Thermal stability of single-domain antibodies estimated by molecular dynamics simulations

Molecular Dynamics Simulation of an Iron(III) Binding Site on the Fc Domain of IgG1 Relevant for Visible Light-Induced Protein Fragmentation

Molecular dynamics simulations were employed to investigate the interaction between Fe(III) and an iron-binding site composed of THR259, ASP252, and GLU261 on the Fc domain of an IgG1. The goal was to provide microscopic mechanistic information for the photochemical, iron-dependent site-specific oxidative fragmentation of IgG1 at THR259 reported in our previous paper. The distance between Fe(III) and residues of interest as well as the binding pocket size was examined for both protonated and deprotonated THR259. The Fe(III) binding free energy (ΔG) was estimated by using an umbrella sampling approach. The pKa shift of the THR259 hydroxyl group caused by the presence of nearby Fe(III) was estimated based on a thermodynamic cycle. The simulation results show that Fe(III) resides inside the proposed binding pocket and profoundly changes the pocket configuration. The ΔG values indicate that the pocket possesses a strong binding affinity for Fe(III). Furthermore, Fe(III) profoundly lowers the pKa value of the THR259 hydroxyl group by 5.4 pKa units.

Binding Mechanism of Riboswitch to Natural Ligand Elucidated by McMD-Based Dynamic Docking Simulations

Flavin mononucleotide riboswitches are common among many pathogenic bacteria and are therefore considered to be an attractive target for antibiotics development. The riboswitch binds riboflavin (RBF, also known as vitamin B2), and although an experimental structure of their complex has been solved with the ligand bound deep inside the RNA molecule in a seemingly unreachable state, the binding mechanism between these molecules is not yet known. We have therefore used our Multicanonical Molecular Dynamics (McMD)-based dynamic docking protocol to analyze their binding mechanism by simulating the binding process between the riboswitch aptamer domain and the RBF, starting from the apo state of the riboswitch. Here, the refinement stage was crucial to identify the native binding configuration, as several other binding configurations were also found by McMD-based docking simulations. RBF initially binds the interface between P4 and P6 including U61 and G62, which forms a gateway where the ligand lingers until this gateway opens sufficiently to allow the ligand to pass through and slip into the hidden binding site including A48, A49, and A85.

Elucidation of binding mechanism, affinity, and complex structure between mWT1 tumor-associated antigen peptide and HLA-A*24:02

We have applied our advanced computational and experimental methodologies to investigate the complex structure and binding mechanism of a modified Wilms' Tumor 1 (mWT1) protein epitope to the understudied Asian-dominant allele HLA-A*24:02 (HLA-A24) in aqueous solution. We have applied our developed multicanonical molecular dynamics (McMD)-based dynamic docking method to analyze the binding pathway and mechanism, which we verified by comparing the highest probability structures from simulation with our experimentally solved x-ray crystal structure. Subsequent path sampling MD simulations elucidated the atomic details of the binding process and indicated that first an encounter complex is formed between the N-terminal's positive charge of the 9-residue mWT1 fragment peptide and a cluster of negative residues on the surface of HLA-A24, with the major histocompatibility complex (MHC) molecule preferring a predominantly closed conformation. The peptide first binds to this closed MHC conformation, forming an encounter complex, after which the binding site opens due to increased entropy of the binding site, allowing the peptide to bind to form the native complex structure. Further sequence and structure analyses also suggest that although the peptide loading complex would help with stabilizing the MHC molecule, the binding depends in a large part on the intrinsic affinity between the MHC molecule and the antigen peptide. Finally, our computational tools and analyses can be of great benefit to study the binding mechanism of different MHC types to their antigens, where it could also be useful in the development of higher affinity variant peptides and for personalized medicine.

Effect of Disulfide Bonds on the Thermal Stability of Pediocin: In-silico Screening Using Molecular Dynamics Simulation

The thermal stability properties of pediocin at 310, 313, 323, 333, 343, and 348 K (37, 40, 50, 60, 70, and 75°C, respectively) are reported in this study. A theoretical approach, such as the molecular dynamics method, was used to analyze the structure. Molecular dynamics simulation confirms the stability of molecules with Cys. Furthermore, this study reveals that Cys residues play an essential role in structure stability at high temperatures. To understand the structural basis for the stability of pediocin, a detailed in-silico analysis using molecular dynamics simulations to explore the thermal stability profiles of the compounds was conducted. This study shows that thermal effects fundamentally alter the functionally crucial secondary structure of pediocin. However, as previously reported, pediocin's activity was strictly conserved due to the disulfide bond between Cys residues. These findings reveal, for the first time, the dominant factor behind the thermodynamic stability of pediocin.

