Experimental and Computational Analysis of Protein Stabilization by Gly-to-d-Ala Substitution: A Convolution of Native State and Unfolded State Effects

Exploring the Transferability of Replica Exchange Structure Reservoirs to Accelerate Generation of Ensembles for Alternate Hamiltonians or Protein Mutations

Generating precise ensembles is commonly a prerequisite to understand the energetics of biological processes using Molecular Dynamics (MD) simulations. Previously, we have shown how unweighted reservoirs built from high temperature MD simulations can accelerate convergence of Boltzmann-weighted ensembles by at least 10× with the Reservoir Replica Exchange MD (RREMD) method. Therefore, in this work, we explore whether an unweighted structure reservoir generated with one Hamiltonian (solute force field plus solvent model) can be reused to quickly generate accurately weighted ensembles for Hamiltonians other than the one that was used to generate the reservoir. We also extended this methodology to rapidly estimate the effects of mutations on peptide stability by using a reservoir of diverse structures obtained from wild-type simulations. These results suggest that structures generated via fast methods such as coarse-grained models or structures predicted by Rosetta or deep learning approaches could be integrated into a reservoir to accelerate generation of ensembles using more accurate representations.

Exploring Glycan Binding Specificity of Odorranalectin by Alanine Scanning Library

Fluorescently labelled alanine scan analogues of odorranalectin (OL), a cyclic peptide that exhibits lectin like properties, were screened for binding BSA-conjugated monosaccharides using an enzyme-linked lectin assay (ELLA). Results revealed that Lys⁵, Phe⁷, Tyr⁹, Gly¹², Leu¹⁴, and Thr¹⁷ were crucial for binding BSA-L-fucose, BSA-D-galactose and BSA-N-acetyl-D-galactosamine. Notably, Ala substitution of Ser³, Pro⁴, and Val¹³ resulted in higher binding affinities compared to the native OL. The obtained data also indicated that Arg⁸ plays an important role in differentiation of binding for BSA-L-fucose/D-galactose from BSA-N-acetyl-D-galactosamine. The thermodynamics of binding of the selected alanine analogues was evaluated by isothermal titration calorimetry. Low to moderate binding affinities were determined for the tetravalent MUC1 glycopeptide and asialofetuin, respectively, and high for the fucose rich polysaccharide, fucoidan. The thermodynamic profile of interactions with asialofetuin exhibits shift to an entropy-driven mechanism compared to the fucoidan, which displayed an enthalpyentropy compensation, typically associated with the carbohydratelectin recognition process.

Scaffold Hopping Transformations Using Auxiliary Restraints for Calculating Accurate Relative Binding Free Energies

In silico screening of drug-target interactions is a key part of the drug discovery process. Changes in the drug scaffold via contraction or expansion of rings, the breaking of rings, and the introduction of cyclic structures from acyclic structures are commonly applied by medicinal chemists to improve binding affinity and enhance favorable properties of candidate compounds. These processes, commonly referred to as scaffold hopping, are challenging to model computationally. Although relative binding free energy (RBFE) calculations have shown success in predicting binding affinity changes caused by perturbing R-groups attached to a common scaffold, applications of RBFE calculations to modeling scaffold hopping are relatively limited. Scaffold hopping inevitably involves breaking and forming bond interactions of quadratic functional forms, which is highly challenging. A novel method for handling ring opening/closure/contraction/expansion and linker contraction/expansion is presented here. To the best of our knowledge, RBFE calculations on linker contraction/expansion have not been previously reported. The method uses auxiliary restraints to hold the atoms at the ends of a bond in place during the breaking and forming of the bonds. The broad applicability of the method was demonstrated by examining perturbations involving small-molecule macrocycles and mutations of proline in proteins. High accuracy was obtained using the method for most of the perturbations studied. The rigor of the method was isolated from the force field by validating the method using relative and absolute hydration free energy calculations compared to standard simulation results. Unlike other methods that rely on λ-dependent functional forms for bond interactions, the method presented here can be employed using modern molecular dynamics software without modification of codes or force field functions.

