Insights into membrane-bound presenilin 2 from all-atom molecular dynamics simulations

Machine learning and classical MD simulation to identify inhibitors against the P37 envelope protein of monkeypox virus

Monkeypox virus (MPXV) outbreak is a serious public health concern that requires international attention. P37 of MPXV plays a pivotal role in DNA replication and acts as one of the promising targets for antiviral drug design. In this study, we intent to screen potential analogs of existing FDA approved drugs of MPXV against P37 using state-of-the-art machine learning and computational biophysical techniques. AlphaFold2 guided all-atoms molecular dynamics simulations optimized P37 structure is used for molecular docking and binding free energy calculations. Similar to members of Phospholipase-D family , the predicted P37 structure also adopts a β-α-β-α-β sandwich fold, harbouring strongly conserved HxKxxxxD motif. The binding pocket comprises of Tyr48, Lys86, His115, Lys117, Ser130, Asn132, Trp280, Asn240, His325, Lys327 and Tyr346 forming strong hydrogen bonds and dense hydrophobic contacts with the screened analogs and is surrounded by positively charged patches. Loops connecting the two domains and C-terminal region exhibit high degree of flexibility. In some structural ensembles, the partial disorderness in the C-terminal region is presumed to be due to its low confidence score, acquired during structure prediction. Transition from loop to β-strands (244-254 aa) in P37-Cidofovir and its analog complexes advocates the need for further investigations. MD simulations support the accuracy of the molecular docking results, indicating the potential of analogs as potent binders of P37. Taken together, our results provide preferable understanding of molecular recognition and dynamics of ligand-bound states of P37, offering opportunities for development of new antivirals against MPXV. However, the need of in vitro and in vivo assays for confirmation of these results still persists.Communicated by Ramaswamy H. Sarma.

Structural bioinformatics studies of glutamate transporters and their AlphaFold2 predicted water-soluble QTY variants and uncovering the natural mutations of L->Q, I->T, F->Y and Q->L, T->I and Y->F

Glutamate transporters play key roles in nervous physiology by modulating excitatory neurotransmitter levels, when malfunctioning, involving in a wide range of neurological and physiological disorders. However, integral transmembrane proteins including the glutamate transporters remain notoriously difficult to study, due to their localization within the cell membrane. Here we present the structural bioinformatics studies of glutamate transporters and their water-soluble variants generated through QTY-code, a protein design strategy based on systematic amino acid substitutions. These include 2 structures determined by X-ray crystallography, cryo-EM, and 6 predicted by AlphaFold2, and their predicted water-soluble QTY variants. In the native structures of glutamate transporters, transmembrane helices contain hydrophobic amino acids such as leucine (L), isoleucine (I), and phenylalanine (F). To design water-soluble variants, these hydrophobic amino acids are systematically replaced by hydrophilic amino acids, namely glutamine (Q), threonine (T) and tyrosine (Y). The QTY variants exhibited water-solubility, with four having identical isoelectric focusing points (pI) and the other four having very similar pI. We present the superposed structures of the native glutamate transporters and their water-soluble QTY variants. The superposed structures displayed remarkable similarity with RMSD 0.528Å-2.456Å, despite significant protein transmembrane sequence differences (41.1%->53.8%). Additionally, we examined the differences of hydrophobicity patches between the native glutamate transporters and their QTY variants. Upon closer inspection, we discovered multiple natural variations of L->Q, I->T, F->Y and Q->L, T->I, Y->F in these transporters. Some of these natural variations were benign and the remaining were reported in specific neurological disorders. We further investigated the characteristics of hydrophobic to hydrophilic substitutions in glutamate transporters, utilizing variant analysis and evolutionary profiling. Our structural bioinformatics studies not only provided insight into the differences between the hydrophobic helices and hydrophilic helices in the glutamate transporters, but they are also expected to stimulate further study of other water-soluble transmembrane proteins.