Theoretical charge plots as a tool for targeted and accelerated ion exchange chromatography method development of NANOBODYⓇ molecules

NANOBODYⓇ molecules are an innovative class of biotherapeutics based on heavy chain only VHH immunoglobulins. Much like canonical antibodies, they are prone to the formation of charge variants and other post-translational modifications, which can potentially impact their critical quality attributes. Therefore, establishing high-resolution product-specific methods, such as IEX chromatography, is essential for evaluating the purity of these molecules. However, due to the lower surface charge of NANOBODYⓇ molecules, their charge-based elution behavior can differ considerably from that of classical antibodies, resulting in a more extensive method development set-up for these smaller molecules. Using an initial pH screening gradient based on theoretical protein charge plots, we investigated the IEX retention behavior of eight NANOBODYⓇ molecules with a wide range of pI values (pI 5.0 to 10.0). Our findings reveal that the charge-based chromatographic behavior of NANOBODYⓇ molecules cannot be solely attributed to the isoelectric point (pI) of the protein. Rather, a molecule-specific charge threshold was identified as a critical parameter for NANOBODYⓇ molecule retention. Furthermore, the protein charge plot also showed that NANOBODYⓇ molecule elution can be characterized by a charge plateau where the net charge of the protein remains constant over a certain pH range (∼ pH 5.5 to pH 8.0), further challenging the paradigm that elution pH and pI are fixed values. The application of this theoretical approach using protein charge plots to define NANOBODYⓇ molecule charge threshold and charge plateau parameters, can reduce overall IEX method development turnaround time by at least 2-fold.

Multiway Analysis Reveals Hydrophobicity as the Sole Determinant of Dynamic Peptide-Acetonitrile-Water Association Behavior

Multiway analysis, a class of techniques developed for the purpose of studying multi-dimensional multivariate data, has been applied to study the dynamical structure of the first solvation layer of Ace-Gly-X-Gly-Nme peptides (where X is any amino acid) perturbed with the increase in concentrations of acetonitrile. Separate MD simulations of each peptide were carried out in five different concentrations of acetonitrile. Association of peptide, water, and acetonitrile atoms was quantified in terms of the relative abundance of Delaunay tetrahedra whose vertices could be centered on either the peptide, acetonitrile, or water atoms. A three-way data set comprising nine types of Delaunay tetrahedra in the first dimension, five concentrations of acetonitrile in the second dimension, and 26 different peptides in the third dimension was subjected to two different multiway methods viz., the constrained PARAFAC and the unconstrained Tucker3 analysis. The results unequivocally show that the dynamic peptide-acetonitrile-water association behavior could be solely explained by the hydrophobicity of the central amino acid. The study also demonstrates the utility of multiway analysis for the integration and interpretation of large number of separate MD simulations.

Molecular Dynamics-Based Design and Biophysical Evaluation of Thermostable Single-Chain Fv Antibody Mutants Derived from Pharmaceutical Antibodies

Antibody drugs are denatured under physical stress, e.g., friction, heat, and freezing, which triggers formation of aggregates and resultant allergic reactions. Design of a stable antibody is thus critical for the development of antibody drugs. Here, we obtained a thermostable single-chain Fv (scFv) antibody clone by rigidifying the flexible region. We first conducted a short molecular dynamics (MD) simulation (3 runs of 50 ns) to search for weak spots in the scFv antibody, i.e., flexible regions located outside the CDR (complementarity determining region) and the interface between the heavy-chain and light-chain variable regions. We then designed a thermostable mutant and evaluated it by means of a short MD simulation (3 runs of 50 ns) based on reductions in the root-mean-square fluctuation (RMSF) values and formation of new hydrophilic interactions around the weak spot. Finally, we designed the VL-R66G mutant by applying our strategy to scFv derived from trastuzumab. Trastuzumab scFv variants were prepared by using an Escherichia coli expression system, and the melting temperature-measured as a thermostability index-was 5 °C higher than that of the wild-type trastuzumab scFv, while the antigen-binding affinity was unchanged. Our strategy required few computational resources, and would be applicable to antibody drug discovery.

Isothermal Compressibility Perturbation as a Protein Design Principle for T1 Lipase Stability-Activity Trade-Off Counteracting

Given the widely existing stability-activity trade-off in enzyme evolution, it is still a goal to obtain enzymes embracing both high activity and stability. Herein, we employed an isothermal compressibility (β_T) perturbation engineering (ICPE) strategy to comprehensively understand the stability-activity seesaw-like mechanism. The stability and activity of mutants derived from ICPE uncovered a high Pearson correlation (r = 0.93) in a prototypical enzyme T1 lipase. The best variant A186L/L188M/A190Y exhibited a high T_m value up to 78.70 °C, catalytic activity of 474.04 U/mg, and a 73.33% increase in dimethyl sulfoxide resistance compared to the wild type, one of the highest comprehensive performances reported to date. The elastic activation mechanism mediated by conformational change with a Δβ_T range of -6.81 × 10^-6 to -1.90 × 10^-6 bar^-1 may account for the balancing of stability and activity to achieve better performing enzymes. The ICPE strategy deepens our understanding of stability-activity trade-off and boosts its applications in enzyme engineering.

