Accurate structure prediction of biomolecular interactions with AlphaFold 3.

The introduction of AlphaFold 21 has spurred a revolution in modelling the structure of proteins and their interactions, enabling a huge range of applications in protein modelling and design2-6. Here we describe our AlphaFold 3 model with a substantially updated diffusion-based architecture that is capable of predicting the joint structure of complexes including proteins, nucleic acids, small molecules, ions and modified residues. The new AlphaFold model demonstrates substantially improved accuracy over many previous specialized tools: far greater accuracy for protein-ligand interactions compared with state-of-the-art docking tools, much higher accuracy for protein-nucleic acid interactions compared with nucleic-acid-specific predictors and substantially higher antibody-antigen prediction accuracy compared with AlphaFold-Multimer v.2.37,8. Together, these results show that high-accuracy modelling across biomolecular space is possible within a single unified deep-learning framework.

On the Interpretation of Force-Induced Unfolding Studies of Membrane Proteins Using Fast Simulations.

Single-molecule force spectroscopy has proven extremely beneficial in elucidating folding pathways for membrane proteins. Here, we simulate these measurements, conducting hundreds of unfolding trajectories using our fast Upside algorithm for slow enough speeds to reproduce key experimental features that may be missed using all-atom methods. The speed also enables us to determine the logarithmic dependence of pulling velocities on the rupture levels to better compare to experimental values. For simulations of atomic force microscope measurements in which force is applied vertically to the C-terminus of bacteriorhodopsin, we reproduce the major experimental features including even the back-and-forth unfolding of single helical turns. When pulling laterally on GlpG to mimic the experiment, we observe quite different behavior depending on the stiffness of the spring. With a soft spring, as used in the experimental studies with magnetic tweezers, the force remains nearly constant after the initial unfolding event, and a few pathways and a high degree of cooperativity are observed in both the experiment and simulation. With a stiff spring, however, the force drops to near zero after each major unfolding event, and numerous intermediates are observed along a wide variety of pathways. Hence, the mode of force application significantly alters the perception of the folding landscape, including the number of intermediates and the degree of folding cooperativity, important issues that should be considered when designing experiments and interpreting unfolding data.

Trajectory-based training enables protein simulations with accurate folding and Boltzmann ensembles in cpu-hours.

An ongoing challenge in protein chemistry is to identify the underlying interaction energies that capture protein dynamics. The traditional trade-off in biomolecular simulation between accuracy and computational efficiency is predicated on the assumption that detailed force fields are typically well-parameterized, obtaining a significant fraction of possible accuracy. We re-examine this trade-off in the more realistic regime in which parameterization is a greater source of error than the level of detail in the force field. To address parameterization of coarse-grained force fields, we use the contrastive divergence technique from machine learning to train from simulations of 450 proteins. In our procedure, the computational efficiency of the model enables high accuracy through the precise tuning of the Boltzmann ensemble. This method is applied to our recently developed Upside model, where the free energy for side chains is rapidly calculated at every time-step, allowing for a smooth energy landscape without steric rattling of the side chains. After this contrastive divergence training, the model is able to de novo fold proteins up to 100 residues on a single core in days. This improved Upside model provides a starting point both for investigation of folding dynamics and as an inexpensive Bayesian prior for protein physics that can be integrated with additional experimental or bioinformatic data.

Accurate calculation of side chain packing and free energy with applications to protein molecular dynamics.

To address the large gap between time scales that can be easily reached by molecular simulations and those required to understand protein dynamics, we present a rapid self-consistent approximation of the side chain free energy at every integration step. In analogy with the adiabatic Born-Oppenheimer approximation for electronic structure, the protein backbone dynamics are simulated as preceding according to the dictates of the free energy of an instantaneously-equilibrated side chain potential. The side chain free energy is computed on the fly, allowing the protein backbone dynamics to traverse a greatly smoothed energetic landscape. This computation results in extremely rapid equilibration and sampling of the Boltzmann distribution. Our method, termed Upside, employs a reduced model involving the three backbone atoms, along with the carbonyl oxygen and amide proton, and a single (oriented) side chain bead having multiple locations reflecting the conformational diversity of the side chain's rotameric states. We also introduce a novel, maximum-likelihood method to parameterize the side chain interactions using protein structures. We demonstrate state-of-the-art accuracy for predicting χ1 rotamer states while consuming only milliseconds of CPU time. Our method enables rapidly equilibrating coarse-grained simulations that can nonetheless contain significant molecular detail. We also show that the resulting free energies of the side chains are sufficiently accurate for de novo folding of some proteins.

A Membrane Burial Potential with H-Bonds and Applications to Curved Membranes and Fast Simulations.

We use the statistics of a large and curated training set of transmembrane helical proteins to develop a knowledge-based potential that accounts for the dependence on both the depth of burial of the protein in the membrane and the degree of side-chain exposure. Additionally, the statistical potential includes depth-dependent energies for unsatisfied backbone hydrogen bond donors and acceptors, which are found to be relatively small, ∼2 RT. Our potential accurately places known proteins within the bilayer. The potential is applied to the mechanosensing MscL channel in membranes of varying thickness and curvature, as well as to the prediction of protein structure. The potential is incorporated into our new Upside molecular dynamics algorithm. Notably, we account for the exchange of protein-lipid interactions for protein-protein interactions as helices contact each other, thereby avoiding overestimating the energetics of helix association within the membrane. Simulations of most multimeric complexes find that isolated monomers and the oligomers retain the same orientation in the membrane, suggesting that the assembly of prepositioned monomers presents a viable mechanism of oligomerization.

