Propensities Of Residues Research Articles

Underlying features for the enhanced electrostatic strength of the extremophilic malate dehydrogenase interface salt-bridge compared to the mesophilic one

Malate dehydrogenase (MDH) exists in multimeric form in normal and extreme solvent conditions where residues of the interface are involved in specific interactions. The interface salt-bridge (ISB) and its microenvironment (ME) residues may have a crucial role in the stability and specificity of the interface. To gain insight into this, we have analyzed 218 ISBs from 42 interfaces of 15 crystal structures along with their sequences. Comparative analyses demonstrate that the ISB strength is ∼30 times greater in extremophilic cases than that of the normal one. To this end, the interface residue propensity, ISB design and pair selection, and ME-residue’s types, i.e., type-I and type-II, are seen to be intrinsically involved. Although Type-I is a common type, Type-II appears to be extremophile-specific, where the net ME-residue count is much lower with an excessive net ME-energy contribution, which seems to be a novel interface compaction strategy. Furthermore, the interface strength can be enhanced by selecting the desired mutant from the net-energy profile of all possible mutations of an unfavorable ME-residue. The study that applies to other similar systems finds applications in protein–protein interaction and protein engineering. Communicated by Ramaswamy H. Sarma

Disallowed spots in protein structures

Ramachandran (ϕ, ψ) steric map was introduced in 1963 to describe available conformation space for protein structures. Subsequently, residues were observed in high-energy disallowed regions of the map. To unequivocally identify the locations of disallowed conformations of residues, we got 36 noise-free protein structures (resolution ≤1 Å, Rwork/Rfree ≤ 0.10). These stringent criteria were applied to rule out data or model errors or any crystallographic disorders. No disallowed conformation was found in the dataset. Further, we also examined disallowed conformations in a larger dataset (resolution ≤1.5 Å, devoid of any model errors, or disorders). The observed locations of disallowed residues are referred as disallowed spots. These spots include short loops of 3–5 residues, and locations where residues participate in disulfide bonding or intramolecular interactions or inter-molecular interactions with neighboring water, metals or ligands. Conformational sampling revealed that short loops in between secondary structures hardly have any opportunity to relieve from conformational strain. Residues involved in interactions, which provide energetic compensation for high-energy conformational states, were relieved from strain once the causative interaction was removed. The present study aims to identify disallowed spots in the native state of proteins, wherein residues are forced to be trapped in high-energy disallowed conformations. Moreover, it was also observed that pre-Pro, Ser, Asp, trans-Pro, Val, Asn & Gly have higher tendency to occur in disallowed conformation, which could be attributed to factors such as conformational restrictions, residue propensity of secondary structures and compensating sidechain and mainchain interactions, stabilizing turn-mimics.

Coking propensity and reactive radical evolution during thermal reaction of heavy residue and SARA fractions at 250–400 °C

In this study, the coking propensity and reactive radical evolution of heavy residue and SARA fractions at 250–400 °C were studied to gain an insight into the coke induction process. The interactions among fractions for thermal coking were discussed. Results showed that residue started to form coke (i.e. toluene-insoluble at room temperature) at 350 °C for longer times while resins and asphaltenes have already extensively produced coke at 250 °C with no coke induction period. This indicates that the coke induction depends on the solubility gap of reactant and coke. The coking process can be fitted with the second-order and autocatalytic kinetics for residue while second-order kinetics for heavy fractions with initial coke precursor amount varying with temperature. Asphaltenes are the sole fraction that generated reactive radicals at 250 °C and responsible for the radicals of thermal residue. The explosion of reactive radicals began when the temperature exceeded 350 °C and became steady as the thermal reaction proceeded. The coke at low temperatures mainly originated from the molecules aggregation with faint radical emergence while that at high temperatures is strongly correlated with the radical evolution. The maltene subfractions could suppress the coking of asphaltenes possibly because of the physical solvation and chemical hydrogen donating behavior to cap reactive radicals. It can serve as the intrinsic immunity for coke inhibition of heavy residue during thermal partial upgrading.

The relevance of short peptides for an understanding of unfolded and intrinsically disordered proteins.

