Frustrated peptide chains at the fibril tip control the kinetics of growth of amyloid-ß fibrils.

Amyloid fibrillization is an exceedingly complex process in which incoming peptide chains bind to the fibril while concertedly folding. The coupling between folding and binding is not fully understood. We explore the molecular pathways of association of Aß40 monomers to fibril tips by combining time-resolved in situ scanning probe microscopy with molecular modeling. The comparison between experimental and simulation results shows that a complex supported by nonnative contacts is present in the equilibrium structure of the fibril tip and impedes fibril growth in a supersaturated solution. The unraveling of this frustrated state determines the rate of fibril growth. The kinetics of growth of freshly cut fibrils, in which the bulk fibril structure persists at the tip, complemented by molecular simulations, indicate that this frustrated complex comprises three or four monomers in nonnative conformations and likely is contained on the top of a single stack of peptide chains in the fibril structure. This pathway of fibril growth strongly deviates from the common view that the conformational transformation of each captured peptide chain is templated by the previously arrived peptide. The insights into the ensemble structure of the frustrated complex may guide the search for suppressors of Aß fibrillization. The uncovered dynamics of coupled structuring and assembly during fibril growth are more complex than during the folding of most globular proteins, as they involve the collective motions of several peptide chains that are not guided by a funneled energy landscape.

Mesoscopic protein-rich clusters host the nucleation of mutant p53 amyloid fibrils.

The protein p53 is a crucial tumor suppressor, often called "the guardian of the genome"; however, mutations transform p53 into a powerful cancer promoter. The oncogenic capacity of mutant p53 has been ascribed to enhanced propensity to fibrillize and recruit other cancer fighting proteins in the fibrils, yet the pathways of fibril nucleation and growth remain obscure. Here, we combine immunofluorescence three-dimensional confocal microscopy of human breast cancer cells with light scattering and transmission electron microscopy of solutions of the purified protein and molecular simulations to illuminate the mechanisms of phase transformations across multiple length scales, from cellular to molecular. We report that the p53 mutant R248Q (R, arginine; Q, glutamine) forms, both in cancer cells and in solutions, a condensate with unique properties, mesoscopic protein-rich clusters. The clusters dramatically diverge from other protein condensates. The cluster sizes are decoupled from the total cluster population volume and independent of the p53 concentration and the solution concentration at equilibrium with the clusters varies. We demonstrate that the clusters carry out a crucial biological function: they host and facilitate the nucleation of amyloid fibrils. We demonstrate that the p53 clusters are driven by structural destabilization of the core domain and not by interactions of its extensive unstructured region, in contradistinction to the dense liquids typical of disordered and partially disordered proteins. Two-step nucleation of mutant p53 amyloids suggests means to control fibrillization and the associated pathologies through modifying the cluster characteristics. Our findings exemplify interactions between distinct protein phases that activate complex physicochemical mechanisms operating in biological systems.

Braiding topology and the energy landscape of chromosome organization proteins.

Assemblies of structural maintenance of chromosomes (SMC) proteins and kleisin subunits are essential to chromosome organization and segregation across all kingdoms of life. While structural data exist for parts of the SMC-kleisin complexes, complete structures of the entire complexes have yet to be determined, making mechanistic studies difficult. Using an integrative approach that combines crystallographic structural information about the globular subdomains, along with coevolutionary information and an energy landscape optimized force field (AWSEM), we predict atomic-scale structures for several tripartite SMC-kleisin complexes, including prokaryotic condensin, eukaryotic cohesin, and eukaryotic condensin. The molecular dynamics simulations of the SMC-kleisin protein complexes suggest that these complexes exist as a broad conformational ensemble that is made up of different topological isomers. The simulations suggest a critical role for the SMC coiled-coil regions, where the coils intertwine with various linking numbers. The twist and writhe of these braided coils are coupled with the motion of the SMC head domains, suggesting that the complexes may function as topological motors. Opening, closing, and translation along the DNA of the SMC-kleisin protein complexes would allow these motors to couple to the topology of DNA when DNA is entwined with the braided coils.

OpenAWSEM with Open3SPN2: A fast, flexible, and accessible framework for large-scale coarse-grained biomolecular simulations.

