Protein thermostability calculations using alchemical free energy simulations

Dimerization of the deaminase domain and locking interactions with Cas9 boost base editing efficiency in ABE8e

CRISPR-based DNA adenine base editors (ABEs) hold remarkable promises to address human genetic diseases caused by point mutations. ABEs were developed by combining CRISPR-Cas9 with a transfer RNA (tRNA) adenosine deaminase enzyme and through directed evolution, conferring the ability to deaminate DNA. However, the molecular mechanisms driving the efficient DNA deamination in the evolved ABEs remain unresolved. Here, extensive molecular simulations and biochemical experiments reveal the biophysical basis behind the astonishing base editing efficiency of ABE8e, the most efficient ABE to date. We demonstrate that the ABE8e's DNA deaminase domain, TadA8e, forms remarkably stable dimers compared to its tRNA-deaminating progenitor and that the strength of TadA dimerization is crucial for DNA deamination. The TadA8e dimer forms robust interactions involving its R98 and R129 residues, the RuvC domain of Cas9 and the DNA. These locking interactions are exclusive to ABE8e, distinguishing it from its predecessor, ABE7.10, and are indispensable to boost DNA deamination. Additionally, we identify three critical residues that drive the evolution of ABE8e toward improved base editing by balancing the enzyme's activity and stability, reinforcing the TadA8e dimer and improving the ABE8e's functionality. These insights offer new directions to engineer superior ABEs, advancing the design of safer precision genome editing tools.

A fully human IgG1 antibody targeting connexin 32 extracellular domain blocks CMTX1 hemichannel dysfunction in an in vitro model

Connexins (Cxs) are fundamental in cell-cell communication, functioning as gap junction channels (GJCs) that facilitate solute exchange between adjacent cells and as hemichannels (HCs) that mediate solute exchange between the cytoplasm and the extracellular environment. Mutations in the GJB1 gene, which encodes Cx32, lead to X-linked Charcot-Marie-Tooth type 1 (CMTX1), a rare hereditary demyelinating disorder of the peripheral nervous system (PNS) without an effective cure or treatment. In Schwann cells, Cx32 HCs are thought to play a role in myelination by enhancing intracellular and intercellular Ca2+ signaling, which is crucial for proper PNS myelination. Single-point mutations (p.S85C, p.D178Y, p.F235C) generate pathological Cx32 HCs characterized by increased permeability ("leaky") or excessive activity ("hyperactive").We investigated the effects of abEC1.1-hIgG1, a fully human immunoglobulin G1 (hIgG1) monoclonal antibody, on wild-type (WT) and mutant Cx32D178Y HCs. Using HeLa DH cells conditionally co-expressing Cx and a genetically encoded Ca2+ biosensor (GCaMP6s), we demonstrated that mutant HCs facilitated 58% greater Ca2+ uptake in response to elevated extracellular Ca2+ concentrations ([Ca2+]ex) compared to WT HCs. abEC1.1-hIgG1 dose-dependently inhibited Ca2+ uptake, achieving a 50% inhibitory concentration (EC50) of ~ 10 nM for WT HCs and ~ 80 nM for mutant HCs. Additionally, the antibody suppressed DAPI uptake and ATP release. An atomistic computational model revealed that serine 56 (S56) of the antibody interacts with aspartate 178 (D178) of WT Cx32 HCs, contributing to binding affinity. Despite the p.D178Y mutation weakening this interaction, the antibody maintained binding to the mutant HC epitope at sub-micromolar concentrations.In conclusion, our study shows that abEC1.1-hIgG1 effectively inhibits both WT and mutant Cx32 HCs, highlighting its potential as a therapeutic approach for CMTX1. These findings expand the antibody's applicability for treating diseases associated with Cx HCs and inform the rational design of next-generation antibodies with enhanced affinity and efficacy against mutant HCs.

Theoretical Investigations of a point mutation affecting H5 Hemagglutinin's receptor binding preference

The avian influenza A H5N1 virus is a subtype of influenza A virus (IAV) that causes a highly infectious and severe respiratory illness in birds and poses significant economic losses in poultry farming. To infect host cell, the virus uses its surface glycoprotein named Hemagglutinin (HA) to recognize and to interact with the host cell receptor containing either α2,6- (SAα2,6 Gal) or α2,3-linked Sialic Acid (SAα2,3 Gal). The H5N1 virus has not yet acquired the capability for efficient human-to-human transmission. However, studies have demonstrated that even a single amino acid substitution in the HA can switch its glycan receptor preference from the avian-type SAα2,3 Gal to the human-type SAα2,6 Gal. The present study aims to explain the underlying mechanism of a mutation (D94N) on the H5 HA that causes the protein to change its glycan receptor-binding preference using molecular dynamics (MD) simulations. Our results reveal that the mutation alters the electrostatic interactions pattern near the HA receptor binding pocket, leading to a reduced stability for the HA-avian-type SAα2,3 Gal complex. On the other hand, the detrimental effect of the mutation D94N is not observed in the HA-human-type SAα2,6 Gal complex due to the glycan's capability to switch its topology.

Beyond IC50-A computational dynamic model of drug resistance in enzyme inhibition treatment

Resistance to therapy is a major clinical obstacle to treatment of cancer and communicable diseases. Drug selection in treatment of patients where the disease is showing resistance to therapy is often guided by IC50 or fold-IC50 values. In this work, through a model of the treatment of chronic myeloid leukaemia (CML), we contest using fold-IC50 values as a guide for treatment selection. CML is a blood cancer that is treated with Abl1 inhibitors, and is often seen as a model for targeted therapy and drug resistance. Resistance to the first-line treatment occurs in approximately one in four patients. The most common cause of resistance is mutations in the Abl1 enzyme. Different mutant Abl1 enzymes show resistance to different Abl1 inhibitors and the mechanisms that lead to resistance for various mutation and inhibitor combinations are not fully known, making the selection of Abl1 inhibitors for treatment a difficult task. We developed a model based on information of catalysis, inhibition and pharmacokinetics, and applied it to study the effect of three Abl1 inhibitors on mutants of the Abl1 enzyme. From this model, we show that the relative decrease of product formation rate (defined in this work as "inhibitory reduction prowess") is a better indicator of resistance than an examination of the size of the product formation rate or fold-IC50 values for the mutant. We also examine current ideas and practices that guide treatment choice and suggest a new parameter for selecting treatments that could increase the efficacy and thus have a positive impact on patient outcomes.

Origin of Pressure Resistance in Deep-Sea Lactate Dehydrogenase

High hydrostatic pressure has a dramatic effect on biochemical systems, as exposure to high pressure can result in structural perturbations ranging from dissociation of protein complexes to complete denaturation. The deep ocean presents an interesting paradox since it is teeming with life despite the high-pressure environment. This is due to evolutionary adaptations in deep-sea organisms, such as amino acid substitutions in their proteins, which aid in resisting the denaturing effects of pressure. However, the physicochemical mechanism by which these substitutions can induce pressure resistance remains unknown. Here, we use molecular dynamics simulations to study pressure-adapted lactate dehydrogenase from the deep-sea abyssal grenadier (Coryphaenoides armatus), in comparison with that of the shallow-water Atlantic cod (Gadus morhua). We examined structural, thermodynamic and volumetric contributions to pressure resistance, and report that the amino acid substitutions result in a decrease in volume of the deep-sea protein accompanied by a decrease in thermodynamic stability of the native protein. Our simulations at high pressure also suggest that differences in compressibility may be important for understanding pressure resistance in deep-sea proteins.