A Cloud Computing Platform for Scalable Relative and Absolute Binding Free Energy Predictions: New Opportunities and Challenges for Drug Discovery

Free energy perturbation (FEP) has become widely used in drug discovery programs for binding affinity prediction between candidate compounds and their biological targets. However, limitations of FEP applications also exist, including, but not limited to, high cost, long waiting time, limited scalability, and breadth of application scenarios. To overcome these problems, we have developed XFEP, a scalable cloud computing platform for both relative and absolute free energy predictions using optimized simulation protocols. XFEP enables large-scale FEP calculations in a more efficient, scalable, and affordable way, for example, the evaluation of 5000 compounds can be performed in 1 week using 50-100 GPUs with a computing cost roughly equivalent to the cost for the synthesis of only one new compound. By combining these capabilities with artificial intelligence techniques for goal-directed molecule generation and evaluation, new opportunities can be explored for FEP applications in the drug discovery stages of hit identification, hit-to-lead, and lead optimization based not only on structure exploitation within the given chemical series but also including evaluation and comparison of completely unrelated molecules during structure exploration in a larger chemical space. XFEP provides the basis for scalable FEP applications to become more widely used in drug discovery projects and to speed up the drug discovery process from hit identification to preclinical candidate compound nomination.

Protein storytelling through physics

Every protein has a story-how it folds, what it binds, its biological actions, and how it misbehaves in aging or disease. Stories are often inferred from a protein's shape (i.e., its structure). But increasingly, stories are told using computational molecular physics (CMP). CMP is rooted in the principled physics of driving forces and reveals granular detail of conformational populations in space and time. Recent advances are accessing longer time scales, larger actions, and blind testing, enabling more of biology's stories to be told in the language of atomistic physics.

Computational design of noncanonical amino acid-based thioether staples at N/C-terminal domains of multi-modular pullulanase for thermostabilization in enzyme catalysis

Enzyme thermostabilization is considered a critical and often obligatory step in biosynthesis, because thermostability is a significant property of enzymes that can be used to evaluate their feasibility for industrial applications. However, conventional strategies for thermostabilizing enzymes generally introduce non-covalent interactions and/or natural covalent bonds caused by natural amino acid substitutions, and the trade-off between the activity and stability of enzymes remains a challenge. Here, we developed a computationally guided strategy for constructing thioether staples by incorporating noncanonical amino acid (ncAA) into the more flexible N/C-terminal domains of the multi-modular pullulanase from Bacillus thermoleovorans (BtPul) to enhance its thermostability. First, potential thioether staples located in the N/C-terminal domains of BtPul were predicted using RosettaMatch. Next, eight variants involving stable thioether staples were precisely predicted using FoldX and Rosetta ddg_monomer. Six positive variants were obtained, of which T73(O2beY)-171C had a 157% longer half-life at 70 °C and an increase of 7.0 °C in T m, when compared with the wild-type (WT). T73(O2beY)-171C/T126F/A72R exhibited an even more improved thermostability, with a 211% increase in half-life at 70 °C and a 44% enhancement in enzyme activity compared with the WT, which was attributed to further optimization of the local interaction network. This work introduces and validates an efficient strategy for enhancing the thermostability and activity of multi-modular enzymes.

Conformational editing of intrinsically disordered protein by α-methylation

Intrinsically disordered proteins (IDPs) constitute a large portion of “Dark Proteome” – difficult to characterize or yet to be discovered protein structures. Here we used conformationally constrained α-methylated amino acids to bias the conformational ensemble in the free unstructured activation domain of transcriptional coactivator ACTR. Different sites and patterns of substitutions were enabled by chemical protein synthesis and led to distinct populations of α-helices. A specific substitution pattern resulted in a substantially higher binding affinity to nuclear coactivator binding domain (NCBD) of CREB-binding protein, a natural binding partner of ACTR. The first X-ray structure of the modified ACTR domain - NCBD complex visualized a unique conformation of ACTR and confirmed that the key α-methylated amino acids are localized within α-helices in the bound state. This study demonstrates a strategy for characterization of individual conformational states of IDPs.

Control of protein conformation was achieved for intrinsically disordered protein by incorporation of α-methylated amino acids.

