dimensional structure, and facilitated by its dynamics. Studies have suggested that protein structures with higher sequence similarity could have more similar patterns of dynamics. However, such studies of protein dynamics within and across protein families typically rely on coarse-grained models, or approximate metrics like crystallographic B-factors. This study uses µs scale molecular dynamics (MD) simulations to explore the conservation of dynamics among homologs of ∼50 kDa N-terminal module of Spt16 (Spt16N). Spt16N from Saccharomyces cerevisiae (Sc-Spt16N) and three of its homologs with 30-40% sequence identities were available in the PDB. To make our data-set more comprehensive, the crystal structure of an additional homolog (62% sequence identity with Sc-Spt16N) was solved at 1.7 Å resolution. Cumulative MD simulations of 6 µs were carried out on these Spt16N structures and on two additional protein structures with varying degrees of similarity to it. The simulations revealed that correlation in patterns of backbone fluctuations vary linearly with sequence identity. This trend could not be inferred using crystallographic B-factors. Further, normal mode analysis suggested a similar pattern of inter-domain (inter-lobe) motions not only among Spt16N homologs, but also in the M24 peptidase structure. On the other hand, MD simulation results highlighted conserved motions that were found unique for Spt16N protein, this along with electrostatics trends shed light on functional aspects of Spt16N.Communicated by Ramaswamy H. Sarma.

Configurational entropy, transition rates, and optimal interactions for rapid folding in coarse-grained model proteins

Under certain conditions, the dynamics of coarse-grained models of solvated proteins can be described using a Markov state model, which tracks the evolution of populations of configurations. The transition rates among states that appear in the Markov model can be determined by computing the relative entropy of states and their mean first passage times. In this paper, we present an adaptive method to evaluate the configurational entropy and the mean first passage times for linear chain models with discontinuous potentials. The approach is based on event-driven dynamical sampling in a massively parallel architecture. Using the fact that the transition rate matrix can be calculated for any choice of interaction energies at any temperature, it is demonstrated how each state's energy can be chosen such that the average time to transition between any two states is minimized. The methods are used to analyze the optimization of the folding process of two protein systems: the crambin protein and a model with frustration and misfolding. It is shown that the folding pathways for both systems are comprised of two regimes: first, the rapid establishment of local bonds, followed by the subsequent formation of more distant contacts. The state energies that lead to the most rapid folding encourage multiple pathways, and they either penalize folding pathways through kinetic traps by raising the energies of trapping states or establish an escape route from the trapping states by lowering free energy barriers to other states that rapidly reach the native state.

Protein Model Quality Estimation Using Molecular Dynamics Simulation

The estimation of protein model quality remains a challenging task and is important for protein structural model utilization. In the last decade, existing methods that rely on machine learning to deep learning have been developed and shown progressive improvement. Despite utilizing more sophisticated techniques and introducing new features, none of these methods employ explicit protein structure stability information. Hypothetically, protein model quality might be indicated by its structural stability in an in silico system disclosed by the structural difference from its initial structure. One of the possible methods to exploit such information is by implementing molecular dynamics simulations that have shown successful applications in many research fields. We present a novel approach by introducing explicit protein structure stability information using molecular dynamics simulation. Despite using only simple features, small data with no training process required, and a short molecular dynamics simulation time, our method shows comparable performance to the state-of-the-art deep learning-based method.

Insights of conformational dynamics on catalytic activity in the computational stability design of Bacillus subtilis LipA

In protein engineering, the contributions of individual mutations to designed combinatorial mutants are unpredictable. Screening designed mutations that affect enzyme catalytic activity enables evolutions towards efficient activities. Here, Bacillus subtilis LipA (BSLA) was selected as a model protein for thermostabilization designs, and the circular dichroism measurements showed six combinatorial designs with improved stability (from 5.81 °C to 13.61 °C). Based on molecular dynamic simulations, the conformational dynamics of the mutants revealed that mutations alter the populations of conformational states and the increased ensembles of inactive conformations might lead to a reduction in activity. We further demonstrated that the mutations responsible for the reduced enzyme catalytic activity involved a short dynamic correlation path to disturbing the equilibrium conformation of active sites. By removing N82V, which had a close dynamic correlation to the active sites in mutant D3, the redesigned mutant RD3 had an increased activity of 57.6%. By combining computational simulation with experimental verification, this work established that essential sites to counteract the activity-stability trade-off in multipoint combinatorial mutants could be computationally predicted and thus provide a possible strategy by which to indirectly or directly guide protein design.

