Acid Pairs Research Articles

Position-Regulated Electrostatic Interactions for Single Amino Acid Revealed by Aspartic Acid-Scanning Mutagenesis.

We have examinedin this contribution the electrostatic interactions between single arginine and aspartic acid by analyzing the peptide-peptide binding characteristics involving arginine-aspartic acid, arginine-glycine, arginine-tryptophan and tryptophan-glycine interactions. The results of aspartic acid mutagenesis revealed that the interactions between arginine and aspartic acid have significant dependence on the position and composition of amino acids. While the primary interaction can be attributed to arginine-tryptophan contacts originated from the indolemoieties with the main chains of 14-merscontaining N-H and C=O moieties, pronounced enhancement could be identified in association with the electrostatic side-chain-side-chaininteractions between arginine and aspartic acid. An optimal separation of 2~4 amino acids between two adjacent aspartic acid and tryptophanbinding sites can be identified to achievemaximalenhancement of binding interactions.Such observed separation dependence may be utilized to unravel cooperative effects in heterogeneous interactionsbetween single pair of amino acids.

Stacking based ensemble learning framework for identification of nitrotyrosine sites

Protein nitrotyrosine is an essential post-translational modification that results from the nitration of tyrosine amino acid residues. This modification is known to be associated with the regulation and characterization of several biological functions and diseases. Therefore, accurate identification of nitrotyrosine sites plays a significant role in the elucidating progress of associated biological signs. In this regard, we reported an accurate computational tool known as iNTyro-Stack for the identification of protein nitrotyrosine sites. iNTyro-Stack is a machine-learning model based on a stacking algorithm. The base classifiers in stacking are selected based on the highest performance. The feature map employed is a linear combination of the amino composition encoding schemes, including the composition of k-spaced amino acid pairs and tri-peptide composition. The recursive feature elimination technique is used for significant feature selection. The performance of the proposed method is evaluated using k-fold cross-validation and independent testing approaches. iNTyro-Stack achieved an accuracy of 86.3% and a Matthews correlation coefficient (MCC) of 72.6% in cross-validation. Its generalization capability was further validated on an imbalanced independent test set, where it attained an accuracy of 69.32%. iNTyro-Stack outperforms existing state-of-the-art methods across both evaluation techniques. The github repository is create to reproduce the method and results of iNTyro-Stack, accessible on: https://github.com/waleed551/iNTyro-Stack/

Structure-guided development of selective caseinolytic protease P agonists as antistaphylococcal agents.

Methicillin-resistant Staphylococcus aureus is a ubiquitous pathogen, posing a serious threat to human health worldwide. Thus, there is a high demand for antibiotics with distinct targets. Caseinolytic protease P (ClpP) is a promising target for combating staphylococcal infections; however, selectively activating S.aureus ClpP (SaClpP) rather than Homo sapiens ClpP (HsClpP) remains challenging. Herein, we rationally design and identify ZG297 by structure-based strategy. It binds and activates SaClpP instead of HsClpP. This is due to differentiated ligand binding attributed to crossed "tyrosine/histidine" amino acid pairs. ZG297 substantially inhibits the growth of a broad panel of S.aureus strains invitro, outperforming the selective (R)-ZG197 agonist. ZG297 also functions as a potent antibiotic against multidrug-resistant S.aureus infections in Galleria mellonella larvae, zebrafish, murine skin, and thigh infection models. Collectively, we demonstrate that ZG297 is a safer and more potent antistaphylococcal agent than acyldepsipeptide 4 and (R)-ZG197.

Human essential gene identification based on feature fusion and feature screening.