Thermodynamic Details of Pinholin S2168 Activation Revealed Using Alchemical Free Energy Simulations

Pinholin S2168 is a viral integral membrane protein whose function is to form nanoscopic "pinholes" in bacterial cell membranes to induce cell lysis as part of the viral replication cycle. Pinholin can transition from an inactive to an active conformation by exposing a transmembrane domain (TMD1) to the extracellular fluid. Upon activation, several copies of the protein assemble via interactions among a second transmembrane domain (TMD2) to form a single pore, thus hastening cell lysis and viral escape. The following experiments provide conformational descriptors of pinholin in active and inactive states and elucidate the molecular driving forces that control pinholin activity. In the present study, molecular dynamics (MD) simulations have been used to refine experimentally derived conformational descriptors into an atomistically detailed model of irsS2168, an antiholin mutant. To provide additional details about the thermodynamics of pinholin activation and to overcome large intrinsic kinetic barriers to activation, alchemical free energy simulations have been conducted. Alchemical mutations reveal the change in folding free energy upon mutation. The results suggest that alchemical mutations are an effective tool to rationalize experimental observations and predict the effects of site mutations on conformational states for proteins integrated into lipid bilayers. S16F, A17Q, A17Q+G21Q, and A17Q+G21Q+G14Q mutants reveal how changes in hydrophilicity and disruption of the glycine zipper motif influence pinholin's thermodynamic equilibrium, favoring the active conformation. These findings align with experimental observations from DEER spectroscopy, demonstrating that mutations increasing the hydrophilicity of TMD1 promote activation by making TMD1 more likely to exit the membrane and enter the extracellular fluid.

Simulation-driven design of stabilized SARS-CoV-2 spike S2 immunogens

The full-length prefusion-stabilized SARS-CoV-2 spike (S) is the principal antigen of COVID-19 vaccines. Vaccine efficacy has been impacted by emerging variants of concern that accumulate most of the sequence modifications in the immunodominant S1 subunit. S2, in contrast, is the most evolutionarily conserved region of the spike and can elicit broadly neutralizing and protective antibodies. Yet, S2's usage as an alternative vaccine strategy is hampered by its general instability. Here, we use a simulation-driven approach to design S2-only immunogens stabilized in a closed prefusion conformation. Molecular simulations provide a mechanistic characterization of the S2 trimer's opening, informing the design of tryptophan substitutions that impart kinetic and thermodynamic stabilization. Structural characterization via cryo-EM shows the molecular basis of S2 stabilization in the closed prefusion conformation. Informed by molecular simulations and corroborated by experiments, we report an engineered S2 immunogen that exhibits increased protein expression, superior thermostability, and preserved immunogenicity against sarbecoviruses.

How to Sample Dozens of Substitutions per Site with λ Dynamics

Alchemical free energy methods are useful in computer-aided drug design and computational protein design because they provide rigorous statistical mechanics-based estimates of free energy differences from molecular dynamics simulations. λ dynamics is a free energy method with the ability to characterize combinatorial chemical spaces spanning thousands of related systems within a single simulation, which gives it a distinct advantage over other alchemical free energy methods that are mostly limited to pairwise comparisons. Recently developed methods have improved the scalability of λ dynamics to perturbations at many sites; however, the size of chemical space that can be explored at each individual site has previously been limited to fewer than ten substituents. As the number of substituents increases, the volume of alchemical space corresponding to nonphysical alchemical intermediates grows exponentially relative to the size corresponding to the physical states of interest. Beyond nine substituents, λ dynamics simulations become lost in an alchemical morass of intermediate states. In this work, we introduce new biasing potentials that circumvent excessive sampling of intermediate states by favoring sampling of physical end points relative to alchemical intermediates. Additionally, we present a more scalable adaptive landscape flattening algorithm for these larger alchemical spaces. Finally, we show that this potential enables more efficient sampling in both protein and drug design test systems with up to 24 substituents per site, enabling, for the first time, simultaneous simulation of all 20 amino acids.

Inhibition of DEF-p65 Interactions as a Potential Avenue to Suppress Tumor Growth in Pancreatic Cancer

The limited success of current targeted therapies for pancreatic cancer underscores an urgent demand for novel treatment modalities. The challenge in mitigating this malignancy can be attributed to the digestive organ expansion factor (DEF), a pivotal yet underexplored factor in pancreatic tumorigenesis. The study uses a blend of in vitro and in vivo approaches, complemented by the theoretical analyses, to propose DEF as a promising anti-tumor target. Analysis of clinical samples reveals that high expression of DEF is correlated with diminished survival in pancreatic cancer patients. Crucially, the depletion of DEF significantly impedes tumor growth. The study further discovers that DEF binds to p65, shielding it from degradation mediated by the ubiquitin-proteasome pathway in cancer cells. Based on these findings and computational approaches, the study formulates a DEF-mimicking peptide, peptide-031, designed to disrupt the DEF-p65 interaction. The effectiveness of peptide-031 in inhibiting tumor proliferation has been demonstrated both in vitro and in vivo. This study unveils the oncogenic role of DEF while highlighting its prognostic value and therapeutic potential in pancreatic cancer. In addition, peptide-031 is a promising therapeutic agent with potent anti-tumor effects.

Application of an alchemical free energy method for the prediction of thermostable DuraPETase variants

Non-equilibrium (NEQ) alchemical free energy calculations are an emerging tool for accurately predicting changes in protein folding free energy resulting from amino acid mutations. In this study, this method in combination with the Rosetta ddg monomer tool was applied to predict more thermostable variants of the polyethylene terephthalate (PET) degrading enzyme DuraPETase. The Rosetta ddg monomer tool efficiently enriched promising mutations prior to more accurate prediction by NEQ alchemical free energy calculations. The relative change in folding free energy of 96 single amino acid mutations was calculated by NEQ alchemical free energy calculation. Experimental validation of ten of the highest scoring variants identified two mutations (DuraPETaseS61M and DuraPETaseS223Y) that increased the melting temperature (Tm) of the enzyme by up to 1 °C. The calculated relative change in folding free energy showed an excellent correlation with experimentally determined Tm resulting in a Pearson's correlation coefficient of r =  - 0.84. Limitations in the prediction of strongly stabilizing mutations were, however, encountered and are discussed. Despite these challenges, this study demonstrates the practical applicability of NEQ alchemical free energy calculations in prospective enzyme engineering projects. KEY POINTS: • Rosetta ddg monomer enriches stabilizing mutations in a library of DuraPETase variants • NEQ free energy calculations accurately predict changes in Tm of DuraPETase • The DuraPETase variants S223Y, S42M, and S61M have increased Tm.

Kartograf: A Geometrically Accurate Atom Mapper for Hybrid-Topology Relative Free Energy Calculations

Relative binding free energy (RBFE) calculations have emerged as a powerful tool that supports ligand optimization in drug discovery. Despite many successes, the use of RBFEs can often be limited by automation problems, in particular, the setup of such calculations. Atom mapping algorithms are an essential component in setting up automatic large-scale hybrid-topology RBFE calculation campaigns. Traditional algorithms typically employ a 2D subgraph isomorphism solver (SIS) in order to estimate the maximum common substructure. SIS-based approaches can be limited by time-intensive operations and issues with capturing geometry-linked chemical properties, potentially leading to suboptimal solutions. To overcome these limitations, we have developed Kartograf, a geometric-graph-based algorithm that uses primarily the 3D coordinates of atoms to find a mapping between two ligands. In free energy approaches, the ligand conformations are usually derived from docking or other previous modeling approaches, giving the coordinates a certain importance. By considering the spatial relationships between atoms related to the molecule coordinates, our algorithm bypasses the computationally complex subgraph matching of SIS-based approaches and reduces the problem to a much simpler bipartite graph matching problem. Moreover, Kartograf effectively circumvents typical mapping issues induced by molecule symmetry and stereoisomerism, making it a more robust approach for atom mapping from a geometric perspective. To validate our method, we calculated mappings with our novel approach using a diverse set of small molecules and used the mappings in relative hydration and binding free energy calculations. The comparison with two SIS-based algorithms showed that Kartograf offers a fast alternative approach. The code for Kartograf is freely available on GitHub (https://github.com/OpenFreeEnergy/kartograf). While developed for the OpenFE ecosystem, Kartograf can also be utilized as a standalone Python package.