Mutual induced-fit mechanism drives binding between intrinsically disordered Bim and cryptic binding site of Bcl-xL

The intrinsically disordered region (IDR) of Bim binds to the flexible cryptic site of Bcl-xL, a pro-survival protein involved in cancer progression that plays an important role in initiating apoptosis. However, their binding mechanism has not yet been elucidated. We have applied our dynamic docking protocol, which correctly reproduced both the IDR properties of Bim and the native bound configuration, as well as suggesting other stable/meta-stable binding configurations and revealed the binding pathway. Although the cryptic site of Bcl-xL is predominantly in a closed conformation, initial binding of Bim in an encounter configuration leads to mutual induced-fit binding, where both molecules adapt to each other; Bcl-xL transitions to an open state as Bim folds from a disordered to an α-helical conformation while the two molecules bind each other. Finally, our data provides new avenues to develop novel drugs by targeting newly discovered stable conformations of Bcl-xL.

Increasing the melting temperature of VHH with the in silico free energy score

VHH, the antigen-binding fragment of a heavy chain-only antibody, is a useful component of antibody-based therapeutics. Thermal stability, represented by the melting temperature (Tm), is one of the key factors affecting the developability of antibody-based therapeutics. In this study, we examined whether the in silico free energy score dStability can be used to design mutants with improved Tm compared to the anti-lysozyme VHH, D3-L11. After verifying that exhaustive mutagenesis was inefficient for improving Tm, we performed a two-round rational approach that combined dStability calculations with a small number of experiments. This method improved the Tm by more than 5 °C in several single mutants including A79I. It reduced the affinity for the antigen by less than 1.6-fold. We speculate that stabilization of A79I required exquisite compatibility among neighboring residues to fill in the internal cavity in the protein. Given that we identified only one mutation that could simultaneously improve Tm and almost maintain affinity, we concluded that achieving both is extremely difficult, even with single mutations that are not located in the paratope. Therefore, we recommend using a variety of approaches when trying to achieve such a feat. Our method will be a useful complementary approach to other existing methods.

General Trends of the Camelidae Antibody VHHs Domain Dynamics

Conformational flexibility plays an essential role in antibodies' functional and structural stability. They facilitate and determine the strength of antigen-antibody interactions. Camelidae express an interesting subtype of single-chain antibody, named Heavy Chain only Antibody. They have only one N-terminal Variable domain (V_HH) per chain, composed of Frameworks (FRs) and Complementarity Determining regions (CDRs) like their VH and VL counterparts in IgG. Even when expressed independently, V_HH domains display excellent solubility and (thermo)stability, which helps them to retain their impressive interaction capabilities. Sequence and structural features of V_HH domains contributing to these abilities have already been studied compared to classical antibodies. To have the broadest view and understand the changes in dynamics of these macromolecules, large-scale molecular dynamics simulations for a large number of non-redundant V_HH structures have been performed for the first time. This analysis reveals the most prevalent movements in these domains. It reveals the four main classes of V_HHs dynamics. Diverse local changes were observed in CDRs with various intensities. Similarly, different types of constraints were observed in CDRs, while FRs close to CDRs were sometimes primarily impacted. This study sheds light on the changes in flexibility in different regions of V_HH that may impact their in silico design.

Application Progress of the Single Domain Antibody in Medicine

The camelid-derived single chain antibody (sdAb), also termed VHH or nanobody, is a unique, functional heavy (H)-chain antibody (HCAb). In contrast to conventional antibodies, sdAb is a unique antibody fragment consisting of a heavy-chain variable domain. It lacks light chains and a first constant domain (CH1). With a small molecular weight of only 12~15 kDa, sdAb has a similar antigen-binding affinity to conventional Abs but a higher solubility, which exerts unique advantages for the recognition and binding of functional, versatile, target-specific antigen fragments. In recent decades, with their unique structural and functional features, nanobodies have been considered promising agents and alternatives to traditional monoclonal antibodies. As a new generation of nano-biological tools, natural and synthetic nanobodies have been used in many fields of biomedicine, including biomolecular materials, biological research, medical diagnosis and immune therapies. This article briefly overviews the biomolecular structure, biochemical properties, immune acquisition and phage library construction of nanobodies and comprehensively reviews their applications in medical research. It is expected that this review will provide a reference for the further exploration and unveiling of nanobody properties and function, as well as a bright future for the development of drugs and therapeutic methods based on nanobodies.

Thermal Stability Estimation of Single Domain Antibodies Using Molecular Dynamics Simulations

In this chapter, we describe a protocol to estimate the thermal stability of single domain antibodies (sdAbs) using molecular dynamics (MD) simulations. This method measures the Q-value, the fraction of the native contacts, along the trajectory of high-temperature MD simulations starting from the experimental X-ray structure. We show a good correlation between the Q-value and the experimental melting temperature (T_m) in seven sdAbs. Assessing the Q-value on a per-residue level enabled us to identify residues that contribute to the instability and thus demonstrate which residues could be mutated to improve the stability and have later been validated by experiments. Our protocol extends beyond the application on sdAbs, as it is also suitable for other proteins and to determine the interfacial stability between protein and ligand.