All-atoms molecular dynamics study to screen potent efflux pump inhibitors against KpnE protein of Klebsiella pneumoniae

The Small Multidrug Resistance efflux pump protein KpnE, plays a pivotal role in multi-drug resistance in Klebsiella pneumoniae. Despite well-documented study of its close homolog, EmrE, from Escherichia coli, the mechanism of drug binding to KpnE remains obscure due to the absence of a high-resolution experimental structure. Herein, we exclusively elucidate its structure-function mechanism and report some of the potent inhibitors through drug repurposing. We used molecular dynamics simulation to develop a dimeric structure of KpnE and explore its dynamics in lipid-mimetic bilayers. Our study identified both semi-open and open conformations of KpnE, highlighting its importance in transport process. Electrostatic surface potential map suggests a considerable degree of similarity between KpnE and EmrE at the binding cleft, mostly occupied by negatively charged residues. We identify key amino acids Glu14, Trp63 and Tyr44, indispensable for ligand recognition. Molecular docking and binding free energy calculations recognizes potential inhibitors like acarbose, rutin and labetalol. Further validations are needed to confirm the therapeutic role of these compounds. Altogether, our membrane dynamics study uncovers the crucial charged patches, lipid-binding sites and flexible loop that could potentiate substrate recognition, transport mechanism and pave the way for development of novel inhibitors against K. pneumoniae.Communicated by Ramaswamy H. Sarma.

Analysis of E2F1 single-nucleotide polymorphisms reveals deleterious non-synonymous substitutions that disrupt E2F1-RB protein interaction in cancer

Cancer is a medical condition that is caused by the abnormal growth and division of cells, leading to the formation of tumors. The E2F1 and RB pathways are critical in regulating cell cycle, and their dysregulation can contribute to the development of cancer. In this study, we analyzed experimentally reported SNPs in E2F1 and assessed their effects on the binding affinity with RB. Out of 46, nine mutations were predicted as deleterious, and further analysis revealed four highly destabilizing mutations (L206W, R232C, I254T, A267T) that significantly altered the protein structure. Molecular docking of wild-type and mutant E2F1 with RB revealed a docking score of -242 kcal/mol for wild-type, while the mutant complexes had scores ranging from -217 to -220 kcal/mol. Molecular simulation analysis revealed variations in the dynamics features of both mutant and wild-type complexes due to the acquired mutations. Furthermore, the total binding free energy for the wild-type E2F1-RB complex was -64.89 kcal/mol, while those of the L206W, R232C, I254T, and A267T E2F1-RB mutants were -45.90 kcal/mol, -53.52 kcal/mol, -55.67 kcal/mol, and -61.22 kcal/mol, respectively. Our study is the first to extensively analyze E2F1 gene mutations and identifies candidate mutations for further validation and potential targeting for cancer therapeutics.

Predicting virus Fitness: Towards a structure-based computational model

Predicting the impact of new emerging virus mutations is of major interest in surveillance and for understanding the evolutionary forces of the pathogens. The SARS-CoV-2 surface spike-protein (S-protein) binds to human ACE2 receptors as a critical step in host cell infection. At the same time, S-protein binding to human antibodies neutralizes the virus and prevents interaction with ACE2. Here we combine these two binding properties in a simple virus fitness model, using structure-based computation of all possible mutation effects averaged over 10 ACE2 complexes and 10 antibody complexes of the S-protein (∼380,000 computed mutations), and validated the approach against diverse experimental binding/escape data of ACE2 and antibodies. The ACE2-antibody selectivity change caused by mutation (i.e., the differential change in binding to ACE2 vs. immunity-inducing antibodies) is proposed to be a key metric of fitness model, enabling systematic error cancelation when evaluated. In this model, new mutations become fixated if they increase the selective binding to ACE2 relative to circulating antibodies, assuming that both are present in the host in a competitive binding situation. We use this model to categorize viral mutations that may best reach ACE2 before being captured by antibodies. Our model may aid the understanding of variant-specific vaccines and molecular mechanisms of viral evolution in the context of a human host.

Computational analysis of the functional and structural impact of the most deleterious missense mutations in the human Protein C

