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The design of vaccines against highly mutable pathogens, such as HIV and influenza, requires a detailed understanding of how the adaptive immune system responds to encountering multiple variant antigens (Ags). Here, we describe a multiscale model of B cell receptor (BCR) affinity maturation that employs actual BCR nucleotide sequences and treats BCR/Ag interactions in atomistic detail. We apply the model to simulate the maturation of a broadly neutralizing Ab (bnAb) against HIV. Starting from a germline precursor sequence of the VRC01 anti-HIV Ab, we simulate BCR evolution in response to different vaccination protocols and different Ags, which were previously designed by us. The simulation results provide qualitative guidelines for future vaccine design and reveal unique insights into bnAb evolution against the CD4 binding site of HIV. Our model makes possible direct comparisons of simulated BCR populations with results of deep sequencing data, which will be explored in future applications.Author SummaryVaccination has saved more lives than any other medical procedure, and the impending end of the COVID-19 pandemic is also due to the rapid development of highly efficacious vaccines. But, we do not have robust ways to develop vaccines against highly mutable pathogens. For example, there is no effective vaccine against HIV, and a universal vaccine against diverse strains of influenza is also not available. The development of immunization strategies to elicit antibodies that can neutralize diverse strains of highly mutable pathogens (so-called ‘broadly neutralizing antibodies’, or bnAbs) would enable the design of universal vaccines against such pathogens, as well as other viruses that may emerge in the future. In this paper, we present an agent-based model of affinity maturation – the Darwinian process by which antibodies evolve against a pathogen – that, for the first time, enables thein silicoinvestigation of real germline nucleotide sequences of antibodies known to evolve into potent bnAbs, evolving against real amino acid sequences of HIV-based vaccine-candidate proteins. Our results provide new insights into bnAb evolution against HIV, and can be used to qualitatively guide the future design of vaccines against highly mutable pathogens.

The design of vaccines against highly mutable pathogens, such as HIV and influenza, requires a detailed understanding of how the adaptive immune system responds to encountering multiple variant antigens (Ags). Here, we describe a multiscale model of B cell receptor (BCR) affinity maturation that employs actual BCR nucleotide sequences and treats BCR/Ag interactions in atomistic detail. We apply the model to simulate the maturation of a broadly neutralizing Ab (bnAb) against HIV. Starting from a germline precursor sequence of the VRC01 anti-HIV Ab, we simulate BCR evolution in response to different vaccination protocols and different Ags, which were previously designed by us. The simulation results provide qualitative guidelines for future vaccine design and reveal unique insights into bnAb evolution against the CD4 binding site of HIV. Our model makes possible direct comparisons of simulated BCR populations with results of deep sequencing data, which will be explored in future applications.

Monoclonal antibodies (mAbs) are an important class of therapeutics used to treat cancer, inflammation, and infectious diseases. Identifying highly developable mAb sequences in silico could greatly reduce the time and cost required for therapeutic mAb development. Here, we present position-specific scoring matrices (PSSMs) for antibody framework mutations developed using natural human antibody repertoire sequences. Our analysis shows that natural human antibody repertoire-based PSSMs are consistent across individuals and demonstrate high correlations between related germlines. We show that mutations in existing therapeutic antibodies can be accurately predicted solely from natural human antibody sequence data. mAbs developed using humanized mice had more human-like FR mutations than mAbs originally developed by hybridoma technology. A quantitative assessment of entire framework regions of therapeutic antibodies revealed that there may be potential for improving the properties of existing therapeutic antibodies by incorporating additional mutations of high frequency in natural human antibody repertoires. In addition, high frequency mutations in natural human antibody repertoires were predicted in silico to reduce immunogenicity in therapeutic mAbs due to the removal of T cell epitopes. Several therapeutic mAbs were identified to have common, universally high-scoring framework mutations, and molecular dynamics simulations revealed the mechanistic basis for the evolutionary selection of these mutations. Our results suggest that natural human antibody repertoires may be useful as predictive tools to guide mAb development in the future.

