De novo design of transmembrane helix–helix interactions and measurement of stability in a biological membrane

CSSP-2.0: A refined consensus method for accurate protein secondary structure prediction

Studying the relationship between sequences and their corresponding three-dimensional structure assists structural biologists in solving the protein-folding problem. Despite several experimental and in-silico approaches, still understanding or decoding the three-dimensional structures from the sequence remains a mystery. In such cases, the accuracy of the structure prediction plays an indispensable role. To address this issue, an updated web server (CSSP-2.0) has been created to improve the accuracy of our previous version of CSSP by deploying the existing algorithms. It uses input as probabilities and predicts the consensus for the secondary structure as a highly accurate three-state Q3 (helix, strand, and coil). This prediction is achieved using six recent top-performing methods: MUFOLD-SS, RaptorX, PSSpred v4, PSIPRED, JPred v4, and Porter 5.0. CSSP-2.0 validation includes datasets involving various protein classes from the PDB, CullPDB, and AlphaFold databases. Our results indicate a significant improvement in the accuracy of the consensus Q3 prediction. Using CSSP-2.0, crystallographers can sort out the stable regular secondary structures from the entire complex structure, which would aid in inferring the functional annotation of hypothetical proteins. The web server is freely available at https://bioserver3.physics.iisc.ac.in/cgi-bin/cssp-2/.

Machine Learning Derived Collective Variables for the Study of Protein Homodimerization in Membrane

The accurate calculation of equilibrium constants for protein-protein association is of fundamental importance to quantitative biology and remains an outstanding challenge for computational biophysics. Traditionally, equilibrium constants have been computed from one-dimensional free energy surfaces derived from sampling along a single collective variable. Importantly, recent advances in enhanced sampling methodology have facilitated the characterization of multidimensional free energy landscapes, often exposing multiple thermodynamically important minima missed by more restrictive sampling methods. A key to the effectiveness of this multidimensional sampling approach is the identification of collective variables that effectively define the configurational space of dissociated and associated states. Here we present the application of two machine learning methods for the unbiased determination of collective variables for enhanced sampling and analysis of protein-protein association. Our results both validate prior work, based on intuition derived collective variables, and demonstrate the effectiveness of the machine learning methods for the identification of collective variables for association reactions in complex biomolecular systems.

A Rigorous Framework for Calculating Protein-Protein Binding Affinities in Membranes

Calculating the binding free energy of integral transmembrane (TM) proteins is crucial for understanding the mechanisms by which they recognize one another and reversibly associate. The glycophorin A (GpA) homodimer, composed of two α-helical segments, has long served as a model system for studying TM protein reversible association. The present work establishes a methodological framework for calculating the binding affinity of the GpA homodimer in the heterogeneous environment of a membrane. Our investigation carefully considered a variety of protocols, including the appropriate choice of the force field, rigorous standardization reflecting the experimental conditions, sampling algorithm, anisotropic environment, and collective variables, to accurately describe GpA dimerization via molecular dynamics-based approaches. Specifically, two strategies were explored: (i) an unrestrained potential mean force (PMF) calculation, which merely enhances sampling along the separation of the two binding partners without any restraint, and (ii) a so-called "geometrical route", whereby the α-helices are progressively separated with imposed restraints on their orientational, positional, and conformational degrees of freedom to accelerate convergence. Our simulations reveal that the simplified, unrestrained PMF approach is inadequate for the description of GpA dimerization. Instead, the geometrical route, tailored specifically to GpA in a membrane environment, yields excellent agreement with experimental data within a reasonable computational time. A dimerization free energy of -10.7 kcal/mol is obtained, in fairly good agreement with available experimental data. The geometrical route further helps elucidate how environmental forces drive association before helical interactions stabilize it. Our simulations also brought to light a distinct, long-lived spatial arrangement that potentially serves as an intermediate state during dimer formation. The methodological advances in the generalized geometrical route provide a powerful tool for accurate and efficient binding-affinity calculations of intricate TM protein complexes in inhomogeneous environments.

The role of structural heterogeneity in the homodimerization of transmembrane proteins

The equilibrium association of transmembrane proteins plays a fundamental role in membrane protein function and cellular signaling. While the study of the equilibrium binding of single pass transmembrane proteins has received significant attention in experiment and simulation, the accurate assessment of equilibrium association constants remains a challenge to experiment and simulation. In experiment, there remain wide variations in association constants derived from experimental studies of the most widely studied transmembrane proteins. In simulation, state-of-the art methods have failed to adequately sample the thermodynamically relevant structures of the dimer state ensembles using coarse-grained models. In addition, all-atom force fields often fail to accurately assess the relative free energies of the dimer and monomer states. Given the importance of this fundamental biophysical process, it is essential to address these shortcomings. In this work, we establish an effective computational protocol for the calculation of equilibrium association constants for transmembrane homodimer formation. A set of transmembrane protein homodimers, used in the parameterization of the MARTINI v3 force field, are simulated using metadynamics, based on three collective variables. The method is found to be accurate and computationally efficient, providing a standard to be used in the future simulation studies using coarse-grained or all-atom models.

