Engineering the Mechanical Stability of a Therapeutic Affibody/PD-L1 Complex by Anchor Point Selection.

Protein-protein complexes can vary in mechanical stability depending on the direction from which force is applied. Here we investigated the anisotropic mechanical stability of a molecular complex between a therapeutic non-immunoglobulin scaffold called Affibody and the extracellular domain of the immune checkpoint protein PD-L1. We used a combination of single-molecule AFM force spectroscopy (AFM-SMFS) with bioorthogonal clickable peptide handles, shear stress bead adhesion assays, molecular modeling, and steered molecular dynamics (SMD) simulations to understand the pulling point dependency of mechanostability of the Affibody:(PD-L1) complex. We observed diverse mechanical responses depending on the anchor point. For example, pulling from residue #22 on Affibody generated an intermediate unfolding event attributed to partial unfolding of PD-L1, while pulling from Affibody's N-terminus generated force-activated catch bond behavior. We found that pulling from residue #22 or #47 on Affibody generated the highest rupture forces, with the complex breaking at up to ~ 190 pN under loading rates of ~104-105 pN/sec, representing a ~4-fold increase in mechanostability as compared with low force N-terminal pulling. SMD simulations provided consistent tendencies in rupture forces, and through visualization of force propagation networks provided mechanistic insights. These results demonstrate how mechanostability of therapeutic protein-protein interfaces can be controlled by informed selection of anchor points within molecules, with implications for optimal bioconjugation strategies in drug delivery vehicles.

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.

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.

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.

A tethered ligand assay to probe SARS-CoV-2:ACE2 interactions.

SignificanceIn the dynamic environment of the airways, where SARS-CoV-2 infections are initiated by binding to human host receptor ACE2, mechanical stability of the viral attachment is a crucial fitness advantage. Using single-molecule force spectroscopy techniques, we mimic the effect of coughing and sneezing, thereby testing the force stability of SARS-CoV-2 RBD:ACE2 interaction under physiological conditions. Our results reveal a higher force stability of SARS-CoV-2 binding to ACE2 compared to SARS-CoV-1, causing a possible fitness advantage. Our assay is sensitive to blocking agents preventing RBD:ACE2 bond formation. It will thus provide a powerful approach to investigate the modes of action of neutralizing antibodies and other agents designed to block RBD binding to ACE2 that are currently developed as potential COVID-19 therapeutics.

Generalized correlation-based dynamical network analysis: a new high-performance approach for identifying allosteric communications in molecular dynamics trajectories.

Molecular interactions are essential for regulation of cellular processes from the formation of multi-protein complexes to the allosteric activation of enzymes. Identifying the essential residues and molecular features that regulate such interactions is paramount for understanding the biochemical process in question, allowing for suppression of a reaction through drug interventions or optimization of a chemical process using bioengineered molecules. In order to identify important residues and information pathways within molecular complexes, the dynamical network analysis method was developed and has since been broadly applied in the literature. However, in the dawn of exascale computing, this method is frequently limited to relatively small biomolecular systems. In this work, we provide an evolution of the method, application, and interface. All data processing and analysis are conducted through Jupyter notebooks, providing automatic detection of important solvent and ion residues, an optimized and parallel generalized correlation implementation that is linear with respect to the number of nodes in the system, and subsequent community clustering, calculation of betweenness of contacts, and determination of optimal paths. Using the popular visualization program visual molecular dynamics (VMD), high-quality renderings of the networks over the biomolecular structures can be produced. Our new implementation was employed to investigate three different systems, with up to 2.5M atoms, namely, the OMP-decarboxylase, the leucyl-tRNA synthetase complexed with its cognate tRNA and adenylate, and respiratory complex I in a membrane environment. Our enhanced and updated protocol provides the community with an intuitive and interactive interface, which can be easily applied to large macromolecular complexes.

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.

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.

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.

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.

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.

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.

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 ß. 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.

NAMD goes quantum: an integrative suite for hybrid simulations.

Hybrid methods that combine quantum mechanics (QM) and molecular mechanics (MM) can be applied to studies of reaction mechanisms in locations ranging from active sites of small enzymes to multiple sites in large bioenergetic complexes. By combining the widely used molecular dynamics and visualization programs NAMD and VMD with the quantum chemistry packages ORCA and MOPAC, we created an integrated, comprehensive, customizable, and easy-to-use suite (http://www.ks.uiuc.edu/Research/qmmm). Through the QwikMD interface, setup, execution, visualization, and analysis are streamlined for all levels of expertise.