The potential emergence of SARS-CoV-2 Spike (S) escape mutants is a threat to reduce the efficacy of existing vaccines and neutralizing antibody (nAb) therapies. An understanding of the antibody/S escape mutation landscape is urgently needed to preemptively address this threat. Here we describe a rapid method to identify escape mutants for nAbs targeting the S receptor binding site. We identified escape mutants for five nAbs, including three from the public germline class VH3-53 elicited by natural COVID-19 infection. Escape mutations predominantly mapped to the periphery of the ACE2 recognition site on the RBD with K417, D420, Y421, F486, and Q493 as notable hotspots. We provide libraries, methods, and software as an openly available community resource to accelerate new therapeutic strategies against SARS-CoV-2., Graphical Abstract, Francino-Urdaniz el al. describe a deep mutational scanning method to identify escape mutants on SARS-CoV-2 S RBD. Escape mutants were identified for five neutralizing antibodies elicited from natural infection, revealing hotspots at positions K417, D420, Y421, F486, and Q493 at the periphery of the ACE2 recognition site.

Natural infections and vaccination with a pathogen typically stimulate the production of potent antibodies specific for the pathogen through a Darwinian evolutionary process known as affinity maturation. Such antibodies provide protection against reinfection by the same strain of a pathogen. A highly mutable virus, like HIV or influenza, evades recognition by these strain-specific antibodies via the emergence of new mutant strains. A vaccine that elicits antibodies that can bind to many diverse strains of the virus-known as broadly neutralizing antibodies (bnAbs)-could protect against highly mutable pathogens. Despite much work, the mechanisms by which bnAbs emerge remain uncertain. Using a computational model of affinity maturation, we studied a wide variety of vaccination strategies. Our results suggest that an effective strategy to maximize bnAb evolution is through a sequential immunization protocol, wherein each new immunization optimally increases the pressure on the immune system to target conserved antigenic sites, thus conferring breadth. We describe the mechanisms underlying why sequentially driving the immune system increasingly further from steady state, in an optimal fashion, is effective. The optimal protocol allows many evolving B cells to become bnAbs via diverse evolutionary paths.

Biological organisms experience constantly changing environments, from sudden changes in physiology brought about by feeding, to the regular rising and setting of the Sun, to ecological changes over evolutionary timescales. Living organisms have evolved to thrive in this changing world but the general principles by which organisms shape and are shaped by time varying environments remain elusive. Our understanding is particularly poor in the intermediate regime with no separation of timescales, where the environment changes on the same timescale as the physiological or evolutionary response. Experiments to systematically characterize the response to dynamic environments are challenging since such environments are inherently high dimensional. This roadmap deals with the unique role played by time varying environments in biological phenomena across scales, from physiology to evolution, seeking to emphasize the commonalities and the challenges faced in this emerging area of research.

Solid-binding peptides (SBPs) recognizing inorganic and synthetic interfaces have enabled a broad range of materials science applications and hold promise as adhesive or morphogenetic control units that can be genetically encoded within desirable or designed protein frameworks. To date, the underlying relationships governing both SBP-surface and SBP-SBP interactions and how they give rise to different adsorption mechanisms remain unclear. Here, we combine protein engineering, surface plasmon resonance characterization, and molecular dynamics (MD) simulations initiated from Rosetta predictions to gain insights on the interplay of amino acid composition, structure, self-association, and adhesion modality in a panel of variants of the Car9 silica-binding peptide (DSARGFKKPGKR) fused to the C-terminus of superfolder green fluorescent protein (sfGFP). Analysis of kinetics, energetics, and MD-predicted structures shows that the high-affinity binding of Car9 to the silanol-rich surface of silica is dominated by electrostatic contributions and a spectrum of several persistent interactions that, along with a high surface population of bound molecules, promote cooperative interactions between neighboring SBPs and higher order structure formation. Transition from cooperative to Langmuir adhesion in sfGFP-Car9 variants occurs in concert with a reduction of stable surface interactions and self-association, as confirmed by atomic force microscopy imaging of proteins exhibiting the two different binding behaviors. We discuss the implications of these results for the de novo design of SBP-surface binding systems.