On Computing Equilibrium Binding Constants for Protein-Protein Association in Membranes

Protein association in lipid membranes is fundamental to membrane protein function and of great biomedical relevance. All-atom and coarse-grained models have been extensively used to understand the protein-protein interactions in the membrane and to compute equilibrium association constants. However, slow translational and rotational diffusion of protein in membrane presents challenges to the effective sampling of conformations defining the ensembles of free and bound states contributing to the association equilibrium and the free energy of dimerization. We revisit the homodimerization equilibrium of the TM region of glycophorin A. Conformational sampling is performed using umbrella sampling along previously proposed one-dimensional collective variables and compared with sampling over a two-dimensional collective variable space using the MARTINI v2.2 force field. We demonstrate that the one-dimensional collective variables suffer from restricted sampling of the native homodimer conformations leading to a biased free energy landscape. Conversely, simulations along the two-dimensional collective variable effectively characterize the thermodynamically relevant native and non-native interactions contributing to the association equilibrium. These results demonstrate the challenges associated with accurately characterizing binding equilibria when multiple poses contribute to the bound state ensemble.

Characterization of interactions within the Igα/Igβ transmembrane domains of the human B-cell receptor provides insights into receptor assembly

The B-cell receptor (BCR), a complex comprised of a membrane-associated immunoglobulin and the Igα/β heterodimer, is one of the most important immune receptors in humans and controls B-cell development, activity, selection, and death. BCR signaling plays key roles in autoimmune diseases and lymphoproliferative disorders, yet, despite the clinical significance of this protein complex, key regions (i.e., the transmembrane domains) have yet to be structurally characterized. The mechanism for BCR signaling also remains unclear and has been variously described by the mutually exclusive cross-linking and dissociation activation models. Common to these models is the significance of local plasma membrane composition, which implies that interactions between BCR transmembrane domains (TMDs) play a role in receptor functionality. Here we used an in vivo assay of TMD oligomerization called GALLEX alongside spectroscopic and computational methods to characterize the structures and interactions of human Igα and Igβ TMDs in detergent micelles and natural membranes. We observed weak self-association of the Igβ TMD and strong self-association of the Igα TMD, which scanning mutagenesis revealed was entirely stabilized by an E–X₁₀–P motif. We also demonstrated strong heterotypic interactions between the Igα and Igβ TMDs both in vitro and in vivo, which scanning mutagenesis and computational models suggest is multiconfigurational but can accommodate distinct interaction sites for self-interactions and heterotypic interactions of the Igα TMD. Taken together, these results demonstrate that the TMDs of the human BCR are sites of strong protein–protein interactions that may direct BCR assembly, endoplasmic reticulum retention, and immune signaling.

Martini 3: a general purpose force field for coarse-grained molecular dynamics

The coarse-grained Martini force field is widely used in biomolecular simulations. Here we present the refined model, Martini 3 ( http://cgmartini.nl ), with an improved interaction balance, new bead types and expanded ability to include specific interactions representing, for example, hydrogen bonding and electronic polarizability. The updated model allows more accurate predictions of molecular packing and interactions in general, which is exemplified with a vast and diverse set of applications, ranging from oil/water partitioning and miscibility data to complex molecular systems, involving protein-protein and protein-lipid interactions and material science applications as ionic liquids and aedamers.

Thermodynamic and kinetic stabilities of transmembrane helix bundles as revealed by single-pair FRET analysis: Effects of the number of membrane-spanning segments and cholesterol

The tertiary structures and conformational dynamics of transmembrane (TM) helical proteins are maintained by the interhelical interaction network in membranes, although it is complicated to analyze the underlying driving forces because the amino acid sequences can involve multiple and various types of interactions. To obtain insights into basal and common effects of the number of membrane-spanning segments and membrane cholesterol, we measured stabilities of helix bundles composed of simple TM helices (AALALAA)₃ (1TM) and (AALALAA)₃-G₅-(AALALAA)₃ (2TM). Association-dissociation dynamics for 1TM-1TM, 1TM-2TM, and 2TM-2TM pairs were monitored to compare stabilities of 2-, 3-, and 4-helical bundles, respectively, with single-pair fluorescence resonance energy transfer (sp-FRET) in liposome membranes. Both thermodynamic and kinetic stabilities of the helix bundles increased with a greater number of membrane-spanning segments in POPC. The presence of 30 mol% cholesterol strongly enhanced the formation of 1TM-1TM and 1TM-2TM bundles (~ - 9 kJ mol^-1), whereas it only weakly stabilized the 2TM-2TM bundle (~ - 3 kJ mol^-1). Fourier transform infrared-polarized attenuated total reflection (ATR-FTIR) spectroscopy revealed an ~30° tilt of the helix axis relative to bilayer normal for the 1TM-2TM pair in the presence of cholesterol, suggesting the formation of a tilted helix bundle to release high lateral pressure at the center of cholesterol-containing membranes. These results demonstrate that the number of membrane-spanning segments affects the stability and structure of the helix bundle, and their cholesterol-dependences. Such information is useful to understand the basics of folding and assembly of multispanning TM proteins.