The Deinococcus protease PprI senses DNA damage by directly interacting with single-stranded DNA

Bacteria have evolved various response systems to adapt to environmental stress. A protease-based derepression mechanism in response to DNA damage was characterized in Deinococcus, which is controlled by the specific cleavage of repressor DdrO by metallopeptidase PprI (also called IrrE). Despite the efforts to document the biochemical, physiological, and downstream regulation of PprI-DdrO, the upstream regulatory signal activating this system remains unclear. Here, we show that single-stranded DNA physically interacts with PprI protease, which enhances the PprI-DdrO interactions as well as the DdrO cleavage in a length-dependent manner both in vivo and in vitro. Structures of PprI, in its apo and complexed forms with single-stranded DNA, reveal two DNA-binding interfaces shaping the cleavage site. Moreover, we show that the dynamic monomer-dimer equilibrium of PprI is also important for its cleavage activity. Our data provide evidence that single-stranded DNA could serve as the signal for DNA damage sensing in the metalloprotease/repressor system in bacteria. These results also shed light on the survival and acquired drug resistance of certain bacteria under antimicrobial stress through a SOS-independent pathway.

Interpreting the effect of mutations to protein binding sites from large-scale genomic screens

Predicting the functionality of missense mutations is extremely difficult. Large-scale genomic screens are commonly performed to identify mutational correlates or drivers of disease and treatment resistance, but interpretation of how these mutations impact protein function is limited. One such consequence of mutations to a protein is to impact its ability to bind and interact with partners or small molecules such as ATP, thereby modulating its function. Multiple methods exist for predicting the impact of a single mutation on protein-protein binding energy, but it is difficult in the context of a genomic screen to understand if these mutations with large impacts on binding are more common than statistically expected. We present a methodology for taking mutational data from large-scale genomic screens and generating functional and statistical insights into their role in the binding of proteins both with each other and their small molecule ligands. This allows a quantitative and statistical analysis to determine whether mutations impacting protein binding or ligand interactions are occurring more or less frequently than expected by chance. We achieve this by calculating the potential impact of any possible mutation and comparing an expected distribution to the observed mutations. This method is applied to examples demonstrating its ability to interpret mutations involved in protein-protein binding, protein-DNA interactions, and the evolution of therapeutic resistance.

Selection pressures on evolution of ribonuclease H explored with rigorous free-energy-based design

Understanding natural protein evolution and designing novel proteins are motivating interest in development of high-throughput methods to explore large sequence spaces. In this work, we demonstrate the application of multisite λ dynamics (MSλD), a rigorous free energy simulation method, and chemical denaturation experiments to quantify evolutionary selection pressure from sequence-stability relationships and to address questions of design. This study examines a mesophilic phylogenetic clade of ribonuclease H (RNase H), furthering its extensive characterization in earlier studies, focusing on E. coli RNase H (ecRNH) and a more stable consensus sequence (AncCcons) differing at 15 positions. The stabilities of 32,768 chimeras between these two sequences were computed using the MSλD framework. The most stable and least stable chimeras were predicted and tested along with several other sequences, revealing a designed chimera with approximately the same stability increase as AncCcons, but requiring only half the mutations. Comparing the computed stabilities with experiment for 12 sequences reveals a Pearson correlation of 0.86 and root mean squared error of 1.18 kcal/mol, an unprecedented level of accuracy well beyond less rigorous computational design methods. We then quantified selection pressure using a simple evolutionary model in which sequences are selected according to the Boltzmann factor of their stability. Selection temperatures from 110 to 168 K are estimated in three ways by comparing experimental and computational results to evolutionary models. These estimates indicate selection pressure is high, which has implications for evolutionary dynamics and for the accuracy required for design, and suggests accurate high-throughput computational methods like MSλD may enable more effective protein design.

Titin domains with reduced core hydrophobicity cause dilated cardiomyopathy

The underlying genetic defect in most cases of dilated cardiomyopathy (DCM), a common inherited heart disease, remains unknown. Intriguingly, many patients carry single missense variants of uncertain pathogenicity targeting the giant protein titin, a fundamental sarcomere component. To explore the deleterious potential of these variants, we first solved the wild-type and mutant crystal structures of I21, the titin domain targeted by pathogenic variant p.C3575S. Although both structures are remarkably similar, the reduced hydrophobicity of deeply buried position 3575 strongly destabilizes the mutant domain, a scenario supported by molecular dynamics simulations and by biochemical assays that show no disulfide involving C3575. Prompted by these observations, we have found that thousands of similar hydrophobicity-reducing variants associate specifically with DCM. Hence, our results imply that titin domain destabilization causes DCM, a conceptual framework that not only informs pathogenicity assessment of gene variants but also points to therapeutic strategies counterbalancing protein destabilization.

MEnTaT: A machine-learning approach for the identification of mutations to increase protein stability

Enhancing protein thermal stability is important for biomedical and industrial applications as well as in the research laboratory. Here, we describe a simple machine-learning method which identifies amino acid substitutions that contribute to thermal stability based on comparison of the amino acid sequences of homologous proteins derived from bacteria that grow at different temperatures. A key feature of the method is that it compares the sequences based not simply on the amino acid identity, but rather on the structural and physicochemical properties of the side chain. The method accurately identified stabilizing substitutions in three well-studied systems and was validated prospectively by experimentally testing predicted stabilizing substitutions in a polyamine oxidase. In each case, the method outperformed the widely used bioinformatic consensus approach. The method can also provide insight into fundamental aspects of protein structure, for example, by identifying how many sequence positions in a given protein are relevant to temperature adaptation.

Assessing the Performance of Non-Equilibrium Thermodynamic Integration in Flavodoxin Redox Potential Estimation

Flavodoxins are enzymes that contain the redox-active flavin mononucleotide (FMN) cofactor and play a crucial role in numerous biological processes, including energy conversion and electron transfer. Since the redox characteristics of flavodoxins are significantly impacted by the molecular environment of the FMN cofactor, the evaluation of the interplay between the redox properties of the flavin cofactor and its molecular surroundings in flavoproteins is a critical area of investigation for both fundamental research and technological advancements, as the electrochemical tuning of flavoproteins is necessary for optimal interaction with redox acceptor or donor molecules. In order to facilitate the rational design of biomolecular devices, it is imperative to have access to computational tools that can accurately predict the redox potential of both natural and artificial flavoproteins. In this study, we have investigated the feasibility of using non-equilibrium thermodynamic integration protocols to reliably predict the redox potential of flavodoxins. Using as a test set the wild-type flavodoxin from Clostridium Beijerinckii and eight experimentally characterized single-point mutants, we have computed their redox potential. Our results show that 75% (6 out of 8) of the calculated reaction free energies are within 1 kcal/mol of the experimental values, and none exceed an error of 2 kcal/mol, confirming that non-equilibrium thermodynamic integration is a trustworthy tool for the quantitative estimation of the redox potential of this biologically and technologically significant class of enzymes.

Computational Design of Potent and Selective d-Peptide Agonists of the Glucagon-like Peptide-2 Receptor

Here, we designed three d-GLP-2 agonists that activated the glucagon-like peptide-2 receptor (GLP-2R) cyclic adenosine monophosphate (cAMP) accumulation without stimulating the glucagon-like peptide-1 receptor (GLP-1R). All the d-GLP-2 agonists increased the protein kinase B phosphorylated (p-AKT) expression levels in a time- and concentration-dependent manner in vitro. The most effective d-GLP-2 analogue boosted the AKT phosphorylation 2.28 times more effectively compared to the native l-GLP-2. The enhancement in the p-AKT levels induced by the d-GLP-2 analogues could be explained by GLP-2R's more prolonged activation, given that the d-GLP-2 analogues induce a lower β-arrestin recruitment. The higher stability to protease degradation of our d-GLP-2 agonists helps us envision their potential applications in enhancing intestinal absorption and treating inflammatory bowel illness while lowering the high dosage required by the current treatments.