Essential genes are necessary to sustain the life of a species under adequate nutritional conditions. These genes have attracted significant attention for their potential as drug targets, especially in developing broad-spectrum antibacterial drugs. However, studying essential genes remains challenging due to their variability in specific environmental conditions. In this study, the authors aim to develop a powerful prediction model for identifying essential genes in humans. The authors first obtained the essential gene data from human cancer cell lines and characterised gene sequences using 7 feature encoding methods such as Kmer, the Composition of K-spaced Nucleic Acid Pairs, and Z-curve. Subsequently, feature fusion and feature optimisation strategies were employed to select the impactful features. Finally, machine learning algorithms were applied to construct the prediction models and evaluate their performance. The single-feature-based model achieved the highest area under the Receiver Operating Characteristic curve (AUC) of 0.830. After fusing and filtering these features, the classical machine learning models achieved the highest AUC at 0.823 while the deep learning model reached 0.860. Results obtained by the authors show that compared to using individual features, feature fusion and feature optimisation strategies significantly improved model performance. Moreover, the study provided an advantageous method for essential gene identification compared to other methods.

Identifying the minimal sets of distance restraints for FRET-assisted protein structural modeling.

Proteins naturally occur in crowded cellular environments and interact with other proteins, nucleic acids, and organelles. Since most previous experimental protein structure determination techniques require that proteins occur in idealized, non-physiological environments, the effects of realistic cellular environments on protein structure are largely unexplored. Recently, Förster resonance energy transfer (FRET) has been shown to be an effective experimental method for investigating protein structure in vivo. Inter-residue distances measured in vivo can be incorporated as restraints in molecular dynamics (MD) simulations to model protein structural dynamics in vivo. Since most FRET studies only obtain inter-residue separations for a small number of amino acid pairs, it is important to determine the minimum number of restraints in the MD simulations that are required to achieve a given root-mean-square deviation (RMSD) from the experimental structural ensemble. Further, what is the optimal method for selecting these inter-residue restraints? Here, we implement several methods for selecting the most important FRET pairs and determine the number of pairs that are needed to induce conformational changes in proteins between two experimentally determined structures. We find that enforcing only a small fraction of restraints, , where is the number of amino acids, can induce the conformational changes. These results establish the efficacy of FRET-assisted MD simulations for atomic scale structural modeling of proteins in vivo.

Selective Production of C3 Polyols from Cellulose over Hydrogen Spillover Promoted Pd–Mo/TiO2 Catalyst with Adjacent Lewis Acid Pairs

Selective Production of C₃ Polyols from Cellulose over Hydrogen Spillover Promoted Pd–Mo/TiO₂ Catalyst with Adjacent Lewis Acid Pairs

Two amino acid pairs in the Gc glycoprotein of severe fever with thrombocytopenia syndrome virus responsible for the enhanced virulence

Severe fever with thrombocytopenia syndrome (SFTS) is a significant public health concern, with a high fatality rate in humans and cats. In this study, we explored the genetic determinants that contribute to the different virulence of SFTS virus (SFTSV) based on Tk-F123 and Ng-F264 strains isolated from cats. Tk-F123 was 100% lethal in type I interferon receptor-knockout mice, whereas Ng-F264 exhibited no fatality. We identified a pair of amino acid residues in the Gc protein, glycine and serine, at residues 581 and 934, respectively, derived from Tk-F123, leading to a fatal infection. Those in Ng-F264 were arginine and asparagine. These results suggest that this pair of residues affects the Gc protein function and regulates SFTSV virulence. Our findings provide useful clues for the elucidation of viral pathogenicity and the development of effective live-attenuated vaccines and antiviral strategies.

Constructing Ru-Co2P Lewis Acid-Base Pairs to Prompt Hydrogen Evolution Reaction in Alkaline Seawater Electrolyte.

