MVSF-AB: accurate antibody-antigen binding affinity prediction via multi-view sequence feature learning

MOTIVATION

Predicting the binding affinity between antigens and antibodies accurately is crucial for assessing therapeutic antibody effectiveness and enhancing antibody engineering and vaccine design. Traditional machine learning methods have been widely used for this purpose, relying on interfacial amino acids' structural information. Nevertheless, due to technological limitations and high costs of acquiring structural data, the structures of most antigens and antibodies are unknown, and sequence-based methods have gained attention. Existing sequence-based approaches designed for protein-protein affinity prediction exhibit a significant drop in performance when applied directly to antibody-antigen affinity prediction due to imbalanced training data and lacking design in the model framework specifically for antibody-antigen, hindering the learning of key features of antibodies and antigens. Therefore, we propose MVSF-AB, a Multi-View Sequence Feature learning for accurate Antibody-antigen Binding affinity prediction.

RESULTS

MVSF-AB designs a multi-view method that fuses semantic features and residue features to fully utilize the sequence information of antibody-antigen and predicts the binding affinity. Experimental results demonstrate that MVSF-AB outperforms existing approaches in predicting unobserved natural antibody-antigen affinity and maintains its effectiveness when faced with mutant strains of antibodies.

AVAILABILITY AND IMPLEMENTATION

Datasets we used and source code are available on our public GitHub repository https://github.com/TAI-Medical-Lab/MVSF-AB.

PPI-Graphomer: enhanced protein-protein affinity prediction using pretrained and graph transformer models

Protein-protein interactions (PPIs) refer to the phenomenon of protein binding through various types of bonds to execute biological functions. These interactions are critical for understanding biological mechanisms and drug research. Among these, the protein binding interface is a critical region involved in protein-protein interactions, particularly the hotspot residues on it that play a key role in protein interactions. Current deep learning methods trained on large-scale data can characterize proteins to a certain extent, but they often struggle to adequately capture information about protein binding interfaces. To address this limitation, we propose the PPI-Graphomer module, which integrates pretrained features from large-scale language models and inverse folding models. This approach enhances the characterization of protein binding interfaces by defining edge relationships and interface masks on the basis of molecular interaction information. Our model outperforms existing methods across multiple benchmark datasets and demonstrates strong generalization capabilities.

Systematic investigation of machine learning on limited data: A study on predicting protein-protein binding strength

The application of machine learning techniques in biological research, especially when dealing with limited data availability, poses significant challenges. In this study, we leveraged advancements in method development for predicting protein-protein binding strength to conduct a systematic investigation into the application of machine learning on limited data. The binding strength, quantitatively measured as binding affinity, is vital for understanding the processes of recognition, association, and dysfunction that occur within protein complexes. By incorporating transfer learning, integrating domain knowledge, and employing both deep learning and traditional machine learning algorithms, we mitigated the impact of data limitations and made significant advancements in predicting protein-protein binding affinity. In particular, we developed over 20 models, ultimately selecting three representative best-performing ones that belong to distinct categories. The first model is structure-based, consisting of a random forest regression and thirteen handcrafted features. The second model is sequence-based, employing an architecture that combines transferred embedding features with a multilayer perceptron. Finally, we created an ensemble model by averaging the predictions of the two aforementioned models. The comparison with other predictors on three independent datasets confirms the significant improvements achieved by our models in predicting protein-protein binding affinity. The programs for running these three models are available at https://github.com/minghuilab/BindPPI.

Structural insights into epitope-paratope interactions of a monoclonal antibody targeting CEACAM5-expressing tumors

Carcinoembryonic antigen-related cell adhesion molecules (CEACAMs) are overexpressed in some tumor types. The antibody-drug conjugate tusamitamab ravtansine specifically recognizes the A3-B3 domains of human CEACAM5 (hCEACAM5). To understand this specificity, here we map the epitope-paratope interface between the A3-B3 domains of hCEACAM5 (hCEACAM5A3-B3) and the antigen-binding fragment of tusamitamab (tusa Fab). We use hydrogen/deuterium exchange mass spectrometry to identify the tusa Fab paratope, which involves heavy chain (HC) residues 101-109 and light chain residues 48-54 and 88-104. Using surface plasmon resonance, we demonstrate that alanine variants of HC residues 96-108 abolish binding to hCEACAM5, suggesting that these residues are critical for tusa-Fab-antigen complex formation. The cryogenic electron microscopy structure of the hCEACAM5A3-B3- tusa Fab complex (3.11 Å overall resolution) reveals a discontinuous epitope involving residues in the A3-B3 domains and an N-linked mannose at residue Asn612. Conformational constraints on the epitope-paratope interface enable tusamitamab to target hCEACAM5A3-B3 and distinguish CEACAM5 from other CEACAMs.

The Rapid Evolution of De Novo Proteins in Structure and Complex

Recent studies in the rice genome-wide have established that de novo genes, evolving from noncoding sequences, enhance protein diversity through a stepwise process. However, the pattern and rate of their evolution in protein structure over time remain unclear. Here, we addressed these issues within a surprisingly short evolutionary timescale (<1 million years for 97% of Oryza de novo genes) with comparative approaches to gene duplicates. We found that de novo genes evolve faster than gene duplicates in the intrinsically disordered regions (such as random coils), secondary structure elements (such as α helix and β strand), hydrophobicity, and molecular recognition features. In de novo proteins, specifically, we observed an 8% to 14% decay in random coils and intrinsically disordered region lengths and a 2.3% to 6.5% increase in structured elements, hydrophobicity, and molecular recognition features, per million years on average. These patterns of structural evolution align with changes in amino acid composition over time as well. We also revealed higher positive charges but smaller molecular weights for de novo proteins than duplicates. Tertiary structure predictions showed that most de novo proteins, though not typically well folded on their own, readily form low-energy and compact complexes with other proteins facilitated by extensive residue contacts and conformational flexibility, suggesting a faster-binding scenario in de novo proteins to promote interaction. These analyses illuminate a rapid evolution of protein structure in de novo genes in rice genomes, originating from noncoding sequences, highlighting their quick transformation into active, protein complex-forming components within a remarkably short evolutionary timeframe.

Gossypium hirsutum calmodulin-like protein (CML 11) interaction with geminivirus encoded protein using bioinformatics and molecular techniques

Plant viruses are the most abundant destructive agents that exist in every ecosystem, causing severe diseases in multiple crops worldwide. Currently, a major gap is present in computational biology determining plant viruses interaction with its host. We lay out a strategy to extract virus-host protein interactions using various protein binding and interface methods for Geminiviridae, a second largest virus family. Using this approach, transcriptional activator protein (TrAP/C2) encoded by Cotton leaf curl Kokhran virus (CLCuKoV) and Cotton leaf curl Multan virus (CLCuMV) showed strong binding affinity with calmodulin-like (CML) protein of Gossypium hirsutum (Gh-CML11). Higher negative value for the change in Gibbs free energy between TrAP and Gh-CML11 indicated strong binding affinity. Consensus from gene ontology database and in-silico nuclear localization signal (NLS) tools identified subcellular localization of TrAP in the nucleus associated with Gh-CML11 for virus infection. Data based on interaction prediction and docking methods present evidences that full length and truncated C2 strongly binds with Gh-CML11. This computational data was further validated with molecular results collected from yeast two-hybrid, bimolecular fluorescence complementation system and pull down assay. In this work, we also show the outcomes of full length and truncated TrAP on plant machinery. This is a first extensive report to delineate a role of CML protein from cotton with begomoviruses encoded transcription activator protein.

DeepPPAPredMut: deep ensemble method for predicting the binding affinity change in protein-protein complexes upon mutation

MOTIVATION

Protein-protein interactions underpin many cellular processes and their disruption due to mutations can lead to diseases. With the evolution of protein structure prediction methods like AlphaFold2 and the availability of extensive experimental affinity data, there is a pressing need for updated computational tools that can efficiently predict changes in binding affinity caused by mutations in protein-protein complexes.