Membrane receptor activation mechanisms and transmembrane peptide tools to elucidate them

Single-pass membrane receptors contain extracellular domains that respond to external stimuli and transmit information to intracellular domains through a single transmembrane (TM) α-helix. Because membrane receptors have various roles in homeostasis, signaling malfunctions of these receptors can cause disease. Despite their importance, there is still much to be understood mechanistically about how single-pass receptors are activated. In general, single-pass receptors respond to extracellular stimuli via alterations in their oligomeric state. The details of this process are still the focus of intense study, and several lines of evidence indicate that the TM domain (TMD) of the receptor plays a central role. We discuss three major mechanistic hypotheses for receptor activation: ligand-induced dimerization, ligand-induced rotation, and receptor clustering. Recent observations suggest that receptors can use a combination of these activation mechanisms and that technical limitations can bias interpretation. Short peptides derived from receptor TMDs, which can be identified by screening or rationally developed on the basis of the structure or sequence of their targets, have provided critical insights into receptor function. Here, we explore recent evidence that, depending on the target receptor, TMD peptides cannot only inhibit but also activate target receptors and can accommodate novel, bifunctional designs. Furthermore, we call for more sharing of negative results to inform the TMD peptide field, which is rapidly transforming into a suite of unique tools with the potential for future therapeutics.

Excessive aggregation of membrane proteins in the Martini model

The coarse-grained Martini model is employed extensively to study membrane protein oligomerization. While this approach is exceptionally promising given its computational efficiency, it is alarming that a significant fraction of these studies demonstrate unrealistic protein clusters, whose formation is essentially an irreversible process. This suggests that the protein-protein interactions are exaggerated in the Martini model. If this held true, then it would limit the applicability of Martini to study multi-protein complexes, as the rapidly clustering proteins would not be able to properly sample the correct dimerization conformations. In this work we first demonstrate the excessive protein aggregation by comparing the dimerization free energies of helical transmembrane peptides obtained with the Martini model to those determined from FRET experiments. Second, we show that the predictions provided by the Martini model for the structures of transmembrane domain dimers are in poor agreement with the corresponding structures resolved using NMR. Next, we demonstrate that the first issue can be overcome by slightly scaling down the Martini protein-protein interactions in a manner, which does not interfere with the other Martini interaction parameters. By preventing excessive, irreversible, and non-selective aggregation of membrane proteins, this approach renders the consideration of lateral dynamics and protein-lipid interactions in crowded membranes by the Martini model more realistic. However, this adjusted model does not lead to an improvement in the predicted dimer structures. This implicates that the poor agreement between the Martini model and NMR structures cannot be cured by simply uniformly reducing the interactions between all protein beads. Instead, a careful amino-acid specific adjustment of the protein-protein interactions is likely required.

Role of GxxxG Motifs in Transmembrane Domain Interactions

Transmembrane (TM) helices of integral membrane proteins can facilitate strong and specific noncovalent protein-protein interactions. Mutagenesis and structural analyses have revealed numerous examples in which the interaction between TM helices of single-pass membrane proteins is dependent on a GxxxG or (small)xxx(small) motif. It is therefore tempting to use the presence of these simple motifs as an indicator of TM helix interactions. In this Current Topic review, we point out that these motifs are quite common, with more than 50% of single-pass TM domains containing a (small)xxx(small) motif. However, the actual interaction strength of motif-containing helices depends strongly on sequence context and membrane properties. In addition, recent studies have revealed several GxxxG-containing TM domains that interact via alternative interfaces involving hydrophobic, polar, aromatic, or even ionizable residues that do not form recognizable motifs. In multipass membrane proteins, GxxxG motifs can be important for protein folding, and not just oligomerization. Our current knowledge thus suggests that the presence of a GxxxG motif alone is a weak predictor of protein dimerization in the membrane.

Biologically active LIL proteins built with minimal chemical diversity

We have constructed 26-amino acid transmembrane proteins that specifically transform cells but consist of only two different amino acids. Most proteins are long polymers of amino acids with 20 or more chemically distinct side-chains. The artificial transmembrane proteins reported here are the simplest known proteins with specific biological activity, consisting solely of an initiating methionine followed by specific sequences of leucines and isoleucines, two hydrophobic amino acids that differ only by the position of a methyl group. We designate these proteins containing leucine (L) and isoleucine (I) as LIL proteins. These proteins functionally interact with the transmembrane domain of the platelet-derived growth factor β-receptor and specifically activate the receptor to transform cells. Complete mutagenesis of these proteins identified individual amino acids required for activity, and a protein consisting solely of leucines, except for a single isoleucine at a particular position, transformed cells. These surprisingly simple proteins define the minimal chemical diversity sufficient to construct proteins with specific biological activity and change our view of what can constitute an active protein in a cellular context.