Machine learning approaches in predicting allosteric sites.

Allosteric regulation is a fundamental biological mechanism that can control critical cellular processes via allosteric modulator binding to protein distal functional sites. The advantages of allosteric modulators over orthosteric ones have sparked the development of numerous computational approaches, such as the identification of allosteric binding sites, to facilitate allosteric drug discovery. Building on the success of machine learning (ML) models for solving complex problems in biology and chemistry, several ML models for predicting allosteric sites have been developed. In this review, we provide an overview of these models and discuss future perspectives powered by the field of artificial intelligence such as protein language models.

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.

Martini 3 Force Field Parameters for Protein Lipidation Post-Translational Modifications.

Protein lipidations are vital co/post-translational modifications that tether lipid tails to specific protein amino acids, allowing them to anchor to biological membranes, switch their subcellular localization, and modulate association with other proteins. Such lipidations are thus crucial for multiple biological processes including signal transduction, protein trafficking, and membrane localization and are implicated in various diseases as well. Examples of lipid-anchored proteins include the Ras family of proteins that undergo farnesylation; actin and gelsolin that are myristoylated; phospholipase D that is palmitoylated; glycosylphosphatidylinositol-anchored proteins; and others. Here, we develop parameters for cysteine-targeting farnesylation, geranylgeranylation, and palmitoylation, as well as glycine-targeting myristoylation for the latest version of the Martini 3 coarse-grained force field. The parameters are developed using the CHARMM36m all-atom force field parameters as reference. The behavior of the coarse-grained models is consistent with that of the all-atom force field for all lipidations and reproduces key dynamical and structural features of lipid-anchored peptides, such as the solvent-accessible surface area, bilayer penetration depth, and representative conformations of the anchors. The parameters are also validated in simulations of the lipid-anchored peripheral membrane proteins Rheb and Arf1, after comparison with independent all-atom simulations. The parameters, along with mapping schemes for the popular martinize2 tool, are available for download at 10.5281/zenodo.7849262 and also as supporting information.

Alchemical Free Energy Calculations on Membrane-Associated Proteins.

Membrane proteins have diverse functions within cells and are well-established drug targets. The advances in membrane protein structural biology have revealed drug and lipid binding sites on membrane proteins, while computational methods such as molecular simulations can resolve the thermodynamic basis of these interactions. Particularly, alchemical free energy calculations have shown promise in the calculation of reliable and reproducible binding free energies of protein-ligand and protein-lipid complexes in membrane-associated systems. In this review, we present an overview of representative alchemical free energy studies on G-protein-coupled receptors, ion channels, transporters as well as protein-lipid interactions, with emphasis on best practices and critical aspects of running these simulations. Additionally, we analyze challenges and successes when running alchemical free energy calculations on membrane-associated proteins. Finally, we highlight the value of alchemical free energy calculations calculations in drug discovery and their applicability in the pharmaceutical industry.

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.

The orchestrated signaling by PI3Kα and PTEN at the membrane interface.

The oncogene PI3Kα and the tumor suppressor PTEN represent two antagonistic enzymatic activities that regulate the interconversion of the phosphoinositide lipids PI(4,5)P2 and PI(3,4,5)P3 in membranes. As such, they are defining components of phosphoinositide-based cellular signaling and membrane trafficking pathways that regulate cell survival, growth, and proliferation, and are often deregulated in cancer. In this review, we highlight aspects of PI3Kα and PTEN interplay at the intersection of signaling and membrane trafficking. We also discuss the mechanisms of PI3Kα- and PTEN- membrane interaction and catalytic activation, which are fundamental for our understanding of the structural and allosteric implications on signaling at the membrane interface and may aid current efforts in pharmacological targeting of these proteins.

Comparative Interaction Studies of Quercetin with 2-Hydroxyl-propyl-ß-cyclodextrin and 2,6-Methylated-ß-cyclodextrin.

Quercetin (QUE) is a well-known natural product that can exert beneficial properties on human health. However, due to its low solubility its bioavailability is limited. In the present study, we examine whether its formulation with two cyclodextrins (CDs) may enhance its pharmacological profile. Comparative interaction studies of quercetin with 2-hydroxyl-propyl-ß-cyclodextrin (2HP-ß-CD) and 2,6-methylated cyclodextrin (2,6Me-ß-CD) were performed using NMR spectroscopy, DFT calculations, and in silico molecular dynamics (MD) simulations. Using T1 relaxation experiments and 2D DOSY it was illustrated that both cyclodextrin vehicles can host quercetin. Quantum mechanical calculations showed the formation of hydrogen bonds between QUE with 2HP-ß-CD and 2,6Μe-ß-CD. Six hydrogen bonds are formed ranging between 2 to 2.8 Å with 2HP-ß-CD and four hydrogen bonds within 2.8 Å with 2,6Μe-ß-CD. Calculations of absolute binding free energies show that quercetin binds favorably to both 2,6Me-ß-CD and 2HP-ß-CD. MM/GBSA results show equally favorable binding of quercetin in the two CDs. Fluorescence spectroscopy shows moderate binding of quercetin in 2HP-ß-CD (520 M-1) and 2,6Me-ß-CD (770 M-1). Thus, we propose that both formulations (2HP-ß-CD:quercetin, 2,6Me-ß-CD:quercetin) could be further explored and exploited as small molecule carriers in biological studies.