RESULTS

We developed a deep ensemble model that leverages protein sequences, predicted structure-based features, and protein functional classes to accurately predict the change in binding affinity due to mutations. The model achieved a correlation of 0.97 and a mean absolute error (MAE) of 0.35 kcal/mol on the training dataset, and maintained robust performance on the test set with a correlation of 0.72 and a MAE of 0.83 kcal/mol. Further validation using Leave-One-Out Complex (LOOC) cross-validation exhibited a correlation of 0.83 and a MAE of 0.51 kcal/mol, indicating consistent performance.

AVAILABILITY AND IMPLEMENTATION

https://web.iitm.ac.in/bioinfo2/DeepPPAPredMut/index.html.

PagEXPA1 combines with PagCDKB2;1 to regulate plant growth and the elongation of fibers in Populus alba × Populus glandulosa

Expansins are important plant cell wall proteins. They can loosen and soften the cell walls and lead to wall extension and cell expansion. To investigate their role in wood formation and fiber elongation, the PagEXPA1 that highly expressed in cell differentiation and expansion tissues was cloned from 84K poplar (Populus alba × P. glandulosa). The subcellular localization showed that PagEXPA1 located in the cell wall and it was highly expressed in primary stems and young leaves. Compared with non-transgenic 84K poplar, overexpression of PagEXPA1 can promote plant-growth, lignification, and fiber cell elongation, while PagEXPA1 Cas9-editing mutant lines exhibited the opposite phenotype. Transcriptome analysis revealed that DEGs were mainly enriched in some important processes, which are associated with cell wall formation and cellulose synthesis. The protein interaction prediction and expression analysis showed that PagCDKB2:1 and PagEXPA1 might have an interaction relationship. The luciferase complementary assay and bimolecular fluorescence complementary assay validated that PagEXPA1 can combined with PagCDKB2;1. So they promoted the expansion of xylem vascular tissues and the development of poplar though participating in the regulation of cell division and differentiation by programming the cell-cycle. It provides good foundation for molecular breeding of fast-growing and high-quality poplar varieties.

MPA-Pred: A machine learning approach for predicting the binding affinity of membrane protein-protein complexes

Membrane protein-protein interactions are essential for several functions including cell signaling, ion transport, and enzymatic activity. These interactions are mainly dictated by their binding affinities. Although several methods are available for predicting the binding affinity of protein-protein complexes, there exists no specific method for membrane protein-protein complexes. In this work, we collected the experimental binding affinity data for a set of 114 membrane protein-protein complexes and derived several structure and sequence-based features. Our analysis on the relationship between binding affinity and the features revealed that the important factors mainly depend on the type of membrane protein and the functional class of the protein. Specifically, aromatic and charged residues at the interface, and aromatic-aromatic and electrostatic interactions are found to be important to understand the binding affinity. Further, we developed a method, MPA-Pred, for predicting the binding affinity of membrane protein-protein complexes using a machine learning approach. It showed an average correlation and mean absolute error of 0.83 and 0.91 kcal/mol, respectively, using the jack-knife test on a set of 114 complexes. We have also developed a web server and it is available at https://web.iitm.ac.in/bioinfo2/MPA-Pred/. This method can be used for predicting the affinity of membrane protein-protein complexes at a large scale and aid to improve drug design strategies.

Sequence-based prediction of the effects of histones H1 post-translational modifications: impact on the features related to the function

Post-translational modifications modulate histones H1 activity but their impact on proteins features was not studied so far. Therefore, this work was intended to answer how the most common modifications, i.e. acetylation, methylation, phosphorylation and ubiquitination, can influence on histones H1 to alter their physicochemical and molecular properties. Investigations were done with the use of sequence-based predictors trained on various protein features. Because a full set of histones H1 modifications is not included in the databases of histone proteins, the survey was performed on the human, animals, plants, fungi and protist sequences selected from UniProtKB/Swiss-Prot database. Quantitative proportions of modifications were similar between the groups of organisms (CV = 0.11) but different within the group (p < 0.05). The effects of modifications were evaluated with the use of mutated sequences obtained through the substitution of modified residue of Lys, Ser and Thr by a neutral residue of the Ala. An advantage of deleterious mutations at the sites of acetylation, methylation and ubiquitination over the sites of phosphorylation (p < 0.05) indicate that this modification have more redundant character. Modifications evoke an increase of protein solubility and stability as well as acceleration of folding kinetics and a weaken of binding affinity. Besides, they also maintain a higher extent of intrinsic structural disorder. The obtained results prove that modifications should be perceived as relevant factors influencing physicochemical features determining molecular properties. Thus, histones H1 functioning is strictly correlated with the status of modifications.

Predictive Models and Impact of Interfacial Contacts and Amino Acids on Protein-Protein Binding Affinity

Protein-protein interactions (PPIs) play a central role in nearly all cellular processes. The strength of the binding in a PPI is characterized by the binding affinity (BA) and is a key factor in controlling protein-protein complex formation and defining the structure-function relationship. Despite advancements in understanding protein-protein binding, much remains unknown about the interfacial region and its association with BA. New models are needed to predict BA with improved accuracy for therapeutic design. Here, we use machine learning approaches to examine how well different types of interfacial contacts can be used to predict experimentally determined BA and to reveal the impact of the specific amino acids at the binding interface on BA. We create a series of multivariate linear regression models incorporating different contact features at both residue and atomic levels and examine how different methods of identifying and characterizing these properties impact the performance of these models. Particularly, we introduce a new and simple approach to predict BA based on the quantities of specific amino acids at the protein-protein interface. We found that the numbers of specific amino acids at the protein-protein interface were correlated with BA. We show that the interfacial numbers of amino acids can be used to produce models with consistently good performance across different data sets, indicating the importance of the identities of interfacial amino acids in underlying BA. When trained on a diverse set of complexes from two benchmark data sets, the best performing BA model was generated with an explicit linear equation involving six amino acids. Tyrosine, in particular, was identified as the key amino acid in controlling BA, as it had the strongest correlation with BA and was consistently identified as the most important amino acid in feature importance studies. Glycine and serine were identified as the next two most important amino acids in predicting BA. The results from this study further our understanding of PPIs and can be used to make improved predictions of BA, giving them implications for drug design and screening in the pharmaceutical industry.

Efflux pump Rv1258c activates novel functions of the oxidative stress and via the VII secretion system ESX-3-mediated iron metabolic pathway in Mycobacterium tuberculosis

Oxidative stress and iron metabolism are essential for Mycobacterium tuberculosis (M.tb) survival in host cells. The efflux pump Rv1258c belongs to the major facilitator superfamily (MFS) and can actively pump drugs to promote certain drug resistance in M.tb. In this study, we compared H37RvΔRv1258c with wild-type (WT) M.tb H37Rv. The qRT-PCR results suggested that Rv1258c is potentially involved in the iron metabolic pathway by regulating the expression of ESX-3, which is required for iron uptake. Protein-Protein Affinity Predictor (PPA-Pred2) and the artificial intelligence program AlphaFold 2 were used for prediction and showed that Rv1258c has direct interactions with PPE4 and EccD3 but weak interactions with EccB3. This was further determined via protein-protein interaction analysis of the yeast two-hybrid expression system. By comparing mutant H37RvΔRv1258c strains with WT strains, we discovered that the absence of Rv1258c led to elevated intracellular H+ potential and NAD+/NADH ratios in M.tb, thereby resulting in oxidative stress. We hypothesize that the efflux pump Rv1258c not only has the function of regulating drug resistance in M.tb but also has a novel function in activating oxidative stress and regulating ESX-3-associated iron metabolism in M.tb.

Software for Predicting Binding Free Energy of Protein-Protein Complexes and Their Mutants

Protein-protein binding affinity prediction is important for understanding complex biochemical pathways and to uncover protein interaction networks. Quantitative estimation of the binding affinity changes caused by mutations can provide critical information for protein function annotation and genetic disease diagnoses. The binding free energies of protein-protein complexes can be predicted using several computational tools. This chapter is a summary of software developed for the prediction of binding free energies for protein-protein complexes and their mutants.

