Single-molecule force stability of the SARS-CoV-2-ACE2 interface in variants-of-concern

Mutations in SARS-CoV-2 have shown effective evasion of population immunity and increased affinity to the cellular receptor angiotensin-converting enzyme 2 (ACE2). However, in the dynamic environment of the respiratory tract, forces act on the binding partners, which raises the question of whether not only affinity but also force stability of the SARS-CoV-2-ACE2 interaction might be a selection factor for mutations. Using magnetic tweezers, we investigate the impact of amino acid substitutions in variants of concern (Alpha, Beta, Gamma and Delta) and on force-stability and bond kinetic of the receptor-binding domain-ACE2 interface at a single-molecule resolution. We find a higher affinity for all of the variants of concern (>fivefold) compared with the wild type. In contrast, Alpha is the only variant of concern that shows higher force stability (by 17%) compared with the wild type. Using molecular dynamics simulations, we rationalize the mechanistic molecular origins of this increase in force stability. Our study emphasizes the diversity of contributions to the transmissibility of variants and establishes force stability as one of the several factors for fitness. Understanding fitness advantages opens the possibility for the prediction of probable mutations, allowing a rapid adjustment of therapeutics, vaccines and intervention measures.

Integrating Dynamic Network Analysis with AI for Enhanced Epitope Prediction in PD-L1:Affibody Interactions

Understanding binding epitopes involved in protein-protein interactions and accurately determining their structure is a long standing goal with broad applicability in industry and biomedicine. Although various experimental methods for binding epitope determination exist, these approaches are typically low throughput and cost intensive. Computational methods have potential to accelerate epitope predictions, however, recently developed artificial intelligence (AI)-based methods frequently fail to predict epitopes of synthetic binding domains with few natural homologs. Here we have developed an integrated method employing generalized-correlation-based dynamic network analysis on multiple molecular dynamics (MD) trajectories, initiated from AlphaFold2 Multimer structures, to unravel the structure and binding epitope of the therapeutic PD-L1:Affibody complex. Both AlphaFold2 and conventional molecular dynamics trajectory analysis alone each proved ineffectual in differentiating between two putative binding models referred to as parallel and perpendicular. However, our integrated approach based on dynamic network analysis showed that the perpendicular mode was significantly more stable. These predictions were validated using a suite of experimental epitope mapping protocols including cross linking mass spectrometry and next-generation sequencing-based deep mutational scanning. Our research highlights the potential of deploying dynamic network analysis to refine AI-based structure predictions for precise predictions of protein-protein interaction interfaces.

VMD as a Platform for Interactive Small Molecule Preparation and Visualization in Quantum and Classical Simulations

Modeling and simulation of small molecules such as drugs and biological cofactors have been both a major focus of computational chemistry for decades and a growing need among computational biophysicists who seek to investigate the interaction of different types of ligands with biomolecules. Of particular interest in this regard are quantum mechanical (QM) calculations that are used to more accurately describe such small molecules, which can be of heterogeneous structures and chemistry, either in purely QM calculations or in hybrid QM/molecular mechanics (MM) simulations. QM programs are also used to develop MM force field parameters for small molecules to be used along with established force fields for biomolecules in classical simulations. With this growing need in mind, here we report a set of software tools developed and closely integrated within the broadly used molecular visualization/analysis program, VMD, that allow the user to construct, modify, and parametrize small molecules and prepare them for QM, hybrid QM/MM, or classical simulations. The tools also provide interactive analysis and visualization capabilities in an easy-to-use and integrated environment. In this paper, we briefly report on these tools and their major features and capabilities, along with examples of how they can facilitate molecular research in computational biophysics that might be otherwise prohibitively complex.