Learning Strategies in Protein Directed Evolution

Synthetic biology is a fast-evolving research field that combines biology and engineering principles to develop new biological systems for medical, pharmacological, and industrial applications. Synthetic biologists use iterative "design, build, test, and learn" cycles to efficiently engineer genetic systems that are reliable, reproducible, and predictable. Protein engineering by directed evolution can benefit from such a systematic engineering approach for various reasons. Learning can be carried out before starting, throughout or after finalizing a directed evolution project. Computational tools, bioinformatics, and scanning mutagenesis methods can be excellent starting points, while molecular dynamics simulations and other strategies can guide engineering efforts. Similarly, studying protein intermediates along evolutionary pathways offers fascinating insights into the molecular mechanisms shaped by evolution. The learning step of the cycle is not only crucial for proteins or enzymes that are not suitable for high-throughput screening or selection systems, but it is also valuable for any platform that can generate a large amount of data that can be aided by machine learning algorithms. The main challenge in protein engineering is to predict the effect of a single mutation on one functional parameter-to say nothing of several mutations on multiple parameters. This is largely due to nonadditive mutational interactions, known as epistatic effects-beneficial mutations present in a genetic background may not be beneficial in another genetic background. In this work, we provide an overview of experimental and computational strategies that can guide the user to learn protein function at different stages in a directed evolution project. We also discuss how epistatic effects can influence the success of directed evolution projects. Since machine learning is gaining momentum in protein engineering and the field is becoming more interdisciplinary thanks to collaboration between mathematicians, computational scientists, engineers, molecular biologists, and chemists, we provide a general workflow that familiarizes nonexperts with the basic concepts, dataset requirements, learning approaches, model capabilities and performance metrics of this intriguing area. Finally, we also provide some practical recommendations on how machine learning can harness epistatic effects for engineering proteins in an "outside-the-box" way.

Toward the Specificity of Bare Nanomaterial Surfaces for Protein Corona Formation

Aiming at creating smart nanomaterials for biomedical applications, nanotechnology aspires to develop a new generation of nanomaterials with the ability to recognize different biological components in a complex environment. It is common opinion that nanomaterials must be coated with organic or inorganic layers as a mandatory prerequisite for applications in biological systems. Thus, it is the nanomaterial surface coating that predominantly controls the nanomaterial fate in the biological environment. In the last decades, interdisciplinary studies involving not only life sciences, but all branches of scientific research, provided hints for obtaining uncoated inorganic materials able to interact with biological systems with high complexity and selectivity. Herein, the fragmentary literature on the interactions between bare abiotic materials and biological components is reviewed. Moreover, the most relevant examples of selective binding and the conceptualization of the general principles behind recognition mechanisms were provided. Nanoparticle features, such as crystalline facets, density and distribution of surface chemical groups, and surface roughness and topography were encompassed for deepening the comprehension of the general concept of recognition patterns.

Bcl-xL Dynamics under the Lens of Protein Structure Networks

Understanding the finely orchestrated interactions leading to or preventing programmed cell death (apoptosis) is of utmost importance in cancer research because the failure of these systems could eventually lead to the onset of the disease. In this regard, the maintenance of a delicate balance between the promoters and inhibitors of mitochondrial apoptosis is crucial, as demonstrated by the interplay among the Bcl-2 family members. In particular, B-cell lymphoma extra-large (Bcl-x_L) is a target of interest due to the forefront role of its dysfunctions in cancer development. Bcl-x_L prevents apoptosis by binding both the pro-apoptotic BH3-only proteins, like PUMA, and the noncanonical partners, such as p53, at different sites. An allosteric communication between the BH3-only protein binding pocket and the p53 binding site, mediating the release of p53 from Bcl-x_L upon PUMA binding, has been postulated and supported by nuclear magnetic resonance and other biophysical data. The molecular details of this mechanism, especially at the residue level, remain unclear. In this work, we investigated the distal communication between these two sites in Bcl-x_L in its free state and when bound to PUMA. We also evaluated how missense mutations of Bcl-x_L found in cancer samples might impair this communication and therefore the allosteric mechanism. We employed all-atom explicit solvent microsecond molecular dynamics simulations, analyzed through a Protein Structure Network approach and integrated with calculations of changes in free energies upon cancer-related mutations identified by genomics studies. We found a subset of candidate residues responsible for both maintaining protein stability and for conveying structural information between the two binding sites and hypothesized possible communication routes between specific residues at both sites.

