Computationally Empowered Workflow Identifies Novel Covalent Allosteric Binders for KRASG12C

Alchemical Transformations and Beyond: Recent Advances and Real-World Applications of Free Energy Calculations in Drug Discovery

Computational methods constitute efficient strategies for screening and optimizing potential drug molecules. A critical factor in this process is the binding affinity between candidate molecules and targets, quantified as binding free energy. Among various estimation methods, alchemical transformation methods stand out for their theoretical rigor. Despite challenges in force field accuracy and sampling efficiency, advancements in algorithms, software, and hardware have increased the application of free energy perturbation (FEP) calculations in the pharmaceutical industry. Here, we review the practical applications of FEP in drug discovery projects since 2018, covering both ligand-centric and residue-centric transformations. We show that relative binding free energy calculations have steadily achieved chemical accuracy in real-world applications. In addition, we discuss alternative physics-based simulation methods and the incorporation of deep learning into free energy calculations.

Alchemical Free Energy Workflows for the Computation of Protein-Ligand Binding Affinities

Alchemical free energy methods can be used for the efficient computation of relative binding free energies during preclinical drug discovery stages. In recent years, this has been facilitated further by the implementation of workflows that enable non-experts to quickly and consistently set up the required simulations. Given the correct input structures, workflows handle the difficult aspects of setting up perturbations, including consistently defining the perturbable molecule, its atom mapping and topology generation, perturbation network generation, running of the simulations via different sampling methods, and analysis of the results. Different academic and commercial workflows are discussed, including FEW, FESetup, FEPrepare, CHARMM-GUI, Transformato, PMX, QLigFEP, TIES, ProFESSA, PyAutoFEP, BioSimSpace, FEP+, Flare, and Orion. These workflows differ in various aspects, such as mapping algorithms or enhanced sampling methods. Some workflows can accommodate more than one molecular dynamics (MD) engine and use external libraries for tasks. Differences between workflows can present advantages for different use cases, however a lack of interoperability of the workflows' components hinders systematic comparisons.

Alchemical Free Energy Calculations on Membrane-Associated Proteins

Membrane proteins have diverse functions within cells and are well-established drug targets. The advances in membrane protein structural biology have revealed drug and lipid binding sites on membrane proteins, while computational methods such as molecular simulations can resolve the thermodynamic basis of these interactions. Particularly, alchemical free energy calculations have shown promise in the calculation of reliable and reproducible binding free energies of protein-ligand and protein-lipid complexes in membrane-associated systems. In this review, we present an overview of representative alchemical free energy studies on G-protein-coupled receptors, ion channels, transporters as well as protein-lipid interactions, with emphasis on best practices and critical aspects of running these simulations. Additionally, we analyze challenges and successes when running alchemical free energy calculations on membrane-associated proteins. Finally, we highlight the value of alchemical free energy calculations calculations in drug discovery and their applicability in the pharmaceutical industry.

The maximal and current accuracy of rigorous protein-ligand binding free energy calculations

Computational techniques can speed up the identification of hits and accelerate the development of candidate molecules for drug discovery. Among techniques for predicting relative binding affinities, the most consistently accurate is free energy perturbation (FEP), a class of rigorous physics-based methods. However, uncertainty remains about how accurate FEP is and can ever be. Here, we present what we believe to be the largest publicly available dataset of proteins and congeneric series of small molecules, and assess the accuracy of the leading FEP workflow. To ascertain the limit of achievable accuracy, we also survey the reproducibility of experimental relative affinity measurements. We find a wide variability in experimental accuracy and a correspondence between binding and functional assays. When careful preparation of protein and ligand structures is undertaken, FEP can achieve accuracy comparable to experimental reproducibility. Throughout, we highlight reliable protocols that can help maximize the accuracy of FEP in prospective studies.