Deciphering molecular mechanisms stabilizing the reovirus-binding complex

Mammalian orthoreoviruses (reoviruses) serve as potential triggers of celiac disease and have oncolytic properties, making these viruses potential cancer therapeutics. Primary attachment of reovirus to host cells is mainly mediated by the trimeric viral protein, σ1, which engages cell-surface glycans, followed by high-affinity binding to junctional adhesion molecule-A (JAM-A). This multistep process is thought to be accompanied by major conformational changes in σ1, but direct evidence is lacking. By combining biophysical, molecular, and simulation approaches, we define how viral capsid protein mechanics influence virus-binding capacity and infectivity. Single-virus force spectroscopy experiments corroborated by in silico simulations show that GM2 increases the affinity of σ1 for JAM-A by providing a more stable contact interface. We demonstrate that conformational changes in σ1 that lead to an extended rigid conformation also significantly increase avidity for JAM-A. Although its associated lower flexibility impairs multivalent cell attachment, our findings suggest that diminished σ1 flexibility enhances infectivity, indicating that fine-tuning of σ1 conformational changes is required to successfully initiate infection. Understanding properties underlying the nanomechanics of viral attachment proteins offers perspectives in the development of antiviral drugs and improved oncolytic vectors.

May the force be with you: The role of hyper-mechanostability of the bone sialoprotein binding protein during early stages of Staphylococci infections

The bone sialoprotein-binding protein (Bbp) is a mechanoactive MSCRAMM protein expressed on the surface of Staphylococcus aureus that mediates adherence of the bacterium to fibrinogen-α (Fgα), a component of the bone and dentine extracellular matrix of the host cell. Mechanoactive proteins like Bbp have key roles in several physiological and pathological processes. Particularly, the Bbp: Fgα interaction is important in the formation of biofilms, an important virulence factor of pathogenic bacteria. Here, we investigated the mechanostability of the Bbp: Fgα complex using in silico single-molecule force spectroscopy (SMFS), in an approach that combines results from all-atom and coarse-grained steered molecular dynamics (SMD) simulations. Our results show that Bbp is the most mechanostable MSCRAMM investigated thus far, reaching rupture forces beyond the 2 nN range in typical experimental SMFS pulling rates. Our results show that high force-loads, which are common during initial stages of bacterial infection, stabilize the interconnection between the protein's amino acids, making the protein more "rigid". Our data offer new insights that are crucial on the development of novel anti-adhesion strategies.

Fostering discoveries in the era of exascale computing: How the next generation of supercomputers empowers computational and experimental biophysics alike

Over a century ago, physicists started broadly relying on theoretical models to guide new experiments. Soon thereafter, chemists began doing the same. Now, biological research enters a new era when experiment and theory walk hand in hand. Novel software and specialized hardware became essential to understand experimental data and propose new models. In fact, current petascale computing resources already allow researchers to reach unprecedented levels of simulation throughput to connect in silico and in vitro experiments. The reduction in cost and improved access allowed a large number of research groups to adopt supercomputing resources and techniques. Here, we outline how large-scale computing has evolved to expand decades-old research, spark new research efforts, and continuously connect simulation and observation. For instance, multiple publicly and privately funded groups have dedicated extensive resources to develop artificial intelligence tools for computational biophysics, from accelerating quantum chemistry calculations to proposing protein structure models. Moreover, advances in computer hardware have accelerated data processing from single-molecule experimental observations and simulations of chemical reactions occurring throughout entire cells. The combination of software and hardware has opened the way for exascale computing and the production of the first public exascale supercomputer, Frontier, inaugurated by the Oak Ridge National Laboratory in 2022. Ultimately, the popularization and development of computational techniques and the training of researchers to use them will only accelerate the diversification of tools and learning resources for future generations.

Molecular Origins of Force-Dependent Protein Complex Stabilization during Bacterial Infections

The unbinding pathway of a protein complex can vary significantly depending on biochemical and mechanical factors. Under mechanical stress, a complex may dissociate through a mechanism different from that used in simple thermal dissociation, leading to different dissociation rates under shear force and thermal dissociation. This is a well-known phenomenon studied in biomechanics whose molecular and atomic details are still elusive. A particularly interesting case is the complex formed by bacterial adhesins with their human peptide target. These protein interactions have a force resilience equivalent to those of covalent bonds, an order of magnitude stronger than the widely used streptavidin:biotin complex, while having an ordinary affinity, much lower than that of streptavidin:biotin. Here, in an in silico single-molecule force spectroscopy approach, we use molecular dynamics simulations to investigate the dissociation mechanism of adhesin/peptide complexes. We show how the Staphylococcus epidermidis adhesin SdrG uses a catch-bond mechanism to increase complex stability with increasing mechanical stress. While allowing for thermal dissociation in a low-force regime, an entirely different mechanical dissociation path emerges in a high-force regime, revealing an intricate mechanism that does not depend on the peptide's amino acid sequence. Using a dynamic network analysis approach, we identified key amino acid contacts that describe the mechanics of this complex, revealing differences in dynamics that hinder thermal dissociation and establish the mechanical dissociation path. We then validate the information content of the selected amino acid contacts using their dynamics to successfully predict the rupture forces for this complex through a machine learning model.