Investigating the Bioactive Conformation of Angiotensin II Using Markov State Modeling Revisited with Web-Scale Clustering.

Molecular dynamics simulation is a powerful technique for studying the structure and dynamics of biomolecules in atomic-level detail by sampling their various conformations in real time. Because of the long timescales that need to be sampled to study biomolecular processes and the big and complex nature of the corresponding data, relevant analyses of important biophysical phenomena are challenging. Clustering and Markov state models (MSMs) are efficient computational techniques that can be used to extract dominant conformational states and to connect those with kinetic information. In this work, we perform Molecular Dynamics simulations to investigate the free energy landscape of Angiotensin II (AngII) in order to unravel its bioactive conformations using different clustering techniques and Markov state modeling. AngII is an octapeptide hormone, which binds to the AT1 transmembrane receptor, and plays a vital role in the regulation of blood pressure, conservation of total blood volume, and salt homeostasis. To mimic the water-membrane interface as AngII approaches the AT1 receptor and to compare our findings with available experimental results, the simulations were performed in water as well as in water-ethanol mixtures. Our results show that in the water-ethanol environment, AngII adopts more compact U-shaped (folded) conformations than in water, which resembles its structure when bound to the AT1 receptor. For clustering of the conformations, we validate the efficiency of an inverted-quantized k-means algorithm, as a fast approximate clustering technique for web-scale data (millions of points into thousands or millions of clusters) compared to k-means, on data from trajectories of molecular dynamics simulations with reasonable trade-offs between time and accuracy. Finally, we extract MSMs using various clustering techniques for the generation of microstates and macrostates and for the selection of the macrostate representatives.

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.

Inactive-to-Active Transition of Human Thymidine Kinase 1 Revealed by Molecular Dynamics Simulations.

Despite its importance in the nucleoside (and nucleoside prodrug) metabolism, the structure of the active conformation of human thymidine kinase 1 (hTK1) remains elusive. We perform microsecond molecular dynamics simulations of the inactive enzyme form bound to a bisubstrate inhibitor that was shown experimentally to activate another TK1-like kinase, Thermotoga maritima TK (TmTK). Our results are in excellent agreement with the experimental findings for the TmTK closed-to-open state transition. We show that the inhibitor induces an increase of the enzyme radius of gyration due to the expansion on one of the dimer interfaces; the structural changes observed, including the active site pocket volume increase and the decrease in the monomer-monomer buried surface area and of the number of hydrogen bonds (as compared to the inactive enzyme control simulation), indicate that the catalytically competent (open) conformation of hTK1 can be assumed in the presence of an activating ligand.

The effect of S427F mutation on RXRα activity depends on its dimeric partner.

RXRs are nuclear receptors acting as transcription regulators that control key cellular processes in all tissues. All type II nuclear receptors require RXRs for transcriptional activity by forming heterodimeric complexes. Recent whole-exome sequencing studies have identified the RXRα S427F hotspot mutation in 5% of the bladder cancer patients, which is always located at the interface of RXRα with its obligatory dimerization partners. Here, we show that mutation of S427 deregulates transcriptional activity of RXRα dimers, albeit with diverse allosteric mechanisms of action depending on its dimeric partner. S427F acts by allosteric mechanisms, which range from inducing the collapse of the binding pocket to allosteric stabilization of active co-activator competent RXRα states. Unexpectedly, RXR S427F heterodimerization leads to either loss- or gain-of-function complexes, in both cases likely compromising its tumor suppressor activity. This is the first report of a cancer-associated single amino acid substitution that affects the function of the mutant protein variably depending on its dimerization partner.

N-terminal domain mutations of the spike protein are structurally implicated in epitope recognition in emerging SARS-CoV-2 strains.

During the past two years, the world has been ravaged by a global pandemic caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Acquired mutations in the SARS-CoV-2 genome affecting virus infectivity and/or immunogenicity have led to a number of novel strains with higher transmissibility compared to the original Wuhan strain. Mutations in the receptor binding domain (RBD) of the SARS-CoV-2 spike protein have been extensively studied in this context. However, mutations and deletions within the N-terminal domain (NTD) located adjacent to the RBD are less studied. Many of these are found within certain ß sheet-linking loops, which are surprisingly long in SARS-CoV-2 in comparison to SARS-CoV and other related ß coronaviruses. Here, we perform a structural and epidemiological study of novel strains carrying mutations and deletions within these loops. We identify short and long-distance interactions that stabilize the NTD loops and form a critical epitope that is essential for the recognition by a wide variety of neutralizing antibodies from convalescent plasma. Among the different mutations/deletions found in these loops, Ala 67 and Asp 80 mutations as well as His 69/Val 70 and Tyr 144 deletions have been identified in different fast-spreading strains. Similarly, deletions in amino acids 241-243 and 246-252 have been found to affect the network of NTD loops in strains with high transmissibility. Our structural findings provide insight regarding the role of these mutations/deletions in altering the epitope structure and thus affecting the immunoreactivity of the NTD region of spike protein.

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/.