Solvent Accessibility Research Articles

Peptide and Peptidomimetic Inhibitors Targeting the Interaction of Collapsin Response Mediator Protein 2 with the N-Type Calcium Channel for Pain Relief.

Ion channels serve pleiotropic functions. Often found in complexes, their activities and functions are sculpted by auxiliary proteins. We discovered that collapsin response mediator protein 2 (CRMP2) is a binding partner and regulator of the N-type voltage-gated calcium channel (CaV2.2), a genetically validated contributor to chronic pain. Herein, we trace the discovery of a new peptidomimetic modulator of this interaction, starting from the identification and development of CBD3, a CRMP2-derived CaV binding domain peptide. CBD3 uncouples CRMP2-CaV2.2 binding to decrease CaV2.2 surface localization and calcium currents. These changes occur at presynaptic sites of nociceptive neurons and indeed, CBD3 ameliorates chronic pain in preclinical models. In pursuit of a CBD3 peptidomimetic, we exploited a unique approach to identify a dipeptide with low conformational flexibility and high solvent accessibility that anchors binding to CaV2.2. From a pharmacophore screen, we obtained CBD3063, a small-molecule that recapitulated CBD3's activity, reversing nociceptive behaviors in rodents of both sexes without sensory, affective, or cognitive effects. By disrupting the CRMP2-CaV2.2 interaction, CBD3063 exerts these effects indirectly through modulating CaV2.2 trafficking, supporting CRMP2 as an auxiliary subunit of CaV2.2. The parent peptide CBD3 was also found by us and others to have neuroprotective properties at postsynaptic sites, through N-methyl-d-aspartate receptor and plasmalemmal Na+/Ca2+ exchanger 3, potentially acting as an auxiliary subunit for these pathways as well. Our new compound is poised to address several open questions regarding CRMP2's role in regulating the CaV2.2 pathways to treat pain with the potential added benefit of neuroprotection.

AggreProt: a web server for predicting and engineering aggregation prone regions in proteins.

Recombinant proteins play pivotal roles in numerous applications including industrial biocatalysts or therapeutics. Despite the recent progress in computational protein structure prediction, protein solubility and reduced aggregation propensity remain challenging attributes to design. Identification of aggregation-prone regions is essential for understanding misfolding diseases or designing efficient protein-based technologies, and as such has a great socio-economic impact. Here, we introduce AggreProt, a user-friendly webserver that automatically exploits an ensemble of deep neural networks to predict aggregation-prone regions (APRs) in protein sequences. Trained on experimentally evaluated hexapeptides, AggreProt compares to or outperforms state-of-the-art algorithms on two independent benchmark datasets. The server provides per-residue aggregation profiles along with information on solvent accessibility and transmembrane propensity within an intuitive interface with interactive sequence and structure viewers for comprehensive analysis. We demonstrate AggreProt efficacy in predicting differential aggregation behaviours in proteins on several use cases, which emphasize its potential for guiding protein engineering strategies towards decreased aggregation propensity and improved solubility. The webserver is freely available and accessible at https://loschmidt.chemi.muni.cz/aggreprot/.

Predictive and Experimental Motif Interaction Analysis Identifies Functions of the WNK-OSR1/SPAK Pathway.

The WNK-OSR1/SPAK protein kinase signaling pathway regulates ion homeostasis and cell volume, but its other functions are poorly understood. To uncover undefined signaling functions of the pathway we analyzed the binding specificity of the conserved C-terminal (CCT) domains of OSR1 and SPAK to find all possible interaction motifs in human proteins. These kinases bind the core consensus sequences R-F-x-V/I and R-x-F-x-V/I. Motifs were ranked based on sequence, conservation, cellular localization, and solvent accessibility. Out of nearly 3,700 motifs identified, 90% of previously published motifs were within the top 2% of those predicted. Selected candidates (TSC22D1, CAVIN1, ATG9A, NOS3, ARHGEF5) were tested. Upstream kinases WNKs 1-4 and their close relatives, the pseudokinases NRBP1/2, contain CCT-like domains as well. We identified additional distinct motif variants lacking the conserved arginine previously thought to be required, and found that the NRBP1 CCT-like domain binds TSC22D1 via the same motif as OSR1 and SPAK. Our results further highlight the rich and diverse functionality of CCT and CCT-like domains in connecting WNK signaling to cellular processes.