Protein structure prediction in the era of AI: Challenges and limitations when applying to in silico force spectroscopy

Mechanoactive proteins are essential for a myriad of physiological and pathological processes. Guided by the advances in single-molecule force spectroscopy (SMFS), we have reached a molecular-level understanding of how mechanoactive proteins sense and respond to mechanical forces. However, even SMFS has its limitations, including the lack of detailed structural information during force-loading experiments. That is where molecular dynamics (MD) methods shine, bringing atomistic details with femtosecond time-resolution. However, MD heavily relies on the availability of high-resolution structural data, which is not available for most proteins. For instance, the Protein Data Bank currently has 192K structures deposited, against 231M protein sequences available on Uniprot. But many are betting that this gap might become much smaller soon. Over the past year, the AI-based AlphaFold created a buzz on the structural biology field by being able to predict near-native protein folds from their sequences. For some, AlphaFold is causing the merge of structural biology with bioinformatics. Here, using an in silico SMFS approach pioneered by our group, we investigate how reliable AlphaFold structure predictions are to investigate mechanical properties of Staphylococcus bacteria adhesins proteins. Our results show that AlphaFold produce extremally reliable protein folds, but in many cases is unable to predict high-resolution protein complexes accurately. Nonetheless, the results show that AlphaFold can revolutionize the investigation of these proteins, particularly by allowing high-throughput scanning of protein structures. Meanwhile, we show that the AlphaFold results need to be validated and should not be employed blindly, with the risk of obtaining an erroneous protein mechanism.

High force catch bond mechanism of bacterial adhesion in the human gut

Bacterial colonization of the human intestine requires firm adhesion of bacteria to insoluble substrates under hydrodynamic flow. Here we report the molecular mechanism behind an ultrastable protein complex responsible for resisting shear forces and adhering bacteria to cellulose fibers in the human gut. Using single-molecule force spectroscopy (SMFS), single-molecule FRET (smFRET), and molecular dynamics (MD) simulations, we resolve two binding modes and three unbinding reaction pathways of a mechanically ultrastable R. champanellensis (Rc) Dockerin:Cohesin (Doc:Coh) complex. The complex assembles in two discrete binding modes with significantly different mechanical properties, with one breaking at ~500 pN and the other at ~200 pN at loading rates from 1-100 nN s-1. A neighboring X-module domain allosterically regulates the binding interaction and inhibits one of the low-force pathways at high loading rates, giving rise to a catch bonding mechanism that manifests under force ramp protocols. Multi-state Monte Carlo simulations show strong agreement with experimental results, validating the proposed kinetic scheme. These results explain mechanistically how gut microbes regulate cell adhesion strength at high shear stress through intricate molecular mechanisms including dual-binding modes, mechanical allostery and catch bonds.