Assessing developability early in the discovery process for novel biologics

Beyond potency, a good developability profile is a key attribute of a biological drug. Selecting and screening for such attributes early in the drug development process can save resources and avoid costly late-stage failures. Here, we review some of the most important developability properties that can be assessed early on for biologics. These include the influence of the source of the biologic, its biophysical and pharmacokinetic properties, and how well it can be expressed recombinantly. We furthermore present in silico, in vitro, and in vivo methods and techniques that can be exploited at different stages of the discovery process to identify molecules with liabilities and thereby facilitate the selection of the most optimal drug leads. Finally, we reflect on the most relevant developability parameters for injectable versus orally delivered biologics and provide an outlook toward what general trends are expected to rise in the development of biologics.

Construction of scFv Antibodies against the Outer Loops of the Microsporidium Nosema bombycis ATP/ADP-Transporters and Selection of the Fragment Efficiently Inhibiting Parasite Growth

Traditional sanitation practices remain the main strategy for controlling Bombyx mori infections caused by microsporidia Nosema bombycis. This actualizes the development of new approaches to increase the silkworm resistance to this parasite. Here, we constructed a mouse scFv library against the outer loops of N. bombycis ATP/ADP carriers and selected nine scFv fragments to the transporter, highly expressed in the early stages of the parasite intracellular growth. Expression of selected scFv genes in Sf9 cells, their infection with different ratios of microsporidia spores per insect cell, qPCR analysis of N. bombycis PTP2 and Spodoptera frugiperda COXI transcripts in 100 infected cultures made it possible to select the scFv fragment most effectively inhibiting the parasite growth. Western blot analysis of 42 infected cultures with Abs against the parasite β-tubulin confirmed its inhibitory efficiency. Since the VL part of this scFv fragment was identified as a human IgG domain retained from the pSEX81 phagemid during library construction, its VH sequence should be a key antigen-recognizing determinant. Along with the further selection of new recombinant Abs, this suggests the searching for its natural mouse VL domain or "camelization" of the VH fragment by introducing cysteine and hydrophilic residues, as well as the randomization of its CDRs.

Advancing the field of computational drug design using multicanonical molecular dynamics-based dynamic docking

Multicanonical molecular dynamics (McMD)-based dynamic docking is a powerful tool to not only predict the native binding configuration between two flexible molecules, but it can also be used to accurately simulate the binding/unbinding pathway. Furthermore, it can also predict alternative binding sites, including allosteric ones, by employing an exhaustive sampling approach. Since McMD-based dynamic docking accurately samples binding/unbinding events, it can thus be used to determine the molecular mechanism of binding between two molecules. We developed the McMD-based dynamic docking methodology based on the powerful, but woefully underutilized McMD algorithm, combined with a toolset to perform the docking and to analyze the results. Here, we showcase three of our recent works, where we have applied McMD-based dynamic docking to advance the field of computational drug design. In the first case, we applied our method to perform an exhaustive search between Hsp90 and one of its inhibitors to successfully predict the native binding configuration in its binding site, as we refined our analysis methods. For our second case, we performed an exhaustive search of two medium-sized ligands and Bcl-xL, which has a cryptic binding site that differs greatly between the apo and holo structures. Finally, we performed a dynamic docking simulation between a membrane-embedded GPCR molecule and a high affinity ligand that binds deep within its receptor's pocket. These advanced simulations showcase the power that the McMD-based dynamic docking method has, and provide a glimpse of the potential our methodology has to unravel and solve the medical and biophysical issues in the modern world.

Supplementary Information

The online version contains supplementary material available at 10.1007/s12551-022-01010-z.

Protein-Glutaminase Engineering Based on Isothermal Compressibility Perturbation for Enhanced Modification of Soy Protein Isolate

Protein-glutaminase plays a significant role in future food (e.g., plant-based meat) processing as a result of its ability to improve the solubility, foaming, emulsifying, and gel properties of plant-based proteins. However, poor stability, activity, high pressure, and high shear processing environments hinder its application. Therefore, we developed an application-oriented method isothermal compressibility perturbation engineering strategy to improve enzyme performance by simulating the high-pressure environment. The best variant with remarkable improvement in specific activity and half-time, N16M/Q21H/T113E, exhibited a 4.28-fold increase compared to the wild type in specific activity (117.18 units/mg) and a 1.23-fold increase in half-time (472 min), as one of the highest comprehensive performances ever reported. The solubility of the soy protein isolate deaminated by the N16M/Q21H/T113E mutant was 55.74% higher than that deaminated by the wild type, with a tinier particle size and coarser texture. Overall, this strategy has the potential to improve the functional performance of enzymes under complex food processing conditions.

Large-scale application of free energy perturbation calculations for antibody design

Alchemical free energy perturbation (FEP) is a rigorous and powerful technique to calculate the free energy difference between distinct chemical systems. Here we report our implementation of automated large-scale FEP calculations, using the Amber software package, to facilitate antibody design and evaluation. In combination with Hamiltonian replica exchange, our FEP simulations aim to predict the effect of mutations on both the binding affinity and the structural stability. Importantly, we incorporate multiple strategies to faithfully estimate the statistical uncertainties in the FEP results. As a case study, we apply our protocols to systematically evaluate variants of the m396 antibody for their conformational stability and their binding affinity to the spike proteins of SARS-CoV-1 and SARS-CoV-2. By properly adjusting relevant parameters, the particle collapse problems in the FEP simulations are avoided. Furthermore, large statistical errors in a small fraction of the FEP calculations are effectively reduced by extending the sampling, such that acceptable statistical uncertainties are achieved for the vast majority of the cases with a modest total computational cost. Finally, our predicted conformational stability for the m396 variants is qualitatively consistent with the experimentally measured melting temperatures. Our work thus demonstrates the applicability of FEP in computational antibody design.