Protein C (PC) is a vitamin K-dependent factor that plays a crucial role in controlling anticoagulant processes and acts as a cytoprotective agent to promote cell survival. Several mutations in human PC are associated with decreased protein production or altered protein structure, resulting in PC deficiency. In this study, we conducted a comprehensive analysis of nonsynonymous single nucleotide polymorphisms in human PC to prioritize and confirm the most high-risk mutations predicted to cause disease. Of the 340 missense mutations obtained from the NCBI database, only 26 were classified as high-risk mutations using various bioinformatic tools. Among these, we identified that 12 mutations reduced the stability of protein, and thereby had the greatest potential to disturb protein structure and function. Molecular dynamics simulations revealed moderate alterations in the structural stability, flexibility, and secondary structural organization of the serine protease domain of human PC for five missense mutations (L305R, W342C, G403R, V420E, and W444C) when compared to the native structure that could maybe influence its interaction with other molecules. Protein-protein interaction analyses demonstrated that the occurrence of these five mutations can affect the regular interaction between PC and activated factor V. Therefore, our findings assume that these mutants can be used in the identification and development of therapeutics for diseases associated with PC dysfunction, although assessment the effect of these mutations need to be proofed in in-vitro.

Modelling SARS-CoV-2 spike-protein mutation effects on ACE2 binding

The binding affinity of the SARS-CoV-2 spike (S)-protein to the human membrane protein ACE2 is critical for virus function. Computational structure-based screening of new S-protein mutations for ACE2 binding lends promise to rationalize virus function directly from protein structure and ideally aid early detection of potentially concerning variants. We used a computational protocol based on cryo-electron microscopy structures of the S-protein to estimate the change in ACE2-affinity due to S-protein mutation (ΔΔG_bind) in good trend agreement with experimental ACE2 affinities. We then expanded predictions to all possible S-protein mutations in 21 different S-protein-ACE2 complexes (400,000 ΔΔG_bind data points in total), using mutation group comparisons to reduce systematic errors. The results suggest that mutations that have arisen in major variants as a group maintain ACE2 affinity significantly more than random mutations in the total protein, at the interface, and at evolvable sites. Omicron mutations as a group had a modest change in binding affinity compared to mutations in other major variants. The single-mutation effects seem consistent with ACE2 binding being optimized and maintained in omicron, despite increased importance of other selection pressures (antigenic drift), however, epistasis, glycosylation and in vivo conditions will modulate these effects. Computational prediction of SARS-CoV-2 evolution remains far from achieved, but the feasibility of large-scale computation is substantially aided by using many structures and mutation groups rather than single mutation effects, which are very uncertain. Our results demonstrate substantial challenges but indicate ways forward to improve the quality of computer models for assessing SARS-CoV-2 mutation effects.

Structural and energetic basis of interaction between human estrogen-related receptor γ and environmental endocrine disruptors from multiple molecular dynamics simulations and free energy predictions

Environmental endocrine disruptors (EEDs), a class of molecules that are widespread in our environment, may adversely affect the endocrine system. Exploring the interactions between these compounds and their potential targets is important for assessing their role in the organism. Focused on the human estrogen-related receptor γ (hERRγ) with BPA, BPB, HPTE, BPE, BP(2,2)(Et), and BP(2,2)(MeO) complexes, respectively, we groped for the mechanisms of conformational changes and interactions of hERRγ when binding to these six EEDs by combining multiple molecular dynamics (MD) simulations with energy prediction (MM-PBSA and solvated interaction energy (SIE)). Dynamics analysis results revealed these six EEDs have different effects on the internal dynamics of hERRγ, resulting in significant changes in the interaction of key residues around Leu268, Val313, Leu345, and Phe435 with EEDs, and thus affected its binding energy with these EEDs. The energy calculations further demonstrated that van der Waals interactions are critical for these EEDs binding to hERRγ. These results present detailed molecular insight into the interaction features between EEDs and hERRγ and help guide the search for safer alternatives to BPA.

Sequence-structure functional implications and molecular simulation of high deleterious nonsynonymous substitutions in IDH1 revealed the mechanism of drug resistance in glioma

In the past few years, various somatic point mutations of isocitrate dehydrogenase (IDH) encoding genes (IDH1 and IDH2) have been identified in a broad range of cancers, including glioma. Despite the important function of IDH1 in tumorigenesis and its very polymorphic nature, it is not yet clear how different nsSNPs affect the structure and function of IDH1. In the present study, we employed different machine learning algorithms to screen nsSNPs in the IDH1 gene that are highly deleterious. From a total of 207 SNPs, all of the servers classified 80 mutations as deleterious. Among the 80 deleterious mutations, 14 were reported to be highly destabilizing using structure-based prediction methods. Three highly destabilizing mutations G15E, W92G, and I333S were further subjected to molecular docking and simulation validation. The docking results and molecular simulation analysis further displayed variation in dynamics features. The results from molecular docking and binding free energy demonstrated reduced binding of the drug in contrast to the wild type. This, consequently, shows the impact of these deleterious substitutions on the binding of the small molecule. PCA (principal component analysis) and FEL (free energy landscape) analysis revealed that these mutations had caused different arrangements to bind small molecules than the wild type where the total internal motion is decreased, thus consequently producing minimal binding effects. This study is the first extensive in silico analysis of the IDH1 gene that can narrow down the candidate mutations for further validation and targeting for therapeutic purposes.