Pharmacophoric analogs of sotorasib-entrapped KRAS G12C in its inactive GDP-bound conformation: covalent docking and molecular dynamics investigations

For decades, KRAS G12C was considered an undruggable target. However, in recent times, a covalent inhibitor known as sotorasib was discovered and approved for the treatment of patients with KRAS G12C-driven cancers. Ever since the discovery of this drug, several preclinical efforts have focused on identifying novel therapeutic candidates that could act as covalent binders of KRAS G12C. Despite these intensive efforts, only a few KRAS G12C inhibitors have entered clinical trials. Hence, this highlights the need to develop effective drug candidates that could be used in the treatment of KRAS G12C-driven cancers. Herein, we embarked on a virtual screening campaign that involves the identification of pharmacophores of sotorasib that could act as covalent arsenals against the KRAS G12C target. To our knowledge, this is the first computational study that involves the compilation of sotorasib pharmacophores from an online chemical database against KRAS G12C. After this library of chemical entities was compiled, we conducted a covalent docking-based virtual screening that revealed three promising drug candidates (CID_146235944, CID_160070181, and CID_140956845) binding covalently to the crucial nucleophilic side chain of Cys12 and interact with the residues that form the cryptic allosteric pocket of KRAS G12C in its inactive GDP-bound conformation. Subsequently, ADMET profiling portrayed the covalent inhibitors as lead-like candidates, while 100 ns molecular dynamics was used to substantiate their stability. Although our overall computational study has shown the promising potential of the lead-like candidates in impeding oncogenic RAS signaling, more experimental efforts are needed to validate and establish their preclinical relevance. Implication of KRAS G12C in cancer and computational approach towards impeding the KRAS G12C RAS signaling.

In Silico Evaluation of the Thr58-Associated Conserved Water with KRAS Switch-II Pocket Binders

The KRAS switch-II pocket (SII-P) has proven to be one of the most successful tools for targeting KRAS with small molecules to date. This has been demonstrated with several KRAS(G12C)-targeting covalent inhibitors, already resulting in two FDA-approved drugs. Several earlier-stage compounds have also been reported to engage KRAS SII-P with other position 12 mutants, including G12D, G12S, and G12R. A highly conserved water molecule exists in the KRAS SII-P, linking Thr58 of switch-II and Gly10 of β1 sheet. This conserved water is also present in the cocrystal structures of most of the disclosed small-molecule inhibitors but is only displaced by a handful of SII-P binders. Here, we evaluated the conserved water molecule energetics by the WaterMap for the SII-P binders with publicly disclosed structures and studied the water behavior in the presence of selected inhibitors by microsecond timescale molecular dynamics (MD) simulations using two water models (total simulation time of 120 μs). Our data revealed the high-energy nature of this hydration site when coexisting with an SII-P binder and that there is a preference for a single isolated hydration site in this location within the most advanced compounds. Furthermore, water displacement was only achieved with a few disclosed compounds and was suboptimal, as for instance a cyanomethyl group as a water displacer appears to introduce repulsion with the native conformation of Thr58. These results suggested that this conserved water should be considered more central when designing new inhibitors, especially in the design of noncovalent inhibitors targeting the SII-P.

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.

Computer-Aided Drug Design Boosts RAS Inhibitor Discovery

The Rat Sarcoma (RAS) family (NRAS, HRAS, and KRAS) is endowed with GTPase activity to regulate various signaling pathways in ubiquitous animal cells. As proto-oncogenes, RAS mutations can maintain activation, leading to the growth and proliferation of abnormal cells and the development of a variety of human cancers. For the fight against tumors, the discovery of RAS-targeted drugs is of high significance. On the one hand, the structural properties of the RAS protein make it difficult to find inhibitors specifically targeted to it. On the other hand, targeting other molecules in the RAS signaling pathway often leads to severe tissue toxicities due to the lack of disease specificity. However, computer-aided drug design (CADD) can help solve the above problems. As an interdisciplinary approach that combines computational biology with medicinal chemistry, CADD has brought a variety of advances and numerous benefits to drug design, such as the rapid identification of new targets and discovery of new drugs. Based on an overview of RAS features and the history of inhibitor discovery, this review provides insight into the application of mainstream CADD methods to RAS drug design.