VHH Structural Modelling Approaches: A Critical Review

V_HH, i.e., VH domains of camelid single-chain antibodies, are very promising therapeutic agents due to their significant physicochemical advantages compared to classical mammalian antibodies. The number of experimentally solved V_HH structures has significantly improved recently, which is of great help, because it offers the ability to directly work on 3D structures to humanise or improve them. Unfortunately, most V_HHs do not have 3D structures. Thus, it is essential to find alternative ways to get structural information. The methods of structure prediction from the primary amino acid sequence appear essential to bypass this limitation. This review presents the most extensive overview of structure prediction methods applied for the 3D modelling of a given V_HH sequence (a total of 21). Besides the historical overview, it aims at showing how model software programs have been shaping the structural predictions of V_HHs. A brief explanation of each methodology is supplied, and pertinent examples of their usage are provided. Finally, we present a structure prediction case study of a recently solved V_HH structure. According to some recent studies and the present analysis, AlphaFold 2 and NanoNet appear to be the best tools to predict a structural model of V_HH from its sequence.

Progress and challenges for the machine learning-based design of fit-for-purpose monoclonal antibodies

Although the therapeutic efficacy and commercial success of monoclonal antibodies (mAbs) are tremendous, the design and discovery of new candidates remain a time and cost-intensive endeavor. In this regard, progress in the generation of data describing antigen binding and developability, computational methodology, and artificial intelligence may pave the way for a new era of in silico on-demand immunotherapeutics design and discovery. Here, we argue that the main necessary machine learning (ML) components for an in silico mAb sequence generator are: understanding of the rules of mAb-antigen binding, capacity to modularly combine mAb design parameters, and algorithms for unconstrained parameter-driven in silico mAb sequence synthesis. We review the current progress toward the realization of these necessary components and discuss the challenges that must be overcome to allow the on-demand ML-based discovery and design of fit-for-purpose mAb therapeutic candidates.

Enhanced Conformational Sampling of Nanobody CDR H3 Loop by Generalized Replica-Exchange with Solute Tempering

The variable domains of heavy-chain antibodies, known as nanobodies, are potential substitutes for IgG antibodies. They have similar affinities to antigens as antibodies, but are more heat resistant. Their small size allows us to exploit computational approaches for structural modeling or design. Here, we investigate the applicability of an enhanced sampling method, a generalized replica-exchange with solute tempering (gREST) for sampling CDR-H3 loop structures of nanobodies. In the conventional replica-exchange methods, temperatures of only a whole system or scaling parameters of a solute molecule are selected for temperature or parameter exchange. In gREST, we can flexibly select a part of a solute molecule and a part of the potential energy terms as a parameter exchange region. We selected the CDR-H3 loop and investigated which potential energy term should be selected for the efficient sampling of the loop structures. We found that the gREST with dihedral terms can explore a global conformational space, but the relaxation to the global equilibrium is slow. On the other hand, gREST with all the potential energy terms can sample the equilibrium distribution, but the structural exploration is slower than with dihedral terms. The lessons learned from this study can be applied to future studies of loop modeling.

N-Terminal-Driven Binding Mechanism of an Antigen Peptide to Human Leukocyte Antigen-A*2402 Elucidated by Multicanonical Molecular Dynamic-Based Dynamic Docking and Path Sampling Simulations

We have applied our advanced multicanonical molecular dynamics (McMD)-based dynamic docking methodology to investigate the binding mechanism of an HIV-1 Nef protein epitope to the Asian-dominant allele human leukocyte antigen (HLA)-A*2402. Even though pMHC complex formation [between a Major histocompatibility complex (MHC) class I molecule, which is encoded by an HLA allele, and an antigen peptide] is one of the fundamental processes of the adaptive human immune response, its binding mechanism has not yet been well studied, partially due to the high allelic variation of HLAs in the population. We have used our developed McMD-based dynamic docking method and have successfully reproduced the native complex structure, which is located near the free energy global minimum. Subsequent path sampling MD simulations elucidated the atomic details of the binding process and indicated that the peptide binding is initially driven by the highly positively charged N-terminus of the peptide that is attracted to the various negatively charged residues on the MHC molecule's surface. Upon nearing the pocket, the second tyrosine residue of the peptide anchors the peptide by strongly binding to the B-site of the MHC molecule via hydrophobic driven interactions, resulting in a very strong bound complex structure. Our methodology can be effectively used to predict the bound complex structures between MHC molecules and their antigens to study their binding mechanism in close detail, which would help with the development of new vaccines against cancers, as well as viral infections such as HIV and COVID-19.