Genome sequencing of cucumber mosaic virus (CMV) isolates infecting chilli and its interaction with host ferredoxin protein of different host for causing mosaic symptoms

Chilli (Capsicum annuum L.) is an important vegetable crop grown in the Indian sub-continent and is prone to viral infections under field conditions. During the field survey, leaf samples from chilli plants showing typical symptoms of disease caused by cucumber mosaic virus (CMV) such as mild mosaic, mottling and leaf distortion were collected. DAC-ELISA analysis confirmed the presence of CMV in 71 out of 100 samples, indicating its widespread prevalence in the region. Five CMV isolates, named Gu1, Gu2, BA, Ho, and Sal were mechanically inoculated onto cucumber and Nicotiana glutinosa plants to study their virulence. Inoculated plants expressed the characteristic symptoms of CMV such as chlorotic spots followed by mild mosaic and leaf distortion. Complete genomes of the five CMV isolates were amplified, cloned, and sequenced, revealing RNA1, RNA2, and RNA3 sequences with 3358, 3045, and 2220 nucleotides, respectively. Phylogenetic analysis classified the isolates as belonging to the CMV-IB subgroup, distinguishing them from subgroup IA and II CMV isolates. Recombination analysis showed intra and interspecific recombination in all the three RNA segments of these isolates. In silico protein-protein docking approach was used to decipher the mechanism behind the production of mosaic symptoms during the CMV-host interaction in 13 host plants. Analysis revealed that the production of mosaic symptoms could be due to the interaction between the coat protein (CP) of CMV and chloroplast ferredoxin proteins. Further, in silico prediction was validated in 13 host plants of CMV by mechanical sap inoculation. Twelve host plants produced systemic symptoms viz., chlorotic spot, chlorotic ringspot, chlorotic local lesion, mosaic and mild mosaic and one host plant, Solanum lycopersicum produced mosaic followed by shoestring symptoms.

Supplementary Information

The online version contains supplementary material available at 10.1007/s13205-023-03777-8.

Modeling and monitoring the effects of three partly conserved Ile residues in the dimerization domain of a Mip-like virulence factor from Escherichia coli

FKBP22, an Escherichia coli-made peptidyl-prolyl cis-trans isomerase, has shown considerable homology with Mip-like virulence factors. While the C-terminal domain of this enzyme is used for executing catalytic function and binding inhibitor, the N-terminal domain is employed for its dimerization. To precisely determine the underlying factors of FKBP22 dimerization, its structural model, developed using a suitable template, was carefully inspected. The data show that the dimeric FKBP22, like dimeric Mip proteins, has a V-like shape. Further, it dimerizes using 40 amino acid residues including Ile 9, Ile 17, Ile 42, and Ile 65. All of the above Ile residues except Ile 9 are partly conserved in the Mip-like proteins. To confirm the roles of the partly conserved Ile residues, three FKBP22 mutants, constructed by substituting them with an Ala residue, were studied as well. The results together indicate that Ile 65 has little role in maintaining the dimeric state or enzymatic activity of FKBP22. Conversely, both Ile 17 and Ile 42 are essential for preserving the structure, enzymatic activity, and dimerization ability of FKBP22. Ile 42 in particular looks more essential to FKBP22. However, none of these two Ile residues is required for binding the cognate inhibitor. Additional computational studies also indicated the change of V-shape and the dimeric state of FKBP22 due to the Ala substitution at position 42. The ways Ile 17 and Ile 42 protect the structure, function, and dimerization of FKBP22 have been discussed at length.Communicated by Ramaswamy H. Sarma.

An updated dataset and a structure-based prediction model for protein-RNA binding affinity

Understanding the process of protein-RNA interaction is essential for structural biology. The thermodynamic process is an important part to uncover the protein-RNA interaction mechanism. The regulatory networks between protein and RNA in organisms are dominated by the binding or dissociation in the cells. Therefore, determining the binding affinity for protein-RNA complexes can help us to understand the regulation mechanism of protein-RNA interaction. Since it is time-consuming and labor-intensive to determine the binding affinity for protein-RNA complexes by experimental methods, it is necessary and urgent to develop computational methods to predict that. To develop a binding affinity prediction model, first we update the dataset of protein-RNA binding affinity benchmark (PRBAB), which includes 145 complexes now. Second, we extract the structural features based on complex structure, and then we analyze and select the representative structural features to train the regression model. Third, we random select the subset from the PRBAB2.0 to fit the protein-RNA binding affinity determined by experiment. In the end, we tested our model on the nonredundant PDBbind dataset, and the results showed that Pearson correlation coefficient r = .57 and RMSE = 2.51 kcal/mol. The Pearson correlation coefficient achieves 0.7 while removing 5 complex structures with modified residues/nucleotides and metal ions. While testing on ProNAB, the results showed that 71.60% of the prediction achieves Pearson correlation coefficient r = .61 and RMSE = 1.56 kcal/mol with experiment values.

emPDBA: protein-DNA binding affinity prediction by combining features from binding partners and interface learned with ensemble regression model

Protein-deoxyribonucleic acid (DNA) interactions are important in a variety of biological processes. Accurately predicting protein-DNA binding affinity has been one of the most attractive and challenging issues in computational biology. However, the existing approaches still have much room for improvement. In this work, we propose an ensemble model for Protein-DNA Binding Affinity prediction (emPDBA), which combines six base models with one meta-model. The complexes are classified into four types based on the DNA structure (double-stranded or other forms) and the percentage of interface residues. For each type, emPDBA is trained with the sequence-based, structure-based and energy features from binding partners and complex structures. Through feature selection by the sequential forward selection method, it is found that there do exist considerable differences in the key factors contributing to intermolecular binding affinity. The complex classification is beneficial for the important feature extraction for binding affinity prediction. The performance comparison of our method with other peer ones on the independent testing dataset shows that emPDBA outperforms the state-of-the-art methods with the Pearson correlation coefficient of 0.53 and the mean absolute error of 1.11 kcal/mol. The comprehensive results demonstrate that our method has a good performance for protein-DNA binding affinity prediction. Availability and implementation: The source code is available at https://github.com/ChunhuaLiLab/emPDBA/.

MutBLESS: A tool to identify disease-prone sites in cancer using deep learning

Understanding the molecular basis and impact of mutations at different stages of cancer are long-standing challenges in cancer biology. Identification of driver mutations from experiments is expensive and time intensive. In the present study, we collected the data for experimentally known driver mutations in 22 different cancer types and classified them into six categories: breast cancer (BRCA), acute myeloid leukaemia (LAML), endometrial carcinoma (EC), stomach cancer (STAD), skin cancer (SKCM), and other cancer types which contains 5747 disease prone and 5514 neutral sites in 516 proteins. The analysis of amino acid distribution along mutant sites revealed that the motifs AAA and LR are preferred in disease-prone sites whereas QPP and QF are dominant in neutral sites. Further, we developed a method using deep neural networks to predict disease-prone sites with amino acid sequence-based features such as physicochemical properties, secondary structure, tri-peptide motifs and conservation scores. We obtained an average AUC of 0.97 in five cancer types BRCA, LAML, EC, STAD and SKCM in a test dataset and 0.72 in all other cancer types together. Our method showed excellent performance for identifying cancer-specific mutations with an average sensitivity, specificity, and accuracy of 96.56 %, 97.39 %, and 97.64 %, respectively. We developed a web server for identifying cancer-prone sites, and it is available at https://web.iitm.ac.in/bioinfo2/MutBLESS/index.html. We suggest that our method can serve as an effective method to identify disease-prone sites and assist to develop therapeutic strategies.