Modeling receptor flexibility in the structure-based design of KRASG12C inhibitors

KRAS has long been referred to as an ‘undruggable’ target due to its high affinity for its cognate ligands (GDP and GTP) and its lack of readily exploited allosteric binding pockets. Recent progress in the development of covalent inhibitors of KRAS^G12C has revealed that occupancy of an allosteric binding site located between the α3-helix and switch-II loop of KRAS^G12C—sometimes referred to as the ‘switch-II pocket’—holds great potential in the design of direct inhibitors of KRAS^G12C. In studying diverse switch-II pocket binders during the development of sotorasib (AMG 510), the first FDA-approved inhibitor of KRAS^G12C, we found the dramatic conformational flexibility of the switch-II pocket posing significant challenges toward the structure-based design of inhibitors. Here, we present our computational approaches for dealing with receptor flexibility in the prediction of ligand binding pose and binding affinity. For binding pose prediction, we modified the covalent docking program CovDock to allow for protein conformational mobility. This new docking approach, termed as FlexCovDock, improves success rates from 55 to 89% for binding pose prediction on a dataset of 10 cross-docking cases and has been prospectively validated across diverse ligand chemotypes. For binding affinity prediction, we found standard free energy perturbation (FEP) methods could not adequately handle the significant conformational change of the switch-II loop. We developed a new computational strategy to accelerate conformational transitions through the use of targeted protein mutations. Using this methodology, the mean unsigned error (MUE) of binding affinity prediction were reduced from 1.44 to 0.89 kcal/mol on a set of 14 compounds. These approaches were of significant use in facilitating the structure-based design of KRAS^G12C inhibitors and are anticipated to be of further use in the design of covalent (and noncovalent) inhibitors of other conformationally labile protein targets.

Targeting RAS oncogenesis with SOS1 inhibitors

RAS proteins play major roles in many human cancers, but programs to develop direct RAS inhibitors so far have only been successful for the oncogenic KRAS mutant G12C. As an alternative approach, inhibitors for the RAS guanine nucleotide exchange factor SOS1 have been investigated by several academic groups and companies, and major progress has been achieved in recent years in the optimization of small molecule activators and inhibitors of SOS1. Here, we review the discovery and development of small molecule modulators of SOS1 and their molecular binding modes and modes of action. As targeting the RAS pathway is expected to result in the development of resistance mechanisms, SOS1 inhibitors will most likely be best applied in vertical combination approaches where two nodes of the RAS signaling pathway are hit simultaneously. We summarize the current understanding of which combination partners may be most beneficial for patients with RAS driven tumors.

KRAS(G12D) can be targeted by potent inhibitors via formation of salt bridge

KRAS mutation occurs in nearly 30% of human cancers, yet the most prevalent and oncogenic KRAS(G12D) variant still lacks inhibitors. Herein, we designed a series of potent inhibitors that can form a salt bridge with KRAS’s Asp12 residue. Our ITC results show that these inhibitors have similar binding affinity with both GDP-bound and GTP-bound KRAS(G12D), and our crystallographic studies reveal the structural basis of inhibitor binding-induced switch-II pocket in KRAS(G12D), experimentally confirming the formation of a salt bridge between the piperazine moiety of the inhibitors and the Asp12 residue of the mutant protein. Among KRAS family proteins and mutants, both ITC and enzymatic assays demonstrate the selectivity of the inhibitors for KRAS(G12D); and the inhibitors disrupt the KRAS–CRAF interaction. We also observed the inhibition of cancer cell proliferation as well as MAPK signaling by a representative inhibitor (TH-Z835). However, since the inhibition was not fully dependent on KRAS mutation status, it is possible that our inhibitors may have off-target effects via targeting non-KRAS small GTPases. Experiments with mouse xenograft models of pancreatic cancer showed that TH-Z835 significantly reduced tumor volume and synergized with an anti-PD-1 antibody. Collectively, our study demonstrates proof-of-concept for a strategy based on salt-bridge and induced-fit pocket formation for KRAS(G12D) targeting, which warrants future medicinal chemistry efforts for optimal efficacy and minimized off-target effects.