Scalable molecular dynamics on CPU and GPU architectures with NAMD

NAMDis a molecular dynamics program designed for high-performance simulations of very large biological objects on CPU- and GPU-based architectures. NAMD offers scalable performance on petascale parallel supercomputers consisting of hundreds of thousands of cores, as well as on inexpensive commodity clusters commonly found in academic environments. It is written in C++ and leans on Charm++ parallel objects for optimal performance on low-latency architectures. NAMD is a versatile, multipurpose code that gathers state-of-the-art algorithms to carry out simulations in apt thermodynamic ensembles, using the widely popular CHARMM, AMBER, OPLS, and GROMOS biomolecular force fields. Here, we review the main features of NAMD that allow both equilibrium and enhanced-sampling molecular dynamics simulations with numerical efficiency. We describe the underlying concepts utilized by NAMD and their implementation, most notably for handling long-range electrostatics; controlling the temperature, pressure, and pH; applying external potentials on tailored grids; leveraging massively parallel resources in multiple-copy simulations; and hybrid quantum-mechanical/molecular-mechanical descriptions. We detail the variety of options offered by NAMD for enhanced-sampling simulations aimed at determining free-energy differences of either alchemical or geometrical transformations and outline their applicability to specific problems. Last, we discuss the roadmap for the development of NAMD and our current efforts toward achieving optimal performance on GPU-based architectures, for pushing back the limitations that have prevented biologically realistic billion-atom objects to be fruitfully simulated, and for making large-scale simulations less expensive and easier to set up, run, and analyze. NAMD is distributed free of charge with its source code at www.ks.uiuc.edu.

Thermophilic Degradation of Hemicellulose, a Critical Feedstock in the Production of Bioenergy and Other Value-Added Products

Renewable fuels have gained importance as the world moves toward diversifying its energy portfolio. A critical step in the biomass-to-bioenergy initiative is deconstruction of plant cell wall polysaccharides to their unit sugars for subsequent fermentation to fuels. To acquire carbon and energy for their metabolic processes, diverse microorganisms have evolved genes encoding enzymes that depolymerize polysaccharides to their carbon/energy-rich building blocks. The microbial enzymes mostly target the energy present in cellulose, hemicellulose, and pectin, three major forms of energy storage in plants. In the effort to develop bioenergy as an alternative to fossil fuel, a common strategy is to harness microbial enzymes to hydrolyze cellulose to glucose for fermentation to fuels. However, the conversion of plant biomass to renewable fuels will require both cellulose and hemicellulose, the two largest components of the plant cell wall, as feedstock to improve economic feasibility. Here, we explore the enzymes and strategies evolved by two well-studied bacteria to depolymerize the hemicelluloses xylan/arabinoxylan and mannan. The sets of enzymes, in addition to their applications in biofuels and value-added chemical production, have utility in animal feed enzymes, a rapidly developing industry with potential to minimize adverse impacts of animal agriculture on the environment.

Streptavidin/biotin: Tethering geometry defines unbinding mechanics

Macromolecules tend to respond to applied forces in many different ways. Chemistry at high shear forces can be intriguing, with relatively soft bonds becoming very stiff in specific force-loading geometries. Largely used in bionanotechnology, an important case is the streptavidin (SA)/biotin interaction. Although SA's four subunits have the same affinity, we find that the forces required to break the SA/biotin bond depend strongly on the attachment geometry. With AFM-based single-molecule force spectroscopy (SMFS), we measured unbinding forces of biotin from different SA subunits to range from 100 to more than 400 pN. Using a wide-sampling approach, we carried out hundreds of all-atom steered molecular dynamics (SMD) simulations for the entire system, including molecular linkers. Our strategy revealed the molecular mechanism that causes a fourfold difference in mechanical stability: Certain force-loading geometries induce conformational changes in SA's binding pocket lowering the energy barrier, which biotin has to overcome to escape the pocket.

Mechanisms of Nanonewton Mechanostability in a Protein Complex Revealed by Molecular Dynamics Simulations and Single-Molecule Force Spectroscopy

Can molecular dynamics simulations predict the mechanical behavior of protein complexes? Can simulations decipher the role of protein domains of unknown function in large macromolecular complexes? Here, we employ a wide-sampling computational approach to demonstrate that molecular dynamics simulations, when carefully performed and combined with single-molecule atomic force spectroscopy experiments, can predict and explain the behavior of highly mechanostable protein complexes. As a test case, we studied a previously unreported homologue from Ruminococcus flavefaciens called X-module-Dockerin (XDoc) bound to its partner Cohesin (Coh). By performing dozens of short simulation replicas near the rupture event, and analyzing dynamic network fluctuations, we were able to generate large simulation statistics and directly compare them with experiments to uncover the mechanisms involved in mechanical stabilization. Our single-molecule force spectroscopy experiments show that the XDoc-Coh homologue complex withstands forces up to 1 nN at loading rates of 10⁵ pN/s. Our simulation results reveal that this remarkable mechanical stability is achieved by a protein architecture that directs molecular deformation along paths that run perpendicular to the pulling axis. The X-module was found to play a crucial role in shielding the adjacent protein complex from mechanical rupture. These mechanisms of protein mechanical stabilization have potential applications in biotechnology for the development of systems exhibiting shear enhanced adhesion or tunable mechanics.