Douglas-fir forestry residues are a potential feedstock for saccharification-based biofuels, and condensed tannins are expected to make up ∼3% of the dry mass of this feedstock. Condensed tannins are well-known for their ability to interact with proteins and can bind and inhibit cellulase enzymes used in saccharification. In this study, we use molecular docking and classical molecular dynamics simulations to investigate how a characterized condensed tannin from Douglas-fir bark binds to the exoglucanase Cel7A from Trichoderma reesei. Through looking at the "occupancy" and "residency" of specific amino acid residue-tannin interactions, we find that the binding sites are characterized by many simultaneous tannin-enzyme interactions with the strongest occurring on the catalytic module as opposed to the carbohydrate-binding module. The simulations indicate that tannin inhibition can result from binding at or near the catalytic tunnel's entrance and exit. The analyzed tannin further prefers to bind to loops around the catalytic region and has affinity for aromatic and charged amino acid residues. These insights provide direction for the rational design of tannin-resistant cellulases.

Many proteins exhibit strong binding affinities to surfaces, with binding energies much greater than thermal fluctuations. When modelling these protein-surface systems with classical molecular dynamics (MD) simulations, the large forces that exist at the protein/surface interface generally confine the system to a single free energy minimum. Exploring the full conformational space of the protein, especially finding other stable structures, becomes prohibitively expensive. Coupling MD simulations with metadynamics (enhanced sampling) has fast become a common method for sampling the adsorption of such proteins. In this paper, we compare three different flavors of metadynamics, specifically well-tempered, parallel-bias, and parallel-tempering in the well-tempered ensemble, to exhaustively sample the conformational surface-binding landscape of model peptide GGKGG. We investigate the effect of mobile ions and ion charge, as well as the choice of collective variable (CV), on the binding free energy of the peptide. We make the case for explicitly biasing ions to sample the true binding free energy of biomolecules when the ion concentration is high and the binding free energies of the solute and ions are similar. We also make the case for choosing CVs that apply bias to all atoms of the solute to speed up calculations and obtain the maximum possible amount of information about the system.

Ionic liquids (ILs) are well known to have significant impacts on the structure and dynamics of proteins and enzymes; however, there is still much to be done to understand the molecular scale mechanisms of these processes. Using classical molecular dynamics (MD) and metadynamics simulations, we have studied how multiple IL solutions change the structure and behavior of two short, model, leucine-lysine (LK) peptides at a vacuum/solvent interface. These peptides - LKα14 and LKβ15 - have been researched extensively through both experiments and simulations in pure water, but have not yet been examined when exposed to ILs. In our study, we found that the two 50% 1-butyl-3-methylimidazolium ([BMIM])-based solutions we tested had a substantial effect on both the interfacial adsorption affinity and secondary structure of our peptides when compared to water. Metadynamics simulations revealed a nearly 40 kJ/mol difference in interfacial adsorption free energy for the same peptide when solvated in water versus in IL solution. Additionally, we saw IL solutions prevent LKβ15 from forming extended β-sheet structures that were found to form at the vacuum/water interface. In place of these β-sheet structures, we observed the formation of a structure similar to a left-handed helix for lone LKβ15 peptides at the interface. The presence of IL also had a subtle effect on LKα14's strong α-helical character, reducing it for peptides at the interface and increasing it for peptides in bulk solution. This work provides several new testable hypotheses about IL action on biomolecular structure that can be studied in detail using experimental surface-specific analysis techniques such as sum frequency generation (SFG) spectroscopy.