We present OpenAWSEM and Open3SPN2, new cross-compatible implementations of coarse-grained models for protein (AWSEM) and DNA (3SPN2) molecular dynamics simulations within the OpenMM framework. These new implementations retain the chemical accuracy and intrinsic efficiency of the original models while adding GPU acceleration and the ease of forcefield modification provided by OpenMM's Custom Forces software framework. By utilizing GPUs, we achieve around a 30-fold speedup in protein and protein-DNA simulations over the existing LAMMPS-based implementations running on a single CPU core. We showcase the benefits of OpenMM's Custom Forces framework by devising and implementing two new potentials that allow us to address important aspects of protein folding and structure prediction and by testing the ability of the combined OpenAWSEM and Open3SPN2 to model protein-DNA binding. The first potential is used to describe the changes in effective interactions that occur as a protein becomes partially buried in a membrane. We also introduced an interaction to describe proteins with multiple disulfide bonds. Using simple pairwise disulfide bonding terms results in unphysical clustering of cysteine residues, posing a problem when simulating the folding of proteins with many cysteines. We now can computationally reproduce Anfinsen's early Nobel prize winning experiments by using OpenMM's Custom Forces framework to introduce a multi-body disulfide bonding term that prevents unphysical clustering. Our protein-DNA simulations show that the binding landscape is funneled towards structures that are quite similar to those found using experiments. In summary, this paper provides a simulation tool for the molecular biophysics community that is both easy to use and sufficiently efficient to simulate large proteins and large protein-DNA systems that are central to many cellular processes. These codes should facilitate the interplay between molecular simulations and cellular studies, which have been hampered by the large mismatch between the time and length scales accessible to molecular simulations and those relevant to cell biology.

AWSEM-Suite: a protein structure prediction server based on template-guided, coevolutionary-enhanced optimized folding landscapes.

The accurate and reliable prediction of the 3D structures of proteins and their assemblies remains difficult even though the number of solved structures soars and prediction techniques improve. In this study, a free and open access web server, AWSEM-Suite, whose goal is to predict monomeric protein tertiary structures from sequence is described. The model underlying the server's predictions is a coarse-grained protein force field which has its roots in neural network ideas that has been optimized using energy landscape theory. Employing physically motivated potentials and knowledge-based local structure biasing terms, the addition of homologous template and co-evolutionary restraints to AWSEM-Suite greatly improves the predictive power of pure AWSEM structure prediction. From the independent evaluation metrics released in the CASP13 experiment, AWSEM-Suite proves to be a reasonably accurate algorithm for free modeling, standing at the eighth position in the free modeling category of CASP13. The AWSEM-Suite server also features a front end with a user-friendly interface. The AWSEM-Suite server is a powerful tool for predicting monomeric protein tertiary structures that is most useful when a suitable structure template is not available. The AWSEM-Suite server is freely available at: https://awsem.rice.edu.

Unusual Organization of I-BAR Proteins on Tubular and Vesicular Membranes.

Protein-mediated membrane remodeling is a ubiquitous and critical process for proper cellular function. Inverse Bin/Amphiphysin/Rvs (I-BAR) domains drive local membrane deformation as a precursor to large-scale membrane remodeling. We employ a multiscale approach to provide the molecular mechanism of unusual I-BAR domain-driven membrane remodeling at a low protein surface concentration with near-atomistic detail. We generate a bottom-up coarse-grained model that demonstrates similar membrane-bound I-BAR domain aggregation behavior as our recent Mesoscopic Membrane with Explicit Proteins model. Together, these models bridge several length scales and reveal an aggregation behavior of I-BAR domains. We find that at low surface coverage (i.e., low bound protein density), I-BAR domains form transient, tip-to-tip strings on periodic flat membrane sheets. Inside of lipid bilayer tubules, we find linear aggregates parallel to the axis of the tubule. Finally, we find that I-BAR domains form tip-to-tip aggregates around the edges of membrane domes. These results are supported by in vitro experiments showing low curvature bulges surrounded by I-BAR domains on giant unilamellar vesicles. Overall, our models reveal new I-BAR domain aggregation behavior in membrane tubules and on the surface of vesicles at low surface concentration that add insight into how I-BAR domain proteins may contribute to certain aspects of membrane remodeling in cells.

Theoretical insights into mechanisms of channel-facilitated molecular transport in the presence of stochastic gating.