Seawater electrolysis is an ideal approach to generating green hydrogen. Nevertheless, the sluggish kinetics of water dissociation and the detrimental chlorine chemistry environment are serious obstructions for industrial applications. Herein, constructing unique (Co) Lewis acid and (Ru-P) base pair sites in Ru-Co2P decorated on nitrogen and phosphorus co-doped carbon (Ru-Co2P/NPC) significantly optimizes the energy barrier of water dissociation and enhances the anti-corrosive ability for alkaline seawater splitting. As expected, the optimal Ru-Co2P/NPC-2 exhibits exceptional hydrogen evolution reaction (HER) performances with overpotentials as low as 22.0 and 26.0mV to derive 10 mAcm-2 and operate steadily (@ 50 mAcm-2) over 30 h in alkaline and alkaline seawater electrolytes. The experimental and theoretical results elucidate that Co acting as Lewis acid sites prompts the water adsorption and breakage of the H─O bond, whereas Ru-P as Lewis base sites facilitates the hydrogen desorption in alkaline media. Furthermore, modulated chemical microenvironments can be beneficial to hinder chloride corrosion on the active sites of catalysts. This work sheds light on the rational construction of a highly efficient electrocatalyst for alkaline HER in seawater.

Deducing formation routes of oxylipins by quantitative multiple heart-cutting achiral-chiral 2D-LC-MS

Several oxylipins are regulators of inflammation. They are formed by enzymes such as lipoxygenases or cyclooxygenases, but also stereorandomly by autoxidation. Reversed-phase liquid chromatography-tandem-mass-spectrometry (LC-MS/MS) methods for oxylipin quantification do not separate enantiomers. Here, we combine sensitive and selective oxylipin analysis with chiral separation using two-dimensional (2D)-LC-MS/MS. By multiple heart-cutting, the oxylipin peaks are transferred onto a chiral column. 45 enantiomeric pairs of (di-)hydroxy-fatty acids are separated with full gradient elution within 1.80min, yielding lower limits of quantification <1pg on column. Concentrations as well as enantiomeric fractions of oxylipins can be determined, even at low concentrations or at high enantiomeric excess of one isomer. The developed achiral-chiral multiple heart-cutting 2D-LC-MS/MS method offers unprecedented selectivity, enabling a better understanding of the formation route of these lipid mediators. This is demonstrated by distinguishing the formation of hydroxy-fatty acids by (acetylated) cyclooxygenase-2 and radical-mediated autoxidation. Applying the method to human M2-like-macrophages, we show that the so-called specialized pro-resolving mediators (SPM) 5,15-DiHEPE and 7,17-DiHDHA as well as 5,15-DiHETE were present as (S,S)-enantiomers, supporting their enzymatic formation. In contrast, at least eight isomers (including protectin DX but not neutroprotectin D1) of 10,17-DiHDHA are present in immune cells, indicating formation by autoxidation. In human plasma of healthy subjects, none of these dihydroxy-fatty acids are not present. However, we demonstrate that all four isomers quickly form via autoxidation if the samples are stored improperly. Thus, dihydroxy-FA should only be reported as SPM, such as resolvin D5 or resolvin E4, if an enantioselective analysis has been carried out.

A fitness distribution law for amino-acid replacements.

The effect of replacing the amino acid at a given site in a protein is difficult to predict. Yet, evolutionary comparisons have revealed highly regular patterns of interchangeability between pairs of amino acids, and such patterns have proved enormously useful in a range of applications in bioinformatics, evolutionary inference, and protein design. Here we reconcile these apparently contradictory observations using fitness data from over 350,000 experimental amino acid replacements. Almost one-quarter of the 20 × 19 = 380 types of replacements have broad distributions of fitness effects (DFEs) that closely resemble the background DFE for random changes, indicating an overwhelming influence of protein context in determining mutational effects. However, we also observe that the 380 pair-specific DFEs closely follow a maximum entropy distribution, specifically a truncated exponential distribution. The shape of this distribution is determined entirely by its mean, which is equivalent to the chance that a replacement of the given type is fitter than a random replacement. In this type of distribution, modest deviations in the mean correspond to much larger changes in the probability of falling in the far right tail, so that modest differences in mean exchangeability may result in much larger differences in the chance of a highly fit mutation. Indeed, we show that under the assumption that purifying selection filters out the vast majority of mutations, the maximum entropy distributions of fitness effects inferred from deep mutational scanning experiments predict the characteristic patterns of amino acid change observed in molecular evolution. These maximum entropy distributions of mutational effects not only provide a tuneable model for molecular evolution, but also have implications for mutational effect prediction and protein engineering.