Construction of the small intestine on molecular dynamics simulation and preliminary exploration of drug intestinal absorption prediction

In this study, molecular dynamics simulation was applied to the construction of the small intestinal epithelial cell membrane and prediction of drug absorption. First, we constructed a system of a small intestinal epithelial cell membrane that was close to the real proportion and investigated the effects of temperature, water layer thickness, and ionic strength on membrane properties to optimize environmental parameters. Next, three drugs with different absorptivity, including Ephedrine (EPH), Quercetin (QUE), and Baicalin (BAI), were selected as model drugs to study the ability of drugs through the membrane by the free diffusion and umbrella sampling simulation, and the drug permeation ability was characterized by the free diffusion coefficient D and free energy barrier (△G) in the processes. The results showed that the free diffusion coefficient D and △G orders of the three drugs were consistent with the classical experimental absorption order, indicating that these two parameters could be used to jointly characterize the membrane permeability of the drugs.

Angiostrongilus cantonensis an Atypical Presenilin: Epitope Mapping, Characterization, and Development of an ELISA Peptide Assay for Specific Diagnostic of Angiostrongyliasis

Background: Angiostrongyliasis, the leading cause universal of eosinophilic meningitis, is an emergent disease due to Angiostrongylus cantonensis (rat lungworm) larvae, transmitted accidentally to humans. The diagnosis of human angiostrongyliasis is based on epidemiologic characteristics, clinical symptoms, medical history, and laboratory findings, particularly hypereosinophilia in blood and cerebrospinal fluid. Thus, the diagnosis is difficult and often confused with those produced by other parasitic diseases. Therefore, the development of a fast and specific diagnostic test for angiostrongyliasis is a challenge mainly due to the lack of specificity of the described tests, and therefore, the characterization of a new target is required. Material and Methods: Using bioinformatics tools, the putative presenilin (PS) protein C7BVX5-1 was characterized structurally and phylogenetically. A peptide microarray approach was employed to identify single and specific epitopes, and tetrameric epitope peptides were synthesized to evaluate their performance in an ELISA-peptide assay. Results: The data showed that the A. cantonensis PS protein presents nine transmembrane domains, the catalytic aspartyl domain [(XD (aa 241) and GLGD (aa 332–335)], between TM6 and TM7 and the absence of the PALP and other characteristics domains of the class A22 and homologous presenilin (PSH). These individualities make it an atypical sub-branch of the PS family, located in a separate subgroup along with the enzyme Haemogonchus contournus and separated from other worm subclasses. Twelve B-linear epitopes were identified by microarray of peptides and validated by ELISA using infected rat sera. In addition, their diagnostic performance was demonstrated by an ELISA-MAP4 peptide. Conclusions: Our data show that the putative AgPS is an atypical multi-pass transmembrane protein and indicate that the protein is an excellent immunological target with two (PsAg3 and PsAg9) A. costarisencis cross-reactive epitopes and eight (PsAg1, PsAg2, PsAg6, PsAg7, PsAg8, PsAg10, PsAg11, PsAg12) apparent unique A. cantonensis epitopes. These epitopes could be used in engineered receptacle proteins to develop a specific immunological diagnostic assay for angiostrongyliasis caused by A. cantonensis.