Crystal structures of human MGST2 reveal synchronized conformational changes regulating catalysis

Microsomal glutathione S-transferase 2 (MGST2) produces leukotriene C₄, key for intracrine signaling of endoplasmic reticulum (ER) stress, oxidative DNA damage and cell death. MGST2 trimer restricts catalysis to only one out of three active sites at a time, but the molecular basis is unknown. Here, we present crystal structures of human MGST2 combined with biochemical and computational evidence for a concerted mechanism, involving local unfolding coupled to global conformational changes that regulate catalysis. Furthermore, synchronized changes in the biconical central pore modulate the hydrophobicity and control solvent influx to optimize reaction conditions at the active site. These unique mechanistic insights pertain to other, structurally related, drug targets.

Microsomal glutathione S-transferase 2 (MGST2) produces leukotriene C₄, an intracrine mediator of cell death. Structural, biochemical and computational analyses of human MGST2 suggest a mechanism employed by the enzyme to restrict catalysis to only one active site within the MGST2 trimer.

Conformational Landscapes of Halohydrin Dehalogenases and Their Accessible Active Site Tunnels

Halohydrin dehalogenases (HHDH) are industrially relevant biocatalysts exhibiting a promiscuous epoxide-ring opening reactivity in the presence of small nucleophiles, thus giving access to novel carbon–carbon, carbon–oxygen, carbon–nitrogen, and carbon–sulfur bonds. Recently, the repertoire of HHDH has been expanded, providing access to some novel HHDH subclasses exhibiting a broader epoxide substrate scope. In this work, we develop a computational approach based on the application of linear and non-linear dimensionality reduction techniques to long time-scale Molecular Dynamics (MD) simulations to study the HHDH conformational landscapes. We couple the analysis of the conformational landscapes to CAVER calculations to assess their impact on the active site tunnels and potential ability towards bulky epoxide ring opening reaction. Our study indicates that the analyzed HHDHs subclasses share a common breathing motion of the halide binding pocket, but present large deviations in the loops adjacent to the active site pocket and N-terminal regions. Such conformational differences affect the available tunnels for epoxide binding to the active site. The superior activity of the HHDH G subclass towards bulkier substrates is explained by the additional structural elements delimiting the active site region, its rich conformational heterogeneity, and the substantially wider and frequently observed active site tunnels. This study therefore provides key information for HHDH promiscuity and engineering.

A Coarse-Grained Methodology Identifies Intrinsic Mechanisms That Dissociate Interacting Protein Pairs

We address the problem of triggering dissociation events between proteins that have formed a complex. We have collected a set of 25 non-redundant, functionally diverse protein complexes having high-resolution three-dimensional structures in both the unbound and bound forms. We unify elastic network models with perturbation response scanning (PRS) methodology as an efficient approach for predicting residues that have the propensity to trigger dissociation of an interacting protein pair, using the three-dimensional structures of the bound and unbound proteins as input. PRS reveals that while for a group of protein pairs, residues involved in the conformational shifts are confined to regions with large motions, there are others where they originate from parts of the protein unaffected structurally by binding. Strikingly, only a few of the complexes have interface residues responsible for dissociation. We find two main modes of response: In one mode, remote control of dissociation in which disruption of the electrostatic potential distribution along protein surfaces play the major role; in the alternative mode, mechanical control of dissociation by remote residues prevail. In the former, dissociation is triggered by changes in the local environment of the protein, e.g., pH or ionic strength, while in the latter, specific perturbations arriving at the controlling residues, e.g., via binding to a third interacting partner is required for decomplexation. We resolve the observations by relying on an electromechanical coupling model which reduces to the usual elastic network result in the limit of the lack of coupling. We validate the approach by illustrating the biological significance of top residues selected by PRS on select cases where we show that the residues whose perturbation leads to the observed conformational changes correspond to either functionally important or highly conserved residues in the complex.