Evaluating Chemical Footprinting-Induced Perturbation of Protein Higher Order Structure.

Specific amino acid footprinting mass spectrometry (MS) is an increasingly utilized method for elucidating protein higher order structure (HOS). It does this by adding to certain amino acid residues a mass tag, whose reaction extent depends on solvent accessibility and microenvironment of the protein. Unlike reactive free radicals and carbenes, these specific footprinters react slower than protein unfolding. Thus, their footprinting, under certain conditions, provokes structural changes to the protein, leading to labeling on non-native structures. It is critical to establish conditions (i.e., reagent concentrations, time of reaction) to ensure that the structure of the protein following footprinting remains native. Here, we compare the efficacy of five methods in assessing protein HOS following footprinting at the intact protein level and then further localize the perturbation at the peptide level. Three are MS-based methods that provide dose-response plot analysis, evaluation of Poisson distributions of precursor and products, and determination of the average number of modifications. These MS-based methods reliably and effectively indicate HOS perturbation at the intact protein level, whereas spectroscopic methods (circular dichroism (CD) and dynamic light scattering (DLS)) are less sensitive in monitoring subtle HOS perturbation caused by footprinting. Evaluation of HOS at the peptide level indicates regions that are sensitive to localized perturbations. Peptide-level analysis also provides higher resolution of the HOS perturbation, and we recommend using it for future footprinting studies. Overall, this work shows conclusive evidence for HOS perturbation caused by footprinting. Implementation of quality control workflows can identify conditions to avoid the perturbation, for footprinting, allowing accurate and reliable identification of protein structural changes that accompany, for example, ligand interactions, mutations, and changes in solution environment.

Native dynamics and allosteric responses in PTP1B probed by high-resolution HDX-MS.

Protein tyrosine phosphatase 1B (PTP1B) is a validated therapeutic target for obesity, diabetes, and certain types of cancer. In particular, allosteric inhibitors hold potential for therapeutic use, but an incomplete understanding of conformational dynamics and allostery in this protein has hindered their development. Here, we interrogate solution dynamics and allosteric responses in PTP1B using high-resolution hydrogen-deuterium exchange mass spectrometry (HDX-MS), an emerging and powerful biophysical technique. Using HDX-MS, we obtain a detailed map of backbone amide exchange that serves as a proxy for the solution dynamics of apo PTP1B, revealing several flexible loops interspersed among more constrained and rigid regions within the protein structure, as well as local regions that exchange faster than expected from their secondary structure and solvent accessibility. We demonstrate that our HDX rate data obtained in solution adds value to estimates of conformational heterogeneity derived from a pseudo-ensemble constructed from ~200 crystal structures of PTP1B. Furthermore, we report HDX-MS maps for PTP1B with active-site versus allosteric small-molecule inhibitors. These maps suggest distinct and widespread effects on protein dynamics relative to the apo form, including changes in locations distal (>35 Å) from the respective ligand binding sites. These results illuminate that allosteric inhibitors of PTP1B can induce unexpected changes in dynamics that extend beyond the previously understood allosteric network. Together, our data suggest a model of BB3 allostery in PTP1B that combines conformational restriction of active-site residues with compensatory liberation of distal residues that aid in entropic balancing. Overall, our work showcases the potential of HDX-MS for elucidating aspects of protein conformational dynamics and allosteric effects of small-molecule ligands and highlights the potential of integrating HDX-MS alongside other complementary methods, such as room-temperature X-ray crystallography, NMR spectroscopy, and molecular dynamics simulations, to guide the development of new therapeutics.