Understanding the Stabilization Mechanism of a Thermostable Mutant of Hygromycin B Phosphotransferase by Protein Sector-Guided Dynamic Analysis

Point mutations can exert beneficial effects on proteins, including stabilization. The stabilizing effects of mutations are typically attributed to changes in free energy and residue interactions. However, these explanations lack detail and physical insights, which hinder the mechanistic study of protein stabilization and prevent accurate computational prediction of stabilizing mutations. Here, we investigate the physical mechanism underlying the enhanced thermostability of a Hygromycin B phosphotransferase mutant, Hph5. We find that the unpredictable mutation A118V induces rotation of F199, allowing it to establish an aromatic-aromatic interaction with W235. In contrast, the predictable mutation T246A acts through static hydrophobic interactions within the protein core. These discoveries were accelerated by a residue-coevolution-based theory, which links mutational effects to stability-associated local structures, providing valuable guidance for mechanistic exploration. The established workflow will benefit the development of accurate stability prediction programs and can be used to mine a protein stability database for undiscovered physical mechanisms.

A FRET-Based Assay for the Identification of PCNA Inhibitors

Proliferating cell nuclear antigen (PCNA) is the key regulator of human DNA metabolism. One important interaction partner is p15, involved in DNA replication and repair. Targeting the PCNA-p15 interaction is a promising therapeutic strategy against cancer. Here, a Förster resonance energy transfer (FRET)-based assay for the analysis of the PCNA-p15 interaction was developed. Next to the application as screening tool for the identification and characterization of PCNA-p15 interaction inhibitors, the assay is also suitable for the investigation of mutation-induced changes in their affinity. This is particularly useful for analyzing disease associated PCNA or p15 variants at the molecular level. Recently, the PCNA variant C148S has been associated with Ataxia-telangiectasia-like disorder type 2 (ATLD2). ATLD2 is a neurodegenerative disease based on defects in DNA repair due to an impaired PCNA. Incubation time dependent FRET measurements indicated no effect on PCNA^C148S-p15 affinity, but on PCNA stability. The impaired stability and increased aggregation behavior of PCNA^C148S was confirmed by intrinsic tryptophan fluorescence, differential scanning fluorimetry (DSF) and asymmetrical flow field-flow fractionation (AF4) measurements. The analysis of the disease associated PCNA variant demonstrated the versatility of the interaction assay as developed.

Development and Benchmarking of Open Force Field 2.0.0: The Sage Small Molecule Force Field

We introduce the Open Force Field (OpenFF) 2.0.0 small molecule force field for drug-like molecules, code-named Sage, which builds upon our previous iteration, Parsley. OpenFF force fields are based on direct chemical perception, which generalizes easily to highly diverse sets of chemistries based on substructure queries. Like the previous OpenFF iterations, the Sage generation of OpenFF force fields was validated in protein-ligand simulations to be compatible with AMBER biopolymer force fields. In this work, we detail the methodology used to develop this force field, as well as the innovations and improvements introduced since the release of Parsley 1.0.0. One particularly significant feature of Sage is a set of improved Lennard-Jones (LJ) parameters retrained against condensed phase mixture data, the first refit of LJ parameters in the OpenFF small molecule force field line. Sage also includes valence parameters refit to a larger database of quantum chemical calculations than previous versions, as well as improvements in how this fitting is performed. Force field benchmarks show improvements in general metrics of performance against quantum chemistry reference data such as root-mean-square deviations (RMSD) of optimized conformer geometries, torsion fingerprint deviations (TFD), and improved relative conformer energetics (ΔΔE). We present a variety of benchmarks for these metrics against our previous force fields as well as in some cases other small molecule force fields. Sage also demonstrates improved performance in estimating physical properties, including comparison against experimental data from various thermodynamic databases for small molecule properties such as ΔHmix, ρ(x), ΔGsolv, and ΔGtrans. Additionally, we benchmarked against protein-ligand binding free energies (ΔGbind), where Sage yields results statistically similar to previous force fields. All the data is made publicly available along with complete details on how to reproduce the training results at https://github.com/openforcefield/openff-sage.

How Single Amino Acid Substitutions Can Disrupt a Protein Hetero-Dimer Interface: Computational and Experimental Studies of the LigAB Dioxygenase from Sphingobium sp. Strain SYK-6

Protocatechuate 4,5-dioxygenase (LigAB) is a heterodimeric enzyme that catalyzes the dioxygenation of multiple lignin derived aromatic compounds. The active site of LigAB is at the heterodimeric interface, with specificity conferred by the alpha subunit and catalytic residues contributed by the beta subunit. Previous research has indicated that the phenylalanine at the 103 position of the alpha subunit (F103α) controls selectivity for the C5 position of the aromatic substrates, and mutations of this residue can enhance the rate of catalysis for substrates with larger functional groups at this position. While several of the mutations to this position (Valine, V; Threonine, T; Leucine, L; and Histidine, H) were catalytically active, other mutations (Alanine, A; and Serine, S) were found to have reduced dimer interface affinity, leading to challenges in copurifing the catalytically active enzyme complex under high salt conditions. In this study, we aimed to experimentally and computationally interrogate residues at the dimer interface to discern the importance of position 103α for maintaining the integrity of the heterodimer. Molecular dynamic simulations and electrophoretic mobility assays revealed a preference for nonpolar/aromatic amino acids in this position, suggesting that while substitutions to polar amino acids may produce a dioxygenase with a useful substrate utilization profile, those considerations may be off-set by potential destabilization of the catalytically active oligomer. Understanding the dimerization of LigAB provides insight into the multimeric proteins within the largely uncharacterized superfamily and characteristics to consider when engineering proteins that can degrade lignin efficiently. These results shed light on the challenges associated with engineering proteins for broader substrate specificity.

APOBEC3A Catalytic Inactivity Mutation Induces Tertiary Structure Destabilization

APOBEC3A (A3A)-catalyzed DNA cytosine deamination is implicated in virus and cancer mutagenesis, and A3A is a target for small molecule drug discovery. The catalytic glutamic acid (E72) is frequently mutated in biochemical studies to characterize deamination-dependent versus deamination-independent A3A functions. Additionally, catalytically active A3A is toxic in bacterial expression systems, which adversely affects yield during recombinant A3A expression. Here, we demonstrate that mutating the catalytic glutamic acid to an isosteric glutamine (E72Q) significantly decreases the thermal stability of the protein, compared to the alanine-inactivating mutation (E72A). Differential scanning fluorimetry and mass spectrometry reveal that A3A E72Q is less thermally stable than A3A E72A or wild-type A3A. Strikingly, A3A E72Q is partially denatured at 37 °C and binds single-stranded DNA with significantly poorer affinity compared to A3A E72A. This study constitutes an important cautionary note on A3A protein design and informs that A3A E72A is the preferred catalytic inactivation mutation for most applications.

Resistance to a tyrosine kinase inhibitor mediated by changes to the conformation space of the kinase

Gilteritinib is a highly selective and effective inhibitor of the FLT3/ITD mutated protein, and is used successfully in treating acute myeloid leukaemia (AML). Unfortunately, tumour cells gradually develop resistance to gilteritinib due to mutations in the molecular drug target. The atomistic details behind this observed resistance are not clear, since the protein structure of the complex is only available in the inactive state, while the drug binds better to the active state. To overcome this limitation, we used a computer-aided approach where we docked gilteritinib to the active site of FLT3/ITD and calculated the Gibbs free energy difference between the binding energies of the parental and mutant enzymes. These calculations agreed with experimental estimations for one mutation (F691L) but not the other (D698N). To further understand how these mutations operate, we used metadynamics simulations to study the conformational landscape of the activation process. Both mutants show a lower activation energy barrier which suggests that they are more likely to adopt an active state until inhibited, making the mutant enzymes more active. This suggests that a higher efficiency of tyrosine kinases contributes to resistance not only against type 2 but also against type 1 kinase inhibitors.

