PyLipID: A Python Package for Analysis of Protein–LipidInteractions from Molecular Dynamics Simulations

Calcium-dependent lectin I from Pseudomonas aeruginosa (PA-IL) binds specifically to oligosaccharides presenting an alpha-galactose residue at their nonreducing end, such as the disaccharides alphaGal1-2betaGalOMe, alphaGal1-3betaGalOMe, and alphaGal1-4betaGalOMe. This provides a unique model for studying the effect of the glycosidic linkage of the ligands on structure and thermodynamics of the complexes by means of experimental and theoretical tools. The structural features of PA-IL in complex with the three disaccharides were established by docking and molecular dynamics simulations and compared with those observed in available crystal structures, including PA-IL.alphaGal1-2betaGalOMe complex, which was solved at 2.4 A resolution and reported herein. The role of a structural bridge water molecule in the binding site of PA-IL was also elucidated through molecular dynamics simulations and free energy calculations. This water molecule establishes three very stable hydrogen bonds with O6 of nonreducing galactose, oxygen from Pro-51 main chain, and nitrogen from Gln-53 main chain of the lectin binding site. Binding free energies for PA-IL in complex with the three disaccharides were investigated, and the results were compared with the experimental data determined by titration microcalorimetry. When the bridge water molecule was included in the free energy calculations, the simulations predicted the correct binding affinity trends with the 1-2-linked disaccharide presenting three times stronger affinity ligand than the other two. These results highlight the role of the water molecule in the binding site of PA-IL and indicate that it should be taken into account when designing glycoderivatives active against P. aeruginosa adhesion.

Lipid–protein interactions are central to maintaining the structural and functional balance of biological membranes, influencing a wide array of cellular processes. These interactions, however, become pathological in neurodegenerative diseases (NDDs), such as Alzheimer’s, Parkinson’s, and Huntington’s diseases. In these disorders, the misfolding and aggregation of proteins like amyloid-beta (Aβ), alphasynuclein (α-syn), and mutant huntingtin (mHTT) disrupt the lipid bilayer, compromising membrane integrity, fluidity, and signaling. In this review we explore the critical role of lipid–protein interactions in NDDs, emphasizing how protein misfolding leads to toxic aggregates that embed into membranes, triggering neurotoxic events. Advanced spectroscopic techniques have been instrumental in studying these molecular interactions. Photon-based methods, including Förster resonance energy transfer (FRET), circular dichroism (CD), and Raman spectroscopy, provide real-time insights into protein aggregation and lipid membrane dynamics. Neutron-based techniques, such as neutron reflectometry and small-angle neutron scattering (SANS), further enhance the resolution of lipid–protein interactions, particularly in the context of neurodegenerative aggregation. Moreover, the review highlights the significance of lipid microdomains, particularly cholesterol-rich lipid rafts, which act as platforms for protein aggregation, influencing disease progression. Therapeutic strategies aimed at targeting these lipid–protein interfaces are also discussed, with a focus on how spectroscopic insights have driven the development of drugs that stabilize membrane integrity or prevent toxic aggregation. Finally, the integration of spectroscopy with computational models, such as molecular dynamics (MD) simulations, is proposed as a promising approach to further unravel the complex dynamics of lipid–protein interactions, providing a more complete picture of disease mechanisms.

Cdc25 phosphatase B, a potential target for cancer therapy, is inhibited by a series of quinones. The binding site and mode of quinone inhibitors to Cdc25B remains unclear, whereas this information is important for structure-based drug design. We investigated the potential binding site of NSC663284 [DA3003-1 or 6-chloro-7-(2-morpholin-4-yl-ethylamino)-quinoline-5, 8-dione] through docking and molecular dynamics simulations. Of the two main binding sites suggested by docking, the molecular dynamics simulations only support one site for stable binding of the inhibitor. Binding sites in and near the Cdc25B catalytic site that have been suggested previously do not lead to stable binding in 50ns molecular dynamics (MD) simulations. In contrast, a shallow pocket between the C-terminal helix and the catalytic site provides a favourable binding site that shows high stability. Two similar binding modes featuring protein-inhibitor interactions involving Tyr428, Arg482, Thr547 and Ser549 are identified by clustering analysis of all stable MD trajectories. The relatively flexible C-terminal region of Cdc25B contributes to inhibitor binding. The binding mode of NSC663284, identified through MD simulation, likely prevents the binding of protein substrates to Cdc25B. The present results provide useful information for the design of quinone inhibitors and their mechanism of inhibition.