Direction Matters: Monovalent Streptavidin/Biotin Complex under Load

Novel site-specific attachment strategies combined with improvements of computational resources enable new insights into the mechanics of the monovalent biotin/streptavidin complex under load and forced us to rethink the diversity of rupture forces reported in the literature. We discovered that the mechanical stability of this complex depends strongly on the geometry in which force is applied. By atomic force microscopy-based single molecule force spectroscopy we found unbinding of biotin to occur beyond 400 pN at force loading rates of 10 nN/s when monovalent streptavidin was tethered at its C-terminus. This value is about twice as high than that for N-terminal attachment. Steered molecular dynamics simulations provided a detailed picture of the mechanics of the unbinding process in the corresponding force loading geometries. Using machine learning techniques, we connected findings from hundreds of simulations to the experimental results, identifying different force propagation pathways. Interestingly, we observed that depending on force loading geometry, partial unfolding of N-terminal region of monovalent streptavidin occurs before biotin is released from the binding pocket.

Non-syndromic Mitral Valve Dysplasia Mutation Changes the Force Resilience and Interaction of Human Filamin A

Filamin A (FLNa), expressed in endocardial endothelia during fetal valve morphogenesis, is key in cardiac development. Missense mutations in FLNa cause non-syndromic mitral valve dysplasia (FLNA-MVD). Here, we aimed to reveal the currently unknown underlying molecular mechanism behind FLNA-MVD caused by the FLNa P637Q mutation. The solved crystal structure of the FLNa3-5 P637Q revealed that this mutation causes only minor structural changes close to mutation site. These changes were observed to significantly affect FLNa's ability to transmit cellular force and to interact with its binding partner. The performed steered molecular dynamics simulations showed that significantly lower forces are needed to split domains 4 and 5 in FLNA-MVD than with wild-type FLNa. The P637Q mutation was also observed to interfere with FLNa's interactions with the protein tyrosine phosphatase PTPN12. Our results provide a crucial step toward understanding the molecular bases behind FLNA-MVD, which is critical for the development of drug-based therapeutics.

Molecular mechanism of extreme mechanostability in a pathogen adhesin

High resilience to mechanical stress is key when pathogens adhere to their target and initiate infection. Using atomic force microscopy-based single-molecule force spectroscopy, we explored the mechanical stability of the prototypical staphylococcal adhesin SdrG, which targets a short peptide from human fibrinogen β. Steered molecular dynamics simulations revealed, and single-molecule force spectroscopy experiments confirmed, the mechanism by which this complex withstands forces of over 2 nanonewtons, a regime previously associated with the strength of a covalent bond. The target peptide, confined in a screwlike manner in the binding pocket of SdrG, distributes forces mainly toward the peptide backbone through an intricate hydrogen bond network. Thus, these adhesins can attach to their target with exceptionally resilient mechanostability, virtually independent of peptide side chains.

PyContact: Rapid, Customizable, and Visual Analysis of Noncovalent Interactions in MD Simulations

Molecular dynamics (MD) simulations have become ubiquitous in all areas of life sciences. The size and model complexity of MD simulations are rapidly growing along with increasing computing power and improved algorithms. This growth has led to the production of a large amount of simulation data that need to be filtered for relevant information to address specific biomedical and biochemical questions. One of the most relevant molecular properties that can be investigated by all-atom MD simulations is the time-dependent evolution of the complex noncovalent interaction networks governing such fundamental aspects as molecular recognition, binding strength, and mechanical and structural stability. Extracting, evaluating, and visualizing noncovalent interactions is a key task in the daily work of structural biologists. We have developed PyContact, an easy-to-use, highly flexible, and intuitive graphical user interface-based application, designed to provide a toolkit to investigate biomolecular interactions in MD trajectories. PyContact is designed to facilitate this task by enabling identification of relevant noncovalent interactions in a comprehensible manner. The implementation of PyContact as a standalone application enables rapid analysis and data visualization without any additional programming requirements, and also preserves full in-program customization and extension capabilities for advanced users. The statistical analysis representation is interactively combined with full mapping of the results on the molecular system through the synergistic connection between PyContact and VMD. We showcase the capabilities and scientific significance of PyContact by analyzing and visualizing in great detail the noncovalent interactions underlying the ion permeation pathway of the human P2X3 receptor. As a second application, we examine the protein-protein interaction network of the mechanically ultrastable cohesin-dockering complex.