Molecular characterization and structural dynamics of Aquaporin1 from walking catfish in lipid bilayers

Aquaporin's (AQPs) are the major superfamily of small integral membrane proteins that facilitates transportation of water, urea, ammonia, glycerol and ions across biological cell membranes. Despite of recent advancements made in understanding the biology of Aquaporin's, only few isoforms of aquaporin 1 (AQP1) some of the teleost fish species have been characterized at molecular scale. In this study, we made an attempt to elucidate the molecular mechanism of water transportation in AQP1 from walking catfish (Clarias batrachus), a model species capable of breathing in air and inhabits in challenging environments. Using state-of-the-art computational modelling and all-atoms molecular dynamics simulation, we explored the structural dynamics of full-length aquaporin 1 from walking catfish (CbAQP1) in lipid mimetic bilayers. Unlike AQP1 of human and bovine, structural ensembles of CbAQP1 from MD revealed discrete positioning of pore lining residues at the intracellular end. Snapshots from MD simulation displayed differential dynamics of aromatic/arginine (ar/R) filter and extracellular loop C bridging transmembrane (TM) helix H3 and H4. Distinct conformation of large extracellular loops, loop bridging TM2 domain and HB helix along with positioning of selectivity filter lining residues controls the permeability of water across the bilayer. Moreover, the identified unique and conserved lipid binding sites with 100% lipid occupancy signifies lipid mediated structural dynamics of CbAQP1. All-together, this is the first ever report on structural-dynamics of aquaporin 1 in walking catfish which will be useful to understand the molecular basis of transportation of water and other small molecules under varying degree of hyperosmotic environment.

Characterizing the Chemical Space of γ-Secretase Inhibitors and Modulators

γ-Secretase (GS) is one of the most attractive molecular targets for the treatment of Alzheimer's disease (AD). Its key role in the final step of amyloid-β peptides generation and its relationship in the cascade of events for disease development have caught the attention of many pharmaceutical groups. Over the past years, different inhibitors and modulators have been evaluated as promising therapeutics against AD. However, despite the great chemical diversity of the reported compounds, a global classification and visual representation of the chemical space for GS inhibitors and modulators remain unavailable. In the present work, we carried out a two-dimensional (2D) chemical space analysis from different classes and subclasses of GS inhibitors and modulators based on their structural similarity. Along with the novel structural information available for GS complexes, our analysis opens the possibility to identify compounds with high molecular similarity, critical to finding new chemical structures through the optimization of existing compounds and relating them with a potential binding site.

Insights into structure and dynamics of extracellular domain of Toll-like receptor 5 in Cirrhinus mrigala (mrigala): A molecular dynamics simulation approach

The toll-like receptor 5 (TLR5) is the most conserved important pattern recognition receptors (PRRs) often stimulated by bacterial flagellins and plays a major role in the first-line defense against invading pathogenic bacteria and in immune homeostasis. Experimental crystallographic studies have shown that the extracellular domain (ECD) of TLR5 recognizes flagellin of bacteria and functions as a homodimer in model organism zebrafish. However, no structural information is available on TLR5 functionality in the major carp Cirrhinus mrigala (mrigala) and its interaction with bacterial flagellins. Therefore, the present study was undertaken to unravel the structural basis of TLR5-flagellin recognition in mrigala using structural homodimeric TLR5-flagellin complex of zebrafish as reference. Integrative structural modeling and molecular dynamics simulations were employed to explore the structural and mechanistic details of TLR5 recognition. Results from structural snapshots of MD simulation revealed that TLR5 consistently formed close interactions with the three helices of the D1 domain in flagellin on its lateral side mediated by several conserved amino acids. Results from the intermolecular contact analysis perfectly substantiate with the findings of per residue-free energy decomposition analysis. The differential recognition mediated by flagellin to TLR5 in mrigala involves charged residues at the interface of binding as compared to the zebrafish complex. Overall our results shows TLR5 of mrigala involved in innate immunity specifically recognized a conserved site on flagellin which advocates the scientific community to explore host-specific differences in receptor activation.