Spectroscopy experiments are crucial to study membrane proteins for which traditional structure determination methods still prove challenging. Double electron-electron resonance (DEER) spectroscopy experiments provide protein residue-pair distance distributions that are indicative of their conformational heterogeneity. Atomistic molecular dynamics (MD) simulations are another tool that have been proven to be vital to study the structural dynamics of membrane proteins such as to identify inward-open, occluded, and outward-open conformations of transporter membrane proteins, among other partially open or closed states of the protein. Yet, studies have reported that there is no direct consensus between the distributional data from DEER experiments and MD simulations, which has challenged validation of structures obtained from long-timescale simulations and using simulations to design experiments. Current coping strategies for comparisons rely on heuristics, such as mapping the nearest matching peaks between two ensembles or biased simulations. Here we examine the differences in residue-pair distance distributions arising due to the choice of membranes around the protein and covalent modification of a pair of residues to nitroxide spin labels in DEER experiments. Through comparing MD simulations of two proteins, PepTSo and LeuT-both of which have been characterized using DEER experiments previously-we show that the proteins' dynamics are similar despite the choice of the detergent micelle as a membrane mimetic in DEER experiments. On the other hand, covalently modified residues show slight local differences in their dynamics and a huge divergence when the oxygen atom pair distances between spin labeled residues are measured rather than protein backbone distances. Given the computational expense associated with pairwise MTSSL labeled MD simulations, we examine the use of biased simulations to explore the conformational dynamics of the spin labels only to reveal that such simulations alter the underlying protein dynamics. Our study identifies the main cause for the mismatch between DEER experiments and MD simulations and will accelerate the development of potential mitigation strategies to improve the match.

Position of helical kinks in membrane protein crystal structures and the accuracy of computational prediction

Functional and computational studies reveal that the allosteric inhibition mechanism is conserved between glutamate and neutral amino acid transporters of the SLC1 family, and identify a novel allosteric inhibitor.

Machine learning for Big Data analytics in plants.

Computational approaches could decrease the need for the laborious high-throughput experimental screening that is often required to improve enzymes by mutagenesis. Here, we report that using multiple short molecular dynamics (MD) simulations makes it possible to accurately model enantioselectivity for large numbers of enzyme-substrate combinations at low computational costs. We chose four different haloalkane dehalogenases as model systems because of the availability of a large set of experimental data on the enantioselective conversion of 45 different substrates. To model the enantioselectivity, we quantified the frequency of occurrence of catalytically productive conformations (near attack conformations) for pairs of enantiomers during MD simulations. We found that the angle of nucleophilic attack that leads to carbon-halogen bond cleavage was a critical variable that limited the occurrence of productive conformations; enantiomers for which this angle reached values close to 180° were preferentially converted. A cluster of 20-40 very short (10 ps) MD simulations allowed adequate conformational sampling and resulted in much better agreement to experimental enantioselectivities than single long MD simulations (22 ns), while the computational costs were 50-100 fold lower. With single long MD simulations, the dynamics of enzyme-substrate complexes remained confined to a conformational subspace that rarely changed significantly, whereas with multiple short MD simulations a larger diversity of conformations of enzyme-substrate complexes was observed.

The solvent of membrane proteins is the membrane lipids in which they are embedded. Therefore, the nature of the lipids that surround membrane proteins impacts their dynamics and interactions. Unfortunately, how membrane proteins dynamically interact is difficult to study, and little is experimentally known how membrane proteins interplay in a membrane at the molecular scale. Herein, high‐speed atomic force microscopy (HS‐AFM) is used to dynamically image a well‐controlled bottom‐up system consisting of two aquaporin‐fold membrane proteins, pentameric FocA and tetrameric GlpF, that interact in membranes composed of varying amounts of 1,2‐dioleoyl‐sn‐glycero‐3‐phosphocholine (DOPC) and E. coli lipids. It is found that the lipid environment significantly influences membrane protein mobility and interaction, where increased E. coli lipid content reduces protein movement, while DOPC‐rich environments promote mobility. Furthermore, the supramolecular structures of the membrane proteins and protomer interactions in clusters are also lipid modulated, where E. coli lipids favor specific protein–protein interactions, whereas greater interaction variability is found in DOPC. These findings highlight the role of lipids in regulating protein diffusion and interactions and suggest that lipid–protein interaction energetics play a significant role in controlling membrane protein interactions and supramolecular assembly.

