MELD-Adapt: On-the-Fly Belief Updating in Integrative Molecular Dynamics.

Integrative structural biology synergizes experimental data with computational methods to elucidate the structures and interactions within biomolecules, a task that becomes critical in the absence of high-resolution structural data. A challenging step for integrating the data is knowing the expected accuracy or belief in the dataset. We previously showed that the Modeling Employing Limited Data (MELD) approach succeeds at predicting structures and finding the best interpretation of the data when the initial belief is equal to or slightly lower than the real value.

Machine Learning in Complex Organic Mixtures: Applying Domain Knowledge Allows for Meaningful Performance with Small Data Sets.

The ability to quantify individual components of complex mixtures is a challenge found throughout the life and physical sciences. An improved capacity to generate large data sets along with the uptake of machine-learning (ML)-based analysis tools has allowed for various "omics" disciplines to realize exceptional advances. Other areas of chemistry that deal with complex mixtures often do not leverage these advances.

Hybrid computational methods combining experimental information with molecular dynamics.

A goal of structural biology is to understand how macromolecules carry out their biological roles by identifying their metastable states, mechanisms of action, pathways leading to conformational changes, and the thermodynamic and kinetic relationships between those states. Integrative modeling brings structural insights into systems where traditional structure determination approaches cannot help. We focus on the synergies and challenges of integrative modeling combining experimental data with molecular dynamics simulations.

CryoFold 2.0: Cryo-EM Structure Determination with MELD.

Cryo-electron microscopy data are becoming more prevalent and accessible at higher resolution levels, leading to the development of new computational tools to determine the atomic structure of macromolecules. However, while existing tools adapted from X-ray crystallography are suitable for the highest-resolution maps, new tools are needed for lower-resolution levels and to account for map heterogeneity. In this article, we introduce CryoFold 2.

An implementation of the Martini coarse-grained force field in OpenMM.

We describe a complete implementation of Martini 2 and Martini 3 in the OpenMM molecular dynamics software package. Martini is a widely used coarse-grained force field with applications in biomolecular simulation, materials, and broader areas of chemistry. It is implemented as a force field but makes extensive use of facilities unique to the GROMACS software, including virtual sites and bonded terms that are not commonly used in standard atomistic force fields.

Understanding FABP7 binding to fatty acid micelles and membranes.

Fatty acid-binding proteins (FABPs) are chaperones that facilitate the transport of long-chain fatty acids within the cell and can provide cargo-dependent localization to specific cellular compartments. Understanding the nature of this transport is important because lipid signaling functions are associated with metabolic pathways impacting disease pathologies including cancer, autism, and schizophrenia. FABPs often associate with cell membranes to acquire and deliver their bound cargo as part of transport.

A Synthetically Accessible Small-Molecule Inhibitor of USP5-Cav3.2 Calcium Channel Interactions with Analgesic Properties.

Cav3.2 calcium channels are important mediators of nociceptive signaling in the primary afferent pain pathway, and their expression is increased in various rodent models of chronic pain. Previous work from our laboratory has shown that this is in part mediated by an aberrant expression of deubiquitinase USP5, which associates with these channels and increases their stability.

Bacterial cyclic diguanylate signaling networks sense temperature.

Many bacteria use the second messenger cyclic diguanylate (c-di-GMP) to control motility, biofilm production and virulence. Here, we identify a thermosensory diguanylate cyclase (TdcA) that modulates temperature-dependent motility, biofilm development and virulence in the opportunistic pathogen Pseudomonas aeruginosa. TdcA synthesizes c-di-GMP with catalytic rates that increase more than a hundred-fold over a ten-degree Celsius change.

Correlation between Labeling Yield and Surface Accessibility in Covalent Labeling Mass Spectrometry.

The functional properties of a protein are strongly influenced by its topography, or the solvent-facing contour map of its surface. Together with crosslinking, covalent labeling mass spectrometry (CL-MS) has the potential to contribute topographical data through the measurement of surface accessibility. However, recent efforts to correlate measures of surface accessibility with labeling yield have been met with mixed success.

From Inhibition to Degradation: Targeting the Antiapoptotic Protein Myeloid Cell Leukemia 1 (MCL1).

Protein-protein interactions (PPIs) have emerged as significant targets for therapeutic development, owing to their critical nature in diverse biological processes. An ideal PPI-based target is the protein myeloid cell leukemia 1 (MCL1), a critical prosurvival factor in cancers such as multiple myeloma where MCL1 levels directly correlate to disease progression. Current strategies for halting the antiapoptotic properties of MCL1 revolve around inhibiting its sequestration of proapoptotic factors.

High Accuracy Protein Structures from Minimal Sparse Paramagnetic Solid-State NMR Restraints.