Molecular recognition between peptides and metal oxide surfaces is a fundamental process in biomineralization, self-assembly, and biocompatibility. Yet, the underlying driving forces and dominant mechanisms remain unclear, bringing obstacles to understand and control this process. To elucidate the mechanism of peptide/surface recognition, specifically the role of serine phosphorylation, we employed molecular dynamics simulation and metadynamics-enhanced sampling to study five artificial peptides, DDD, DSS, DpSpS, DpSpSGKK, and DpSKGpSK, interacting with two surfaces: rutile TiO2 and quartz SiO2. On both surfaces, we observe that phosphorylation increases the binding energy. However, the interfacial peptide conformation reveals a distinct binding mechanism on each surface. We also study the impact of peptide sequence to binding free energy and interfacial conformation on both surfaces, specifically the impact on the behavior of phosphorylated serine. Finally, the results are discussed in context of prior studies investigating the role of serine phosphorylation in peptide binding to silica.

Thermodynamic analyses can provide key insights into the origins of protein self-assembly on surfaces, protein function, and protein stability. However, obtaining quantitative measurements of thermodynamic observables from unbiased classical simulations of peptide or protein adsorption is challenging because of sampling limitations brought on by strong biomolecule/surface binding forces as well as time scale limitations. We used the parallel tempering metadynamics in the well-tempered ensemble (PTMetaD-WTE) enhanced sampling method to study the adsorption behavior and thermodynamics of several explicitly solvated model peptide adsorption systems, providing new molecular-level insight into the biomolecule adsorption process. Specifically studied were peptides LKα14 and LKβ15 and trpcage miniprotein adsorbing onto a charged, hydrophilic self-assembled monolayer surface functionalized with a carboxylic acid/carboxylate headgroup and a neutral, hydrophobic methyl-terminated self-assembled monolayer surface. Binding free energies were calculated as a function of temperature for each system and decomposed into their respective energetic and entropic contributions. We investigated how specific interfacial features such as peptide/surface electrostatic interactions and surface-bound ion content affect the thermodynamic landscape of adsorption and lead to differences in surface-bound conformations of the peptides. Results show that upon adsorption to the charged surface, configurational entropy gains of the released solvent molecules dominate the configurational entropy losses of the bound peptide. This behavior leads to an apparent increase in overall system entropy upon binding and therefore to the surprising and seemingly nonphysical result of an apparent increased binding free energy at elevated temperatures. Opposite effects and conclusions are found for the neutral surface. Additional simulations demonstrate that by adjusting the ionic strength of the solution, results that show the expected physical behavior, i.e., peptide binding strength that decreases with increasing temperature or is independent of temperature altogether, can be recovered on the charged surface. On the basis of this analysis, an overall free energy for the entire thermodynamic cycle for peptide adsorption on charged surfaces is constructed and validated with independent simulations.

Lytic polysaccharide monooxygenases (LPMOs) are a newly discovered family of enzymes proposed to work synergistically with cellulases and aid in the decomposition of cellulose for the creation of environmentally friendly fuels and chemicals. To our knowledge, evaluation of the stability of LPMOs in ionic liquid (IL) solvents at relevant biomass processing conditions has not been explored. Herein, molecular dynamics simulations of ScLPMO10B and ScLPMO10C in three ILs at 10 and 20 wt% in water and in pure water have been performed. Enzyme stability was predicted to be high on the basis of structural and dynamic analyses we performed. We used the simulations to identify key areas that deviate from the crystal structures as a starting place for surface charge modifications to increase stability in ILs. Results show that, in general, both enzymes have a high degree of stability across the range of IL solutions tested. For each enzyme, two regions were identified that showed notable deviations from the crystal structure. In addition to providing a basis for future rational design efforts, this work represents a first step toward engineering LPMOs to function efficiently in enzyme cocktails for use in industrial biomass processing applications with ILs.

We have applied molecular dynamics to calculate thermodynamic and transport properties of a set of 19 room-temperature ionic liquids. Since accurately simulating the thermophysical properties of solvents strongly depends upon the force field of choice, we tested the accuracy of the general AMBER force field, without refinement, for the case of ionic liquids. Electrostatic point charges were developed using ab initio calculations and a charge scaling factor of 0.8 to more accurately predict dynamic properties. The density, heat capacity, molar enthalpy of vaporization, self-diffusivity, and shear viscosity of the ionic liquids were computed and compared to experimentally available data, and good agreement across a wide range of cation and anion types was observed. Results show that, for a wide range of ionic liquids, the general AMBER force field, with no tuning of parameters, can reproduce a variety of thermodynamic and transport properties with similar accuracy to that of other published, often IL-specific, force fields.