Fowl adenovirus serotype 4 52/55k protein triggers PKR degradation by ubiquitin-proteasome system to evade effective innate immunity

The pro- and inflammatory cytokines fail to effectively inhibit FAdV-4, which has always puzzled us. In the current study, the data determined that the mRNA levels of interferons were significantly enhanced in the livers and LMH cells from 24 h to 72 h post FAdV-4 infection. But the viral load of FAdV-4 was still significantly increased, which meant that FAdV-4 evaded innate immune response. We additionally revealed that the protein levels not mRNA levels of PKR were degraded in host cell at 48 h post FAdV-4 infection. Moreover, the results of over expression and silent expression of PKR revealed that PKR could inhibit FAdV-4 proliferation. These results indicated that FAdV-4 degraded the protein levels of PKR to evade innate immune response. We also found that the protein degradation levels of PKR induced by FAdV-4 were recovery in LHM cells after treatment with proteasome inhibitor MG132, and ubiquitin-specific proteases inhibitor DUB-IN-1. Furthermore, our current data presented that FAdV-4 52/55 K protein directly interacted with PKR and degraded it determined by Co-immunoprecipitation and immunofluorescence. We also determined that 52/55 K protein triggered PKR degradation, and the degradation of PKR could be recovery in LHM cells after treatment with MG132, or DUB-IN-1, respectively. Finally, our data demonstrated that 52/55 K protein was a ubiquitylase that could directly degrade PKR protein in host cells via the ubiquitin-proteasome pathway. Therefore, the current study firstly revealed that FAdV-4 52/55 K protein played the key role in triggering PKR degradation by ubiquitin-proteasome system pathway to escape from innate immunity response.

Does the SARS-CoV-2 Spike Receptor-Binding Domain Hamper the Amyloid Transformation of Alpha-Synuclein after All?

Interactions of key amyloidogenic proteins with SARS-CoV-2 proteins may be one of the causes of expanding and delayed post-COVID-19 neurodegenerative processes. Furthermore, such abnormal effects can be caused by proteins and their fragments circulating in the body during vaccination. The aim of our work was to analyze the effect of the receptor-binding domain of the coronavirus S-protein domain (RBD) on alpha-synuclein amyloid aggregation. Molecular modeling showed that the predicted RBD complex with monomeric alpha-synuclein is stable over 100 ns of molecular dynamics. Analysis of the interactions of RBD with the amyloid form of alpha-synuclein showed that during molecular dynamics for 200 ns the number of contacts is markedly higher than that for the monomeric form. The formation of the RBD complex with the alpha-synuclein monomer was confirmed immunochemically by immobilization of RBD on its specific receptor ACE2. Changes in the spectral characteristics of the intrinsic tryptophans of RBD and hydrophobic dye ANS indicate an interaction between the monomeric proteins, but according to the data of circular dichroism spectra, this interaction does not lead to a change in their secondary structure. Data on the kinetics of amyloid fibril formation using several spectral approaches strongly suggest that RBD prevents the amyloid transformation of alpha-synuclein. Moreover, the fibrils obtained in the presence of RBD showed significantly less cytotoxicity on SH-SY5Y neuroblastoma cells.

Binding affinity prediction for antibody-protein antigen complexes: A machine learning analysis based on interface and surface areas

Specific antibodies can bind to protein antigens with high affinity and specificity, and this property makes them one of the best protein-based therapeutics. Accurate prediction of antibody‒protein antigen binding affinity is crucial for designing effective antibodies. The current predictive methods for protein‒protein binding affinity usually fail to predict the binding affinity of an antibody‒protein antigen complex with a comparable level of accuracy. Here, new models specific for antibody‒antigen binding affinity prediction are developed according to the different types of interface and surface areas present in antibody‒antigen complex. The contacts-based descriptors are also employed to construct or train different models specific for antibody‒protein antigen binding affinity prediction. The results of this study show that (i) the area-based descriptors are slightly better than the contacts-based descriptors in terms of the predictive power; (ii) the new models specific for antibody‒protein antigen binding affinity prediction are superior to the previously-used general models for predicting the protein‒protein binding affinities; (iii) the performances of the best area-based and contacts-based models developed in this work are better than the performances of a recently-developed graph-based model (i.e., CSM-AB) specific for antibody‒protein antigen binding affinity prediction. The new models developed in this work would not only help understand the mechanisms underlying antibody‒protein antigen interactions, but would also be of some applicable utility in the design and virtual screening of antibody-based therapeutics.

Recent developments of sequence-based prediction of protein-protein interactions

The identification of protein-protein interactions (PPIs) can lead to a better understanding of cellular functions and biological processes of proteins and contribute to the design of drugs to target disease-causing PPIs. In addition, targeting host-pathogen PPIs is useful for elucidating infection mechanisms. Although several experimental methods have been used to identify PPIs, these methods can yet to draw complete PPI networks. Hence, computational techniques are increasingly required for the prediction of potential PPIs, which have never been seen experimentally. Recent high-performance sequence-based methods have contributed to the construction of PPI networks and the elucidation of pathogenetic mechanisms in specific diseases. However, the usefulness of these methods depends on the quality and quantity of training data of PPIs. In this brief review, we introduce currently available PPI databases and recent sequence-based methods for predicting PPIs. Also, we discuss key issues in this field and present future perspectives of the sequence-based PPI predictions.

Voltage-Gated Sodium Channel NaV1.5 Controls NHE-1-Dependent Invasive Properties in Colon Cancer Cells

Colorectal cancer (CRC) is the second leading cause of death worldwide, with 0.9 million deaths per year. The metastatic stage of the disease is identified in about 20% of cases at the first diagnosis and is associated with low patient-survival rates. Voltage-gated sodium channels (Na_V) are abnormally overexpressed in several carcinomas including CRC and are strongly associated with the metastatic behavior of cancer cells. Acidification of the extracellular space by Na⁺/H⁺ exchangers (NHE) contributes to extracellular matrix degradation and cell invasiveness. In this study, we assessed the expression levels of pore-forming α-subunits of Na_V channels and NHE exchangers in tumor and adjacent non-malignant tissues from colorectal cancer patients, CRC cell lines and primary tumor cells. In all cases, SCN5A (gene encoding for Na_V1.5) was overexpressed and positively correlated with cancer stage and poor survival prognosis for patients. In addition, we identified an anatomical differential expression of SCN5A and SLC9A1 (gene encoding for NHE-1) being particularly relevant for tumors that originated on the sigmoid colon epithelium. The functional activity of Na_V1.5 channels was characterized in CRC cell lines and the primary cells of colon tumors obtained using tumor explant methodologies. Furthermore, we assessed the performance of two new small-molecule Na_V1.5 inhibitors on the reduction of sodium currents, as well as showed that silencing SCN5A and SLC9A1 substantially reduced the 2D invasive capabilities of cancer cells. Thus, our findings show that both Na_V1.5 and NHE-1 represent two promising targetable membrane proteins against the metastatic progression of CRC.

Machine learning methods for protein-protein binding affinity prediction in protein design

Protein-protein interactions govern a wide range of biological activity. A proper estimation of the protein-protein binding affinity is vital to design proteins with high specificity and binding affinity toward a target protein, which has a variety of applications including antibody design in immunotherapy, enzyme engineering for reaction optimization, and construction of biosensors. However, experimental and theoretical modelling methods are time-consuming, hinder the exploration of the entire protein space, and deter the identification of optimal proteins that meet the requirements of practical applications. In recent years, the rapid development in machine learning methods for protein-protein binding affinity prediction has revealed the potential of a paradigm shift in protein design. Here, we review the prediction methods and associated datasets and discuss the requirements and construction methods of binding affinity prediction models for protein design.

A novel protein encoded by ZCRB1-induced circHEATR5B suppresses aerobic glycolysis of GBM through phosphorylation of JMJD5

Background

RNA-binding proteins (RBPs) and circular RNAs (circRNAs) play important roles in glioblastoma multiforme (GBM). Aerobic glycolysis is a metabolic characteristic of GBM. However, the roles of RBPs and circRNAs in aerobic glycolysis in GBM remain unclear. The aim of this study is to explore the mechanisms by which RBPs and circRNAs regulate aerobic glycolysis in GBM cells.