Target-template relationships in protein structure prediction and their effect on the accuracy of thermostability calculations

Improving protein thermostability has been a labor- and time-consuming process in industrial applications of protein engineering. Advances in computational approaches have facilitated the development of more efficient strategies to allow the prioritization of stabilizing mutants. Among these is FEP+, a free energy perturbation implementation that uses a thoroughly tested physics-based method to achieve unparalleled accuracy in predicting changes in protein thermostability. To gauge the applicability of FEP+ to situations where crystal structures are unavailable, here we have applied the FEP+ approach to homology models of 12 different proteins covering 316 mutations. By comparing predictions obtained with homology models to those obtained using crystal structures, we have identified that local rather than global sequence conservation between target and template sequence is a determining factor in the accuracy of predictions. By excluding mutation sites with low local sequence identity (<40%) to a template structure, we have obtained predictions with comparable performance to crystal structures (R2 of 0.67 and 0.63 and an RMSE of 1.20 and 1.16 kcal/mol for crystal structure and homology model predictions, respectively) for identifying stabilizing mutations when incorporating residue scanning into a cascade screening strategy. Additionally, we identify and discuss inherent limitations in sequence alignments and homology modeling protocols that translate into the poor FEP+ performance of a few select examples. Overall, our retrospective study provides detailed guidelines for the application of the FEP+ approach using homology models for protein thermostability predictions, which will greatly extend this approach to studies that were previously limited by structure availability.

High-Throughput Prediction of the Impact of Genetic Variability on Drug Sensitivity and Resistance Patterns for Clinically Relevant Epidermal Growth Factor Receptor Mutations from Atomistic Simulations

Mutations in the kinase domain of the epidermal growth factor receptor (EGFR) can be drivers of cancer and also trigger drug resistance in patients receiving chemotherapy treatment based on kinase inhibitors. A priori knowledge of the impact of EGFR variants on drug sensitivity would help to optimize chemotherapy and design new drugs that are effective against resistant variants before they emerge in clinical trials. To this end, we explored a variety of in silico methods, from sequence-based to "state-of-the-art" atomistic simulations. We did not find any sequence signal that can provide clues on when a drug-related mutation appears or the impact of such mutations on drug activity. Low-level simulation methods provide limited qualitative information on regions where mutations are likely to cause alterations in drug activity, and they can predict around 70% of the impact of mutations on drug efficiency. High-level simulations based on nonequilibrium alchemical free energy calculations show predictive power. The integration of these "state-of-the-art" methods into a workflow implementing an interface for parallel distribution of the calculations allows its automatic and high-throughput use, even for researchers with moderate experience in molecular simulations.

Prioritizing Small Sets of Molecules for Synthesis through in-silico Tools: A Comparison of Common Ranking Methods

Prioritizing molecules for synthesis is a key role of computational methods within medicinal chemistry. Multiple tools exist for ranking molecules, from the cheap and popular molecular docking methods to more computationally expensive molecular-dynamics (MD)-based methods. It is often questioned whether the accuracy of the more rigorous methods justifies the higher computational cost and associated calculation time. Here, we compared the performance on ranking the binding of small molecules for seven scoring functions from five docking programs, one end-point method (MM/GBSA), and two MD-based free energy methods (PMX, FEP+). We investigated 16 pharmaceutically relevant targets with a total of 423 known binders. The performance of docking methods for ligand ranking was strongly system dependent. We observed that MD-based methods predominantly outperformed docking algorithms and MM/GBSA calculations. Based on our results, we recommend the application of MD-based free energy methods for prioritization of molecules for synthesis in lead optimization, whenever feasible.

RosettaDDGPrediction for high-throughput mutational scans: From stability to binding

Reliable prediction of free energy changes upon amino acid substitutions (ΔΔGs) is crucial to investigate their impact on protein stability and protein-protein interaction. Advances in experimental mutational scans allow high-throughput studies thanks to multiplex techniques. On the other hand, genomics initiatives provide a large amount of data on disease-related variants that can benefit from analyses with structure-based methods. Therefore, the computational field should keep the same pace and provide new tools for fast and accurate high-throughput ΔΔG calculations. In this context, the Rosetta modeling suite implements effective approaches to predict folding/unfolding ΔΔGs in a protein monomer upon amino acid substitutions and calculate the changes in binding free energy in protein complexes. However, their application can be challenging to users without extensive experience with Rosetta. Furthermore, Rosetta protocols for ΔΔG prediction are designed considering one variant at a time, making the setup of high-throughput screenings cumbersome. For these reasons, we devised RosettaDDGPrediction, a customizable Python wrapper designed to run free energy calculations on a set of amino acid substitutions using Rosetta protocols with little intervention from the user. Moreover, RosettaDDGPrediction assists with checking completed runs and aggregates raw data for multiple variants, as well as generates publication-ready graphics. We showed the potential of the tool in four case studies, including variants of uncertain significance in childhood cancer, proteins with known experimental unfolding ΔΔGs values, interactions between target proteins and disordered motifs, and phosphomimetics. RosettaDDGPrediction is available, free of charge and under GNU General Public License v3.0, at https://github.com/ELELAB/RosettaDDGPrediction.

Structure of an Alkaline Pectate Lyase and Rational Engineering with Improved Thermo-Alkaline Stability for Efficient Ramie Degumming

Alkaline pectate lyases have biotechnological applications in plant fiber processing, such as ramie degumming. Previously, we characterized an alkaline pectate lyase from Bacillus clausii S10, named BacPelA, which showed potential for enzymatic ramie degumming because of its high cleavage activity toward methylated pectins in alkaline conditions. However, BacPelA displayed poor thermo-alkaline stability. Here, we report the 1.78 Å resolution crystal structure of BacPelA in apo form. The enzyme has the characteristic right-handed β-helix fold of members of the polysaccharide lyase 1 family and shows overall structural similarity to them, but it displays some differences in the details of the secondary structure and Ca²⁺-binding site. On the basis of the structure, 10 sites located in flexible regions and showing high B-factor and positive ΔT_m values were selected for mutation, aiming to improve the thermo-alkaline stability of the enzyme. Following site-directed saturation mutagenesis and screening, mutants A238C, R150G, and R216H showed an increase in the T₅₀¹⁵ value at pH 10.0 of 3.0 °C, 6.5 °C, and 7.0 °C, respectively, compared with the wild-type enzyme, interestingly accompanied by a 24.5%, 46.6%, and 61.9% increase in activity. The combined mutant R150G/R216H/A238C showed an 8.5 °C increase in the T₅₀¹⁵ value at pH 10.0, and an 86.1% increase in the specific activity at 60 °C, with approximately doubled catalytic efficiency, compared with the wild-type enzyme. Moreover, this mutant retained 86.2% activity after incubation in ramie degumming conditions (4 h, 60 °C, pH 10.0), compared with only 3.4% for wild-type BacPelA. The combined mutant increased the weight loss of ramie fibers in degumming by 30.2% compared with wild-type BacPelA. This work provides a thermo-alkaline stable, highly active pectate lyase with great potential for application in the textile industry, and also illustrates an effective strategy for rational design and improvement of pectate lyases.