Use of plasma induced modification of biomolecules (PLIMB) to evaluate hemin dissociation from fish and bovine methemoglobins

Hemin dissociation occurs much faster from fish methemoglobin (metHb) compared to mammalian metHb yet the mechanism remains poorly understood. This may involve enhanced solvent access to His(E7) of fish metHbs by a protonation mechanism. Plasma induced modification of biomolecules (PLIMB) produces free radicals that covalently modify solvent accessible residues of proteins, and so can provide insight regarding accessibility of hydronium ions to protonate His(E7). PLIMB-induced modifications to heme crevice sites of trout IV and bovine metHb were determined using tandem mass spectrometry after generating peptides with Trypsin/Lys-C. αHis(CE3) was more modified in trout attributable to the more dynamic nature of bovine αHis(CE3) from available crystal structures. Although His(E7) was not found to be more modified in trout, aspects of trout peptides containing His(E7) hampered modification determinations. An existing computational structure-based approach was also used to estimate protonation tendencies, suggesting His(E7) of metHbs with low hemin affinity are more protonatable.

Interpretation of Hydrogen/Deuterium Exchange Mass Spectrometry.

This paper sheds light on the meaning of hydrogen/deuterium exchange-mass spectrometry (HDX-MS) data. HDX-MS data provide not structural information but dynamic information on an analyte protein. First, the reaction mechanism of backbone amide HDX reaction is considered and the correlation between the parameters from an X-ray crystal structure and the protection factors of HDX reactions of cytochrome c is evaluated. The presence of H-bonds in a protein structure has a strong influence on HDX rates which represent protein dynamics, while the solvent accessibility only weakly affects the HDX rates. Second, the energy diagrams of the HDX reaction at each residue in the presence and absence of perturbation are described. Whereas the free energy change upon mutation can be directly measured by the HDX rates, the free energy change upon ligand binding may be complicated due to the presence of unbound analyte protein in the protein-ligand mixture. Third, the meanings of HDX and other biophysical techniques are explained using a hypothetical protein folding well. The shape of the protein folding well describes the protein dynamics and provides Boltzmann distribution of open and closed states which yield HDX protection factors, while a protein's crystal structure represents a snapshot near the bottom of the well. All biophysical data should be consistent yet provide different information because they monitor different parts of the same protein folding well.

A stacking-based algorithm for antifreeze protein identification using combined physicochemical, pseudo amino acid composition, and reduction property features

Antifreeze proteins have wide applications in the medical and food industries. In this study, we propose a stacking-based classifier that can effectively identify antifreeze proteins. Initially, feature extraction was performed in three aspects: reduction properties, scalable pseudo amino acid composition, and physicochemical properties. A hybrid feature set comprised of the combined information from these three categories was obtained. Subsequently, we trained the training set based on LightGBM, XGBoost, and RandomForest algorithms, and the training outcomes were passed to the Logistic algorithm for matching, thereby establishing a stacking algorithm. The proposed algorithm was tested on the test set and an independent validation set. Experimental data indicates that the algorithm achieved a recognition accuracy of 98.3 %, and an accuracy of 98.5 % on the validation set. Lastly, we analyzed the reasons why numerical features achieved high recognition capabilities from multiple aspects. Data dimensionality reduction and the analysis from two-dimensional and three-dimensional views revealed separability between positive and negative samples, and the protein three-dimensional structure further demonstrated significant differences in related features between the two samples. Analysis of the classifier revealed that Hr*Hr, HrHr, and Sc-PseAAC_1, 188D(152,116,57,183) were among the seven most important numerical features affecting algorithm recognition. For Hr*Hr and HrHr, supportive sequence level evidence for the reduction dictionary was found in terms of conservation area analysis, multiple sequence alignment, and amino acid conservative substitution. Moreover, the importance of the reduction dictionary was recognized through a comparative analysis of importance before and after the reduction, realizing the effectiveness of the dictionary in improving feature importance. A decision tree model has been utilized to discern the distinctions between dipeptides associated with the physical and chemical properties of His(H), Iso(I), Leu(L), and Lys(K) and other dipeptides. We finally analyzed the other seven features of importance, and data analysis confirmed that hydrophobicity, secondary structure, charge properties, van der Waals forces, and solvent accessibility are also factors affecting the antifreeze capability of proteins.

Mutations in Glycosyltransferases and Glycosidases: Implications for Associated Diseases.