Methods

RNA sequencing and circRNA microarray analysis were performed to identify RBPs and circRNAs for further study. Mass spectrometry validated the encoded protein and its interacting proteins. Quantitative reverse transcription PCR and western blot assays were used to determine the mRNA and protein expression, respectively. Furthermore, immunofluorescence and fluorescence in situ hybridization assays were used to determine the protein and RNA localization, respectively. Glucose and lactate measurement assays, Seahorse XF glycolysis stress assays and cell viability assays were conducted to investigate the effects on glycolysis and proliferation in GBM cells.

Results

We selected zinc finger CCHC-type and RNA-binding motif 1 (ZCRB1) and circRNA HEAT repeat containing 5B (circHEATR5B) as candidates for this study. These genes were expressed at low levels in GBM tissues and cells. Both ZCRB1 and circHEATR5B overexpression suppressed aerobic glycolysis and proliferation in GBM cells. ZCRB1 overexpression promoted the Alu element-mediated formation of circHEATR5B. In addition, circHEATR5B encoded a novel protein HEATR5B-881aa which interacted directly with Jumonji C-domain-containing 5 (JMJD5) and reduced its stability by phosphorylating S361. JMJD5 knockdown increased pyruvate kinase M2 (PKM2) enzymatic activity and suppressed glycolysis and proliferation in GBM cells. Finally, ZCRB1, circHEATR5B and HEATR5B-881aa overexpression inhibited GBM xenograft growth and prolonged the survival time of nude mice.

Conclusions

This study reveals a novel mechanism of regulating aerobic glycolysis and proliferation in GBM cells through the ZCRB1/circHEATR5B/HEATR5B-881aa/JMJD5/PKM2 pathway, which can provide novel strategies and potential targets for GBM therapy.

Cotton leaf curl Multan virus differentially regulates innate antiviral immunity of whitefly (Bemisia tabaci) vector to promote cryptic species-dependent virus acquisition

Begomoviruses represent the largest group of economically important, highly pathogenic, DNA plant viruses that contribute a substantial amount of global crop disease burden. The exclusive transmission of begomoviruses by whiteflies (Bemisia tabaci) requires them to interact and efficiently manipulate host responses at physiological, biological and molecular scales. However, the molecular mechanisms underlying complex begomovirus-whitefly interactions that consequently substantiate efficient virus transmission largely remain unknown. Previously, we found that whitefly Asia II 7 cryptic species can efficiently transmit cotton leaf curl Multan virus (CLCuMuV) while MEAM1 cryptic species is a poor carrier and incompetent vector of CLCuMuV. To investigate the potential mechanism/s that facilitate the higher acquisition of CLCuMuV by its whitefly vector (Asia II 7) and to identify novel whitefly proteins that putatively interact with CLCuMuV-AV1 (coat protein), we employed yeast two-hybrid system, bioinformatics, bimolecular fluorescence complementation, RNA interference, RT-qPCR and bioassays. We identified a total of 21 Asia II 7 proteins putatively interacting with CLCuMuV-AV1. Further analyses by molecular docking, Y2H and BiFC experiments validated the interaction between a whitefly innate immunity-related protein (BTB/POZ) and viral AV1 (coat protein). Gene transcription analysis showed that the viral infection significantly suppressed the transcription of BTB/POZ and enhanced the accumulation of CLCuMuV in Asia II 7, but not in MEAM1 cryptic species. In contrast to MEAM1, the targeted knock-down of BTB/POZ substantially reduced the ability of Asia II 7 to acquire and accumulate CLCuMuV. Additionally, antiviral immune signaling pathways (Toll, Imd, Jnk and Jak/STAT) were significantly suppressed following viral infection of Asia II 7 whiteflies. Taken together, the begomovirus CLCuMuV potentiates efficient virus accumulation in its vector B. tabaci Asia II 7 by targeting and suppressing the transcription of an innate immunity-related BTB/POZ gene and other antiviral immune responses in a cryptic species-specific manner.

Cold spots are universal in protein-protein interactions

Proteins interact with each other through binding interfaces that differ greatly in size and physico-chemical properties. Within the binding interface, a few residues called hot spots contribute the majority of the binding free energy and are hence irreplaceable. In contrast, cold spots are occupied by suboptimal amino acids, providing possibility for affinity enhancement through mutations. In this study, we identify cold spots due to cavities and unfavorable charge interactions in multiple protein-protein interactions (PPIs). For our cold spot analysis, we first use a small affinity database of PPIs with known structures and affinities and then expand our search to nearly 4000 homo- and heterodimers in the Protein Data Bank (PDB). We observe that cold spots due to cavities are present in nearly all PPIs unrelated to their binding affinity, while unfavorable charge interactions are relatively rare. We also find that most cold spots are located in the periphery of the binding interface, with high-affinity complexes showing fewer centrally located colds spots than low-affinity complexes. A larger number of cold spots is also found in non-cognate interactions compared to their cognate counterparts. Furthermore, our analysis reveals that cold spots are more frequent in homo-dimeric complexes compared to hetero-complexes, likely due to symmetry constraints imposed on sequences of homodimers. Finally, we find that glycines, glutamates, and arginines are the most frequent amino acids appearing at cold spot positions. Our analysis emphasizes the importance of cold spot positions to protein evolution and facilitates protein engineering studies directed at enhancing binding affinity and specificity in a wide range of applications.

Comparison of hydrophobicity scales for predicting biophysical properties of antibodies

While antibody-based therapeutics have grown to be one of the major classes of novel medicines, some antibody development candidates face significant challenges regarding expression levels, solubility, as well as stability and aggregation, under physiological and storage conditions. A major determinant of those properties is surface hydrophobicity, which promotes unspecific interactions and has repeatedly proven problematic in the development of novel antibody-based drugs. Multiple computational methods have been devised for in-silico prediction of antibody hydrophobicity, often using hydrophobicity scales to assign values to each amino acid. Those approaches are usually validated by their ability to rank potential therapeutic antibodies in terms of their experimental hydrophobicity. However, there is significant diversity both in the hydrophobicity scales and in the experimental methods, and consequently in the performance of in-silico methods to predict experimental results. In this work, we investigate hydrophobicity of monoclonal antibodies using hydrophobicity scales. We implement several scoring schemes based on the solvent-accessibility and the assigned hydrophobicity values, and compare the different scores and scales based on their ability to predict retention times from hydrophobic interaction chromatography. We provide an overview of the strengths and weaknesses of several commonly employed hydrophobicity scales, thereby improving the understanding of hydrophobicity in antibody development. Furthermore, we test several datasets, both publicly available and proprietary, and find that the diversity of the dataset affects the performance of hydrophobicity scores. We expect that this work will provide valuable guidelines for the optimization of biophysical properties in future drug discovery campaigns.

PPI-Affinity: A Web Tool for the Prediction and Optimization of Protein-Peptide and Protein-Protein Binding Affinity

Virtual screening of protein–protein and protein–peptide interactions is a challenging task that directly impacts the processes of hit identification and hit-to-lead optimization in drug design projects involving peptide-based pharmaceuticals. Although several screening tools designed to predict the binding affinity of protein–protein complexes have been proposed, methods specifically developed to predict protein–peptide binding affinity are comparatively scarce. Frequently, predictors trained to score the affinity of small molecules are used for peptides indistinctively, despite the larger complexity and heterogeneity of interactions rendered by peptide binders. To address this issue, we introduce PPI-Affinity, a tool that leverages support vector machine (SVM) predictors of binding affinity to screen datasets of protein–protein and protein–peptide complexes, as well as to generate and rank mutants of a given structure. The performance of the SVM models was assessed on four benchmark datasets, which include protein–protein and protein–peptide binding affinity data. In addition, we evaluated our model on a set of mutants of EPI-X4, an endogenous peptide inhibitor of the chemokine receptor CXCR4, and on complexes of the serine proteases HTRA1 and HTRA3 with peptides. PPI-Affinity is freely accessible at https://protdcal.zmb.uni-due.de/PPIAffinity.

