A protein sequence fitness function for identifying natural and nonnatural proteins

Structure-based drug discovery to identify SARS-CoV2 spike protein-ACE2 interaction inhibitors

After the emergence of the COVID-19 pandemic in late 2019, the severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) has undergone a dynamic evolution driven by the acquisition of genetic modifications, resulting in several variants that are further classified as variants of interest (VOIs), variants under monitoring (VUM) and variants of concern (VOC) by World Health Organization (WHO). Currently, there are five SARS-CoV-2 VOCs (Alpha, Beta, Delta, Gamma and Omicron), two VOIs (Lambda and Mu) and several other VOIs that have been reported globally. In this study, we report a natural compound, Curcumin, as the potential inhibitor to the interactions between receptor binding domain (RBD(S1)) and human angiotensin-converting enzyme 2 (hACE2) domains and showcased its inhibitory potential for the Delta and Omicron variants through a computational approach by implementing state of the art methods. The study for the first time revealed a higher efficiency of Curcumin, especially for hindering the interaction between RBD(S1) and hACE-2 domains of Delta and Omicron variants as compared to other lead compounds. We investigated that the mutations in the RBD(S1) of VOC especially Delta and Omicron variants affect its structure compared to that of the wild type and other variants and therefore altered its binding to the hACE2 receptor. Molecular docking and molecular dynamics (MD) simulation analyses substantially supported the findings in terms of the stability of the docked complexes. This study offers compelling evidence, warranting a more in-depth exploration into the impact of these alterations on the binding of identified drug molecules with the Spike protein. Further investigation into their potential therapeutic effects in vivo is highly recommended.Communicated by Ramaswamy H. Sarma.

Role of environmental specificity in CASP results

BACKGROUND

Recently, significant progress has been made in the field of protein structure prediction by the application of artificial intelligence techniques, as shown by the results of the CASP13 and CASP14 (Critical Assessment of Structure Prediction) competition. However, the question of the mechanism behind the protein folding process itself remains unanswered. Correctly predicting the structure also does not solve the problem of, for example, amyloid proteins, where a polypeptide chain with an unaltered sequence adopts a different 3D structure.

RESULTS

This work was an attempt at explaining the structural variation by considering the contribution of the environment to protein structuring. The application of the fuzzy oil drop (FOD) model to assess the validity of the selected models provided in the CASP13, CASP14 and CASP15 projects reveals the need for an environmental factor to determine the 3D structure of proteins. Consideration of the external force field in the form of polar water (Fuzzy Oil Drop) and a version modified by the presence of the hydrophobic compounds, FOD-M (FOD-Modified) reveals that the protein folding process is environmentally dependent. An analysis of selected models from the CASP competitions indicates the need for structure prediction as dependent on the consideration of the protein folding environment.

CONCLUSIONS

The conditions governed by the environment direct the protein folding process occurring in a certain environment. Therefore, the variation of the external force field should be taken into account in the models used in protein structure prediction.

Biomimetic Construction of Artificial Selenoenzymes

Selenium exists in the form of selenocysteines in selenoproteins and plays a pivotal role in the catalytic process of the antioxidative enzymes. In order to study the structural and functional properties of selenium in selenoproteins, explore the significance of the role of selenium in the fields of biology and chemistry, scientists conducted a series of artificial simulations on selenoproteins. In this review, we sum up the progress and developed strategies in the construction of artificial selenoenzyme. Using different mechanisms from different catalytic angles, selenium-containing catalytic antibodies, semi-synthetic selenonezyme, and the selenium-containing molecularly imprinted enzymes have been constructed. A variety of synthetic selenoenzyme models have been designed and constructed by selecting host molecules such as cyclodextrins, dendrimers, and hyperbranched polymers as the main scaffolds. Then, a variety of selenoprotein assemblies as well as cascade antioxidant nanoenzymes were built by using electrostatic interaction, metal coordination, and host-guest interaction. The unique redox properties of selenoenzyme glutathione peroxidase (GPx) can be reproduced.