Stress-dependent flexibility of a full-length human monoclonal antibody: Insights from molecular dynamics to support biopharmaceutical development

After several decades of advancements in drug discovery, product development of biopharmaceuticals remains a time- and resource-consuming endeavor. One of the main reasons is associated to the lack of fundamental understanding of conformational dynamics of such biologic entities, and how they respond to various stresses encountered during manufacturing. In this work, we have studied the conformational dynamics of human IgG₁κ b12 monoclonal antibody (mAb) using molecular dynamics simulations. The hundreds of nanoseconds long trajectories reveal that b12 mAb is highly flexible. Its variable domains show greater conformational fluctuations than the constant domains. Additionally, it collapses towards a more globular shape in response to thermal stress, leading to decrease in the total solvent exposed surface area and radius of gyration. This behavior is more pronounced for the deglycosylated b12 mAb, and it appears to correlate with increase in inter-domain contacts between specific regions of the antibody. Conformational fluctuations also cause temporary formation and disruption of hydrophobic and charged patches on the antibody surface, which is particularly important for the prediction of CMC properties during development phases of antibody-based biotherapeutics. The insights gained through these simulations may help the development of biologic drugs, especially with regards to manufacturing processes where antibodies may undergo significant thermal stress.

Accurate Binding Configuration Prediction of a G-Protein-Coupled Receptor to Its Antagonist Using Multicanonical Molecular Dynamics-Based Dynamic Docking

We have performed dynamic docking between a prototypic G-protein-coupled receptor (GPCR) system, the β₂-adrenergic receptor, and its antagonist, alprenolol, using one of the enhanced conformation sampling methods, multicanonical molecular dynamics (McMD), which does not rely on any prior knowledge for the definition of the reaction coordinate. Although we have previously applied our McMD-based dynamic docking protocol to various globular protein systems, its application to GPCR systems would be difficult because of their complicated design, which include a lipid bilayer, and because of the difficulty in sampling the configurational space of a binding site that exists deep inside the GPCR. Our simulations sampled a wide array of ligand-bound and ligand-unbound structures, and we measured 427 binding events during our 48 μs production run. Analysis of the ensemble revealed several stable and meta-stable structures, where the most stable structure at the global free energy minimum matches the experimental one. Additional canonical MD simulations were used for refinement and validation of the structures, revealing that most of the intermediates are sufficiently stable to trap the ligand in these intermediary states and furthermore validated our prediction results. Given the difficulty in reaching the orthosteric binding site, chemical optimization of the compound for the second ranking configuration, which binds near the pocket's entrance, might lead to a high-affinity allosteric inhibitor. Accordingly, we show that the application of our methodology can be used to provide crucial insights for the rational design of drugs that target GPCRs.

Delicate balance among thermal stability, binding affinity, and conformational space explored by single-domain VHH antibodies

The high binding affinities and specificities of antibodies have led to their use as drugs and biosensors. Single-domain V_HH antibodies exhibit high specificity and affinity but have higher stability and solubility than conventional antibodies as they are single-domain proteins. In this work, based on physicochemical measurements and molecular dynamics (MD) simulations, we have gained insight that will facilitate rational design of single-chain V_HH antibodies. We first assessed two homologous V_HH antibodies by differential scanning calorimetry (DSC); one had a high (64.8 °C) and the other a low (58.6 °C) melting temperature. We then generated a series of the variants of the low stability antibody and analyzed their thermal stabilities by DSC and characterized their structures through MD simulations. We found that a single mutation that resulted in 8.2 °C improvement in melting temperature resulted in binding affinity an order of magnitude lower than the parent antibody, likely due to a shift of conformational space explored by the single-chain V_HH antibody. These results suggest that the delicate balance among conformational stability, binding capability, and conformational space explored by antibodies must be considered in design of fully functional single-chain V_HH antibodies.

Direct Immobilization of Engineered Nanobodies on Gold Sensors

Single-domain antibodies, known as nanobodies, have great potential as biorecognition elements for sensors because of their small size, affinity, specificity, and robustness. However, facile and efficient methods of nanobody immobilization are sought that retain their maximum functionality. Herein, we describe the direct immobilization of nanobodies on gold sensors by exploiting a modified cysteine strategically positioned at the C-terminal end of the nanobody. The experimental data based on secondary ion mass spectrometry, circular dichroism, and surface plasmon resonance, taken together with a detailed computational work (molecular dynamics simulations), support the formation of stable and well-oriented nanobody monolayers. Furthermore, the nanobody structure and activity is preserved, wherein the nanobody is immobilized at a high density (approximately 1 nanobody per 13 nm²). The strategy for the spontaneous nanobody self-assembly is simple and effective and possesses exceptional potential to be used in numerous sensing platforms, ranging from clinical diagnosis to environmental monitoring.