Combining in Vitro and in Silico Single-Molecule Force Spectroscopy to Characterize and Tune Cellulosomal Scaffoldin Mechanics

Cellulosomes are polyprotein machineries that efficiently degrade cellulosic material. Crucial to their function are scaffolds consisting of highly homologous cohesin domains, which serve a dual role by coordinating a multiplicity of enzymes as well as anchoring the microbe to its substrate. Here we combined two approaches to elucidate the mechanical properties of the main scaffold ScaA of Acetivibrio cellulolyticus. A newly developed parallelized one-pot in vitro transcription-translation and protein pull-down protocol enabled high-throughput atomic force microscopy (AFM)-based single-molecule force spectroscopy (SMFS) measurements of all cohesins from ScaA with a single cantilever, thus promising improved relative force comparability. Albeit very similar in sequence, the hanging cohesins showed considerably lower unfolding forces than the bridging cohesins, which are subjected to force when the microbe is anchored to its substrate. Additionally, all-atom steered molecular dynamics (SMD) simulations on homology models offered insight into the process of cohesin unfolding under force. Based on the differences among the individual force propagation pathways and their associated correlation communities, we designed mutants to tune the mechanical stability of the weakest hanging cohesin. The proposed mutants were tested in a second high-throughput AFM SMFS experiment revealing that in one case a single alanine to glycine point mutation suffices to more than double the mechanical stability. In summary, we have successfully characterized the force induced unfolding behavior of all cohesins from the scaffoldin ScaA, as well as revealed how small changes in sequence can have large effects on force resilience in cohesin domains. Our strategy provides an efficient way to test and improve the mechanical integrity of protein domains in general.

Skeletal Dysplasia Mutations Effect on Human Filamins' Structure and Mechanosensing

Cells' ability to sense mechanical cues in their environment is crucial for fundamental cellular processes, leading defects in mechanosensing to be linked to many diseases. The actin cross-linking protein Filamin has an important role in the conversion of mechanical forces into biochemical signals. Here, we reveal how mutations in Filamin genes known to cause Larsen syndrome and Frontometaphyseal dysplasia can affect the structure and therefore function of Filamin domains 16 and 17. Employing X-ray crystallography, the structure of these domains was first solved for the human Filamin B. The interaction seen between domains 16 and 17 is broken by shear force as revealed by steered molecular dynamics simulations. The effects of skeletal dysplasia associated mutations of the structure and mechanosensing properties of Filamin were studied by combining various experimental and theoretical techniques. The results showed that Larsen syndrome associated mutations destabilize or even unfold domain 17. Interestingly, those Filamin functions that are mediated via domain 17 interactions with other proteins are not necessarily affected as strongly interacting peptide binding to mutated domain 17 induces at least partial domain folding. Mutation associated to Frontometaphyseal dysplasia, in turn, transforms 16-17 fragment from compact to an elongated form destroying the force-regulated domain pair.

Computational Methodologies for Real-Space Structural Refinement of Large Macromolecular Complexes

The rise of the computer as a powerful tool for model building and refinement has revolutionized the field of structure determination for large biomolecular systems. Despite the wide availability of robust experimental methods capable of resolving structural details across a range of spatiotemporal resolutions, computational hybrid methods have the unique ability to integrate the diverse data from multimodal techniques such as X-ray crystallography and electron microscopy into consistent, fully atomistic structures. Here, commonly employed strategies for computational real-space structural refinement are reviewed, and their specific applications are illustrated for several large macromolecular complexes: ribosome, virus capsids, chemosensory array, and photosynthetic chromatophore. The increasingly important role of computational methods in large-scale structural refinement, along with current and future challenges, is discussed.