Molecular motion through pores plays a crucial role in various natural and industrial processes. One of the most fascinating features of biological channel-facilitated transport is a stochastic gating process, when the channels dynamically fluctuate between several conformations during the translocation. Although this phenomenon has been intensively investigated, many properties of translocation in a dynamically changing environment remain not well understood microscopically. We developed a discrete-state stochastic framework to analyze the molecular mechanisms of transport processes with stochastic gating by explicitly calculating molecular fluxes through the pores. Two scenarios are specifically investigated: (1) symmetry preserving stochastic gating with free-energy changes and (2) stochastic gating with symmetry changes but without modifications in the overall particle-pore interactions. It is found that stochastic gating can either accelerate or slow down the molecular translocation depending on the specific parameters of the system. We argue that biological systems might optimize their performance by utilizing conformational fluctuations of channels. Our theoretical analysis clarifies physical-chemical aspects of the molecular mechanisms of transport with stochastic gating.

Insights into the Cooperative Nature of ATP Hydrolysis in Actin Filaments.

Actin filaments continually assemble and disassemble within a cell. Assembled filaments "age" as a bound nucleotide ATP within each actin subunit quickly hydrolyzes followed by a slower release of the phosphate Pi, leaving behind a bound ADP. This subtle change in nucleotide state of actin subunits affects filament rigidity as well as its interactions with binding partners. We present here a systematic multiscale ultra-coarse-graining approach that provides a computationally efficient way to simulate a long actin filament undergoing ATP hydrolysis and phosphate-release reactions while systematically taking into account available atomistic details. The slower conformational changes and their dependence on the chemical reactions are simulated with the ultra-coarse-graining model by assigning internal states to the coarse-grained sites. Each state is represented by a unique potential surface of a local heterogeneous elastic network. Internal states undergo stochastic transitions that are coupled to conformations of the underlying molecular system. The model reproduces mechanical properties of the filament and allows us to study whether conformational fluctuations in actin subunits produce cooperative filament aging. We find that the nucleotide states of neighboring subunits modulate the reaction kinetics, implying cooperativity in ATP hydrolysis and Pi release. We further systematically coarse grain the system into a Markov state model that incorporates assembly and disassembly, facilitating a direct comparison with previously published models. We find that cooperativity in ATP hydrolysis and Pi release significantly affects the filament growth dynamics only near the critical G-actin concentration, whereas far from it, both cooperative and random mechanisms show similar growth dynamics. In contrast, filament composition in terms of the bound nucleotide distribution varies significantly at all monomer concentrations studied. These results provide new insights, to our knowledge, into the cooperative nature of ATP hydrolysis and Pi release and the implications it has for actin filament properties, providing novel predictions for future experimental studies.

The mesoscopic membrane with proteins (MesM-P) model.

We present the Mesoscopic Membrane with Proteins (MesM-P) model, an extension of a previously developed elastic membrane model for mesoscale simulations of lipid membranes. MesM-P employs a discrete mesoscopic quasi-particle approach to model protein-facilitated shape and topology changes of the lipid membrane on length and time scales inaccessible to all-atom and quasimolecular coarse-grained molecular dynamics simulations. We investigate the ability of MesM-P to model the behavior of large lipid vesicles as a function of bound protein density. We find four distinct mechanisms for protein aggregation on the surface of the membrane, depending on membrane stiffness and protein spontaneous curvature. We also establish a connection between MesM-P and the results of higher resolution coarse-grained molecular dynamics simulations.

Multiscale simulations of protein-facilitated membrane remodeling.

Protein-facilitated shape and topology changes of cell membranes are crucial for many biological processes, such as cell division, protein trafficking, and cell signaling. However, the inherently multiscale nature of membrane remodeling presents a considerable challenge for understanding the mechanisms and physics that drive this process. To address this problem, a multiscale approach that makes use of a diverse set of computational and experimental techniques is required. The atomistic simulations provide high-resolution information on protein-membrane interactions. Experimental techniques, like electron microscopy, on the other hand, resolve high-order organization of proteins on the membrane. Coarse-grained (CG) and mesoscale computational techniques provide the intermediate link between the two scales and can give new insights into the underlying mechanisms. In this Review, we present the recent advances in multiscale computational approaches established in our group. We discuss various CG and mesoscale approaches in studying the protein-mediated large-scale membrane remodeling.

Stochastic Resonance in Protein Folding Dynamics.

