Electrostatic energy has a significant contribution to intermolecular interaction energy, especially in biological systems. Unfortunately, precise quantum mechanics calculations are not feasible for large biological systems; hence, simpler calculation methods are required. We propose a method called UBDB+EPMM (University at Buffalo Pseudoatom DataBank + Exact Potential Multipole Moments), which shortens computational time without losing accuracy. Here, we characterize electrostatic interactions in selected complexes of IFIT proteins with RNA. IFIT proteins are effectors of the innate immune system, and by binding foreign RNA, they prevent the synthesis of viral proteins in human host cells; hence, they block the propagation of viruses. We show that by using the UBDB+EPMM method it is possible to describe protein-RNA interactions not only qualitatively but also quantitatively. Looking at the charge penetration contribution to electrostatic interactions, we find all amino acid residues with strong local interactions. Moreover, we confirm that electrostatic interaction of IFIT5 with pppRNA does not depend on the sequence of the RNA.

Alzheimer’s disease is the most common progressive neurodegenerative disorder and is characterized by the presence of amyloid β (Aβ) plaques in the brain. The γ-secretase complex, which produces Aβ, is an intramembrane-cleaving protease consisting of four membrane proteins. In this paper we investigated the amyloidogenic fragments of amyloid precursor protein (substrates Aβ₄₃ and Aβ₄₅, leading to less amyloidogenic Aβ₄₀ and more amyloidogenic Aβ₄₂, respectively) docked to the binding site of presenilin, the catalytic subunit of γ-secretase. In total, we performed 9 μs of all-atom molecular dynamics simulations of the whole γ-secretase complex with both substrates in low (10%) and high (50%) concentrations of cholesterol in the membrane. We found that, at the high cholesterol level, the Aβ₄₅ helix was statistically more flexible in the binding site of presenilin than Aβ₄₃. An increase in the cholesterol concentration was also correlated with a higher flexibility of the Aβ₄₅ helix, which suggests incompatibility between Aβ₄₅ and the binding site of presenilin potentiated by a high cholesterol level. However, at the C-terminal part of Aβ₄₅, the active site of presenilin was more compact in the case of a high cholesterol level, which could promote processing of this substrate. We also performed detailed mapping of the cholesterol binding sites at low and high cholesterol concentrations, which were independent of the typical cholesterol binding motifs.

The presented methodology is based on coarse-grained representation of biomolecules in implicit environments and is designed for the molecular dynamics simulations of membrane proteins and their complexes. The membrane proteins are not only found in the cell membrane but also in all membranous compartments of the cell: Golgi apparatus, mitochondria, endosomes and lysosomes, and they usually form large complexes. To investigate such systems the methodology is proposed based on two independent approaches combining the coarse-grained MARTINI model for proteins and the effective energy function to mimic the water/membrane environments. The latter is based on the implicit environment developed for all-atom simulations in the IMM1 method. The force field solvation parameters for COGRIMEN were initially calculated from IMM1 all-atom parameters and then optimized using Genetic Algorithms. The new methodology was tested on membrane proteins, their complexes and oligomers. COGRIMEN method is implemented as a patch for NAMD program and can be useful for fast and brief studies of large membrane protein complexes.

Alzheimer’s disease (AD) is the leading cause of dementia and is characterized by a presence of amyloid plaques, composed mostly of the amyloid-β (Aβ) peptides, in the brains of AD patients. The peptides are generated from the amyloid precursor protein (APP), which undergoes a sequence of cleavages, referred as trimming, performed by γ-secretase. Here, we investigated conformational changes in a series of β-amyloid substrates (from less and more amyloidogenic pathways) in the active site of presenilin-1, the catalytic subunit of γ-secretase. The substrates are trimmed every three residues, finally leading to Aβ₄₀ and Aβ₄₂, which are the major components of amyloid plaques. To study conformational changes, we employed all-atom molecular dynamics simulations, while for unfolding, we used steered molecular dynamics simulations in an implicit membrane-water environment to accelerate changes. We have found substantial differences in the flexibility of extended C-terminal parts between more and less amyloidogenic pathway substrates. We also propose that the positively charged residues of presenilin-1 may facilitate the stretching and unfolding of substrates. The calculated forces and work/energy of pulling were exceptionally high for Aβ₄₀, indicating why trimming of this substrate is so infrequent.

Background Protein repeats can confound sequence analyses because the repetitiveness of their amino acid sequences lead to difficulties in identifying whether similar repeats are due to convergent or divergent evolution. We noted that the patterns derived from traditional “dot plot” protein sequence self-similarity analysis tended to be conserved in sets of related repeat proteins and this conservation could be quantitated using a Jaccard metric. Results Comparison of these dot plots obviated the issues due to sequence similarity for analysis of repeat proteins. A high Jaccard similarity score was suggestive of a conserved relationship between closely related repeat proteins. The dot plot patterns decay quickly in the absence of selective pressure with an expected loss of 50% of Jaccard similarity due to a loss of 8.2 % sequence identity. We assembled a standard set of 79 repeat proteins representing all the subgroups in RepeatsDB to perform method testing on. Comparison of known repeat and non-repeat proteins from the PDB suggested that the information content in dot plots could be used to identify repeat proteins from pure sequence without needing structural information. Analysis of the UniRef90 database suggested that 16.9% of all known proteins could be classified as repeat proteins. These 13.3 million putative repeat protein chains were clustered and a significant amount (82.9%) of clusters containing between 5 and 200 members were of a single functional type. Conclusions Dot plot analysis of repeat proteins attempts to obviate issues that arise due to the sequence degeneracy of repeat proteins. These results show that this kind of analysis can efficiently be applied to analyze repeat proteins on a large scale.