Mapping Mechanical Force Propagation through Biomolecular Complexes

Here we employ single-molecule force spectroscopy with an atomic force microscope (AFM) and steered molecular dynamics (SMD) simulations to reveal force propagation pathways through a mechanically ultrastable multidomain cellulosome protein complex. We demonstrate a new combination of network-based correlation analysis supported by AFM directional pulling experiments, which allowed us to visualize stiff paths through the protein complex along which force is transmitted. The results implicate specific force-propagation routes nonparallel to the pulling axis that are advantageous for achieving high dissociation forces.

Ultrastable cellulosome-adhesion complex tightens under load

Challenging environments have guided nature in the development of ultrastable protein complexes. Specialized bacteria produce discrete multi-component protein networks called cellulosomes to effectively digest lignocellulosic biomass. While network assembly is enabled by protein interactions with commonplace affinities, we show that certain cellulosomal ligand-receptor interactions exhibit extreme resistance to applied force. Here, we characterize the ligand-receptor complex responsible for substrate anchoring in the Ruminococcus flavefaciens cellulosome using single-molecule force spectroscopy and steered molecular dynamics simulations. The complex withstands forces of 600-750 pN, making it one of the strongest bimolecular interactions reported, equivalent to half the mechanical strength of a covalent bond. Our findings demonstrate force activation and inter-domain stabilization of the complex, and suggest that certain network components serve as mechanical effectors for maintaining network integrity. This detailed understanding of cellulosomal network components may help in the development of biocatalysts for production of fuels and chemicals from renewable plant-derived biomass.

Enhanced sampling techniques in molecular dynamics simulations of biological systems

BACKGROUND

Molecular dynamics has emerged as an important research methodology covering systems to the level of millions of atoms. However, insufficient sampling often limits its application. The limitation is due to rough energy landscapes, with many local minima separated by high-energy barriers, which govern the biomolecular motion.

SCOPE OF REVIEW

In the past few decades methods have been developed that address the sampling problem, such as replica-exchange molecular dynamics, metadynamics and simulated annealing. Here we present an overview over theses sampling methods in an attempt to shed light on which should be selected depending on the type of system property studied.

MAJOR CONCLUSIONS

Enhanced sampling methods have been employed for a broad range of biological systems and the choice of a suitable method is connected to biological and physical characteristics of the system, in particular system size. While metadynamics and replica-exchange molecular dynamics are the most adopted sampling methods to study biomolecular dynamics, simulated annealing is well suited to characterize very flexible systems. The use of annealing methods for a long time was restricted to simulation of small proteins; however, a variant of the method, generalized simulated annealing, can be employed at a relatively low computational cost to large macromolecular complexes.

GENERAL SIGNIFICANCE

Molecular dynamics trajectories frequently do not reach all relevant conformational substates, for example those connected with biological function, a problem that can be addressed by employing enhanced sampling algorithms. This article is part of a Special Issue entitled Recent developments of molecular dynamics.

Molecular dynamics study of enhanced Man5B enzymatic activity

BACKGROUND

Biofuels are a well-known alternative to the largely used fossil-derived fuels, however the competition with food production is an ethical dilemma. Fortunately a solution is offered by second-generation biofuels which can be produced from agricultural waste or, more specifically, from plant cell wall polysaccharides. The conversion process involves typically enzymatic hydrolysis of lignocellulosic biomass and then separation of its constituent sugars that are further fermented to produce ethanol. Over the years several technologies have been developed that allow this conversion process to occur and the objective is now to make this process cost-competitive in today's markets.

RESULTS

We observe that reduction of enzymatic efficiency in the presence of gluco-oligosaccharides is associated with a loss of the enzyme's flexibility, the latter being required to bind new substrate, while the presence of manno-oligosaccharides does not pose this problem. Molecular dynamics simulations identify key contacts between substrates and the enzyme catalytic pocket that might be modified through site-directed mutagenesis to prevent loss of enzymatic efficiency.