Over the last thirty years the unfolded state of proteins has attracted considerable interest owing to the discovery of intrinsically disordered proteins which perform a plethora of functions despite resembling unfolded proteins to a significant extent. Research on both, unfolded and disordered proteins has revealed that their conformational properties can deviate locally from random coil behavior. In this context results from work on short oligopeptides suggest that individual amino acid residues sample the sterically allowed fraction of the Ramachandran plot to a different extent. Alanine has been found to exhibit a peculiarity in that it has a very high propensity for adopting polyproline II like conformations. This Perspectives article reviews work on short peptides aimed at exploring the Ramachandran distributions of amino acid residues in different contexts with experimental and computational means. Based on the thus provided overview the article discussed to what extent short peptides can serve as tools for exploring unfolded and disordered proteins and as benchmarks for the development of a molecular dynamics force field.

UnderstandingRamachandranplot for dipeptide: A density functional theory and information‐theoreticapproach study

AbstractThe Ramachandran plot in biochemistry visualizes the propensity of amino acid residues to assume different secondary structures. It can be obtained by different approaches such as bioinformatics and molecular dynamics. In this work, we systematically scan the two single bonds around the α‐carbon of a peptide to generate the three‐dimensional potential energy surface and then project it onto the coordinate plane to generate the Ramachandran plot. Using the two schemes of the total energy partition and information‐theoretic approach in density functional theory, we analyze the plot and find out that the dominant contributor to determine the local minima of secondary structures, especially β‐sheets, in the Ramachandran plot is the electrostatic interaction, whereas steric and exchange‐correlation contributions play minor yet indispensable roles, especially for determining α‐helixes. As the generalization of our prior studies for systems with one rotatable bond, our current results confirm what we yielded before from the viewpoint of energetic contributions, and at meanwhile provide an in‐depth understanding about the nature and origin of secondary structure propensities from the Ramachandran plot.

Probing conformational landscapes and mechanisms of allosteric communication in the functional states of the ABL kinase domain using multiscale simulations and network-based mutational profiling of allosteric residue potentials.

In the current study, multiscale simulation approaches and dynamic network methods are employed to examine the dynamic and energetic details of conformational landscapes and allosteric interactions in the ABL kinase domain that determine the kinase functions. Using a plethora of synergistic computational approaches, we elucidate how conformational transitions between the active and inactive ABL states can employ allosteric regulatory switches to modulate intramolecular communication networks between the ATP site, the substrate binding region, and the allosteric binding pocket. A perturbation-based network approach that implements mutational profiling of allosteric residue propensities and communications in the ABL states is proposed. Consistent with biophysical experiments, the results reveal functionally significant shifts of the allosteric interaction networks in which preferential communication paths between the ATP binding site and substrate regions in the active ABL state become suppressed in the closed inactive ABL form, which in turn features favorable allosteric coupling between the ATP site and the allosteric binding pocket. By integrating the results of atomistic simulations with dimensionality reduction methods and Markov state models, we analyze the mechanistic role of macrostates and characterize kinetic transitions between the ABL conformational states. Using network-based mutational scanning of allosteric residue propensities, this study provides a comprehensive computational analysis of long-range communications in the ABL kinase domain and identifies conserved regulatory hotspots that modulate kinase activity and allosteric crosstalk between the allosteric pocket, ATP binding site, and substrate binding regions.

AAPred-CNN: Accurate predictor based on deep convolution neural network for identification of anti-angiogenic peptides.

Recently, deep learning techniques have been developed for various bioactive peptide prediction tasks. However, there are only conventional machine learning-based methods for the prediction of anti-angiogenic peptides (AAP), which play an important role in cancer treatment. The main reason why no deep learning method has been involved in this field is that there are too few experimentally validated AAPs to support the training of deep models but researchers have believed that deep learning seriously depends on the amounts of labeled data. In this paper, as a tentative work, we try to predict AAP by constructing different classical deep learning models and propose the first deep convolution neural network-based predictor (AAPred-CNN) for AAP. Contrary to intuition, the experimental results show that deep learning models can achieve superior or comparable performance to the state-of-the-art model, although they are given a few labeled sequences to train. We also decipher the influence of hyper-parameters and training samples on the performance of deep learning models to help understand how the model work. Furthermore, we also visualize the learned embeddings by dimension reduction to increase the model interpretability and reveal the residue propensity of AAP through the statistics of convolutional features for different residues. In summary, this work demonstrates the powerful representation ability of AAPred-CNNfor AAP prediction, further improving the prediction accuracy of AAP.