There is a pressing need for new computational tools to integrate data from diverse experimental approaches in structural biology. We present a strategy that combines sparse paramagnetic solid-state NMR restraints with physics-based atomistic simulations. Our approach explicitly accounts for uncertainty in the interpretation of experimental data through the use of a semi-quantitative mapping between the data and the restraint energy that is calibrated by extensive simulations.

Online Optimization of Total Acceptance in Hamiltonian Replica Exchange Simulations.

Replica exchange is a widely used sampling strategy in molecular simulation. While a variety of methods exist to optimize parameters for temperature replica exchange, less is known about how to optimize parameters for more general Hamiltonian replica exchange simulations. We present an algorithm for the online optimization of total acceptance for both temperature and Hamiltonian replica exchange simulations using stochastic gradient descent.

The emerging role of physical modeling in the future of structure determination.

Biomolecular structure determination has long relied on heuristics based on physical insight; however, recent efforts to model conformational ensembles and to make sense of sparse, ambiguous, and noisy data have revealed the value of detailed, quantitative physical models in structure determination. We review these two key challenges, describe different approaches to physical modeling in structure determination, and illustrate several successes and emerging technologies enabled by physical modeling.

Molecular modeling of biomolecules by paramagnetic NMR and computational hybrid methods.

The 3D atomic structures of biomolecules and their complexes are key to our understanding of biomolecular function, recognition, and mechanism. However, it is often difficult to obtain structures, particularly for systems that are complex, dynamic, disordered, or exist in environments like cell membranes. In such cases sparse data from a variety of paramagnetic NMR experiments offers one possible source of structural information.

Blind protein structure prediction using accelerated free-energy simulations.

We report a key proof of principle of a new acceleration method [Modeling Employing Limited Data (MELD)] for predicting protein structures by molecular dynamics simulation. It shows that such Boltzmann-satisfying techniques are now sufficiently fast and accurate to predict native protein structures in a limited test within the Critical Assessment of Structure Prediction (CASP) community-wide blind competition.

Constraint methods that accelerate free-energy simulations of biomolecules.

Atomistic molecular dynamics simulations of biomolecules are critical for generating narratives about biological mechanisms. The power of atomistic simulations is that these are physics-based methods that satisfy Boltzmann's law, so they can be used to compute populations, dynamics, and mechanisms. But physical simulations are computationally intensive and do not scale well to the sizes of many important biomolecules.

Grid-based backbone correction to the ff12SB protein force field for implicit-solvent simulations.

Force fields, such as Amber's ff12SB, can be fairly accurate models of the physical forces in proteins and other biomolecules. When coupled with accurate solvation models, force fields are able to bring insight into the conformational preferences, transitions, pathways, and free energies for these biomolecules. When computational speed/cost matters, implicit solvent is often used but at the cost of accuracy.

Accelerating molecular simulations of proteins using Bayesian inference on weak information.

Atomistic molecular dynamics (MD) simulations of protein molecules are too computationally expensive to predict most native structures from amino acid sequences. Here, we integrate "weak" external knowledge into folding simulations to predict protein structures, given their sequence. For example, we instruct the computer "to form a hydrophobic core," "to form good secondary structures," or "to seek a compact state.

Determining protein structures by combining semireliable data with atomistic physical models by Bayesian inference.

More than 100,000 protein structures are now known at atomic detail. However, far more are not yet known, particularly among large or complex proteins. Often, experimental information is only semireliable because it is uncertain, limited, or confusing in important ways.

Extracting representative structures from protein conformational ensembles.

A large number of methods generate conformational ensembles of biomolecules. Often one structure is selected to be representative of the whole ensemble, usually by clustering and selecting the structure closest to the center of the most populated cluster. We find that this structure is not necessarily the best representation of the cluster and present here two computationally inexpensive averaging protocols that can systematically provide better representations of the system, which can be more directly compared with structures from X-ray crystallography.

Single molecule conformational memory extraction: p5ab RNA hairpin.

Extracting kinetic models from single molecule data is an important route to mechanistic insight in biophysics, chemistry, and biology. Data collected from force spectroscopy can probe discrete hops of a single molecule between different conformational states. Model extraction from such data is a challenging inverse problem because single molecule data are noisy and rich in structure.

Computing the relative stabilities and the per-residue components in protein conformational changes.

Protein molecules often undergo conformational changes. In order to gain insights into the forces that drive such changes, it would be useful to have a method that computes the per-residue contributions to the conversion free energy. Here, we describe the "confine-convert-release" (CCR) method, which is applicable to large conformational changes.

The protein-folding problem, 50 years on.

The protein-folding problem was first posed about one half-century ago. The term refers to three broad questions: (i) What is the physical code by which an amino acid sequence dictates a protein's native structure? (ii) How can proteins fold so fast? (iii) Can we devise a computer algorithm to predict protein structures from their sequences? We review progress on these problems. In a few cases, computer simulations of the physical forces in chemically detailed models have now achieved the accurate folding of small proteins.