Membrane dynamics of γ-secretase with the anterior pharynx-defective 1B subunit

The four-subunit protease complex γ-secretase cleaves many single-pass transmembrane (TM) substrates, including Notch and β-amyloid precursor protein to generate amyloid-β (Aβ), central to Alzheimer's disease. Two of the subunits anterior pharynx-defective 1 (APH-1) and presenilin (PS) exist in two homologous forms APH1-A and APH1-B, and PS1 and PS2. The consequences of these variations are poorly understood and could affect Aβ production and γ-secretase medicine. Here, we developed the first complete structural model of the APH-1B subunit using the published cryo-electron microscopy (cryo-EM) structures of APH1-A (Protein Data Bank: 5FN2, 5A63, and 6IYC). We then performed all-atom molecular dynamics simulations at 303 K in a realistic bilayer system to understand both APH-1B alone and in γ-secretase without and with substrate C83-bound. We show that APH-1B adopts a 7TM topology with a water channel topology similar to APH-1A. We demonstrate direct transport of water through this channel, mainly via Glu84, Arg87, His170, and His196. The apo and holo states closely resemble the experimental cryo-EM structures with APH-1A, however with subtle differences: The substrate-bound APH-1B γ-secretase was quite stable, but some TM helices of PS1 and APH-1B rearranged in the membrane consistent with the disorder seen in the cryo-EM data. This produces different accessibility of water molecules for the catalytic aspartates of PS1, critical for Aβ production. In particular, we find that the typical distance between the catalytic aspartates of PS1 and the C83 cleavage sites are shorter in APH-1B, that is, it represents a more closed state, due to interactions with the C-terminal fragment of PS1. Our structural-dynamic model of APH-1B alone and in γ-secretase suggests generally similar topology but some notable differences in water accessibility which may be relevant to the protein's existence in two forms and their specific function and location.

Side-by-side comparison of Notch- and C83 binding to γ-secretase in a complete membrane model at physiological temperature

γ-Secretase cleaves the C99 fragment of the amyloid precursor protein, leading to formation of aggregated β-amyloid peptide central to Alzheimer's disease, and Notch, essential for cell regulation. Recent cryogenic electron microscopy (cryo-EM) structures indicate major changes upon substrate binding, a β-sheet recognition motif, and a possible helix unwinding to expose peptide bonds towards nucleophilic attack. Here we report side-by-side comparison of the 303 K dynamics of the two proteins in realistic membranes using molecular dynamics simulations. Our ensembles agree with the cryo-EM data (full-protein Cα-RMSD = 1.62–2.19 Å) but reveal distinct presenilin helix conformation states and thermal β-strand to coil transitions of C83 and Notch100. We identify distinct 303 K hydrogen bond dynamics and water accessibility of the catalytic sites. The RKRR motif (1758–1761) contributes significantly to Notch binding and serves as a “membrane anchor” that prevents Notch displacement. Water that transiently hydrogen bonds to G1753 and V1754 probably represents the catalytic nucleophile. At 303 K, Notch and C83 binding induce different conformation states, with Notch mostly present in a closed state with shorter Asp–Asp distance. This may explain the different outcome of Notch and C99 cleavage, as the latter is more imprecise with many products. Our identified conformation states may aid efforts to develop conformation-selective drugs that target C99 and Notch cleavage differently, e.g. Notch-sparing γ-secretase modulators.

Distinct membrane dynamics and conformations of C83- and Notch-bound γ-secretase may aid the development of Notch-sparing treatments of Alzheimer's disease.

Identification of Structural Calcium Binding Sites in Membrane-Bound Presenilin 1 and 2

Variants of presenilin (PS1 and PS2) are the main genetic risk factors of familial Alzheimer's disease and thus central to the disease etiology. Although mostly studied as catalytic units of γ-secretase controlling Aβ production, presenilins also affect calcium levels, which are disturbed in Alzheimer's disease. We investigated the interaction of calcium with both PS1 and PS2 using all-atom molecular dynamics (MD) simulations in realistic membrane models, with the specific aim to identify any Ca²⁺ sites. We did not observe any complete Ca²⁺ leak event, but we identified four persistent Ca²⁺ sites in membrane-bound PS1 and PS2: One in HL2 near the C-terminal of TM6, one in HL2 toward the N-terminal of TM7, a site at the catalytic aspartate on TM7, and a site at the PALP motif on TM9. The sites feature negatively charged glutamates and aspartates typical of calcium binding. Structural homology to diaspartate calcium transport proteins and mutation studies of calcium efflux support our identified calcium sites. Calcium consistently dampens HL2 motions in all comparisons (PS1, protonated PS1, PS2, protonated PS2). Due to their location in HL2 and the active site, we propose that the calcium sites control autoproteolytic maturation of presenilin by a pH-dependent conformational restriction of the HL2 recognition loop, which also regulates calcium transport proteins such as inositol 1,4,5-triphosphate receptor and ryanodine receptor. Our structural dynamics could provide a possible molecular basis for the need of both calcium and presenilin for lysosome proteolytic function, perhaps relevant also to other protein misfolding diseases.