An efficient method to predict protein thermostability in alanine mutation

The relationship between protein sequence and its thermodynamic stability is a critical aspect of computational protein design. In this work, we present a new theoretical method to calculate the free energy change (ΔΔG) resulting from a single-point amino acid mutation to alanine in a protein sequence. The method is derived based on physical interactions and is very efficient in estimating the free energy changes caused by a series of alanine mutations from just a single molecular dynamics (MD) trajectory. Numerical calculations are carried out on a total of 547 alanine mutations in 19 diverse proteins whose experimental results are available. The comparison between the experimental ΔΔG_exp and the calculated values shows a generally good correlation with a correlation coefficient of 0.67. Both the advantages and limitations of this method are discussed. This method provides an efficient and valuable tool for protein design and engineering.

Toward the design and development of peptidomimetic inhibitors of the Ataxin-1 aggregation pathway

Spinocerebellar ataxia type 1 is a degenerative disorder caused by polyglutamine expansions and aggregation of Ataxin-1. The interaction between Capicua (CIC) and the AXH domain of Ataxin-1 protein has been suggested as a possible driver of aggregation for the expanded Ataxin-1 protein and the subsequent onset of spinocerebellar ataxia 1. Experimental studies have demonstrated that short constructs of CIC may prevent such aggregation and suggested this as a possible candidate to inspire the rational design of peptidomimetics. In this work, molecular modeling techniques, namely the alchemical mutation and force field-based molecular dynamics, have been employed to propose a pipeline for the rational design of a CIC-inspired inhibitor of the ataxin-1 aggregation pathway. In particular, this study has shown that the alchemical mutation can estimate the affinity between AXH and CIC with good correlation with experimental data, while molecular dynamics shed light on molecular mechanisms that occur for stabilization of the interaction between the CIC-inspired construct and the AXH domain of Ataxin-1. This work lays the foundation for a rational methodology for the in silico screening and design of peptidomimetics, which can expedite and streamline experimental studies to identify strategies for inhibiting the ataxin-1 aggregation pathway.

Prediction of polyreactive and nonspecific single-chain fragment variables through structural biochemical features and protein language-based descriptors

BACKGROUND

Monoclonal antibodies (mAbs) have been used as therapeutic agents, which must overcome many developability issues after the discovery from in vitro display libraries. Especially, polyreactive mAbs can strongly bind to a specific target and weakly bind to off-target proteins, which leads to poor antibody pharmacokinetics in clinical development. Although early assessment of polyreactive mAbs is important in the early discovery stage, experimental assessments are usually time-consuming and expensive. Therefore, computational approaches for predicting the polyreactivity of single-chain fragment variables (scFvs) in the early discovery stage would be promising for reducing experimental efforts.

RESULTS

Here, we made prediction models for the polyreactivity of scFvs with the known polyreactive antibody features and natural language model descriptors. We predicted 19,426 protein structures of scFvs with trRosetta to calculate the polyreactive antibody features and investigated the classifying performance of each factor for polyreactivity. In the known polyreactive features, the net charge of the CDR2 loop, the tryptophan and glycine residues in CDR-H3, and the lengths of the CDR1 and CDR2 loops, importantly contributed to the performance of the models. Additionally, the hydrodynamic features, such as partial specific volume, gyration radius, and isoelectric points of CDR loops and scFvs, were newly added to improve model performance. Finally, we made the prediction model with a robust performance ([Formula: see text]) with an ensemble learning of the top 3 best models.

CONCLUSION

The prediction models for polyreactivity would help assess polyreactive scFvs in the early discovery stage and our approaches would be promising to develop machine learning models with quantitative data from high throughput assays for antibody screening.

The Thermostability of Nucleoside Phosphorylases from Prokaryotes. I. The Role of the Primary Structure of the N-terminal fragment of the Protein in the Thermostability of Uridine Phosphorylases

Mutant uridine phosphorylase genes from Shewanella oneidensis MR-1 (S. oneidensis) were constructed by site-directed mutagenesis and strains-producers of the corresponding recombinant (F5I and F5G) proteins were obtained on the basis of Escherichia coli cells. The mutant proteins were purified and their physicochemical and enzymatic properties were studied. It was shown that the N-terminal fragment of uridine phosphorylase plays an important role in the thermal stabilization of the enzyme as a whole. The role of the aminoacid (a.a.) residue phenylalanine (F5) in the formation of thermotolerance of uridine phosphorylases from gamma-proteobacteria was revealed.

High-Throughput Molecular Dynamics-Based Alchemical Free Energy Calculations for Predicting the Binding Free Energy Change Associated with the Selected Omicron Mutations in the Spike Receptor-Binding Domain of SARS-CoV-2

The ongoing pandemic caused by SARS-CoV-2 has gone through various phases. Since the initial outbreak, the virus has mutated several times, with some lineages showing even stronger infectivity and faster spread than the original virus. Among all the variants, omicron is currently classified as a variant of concern (VOC) by the World Health Organization, as the previously circulating variants have been replaced by it. In this work, we have focused on the mutations observed in omicron sub lineages BA.1, BA.2, BA.4 and BA.5, particularly at the receptor-binding domain (RBD) of the spike protein that is responsible for the interactions with the host ACE2 receptor and binding of antibodies. Studying such mutations is particularly important for understanding the viral infectivity, spread of the disease and for tracking the escape routes of this virus from antibodies. Molecular dynamics (MD) based alchemical free energy calculations have been shown to be very accurate in predicting the free energy change, due to a mutation that could have a deleterious or a stabilizing effect on either the protein itself or its binding affinity to another protein. Here, we investigated the significance of five spike RBD mutations on the stability of the spike protein binding to ACE2 by free energy calculations using high throughput MD simulations. For comparison, we also used conventional MD simulations combined with a Molecular Mechanics-Generalized Born Surface Area (MM-GBSA) based approach, and compared our results with the available experimental data. Overall, the alchemical free energy calculations performed far better than the MM-GBSA approach in predicting the individual impact of the mutations. When considering the experimental variation, the alchemical free energy method was able to produce a relatively accurate prediction for N501Y, the mutant that has previously been reported to increase the binding affinity to hACE2. On the other hand, the other individual mutations seem not to have a significant effect on the spike RBD binding affinity towards hACE2.

Probing Factor Xa Protein-Ligand Interactions: Accurate Free Energy Calculations and Experimental Validations of Two Series of High-Affinity Ligands

The accurate prediction of protein-ligand binding affinity belongs to one of the central goals in computer-based drug design. Molecular dynamics (MD)-based free energy calculations have become increasingly popular in this respect due to their accuracy and solid theoretical basis. Here, we present a combined study which encompasses experimental and computational studies on two series of factor Xa ligands, which enclose a broad chemical space including large modifications of the central scaffold. Using this integrated approach, we identified several new ligands with different heterocyclic scaffolds different from the previously identified indole-2-carboxamides that show superior or similar affinity. Furthermore, the so far underexplored terminal alkyne moiety proved to be a suitable non-classical bioisosteric replacement for the higher halogen-π aryl interactions. With this challenging example, we demonstrated the ability of the MD-based non-equilibrium free energy calculation approach for guiding crucial modifications in the lead optimization process, such as scaffold replacement and single-site modifications at molecular interaction hot spots.

Relative binding free energy calculations with transformato: A molecular dynamics engine-independent tool

We present the software package transformato for the setup of large-scale relative binding free energy calculations. Transformato is written in Python as an open source project (https://github.com/wiederm/transformato); in contrast to comparable tools, it is not closely tied to a particular molecular dynamics engine to carry out the underlying simulations. Instead of alchemically transforming a ligand L₁ directly into another L₂, the two ligands are mutated to a common core. Thus, while dummy atoms are required at intermediate states, in particular at the common core state, none are present at the physical endstates. To validate the method, we calculated 76 relative binding free energy differences ΔΔGL1→L2bind for five protein–ligand systems. The overall root mean squared error to experimental binding free energies is 1.17 kcal/mol with a Pearson correlation coefficient of 0.73. For selected cases, we checked that the relative binding free energy differences between pairs of ligands do not depend on the choice of the intermediate common core structure. Additionally, we report results with and without hydrogen mass reweighting. The code currently supports OpenMM, CHARMM, and CHARMM/OpenMM directly. Since the program logic to choose and construct alchemical transformation paths is separated from the generation of input and topology/parameter files, extending transformato to support additional molecular dynamics engines is straightforward.