Design and Engineering of Miniproteins.

The potential of miniproteins in the biological and chemical sciences is constantly increasing. Significant progress in the design methodologies has been achieved over the last 30 years. Early approaches based on propensities of individual amino acid residues to form individual secondary structures were subsequently improved by structural analyses using NMR spectroscopy and crystallography. Consequently, computational algorithms were developed, which are now highly successful in designing structures with accuracy often close to atomic range. Further perspectives include construction of miniproteins incorporating non-native secondary structures derived from sequences with units other than α-amino acids. Noteworthy, miniproteins with extended structures, which are now feasibly accessible, are excellent scaffolds for construction of functional molecules.

Electrostatic Interactions Contribute to the Overall Structural Stability in Small Interfaces of Corona Viral Spike Glycoproteins

The coronavirus outbreak and its mutant variants have harmed the health of the human populace and imperiled the world economy. Several studies are initiated across the globe using clinical biomarkers from hematological, immunological, and biochemical experiments. In addition, analysis of protein interfaces provides an understanding of the functioning of the coronavirus target proteins. This study examines the interfaces of spike glycoproteins in terms of large (vdW dominant) and small (vdW subdominant) interfaces. We also calculated Gibbs free energy (ΔG), residue propensity and hot-spot prediction for these interfaces. Dataset consisting of 115 (large interface with vdW dominant) and 18 (small interface with vdW subdominant) were obtained from PDB. Results show that 86% of the total interfaces were vdW dominant, while the rest, 14%, were sub-dominant in vdW energy. Interestingly, on average, we found the Gibbs free energy (ΔG) of large and small interfaces to be -21 and -30 kcal/mol respectively. We also found the interfaces of large and small to be highly pronounced with polar residues followed by hydrophobic residues in case of large interfaces and charged residues in case of small interfaces. We found and report methionine residues to be absent at the small interfaces having subdominant vdW energy. We also observed the majority of the interfaces to be rich in hotspot residues. Thus, the information on heteromeric interactions of glycoproteins may help develop new and productive therapeutic drugs.

O‧‧‧C═O interaction, its occurrence and implications for protein structure and folding.

Various noncovalent interactions, long and short range, stabilize the native protein structure. We had observed a short-range interaction between two adjacent peptide groups in a nearly perpendicular orientation through the involvement of an NH‧‧‧N hydrogen bond. Here we show that the other half of the peptide group, namely the carbonyl moiety, can also be involved through the O‧‧‧C═O interaction. Considering the interacting residues, the second residue of the pair has distinct backbone conformational angles, occurring in four clusters, each engendering well-defined structural motifs. One of the motifs is the γ-turn, another being polyproline II helix. The interacting pair is found mostly in the irregular region in protein structures, and the propensities of residues and the identification of the nearest secondary structure show interesting patterns. The most conspicuous β-turn conformation is built from two consecutive γ-turns, with embedded O‧‧‧C═O and NH‧‧‧N interactions, and there is considerable match of the residue usage at the central positions of the β-turn and the γ-turn components. This clearly exemplifies the hierarchical growth of the protein secondary structures, which would be important in our understanding of protein folding. While the occurrence of the O‧‧‧C═O interaction in α-helices has been well documented, we find it to be equally important in making capping interactions at helix termini.

Recognition of Metal Ion Ligand-Binding Residues by Adding Correlation Features and Propensity Factors.

The realization of many protein functions is inseparable from the interaction with ligands; in particular, the combination of protein and metal ion ligands performs an important biological function. Currently, it is a challenging work to identify the metal ion ligand-binding residues accurately by computational approaches. In this study, we proposed an improved method to predict the binding residues of 10 metal ion ligands (Zn2+, Cu2+, Fe2+, Fe3+, Co2+, Mn2+, Ca2+, Mg2+, Na+, and K+). Based on the basic feature parameters of amino acids, and physicochemical and predicted structural information, we added another two features of amino acid correlation information and binding residue propensity factors. With the optimized parameters, we used the GBM algorithm to predict metal ion ligand-binding residues. In the obtained results, the Sn and MCC values were over 10.17% and 0.297, respectively. Besides, the Sn and MCC values of transition metals were higher than 34.46% and 0.564, respectively. In order to test the validity of our model, another method (Random Forest) was also used in comparison. The better results of this work indicated that the proposed method would be a valuable tool to predict metal ion ligand-binding residues.