DFFR: A New Method for High-Throughput Recalibration of Automatic Force-Fields for Drugs

We present drug force-field recalibration (DFFR), a new method for refining of automatic force-fields used to represent small drugs in docking and molecular dynamics simulations. The method is based on fine-tuning of torsional terms to obtain ensembles that reproduce observables derived from reference data. DFFR is fast and flexible and can be easily automatized for a high-throughput regime, making it useful in drug-design projects. We tested the performance of the method in a few model systems and also in a variety of druglike molecules using reference data derived from: (i) density functional theory coupled to a self-consistent reaction field (DFT/SCRF) calculations on highly populated conformers and (ii) enhanced sampling quantum mechanical/molecular mechanics (QM/MM) where the drug is reproduced at the QM level, while the solvent is represented by classical force-fields. Extension of the method to include other sources of reference data is discussed.

Probing the Structural Dynamics of the Plasmodium falciparum Tunneling-Fold Enzyme 6-Pyruvoyl Tetrahydropterin Synthase to Reveal Allosteric Drug Targeting Sites

The de novo folate synthesis pathway is a well-established drug target in the treatment of many infectious diseases. Antimalarial antifolate drugs have proven to be effective against malaria, however, rapid drug resistance has emerged on the two primary targeted enzymes: dihydrofolate reductase and dihydroptoreate synthase. The need to identify alternative antifolate drugs and novel metabolic targets is of imminent importance. The 6-pyruvol tetrahydropterin synthase (PTPS) enzyme belongs to the tunneling fold protein superfamily which is characterized by a distinct central tunnel/cavity. The enzyme catalyzes the second reaction step of the parasite’s de novo folate synthesis pathway and is responsible for the conversion of 7,8-dihydroneopterin to 6-pyruvoyl-tetrahydropterin. In this study, we examine the structural dynamics of Plasmodium falciparum PTPS using the anisotropic network model, to elucidate the collective motions that drive the function of the enzyme and identify potential sites for allosteric modulation of its binding properties. Based on our modal analysis, we identified key sites in the N-terminal domains and central helices which control the accessibility to the active site. Notably, the N-terminal domains were shown to regulate the open-to-closed transition of the tunnel, via a distinctive wringing motion that deformed the core of the protein. We, further, combined the dynamic analysis with motif discovery which revealed highly conserved motifs that are unique to the Plasmodium species and are located in the N-terminal domains and central helices. This provides essential structural information for the efficient design of drugs such as allosteric modulators that would have high specificity and low toxicity as they do not target the PTPS active site that is highly conserved in humans.

Impact of protein dynamics on secondary structure prediction

Protein 3D structures support their biological functions. As the number of protein structures is negligible in regards to the number of available protein sequences, prediction methodologies relying only on protein sequences are essential tools. In this field, protein secondary structure prediction (PSSPs) is a mature area, and is considered to have reached a plateau. Nonetheless, proteins are highly dynamical macromolecules, a property that could impact the PSSP methods. Indeed, in a previous study, the stability of local protein conformations was evaluated demonstrating that some regions easily changed to another type of secondary structure. The protein sequences of this dataset were used by PSSPs and their results compared to molecular dynamics to investigate their potential impact on the quality of the secondary structure prediction. Interestingly, a direct link is observed between the quality of the prediction and the stability of the assignment to the secondary structure state. The more stable a local protein conformation is, the better the prediction will be. The secondary structure assignment not taken from the crystallized structures but from the conformations observed during the dynamics slightly increase the quality of the secondary structure prediction. These results show that evaluation of PSSPs can be done differently, but also that the notion of dynamics can be included in development of PSSPs and other approaches such as de novo approaches.

Implicit Modeling of the Impact of Adsorption on Solid Surfaces for Protein Mechanics and Activity with a Coarse-Grained Representation

Surface immobilized enzymes play a key role in numerous biotechnological applications such as biosensors, biofuel cells, or biocatalytic synthesis. As a consequence, the impact of adsorption on the enzyme structure, dynamics, and function needs to be understood on the molecular level as it is critical for the improvement of these technologies. With this perspective in mind, we used a theoretical approach for investigating local protein flexibility on the residue scale that couples a simplified protein representation with an elastic network and Brownian dynamics simulations. The impact of protein adsorption on a solid surface is implicitly modeled via additional external constraints between the residues in contact with the surface. We first performed calculations on a redox enzyme, bilirubin oxidase (BOD) from M. verrucaria, to study the impact of adsorption on its mechanical properties. The resulting rigidity profiles show that, in agreement with the available experimental data, the mechanical variations observed in the adsorbed BOD will depend on its orientation and its anchor residues (i.e., residues that are in contact with the functionalized surface). Additional calculations on ribonuclease A and nitroreductase shed light on how seemingly stable adsorbed enzymes can nonetheless display an important decrease in their catalytic activity resulting from a perturbation of their mechanics and internal dynamics.

