Alchemical Free Energy Calculations to Investigate Protein-Protein Interactions: the Case of the CDC42/PAK1 Complex

Comparing Supervised Learning and Rigorous Approach for Predicting Protein Stability upon Point Mutations in Difficult Targets

Accurate prediction of protein stability upon a point mutation has important applications in drug discovery and personalized medicine. It remains a challenging issue in computational biology. Existing computational prediction methods, which range from mechanistic to supervised learning approaches, have experienced limited progress over the last few decades. This stagnation is largely due to their heavy reliance on both the quantity and quality of the training data. This is evident in recent state-of-the-art methods that continue to yield substantial errors on two challenging blind test sets: frataxin and p53, with average root-mean-square errors exceeding 3 and 1.5 kcal/mol, respectively, which is still above the theoretical 1 kcal/mol prediction barrier. Rigorous approaches, on the other hand, offer greater potential for accuracy without relying on training data but are computationally demanding and require both wild-type and mutant structure information. Although they showed high accuracy for conserving mutations, their performance is still limited for charge-changing mutation cases. This might be due to the lack of an available mutant structure, often represented by a simplified capped peptide. The recent advances in protein structure prediction methods now make it possible to obtain structures comparable to experimental ones, including complete mutant structure information. In this work, we compare the performance of supervised learning-based methods and rigorous approaches for predicting protein stability on point mutations in difficult targets: frataxin and p53. The rigorous alchemical method significantly surpasses state-of-the-art techniques in terms of both the root-mean-squared error and Pearson correlation coefficient in these two challenging blind test sets. Additionally, we propose an improved alchemical method that employs the pmx double-system/single-box approach to accurately predict the folding free energy change upon both conserving and charge-changing mutations. The enhanced protocol can accurately predict both types of mutations, thereby outperforming existing state-of-the-art methods in overall performance.

Identifying and Overcoming the Sampling Challenges in Relative Binding Free Energy Calculations of a Model Protein:Protein Complex

Relative alchemical binding free energy calculations are routinely used in drug discovery projects to optimize the affinity of small molecules for their drug targets. Alchemical methods can also be used to estimate the impact of amino acid mutations on protein:protein binding affinities, but these calculations can involve sampling challenges due to the complex networks of protein and water interactions frequently present in protein:protein interfaces. We investigate these challenges by extending a graphics processing unit (GPU)-accelerated open-source relative free energy calculation package (Perses) to predict the impact of amino acid mutations on protein:protein binding. Using the well-characterized model system barnase:barstar, we describe analyses for identifying and characterizing sampling problems in protein:protein relative free energy calculations. We find that mutations with sampling problems often involve charge-changes, and inadequate sampling can be attributed to slow degrees of freedom that are mutation-specific. We also explore the accuracy and efficiency of current state-of-the-art approaches─alchemical replica exchange and alchemical replica exchange with solute tempering─for overcoming relevant sampling problems. By employing sufficiently long simulations, we achieve accurate predictions (RMSE 1.61, 95% CI: [1.12, 2.11] kcal/mol), with 86% of estimates within 1 kcal/mol of the experimentally determined relative binding free energies and 100% of predictions correctly classifying the sign of the changes in binding free energies. Ultimately, we provide a model workflow for applying protein mutation free energy calculations to protein:protein complexes, and importantly, catalog the sampling challenges associated with these types of alchemical transformations. Our free open-source package (Perses) is based on OpenMM and is available at https://github.com/choderalab/perses.

Free Energy Perturbation Calculations of Mutation Effects on SARS-CoV-2 RBD::ACE2 Binding Affinity

The strength of binding between human angiotensin converting enzyme 2 (ACE2) and the receptor binding domain (RBD) of viral spike protein plays a role in the transmissibility of the SARS-CoV-2 virus. In this study we focus on a subset of RBD mutations that have been frequently observed in infected individuals and probe binding affinity changes to ACE2 using surface plasmon resonance (SPR) measurements and free energy perturbation (FEP) calculations. Our SPR results are largely in accord with previous studies but discrepancies do arise due to differences in experimental methods and to protocol differences even when a single method is used. Overall, we find that FEP performance is superior to that of other computational approaches examined as determined by agreement with experiment and, in particular, by its ability to identify stabilizing mutations. Moreover, the calculations successfully predict the observed cooperative stabilization of binding by the Q498R N501Y double mutant present in Omicron variants and offer a physical explanation for the underlying mechanism. Overall, our results suggest that despite the significant computational cost, FEP calculations may offer an effective strategy to understand the effects of interfacial mutations on protein-protein binding affinities and, hence, in a variety of practical applications such as the optimization of neutralizing antibodies.