Oncogenic Mutations in the DNA-Binding Domain of FOXO1 that Disrupt Folding: Quantitative Insights from Experiments and Molecular Simulations

FOXO1, a member of the family of winged-helix motif Forkhead box (FOX) transcription factors, is the most abundantly expressed FOXO member in mature B cells. Sequencing of diffuse large B-cell lymphoma (DLBCL) tumors and cell lines identified specific mutations in the forkhead domain linked to loss of function. Differential scanning calorimetry and thermal shift assays were used to characterize how eight of these mutations affect the stability of the FOX domain. Mutations L183P and L183R were found to be particularly destabilizing. Electrophoresis mobility shift assays show these same mutations also disrupt FOXO1 binding to their canonical DNA sequences, suggesting that the loss of function is due to destabilization of the folded structure. Computational modeling of the effect of mutations on FOXO1 folding was performed using alchemical free energy perturbation (FEP), and a Markov model of the entire folding reaction was constructed from massively parallel molecular simulations, which predicts folding pathways involving the late folding of helix α3. Although FEP can qualitatively predict the destabilization from L183 mutations, we find that a simple hydrophobic transfer model, combined with estimates of unfolded-state solvent-accessible surface areas from molecular simulations, is able to more accurately predict changes in folding free energies due to mutations. These results suggest that the atomic detail provided by simulations is important for the accurate prediction of mutational effects on folding stability. Corresponding disease-associated mutations in other FOX family members support further experimental and computational studies of the folding mechanism of FOX domains.

Studying Disease-Associated UBE3A Missense Variants Using Enhanced Sampling Molecular Simulations

Missense variants in UBE3A underlie neurodevelopmental conditions such as Angelman Syndrome and Autism Spectrum Disorder, but the underlying molecular pathological consequences on protein folding and function are poorly understood. Here, we report a novel, maternally inherited, likely pathogenic missense variant in UBE3A (NM_000462.4(UBE3A_v001):(c.1841T>C) (p.(Leu614Pro))) in a child that presented with myoclonic epilepsy from 14 months, subsequent developmental regression from 16 months, and additional features consistent with Angelman Syndrome. To understand the impact of p.(Leu614Pro) on UBE3A, we used adiabatic biased molecular dynamics and metadynamics simulations to investigate conformational differences from wildtype proteins. Our results suggest that Leu614Pro substitution leads to less efficient binding and substrate processing compared to wildtype. Our results support the use of enhanced sampling molecular simulations to investigate the impact of missense UBE3A variants on protein function that underlies neurodevelopment and human disorders.

Identification of potential inhibitors, conformational dynamics, and mechanistic insights into mutant Kirsten rat sarcoma virus (G13D) driven cancers

The mutations at the hotspot region of K-Ras result in the progression of cancer types. Our study aimed to explore the small molecule inhibitors against the G13D mutant K-Ras model with anti-cancerous activity from food and drug administration (FDA)-approved drug compounds. We implemented several computational strategies such as pharmacophore-based virtual screening, molecular docking, absorption, distribution, metabolism and excretion features, and molecular simulation to ensure the identified hit compounds have potential efficacy against G13D K-Ras. We found that the FDA-approved compounds, namely, azelastine, dihydrocodeine, paroxetine, and tramadol, are potential candidates to inhibit the action of G13D mutant K-Ras. All four compounds exhibited similar binding patterns of sotorasib, and a structural binding mechanism with significant hydrophobic contacts. The descriptor features from the QikProp of all four compounds are within allowable limits compared to sotorasib drug. Consequently, a molecular simulation result emphasized that the dihydrocodeine and tramadol exhibited less fluctuation, minimal basin, significant h-bonds, and potent inhibition against G13D K-Ras. As a result, the current research identifies prospective K-Ras inhibitors that could be further improved with biochemical analysis for precision medicine against K-Ras-driven cancers.

Addressing Intersite Coupling Unlocks Large Combinatorial Chemical Spaces for Alchemical Free Energy Methods

Alchemical free energy methods are playing a growing role in molecular design, both for computer-aided drug design of small molecules and for computational protein design. Multisite λ dynamics (MSλD) is a uniquely scalable alchemical free energy method that enables more efficient exploration of combinatorial alchemical spaces encountered in molecular design, but simulations have typically been limited to a few hundred ligands or sequences. Here, we focus on coupling between sites to enable scaling to larger alchemical spaces. We first discuss updates to the biasing potentials that facilitate MSλD sampling to include coupling terms and show that this can provide more thorough sampling of alchemical states. We then harness coupling between sites by developing a new free energy estimator based on the Potts models underlying direct coupling analysis, a method for predicting contacts from sequence coevolution, and find it yields more accurate free energies than previous estimators. The sampling requirements of the Potts model estimator scale with the square of the number of sites, a substantial improvement over the exponential scaling of the standard estimator. This opens up exploration of much larger alchemical spaces with MSλD for molecular design.

Exploring the Effect of Enhanced Sampling on Protein Stability Prediction

Changes in protein stability due to side-chain mutations are evaluated by alchemical free-energy calculations based on classical molecular dynamics (MD) simulations in explicit solvent using the GROMOS force field. Three proteins of different complexity with a total number of 93 single-point mutations are analyzed, and the relative free-energy differences are discussed with respect to configurational sampling and (dis)agreement with experimental data. For the smallest protein studied, a 34-residue WW domain, the starting structure dependence of the alchemical free-energy changes, is discussed in detail. Deviations from previous simulations for the two other proteins are shown to result from insufficient sampling in the earlier studies. Hamiltonian replica exchange in combination with multiple starting structures and sufficient sampling time of more than 100 ns per intermediate alchemical state is required in some cases to reach convergence.

RestraintMaker: a graph-based approach to select distance restraints in free-energy calculations with dual topology

The calculation of relative binding free energies (RBFE) involves the choice of the end-state/system representation, of a sampling approach, and of a free-energy estimator. System representations are usually termed "single topology" or "dual topology". As the terminology is often used ambiguously in the literature, a systematic categorization of the system representations is proposed here. In the dual-topology approach, the molecules are simulated as separate molecules. Such an approach is relatively easy to automate for high-throughput RBFE calculations compared to the single-topology approach. Distance restraints are commonly applied to prevent the molecules from drifting apart, thereby improving the sampling efficiency. In this study, we introduce the program RestraintMaker, which relies on a greedy algorithm to find (locally) optimal distance restraints between pairs of atoms based on geometric measures. The algorithm is further extended for multi-state methods such as enveloping distribution sampling (EDS) or multi-site [Formula: see text]-dynamics. The performance of RestraintMaker is demonstrated for toy models and for the calculation of relative hydration free energies. The Python program can be used in script form or through an interactive GUI within PyMol. The selected distance restraints can be written out in GROMOS or GROMACS file formats. Additionally, the program provides a human-readable JSON format that can easily be parsed and processed further. The code of RestraintMaker is freely available on GitHub https://github.com/rinikerlab/restraintmaker.