Astronomy datasets are generally terabytes in size and contain hundreds of millions of objects separated into millions of files-factors which makes many analyses impractical to perform on small computers. The key question we answer in this paper is: How can we leverage Grid resources to make the analysis of large astronomy datasets a reality for the astronomy community? To address this question, we have developed a Web Services-based system, AstroPortal, that uses grid computing to federate large computing and storage resources for dynamic analysis of large datasets. Building on the GT4, we have built a prototype and implemented a first analysis, stacking, that sums multiple regions of the sky, a function that can help both identify variable sources and detect faint objects. AstroPortal gives the astronomy community a new tool to advance their research and to open new doors to opportunities never before possible on such a large scale.

A molecular dynamics simulation study to understand the effect of cholesterol and tissue factor palmitoylation on tissue factor-factor VIIa-factor Xa ternary complex in different lipid environments

The efforts of this thesis are directed towards understanding the physical driving forces that mediate membrane protein self-assembly processes within the native environment of membrane proteins, the bilayer milieu. While many defined determinants contribute to the energetics of membrane protein self-assembly, such as electrostatics, non-polar side-chain packing, hydrogen bonding, and solvophobic effects, the roles that these driving forces play have not been well characterized. The inability to describe the role of these physical driving forces in processes involving membrane proteins comes from the lack of quantitative thermodynamic measurements concerning membrane protein self-assembly equilibria within the lipid bilayer environment. These thermodynamic measurements would describe the free energy of self-assembly (∆G°) and the breakdown of this term into enthalpic (∆H°) and entropic components (∆S°). The studies within this thesis bridge the gaps in our knowledge of membrane protein self-assembly by quantifying the contributions of non-polar side-chain packing in the dimerization reaction of the large poly-topic membrane protein CLC-ec1 within lipid bilayers. An emphasis on describing non-polar side-chain packing was chosen due to membrane proteins commonly exhibiting the packing of transmembrane helices and surfaces lined with non-polar residues within lipid bilayers. Furthermore, non-polar side-chain packing is thought to be a significant contributor to membrane protein self-assembly, but its quantification has been limited. An emphasis is placed within the thesis on quantifying non-polar side-chain packing interactions in terms of van der Waals (VDW) interaction energies. Chapter 1 of this thesis describes the pertinent background of the driving forces thought to be involved in membrane protein self-assembly and provides an extensive review of the membrane protein CLC-ec1. The studies within this thesis employed a novel method, called the subunit capture method, to measure CLC-ec1 dimerization within membranes. Chapter 2 describes the subunit capture method and other methods commonly used throughout the thesis. Chapter 3 describes the first study, which characterizes the impact of removing non-polar side-chain contacts on the stoichiometry of the CLC-ec1 homodimer. Computational analysis of the dimerization interface of CLC-ec1 characterized non-polar side-chain packing by measuring the VDW interaction energies of side-chains within a structure of CLC-ec1 determined by x-ray crystallography. Subsequent experiments targeted non-polar side-chains exhibiting favorable VDW interaction energies by substituting 4-5 of the native residues at a time to the smaller residue alanine, depleting the favorable VDW interactions at the dimerization interface. The CLC-ec1 multiple alanine constructs revealed that the stoichiometry of CLC-ec1 is dependent upon the non-polar side-chains at the dimerization interface, as each construct becomes entirely monomeric in lipid bilayers. The studies also found that changes in CLC-ec1 stoichiometry occur in a solvent-dependent manner as three of the four CLC-ec1 multiple alanine constructs remain stable dimers in detergent micelles. Lastly, the study exposes a molecular hot-spot at residue 194, characterizing the substitution to alanine at that position as important for dimer stoichiometry in both detergents and lipids. The contents of Chapter 4 present the findings from a second study that sought answers to two questions. The study begins by posing the first question: Are protein-protein interactions driving the dimerization of CLC-ec1? To answer this question, we performed long all-atom molecular dynamics (MD) simulations to quantify the interactions of CLC-ec1 in a dimeric and monomeric state while embedded in a lipid bilayer. Through the quantification of VDW interaction energies, the measurements revealed that the interactions within each state are similar in magnitude. The findings from the MD simulations present VDW interactions as important to CLC-ec1 dimerization but not as a determinant that excessively stabilizes the CLC-ec1 dimer complex. The results from the analysis of the MD simulations allowed us to form the second question of the study: Will removing side-chain packing interactions at the dimerization interface destabilize the CLC-ec1 homodimer? The second question was thoroughly investigated by measuring the changes in dimerization free energy of CLC-ec1 constructs that contained single substitutions to alanine that generate cavities at the dimerization interface. The various changes in CLC-ec1 dimer stability due to the alanine substitutions were compared to the MD computational analysis to find no correlation between the two datasets. We conclude that while the CLC-ec1 single alanine substitutions destabilize the dimer complex, the removal of non-polar packing interactions from the dimer state of CLC-ec1 does not explain the measured changes in stability. The final chapter in this thesis, Chapter 5, presents a discussion of how the results obtained within this thesis fit into our current understanding of membrane protein self-assembly. Included in the discussion, we reason that CLC-ec1 may not have excessively favorable non-polar packing interactions at the CLC-ec1 dimerization interface in order to retain a flexible interface for function -related activities. Finally, Chapter 5 will conclude by posing a few future directions of study to answer new questions we have encountered while performing the work presented in this thesis.