CONCLUSIONS

Based on previous experimental studies and the new molecular dynamics data, we suggest that cellohexaose in the active site pocket slows down or even inhibits Man5B enzymatic activity. The assumption of such a mechanism is reasonable since when the gluco-oligosaccharide substrate is attached to the catalytic pocket it takes much longer to leave the pocket and thus prevents other substrates from reaching the active site. The insight is of crucial importance since the inhibition of enzymes by the enzymatic product or by an unsuitable substrate is a major technological problem in reducing the competitiveness of second-generation biofuel production.

The structural dynamics of the flavivirus fusion peptide-membrane interaction

Membrane fusion is a crucial step in flavivirus infections and a potential target for antiviral strategies. Lipids and proteins play cooperative roles in the fusion process, which is triggered by the acidic pH inside the endosome. This acidic environment induces many changes in glycoprotein conformation and allows the action of a highly conserved hydrophobic sequence, the fusion peptide (FP). Despite the large volume of information available on the virus-triggered fusion process, little is known regarding the mechanisms behind flavivirus-cell membrane fusion. Here, we evaluated the contribution of a natural single amino acid difference on two flavivirus FPs, FLA(G) ((98)DRGWGNGCGLFGK(110)) and FLA(H) ((98)DRGWGNHCGLFGK(110)), and investigated the role of the charge of the target membrane on the fusion process. We used an in silico approach to simulate the interaction of the FPs with a lipid bilayer in a complementary way and used spectroscopic approaches to collect conformation information. We found that both peptides interact with neutral and anionic micelles, and molecular dynamics (MD) simulations showed the interaction of the FPs with the lipid bilayer. The participation of the indole ring of Trp appeared to be important for the anchoring of both peptides in the membrane model, as indicated by MD simulations and spectroscopic analyses. Mild differences between FLA(G) and FLA(H) were observed according to the pH and the charge of the target membrane model. The MD simulations of the membrane showed that both peptides adopted a bend structure, and an interaction between the aromatic residues was strongly suggested, which was also observed by circular dichroism in the presence of micelles. As the FPs of viral fusion proteins play a key role in the mechanism of viral fusion, understanding the interactions between peptides and membranes is crucial for medical science and biology and may contribute to the design of new antiviral drugs.

GSAFold: a new application of GSA to protein structure prediction

The folding process defines three-dimensional protein structures from their amino acid chains. A protein's structure determines its activity and properties; thus knowing such conformation on an atomic level is essential for both basic and applied studies of protein function and dynamics. However, the acquisition of such structures by experimental methods is slow and expensive, and current computational methods mostly depend on previously known structures to determine new ones. Here we present a new software called GSAFold that applies the generalized simulated annealing (GSA) algorithm on ab initio protein structure prediction. The GSA is a stochastic search algorithm employed in energy minimization and used in global optimization problems, especially those that depend on long-range interactions, such as gravity models and conformation optimization of small molecules. This new implementation applies, for the first time in ab initio protein structure prediction, an analytical inverse for the Visitation function of GSA. It also employs the broadly used NAMD Molecular Dynamics package to carry out energy calculations, allowing the user to select different force fields and parameterizations. Moreover, the software also allows the execution of several simulations simultaneously. Applications that depend on protein structures include rational drug design and structure-based protein function prediction. Applying GSAFold in a test peptide, it was possible to predict the structure of mastoparan-X to a root mean square deviation of 3.00 Å.

Hybrid QM/MM Molecular Dynamics Study of Benzocaine in a Membrane Environment: How Does a Quantum Mechanical Treatment of Both Anesthetic and Lipids Affect Their Interaction

Biomolecular dynamics studies using a QM/MM approach have been largely used especially to study enzymatic reactions. However, to the best of our knowledge, the very same approach has not been used to study the water/membrane interface using a quantum mechanical treatment for the lipids. Since a plethora of biochemical processes take place in this region, we believe that it is of primary importance to understand, at the level of molecular orbitals, the behavior of a drug in such an odd environment. In this work, we take advantage of an integration of the CPMD and the GROMACS code, using the Car-Parrinello method, to treat the benzocaine local anesthetic as well as two of the membrane lipids and the GROMOS force field to treat the remaining lipids and the water molecules.