Cryptic-site binding mechanism of medium-sized Bcl-xL inhibiting compounds elucidated by McMD-based dynamic docking simulations

We have performed multicanonical molecular dynamics (McMD) based dynamic docking simulations to study and compare the binding mechanism between two medium-sized inhibitors (ABT-737 and WEHI-539) that bind to the cryptic site of Bcl-xL, by exhaustively sampling the conformational and configurational space. Cryptic sites are binding pockets that are transiently formed in the apo state or are induced upon ligand binding. Bcl-xL, a pro-survival protein involved in cancer progression, is known to have a cryptic site, whereby the shape of the pocket depends on which ligand is bound to it. Starting from the apo-structure, we have performed two independent McMD-based dynamic docking simulations for each ligand, and were able to obtain near-native complex structures in both cases. In addition, we have also studied their interactions along their respective binding pathways by using path sampling simulations, which showed that the ligands form stable binding configurations via predominantly hydrophobic interactions. Although the protein started from the apo state, both ligands modulated the pocket in different ways, shifting the conformational preference of the sub-pockets of Bcl-xL. We demonstrate that McMD-based dynamic docking is a powerful tool that can be effectively used to study binding mechanisms involving a cryptic site, where ligand binding requires a large conformational change in the protein to occur.

Dynamic Docking Using Multicanonical Molecular Dynamics: Simulating Complex Formation at the Atomistic Level

Multicanonical molecular dynamics (McMD)-based dynamic docking has been applied to predict the native binding configurations for several protein receptors and their ligands. Due to the enhanced sampling capabilities of McMD, it can exhaustively sample bound and unbound ligand configurations, as well as receptor conformations, and thus enables efficient sampling of the conformational and configurational space, not possible using canonical MD simulations. As McMD samples a wide configurational space, extensive analysis is required to study the diverse ensemble consisting of bound and unbound structures. By projecting the reweighted ensemble onto the first two principal axes obtained via principal component analysis of the multicanonical ensemble, the free energy landscape (FEL) can be obtained. Further analysis produces representative structures positioned at the local minima of the FEL, where these structures are then ranked by their free energy. In this chapter, we describe our dynamic docking methodology, which has successfully reproduced the native binding configuration for small compounds, medium-sized compounds, and peptide molecules.

Molecular Dynamics Simulations in Drug Discovery and Pharmaceutical Development

Molecular dynamics (MD) simulations have become increasingly useful in the modern drug development process. In this review, we give a broad overview of the current application possibilities of MD in drug discovery and pharmaceutical development. Starting from the target validation step of the drug development process, we give several examples of how MD studies can give important insights into the dynamics and function of identified drug targets such as sirtuins, RAS proteins, or intrinsically disordered proteins. The role of MD in antibody design is also reviewed. In the lead discovery and lead optimization phases, MD facilitates the evaluation of the binding energetics and kinetics of the ligand-receptor interactions, therefore guiding the choice of the best candidate molecules for further development. The importance of considering the biological lipid bilayer environment in the MD simulations of membrane proteins is also discussed, using G-protein coupled receptors and ion channels as well as the drug-metabolizing cytochrome P450 enzymes as relevant examples. Lastly, we discuss the emerging role of MD simulations in facilitating the pharmaceutical formulation development of drugs and candidate drugs. Specifically, we look at how MD can be used in studying the crystalline and amorphous solids, the stability of amorphous drug or drug-polymer formulations, and drug solubility. Moreover, since nanoparticle drug formulations are of great interest in the field of drug delivery research, different applications of nano-particle simulations are also briefly summarized using multiple recent studies as examples. In the future, the role of MD simulations in facilitating the drug development process is likely to grow substantially with the increasing computer power and advancements in the development of force fields and enhanced MD methodologies.

In Situ Analysis of Weakly Bound Proteins Reveals Molecular Basis of Soft Corona Formation

Few experimental techniques allow the analysis of the protein corona in situ. As a result, little is known on the effects of nanoparticles on weakly bound proteins that form the soft corona. Despite its biological importance, our understanding of the molecular bases driving its formation is limited. Here, we show that hemoglobin can form either a hard or a soft corona on silica nanoparticles depending on the pH conditions. Using cryoTEM and synchrotron-radiation circular dichroism, we show that nanoparticles alter the structure and the stability of weakly bound proteins in situ. Molecular dynamics simulation identified the structural elements driving protein-nanoparticle interaction. Based on thermodynamic analysis, we show that nanoparticles stabilize partially unfolded protein conformations by enthalpy-driven molecular interactions. We suggest that nanoparticles alter weakly bound proteins by shifting the equilibrium toward the unfolded states at physiological temperature. We show that the classical approach based on nanoparticle separation from the biological medium fails to detect destabilization of weakly bound proteins, and therefore cannot be used to fully predict the biological effects of nanomaterials in situ.

Exhaustive search of the configurational space of heat-shock protein 90 with its inhibitor by multicanonical molecular dynamics based dynamic docking

Multicanonical molecular dynamics based dynamic docking was used to exhaustively search the configurational space of an inhibitor binding to the N-terminal domain of heat-shock protein 90 (Hsp90). The obtained structures at 300 K cover a wide structural ensemble, with the top two clusters ranked by their free energy coinciding with the native binding site. The representative structure of the most stable cluster reproduced the experimental binding configuration, but an interesting conformational change in Hsp90 could be observed. The combined effects of solvation and ligand binding shift the equilibrium from a preferred loop-in conformation in the unbound state to an α-helical one in the bound state for the flexible lid region of Hsp90. Thus, our dynamic docking method is effective at predicting the native binding site while exhaustively sampling a wide configurational space, modulating the protein structure upon binding.