DNA Replication across α-l-(3'-2')-Threofuranosyl Nucleotides Mediated by Human DNA Polymerase η.

α-l-(3'-2')-Threofuranosyl nucleic acid (TNA) pairs with itself, cross-pairs with DNA and RNA, and shows promise as a tool in synthetic genetics, diagnostics, and oligonucleotide therapeutics. We studied in vitro primer insertion and extension reactions catalyzed by human trans-lesion synthesis (TLS) DNA polymerase η (hPol η) opposite a TNA-modified template strand without and in combination with O4-alkyl thymine lesions. Across TNA-T (tT), hPol η inserted mostly dAMP and dGMP, dTMP and dCMP with lower efficiencies, followed by extension of the primer to a full-length product. hPol η inserted dAMP opposite O4-methyl and -ethyl analogs of tT, albeit with reduced efficiencies relative to tT. Crystal structures of ternary hPol η complexes with template tT and O4-methyl tT at the insertion and extension stages demonstrated that the shorter backbone and different connectivity of TNA compared to DNA (3' → 2' versus 5' → 3', respectively) result in local differences in sugar orientations, adjacent phosphate spacings, and directions of glycosidic bonds. The 3'-OH of the primer's terminal thymine was positioned at 3.4 Å on average from the α-phosphate of the incoming dNTP, consistent with insertion opposite and extension past the TNA residue by hPol η. Conversely, the crystal structure of a ternary hPol η·DNA·tTTP complex revealed that the primer's terminal 3'-OH was too distant from the tTTP α-phosphate, consistent with the inability of the polymerase to incorporate TNA. Overall, our study provides a better understanding of the tolerance of a TLS DNA polymerase vis-à-vis unnatural nucleotides in the template and as the incoming nucleoside triphosphate.

Decoding the impact of neighboring amino acids on ESI-MS intensity output through deep learning

Peptide-level quantification using mass spectrometry (MS) is no trivial task as the physicochemical properties affect both response and detectability. The specific amino acid (AA) sequence affects these properties, however the connection between sequence and intensity output remains poorly understood. In this work, we explore combinations of amino acid pairs (i.e., dimer motifs) to determine a potential relationship between the local amino acid environment and MS1 intensity. For this purpose, a deep learning (DL) model, consisting of an encoder-decoder with an attention mechanism, was built. The attention mechanism allowed to identify the most relevant motifs. Specific patterns were consistently observed where a bulky/aromatic and hydrophobic AA followed by a cationic AA as well as consecutive bulky/aromatic and hydrophobic AAs were found important for the prediction of the MS1 intensity. Correlating attention weights to mean MS1 intensities revealed that some important motifs, particularly containing Trp, His, and Cys, were linked with low responding peptides whereas motifs containing Lys and most bulky hydrophobic AAs were often associated with high responding peptides. Moreover, Asn-Gly was associated with low response. The model predicts MS1 response with a mean average percentage error of ∼11 % and a Pearson correlation coefficient of ∼0.64. While dimer representation of peptide sequences did not improve predictive capacity compared to single AA representation in earlier work, this work adds valuable insight for a better understanding of peptide response in MS analysis. SignificanceMass spectrometry is not inherently quantitative, and the response of a compound relies not only on its concentration but also on the molecular composition. For mass spectrometry-based analysis of peptides, such as in bottom-up proteomics, this directly implies that the response cannot be used directly to quantify individual peptides. Moreover, the dependency of the response on the amino acid sequence of individual peptides remains poorly understood. Using a deep learning model based on a recurrent neural network with an attention mechanism, we here investigate how the presence of dimer motifs within a peptide affects the MS1 response through the analysis of intended equimolar peptide pools comprising almost 200,000 unique peptides in total. Not only do we identify certain dimer classes and specific dimers that substantially affect the MS1 response, but the model is also able to predict peptide intensity with low error rates within the independent test subset. The findings not only improve our understanding of the link between sequence and response for peptides but also highlight the potential of utilizing deep learning for developing methods allowing for absolute, label-free peptide quantification.