Amino Acid Residues Determine the Response of Flexible Metal-Organic Frameworks to Guests

Flexible metal-organic frameworks (MOFs) undergo structural transformations in response to physical and chemical stimuli. This is hard to control because of feedback between guest uptake and host structure change. We report a family of flexible MOFs based on derivatized amino acid linkers. Their porosity consists of a one-dimensional channel connected to three peripheral pockets. This network structure amplifies small local changes in linker conformation, which are strongly coupled to the guest packing in and the shape of the peripheral pockets, to afford large changes in the global pore geometry that can, for example, segment the pore into four isolated components. The synergy among pore volume, guest packing, and linker conformation that characterizes this family of structures can be determined by the amino acid side chain, because it is repositioned by linker torsion. The resulting control optimizes noncovalent interactions to differentiate the uptake and structure response of host-guest pairs with similar chemistries.

Dissecting the Energetics of Intrinsically Disordered Proteins via a Hybrid Experimental and Computational Approach

Intrinsically disordered proteins (IDPs) play important roles in biology, but little is known about the energetics of their inter-residue interactions. Methods that have been successfully applied to analyze the energetics of globular proteins are not applicable to the fluctuating partially ordered ensembles populated by IDPs. A combined computational experimental strategy is introduced for analyzing the energetic role of individual residues in the free state of IDPs. The approach combines experimental measurements of the binding of wild-type and mutant IDPs to their partners with alchemical free energy calculations of the structured complexes. These data allow quantitative information to be deduced about the free state via a thermodynamic cycle. The approach is validated by the analysis of the effects of mutations upon the binding free energy of the ovomucoid inhibitor third binding domain to its partners and is applied to the C-terminal domain of the measles virus nucleoprotein, a 125-residue IDP involved in the RNA transcription and replication of measles virus. The analysis reveals significant inter-residue interactions in the unbound IDP and suggests a biological role for them. The work demonstrates that advances in force fields and computational hardware have now led to the point where it is possible to develop methods, which integrate experimental and computational techniques to reveal insights that cannot be studied using either technique alone.

Selective enhanced sampling in dihedral energy facilitates overcoming the dihedral energy increase in protein folding and accelerates the searching for protein native structure

A dihedral-energy-based selective enhanced sampling method (D-SITSMD) is presented with improved capabilities for searching a protein's natively folded structure and for providing the underlying folding pathway.

Assessing AMBER force fields for protein folding in an implicit solvent

Molecular dynamics (MD) simulation implemented with a state-of-the-art protein force field and implicit solvent model is an attractive approach to investigate protein folding, one of the most perplexing problems in molecular biology. But how well can force fields developed independently of implicit solvent models work together in reproducing diverse protein native structures and measuring the corresponding folding thermodynamics is not always clear. In this work, we performed enhanced sampling MD simulations to assess the ability of six AMBER force fields (FF99SBildn, FF99SBnmr, FF12SB, FF14ipq, FF14SB, and FF14SBonlysc) as coupled with a recently improved pair-wise GB-Neck2 model in modeling the folding of two helical and two β-sheet peptides. Whilst most of the tested force fields can yield roughly similar features for equilibrium conformational ensembles and detailed folding free-energy profiles for short α-helical TC10b in an implicit solvent, the measured counterparts are significantly discrepant in the cases of larger or β-structured peptides (HP35, 1E0Q, and GTT). Additionally, the calculated folding/unfolding thermodynamic quantities can only partially match the experimental data. Although a combination of the force fields and GB-Neck2 implicit model able to describe all aspects of the folding transitions towards the native structures of all the considered peptides was not identified, we found that FF14SBonlysc coupled with the GB-Neck2 model seems to be a reasonably balanced combination to predict peptide folding preferences.

In silico Design of Laccase Thermostable Mutants From Lacc 6 of Pleurotus Ostreatus

Fungal laccase enzymes have a great biotechnological potential for bioremediation processes due to their ability to degrade compounds such as ρ-diphenol, aminophenols, polyphenols, polyamines, and aryldiamines. These enzymes have activity at different pH and temperature values, however, high temperatures can cause partial or total loss of enzymatic activity, so it is appropriate to do research to modify their secondary and/or tertiary structure to make them more resistant to extreme temperature conditions. In silico, a structure of the Lacc 6 enzyme of Pleurotus ostreatus was constructed using a laccase of Trametes versicolor as a template. From this structure, 16 mutants with possible resistance at high temperature due to ionic interactions, salt bridges and disulfide bonds were also obtained in silico. It was determined that 12 mutants called 4-DB, 3-DB, D233C-T310C, F468P, 3-SB, L132T, N79D, N372D, P203C, P203V, T147E, and W85F, presented the lowest thermodynamic energy. Based on the previous criterion and determining the least flexibility in the protein structures, three mutants (4-DB, 3-DB, and P203C) were selected, which may present high stability at high temperatures without affecting their active site. The obtained results allow the understanding of the molecular base that increase the structural stability of the enzyme Lacc 6 of Pleurotus ostreatus, achieving the in silico generation of mutants, which could have activity at high temperatures.