A novel consensus-based computational pipeline for screening of antibody therapeutics for efficacy against SARS-CoV-2 variants of concern including Omicron variant

Multiple severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) variants continue to evolve carrying flexible amino acid substitutions in the spike protein's receptor binding domain (RBD). These substitutions modify the binding of the SARS-CoV-2 to human angiotensin-converting enzyme 2 (hACE2) receptor and have been implicated in altered host fitness, transmissibility, and efficacy against antibody therapeutics and vaccines. Reliably predicting the binding strength of SARS-CoV-2 variants RBD to hACE2 receptor and neutralizing antibodies (NAbs) can help assessing their fitness, and rapid deployment of effective antibody therapeutics, respectively. Here, we introduced a two-step computational framework with 3-fold validation that first identified dissociation constant as a reliable predictor of binding affinity in hetero- dimeric and trimeric protein complexes. The second step implements dissociation constant as descriptor of the binding strengths of SARS-CoV-2 variants RBD to hACE2 and NAbs. Then, we examined several variants of concerns (VOCs) such as Alpha, Beta, Gamma, Delta, and Omicron and demonstrated that these VOCs RBD bind to the hACE2 with enhanced affinity. Furthermore, the binding affinity of Omicron variant's RBD was reduced with majority of the RBD-directed NAbs, which is highly consistent with the experimental neutralization data. By studying the atomic contacts between RBD and NAbs, we revealed the molecular footprints of four NAbs (GH-12, P2B-1A1, Asarnow_3D11, and C118)-that may likely neutralize the recently emerged Omicron variant-facilitating enhanced binding affinity. Finally, our findings suggest a computational pathway that could aid researchers identify a range of current NAbs that may be effective against emerging SARS-CoV-2 variants.

A novel structure-based approach for identification of vertebrate susceptibility to SARS-CoV-2: Implications for future surveillance programmes

Understanding the origin of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has been a highly debatable and unresolved issue for scientific communities all over the world. Understanding the mechanism of virus entry to the host cells is crucial to deciphering the susceptibility profiles of animal species to SARS-CoV-2. The interaction of SARS-CoV-2 ligands (receptor-binding domain on spike protein) with its host cell receptor, angiotensin-converting enzyme 2 (ACE2), is a critical determinant of host range and cross-species transmission. In this study, we developed and implemented a rigorous computational approach for predicting binding affinity between 299 ACE2 orthologs from diverse vertebrate species and the SARS-CoV-2 spike protein. The findings show that the SARS-CoV-2 spike protein can bind to a wide range of vertebrate species carrying evolutionary divergent ACE2, implying a broad host range at the virus entry level, which may contribute to cross-species transmission and further viral evolution. Furthermore, the current study facilitated the identification of genetic determinants that may differentiate susceptible from resistant host species based on the conservation of ACE2-spike protein interacting residues in vertebrate host species known to facilitate SARS-CoV-2 infection; however, these genetic determinants warrant in vivo experimental confirmation. The molecular interactions associated with varied binding affinity of distinct ACE2 isoforms in a specific bat species were identified using protein structure analysis, implying the existence of diversified bat species' susceptibility to SARS-CoV-2. The current study's findings highlight the importance of intensive surveillance programmes aimed at identifying susceptible hosts, especially those with the potential to transmit zoonotic pathogens, in order to prevent future outbreaks.

ProFitFun: a protein tertiary structure fitness function for quantifying the accuracies of model structures

MOTIVATION

An accurate estimation of the quality of protein model structures typifies as a cornerstone in protein structure prediction regimes. Despite the recent groundbreaking success in the field of protein structure prediction, there are certain prospects for the improvement in model quality estimation at multiple stages of protein structure prediction and thus, to further push the prediction accuracy. Here, a novel approach, named ProFitFun, for assessing the quality of protein models is proposed by harnessing the sequence and structural features of experimental protein structures in terms of the preferences of backbone dihedral angles and relative surface accessibility of their amino acid residues at the tripeptide level. The proposed approach leverages upon the backbone dihedral angle and surface accessibility preferences of the residues by accounting for its N-terminal and C-terminal neighbors in the protein structure. These preferences are used to evaluate protein structures through a machine learning approach and tested on an extensive dataset of diverse proteins.