Bioinformatics and Biosimulations as Toolbox for Peptides and Peptidomimetics Design: Where Are We?

Peptides and peptidomimetics are strongly re-emerging as amenable candidates in the development of therapeutic strategies against a plethora of pathologies. In particular, these molecules are extremely suitable to treat diseases in which a major role is played by protein-protein interactions (PPIs). Unlike small organic compounds, peptides display both a high degree of specificity avoiding secondary off-targets effects and a relatively low degree of toxicity. Further advantages are provided by the possibility to easily conjugate peptides to functionalized nanoparticles, so improving their delivery and cellular uptake. In many cases, such molecules need to assume a specific three-dimensional conformation that resembles the bioactive one of the endogenous ligand. To this end, chemical modifications are introduced in the polypeptide chain to constrain it in a well-defined conformation, and to improve the drug-like properties. In this context, a successful strategy for peptide/peptidomimetics design and optimization is to combine different computational approaches ranging from structural bioinformatics to atomistic simulations. Here, we review the computational tools for peptide design, highlighting their main features and differences, and discuss selected protocols, among the large number of methods available, used to assess and improve the stability of the functional folding of the peptides. Finally, we introduce the simulation techniques employed to predict the binding affinity of the designed peptides for their targets.

Defining the mobility range of a hinge-type connection using molecular dynamics and metadynamics

A designed disulfide-rich β-hairpin peptide that dimerizes spontaneously served as a hinge-type connection between proteins. Here, we analyze the range of dynamics of this hinge dimer with the aim of proposing new applications for the DNA-encodable peptide and establishing guidelines for the computational analysis of other disulfide hinges. A recent structural analysis based on nuclear magnetic resonance spectroscopy and ion mobility spectrometry revealed an averaged conformation in the hinge region which motivated us to investigate the dynamic behavior using a combination of molecular dynamics simulation, metadynamics and free energy surface analysis to characterize the conformational space available to the hinge. Principal component analysis uncovered two slow modes of the peptide, namely, the opening and closing motion and twisting of the two β-hairpins assembling the hinge. Applying a collective variable (CV) that mimics the first dominating mode, led to a major expansion of the conformational space. The description of the dynamics could be achieved by analysis of the opening angle and the twisting of the β-hairpins and, thus, offers a methodology that can also be transferred to other derivatives. It has been demonstrated that the hinge peptide’s lowest energy conformation consists of a large opening angle and strong twist but is separated by small energy barriers and can, thus, adopt a closed and untwisted structure. With the aim of proposing further applications for the hinge peptide, we simulated its behavior in the sterically congested environment of a four-helix bundle. Preliminary investigations show that one helix is pushed out and a three-helix bundle forms. The insights gained into the dynamics of the tetra-disulfide peptide and analytical guidelines developed in this study may contribute to the understanding of the structure and function of more complex hinge-type proteins, such as the IgG antibody family.

Solution structure and oligomeric state of the E. coliglycerol facilitator

Protein dynamics at atomic resolution can provide deep insights into the biological activities of proteins and enzymes but they can also make structure and dynamics studies challenging. Despite their well-known biological and pharmaceutical importance, integral membrane protein structure and dynamics studies lag behind those of water-soluble proteins mainly owing to solubility problems that result upon their removal from the membrane. Escherichia coli glycerol facilitator (GF) is a member of the aquaglyceroporin family that allows for the highly selective passive diffusion of its substrate glycerol across the inner membrane of the bacterium. Previous molecular dynamics simulations and hydrogen-deuterium exchange studies suggested that protein dynamics play an important role in the passage of glycerol through the protein pore. With the aim of studying GF dynamics by solution and solid-state nuclear magnetic resonance (NMR) spectroscopy we optimized the expression of isotope-labelled GF and explored various solubilizing agents including detergents, osmolytes, amphipols, random heteropolymers, lipid nanodiscs, bicelles and other buffer additives to optimize the solubility and polydispersity of the protein. The GF protein is most stable and soluble in lauryl maltose neopentyl glycol (LMNG), where it exists in a tetramer-octamer equilibrium. The solution structures of the GF tetramer and octamer were determined by negative-stain transmission electron microscopy (TEM), size-exclusion chromatography small-angle X-ray scattering (SEC-SAXS) and solid-state magic-angle spinning NMR spectroscopy. Although NMR sample preparation still needs optimization for full structure and dynamics studies, negative stain TEM and SEC-SAXS revealed low-resolution structures of the detergent-solubilized tetramer and octamer particles. The non-native octamer appears to form from the association of the cytoplasmic faces of two tetramers, the interaction apparently mediated by their disordered N- and C-termini. This information may be useful in future studies directed at reducing the heterogeneity and self-association of the protein.