The Unfolded State of the C-Terminal Domain of L9 Expands at Low but Not at Elevated Temperatures

The temperature dependence of the overall dimensions of the denatured state ensemble (DSE) of proteins remains unclear. Some studies indicate compaction of the DSE at high temperatures, whereas others argue that dimensions do not decrease. The degree of compaction or expansion in the cold-denatured state has been less studied. To investigate the temperature dependence of unfolded state dimensions, small angle x-ray scattering measurements were performed in native buffer in the absence of denaturant for a designed point mutant of the C-terminal domain of L9, a small cooperatively folded α-β protein, at 14 different temperatures over the range of 5-60°C. The I98A mutation destabilizes the domain such that both the DSE and the folded state are populated at 25°C in the absence of denaturant or extreme pH. Thermal unfolding as well as cold unfolding can thus be observed in the absence of denaturant, allowing a direct comparison of these regimes for the same protein using the same technique. The temperature of maximal stability, T_s, is 30°C. There is no detectable change in R_g of the unfolded state as the temperature is increased above T_s, but a clear expansion is detected as the temperature is decreased below T_s. The R_g of the DSE populated in buffer was found to be 27.8 ± 1.7 Å at 5°C, 21.8 ± 1.9 Å at 30°C, and 21.7 ± 2.0 Å at 60°C. In contrast, no significant temperature dependence was observed for the value of R_g measured in 6 M guanidine hydrochloride. The small angle x-ray scattering data reported here indicate clear differences between the cold- and thermal-unfolded states and show that there is no significant compaction at elevated temperatures.

Predicting resistance of clinical Abl mutations to targeted kinase inhibitors using alchemical free-energy calculations

The therapeutic effect of targeted kinase inhibitors can be significantly reduced by intrinsic or acquired resistance mutations that modulate the affinity of the drug for the kinase. In cancer, the majority of missense mutations are rare, making it difficult to predict their impact on inhibitor affinity. This complicates the practice of precision medicine, pairing of patients with clinical trials, and development of next-generation inhibitors. Here, we examine the potential for alchemical free-energy calculations to predict how kinase mutations modulate inhibitor affinities to Abl, a major target in chronic myelogenous leukemia (CML). We find these calculations can achieve useful accuracy in predicting resistance for a set of eight FDA-approved kinase inhibitors across 144 clinically-identified point mutations, achieving a root mean square error in binding free energy changes of 1.1 0.9 1.3 kcal/mol (95% confidence interval) and correctly classifying mutations as resistant or susceptible with 88 82 93 % accuracy. Since these calculations are fast on modern GPUs, this benchmark establishes the potential for physical modeling to collaboratively support the rapid assessment and anticipation of the potential for patient mutations to affect drug potency in clinical applications.

Enzyme stabilization via computationally guided protein stapling

Thermostabilization represents a critical and often obligatory step toward enhancing the robustness of enzymes for organic synthesis and other applications. While directed evolution methods have provided valuable tools for this purpose, these protocols are laborious and time-consuming and typically require the accumulation of several mutations, potentially at the expense of catalytic function. Here, we report a minimally invasive strategy for enzyme stabilization that relies on the installation of genetically encoded, nonreducible covalent staples in a target protein scaffold using computational design. This methodology enables the rapid development of myoglobin-based cyclopropanation biocatalysts featuring dramatically enhanced thermostability (ΔT_m = +18.0 °C and ΔT₅₀ = +16.0 °C) as well as increased stability against chemical denaturation [ΔC_m (GndHCl) = 0.53 M], without altering their catalytic efficiency and stereoselectivity properties. In addition, the stabilized variants offer superior performance and selectivity compared with the parent enzyme in the presence of a high concentration of organic cosolvents, enabling the more efficient cyclopropanation of a water-insoluble substrate. This work introduces and validates an approach for protein stabilization which should be applicable to a variety of other proteins and enzymes.