RESULTS

The approach was extensively validated on a large test dataset (n = 25 005) of protein structures, comprising 23 661 models of 82 non-homologous proteins and 1344 non-homologous experimental structures. In addition, an external dataset of 40 000 models of 200 non-homologous proteins was also used for the validation of the proposed method. Both datasets were further used for benchmarking the proposed method with four different state-of-the-art methods for protein structure quality assessment. In the benchmarking, the proposed method outperformed some state-of-the-art methods in terms of Spearman's and Pearson's correlation coefficients, average GDT-TS loss, sum of z-scores and average absolute difference of predictions over corresponding observed values. The high accuracy of the proposed approach promises a potential use of the sequence and structural features in computational protein design.

AVAILABILITY AND IMPLEMENTATION

http://github.com/KYZ-LSB/ProTerS-FitFun.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Obtaining protein foldability information from computational models of AlphaFold2 and RoseTTAFold

The recent breakthrough from AlphaFold2 and RoseTTAFold set a profound milestone for solving the protein folding problem, but they were not explicitly trained to predict protein foldability, i.e., if a protein can really fold into the predicted 3D structure. We wondered if the computational models from AlphaFold2 and RoseTTAFold might carry protein foldability information. Therefore, we predicted the structural models of 159 circular permutants and 158 alanine insertion mutants of the 159-residue dihydrofolate reductase. Our data showed that although AlphaFold2 and RoseTTAFold cannot directly identify unfoldable proteins, the RMSD values of computational models are correlated with protein foldability, with higher RMSD values indicating lower protein foldability. Furthermore, this correlation is independent of secondary structures, and the RMSD values of computational models are quantitatively correlated with protein foldability but not protein functions. Additionally, using a dataset of 129 de novo designed proteins, we showed that inter-model RMSD values between AlphaFold2 models and RoseTTAFold models are a good indicator of protein foldability. At last, we showed that inter-model RMSD values are also useful for evaluating protein solubility by modeling 1664 natural proteins. Our work could be of great value to the design of novel proteins and the prediction of protein foldability.

Tools for Characterizing Proteins: Circular Variance, Mutual Proximity, Chameleon Sequences, and Subsequence Propensities

For the characterization of various aspects of protein structures, four useful concepts are discussed: chameleon sequences, circular variance, mutual proximity, and a subsequence-based foldability score. These concepts were used in estimating foldability of globular, intrinsically disordered and fold-switching proteins, properties of protein-protein interfaces, quantifying sphericity, helping to improve protein-protein docking scores, and estimating the effect of mutations on stability. A conjecture about the Achilles' heel of proteins is presented as well.

Hierarchical Structure of Protein Sequence

Most non-communicable diseases are associated with dysfunction of proteins or protein complexes. The relationship between sequence and structure has been analyzed for a long time, and the analysis of the sequences organization in domains and motifs remains an actual research area. Here, we propose a mathematical method for revealing the hierarchical organization of protein sequences. The method is based on the pentapeptide as a unit of protein sequences. Employing the frequency of occurrence of pentapeptides in sequences of natural proteins and a special mathematical approach, this method revealed a hierarchical structure in the protein sequence. The method was applied to 24,647 non-homologous protein sequences with sizes ranging from 50 to 400 residues from the NRDB90 database. Statistical analysis of the branching points of the graphs revealed 11 characteristic values of y (the width of the inscribed function), showing the relationship of these multiple fragments of the sequences. Several examples illustrate how fragments of the protein spatial structure correspond to the elements of the hierarchical structure of the protein sequence. This methodology provides a promising basis for a mathematically-based classification of the elements of the spatial organization of proteins. Elements of the hierarchical structure of different levels of the hierarchy can be used to solve biotechnological and medical problems.