A Study on Novel Amino Acid Pair Features for Protein Evolutionary Classifications

Protein evolutionary classification from amino acid sequence is one of the hot research topics in computational biology and bioinformatics. The amino acid composition and arrangement in a protein sequence embed the hints to its evolutionary origins. The feature extraction from an amino acid sequence to a numerical vector is still a challenging problem. Traditional feature methods extract protein sequence information either from individual amino acids or kmers aspects, which have general performance with limitations in classification accuracy. To further improve the accuracy in protein evolutionary classifications, six new features defined on separated amino acid pairs are proposed for protein evolutionary classification analysis, where composition and arrangement as well as physical properties are considered for the different combinations of separated amino acid pairs. Different from general consideration of amino acid pairs, the new features account for the features of separated amino acid pairs with spatial intervals in the sequence, which may deeper reflect the spatial relationships and characters between the amino acid in pairs. In test of the performances of the new features, five standard protein evolutionary classification examples are employed, where the new features proposed are compared with classical protein sequence features such as averaged property factors (APF), natural vector (NV) and pseudo amino acid composition (PseAAC) as well as kmer versions of these features. The area under precision-recall curve (AUPRC) analysis shows that the new features are efficient in evolutionary classifications, which outperform traditional protein sequence features that are based on individual amino acids and kmers. Parameter analysis on the novel separated amino acid pair features and kmer features show that the features of some medium or longer length of amino acid pair intervals and kmers may achieve higher classification accuracy in evolutionary classifications. From this analysis, the newly proposed separated amino acid pairs with spacial intervals are proved to be efficient units in extracting protein sequences features, which may interpret richer evolutionary information of protein sequences than individual amino acids and kmers.

Accurately identifying positive and negative regulation of apoptosis using fusion features and machine learning methods

Apoptotic proteins play a crucial role in the apoptosis process, ensuring a balance between cell proliferation and death. Thus, further elucidating the regulatory mechanisms of apoptosis will enhance our understanding of their functions. However, the development of computational methods to accurately identify positive and negative regulation of apoptosis remains a significant challenge. This work proposes a machine learning model based on multi-feature fusion to effectively identify the roles of positive and negative regulation of apoptosis. Initially, we constructed a reliable benchmark dataset containing 200 positive regulation of apoptosis and 241 negative regulation of apoptosis proteins. Subsequently, we developed a classifier that combines the support vector machine (SVM) with pseudo composition of k-spaced amino acid pairs (PseCKSAAP), composition transition distribution (CTD), dipeptide deviation from expected mean (DDE), and PSSM-composition to identify these proteins. Analysis of variance (ANOVA) was employed to select optimized features that could yield the maximum prediction performance. Evaluating the proposed model on independent data revealed and achieved an accuracy of 0.781 with an AUROC of 0.837, demonstrating our model's potent capabilities.

SIL-IS LC-ESI-MS/MS method for simultaneous quick detection of amoxicillin and clavulanic acid in human plasma: Development, validation and its application to a pharmacokinetics study.