Large-Scale Conformational Changes and Protein Function: Breaking the in silico Barrier

Large-scale conformational changes are essential to link protein structures with their function at the cell and organism scale, but have been elusive both experimentally and computationally. Over the past few years developments in cryo-electron microscopy and crystallography techniques have started to reveal multiple snapshots of increasingly large and flexible systems, deemed impossible only short time ago. As structural information accumulates, theoretical methods become central to understand how different conformers interconvert to mediate biological function. Here we briefly survey current in silico methods to tackle large conformational changes, reviewing recent examples of cross-validation of experiments and computational predictions, which show how the integration of different scale simulations with biological information is already starting to break the barriers between the in silico, in vitro, and in vivo worlds, shedding new light onto complex biological problems inaccessible so far.

Energetic Self-Folding Mechanism in α-Helices

A novel energetic route driving the folding of a polyalanine peptide from an extended conformation to its α-helix native conformation is described, supported by a new method to compute mean potential energy surfaces accurately in terms of the dihedral angles of the peptide chain from extensive molecular dynamics simulations. The energetic self-folding (ESF) route arises specifically from the balance between the intrinsic propensity of alanine residues toward the α_R conformation and two, opposite, effects: the destabilizing interaction with neighbor residues and the stabilizing formation of native hydrogen bonds, with the latter being dominant for large peptide lengths. The ESF mechanism provides simple but robust support to the nucleation-elongation or zipper models and offers a quantitative energetic funnel picture of the folding process. The mechanism is validated by the reasonable agreement between the computed folding energies and the experimental values.

BioExcel Building Blocks, a software library for interoperable biomolecular simulation workflows

In the recent years, the improvement of software and hardware performance has made biomolecular simulations a mature tool for the study of biological processes. Simulation length and the size and complexity of the analyzed systems make simulations both complementary and compatible with other bioinformatics disciplines. However, the characteristics of the software packages used for simulation have prevented the adoption of the technologies accepted in other bioinformatics fields like automated deployment systems, workflow orchestration, or the use of software containers. We present here a comprehensive exercise to bring biomolecular simulations to the “bioinformatics way of working”. The exercise has led to the development of the BioExcel Building Blocks (BioBB) library. BioBB’s are built as Python wrappers to provide an interoperable architecture. BioBB’s have been integrated in a chain of usual software management tools to generate data ontologies, documentation, installation packages, software containers and ways of integration with workflow managers, that make them usable in most computational environments.

DRAMP 2.0, an updated data repository of antimicrobial peptides

Data Repository of Antimicrobial Peptides (DRAMP, http://dramp.cpu-bioinfor.org/ ) is an open-access comprehensive database containing general, patent and clinical antimicrobial peptides (AMPs). Currently DRAMP has been updated to version 2.0, it contains a total of 19,899 entries (newly added 2,550 entries), including 5,084 general entries, 14,739 patent entries, and 76 clinical entries. The update covers new entries, structures, annotations, classifications and downloads. Compared with APD and CAMP, DRAMP contains 14,040 (70.56% in DRAMP) non-overlapping sequences. In order to facilitate users to trace original references, PubMed_ID of references have been contained in activity information. The data of DRAMP can be downloaded by dataset and activity, and the website source code is also available on dedicatedly designed download webpage. Although thousands of AMPs have been reported, only a few parts have entered clinical stage. In the paper, we described several AMPs in clinical trials, including their properties, indications and clinicaltrials.gov identifiers. Finally, we provide the applications of DRAMP in the development of AMPs.