CRISPR/Cas9-mediated P-CR domain-specific engineering of CESA4 heterodimerization capacity alters cell wall architecture and improves saccharification efficiency in poplar

Cellulose is the most abundant unique biopolymer in nature with widespread applications in bioenergy and high-value bioproducts. The large transmembrane-localized cellulose synthase (CESA) complexes (CSCs) play a pivotal role in the biosynthesis and orientation of the para-crystalline cellulose microfibrils during secondary cell wall (SCW) deposition. However, the hub CESA subunit with high potential homo/heterodimerization capacity and its functional effects on cell wall architecture, cellulose crystallinity, and saccharification efficiency remains unclear. Here, we reported the highly potent binding site containing four residues of Pro435, Trp436, Pro437, and Gly438 in the plant-conserved region (P-CR) of PalCESA4 subunit, which are involved in the CESA4-CESA8 heterodimerization. The CRISPR/Cas9-knockout mutagenesis in the predicted binding site results in physiological abnormalities, stunt growth, and deficient roots. The homozygous double substitution of W436Q and P437S and heterozygous double deletions of W436 and P437 residues potentially reduced CESA4-binding affinity resulting in normal roots, 1.5-2-fold higher plant growth and cell wall regeneration rates, 1.7-fold thinner cell wall, high hemicellulose content, 37%-67% decrease in cellulose content, high cellulose DP, 25%-37% decrease in cellulose crystallinity, and 50% increase in saccharification efficiency. The heterozygous deletion of W436 increases about 2-fold CESA4 homo/heterodimerization capacity led to the 50% decrease in plant growth and increase in cell walls thickness, cellulose content (33%), cellulose DP (20%), and CrI (8%). Our findings provide a strategy for introducing commercial CRISPR/Cas9-mediated bioengineered poplars with promising cellulose applications. We anticipate our results could create an engineering revolution in bioenergy and cellulose-based nanomaterial technologies.

Protein-Protein Binding Free Energy Predictions with the MM/PBSA Approach Complemented with the Gaussian-Based Method for Entropy Estimation

Here, we present a Gaussian-based method for estimation of protein-protein binding entropy to augment the molecular mechanics Poisson-Boltzmann surface area (MM/PBSA) method for computational prediction of binding free energy (ΔG). The method is termed f5-MM/PBSA/E, where "E" stands for entropy and f5 for five adjustable parameters. The enthalpy components of ΔG (molecular mechanics, polar and non-polar solvation energies) are computed from a single implicit solvent generalized Born (GB) energy minimized structure of a protein-protein complex, while the binding entropy is computed using independently GB energy minimized unbound and bound structures. It should be emphasized that the f5-MM/PBSA/E method does not use snapshots, just energy minimized structures, and is thus very fast and computationally efficient. The method is trained and benchmarked in 5-fold validation test over a data set consisting of 46 protein-protein binding cases with experimentally determined dissociation constant K _d values. This data set has been used for benchmarking in recently published protein-protein binding studies that apply conventional MM/PBSA and MM/PBSA with an enhanced sampling method. The f5-MM/PBSA/E tested on the same data set achieves similar or better performance than these computationally demanding approaches, making it an excellent choice for high throughput protein-protein binding affinity prediction studies.

In silico design of ACE2 mutants for competitive binding of SARS-CoV-2 receptor binding domain with hACE2

The receptor binding motif (RBM) within the S-protein of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has been touted as one of the main targets for vaccine/therapeutic development due to its interaction with the human angiotensin II converting enzyme 2 (hACE2) to facilitate virus entry into the host cell. The mechanism of action is based on the disruption of binding between the RBM and the hACE2 to prevent virus uptake for replication. In this work, we applied in silico approaches to design specific competitive binders for SARS-CoV-2 S-protein receptor binding motif (RBM) by using hACE2 peptidase domain (PD) mutants. Online single point mutation servers were utilised to estimate the effect of PD mutation on the binding affinity with RBM. The PD mutants were then modelled and the binding free energy was calculated. Three PD variants were designed with an increased affinity and interaction with SARS-CoV-2-RBM. It is hope that these designs could serve as the initial work for vaccine/drug development and could eventually interfere the preliminary recognition between SARS-CoV-2 and the host cell.

SARS-CoV-2 Proteins Interact with Alpha Synuclein and Induce Lewy Body-like Pathology In Vitro

Growing cases of patients reported have shown a potential relationship between (severe acute respiratory syndrome coronavirus 2) SARS-CoV-2 infection and Parkinson's disease (PD). However, it is unclear whether there is a molecular link between these two diseases. Alpha-synuclein (α-Syn), an aggregation-prone protein, is considered a crucial factor in PD pathology. In this study, bioinformatics analysis confirmed favorable binding affinity between α-Syn and SARS-CoV-2 spike (S) protein and nucleocapsid (N) protein, and direct interactions were further verified in HEK293 cells. The expression of α-Syn was upregulated and its aggregation was accelerated by S protein and N protein. It was noticed that SARS-CoV-2 proteins caused Lewy-like pathology in the presence of α-Syn overexpression. By confirming that SARS-CoV-2 proteins directly interact with α-Syn, our study offered new insights into the mechanism underlying the development of PD on the background of COVID-19.

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.

Bioinformatic analysis and antiviral effect of Periplaneta americana defensins

Due to the lack of an adaptive immune system, insects rely on innate immune mechanisms to fight against pathogenic infections. Two major innate immune pathways, Toll and IMD, orchestrate anti-pathogen responses by regulating the expression of antimicrobial peptide (AMP) genes. Although the antifungal or antibacterial function of AMPs has been well characterized, the antiviral role of AMPs in insects remains largely unclear. Periplaneta americana (P. americana), or the American cockroach, is used in traditional Chinese medicine as an antiviral agent; however, the underlying mechanism of action of P. americana extracts is unclear. Our previous study showed that the P. americana genome encodes multiple antimicrobial peptide genes. Based on these data, we predicted five novel P. americana defensins (PaDefensins) and analyzed their primary structure, secondary structure, and physicochemical properties. The putative antiviral, antifungal, antibacterial, and anticancer activities suggested that PaDefensin5 is a desirable therapeutic candidate against viral diseases. As the first experimental evidence of the antiviral effects of insect defensins, we also showed the antiviral effect of PaDefensin5 in Drosophila Kc cells and Drosophila embryos in vivo . In conclusion, results of both in silico predictions and subsequent antiviral experiments suggested PaDefensin5 a promising antiviral drug.

The Potential Role of Human NME1 in Neuronal Differentiation of Porcine Mesenchymal Stem Cells: Application of NB-hNME1 as a Human NME1 Suppressor

This study aimed to investigate the effects of the human macrophage (MP) secretome in cellular xenograft rejection. The role of human nucleoside diphosphate kinase A (hNME1), from the secretome of MPs involved in the neuronal differentiation of miniature pig adipose tissue-derived mesenchymal stem cells (mp AD-MSCs), was evaluated by proteomic analysis. Herein, we first demonstrate that hNME1 strongly binds to porcine ST8 alpha-N-acetyl-neuraminide alpha-2,8-sialyltransferase 1 (pST8SIA1), which is a ganglioside GD3 synthase. When hNME1 binds with pST8SIA1, it induces degradation of pST8SIA1 in mp AD-MSCs, thereby inhibiting the expression of ganglioside GD3 followed by decreased neuronal differentiation of mp AD-MSCs. Therefore, we produced nanobodies (NBs) named NB-hNME1 that bind to hNME1 specifically, and the inhibitory effect of NB-hNME1 was evaluated for blocking the binding between hNME1 and pST8SIA1. Consequently, NB-hNME1 effectively blocked the binding of hNME1 to pST8SIA1, thereby recovering the expression of ganglioside GD3 and neuronal differentiation of mp AD-MSCs. Our findings suggest that mp AD-MSCs could be a potential candidate for use as an additive, such as an immunosuppressant, in stem cell transplantation.