Although protein folding reactions are usually studied under static external conditions, it is likely that proteins fold in a locally fluctuating cellular environment in vivo. To mimic such behavior in in vitro experiments, the local temperature of the solvent can be modulated either harmonically or using correlated noise. In this study, coarse-grained molecular simulations are used to investigate these possibilities, and it is found that both periodic and correlated random fluctuations of the environment can indeed accelerate folding kinetics if the characteristic frequencies of the applied fluctuations are commensurate with the internal timescale of the folding reaction; this is consistent with the phenomenon of stochastic resonance observed in many other condensed-matter processes. To test this theoretical prediction, the folding dynamics of phosphoglycerate kinase under harmonic temperature fluctuations are experimentally probed using Förster resonance energy transfer fluorescence measurements. To analyze these experiments, a combination of theoretical approaches is developed, including stochastic simulations of folding kinetics and an analytical mean-field kinetic theory. The experimental observations are consistent with the theoretical predictions of stochastic resonance in phosphoglycerate kinase folding. When combined with an alternative experiment on the protein VlsE using a power spectrum analysis, elaborated in Dave et al., ChemPhysChem 2016, 10.1002/cphc.201501041, the overall data overwhelmingly point to the experimental confirmation of stochastic resonance in protein folding dynamics.

Environmental Fluctuations and Stochastic Resonance in Protein Folding.

Stochastic resonance is a mechanism whereby a weak signal becomes detectable through the addition of noise. It is common in many macroscopic biological phenomena, but here we ask whether it can be observed in a microscopic biological phenomenon, protein folding. We investigate the folding kinetics of the protein VlsE, with a folding relaxation time of about 0.7 seconds at 38 °C in vitro. First we show that the VlsE unfolding/refolding reaction can be driven by a periodic thermal excitation above the reaction threshold. We detect the reaction by fluorescence from FRET labels on VlSE and show that accurate rate coefficients and activation barriers can be obtained from modulated kinetics. Then we weaken the periodic temperature modulation below the reaction threshold, and show that addition of artificial thermal noise speeds up the reaction from an undetectable to a detectable rate. We observe a maximum in the recovered signal as a function of thermal noise, a stochastic resonance. Simulation of a small model-protein, analysis in an accompanying theory paper, and our experimental result here all show that correlated noise is a physically and chemically plausible mechanism by which cells could modulate biomolecular dynamics during threshold processes such as signaling.

Dynamic force matching: Construction of dynamic coarse-grained models with realistic short time dynamics and accurate long time dynamics.

We recently developed a dynamic force matching technique for converting a coarse-grained (CG) model of a molecular system, with a CG potential energy function, into a dynamic CG model with realistic dynamics [A. Davtyan et al., J. Chem. Phys. 142, 154104 (2015)]. This is done by supplementing the model with additional degrees of freedom, called "fictitious particles." In that paper, we tested the method on CG models in which each molecule is coarse-grained into one CG point particle, with very satisfactory results. When the method was applied to a CG model of methanol that has two CG point particles per molecule, the results were encouraging but clearly required improvement. In this paper, we introduce a new type (called type-3) of fictitious particle that exerts forces on the center of mass of two CG sites. A CG model constructed using type-3 fictitious particles (as well as type-2 particles previously used) gives a much more satisfactory dynamic model for liquid methanol. In particular, we were able to construct a CG model that has the same self-diffusion coefficient and the same rotational relaxation time as an all-atom model of liquid methanol. Type-3 particles and generalizations of it are likely to be useful in converting more complicated CG models into dynamic CG models.

Dynamic force matching: A method for constructing dynamical coarse-grained models with realistic time dependence.

Coarse-grained (CG) models of molecular systems, with fewer mechanical degrees of freedom than an all-atom model, are used extensively in chemical physics. It is generally accepted that a coarse-grained model that accurately describes equilibrium structural properties (as a result of having a well constructed CG potential energy function) does not necessarily exhibit appropriate dynamical behavior when simulated using conservative Hamiltonian dynamics for the CG degrees of freedom on the CG potential energy surface. Attempts to develop accurate CG dynamic models usually focus on replacing Hamiltonian motion by stochastic but Markovian dynamics on that surface, such as Langevin or Brownian dynamics. However, depending on the nature of the system and the extent of the coarse-graining, a Markovian dynamics for the CG degrees of freedom may not be appropriate. In this paper, we consider the problem of constructing dynamic CG models within the context of the Multi-Scale Coarse-graining (MS-CG) method of Voth and coworkers. We propose a method of converting a MS-CG model into a dynamic CG model by adding degrees of freedom to it in the form of a small number of fictitious particles that interact with the CG degrees of freedom in simple ways and that are subject to Langevin forces. The dynamic models are members of a class of nonlinear systems interacting with special heat baths that were studied by Zwanzig [J. Stat. Phys. 9, 215 (1973)]. The properties of the fictitious particles can be inferred from analysis of the dynamics of all-atom simulations of the system of interest. This is analogous to the fact that the MS-CG method generates the CG potential from analysis of equilibrium structures observed in all-atom simulation data. The dynamic models generate a non-Markovian dynamics for the CG degrees of freedom, but they can be easily simulated using standard molecular dynamics programs. We present tests of this method on a series of simple examples that demonstrate that the method provides realistic dynamical CG models that have non-Markovian or close to Markovian behavior that is consistent with the actual dynamical behavior of the all-atom system used to construct the CG model. Both the construction and the simulation of such a dynamic CG model have computational requirements that are similar to those of the corresponding MS-CG model and are good candidates for CG modeling of very large systems.