Coarse-grain simulations on NMR conformational ensembles highlight functional residues in proteins

Dynamics are a key feature of protein function, and this is especially true of gating residues, which occupy cavity or tunnel lining positions in the protein structure, and will reversibly switch between open and closed conformations in order to control the diffusion of small molecules within a protein's internal matrix. Earlier work on globins and hydrogenases have shown that these gating residues can be detected using a multiscale scheme combining all-atom classic molecular dynamics simulations and coarse-grain calculations of the resulting conformational ensemble mechanical properties. Here, we show that the structural variations observed in the conformational ensembles produced by NMR spectroscopy experiments are sufficient to induce noticeable mechanical changes in a protein, which in turn can be used to identify residues important for function and forming a mechanical nucleus in the protein core. This new approach, which combines experimental data and rapid coarse-grain calculations and no longer needs to resort to time-consuming all-atom simulations, was successfully applied to five different protein families.

Molecular Modelling of the Ni(II)-Responsive Synechocystis PCC 6803 Transcriptional Regulator InrS in the Metal Bound Form

InrS (internal nickel-responsive sensor) is a transcriptional regulator found in cyanobacteria that represses the transcription of the nickel exporter NrsD in the apo form and de-represses expression of the exporter upon Ni(II) binding. Although a crystal structure of apo-InrS from Synechocystis PCC 6803 has been reported, no structure of the protein with metal ions bound is available. Here we report the results of a computational study aimed to reconstruct the metal binding site by taking advantage of recent X-ray absorption spectroscopy (XAS) data and to envisage the structural rearrangements occurring upon Ni(II) binding. The modelled Ni(II) binding site shows a square planar geometry consistent with experimental data. The structural details of the conformational changes occurring upon metal binding are also discussed in the framework of trying to rationalize the different affinity of the apo- and holo-forms of the protein for DNA.

Integration of network models and evolutionary analysis into high-throughput modeling of protein dynamics and allosteric regulation: theory, tools and applications

Proteins are dynamical entities that undergo a plethora of conformational changes, accomplishing their biological functions. Molecular dynamics simulation and normal mode analysis methods have become the gold standard for studying protein dynamics, analyzing molecular mechanism and allosteric regulation of biological systems. The enormous amount of the ensemble-based experimental and computational data on protein structure and dynamics has presented a major challenge for the high-throughput modeling of protein regulation and molecular mechanisms. In parallel, bioinformatics and systems biology approaches including genomic analysis, coevolution and network-based modeling have provided an array of powerful tools that complemented and enriched biophysical insights by enabling high-throughput analysis of biological data and dissection of global molecular signatures underlying mechanisms of protein function and interactions in the cellular environment. These developments have provided a powerful interdisciplinary framework for quantifying the relationships between protein dynamics and allosteric regulation, allowing for high-throughput modeling and engineering of molecular mechanisms. Here, we review fundamental advances in protein dynamics, network theory and coevolutionary analysis that have provided foundation for rapidly growing computational tools for modeling of allosteric regulation. We discuss recent developments in these interdisciplinary areas bridging computational biophysics and network biology, focusing on promising applications in allosteric regulations, including the investigation of allosteric communication pathways, protein–DNA/RNA interactions and disease mutations in genomic medicine. We conclude by formulating and discussing future directions and potential challenges facing quantitative computational investigations of allosteric regulatory mechanisms in protein systems.

Analyzing Biomolecular Ensembles

Several techniques are available to generate conformational ensembles of proteins and other biomolecules either experimentally or computationally. These methods produce a large amount of data that need to be analyzed to identify structure-dynamics-function relationship. In this chapter, we will cover different tools to unveil the information hidden in conformational ensemble data and to guide toward the rationalization of the data. We included routinely used approaches such as dimensionality reduction, as well as new methods inspired by high-order statistics and graph theory.

Modeling of Protein Structural Flexibility and Large-Scale Dynamics: Coarse-Grained Simulations and Elastic Network Models

Fluctuations of protein three-dimensional structures and large-scale conformational transitions are crucial for the biological function of proteins and their complexes. Experimental studies of such phenomena remain very challenging and therefore molecular modeling can be a good alternative or a valuable supporting tool for the investigation of large molecular systems and long-time events. In this minireview, we present two alternative approaches to the coarse-grained (CG) modeling of dynamic properties of protein systems. We discuss two CG representations of polypeptide chains used for Monte Carlo dynamics simulations of protein local dynamics and conformational transitions, and highly simplified structure-based elastic network models of protein flexibility. In contrast to classical all-atom molecular dynamics, the modeling strategies discussed here allow the quite accurate modeling of much larger systems and longer-time dynamic phenomena. We briefly describe the main features of these models and outline some of their applications, including modeling of near-native structure fluctuations, sampling of large regions of the protein conformational space, or possible support for the structure prediction of large proteins and their complexes.