A comprehensive review of the analysis and integration of omics data for SARS-CoV-2 and COVID-19

Since the first report of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) in December 2019, over 100 million people have been infected by COVID-19, millions of whom have died. In the latest year, a large number of omics data have sprung up and helped researchers broadly study the sequence, chemical structure and function of SARS-CoV-2, as well as molecular abnormal mechanisms of COVID-19 patients. Though some successes have been achieved in these areas, it is necessary to analyze and mine omics data for comprehensively understanding SARS-CoV-2 and COVID-19. Hence, we reviewed the current advantages and limitations of the integration of omics data herein. Firstly, we sorted out the sequence resources and database resources of SARS-CoV-2, including protein chemical structure, potential drug information and research literature resources. Next, we collected omics data of the COVID-19 hosts, including genomics, transcriptomics, microbiology and potential drug information data. And subsequently, based on the integration of omics data, we summarized the existing data analysis methods and the related research results of COVID-19 multi-omics data in recent years. Finally, we put forward SARS-CoV-2 (COVID-19) multi-omics data integration research direction and gave a case study to mine deeper for the disease mechanisms of COVID-19.

High-Mass Matrix-Assisted Laser Desorption/Ionization Mass Spectrometry for Absolute Quantitation of Noncovalent Protein-Protein Binding Interactions

Matrix-assisted laser desorption/ionization mass spectrometry (MALDI-MS) is a robust and powerful tool for studying biomacromolecules and their interactions. However, quantitative detection of high-mass analytes (kDa to MDa range) remains challenging for MALDI-MS. Herein, we successfully used commercially available purified proteins (β-galactosidase and BSA) as internal standards for high-mass MALDI-MS analysis and achieved absolute quantification of several high-mass analytes. We systematically evaluated four sample deposition methods, and using the sandwich deposition method with saturated sinapinic acid as the top layer, we performed a robust quantitative analysis by high-mass MALDI-MS. Combined with chemical cross-linking, this quantitative strategy was further used to evaluate the affinity of protein-protein interactions (PPIs), specifically of two soluble protein receptors (interleukin 1 receptor and interleukin 2 receptor) and two membrane protein receptors (rhodopsin and angiotensin 2 receptor 1) with their interaction partners. The measured dissociation constants of the protein complexes formed were between 10 nM and 5 μM. We expect this high-throughput, rapid method, which does not require labeling or immobilization of any of the interaction partners, to become a viable alternative to traditional biophysical methods for studying PPIs.

PANDA: Predicting the change in proteins binding affinity upon mutations by finding a signal in primary structures

Accurately determining a change in protein binding affinity upon mutations is important to find novel therapeutics and to assist mutagenesis studies. Determination of change in binding affinity upon mutations requires sophisticated, expensive, and time-consuming wet-lab experiments that can be supported with computational methods. Most of the available computational prediction techniques depend upon protein structures that bound their applicability to only protein complexes with recognized 3D structures. In this work, we explore the sequence-based prediction of change in protein binding affinity upon mutation and question the effectiveness of [Formula: see text]-fold cross-validation (CV) across mutations adopted in previous studies to assess the generalization ability of such predictors with no known mutation during training. We have used protein sequence information instead of protein structures along with machine learning techniques to accurately predict the change in protein binding affinity upon mutation. Our proposed sequence-based novel change in protein binding affinity predictor called PANDA performs comparably to the existing methods gauged through an appropriate CV scheme and an external independent test dataset. On an external test dataset, our proposed method gives a maximum Pearson correlation coefficient of 0.52 in comparison to the state-of-the-art existing protein structure-based method called MutaBind which gives a maximum Pearson correlation coefficient of 0.59. Our proposed protein sequence-based method, to predict a change in binding affinity upon mutations, has wide applicability and comparable performance in comparison to existing protein structure-based methods. We made PANDA easily accessible through a cloud-based webserver and python code available at https://sites.google.com/view/wajidarshad/software and https://github.com/wajidarshad/panda, respectively.

Functional alterations caused by mutations reflect evolutionary trends of SARS-CoV-2

Since the first report of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) in December 2019, the COVID-19 pandemic has spread rapidly worldwide. Due to the limited virus strains, few key mutations that would be very important with the evolutionary trends of virus genome were observed in early studies. Here, we downloaded 1809 sequence data of SARS-CoV-2 strains from GISAID before April 2020 to identify mutations and functional alterations caused by these mutations. Totally, we identified 1017 nonsynonymous and 512 synonymous mutations with alignment to reference genome NC_045512, none of which were observed in the receptor-binding domain (RBD) of the spike protein. On average, each of the strains could have about 1.75 new mutations each month. The current mutations may have few impacts on antibodies. Although it shows the purifying selection in whole-genome, ORF3a, ORF8 and ORF10 were under positive selection. Only 36 mutations occurred in 1% and more virus strains were further analyzed to reveal linkage disequilibrium (LD) variants and dominant mutations. As a result, we observed five dominant mutations involving three nonsynonymous mutations C28144T, C14408T and A23403G and two synonymous mutations T8782C, and C3037T. These five mutations occurred in almost all strains in April 2020. Besides, we also observed two potential dominant nonsynonymous mutations C1059T and G25563T, which occurred in most of the strains in April 2020. Further functional analysis shows that these mutations decreased protein stability largely, which could lead to a significant reduction of virus virulence. In addition, the A23403G mutation increases the spike-ACE2 interaction and finally leads to the enhancement of its infectivity. All of these proved that the evolution of SARS-CoV-2 is toward the enhancement of infectivity and reduction of virulence.

Structure Prediction and Expression of Modified rCTLA4-Ig as a Blocker for B7 Molecules

CTLA4-Ig (Abatacept) has been produced to suppress immune response by inhibition of T cells functions in autoimmune disease. A new drug, which is called belatacept, has recently been recently developed that is more efficient. The development has been occurred by two substitutions (A29Y, L104E) in the extracellular domain of CTLA4. In the present study, the bioinformatics analysis was used in order to make a new structure that has a better function in comparison with belatacept. Firstly, eight different structures were designed. Thereafter, the secondary and 3D structures, mRNA structure, docking of chimeric proteins with CD80/CD86, antigenicity and affinity of designed chimeric molecules were predicted. Based on the criteria, a new candidate molecule was selected and its gene synthesized. The gene was cloned and expressed in E. coli BL21 (DE3) successfully. The purified rCTLA4-Ig was analyzed by SDS-PAGE, western blotting, and ELISA. Circular dichroism analysis (CD analysis) was used for characterization of the rCTLA4-Ig. Affinity of rCTLA4-Ig was also evaluated by the flow cytometry method. Finally, its biological activity was determined by T cell inhibition test. The results showed rCTLA4-Ig and the belatacept protein have some similarities in structure and function. In addition, rCTLA4-Ig was able to bind CD80/CD86 and inhibit T cell function. Although flow cytomery results showed that the standard protein (CTLA4-Ig), represented better affinity than rCTLA4-Ig, the recombinant protein was able to inhibit T cell proliferation as well as CTLA4-Ig.

Therapeutic peptides for the treatment of cystic fibrosis: Challenges and perspectives

Cystic fibrosis (CF) is the most common amongst rare genetic diseases, affecting more than 70.000 people worldwide. CF is characterized by a dysfunctional chloride channel, termed cystic fibrosis conductance regulator (CFTR), which leads to the production of a thick and viscous mucus layer that clogs the lungs of CF patients and traps pathogens, leading to chronic infections and inflammation and, ultimately, lung damage. In recent years, the use of peptides for the treatment of respiratory diseases, including CF, has gained growing interest. Therapeutic peptides for CF include antimicrobial peptides, inhibitors of proteases, and modulators of ion channels, among others. Peptides display unique features that make them appealing candidates for clinical translation, like specificity of action, high efficacy, and low toxicity. Nevertheless, the intrinsic properties of peptides, together with the need of delivering these compounds locally, e.g. by inhalation, raise a number of concerns in the development of peptide therapeutics for CF lung disease. In this review, we discuss the challenges related to the use of peptides for the treatment of CF lung disease through inhalation, which include retention within mucus, proteolysis, immunogenicity and aggregation. Strategies for overcoming major shortcomings of peptide therapeutics will be presented, together with recent developments in peptide design and optimization, including computational analysis and high-throughput screening.