Predictive energy landscapes for protein-protein association.

We investigate protein-protein association using the associative-memory, water-mediated, structure, and energy model (AWSEM), a coarse-grained protein folding model that has been optimized using energy-landscape theory. The potential was originally parameterized by enforcing a funneled nature for a database of dimeric interfaces but was later further optimized to create funneled folding landscapes for individual monomeric proteins. The ability of the model to predict interfaces was not tested previously. The present results show that simulated annealing of the model indeed is able to predict successfully the native interfaces of eight homodimers and four heterodimers, thus amounting to a flexible docking algorithm. We go on to address the relative importance of monomer geometry, flexibility, and nonnative intermonomeric contacts in the association process for the homodimers. Monomer surface geometry is found to be important in determining the binding interface, but it is insufficient. Using a uniform binding potential rather than the water-mediated potential results in sampling of misbound structures that are geometrically preferred but are nonetheless energetically disfavored by AWSEM, as well as in nature. Depending on the stability of the unbound monomers, nonnative contacts play different roles in the association process. For unstable monomers, thermodynamic states stabilized by nonnative interactions correspond to productive, on-pathway intermediates and can, therefore, catalyze binding through a fly-casting mechanism. For stable monomers, in contrast, states stabilized by nonnative interactions generally correspond to traps that impede binding.

Artificial intelligence-powered discovery of small molecules inhibiting CTLA-4 in cancer.

BACKGROUND/OBJECTIVES: Checkpoint inhibitors, which generate durable responses in many cancer patients, have revolutionized cancer immunotherapy. However, their therapeutic efficacy is limited, and immune-related adverse events are severe, especially for monoclonal antibody treatment directed against cytotoxic T-lymphocyte-associated protein 4 (CTLA-4), which plays a pivotal role in preventing autoimmunity and fostering anticancer immunity by interacting with the B7 proteins CD80 and CD86. Small molecules impairing the CTLA-4/CD80 interaction have been developed; however, they directly target CD80, not CTLA-4. SUBJECTS/METHODS: In this study, we performed artificial intelligence (AI)-powered virtual screening of approximately ten million compounds to identify those targeting CTLA-4. We validated the hits molecules with biochemical, biophysical, immunological, and experimental animal assays. RESULTS: The primary hits obtained from the virtual screening were successfully validated in vitro and in vivo. We then optimized lead compounds and obtained inhibitors (inhibitory concentration, 1 micromole) that disrupted the CTLA-4/CD80 interaction without degrading CTLA-4. CONCLUSIONS: Several compounds inhibited tumor development prophylactically and therapeutically in syngeneic and CTLA-4-humanized mice. Our findings support using AI-based frameworks to design small molecules targeting immune checkpoints for cancer therapy.

AI-powered discovery of a novel p53-Y220C reactivator.

Introduction: The p53-Y220C mutation is one of the most common mutations that play a major role in cancer progression. Methods: In this study, we applied artificial intelligence (AI)-powered virtual screening to identify small-molecule compounds that specifically restore the wild-type p53 conformation from p53-Y220C. From 10 million compounds, the AI algorithm selected a chemically diverse set of 83 high-scoring hits, which were subjected to several experimental assays using cell lines with different p53 mutations. Results: We identified one compound, H3, that preferentially killed cells with the p53-Y220C mutation compared to cells with other p53 mutations. H3 increased the amount of folded mutant protein with wild-type p53 conformation, restored its transcriptional functions, and caused cell cycle arrest and apoptosis. Furthermore, H3 reduced tumorigenesis in a mouse xenograft model with p53-Y220C-positive cells. Conclusion: AI enabled the discovery of the H3 compound that selectively reactivates the p53-Y220C mutant and inhibits tumor development in mice.

Discovery of a cryptic pocket in the AI-predicted structure of PPM1D phosphatase explains the binding site and potency of its allosteric inhibitors.

