A Perspective on the Prospective Use of AI in Protein Structure Prediction.

AlphaFold2 (AF2) and RoseTTaFold (RF) have revolutionized structural biology, serving as highly reliable and effective methods for predicting protein structures. This article explores their impact and limitations, focusing on their integration into experimental pipelines and their application in diverse protein classes, including membrane proteins, intrinsically disordered proteins (IDPs), and oligomers. In experimental pipelines, AF2 models help X-ray crystallography in resolving the phase problem, while complementarity with mass spectrometry and NMR data enhances structure determination and protein flexibility prediction. Predicting the structure of membrane proteins remains challenging for both AF2 and RF due to difficulties in capturing conformational ensembles and interactions with the membrane. Improvements in incorporating membrane-specific features and predicting the structural effect of mutations are crucial. For intrinsically disordered proteins, AF2's confidence score (pLDDT) serves as a competitive disorder predictor, but integrative approaches including molecular dynamics (MD) simulations or hydrophobic cluster analyses are advocated for accurate dynamics representation. AF2 and RF show promising results for oligomeric models, outperforming traditional docking methods, with AlphaFold-Multimer showing improved performance. However, some caveats remain in particular for membrane proteins. Real-life examples demonstrate AF2's predictive capabilities in unknown protein structures, but models should be evaluated for their agreement with experimental data. Furthermore, AF2 models can be used complementarily with MD simulations. In this Perspective, we propose a "wish list" for improving deep-learning-based protein folding prediction models, including using experimental data as constraints and modifying models with binding partners or post-translational modifications. Additionally, a meta-tool for ranking and suggesting composite models is suggested, driving future advancements in this rapidly evolving field.

DREAMM: a web-based server for drugging protein-membrane interfaces as a novel workflow for targeted drug design.

SUMMARY: The allosteric modulation of peripheral membrane proteins (PMPs) by targeting protein-membrane interactions with drug-like molecules represents a new promising therapeutic strategy for proteins currently considered undruggable. However, the accessibility of protein-membrane interfaces by small molecules has been so far unexplored, possibly due to the complexity of the interface, the limited protein-membrane structural information and the lack of computational workflows to study it. Herein, we present a pipeline for drugging protein-membrane interfaces using the DREAMM (Drugging pRotein mEmbrAne Machine learning Method) web server. DREAMM works in the back end with a fast and robust ensemble machine learning algorithm for identifying protein-membrane interfaces of PMPs. Additionally, DREAMM also identifies binding pockets in the vicinity of the predicted membrane-penetrating amino acids in protein conformational ensembles provided by the user or generated within DREAMM. AVAILABILITY AND IMPLEMENTATION: DREAMM web server is accessible via https://dreamm.ni4os.eu. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Membrane Composition and Raf[CRD]-Membrane Attachment Are Driving Forces for K-Ras4B Dimer Stability.

Ras proteins are membrane-anchored GTPases that regulate key cellular signaling networks. It has been recently shown that different anionic lipid types can affect the properties of Ras in terms of dimerization/clustering on the cell membrane. To understand the effects of anionic lipids on key spatiotemporal properties of dimeric K-Ras4B, we perform all-atom molecular dynamics simulations of the dimer K-Ras4B in the presence and absence of Raf[RBD/CRD] effectors on two model anionic lipid membranes: one containing 78% mol DOPC, 20% mol DOPS, and 2% mol PIP2 and another one with enhanced concentration of anionic lipids containing 50% mol DOPC, 40% mol DOPS, and 10% mol PIP2. Analysis of our results unveils the orientational space of dimeric K-Ras4B and shows that the stability of the dimer is enhanced on the membrane containing a high concentration of anionic lipids in the absence of Raf effectors. This enhanced stability is also observed in the presence of Raf[RBD/CRD] effectors although it is not influenced by the concentration of anionic lipids in the membrane, but rather on the ability of Raf[CRD] to anchor to the membrane. We generate dominant K-Ras4B conformations by Markov state modeling and yield the population of states according to the K-Ras4B orientation on the membrane. For the membrane containing anionic lipids, we observe correlations between the diffusion of K-Ras4B and PIP2 and anchoring of anionic lipids to the Raf[CRD] domain. We conclude that the presence of effectors with the Raf[CRD] domain anchoring on the membrane as well as the membrane composition both influence the conformational stability of the K-Ras4B dimer, enabling the preservation of crucial interface interactions.

Predicting protein-membrane interfaces of peripheral membrane proteins using ensemble machine learning.