Innovative scattering analysis shows that hydrophobic disordered proteins are expanded in water.

A substantial fraction of the proteome is intrinsically disordered, and even well-folded proteins adopt non-native geometries during synthesis, folding, transport, and turnover. Characterization of intrinsically disordered proteins (IDPs) is challenging, in part because of a lack of accurate physical models and the difficulty of interpreting experimental results. We have developed a general method to extract the dimensions and solvent quality (self-interactions) of IDPs from a single small-angle x-ray scattering measurement. We applied this procedure to a variety of IDPs and found that even IDPs with low net charge and high hydrophobicity remain highly expanded in water, contrary to the general expectation that protein-like sequences collapse in water. Our results suggest that the unfolded state of most foldable sequences is expanded; we conjecture that this property was selected by evolution to minimize misfolding and aggregation.

Free-Standing Kinked Silicon Nanowires for Probing Inter- and Intracellular Force Dynamics.

Silicon nanowires (SiNWs) have emerged as a new class of materials with important applications in biology and medicine with current efforts having focused primarily on using substrate bound SiNW devices. However, developing devices capable of free-standing inter- and intracellular operation is an important next step in designing new synthetic cellular materials and tools for biophysical characterization. To demonstrate this, here we show that label free SiNWs can be internalized in multiple cell lines, forming robust cytoskeletal interfaces, and when kinked can serve as free-standing inter- and intracellular force probes capable of continuous extended (>1 h) force monitoring. Our results show that intercellular interactions exhibit ratcheting like behavior with force peaks of ∼69.6 pN/SiNW, while intracellular force peaks of ∼116.9 pN/SiNW were recorded during smooth muscle contraction. To accomplish this, we have introduced a simple single-capture dark-field/phase contrast optical imaging modality, scatter enhanced phase contrast (SEPC), which enables the simultaneous visualization of both cellular components and inorganic nanostructures. This approach demonstrates that rationally designed devices capable of substrate-independent operation are achievable, providing a simple and scalable method for continuous inter- and intracellular force dynamics studies.

Loss of conformational entropy in protein folding calculated using realistic ensembles and its implications for NMR-based calculations.

The loss of conformational entropy is a major contribution in the thermodynamics of protein folding. However, accurate determination of the quantity has proven challenging. We calculate this loss using molecular dynamic simulations of both the native protein and a realistic denatured state ensemble. For ubiquitin, the total change in entropy is TΔSTotal = 1.4 kcal⋅mol(-1) per residue at 300 K with only 20% from the loss of side-chain entropy. Our analysis exhibits mixed agreement with prior studies because of the use of more accurate ensembles and contributions from correlated motions. Buried side chains lose only a factor of 1.4 in the number of conformations available per rotamer upon folding (ΩU/ΩN). The entropy loss for helical and sheet residues differs due to the smaller motions of helical residues (TΔShelix-sheet = 0.5 kcal⋅mol(-1)), a property not fully reflected in the amide N-H and carbonyl C=O bond NMR order parameters. The results have implications for the thermodynamics of folding and binding, including estimates of solvent ordering and microscopic entropies obtained from NMR.

Rotational relaxation in ortho-terphenyl: using atomistic simulations to bridge theory and experiment.

Understanding the nature of the glass transition--the dramatic slowing of dynamics and eventual emergence of a disordered solid from a cooling liquid--is a fundamental challenge in physical science. A central characteristic of glass-forming liquids is a non-exponential main relaxation process. The extent of deviation from exponential relaxation typically becomes more pronounced on cooling. Theories that predict a growth of spatially heterogeneous dynamics as temperature is lowered can explain these observations. In apparent contradiction to these theories, however, some experiments suggest that certain substances--notably including the intensely studied molecular glass-former ortho-terphenyl (OTP)--have a main relaxation process whose shape is essentially temperature independent, even though other observables predicted to be correlated with the degree of dynamical heterogeneity are temperature dependent. Here we report the first simulations based on an atomistic model of OTP that reach equilibrium at temperatures well into the supercooled regime. We first show that the results of these simulations are in reasonable quantitative agreement with experimental data for several basic properties over a wide range of temperatures. We then focus on rotational relaxation, finding nearly exponential behavior at high temperatures with clearly increasing deviations as temperature is lowered. The much weaker temperature dependence observed in light-scattering experiments also emerges from the same simulation data when we calculate correlation functions similar to those probed experimentally; this highlights the diversity of temperature dependencies that can be obtained with different probes. Further analysis suggests that the temperature insensitivity observed in the light-scattering experiments stems from the dependence of these measurements on internal as well as rotational molecular motion. Within the temperature range of our OTP simulations, our results strongly suggest that this archetypal glass-former behaves as anticipated by theories of the glass transition that predict increasing non-exponentiality with cooling, and our simulations thus strengthen the evidence supporting such theories.

Atomic-level characterization of the structural dynamics of proteins.

Molecular dynamics (MD) simulations are widely used to study protein motions at an atomic level of detail, but they have been limited to time scales shorter than those of many biologically critical conformational changes. We examined two fundamental processes in protein dynamics--protein folding and conformational change within the folded state--by means of extremely long all-atom MD simulations conducted on a special-purpose machine. Equilibrium simulations of a WW protein domain captured multiple folding and unfolding events that consistently follow a well-defined folding pathway; separate simulations of the protein's constituent substructures shed light on possible determinants of this pathway. A 1-millisecond simulation of the folded protein BPTI reveals a small number of structurally distinct conformational states whose reversible interconversion is slower than local relaxations within those states by a factor of more than 1000.