Glycosylation, a crucial and the most common post-translational modification, coordinates a multitude of biological functions through the attachment of glycans to proteins and lipids. This process, predominantly governed by glycosyltransferases (GTs) and glycoside hydrolases (GHs), decides not only biomolecular functionality but also protein stability and solubility. Mutations in these enzymes have been implicated in a spectrum of diseases, prompting critical research into the structural and functional consequences of such genetic variations. This study compiles an extensive dataset from ClinVar and UniProt, providing a nuanced analysis of 2603 variants within 343 GT and GH genes. We conduct thorough MTR score analyses for the proteins with the most documented variants using MTR3D-AF2 via AlphaFold2 (AlphaFold v2.2.4) predicted protein structure, with the analyses indicating that pathogenic mutations frequently correlate with Beta Bridge secondary structures. Further, the calculation of the solvent accessibility score and variant visualisation show that pathogenic mutations exhibit reduced solvent accessibility, suggesting the mutated residues are likely buried and their localisation is within protein cores. We also find that pathogenic variants are often found proximal to active and binding sites, which may interfere with substrate interactions. We also incorporate computational predictions to assess the impact of these mutations on protein function, utilising tools such as mCSM to predict the destabilisation effect of variants. By identifying these critical regions that are prone to disease-associated mutations, our study opens avenues for designing small molecules or biologics that can modulate enzyme function or compensate for the loss of stability due to these mutations.

Prediction of deleterious non-synonymous SNPs of human MDC1 gene: an in silico approach

MDC1 (Mediator of DNA damage Checkpoint protein 1) functions to facilitate the localization of numerous DNA damage response (DDR) components to DNA double-strand break sites. MDC1 is an integral component in preserving genomic stability and appropriate DDR regulation. There haven’t been systematic investigations of MDC1 mutations that induce cancer and genomic instability. Variations in nsSNPs have the potential to modify the protein chemistry and their function. Describing functional SNPs in disease-associated genes presents a significant conundrum for investigators, it is possible to assess potential functional SNPs before conducting larger population examinations. Multiple sequences and structure-based bioinformatics strategies were implemented in the current in-silico investigation to discern potential nsSNPs of the MDC1 genes. The nsSNPs were identified with SIFT, SNAP2, Align GVGD, PolyPhen-2, and PANTHER, and their stability was determined with MUpro. The conservation, solvent accessibility, and structural effects of the mutations were identified with ConSurf, NetSurfP-2.0, and SAAFEC-SEQ respectively. Cancer-related analysis of the nsSNPs was conducted using cBioPortal and TCGA web servers. The present study appraised five nsSNPs (P1426T, P69S, P194R, P203L, and H131Y) as probably mutilating due to their existence in highly conserved regions and propensity to deplete protein stability. The nsSNPs P194R, P203L, and H131Y were concluded as deleterious and possibly damaging from the 5 prediction tools. The functional nsSNP P194R mutation is associated with skin cutaneous melanoma while no significant records were found for other nsSNPs. The present study concludes that the highly deleterious P194R mutations can potentially induce genomic instability and contribute to various cancers’ pathogenesis. Developing drugs targeting these mutations can undoubtedly be advantageous in large population-based studies, particularly in the development of precision medicine.

Design, Synthesis, and Mechanistic Studies of (R)-3-Amino-5,5-difluorocyclohex-1-ene-1-carboxylic Acid as an Inactivator of Human Ornithine Aminotransferase.

Human ornithine aminotransferase (hOAT), a pyridoxal 5'-phosphate (PLP)-dependent enzyme, has been shown to play an essential role in the metabolic reprogramming and progression of hepatocellular carcinoma (HCC). HCC accounts for approximately 75% of primary liver cancers and is within the top three causes of cancer death worldwide. As a result of treatment limitations, the overall 5-year survival rate for all patients with HCC is under 20%. The prevalence of HCC necessitates continued development of novel and effective treatment methods. In recent years, the therapeutic potential of selective inactivation of hOAT has been demonstrated for the treatment of HCC. Inspired by previous increased selectivity for hOAT by the expansion of the cyclopentene ring scaffold to a cyclohexene, we designed, synthesized, and evaluated a series of novel fluorinated cyclohexene analogues and identified (R)-3-amino-5,5-difluorocyclohex-1-ene-1-carboxylic acid as a time-dependent inhibitor of hOAT. Structural and mechanistic studies have elucidated the mechanism of inactivation of hOAT by 5, resulting in a PLP-inactivator adduct tightly bound to the active site of the enzyme. Intact protein mass spectrometry, 19F NMR spectroscopy, transient state kinetic studies, and X-ray crystallography were used to determine the structure of the final adduct and elucidate the mechanisms of inactivation. Interestingly, despite the highly electrophilic intermediate species conferred by fluorine and structural evidence of solvent accessibility in the hOAT active site, Lys292 and water did not participate in nucleophilic addition during the inactivation mechanism of hOAT by 5. Instead, rapid aromatization to yield the final adduct was favored.