Role of conformational dynamics in the evolution of novel enzyme function

The free energy landscape concept that describes enzymes as an ensemble of differently populated conformational sub-states in dynamic equilibrium is key for evaluating enzyme activity, enantioselectivity, and specificity. Mutations introduced in the enzyme sequence can alter the populations of the pre-existing conformational states, thus strongly modifying the enzyme ability to accommodate alternative substrates, revert its enantiopreferences, and even increase the activity for some residual promiscuous reactions. In this feature article, we present an overview of the current experimental and computational strategies to explore the conformational free energy landscape of enzymes. We provide a series of recent publications that highlight the key role of conformational dynamics for the enzyme evolution towards new functions and substrates, and provide some perspectives on how conformational dynamism should be considered in future computational enzyme design protocols.

Role of Conformational Dynamics in the Evolution of Retro-Aldolase Activity

Enzymes exist as ensembles of conformations that are important for function. Tuning these populations of conformational states through mutation enables evolution toward additional activities. Here we computationally evaluate the population shifts induced by distal and active site mutations in a family of computationally designed and experimentally optimized retro-aldolases. The conformational landscape of these enzymes was significantly altered during evolution, as pre-existing catalytically active conformational substates became major states in the most evolved variants. We further demonstrate that key residues responsible for these substate conversions can be predicted computationally. Significantly, the identified residues coincide with those positions mutated in the laboratory evolution experiments. This study establishes that distal mutations that affect enzyme catalytic activity can be predicted computationally and thus provides the enzyme (re)design field with a rational strategy to determine promising sites for enhancing activity through mutation.

Adding a new separation dimension to MS and LC-MS: What is the utility of ion mobility spectrometry?

Ion mobility spectrometry is an analytical technique known for more than 100 years, which entails separating ions in the gas phase based on their size, shape, and charge. While ion mobility spectrometry alone can be useful for some applications (mostly security analysis for detecting certain classes of narcotics and explosives), it becomes even more powerful in combination with mass spectrometry and high-performance liquid chromatography. Indeed, the limited resolving power of ion mobility spectrometry alone can be tackled when combining this analytical strategy with mass spectrometry or liquid chromatography with mass spectrometry. Over the last few years, the hyphenation of ion mobility spectrometry to mass spectrometry or liquid chromatography with mass spectrometry has attracted more and more interest, with significant progresses in both technical advances and pioneering applications. This review describes the theoretical background, available technologies, and future capabilities of these techniques. It also highlights a wide range of applications, from small molecules (natural products, metabolites, glycans, lipids) to large biomolecules (proteins, protein complexes, biopharmaceuticals, oligonucleotides).

Comparison of NMR and crystal structures of membrane proteins and computational refinement to improve model quality

Membrane proteins are challenging to study and restraints for structure determination are typically sparse or of low resolution because the membrane environment that surrounds them leads to a variety of experimental challenges. When membrane protein structures are determined by different techniques in different environments, a natural question is "which structure is most biologically relevant?" Towards answering this question, we compiled a dataset of membrane proteins with known structures determined by both solution NMR and X-ray crystallography. By investigating differences between the structures, we found that RMSDs between crystal and NMR structures are below 5 Å in the membrane region, NMR ensembles have a higher convergence in the membrane region, crystal structures typically have a straighter transmembrane region, have higher stereo-chemical correctness, and are more tightly packed. After quantifying these differences, we used high-resolution refinement of the NMR structures to mitigate them, which paves the way for identifying and improving the structural quality of membrane proteins.

Computational Design of Protein Function

The computational design of enzymes is a tremendous challenge for both chemistry and biochemistry. The ability to design stable and functional biocatalysts that could operate under different conditions to perform chemical reactions without precedent in nature, allowing the large-scale production of chemicals à la carte, would revolutionise both synthetic, pharmacologic and materials chemistry. Despite the great advances achieved, this highly multidisciplinary area of research is still in its infancy. This chapter describes the ‘inside-out’ protocol for computational enzyme design and both the achievements and limitations of the current technology are highlighted. Furthermore, molecular dynamics simulations have proved to be invaluable in the enzyme design process, constituting an important tool for discovering elusive catalytically relevant conformations of the engineered or designed enzyme. As a complement to the ‘inside-out’ design protocol, different examples where hybrid QM/MM approaches have been directly applied to discover beneficial mutations in rational computational enzyme design are described.