A liquid chromatography electrospray ionization tandem mass spectrometry method with amoxicillin-d4 as the stable isotope-labeled internal standard for simultaneous quick detection of amoxicillin and clavulanic acid in human plasma was developed and validated. Chromatographic separations were performed on a Hedera ODS-2 column (2.1 × 150 mm, 5μm). The mobile phases for gradient elution were aqueous solution containing 0.2% acetic acid (AA) (mobile phase A) together with organic phase solution (acetonitrile and methanol mixed solution, mobile phase B). Mass spectrometry was performed using negative electrospray ionization in multiple reaction monitoring mode. The target fragment ion pairs of amoxicillin, clavulanic acid and amoxicillin-d4 were m/z 364.1 → 223.1, 198.1 → 135.9 and 368.1 → 227.1, respectively. The linear ranges of this method were 40-5,000 ng/ml for amoxicillin and 30-2,500 ng/ml for clavulanic acid, with coefficient of determination > 0.9900. This method validation included selectivity, standard curve, lower limit of quantitation, accuracy, precision, recovery, matrix effect (hemolytic matrix and hyperlipidemic matrix), carryover, stability, dilution reliability and incurred sample reanalysis study. A successful application of this method was realized in a pharmacokinetic study after administration of amoxicillin-clavulanic acid potassium granules.

Aggregation Rules of Short Peptides.

The elucidation of aggregation rules for short peptides (e.g., tetrapeptides and pentapeptides) is crucial for the precise manipulation of aggregation. In this study, we derive comprehensive aggregation rules for tetrapeptides and pentapeptides across the entire sequence space based on the aggregation propensity values predicted by a transformer-based deep learning model. Our analysis focuses on three quantitative aspects. First, we investigate the type and positional effects of amino acids on aggregation, considering both the first- and second-order contributions. By identifying specific amino acids and amino acid pairs that promote or attenuate aggregation, we gain insights into the underlying aggregation mechanisms. Second, we explore the transferability of aggregation propensities between tetrapeptides and pentapeptides, aiming to explore the possibility of enhancing or mitigating aggregation by concatenating or removing specific amino acids at the termini. Finally, we evaluate the aggregation morphologies of over 20,000 tetrapeptides, regarding the morphology distribution and type and positional contributions of each amino acid. This work extends the existing aggregation rules from tripeptide sequences to millions of tetrapeptide and pentapeptide sequences, offering experimentalists an explicit roadmap for fine-tuning the aggregation behavior of short peptides for diverse applications, including hydrogels, emulsions, or pharmaceuticals.

Chirality in Singlet Fission: Controlling Singlet Fission in Aqueous Nanoparticles of Tetracenedicarboxylic Acid Ion Pairs.

The singlet fission characteristics of aqueous nanoparticles, self-assembled from ion pairs of tetracene dicarboxylic acid and various amines with or without chirality, are thoroughly investigated. The structure of the ammonium molecule, the counterion, is found to play a decisive role in determining the molecular orientation of the ion pairs and its regularity, spectroscopic properties, the strength of the intermolecular coupling between the tetracene chromophores, and the consequent singlet fission process. Using chiral amines has led to the formation of crystalline nanosheets and efficient singlet fission with a triplet quantum yield as high as 133% ±20% and a rate constant of 6.99×109s-1. The chiral ion pairs also provide a separation channel to free triplets with yields as high as 33% ±10%. In contrast, nanoparticles with achiral counterions do not show singlet fission, which gave low or high fluorescence quantum yields depending on the size of the counterions. The racemic ion pair produces a correlated triplet pair intermediate by singlet fission, but no decorrelation into two free triplets is observed, as triplet-triplet annihilation dominates. The introduction of chirality enables higher control over orientation and singlet fission in self-assembled chromophores. It provides new design guidelines for singlet fission materials.

PMTPred: machine-learning-based prediction of protein methyltransferases using the composition of k-spaced amino acid pairs.