Role of Dynamics and Mutations in Interactions of a Zinc Finger Antiviral Protein with CG-rich Viral RNA

Zinc finger antiviral protein (ZAP) is a host antiviral factor that selectively inhibits the replication of a variety of viruses. ZAP recognizes the CG-enriched RNA sequences and activates the viral RNA degradation machinery. In this work, we investigated the dynamics of a ZAP/RNA complex and computed the energetics of mutations in ZAP that affect its binding to the viral RNA. The crystal structure of a mouse-ZAP/RNA complex showed that RNA interacts with the zinc finger 2 (ZF2) and ZF3 domains. However, we found that due to the dynamic behavior of the single-stranded RNA, the terminal nucleotides C1 and G2 of RNA change their positions from the ZF3 to the ZF1 domain. Moreover, the electrostatic interactions between the zinc ions and the viral RNA provide further stability to the ZAP/RNA complex. We also provide structural and thermodynamic evidence for seven residue pairs (C1-Arg74, C1-Arg179, G2-Arg74, U3-Lys76, C4-Lys76, G5-Arg95, and U6-Glu204) that show favorable ZAP/RNA interactions, although these interactions were not observed in the ZAP/RNA crystal structure. Consistent with the observations from the mouse-ZAP/RNA crystal structure, we found that four residue pairs (C4-Lys89, C4-Leu90, C4-Tyr108, and G5-Lys107) maintained stable interactions in MD simulations. Based on experimental mutagenesis studies and our residue-level interaction analysis, we chose seven residues (Arg74, Lys76, Lys89, Arg95, Lys107, Tyr108, and Arg179) for individual alanine mutations. In addition, we studied mutations in those residues that are only observed in the crystal structures as interacting with RNA (Tyr98, Glu148, and Arg170). Out of these 10 mutations, we found that the Ala mutation in each of the five residues Arg74, Lys76, Lys89, Lys107, and Glu148 significantly reduced the binding affinity of ZAP to RNA.

Exploring PROTAC Cooperativity with Coarse-Grained Alchemical Methods

Proteolysis targeting chimera (PROTAC) is a novel drug modality that facilitates the degradation of a target protein by inducing proximity with an E3 ligase. In this work, we present a new computational framework to model the cooperativity between PROTAC-E3 binding and PROTAC-target binding principally through protein-protein interactions (PPIs) induced by the PROTAC. Due to the scarcity and low resolution of experimental measurements, the physical and chemical drivers of these non-native PPIs remain to be elucidated. We develop a coarse-grained (CG) approach to model interactions in the target-PROTAC-E3 complexes, which enables converged thermodynamic estimations using alchemical free energy calculation methods despite an unconventional scale of perturbations. With minimal parametrization, we successfully capture fundamental principles of cooperativity, including the optimality of intermediate PROTAC linker lengths that originates from configurational entropy. We qualitatively characterize the dependency of cooperativity on PROTAC linker lengths and protein charges and shapes. Minimal inclusion of sequence- and conformation-specific features in our current force field, however, limits quantitative modeling to reproduce experimental measurements, but further development of the CG model may allow for efficient computational screening to optimize PROTAC cooperativity.

Alchemical Free-Energy Calculations of Watson-Crick and Hoogsteen Base Pairing Interconversion in DNA

Hoogsteen (HG) base pairs have a transient nature and can be structurally similar to Watson-Crick (WC) base pairs, making their occurrence and thermodynamic stability difficult to determine experimentally. Herein, we employed the restrain-free-energy perturbation-release (R-FEP-R) method to calculate the relative free energy of the WC and HG base pairing modes in isolated and bound DNA systems and predict the glycosyl torsion conformational preference of purine bases. Notably, this method does not require prior knowledge of the transition pathway between the two end states. Remarkably, relatively fast convergence was reached, with results in excellent agreement with experimental data for all the examined DNA systems. The R-REP-R method successfully determined the stability of HG base pairing and more generally, the conformational preference of purine bases, in these systems. Therefore, this computational approach can help to understand the dynamic equilibrium between the WC and HG base pairing modes in DNA.