Electric-field-induced electroporation and permeation of reactive oxygen species across a skin membrane

Electroporation processes affect the permeability of cell membranes, which can be utilized for the delivery of plasma species in cancer therapy. By means of computational dynamics, many aspects of membrane electroporation have been unveiled at the atomic level for lipid membranes. Herein, a molecular dynamics simulation study was performed on native and oxidized membrane systems with transversal electric fields. The simulation result shows that the applied electric field mainly affects the membrane properties so that electroporation takes place and these pores are lined by hydrophilic headgroups of the lipid components. The calculated hydrophobic thickness, lateral diffusion and pair correlation revealed the role of 5α-CH in creation of water-pore in an oxidized membrane. Additionally, the permeability of reactive oxygen species was examined through these electroporated systems. The permeability study suggested that water pores in the membrane facilitate the penetration of these species across the membrane to the interior of the cell. These findings may have significance in experimental applications in vivo as once the reactive oxygen species reaches the interior of the cell, they may cause oxidative stress and induce apoptosis.Communicated by Ramaswamy H. Sarma.

Atomic-scale modeling of the effect of lipid peroxidation on the permeability of reactive species

Biomembranes and lipid systems are rich in unsaturated lipid components and are subject to photo-induced lipid peroxidation. The peroxidized lipid products in cellular systems are known to affect the structural organization and function of the biomembrane. We employed molecular dynamics simulations to study the effects of phospholipid peroxidation on membrane properties and the permeability of different reactive species. The results suggest that when the lipids are peroxidized, the peroxide group moves toward the membrane surface, which causes the membrane system to expand laterally and increase in area. The permeability profile revealed that nitrogen species can easily permeate through the native and peroxidized system in comparison to oxygen species, suggesting its importance in plasma-based treatment. Thus, by breaching the energy barrier with lower energy, they can traverse the cell membrane and induce oxidative stress, which leads to apoptosis.Communicated by Ramaswamy H. Sarma.

Prediction of impacts of mutations on protein structure and interactions: SDM, a statistical approach, and mCSM, using machine learning

Next-generation sequencing methods have not only allowed an understanding of genome sequence variation during the evolution of organisms but have also provided invaluable information about genetic variants in inherited disease and the emergence of resistance to drugs in cancers and infectious disease. A challenge is to distinguish mutations that are drivers of disease or drug resistance, from passengers that are neutral or even selectively advantageous to the organism. This requires an understanding of impacts of missense mutations in gene expression and regulation, and on the disruption of protein function by modulating protein stability or disturbing interactions with proteins, nucleic acids, small molecule ligands, and other biological molecules. Experimental approaches to understanding differences between wild-type and mutant proteins are most accurate but are also time-consuming and costly. Computational tools used to predict the impacts of mutations can provide useful information more quickly. Here, we focus on two widely used structure-based approaches, originally developed in the Blundell lab: site-directed mutator (SDM), a statistical approach to analyze amino acid substitutions, and mutation cutoff scanning matrix (mCSM), which uses graph-based signatures to represent the wild-type structural environment and machine learning to predict the effect of mutations on protein stability. Here, we describe DUET that uses machine learning to combine the two approaches. We discuss briefly the development of mCSM for understanding the impacts of mutations on interfaces with other proteins, nucleic acids, and ligands, and we exemplify the wide application of these approaches to understand human genetic disorders and drug resistance mutations relevant to cancer and mycobacterial infections. STATEMENT FOR A BROADER AUDIENCE: Genetic or somatic changes in genes can lead to mutations in human proteins, which give rise to genetic disorders or cancer, or to genes of pathogens leading to drug resistance. Computer software described here, using statistical approaches or machine learning, uses the information from genome sequencing of humans and pathogens, together with experimental or modeled 3D structures of gene products, the proteins, to predict impacts of mutations in genetic disease, cancer and drug resistance.

Interactions of a multifunctional di-triazole derivative with Alzheimer's Aβ42monomer and Aβ42protofibril: a systematic molecular dynamics study

The molecular dynamics simulations results highlighted that the multi-target-directed ligand6nstabilizes the native α-helix conformation of the Aβ₄₂monomer and induces a sizable destabilization in the Aβ₄₂protofibril structure.