A Fast‐Charge Graphite Anode with a Li‐Ion‐Conductive, Electron/Solvent‐Repelling Interface

AbstractGraphite has been serving as the key anode material of rechargeable Li‐ion batteries, yet is difficultly charged within a quarter hour while maintaining stable electrochemistry. In addition to a defective edge structure that prevents fast Li‐ion entry, the high‐rate performance of graphite could be hampered by co‐intercalation and parasitic reduction of solvent molecules at anode/electrolyte interface. Conventional surface modification by pitch‐derived carbon barely isolates the solvent and electrons, and usually lead to inadequate rate capability to meet practical fast‐charge requirements. Here we show that, by applying a MoOx−MoNx layer onto graphite surface, the interface allows fast Li‐ion diffusion yet blocks solvent access and electron leakage. By regulating interfacial mass and charge transfer, the modified graphite anode delivers a reversible capacity of 340.3 mAh g−1 after 4000 cycles at 6 C, showing promises in building 10‐min‐rechargeable batteries with a long operation life.

A Fast-Charge Graphite Anode with a Li-Ion-Conductive, Electron/Solvent-Repelling Interface.

Graphite has been serving as the key anode material of rechargeable Li-ion batteries, yet is difficultly charged within a quarter hour while maintaining stable electrochemistry. In addition to a defective edge structure that prevents fast Li-ion entry, the high-rate performance of graphite could be hampered by co-intercalation and parasitic reduction of solvent molecules at anode/electrolyte interface. Conventional surface modification by pitch-derived carbon barely isolates the solvent and electrons, and usually lead to inadequate rate capability to meet practical fast-charge requirements. Here we show that, by applying a MoOx-MoNx layer onto graphite surface, the interface allows fast Li-ion diffusion yet blocks solvent access and electron leakage. By regulating interfacial mass and charge transfer, the modified graphite anode delivers a reversible capacity of 340.3 mAh g-1 after 4000 cycles at 6 C, showing promises in building 10-min-rechargeable batteries with a long operation life.

Allosteric modulation of fluorescence revealed by hydrogen bond dynamics in a genetically encoded maltose biosensor.

Genetically encoded fluorescent biosensors (GEFBs) proved to be reliable tracers for many metabolites and cellular processes. In the simplest case, a fluorescent protein (FP) is genetically fused to a sensing protein which undergoes a conformational change upon ligand binding. This drives a rearrangement in the chromophore environment and changes the spectral properties of the FP. Structural determinants of successful biosensors are revealed only in hindsight when the crystal structures of both ligand-bound and ligand-free forms are available. This makes the development of new biosensors for desired analytes a long trial-and-error process. In the current study, we conducted μs-long all atom molecular dynamics (MD) simulations of a maltose biosensor in both the apo (dark) and holo (bright) forms. We performed detailed hydrogen bond occupancy analyses to shed light on the mechanism of ligand induced conformational change in the sensor protein and its allosteric effect on the chromophore environment. We find that two strong indicators for distinguishing bright and dark states of biosensors are due to substantial changes in hydrogen bond dynamics in the system and solvent accessibility of the chromophore.

Solid-state nuclear magnetic resonance in the structural study of polyglutamine aggregation.