Virtual screening is a widely used tool for drug discovery, but its predictive power can vary dramatically depending on how much structural data is available. In the best case, crystal structures of a ligand-bound protein can help find more potent ligands. However, virtual screens tend to be less predictive when only ligand-free crystal structures are available, and even less predictive if a homology model or other predicted structure must be used. Here, we explore the possibility that this situation can be improved by better accounting for protein dynamics, as simulations started from a single structure have a reasonable chance of sampling nearby structures that are more compatible with ligand binding. As a specific example, we consider the cancer drug target PPM1D/Wip1 phosphatase, a protein that lacks crystal structures. High-throughput screens have led to the discovery of several allosteric inhibitors of PPM1D, but their binding mode remains unknown. To enable further drug discovery efforts, we assessed the predictive power of an AlphaFold-predicted structure of PPM1D and a Markov state model (MSM) built from molecular dynamics simulations initiated from that structure. Our simulations reveal a cryptic pocket at the interface between two important structural elements, the flap and hinge regions. Using deep learning to predict the pose quality of each docked compound for the active site and cryptic pocket suggests that the inhibitors strongly prefer binding to the cryptic pocket, consistent with their allosteric effect. The predicted affinities for the dynamically uncovered cryptic pocket also recapitulate the relative potencies of the compounds (τ b =0.70) better than the predicted affinities for the static AlphaFold-predicted structure (τ b =0.42). Taken together, these results suggest that targeting the cryptic pocket is a good strategy for drugging PPM1D and, more generally, that conformations selected from simulation can improve virtual screening when limited structural data is available.

Discovery of a cryptic pocket in the AI-predicted structure of PPM1D phosphatase explains the binding site and potency of its allosteric inhibitors.

Virtual screening is a widely used tool for drug discovery, but its predictive power can vary dramatically depending on how much structural data is available. In the best case, crystal structures of a ligand-bound protein can help find more potent ligands. However, virtual screens tend to be less predictive when only ligand-free crystal structures are available, and even less predictive if a homology model or other predicted structure must be used. Here, we explore the possibility that this situation can be improved by better accounting for protein dynamics, as simulations started from a single structure have a reasonable chance of sampling nearby structures that are more compatible with ligand binding. As a specific example, we consider the cancer drug target PPM1D/Wip1 phosphatase, a protein that lacks crystal structures. High-throughput screens have led to the discovery of several allosteric inhibitors of PPM1D, but their binding mode remains unknown. To enable further drug discovery efforts, we assessed the predictive power of an AlphaFold-predicted structure of PPM1D and a Markov state model (MSM) built from molecular dynamics simulations initiated from that structure. Our simulations reveal a cryptic pocket at the interface between two important structural elements, the flap and hinge regions. Using deep learning to predict the pose quality of each docked compound for the active site and cryptic pocket suggests that the inhibitors strongly prefer binding to the cryptic pocket, consistent with their allosteric effect. The predicted affinities for the dynamically uncovered cryptic pocket also recapitulate the relative potencies of the compounds (τb = 0.70) better than the predicted affinities for the static AlphaFold-predicted structure (τb = 0.42). Taken together, these results suggest that targeting the cryptic pocket is a good strategy for drugging PPM1D and, more generally, that conformations selected from simulation can improve virtual screening when limited structural data is available.

CTLA-4 in Regulatory T Cells for Cancer Immunotherapy.

Immune checkpoint inhibitors (ICIs) have obtained durable responses in many cancers, making it possible to foresee their potential in improving the health of cancer patients. However, immunotherapies are currently limited to a minority of patients and there is a need to develop a better understanding of the basic molecular mechanisms and functions of pivotal immune regulatory molecules. Immune checkpoint cytotoxic T-lymphocyte-associated protein 4 (CTLA-4) and regulatory T (Treg) cells play pivotal roles in hindering the anticancer immunity. Treg cells suppress antigen-presenting cells (APCs) by depleting immune stimulating cytokines, producing immunosuppressive cytokines and constitutively expressing CTLA-4. CTLA-4 molecules bind to CD80 and CD86 with a higher affinity than CD28 and act as competitive inhibitors of CD28 in APCs. The purpose of this review is to summarize state-of-the-art understanding of the molecular mechanisms underlining CTLA-4 immune regulation and the correlation of the ICI response with CTLA-4 expression in Treg cells from preclinical and clinical studies for possibly improving CTLA-4-based immunotherapies, while highlighting the knowledge gap.