Protein–protein interactions are at the heart of biological processes. Understanding how proteins interact is key for deciphering their roles in health and disease, and for therapeutic interventions. However, identifying protein interaction sites, especially for intrinsically disordered proteins, is challenging. Here, we developed a deep learning framework to predict potential protein binding sites to 14-3-3 – a ‘central hub’ protein holding a key role in cellular signaling networks. After systematically testing multiple deep learning approaches to predict sequence binding to 14-3-3, we developed an ensemble model that achieved a 75% balanced accuracy on external sequences. Our approach was applied prospectively to identify putative binding sites across medically relevant proteins (ranging from highly structured to intrinsically disordered) for a total of approximately 300 sequences. The top eight predicted peptide sequences were experimentally validated in the wet-lab, and binding to 14-3-3 was confirmed for five out of eight sequences (Kd ranging from 1.6 ± 0.1 μM to 70 ± 5 μM). The relevance of our results was further confirmed by X-ray crystallography and molecular dynamics simulations. These sequences represent potential new binding sites within the 14-3-3 interactome (e.g., relating to Alzheimer's disease as the binding to tau is not the new part), and provide opportunities to investigate their functional relevance. Our results highlight the ability of deep learning to capture intricate patterns underlying protein–protein interactions, even for challenging cases like intrinsically disordered proteins. To further the understanding and targeting of 14-3-3/protein interactions, our model was provided as a freely accessible web resource at the following URL: https://14-3-3-bindsite.streamlit.app/.

Cytidylate kinase (CMK) plays a central role in the phosphorylation of ATP to nucleoside diphosphates in the purine and pyrimidine pathway in Mycobacterium tuberculosis (MTB). The enzyme is specific to bacteria, fungi, and plants and it shares the same pathway with a DNA-directed RNA polymerase subunit beta, the known target of rifampicin (RIF). Therefore, it serves as an attractive alternative target to inhibit the purine and pyrimidine pathway in MTB. In this study, the novel inhibitors of CMK were identified using in silico approach. Homology modeling was used to build the three-dimensional (3D) structure of CMK using program implemented in Modeller 9.16 and based on the template (3R20) obtained from Mycobacterium smegmatis. Structural analysis of CMK showed that it has P-loop or core in the ATP binding domain, nucleoside monophosphate binding (NMP) site, and a LID domain. Inhibition of any of the residues that formed the nucleoside binding site or ATP binding site blocks the catalytic activity of the CMK. Eleven thousand four hundred and fifty-four compounds that had an affinity to bind to the CMK with minimum binding energies were obtained through virtual screening from Zinc and PubChem databases using PyRx 8.0. These compounds were further filtered for Lipinski rule of five, molecular docking analysis, and ADMET properties (absorption distribution, metabolism, excretion, and toxicity). Four compounds (ZINC95485891 = − 8.80 kcal/mol, ZINC95485880 = − 8.44 kcal/mol, ZINC95485900 = − 8.24 kcal/mol, and ZINC95486231 = − 8.20 kcal/mol) with good binding energies and desirable pharmacokinetic properties were selected. These compounds were further subjected to molecular dynamic (MD) simulation and molecular mechanics generalized Born and surface area (MM-GBSA) analyses. The results of the analyses showed that all the four ligands formed a stable complex and had good free binding energy after 50 ns MD simulation. Therefore, these compounds were considered as possible inhibitors of MTB after experimental validation.

The lipid environment of Escherichia coli Aquaporin Z