A conserved arginine residue in a staphylococcal anti-sigma factor is required to preserve its kinase activity, structure, and stability

RsbW, σ^B, and RsbV, encoded by Staphylococcus aureus and related bacteria, act as an anti-sigma factor, an sigma factor, and an anti-anti-sigma factor, respectively. The interaction between RsbW and σ^B blocks the transcription initiation activity of the latter protein. RsbW also functions as a serine kinase and phosphorylates RsbV in the presence of ATP. Our modeling study indicates that the RsbW-RsbV complex is stabilized by twenty-four intermolecular non-covalent bonds. Of the bond-forming RsbW residues, Arg 23, and Glu 49 are conserved residues. To understand the roles of Arg 23 in RsbW, rRsbW[R23A], a recombinant S. aureus RsbW (rRsbW) harboring Arg to Ala change at position 23, was investigated using various probes. The results reveal that rRsbW[R23A], like rRsbW, exists as the dimers in the aqueous solution. However, rRsbW[R23A], unlike rRsbW, neither interacted with a chimeric RsbV (rRsbV) nor formed the phosphorylated rRsbV in the presence of ATP. Furthermore, the tertiary structure and hydrophobic surface area of rRsbW[R23A] matched little with those of rRsbW. Conversely, both rRsbW[R23A] and rRsbW showed interaction with a recombinant σ^B (rσ^B). rRsbW and rRsbW[R23A] were also unfolded via the formation of at least one intermediate in the presence of urea. However, the thermodynamic stability of rRsbW significantly differed from that of rRsbW[R23A]. Our molecular dynamics (MD) simulation study also reveals the substantial change of structure, dimension, and stability of RsbW due to the above mutation. The ways side chain of critical Arg 23 contributes to maintaining the tertiary structure, and stability of RsbW was elaborately discussed.Communicated by Ramaswamy H. Sarma.

APEX2S: A two‐layer machine learning model for discovery of host‐pathogen protein‐protein interactions on cloud‐based multiomics data

Presented by the avalanche of biological interactions data, computational biology is now facing greater challenges on big data analysis and solicits more studies to mine and integrate cloud‐based multiomics data, especially when the data are related to infectious diseases. Meanwhile, machine learning techniques have recently succeeded in different computational biology tasks. In this article, we have calibrated the focus for host‐pathogen protein‐protein interactions study, aiming to apply the machine learning techniques for learning the interactions data and making predictions. A comprehensive and practical workflow to harness different cloud‐based multiomics data is discussed. In particular, a novel two‐layer machine learning model, namely APEX2S, is proposed for discovery of the protein‐protein interactions data. The results show that our model can better learn and predict from the accumulated host‐pathogen protein‐protein interactions.

ISLAND: in-silico proteins binding affinity prediction using sequence information

BACKGROUND

Determining binding affinity in protein-protein interactions is important in the discovery and design of novel therapeutics and mutagenesis studies. Determination of binding affinity of proteins in the formation of protein complexes requires sophisticated, expensive and time-consuming experimentation which can be replaced with computational methods. Most computational prediction techniques require protein structures that limit their applicability to protein complexes with known structures. In this work, we explore sequence-based protein binding affinity prediction using machine learning.

METHOD

We have used protein sequence information instead of protein structures along with machine learning techniques to accurately predict the protein binding affinity.

RESULTS

We present our findings that the true generalization performance of even the state-of-the-art sequence-only predictor is far from satisfactory and that the development of machine learning methods for binding affinity prediction with improved generalization performance is still an open problem. We have also proposed a sequence-based novel protein binding affinity predictor called ISLAND which gives better accuracy than existing methods over the same validation set as well as on external independent test dataset. A cloud-based webserver implementation of ISLAND and its python code are available at https://sites.google.com/view/wajidarshad/software .

CONCLUSION

This paper highlights the fact that the true generalization performance of even the state-of-the-art sequence-only predictor of binding affinity is far from satisfactory and that the development of effective and practical methods in this domain is still an open problem.

ProAffiMuSeq: sequence-based method to predict the binding free energy change of protein-protein complexes upon mutation using functional classification

MOTIVATION

Protein-protein interactions are essential for the cell and mediate various functions. However, mutations can disrupt these interactions and may cause diseases. Currently available computational methods require a complex structure as input for predicting the change in binding affinity. Further, they have not included the functional class information for the protein-protein complex. To address this, we have developed a method, ProAffiMuSeq, which predicts the change in binding free energy using sequence-based features and functional class.

RESULTS

Our method shows an average correlation between predicted and experimentally determined ΔΔG of 0.73 and mean absolute error (MAE) of 0.86 kcal/mol in 10-fold cross-validation and correlation of 0.75 with MAE of 0.94 kcal/mol in the test dataset. ProAffiMuSeq was also tested on an external validation set and showed results comparable to structure-based methods. Our method can be used for large-scale analysis of disease-causing mutations in protein-protein complexes without structural information.

AVAILABILITY AND IMPLEMENTATION

Users can access the method at https://web.iitm.ac.in/bioinfo2/proaffimuseq/.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Bayesian Active Learning for Optimization and Uncertainty Quantification in Protein Docking

Ab initio protein docking represents a major challenge for optimizing a noisy and costly "black box"-like function in a high-dimensional space. Despite progress in this field, there is a lack of rigorous uncertainty quantification (UQ). To fill the gap, we introduce a novel algorithm, Bayesian active learning (BAL), for optimization and UQ of such black-box functions with applications to flexible protein docking. BAL directly models the posterior distribution of the global optimum (i.e., native structures) with active sampling and posterior estimation iteratively feeding each other. Furthermore, it uses complex normal modes to span a homogeneous, Euclidean conformation space suitable for high-dimensional optimization and constructs funnel-like energy models for quality estimation of encounter complexes. Over a protein-docking benchmark set and a CAPRI set including homology docking, we establish that BAL significantly improves against starting points from rigid docking and refinements by particle swarm optimization, providing a top-3 near-native prediction for one third targets. Quality assessment empowered with UQ leads to tight quality intervals with half range around 25% of the actual interface root-mean-square deviation and confidence level at 85%. BAL's estimated probability of a prediction being near-native achieves binary classification AUROC at 0.93 and area under the precision recall curve over 0.60 (compared to 0.50 and 0.14, respectively, by chance), which also improves ranking predictions. This study represents the first UQ solution for protein docking, with rigorous theoretical frameworks and comprehensive empirical assessments.

Energy-based graph convolutional networks for scoring protein docking models

Structural information about protein-protein interactions, often missing at the interactome scale, is important for mechanistic understanding of cells and rational discovery of therapeutics. Protein docking provides a computational alternative for such information. However, ranking near-native docked models high among a large number of candidates, often known as the scoring problem, remains a critical challenge. Moreover, estimating model quality, also known as the quality assessment problem, is rarely addressed in protein docking. In this study, the two challenging problems in protein docking are regarded as relative and absolute scoring, respectively, and addressed in one physics-inspired deep learning framework. We represent protein and complex structures as intra- and inter-molecular residue contact graphs with atom-resolution node and edge features. And we propose a novel graph convolutional kernel that aggregates interacting nodes' features through edges so that generalized interaction energies can be learned directly from 3D data. The resulting energy-based graph convolutional networks (EGCN) with multihead attention are trained to predict intra- and inter-molecular energies, binding affinities, and quality measures (interface RMSD) for encounter complexes. Compared to a state-of-the-art scoring function for model ranking, EGCN significantly improves ranking for a critical assessment of predicted interactions (CAPRI) test set involving homology docking; and is comparable or slightly better for Score_set, a CAPRI benchmark set generated by diverse community-wide docking protocols not known to training data. For Score_set quality assessment, EGCN shows about 27% improvement to our previous efforts. Directly learning from 3D structure data in graph representation, EGCN represents the first successful development of graph convolutional networks for protein docking.