The ability of ionic liquids (ILs) to solubilize cellulose has sparked interest in their use for enzymatic biomass processing. However, this potential is yet to be realized, primarily because ILs inactivate requisite cellulases by mechanisms that are yet to be identified. We used a combination of enzymology, circular dichroism (CD), nuclear magnetic resonance (NMR), and molecular dynamics (MD) methods to investigate the molecular basis for the inactivation of the endocellulase 1 (E1) from Acidothermus cellulolyticus by the imidazolium IL 1-butyl-3-methylimidazolium chloride ([BMIM][Cl]). Enzymatic studies revealed that [BMIM][Cl] inactivates E1 in a biphasic manner that involves rapid, reversible inhibition, followed by slow, irreversible deactivation. Backbone NMR signals of the 40.5 kDa E1 were assigned by triple resonance NMR methods, enabling monitoring of residue-specific perturbations. 1H-15N NMR titration experiments revealed that [BMIM][Cl] binds reversibly to the E1 active site, indicating that reversible deactivation is due to competitive inhibition of substrate binding. Prolonged incubation with [BMIM][Cl] led to substantial global changes in the 1H-15N heteronuclear single quantum coherence NMR and CD spectra of E1 indicative of protein denaturation. Notably, weak interactions between [BMIM][Cl] and residues at the termini of several helices were also observed, which, together with MD simulations, suggest that E1 denaturation is promoted by [BMIM][Cl]-induced destabilization of helix capping structures. In addition to identifying determinants of E1 inactivation, our findings establish a molecular framework for engineering cellulases with improved IL compatibility.

Biomimetic silica formation, a process that is largely driven by proteins, has garnered considerable interest in recent years due to its role in the development of new biotechnologies. However, much remains unknown of the molecular-scale mechanisms underlying the binding of proteins to biomineral surfaces such as silica, or even of the key residue-level interactions between such proteins and surfaces. In this study, we employ molecular dynamics (MD) simulations to study the binding of R5-a 19-residue segment of a native silaffin peptide used for in vitro silica formation-to a silica surface. The metadynamics enhanced sampling method is used to converge the binding behavior of R5 on silica at both neutral (pH 7.5) and acidic (pH 5) conditions. The results show fundamental differences in the mechanism of binding between the two cases, providing unique insight into the pH-dependent ability of R5 and native silaffin to precipitate silica. We also study the effect of phosphorylation of serine residues in R5 on both the binding free energy to silica and the interfacial conformation of the peptide. Results indicate that phosphorylation drastically decreases the binding free energy and changes the structure of silica-adsorbed R5 through the introduction of charge and steric repulsion. New mechanistic insights from this work could inform rational design of new biomaterials and biotechnologies.

For many different frameworks, the structure, function, and dynamics of an enzyme is largely determined by the nature of its interactions with the surrounding host environment, thus a molecular level understanding of enzyme/host interactions is essential to the design of new processes and applications. Ionic liquid (IL) solvents are a popular class of solvents in which to study enzyme behavior, yet it is still not possible to predict how a given enzyme will behave in a given IL solvent. Furthermore, a dearth of experimental data with which to evaluate simulation force fields has prevented the full integration of experimental and computational techniques to gain a complete picture of enzyme/IL interactions. Utilizing recently published crystallographic data of an enzyme in complex with an IL, this study aims to validate the use of current molecular force fields for studying enzyme/IL interactions, and to provide new mechanistic insight into enzyme stabilization in IL solvents. Classical molecular dynamics (MD) simulations have been performed on both the folded and unfolded state of Bacillus subtilis lipase A and a quadruple-mutant version of lipase A, in solutions of aqueous 1-butyl-3-methylimidazolium chloride. Results show classical MD simulations can predict the preferred surface binding locations of IL cations as well as reductions in IL anion binding to mutated surface residues with high accuracy. The results also point to a mechanistic difference between IL binding to the folded and unfolded state of an enzyme, which we call the "counter-ion effect". These findings could have important implications for future rational design efforts to stabilize enzymes in non-conventional media.