Protein methyltransferases (PMTs) are a group of enzymes that help catalyze the transfer of a methyl group to its substrates. These enzymes play an important role in epigenetic regulation and can methylate various substrates with DNA, RNA, protein, and small-molecule secondary metabolites. Dysregulation of methyltransferases is implicated in various human cancers. However, in light of the well-recognized significance of PMTs, reliable and efficient identification methods are essential. In the present work, we propose a machine-learning-based method for the identification of PMTs. Various sequence-based features were calculated, and prediction models were trained using various machine-learning algorithms using a tenfold cross-validation technique. After evaluating each model on the dataset, the SVM-based CKSAAP model achieved the highest prediction accuracy with balanced sensitivity and specificity. Also, this SVM model outperformed deep-learning algorithms for the prediction of PMTs. In addition, cross-database validation was performed to ensure the robustness of the model. Feature importance was assessed using shapley additive explanations (SHAP) values, providing insights into the contributions of different features to the model's predictions. Finally, the SVM-based CKSAAP model was implemented in a standalone tool, PMTPred, due to its consistent performance during independent testing and cross-database evaluation. We believe that PMTPred will be a useful and efficient tool for the identification of PMTs. The PMTPred is freely available for download at https://github.com/ArvindYadav7/PMTPred and http://www.bioinfoindia.org/PMTPred/home.html for research and academic use.

Revisiting Artifacts of Kohn-Sham Density Functionals for Biosimulation.

We revisit the problem of unphysical charge density delocalization/fractionalization induced by the self-interaction error of common approximate Kohn-Sham (KS) density functional theory functionals on simulation of small to medium-sized proteins in a vacuum. Aside from producing unphysical electron densities and total energies, the vanishing of the HOMO-LUMO gap associated with the unphysical charge delocalization leads to an unphysical low-energy spectrum and catastrophic failure of most popular solvers for the KS self-consistent field (SCF) problem. We apply a robust quasi-Newton SCF solver [ Phys. Chem. Chem. Phys. 2024, 26, 6557] to obtain solutions for some of these difficult cases. The anatomy of the charge delocalization is revealed by the natural deformation orbitals obtained from the density matrix difference between the Hartree-Fock and KS solutions; the charge delocalization not only can occur between charged fragments (such as in zwitterionic polypeptides) but also involves neutral fragments. The vanishing-gap phenomenon and troublesome SCF convergence are both attributed to the unphysical KS Fock operator eigenspectra of molecular fragments (e.g., amino acids or their side chains). Analysis of amino acid pairs suggests that the unphysical charge delocalization can be partially ameliorated by the use of some range-separated hybrid functionals but not by semilocal or standard hybrid functionals. Last, we demonstrate that solutions without the unphysical charge delocalization can be located even for semilocal KS functionals highly prone to such defects, but such solutions have non-Aufbau character and are unstable with respect to mixing of the non-overlapping "frontier" orbitals. Caution should be exercised when unexpectedly small (or vanishing) HOMO-LUMO gaps and atypical SCF convergence patterns (e.g., oscillatory) are observed in KS DFT simulations in any context (bio or otherwise).

Unraveling the role of physicochemical differences in predicting protein-protein interactions.

The ability to accurately predict protein-protein interactions is critically important for understanding major cellular processes. However, current experimental and computational approaches for identifying them are technically very challenging and still have limited success. We propose a new computational method for predicting protein-protein interactions using only primary sequence information. It utilizes the concept of physicochemical similarity to determine which interactions will most likely occur. In our approach, the physicochemical features of proteins are extracted using bioinformatics tools for different organisms. Then they are utilized in a machine-learning method to identify successful protein-protein interactions via correlation analysis. It was found that the most important property that correlates most with the protein-protein interactions for all studied organisms is dipeptide amino acid composition (the frequency of specific amino acid pairs in a protein sequence). While current approaches often overlook the specificity of protein-protein interactions with different organisms, our method yields context-specific features that determine protein-protein interactions. The analysis is specifically applied to the bacterial two-component system that includes histidine kinase and transcriptional response regulators, as well as to the barnase-barstar complex, demonstrating the method's versatility across different biological systems. Our approach can be applied to predict protein-protein interactions in any biological system, providing an important tool for investigating complex biological processes' mechanisms.