Common mechanism of thermostability in small α- and β-proteins studied by molecular dynamics

Protein thermostability is important to evolution, diseases, and industrial applications. Proteins use diverse molecular strategies to achieve stability at high temperature, yet reducing the entropy of unfolding seems required. We investigated five small α-proteins and five β-proteins with known, distinct structures and thermostability (T_m ) using multi-seed molecular dynamics simulations at 300, 350, and 400 K. The proteins displayed diverse changes in hydrogen bonding, solvent exposure, and secondary structure with no simple relationship to T_m . Our dynamics were in good agreement with experimental B-factors at 300 K and insensitive to force-field choice. Despite the very distinct structures, the native-state (300 + 350 K) free-energy landscapes (FELs) were significantly broader for the two most thermostable proteins and smallest for the three least stable proteins in both the α- and β-group and with both force fields studied independently (tailed t-test, 95% confidence level). Our results suggest that entropic ensembles stabilize proteins at high temperature due to reduced entropy of unfolding, viz., ΔG = ΔH - TΔS. Supporting this mechanism, the most thermostable proteins were also the least kinetically stable, consistent with broader FELs, typified by villin headpiece and confirmed by specific comparison to a mesophilic ortholog of Thermus thermophilus apo-pyrophosphate phosphohydrolase. We propose that molecular strategies of protein thermostabilization, although diverse, tend to converge toward highest possible entropy in the native state consistent with the functional requirements. We speculate that this tendency may explain why many proteins are not optimally structured and why molten-globule states resemble native proteins so much.

The Biological Structure Model Archive (BSM-Arc): an archive for in silico models and simulations

We present the Biological Structure Model Archive (BSM-Arc, https://bsma.pdbj.org), which aims to collect raw data obtained via in silico methods related to structural biology, such as computationally modeled 3D structures and molecular dynamics trajectories. Since BSM-Arc does not enforce a specific data format for the raw data, depositors are free to upload their data without any prior conversion. Besides uploading raw data, BSM-Arc enables depositors to annotate their data with additional explanations and figures. Furthermore, via our WebGL-based molecular viewer Molmil, it is possible to recreate 3D scenes as shown in the corresponding scientific article in an interactive manner. To submit a new entry, depositors require an ORCID ID to login, and to finally publish the data, an accompanying peer-reviewed paper describing the work must be associated with the entry. Submitting their data enables researchers to not only have an external backup but also provide an opportunity to promote their work via an interactive platform and to provide third-party researchers access to their raw data.

Mutual population-shift driven antibody-peptide binding elucidated by molecular dynamics simulations

Antibody based bio-molecular drugs are an exciting, new avenue of drug development as an alternative to the more traditional small chemical compounds. However, the binding mechanism and the effect on the conformational ensembles of a therapeutic antibody to its peptide or protein antigen have not yet been well studied. We have utilized dynamic docking and path sampling simulations based on all-atom molecular dynamics to study the binding mechanism between the antibody solanezumab and the peptide amyloid-β (Aβ). Our docking simulations reproduced the experimental structure and gave us representative binding pathways, from which we accurately estimated the binding free energy. Not only do our results show why solanezumab has an explicit preference to bind to the monomeric form of Aβ, but that upon binding, both molecules are stabilized towards a specific conformation, suggesting that their complex formation follows a novel, mutual population-shift model, where upon binding, both molecules impact the dynamics of their reciprocal one.

Experimental evaluation of single-domain antibodies predicted by molecular dynamics simulations to have elevated thermal stability

Recently Bekker et al. [Bekker G-J et al. Protein Sci. 2019;28:429-438.] described a computational strategy of applying molecular-dynamics simulations to estimate the relative stabilities of single-domain antibodies, and utilized their method to design changes with the aim of increasing the stability of a single-domain antibody with a known crystal structure. The structure from which they generated potentially stabilizing mutations is an anti-cholera toxin single domain antibody selected from a naïve library which has relatively low thermal stability, reflected by a melting point of 48°C. Their work was purely theoretical, so to examine their predictions, we prepared the parental and predicted stabilizing mutant single domain antibodies and examined their thermal stability, ability to refold and affinity. We found that the mutation that improved stability the most (~7°C) was one which changed an amino acid in CDR1 from an asparagine to an aspartic acid. This change unfortunately was also accompanied by a reduction in affinity. Thus, while their modeling did appear to successfully predict stabilizing mutations, introducing mutations in the binding regions is problematic. Of further interest, the mutations selected via their high temperature simulations, did improve refolding, suggesting that they were successful in stabilizing the structure at high temperatures and thereby decrease aggregation. Our result should permit them to reassess and refine their model and may one day lead to a usefulin silico approach to protein stabilization.