Abnormal protein-membrane attachment is involved in deregulated cellular pathways and in disease. Therefore, the possibility to modulate protein-membrane interactions represents a new promising therapeutic strategy for peripheral membrane proteins that have been considered so far undruggable. A major obstacle in this drug design strategy is that the membrane-binding domains of peripheral membrane proteins are usually unknown. The development of fast and efficient algorithms predicting the protein-membrane interface would shed light into the accessibility of membrane-protein interfaces by drug-like molecules. Herein, we describe an ensemble machine learning methodology and algorithm for predicting membrane-penetrating amino acids. We utilize available experimental data from the literature for training 21 machine learning classifiers and meta-classifiers. Evaluation of the best ensemble classifier model accuracy yields a macro-averaged F1 score = 0.92 and a Matthews correlation coefficient = 0.84 for predicting correctly membrane-penetrating amino acids on unknown proteins of a validation set. The python code for predicting protein-membrane interfaces of peripheral membrane proteins is available at https://github.com/zoecournia/DREAMM.

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/.

Coating of magnetic nanoparticles affects their interactions with model cell membranes.

BACKGROUND: The use of functionalized iron oxide nanoparticles of various chemical properties and architectures offers a new promising direction in theranostic applications. The increasing applications of nanoparticles in medicine require that these engineered nanomaterials will contact human cells without damaging essential tissues. Thus, efficient delivery must be achieved, while minimizing cytotoxicity during passage through cell membranes to reach intracellular target compartments. METHODS: Differential Scanning Calorimetry (DSC), molecular modeling, and atomistic Molecular Dynamics (MD) simulations were performed for two magnetite nanoparticles coated with polyvinyl alcohol (PVA) and polyarabic acid (ARA) in order to assess their interactions with model DPPC membranes. RESULTS: DSC experiments showed that both nanoparticles interact strongly with DPPC lipid head groups, albeit to a different degree, which was further confirmed and quantified by MD simulations. The two systems were simulated, and dynamical and structural properties were monitored. A bimodal diffusion was observed for both nanoparticles, representing the diffusion in the water phase and in the proximity of the lipid bilayer. Nanoparticles did not enter the bilayer, but caused ordering of the head groups and reduced the area per lipid compared to the pure bilayer, with MAG-PVA interacting more strongly and being closer to the lipid bilayer. CONCLUSIONS: Results of DSC experiments and MD simulations were in excellent agreement. Our findings demonstrate that the external coating is a key factor that affects nanoparticle-membrane interactions. Magnetite nanoparticles coated with PVA and ARA did not destabilize the model membrane and can be considered promising platforms for biomedical applications. GENERAL SIGNIFICANCE: Understanding the physico-chemical interactions of different nanoparticle coatings in contact with model cell membranes is the first step for assessing toxic response and could lead to predictive models for estimating toxicity. DSC in combination with MD simulations is an effective strategy to assess physico-chemical interactions of coated nanoparticles with lipid bilayers.

Allostery in membrane proteins.

Membrane proteins are an integral part of signal transduction. To signal, membrane proteins must interact with a variety of lipid species, effectors, and other proteins in the biological membrane leading to an immense number of possible interactions. Despite this inherent complexity, accurate control of signaling must take place. By allowing proteins to adopt a multiplicity of conformations in a process known as allostery, nature is able to transmit a signal from one protein site to another distal, functional site, allowing for modulation of protein properties and regulation of activity. In recent years, an increasing number of reports have pointed to common mechanisms governing the allosteric modulation of membrane proteins, including conformational selection, oligomerization, and the modulation of allosteric sites. In this report, we summarize recent advances in membrane protein allostery.

NanoCrystal: A Web-Based Crystallographic Tool for the Construction of Nanoparticles Based on Their Crystal Habit.

Modeling of nanoparticles is an essential first step to assess their capacities for different uses such as energy storage and drug delivery. However, creating an initial starting conformation for modeling and simulation is tedious because every crystalline material grows with a different crystal habit. In this application note, we describe NanoCrystal, a novel web-based crystallographic tool that creates nanoparticle models from any crystal structure guided by their preferred equilibrium shape under standard conditions according to the Wulff morphology (crystal habit). Users can upload a cif file, define the Miller indices and their corresponding minimum surface energies according to the Wulff construction of a particular crystal, and specify the size of the nanocrystal. As a result, the nanoparticle is constructed and visualized, and the coordinates of the atoms are output to the user. NanoCrystal can be accessed at http://nanocrystal.vi-seem.edu/ .