From computer-aided drug discovery to computer-driven drug discovery

Computational chemistry and structure-based design have traditionally been viewed as a subset of tools that could aid acceleration of the drug discovery process, but were not commonly regarded as a driving force in small molecule drug discovery. In the last decade however, there have been dramatic advances in the field, including (1) development of physics-based computational approaches to accurately predict a broad variety of endpoints from potency to solubility, (2) improvements in artificial intelligence and deep learning methods and (3) dramatic increases in computational power with the advent of GPUs and cloud computing, resulting in the ability to explore and accurately profile vast amounts of drug-like chemical space in silico. There have also been simultaneous advancements in structural biology such as cryogenic electron microscopy (cryo-EM) and computational protein-structure prediction, allowing for access to many more high-resolution 3D structures of novel drug-receptor complexes. The convergence of these breakthroughs has positioned structurally-enabled computational methods to be a driving force behind the discovery of novel small molecule therapeutics. This review will give a broad overview of the synergies in recent advances in the fields of computational chemistry, machine learning and structural biology, in particular in the areas of hit identification, hit-to-lead, and lead optimization.

FEPrepare: A Web-Based Tool for Automating the Setup of Relative Binding Free Energy Calculations

Relative binding free energy calculations in drug design are becoming a useful tool in facilitating lead binding affinity optimization in a cost- and time-efficient manner. However, they have been limited by technical challenges such as the manual creation of large numbers of input files to set up, run, and analyze free energy simulations. In this Application Note, we describe FEPrepare, a novel web-based tool, which automates the setup procedure for relative binding FEP calculations for the dual-topology scheme of NAMD, one of the major MD engines, using OPLS-AA force field topology and parameter files. FEPrepare provides the user with all necessary files needed to run a FEP/MD simulation with NAMD. FEPrepare can be accessed and used at https://feprepare.vi-seem.eu/.

Preliminary modelling as the first stage of targeted organic synthesis

The review aims to present a classification and applicability analysis of methods for preliminary molecular modelling for targeted organic, catalytic and biocatalytic synthesis. The following three main approaches are considered as a primary classification of the methods: modelling of the target – ligand coordination without structural information on both the target and the resulting complex; calculations based on experimentally obtained structural information about the target; and dynamic simulation of the target – ligand complex and the reaction mechanism with calculation of the free energy of the reaction. The review is meant for synthetic chemists to be used as a guide for building an algorithm for preliminary modelling and synthesis of structures with specified properties.

The bibliography includes 353 references.

Structure-based inhibitor design of mutant RAS proteins-a paradigm shift

As a member of small GTPase family, KRAS protein is a key physiological modulator of various cellular activities including proliferation. However, mutations of KRAS present in numerous cancer types, most frequently in pancreatic (> 60%), colorectal (> 40%), and lung cancers, drive oncogenic processes through overactivation of proliferation. The G12C mutation of KRAS protein is especially abundant in the case of these types of malignancies. Despite its key importance in human disease, KRAS was assumed to be non-druggable for a long time since the protein seemingly lacks potential drug-binding pockets except the nucleotide-binding site, which is difficult to be targeted due to the high affinity of KRAS for both GDP and GTP. Recently, a new approach broke the ice and provided evidence that upon covalent targeting of the G12C mutant KRAS, a highly dynamic pocket was revealed. This novel targeting is especially important since it serves with an inherent solution for drug selectivity. Based on these results, various structure-based drug design projects have been launched to develop selective KRAS mutant inhibitors. In addition to the covalent modification strategy mostly applicable for G12C mutation, different innovative solutions have been suggested for the other frequently occurring oncogenic G12 mutants. Here we summarize the latest advances of this field, provide perspectives for novel approaches, and highlight the special properties of KRAS, which might issue some new challenges.

Computationally Empowered Workflow Identifies Novel Covalent Allosteric Binders for KRASG12C

Due to its frequent mutations in multiple lethal cancers, KRAS is one of the most-studied anticancer targets nowadays. Since the discovery of the druggable allosteric binding site containing a G12C mutation, KRASG12C has been the focus of attention in oncology research. We report here a computationally driven approach aimed at identifying novel and selective KRASG12C covalent inhibitors. The workflow involved initial enumeration of virtual molecules tailored for the KRAS allosteric binding site. Tools such as pharmacophore modeling, docking, and free-energy perturbations were deployed to prioritize the compounds with the best profiles. The synthesized naphthyridinone scaffold showed the ability to react with G12C and inhibit KRASG12C . Analogues were prepared to establish structure-activity relationships, while molecular dynamics simulations and crystallization of the inhibitor-KRASG12C complex highlighted an unprecedented binding mode.