Due to their flexible chemical functionality and simple formulation, self-assembled monolayer (SAM) surfaces have become an ideal choice for a multitude of wide-ranging applications. However, a major issue in the preparation of SAM surfaces is naturally occurring defects that manifest in a number of different ways, including depressions in the underlying gold substrate that cause surface roughness or through incorrect self-assembly of the chains that causes domain boundary effects. Molecular simulations can provide valuable insight into the origins of these defects and the effect they have on biological and other processes. Molecular dynamics (MD) simulations have been performed on a SAM surface with a carboxylic acid/carboxylate terminal functionality and induced with two types of experimentally observed defects. The enhanced sampling method PTMetaD-WTE has been used to model the adsorption of LKα14 onto the two types of defective SAM surfaces and onto a control SAM surface with no defective chains. An advanced clustering algorithm has been used to elucidate the effect of the surface defects on the conformations of the adsorbed peptide. Results show significant structural differences arise as a result of the defects. Specifically, both types of defects lead to a near-complete loss of secondary structure of the adsorbed peptide as compared to the control simulation, in which LKα14 adopts a perfect helical structure at the SAM/water interface. On the surface with domain boundary effects, extended conformations of the peptide are stabilized, whereas on the SAM with surface roughness (i.e., chains of various lengths), random coil conformations dominate the ensemble of surface-bound structures.

Enzymes play a critical role in many applications in biology and medicine as potential therapeutics. One specific area of interest is enzyme encapsulation in polymer nanostructures, which have applications in drug delivery and catalysis. A detailed understanding of the mechanisms governing protein/polymer interactions is crucial for optimizing the performance of these complex systems for different applications. Using a combined computational and experimental approach, this study aims to quantify the relative importance of molecular and mesoscale driving forces to protein release from polymeric nanoparticles. Classical molecular dynamics (MD) simulations have been performed on bovine serum albumin (BSA) in aqueous solutions with oligomeric surrogates of poly(lactic-co-glycolic acid) copolymer, poly(styrene)-poly(lactic acid) copolymer, and poly(lactic acid). The simulated strength and location of polymer surrogate binding to the surface of BSA have been compared to experimental BSA release rates from nanoparticles formulated with these same polymers. Results indicate that the self-interaction tendencies of the polymer surrogates and other macroscale properties may play governing roles in protein release. Additional MD simulations of BSA in solution with poly(styrene)-acrylate copolymer reveal the possibility of enhanced control over the enzyme encapsulation process by tuning polymer self-interaction. Last, the authors find consistent protein surface binding preferences across simulations performed with polymer surrogates of varying lengths, demonstrating that protein/polymer interactions can be understood in part by studying the interactions and affinity of proteins with small polymer surrogates in solution.

The conversion of biomass into green fuels and chemicals is of great societal interest. Engineers have been designing new cellulase enzymes for the breakdown of otherwise insoluble cellulose materials. A barrier to the rational design of new enzymes has been our lack of a molecular picture of how cellulase binding occurs. A critical factor is the attachment via the enzyme's carbohydrate binding module (CBM). To elucidate the structural and mechanistic details of cellulase adsorption, we have combined experimental data from sum frequency generation spectroscopy with molecular dynamics simulations to probe the equilibrium structure and surface alignment of a 14-residue peptide mimicking the CBM. The data show that binding is driven by hydrogen bonding and that tyrosine side chains within the CBM align the cellulase with the registry of the cellulose surface. Such an alignment is favorable for the translocation and effective cellulose breakdown and is therefore likely an important parameter for the design of novel enzymes.