A Glance into MTHFR Deficiency at a Molecular Level.

MTHFR deficiency still deserves an investigation to associate the phenotype to protein structure variations. To this aim, considering the MTHFR wild type protein structure, with a catalytic and a regulatory domain and taking advantage of state-of-the-art computational tools, we explore the properties of 72 missense variations known to be disease associated. By computing the thermodynamic ΔΔG change according to a consensus method that we recently introduced, we find that 61% of the disease-related variations destabilize the protein, are present both in the catalytic and regulatory domain and correspond to known biochemical deficiencies. The propensity of solvent accessible residues to be involved in protein-protein interaction sites indicates that most of the interacting residues are located in the regulatory domain, and that only three of them, located at the interface of the functional protein homodimer, are both disease-related and destabilizing. Finally, we compute the protein architecture with Hidden Markov Models, one from Pfam for the catalytic domain and the second computed in house for the regulatory domain. We show that patterns of disease-associated, physicochemical variation types, both in the catalytic and regulatory domains, are unique for the MTHFR deficiency when mapped into the protein architecture.

Elucidating important structural features for the binding affinity of spike - SARS-CoV-2 neutralizing antibody complexes.

The coronavirus disease 2019 (COVID‐19) has affected the lives of millions of people around the world. In an effort to develop therapeutic interventions and control the pandemic, scientists have isolated several neutralizing antibodies against SARS‐CoV‐2 from the vaccinated and convalescent individuals. These antibodies can be explored further to understand SARS‐CoV‐2 specific antigen–antibody interactions and biophysical parameters related to binding affinity, which can be utilized to engineer more potent antibodies for current and emerging SARS‐CoV‐2 variants. In the present study, we have analyzed the interface between spike protein of SARS‐CoV‐2 and neutralizing antibodies in terms of amino acid residue propensity, pair preference, and atomic interaction energy. We observed that Tyr residues containing contacts are highly preferred and energetically favorable at the interface of spike protein–antibody complexes. We have also developed a regression model to relate the experimental binding affinity for antibodies using structural features, which showed a correlation of 0.93. Moreover, several mutations at the spike protein–antibody interface were identified, which may lead to immune escape (epitope residues) and improved affinity (paratope residues) in current/emerging variants. Overall, the work provides insights into spike protein–antibody interactions, structural parameters related to binding affinity and mutational effects on binding affinity change, which can be helpful to develop better therapeutics against COVID‐19.

Diastereoselective Synthesis of (3R,5R)-γ-Hydroxypiperazic Acid

AbstractWe report an asymmetric synthesis of the (3R,5R)-γ-hydroxypiperazic acid (γ-OHPiz) residue encountered in several bioactive nonribosomal peptides. Our strategy relies on a diastereoselective enolate hydroxylation reaction and electrophilic N-amination to provide the acyclic γ-OHPiz precursor. This orthogonally protected α-hydrazino acid intermediate is amenable to late-stage diazinane ring formation following incorporation into a peptide chain. We determined the N-terminal amide rotamer propensity of the γ-OHPiz residue and showed that the γ-OH substituent enhances trans-amide bias relative to piperazic acid.

Dynamic Profiling of Binding and Allosteric Propensities of the SARS-CoV-2 Spike Protein with Different Classes of Antibodies: Mutational and Perturbation-Based Scanning Reveals the Allosteric Duality of Functionally Adaptable Hotspots.