The aggregation of proteins into amyloid-like fibrils is seen in many neurodegenerative diseases. Recent years have seen much progress in our understanding of these misfolded protein inclusions, thanks to advances in techniques such as solid-state nuclear magnetic resonance (ssNMR) spectroscopy and cryogenic electron microscopy (cryo-EM). However, multiple repeat-expansion-related disorders have presented special challenges to structural elucidation. This review discusses the special role of ssNMR analysis in the study of protein aggregates associated with CAG repeat expansion disorders. In these diseases, the misfolding and aggregation affect mutant proteins with expanded polyglutamine segments. The most common disorder, Huntington's disease (HD), is connected to the mutation of the huntingtin protein. Since the discovery of the genetic causes for HD in the 1990s, steady progress in our understanding of the role of protein aggregation has depended on the integrative and interdisciplinary use of multiple types of structural techniques. The heterogeneous and dynamic features of polyQ protein fibrils, and in particular those formed by huntingtin N-terminal fragments, have made these aggregates into challenging targets for structural analysis. ssNMR has offered unique insights into many aspects of these amyloid-like aggregates. These include the atomic-level structure of the polyglutamine core, but also measurements of dynamics and solvent accessibility of the non-core flanking domains of these fibrils' fuzzy coats. The obtained structural insights shed new light on pathogenic mechanisms behind this and other protein misfolding diseases.

Unravelling the link between SARS-CoV-2 mutation frequencies, patient comorbidities, and structural dynamics.

Genomic surveillance is crucial for tracking emergence and spread of novel variants of pathogens, such as SARS-CoV-2, to inform public health interventions and to enforce control measures. However, in some settings especially in low- and middle- income counties, where sequencing platforms are limited, only certain patients get to be selected for sequencing surveillance. Here, we show that patients with multiple comorbidities potentially harbour SARS-CoV-2 with higher mutation rates and thus deserve more attention for genomic surveillance. The relationship between the patient comorbidities, and type of amino acid mutations was assessed. Correlation analysis showed that there was a significant tendency for mutations to occur within the ORF1a region for patients with higher number of comorbidities. Frequency analysis of the amino acid substitution within ORF1a showed that nsp3 P822L of the PLpro protease was one of the highest occurring mutations. Using molecular dynamics, we simulated that the P822L mutation in PLpro represents a system with lower Root Mean Square Deviation (RMSD) fluctuations, and consistent Radius of gyration (Rg), Solvent Accessible Surface Area (SASA) values-indicate a much stabler protein than the wildtype. The outcome of this study will help determine the relationship between the clinical status of a patient and the mutations of the infecting SARS-CoV-2 virus.

Classification of likely functional class for ligand binding sites identified from fragment screening

Fragment screening is used to identify binding sites and leads in drug discovery, but it is often unclear which binding sites are functionally important. Here, data from 37 experiments, and 1309 protein structures binding to 1601 ligands were analysed. A method to group ligands by binding sites is introduced and sites clustered according to profiles of relative solvent accessibility. This identified 293 unique ligand binding sites, grouped into four clusters (C1-4). C1 includes larger, buried, conserved, and population missense-depleted sites, enriched in known functional sites. C4 comprises smaller, accessible, divergent, missense-enriched sites, depleted in functional sites. A site in C1 is 28 times more likely to be functional than one in C4. Seventeen sites, which to the best of our knowledge are novel, in 13 proteins are identified as likely to be functionally important with examples from human tenascin and 5-aminolevulinate synthase highlighted. A multi-layer perceptron, and K-nearest neighbours model are presented to predict cluster labels for ligand binding sites with an accuracy of 96% and 100%, respectively, so allowing functional classification of sites for proteins not in this set. Our findings will be of interest to those studying protein-ligand interactions and developing new drugs or function modulators.

Leveraging Neurospora crassa Fungus and Carboxypeptidase A1 Enzyme to Illuminate Microscale Biodiversity Changes in Response to Global Shifts

The profound impacts of global changes on biodiversity necessitate a more comprehensive documentation, particularly at the microscale level. To achieve precise and rapid insights into this unique diversity, the choice of an ideal species candidate is crucial. Neurospora crassa, a well-established organism in the field of biology, emerges as a promising candidate for this purpose. In our study, we explore the potential of the Carboxypeptidase A1 (CPA1) enzyme as a valuable tool for profiling global diversity. Our investigation has revealed that CPA1 possesses distinctive characteristics, notably its conserved solvent accessibility. This unique feature makes CPA1 an invaluable asset for microscale studies of global changes. The insights presented in our study serve as a practical blueprint, showcasing the application of structural biology in understanding diversity and global changes within microscale environments.