Effect of Cysteine Oxidation in SARS-CoV-2 Receptor-Binding Domain on Its Interaction with Two Cell Receptors: Insights from Atomistic Simulations

Binding of the SARS-CoV-2 S-glycoprotein to cell receptors is vital for the entry of the virus into cells and subsequent infection. ACE2 is the main cell receptor for SARS-CoV-2, which can attach to the C-terminal receptor-binding domain (RBD) of the SARS-CoV-2 S-glycoprotein. The GRP78 receptor plays an anchoring role, which attaches to the RBD and increases the chance of other RBDs binding to ACE2. Although high levels of reactive oxygen and nitrogen species (RONS) are produced during viral infections, it is not clear how they affect the RBD structure and its binding to ACE2 and GRP78. In this research, we apply molecular dynamics simulations to study the effect of oxidation of the highly reactive cysteine (Cys) amino acids of the RBD on its binding to ACE2 and GRP78. The interaction energy of both ACE2 and GRP78 with the whole RBD, as well as with the RBD main regions, is compared in both the native and oxidized RBDs. Our results show that the interaction energy between the oxidized RBD and ACE2 is strengthened by 155 kJ/mol, increasing the binding of the RBD to ACE2 after oxidation. In addition, the interaction energy between the RBD and GRP78 is slightly increased by 8 kJ/mol after oxidation, but this difference is not significant. Overall, these findings highlight the role of RONS in the binding of the SARS-CoV-2 S-glycoprotein to host cell receptors and suggest an alternative mechanism by which RONS could modulate the entrance of viral particles into the cells.

Computational Insights into the Unfolding of a Destabilized Superoxide Dismutase 1 Mutant

In this work, we investigate the β-barrel of superoxide dismutase 1 (SOD1) in a mutated form, the isoleucine 35 to alanine (I35A) mutant, commonly used as a model system to decipher the role of the full-length apoSOD1 protein in amyotrophic lateral sclerosis (ALS). It is known from experiments that the mutation reduces the stability of the SOD1 barrel and makes it largely unfolded in the cell at 37 degrees Celsius. We deploy state-of-the-art computational machinery to examine the thermal destabilization of the I35A mutant by comparing two widely used force fields, Amber a99SB-disp and CHARMM36m. We find that only the latter force field, when combined with the Replica Exchange with Solute Scaling (REST2) approach, reproduces semi-quantitatively the experimentally observed shift in the melting between the original and the mutated SOD1 barrel. In addition, we analyze the unfolding process and the conformational landscape of the mutant, finding these largely similar to those of the wildtype. Nevertheless, we detect an increased presence of partially misfolded states at ambient temperatures. These states, featuring conformational changes in the region of the β-strands β4-β6, might provide a pathway for nonnative aggregation.

Accurate Prediction of Protein Thermodynamic Stability Changes upon Residue Mutation using Free Energy Perturbation

This work describes the application of a physics-based computational approach to predict the relative thermodynamic stability of protein variants, and evaluates the quantitative accuracy of those predictions compared to experimental data obtained from a diverse set of protein systems assayed at variable pH conditions. Physical stability is a key determinant of the clinical and commercial success of biological therapeutics, vaccines, diagnostics, enzymes and other protein-based products. Although experimental techniques for measuring the impact of amino acid residue mutation on the stability of proteins exist, they tend to be time consuming and costly, hence the need for accurate prediction methods. In contrast to many of the commonly available computational methods for stability prediction, the Free Energy Perturbation approach applied in this paper explicitly accounts for solvent effects and samples conformational dynamics using a rigorous molecular dynamics simulation process. On the entire validation dataset, consisting of 328 single point mutations spread across 14 distinct protein structures, our results show good overall correlation with experiment with an R² of 0.65 and a low mean unsigned error of 0.95 kcal/mol. Application of the FEP approach in conjunction with experimental assessment techniques offers opportunities to lower the time and expense of product development and reduce the risk of costly late-stage failures.

BLaDE: A Basic Lambda Dynamics Engine for GPU-Accelerated Molecular Dynamics Free Energy Calculations

There is an accelerating interest in practical applications of alchemical free energy methods to problems in protein design, constant pH simulations, and especially computer-aided drug design. In the present paper, we describe a basic lambda dynamics engine (BLaDE) that enables alchemical free energy simulations, including multisite λ dynamics (MSλD) simulations, on graphical processor units (GPUs). We find that BLaDE is 5 to 8 times faster than the current GPU implementation of MSλD-based free energy calculations in CHARMM. We also demonstrate that BLaDE running standard molecular dynamics attains a performance competitive with and sometimes exceeding that of the highly optimized OpenMM GPU code. BLaDE is available as a standalone program and through an API in CHARMM.

Computational Design of Potent D-Peptide Inhibitors of SARS-CoV-2

Blocking the association between the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) spike protein receptor-binding domain (RBD) and the human angiotensin-converting enzyme 2 (ACE2) is an attractive therapeutic approach to prevent the virus from entering human cells. While antibodies and other modalities have been developed to this end, d-amino acid peptides offer unique advantages, including serum stability, low immunogenicity, and low cost of production. Here, we designed potent novel D-peptide inhibitors that mimic the ACE2 α1-binding helix by searching a mirror-image version of the PDB. The two best designs bound the RBD with affinities of 29 and 31 nM and blocked the infection of Vero cells by SARS-CoV-2 with IC50 values of 5.76 and 6.56 μM, respectively. Notably, both D-peptides neutralized with a similar potency the infection of two variants of concern: B.1.1.7 and B.1.351 in vitro. These potent D-peptide inhibitors are promising lead candidates for developing SARS-CoV-2 prophylactic or therapeutic treatments.

Computational investigation to identify potent inhibitors of the GTPase-Kirsten RAt sarcoma virus (K-Ras) mutants G12C and G12D

K-Ras mutations are frequent in various cancer types, and according to recent research, K-Ras possesses four-drug targeting sites. This increased our interest in finding potential small molecule inhibitors with anticancer activity to treat K-Ras-driven cancers. We utilized integrated bioinformatic strategies, such as XP docking, MM-GBSA, cell-line cytotoxicity prediction, ADMET, and molecular simulation, to discover potential inhibitors of G12C and G12D mutants compared to sotorasib, which is a recent FDA-approved inhibitor of G12C. We identified compounds, such as flupentixol, amlodipine, and fluvoxamine, for the G12C mutant and paroxetine, flupentixol, and zuclopenthixol for the G12D mutant with significant inhibitory functions. All five compounds bound to the H95 cryptic groove of mutant K-Ras with high efficiency and, like sotorasib, retained a novel binding mechanism with additional hydrophobic interactions at the molecular level. Furthermore, the simulation studies suggested that the binding of flupentixol and amlodipine to G12C stabilizes switch I and switch II. In contrast, paroxetine and flupentixol to G12D showed a similar trend compared to sotorasib complexes. Thus, despite the very dynamic functionality of K-Ras switches I and II, the binding of shortlisted compounds is highly stable. Therefore, the reported study provides potential drug candidates for K-Ras inhibition that can be further developed with in vitro and in vivo evidence for targeted therapy.

Expanded Ensemble Methods Can be Used to Accurately Predict Protein-Ligand Relative Binding Free Energies

Alchemical free energy methods have become indispensable in computational drug discovery for their ability to calculate highly accurate estimates of protein-ligand affinities. Expanded ensemble (EE) methods, which involve single simulations visiting all of the alchemical intermediates, have some key advantages for alchemical free energy calculation. However, there have been relatively few examples published in the literature of using expanded ensemble simulations for free energies of protein-ligand binding. In this paper, as a test of expanded ensemble methods, we compute relative binding free energies using the Open Force Field Initiative force field (codename "Parsley") for 24 pairs of Tyk2 inhibitors derived from a congeneric series of 16 compounds. The EE predictions agree well with the experimental values (root-mean-square error (RMSE) of 0.94 ± 0.13 kcal mol^-1 and mean unsigned error (MUE) of 0.75 ± 0.12 kcal mol^-1). We find that while increasing the number of alchemical intermediates can improve the phase space overlap, faster convergence can be obtained with fewer intermediates, as long as acceptance rates are sufficient. We also find that convergence can be improved using more aggressive updating of biases, and that estimates can be improved by performing multiple independent EE calculations. This work demonstrates that EE is a viable option for alchemical free energy calculation. We discuss the implications of these findings for rational drug design, as well as future directions for improvement.