The functional adaptability and conformational plasticity of SARS-CoV-2 spike proteins allow for the efficient modulation of complex phenotypic responses to the host receptor and antibodies. In this study, we combined atomistic simulations with mutational and perturbation-based scanning approaches to examine binding mechanisms of the SARS-CoV-2 spike proteins with three different classes of antibodies. The ensemble-based profiling of binding and allosteric propensities of the SARS-CoV-2 spike protein residues showed that these proteins can work as functionally adaptable and allosterically regulated machines. Conformational dynamics analysis revealed that binding-induced modulation of soft modes can elicit the unique protein response to different classes of antibodies. Mutational scanning heatmaps and sensitivity analysis revealed the binding energy hotspots for different classes of antibodies that are consistent with the experimental deep mutagenesis, showing that differences in the binding affinity caused by global circulating variants in spike positions K417, E484, and N501 are relatively moderate and may not fully account for the observed antibody resistance effects. Through functional dynamics analysis and perturbation-response scanning of the SARS-CoV-2 spike protein residues in the unbound form and antibody-bound forms, we examine how antibody binding can modulate allosteric propensities of spike protein residues and determine allosteric hotspots that control signal transmission and global conformational changes. These results show that residues K417, E484, and N501 targeted by circulating mutations correspond to a group of versatile allosteric centers in which small perturbations can modulate collective motions, alter the global allosteric response, and elicit binding resistance. We suggest that the SARS-CoV-2 S protein may exploit the plasticity of specific allosteric hotspots to generate escape mutants that alter the response to antibody binding without compromising the activity of the spike protein.

Computational analysis of protein stability and allosteric interaction networks in distinct conformational forms of the SARS-CoV-2 spike D614G mutant: reconciling functional mechanisms through allosteric model of spike regulation

In this study, we used an integrative computational approach to examine molecular mechanisms underlying functional effects of the D614G mutation by exploring atomistic modeling of the SARS-CoV-2 spike proteins as allosteric regulatory machines. We combined coarse-grained simulations, protein stability and dynamic fluctuation communication analysis with network-based community analysis to examine structures of the native and mutant SARS-CoV-2 spike proteins in different functional states. Through distance fluctuations communication analysis, we probed stability and allosteric communication propensities of protein residues in the native and mutant SARS-CoV-2 spike proteins, providing evidence that the D614G mutation can enhance long-range signaling of the allosteric spike engine. By combining functional dynamics analysis and ensemble-based alanine scanning of the SARS-CoV-2 spike proteins we found that the D614G mutation can improve stability of the spike protein in both closed and open forms, but shifting thermodynamic preferences towards the open mutant form. Our results revealed that the D614G mutation can promote the increased number of stable communities and allosteric hub centers in the open form by reorganizing and enhancing the stability of the S1-S2 inter-domain interactions and restricting mobility of the S1 regions. This study provides atomistic-based view of allosteric communications in the SARS-CoV-2 spike proteins, suggesting that the D614G mutation can exert its primary effect through allosterically induced changes on stability and communications in the residue interaction networks. Communicated by Ramaswamy H. Sarma

Marker residue types at the structural regions of transmembrane alpha-helical and beta-barrel interfaces.

Membrane proteins play a variety of biological functions to the survival of organisms and functionalities of these proteins are often due to their homo- or hetero-complexation. Encoded by ~30% of the genome in most organisms, they represent the target of over half of nowadays drugs. Spanning the entirety of the cell membrane, transmembrane proteins are the most common type of membrane proteins and can be classified by secondary structures: alpha-helical and beta-barrel structures. Protein-protein interaction (PPI) have been widely studied for globular proteins and many computational tools are available for predicting PPI sites and construct models of complexes. Here, the structural regions of a non-redundant set of 232 alpha-helical and 37 beta-barrel transmembrane complexes and their interfaces are analyzed. Using the residue composition, frequency and propensity, this study brings the light on the marker residue types located at the structural regions of alpha-helical and beta-barrel transmembrane homomeric protein complexes and of their interfaces. This study also shows the necessity to relate the frequency to the composition into a ratio for immediately figuring out residue types presenting high frequencies at the interface and/or at one of its structural regions despite being a minor contributor compared to other residue types to that location's residue composition.

A survey of human histone H1 subtypes interaction networks: Implications for histones H1 functioning.