FCMSTrans: Accurate Prediction of Disease-Associated nsSNPs by Utilizing Multiscale Convolution and Deep Feature Combination within a Transformer Framework.

Nonsynonymous single-nucleotide polymorphisms (nsSNPs), implicated in over 6000 diseases, necessitate accurate prediction for expedited drug discovery and improved disease diagnosis. In this study, we propose FCMSTrans, a novel nsSNP predictor that innovatively combines the transformer framework and multiscale modules for comprehensive feature extraction. The distinctive attribute of FCMSTrans resides in a deep feature combination strategy. This strategy amalgamates evolutionary-scale modeling (ESM) and ProtTrans (PT) features, providing an understanding of protein biochemical properties, and position-specific scoring matrix, secondary structure, predicted relative solvent accessibility, and predicted disorder (PSPP) features, which are derived from four protein sequences and structure-oriented characteristics. This feature combination offers a comprehensive view of the molecular dynamics involving nsSNPs. Our model employs the transformer's self-attention mechanisms across multiple layers, extracting higher-level and abstract representations. Simultaneously, varied-level features are captured by multiscale convolutions, enriching feature abstraction at multiple echelons. Our comparative analyses with existing methodologies highlight significant improvements made possible by the integrated feature fusion approach adopted in FCMSTrans. This is further substantiated by performance assessments based on diverse data sets, such as PredictSNP, MMP, and PMD, with areas under the curve (AUCs) of 0.869, 0.819, and 0.693, respectively. Furthermore, FCMSTrans shows robustness and superiority by outperforming the current best predictor, PROVEAN, in a blind test conducted on a third-party data set, achieving an impressive AUC score of 0.7838. The Python code of FCMSTrans is available at https://github.com/gc212/FCMSTrans for academic usage.

Electrochemical and structural characterization of recombinant respiratory proteins of the acidophilic iron oxidizer Ferrovum sp. PN-J47-F6 suggests adaptations to the acidic pH at protein level.

The tendency of the periplasmic redox proteins in acidophiles to have more positive redox potentials (Em) than their homologous counterparts in neutrophiles suggests an adaptation to acidic pH at protein level, since thermodynamics of electron transfer processes are also affected by acidic pH. Since this conclusion is mainly based on the electrochemical characterization of redox proteins from extreme acidophiles of the genus Acidithiobacillus, we aimed to characterize three recombinant redox proteins of the more moderate acidophile Ferrovum sp. PN-J47-F6. We applied protein film voltammetry and linear sweep voltammetry coupled to UV/Vis spectroscopy to characterize the redox behavior of HiPIP-41, CytC-18, and CytC-78, respectively. The Em-values of HiPIP-41 (571 ± 16 mV), CytC-18 (276 ± 8 mV, 416 ± 2 mV), and CytC-78 (308 ± 7 mV, 399 ± 7 mV) were indeed more positive than those of homologous redox proteins in neutrophiles. Moreover, our findings suggest that the adaptation of redox proteins with respect to their Em occurs more gradually in response to the pH, since there are also differences between moderate and more extreme acidophiles. In order to address structure function correlations in these redox proteins with respect to structural features affecting the Em, we conducted a comparative structural analysis of the Ferrovum-derived redox proteins and homologs of Acidithiobacillus spp. and neutrophilic proteobacteria. Hydrophobic contacts in the redox cofactor binding pockets resulting in a low solvent accessibility appear to be the major factor contributing to the more positive Em-values in acidophile-derived redox proteins. While additional cysteines in HiPIPs of acidophiles might increase the effective shielding of the [4Fe-4S]-cofactor, the tight shielding of the heme centers in acidophile-derived cytochromes is achieved by a drastic increase in hydrophobic contacts (A.f. Cyc41), and by a larger fraction of aromatic residues in the binding pockets (CytC-18, CytC-78).