To show a spectrum of histone H1 subtypes (H1.1-H1.5) activity realized through the protein-protein interactions, data selected from APID resources were processed with sequence-based bioinformatics approaches. Histone H1 subtypes participate in over half a thousand interactions with nuclear and cytosolic proteins (ComPPI database) engaged in the enzymatic activity and binding of nucleic acids and proteins (SIFTER tool). Small-scale networks of H1 subtypes (STRING network) have similar topological parameters (P > .05) which are, however, different for networks hubs between subtype H1.1 and H1.4 and subtype H1.3 and H1.5 (P < .05) (Cytoscape software). Based on enriched GO terms (g:Profiler toolset) of interacting proteins, molecular function and biological process of networks hubs is related to RNA binding and ribosome biogenesis (subtype H1.1 and H1.4), cell cycle and cell division (subtype H1.3 and H1.5) and protein ubiquitination and degradation (subtype H1.2). The residue propensity (BIPSPI predictor) and secondary structures of interacting surfaces (GOR algorithm) as well as a value of equilibrium dissociation constant (ISLAND predictor) indicate that a type of H1 subtypes interactions is transient in term of the stability and medium-strong in relation to the strength of binding. Histone H1 subtypes bind interacting partners in the intrinsic disorder-dependent mode (FoldIndex, PrDOS predictor), according to the coupled folding and binding and mutual synergistic folding mechanism. These results evidence that multifunctional H1 subtypes operate via protein interactions in the networks of crucial cellular processes and, therefore, confirm a new histone H1 paradigm relating to its functioning in the protein-protein interaction networks.

POTN: A Human Leukocyte Antigen-A2 Immunogenic Peptides Screening Model and Its Applications in Tumor Antigens Prediction.

Whole genome/exome sequencing data for tumors are now abundant, and many tumor antigens, especially mutant antigens (neoantigens), have been identified for cancer immunotherapy. However, only a small fraction of the peptides from these antigens induce cytotoxic T cell responses. Therefore, efficient methods to identify these antigenic peptides are crucial. The current models of major histocompatibility complex (MHC) binding and antigenic prediction are still inaccurate. In this study, 360 9-mer peptides with verified immunological activity were selected to construct a prediction of tumor neoantigen (POTN) model, an immunogenic prediction model specifically for the human leukocyte antigen-A2 allele. Based on the physicochemical properties of amino acids, such as the residue propensity, hydrophobicity, and organic solvent/water, we found that the predictive capability of POTN is superior to that of the prediction programs SYPEITHI, IEDB, and NetMHCpan 4.0. We used POTN to screen peptides for the cancer-testis antigen located on the X chromosome, and we identified several peptides that may trigger immunogenicity. We synthesized and measured the binding affinity and immunogenicity of these peptides and found that the accuracy of POTN is higher than that of NetMHCpan 4.0. Identifying the properties related to the T cell response or immunogenicity paves the way to understanding the MHC/peptide/T cell receptor complex. In conclusion, POTN is an efficient prediction model for screening high-affinity immunogenic peptides from tumor antigens, and thus provides useful information for developing cancer immunotherapy.

New facets of larger Nest motifs in proteins.

The Nest is a concave-shaped structural motif in proteins formed by consecutive enantiomeric left-handed (L) and right-handed (R) helical conformation of the backbone. This important motif subsumes many turn and helix capping structures and binds electron-rich ligands. Simple Nests are either RL or LR. Larger Nests (>2 residues long) may be RLR, LRL, RLRL, and so forth, being considered as composed of overlapping simple Nests. The larger Nests remain under-explored despite their widely known contributions to protein function. In our study, we address whether the recurrence of enantiomeric geometry in the larger Nests constrains the peptide backbone such that distinct compositional and conformational preferences are seen compared to simple Nests. Our analysis reveals the critical role of the L helical torsion angle in the formation of larger Nests. This can be observed through the higher propensity of residue or secondary structure combinations in LR and LRL backbone conformation in comparison to RL or RLR, although LR/LRL is considerably lower by occurrence. We also find that the most abundant doublets and triplets in Nests have a propensity for particular secondary structures, suggesting a strong sequence-structure relationship in the larger Nest. Overall, our analysis corroborates distinct features of simple and the larger Nests. Such insights would be helpful towards in-vitro design of peptides and peptidomimetic studies.