CaMELS:In silicoprediction of calmodulin binding proteins and their binding sites

Genome-wide analysis of calmodulin binding Protein60 candidates in the important crop plants

BACKGROUND

Efficient management of environmental stresses is essential for sustainable crop production. Calcium (Ca²⁺) signaling plays a crucial role in regulating responses to both biotic and abiotic stresses, particularly during host-pathogen interactions. In Arabidopsis thaliana, calmodulin-binding protein 60 (CBP60) family members, such as AtCBP60g, AtCBP60a, and AtSARD1, have been well characterized for their involvement in immune regulation. However, a comprehensive understanding of CBP60 genes in major crops remains limited.

METHODS

In this study, we utilized the Phytozome v12.1 database to identify and analyze CBP60 genes in agriculturally important crops. Expression patterns of a Oryza sativa (rice) CBP60 gene, OsCBP60bcd-1, were assessed in resistant and susceptible rice genotypes in response to infection by the bacterial pathogen Xanthomonas oryzae. Localization of CBP60 proteins was analyzed to predict their functional roles, and computational promoter analysis was performed to identify stress-responsive cis-regulatory elements.

RESULTS

Phylogenetic analysis revealed that most CBP60 genes in crops belong to the immune-related clade. Expression analysis showed that OsCBP60bcd-1 was significantly upregulated in the resistant rice genotype upon pathogen infection. Subcellular localization studies suggested that the majority of CBP60 proteins are nuclear-localized, indicating a potential role as transcription factors. Promoter analysis identified diverse stress-responsive cis-regulatory elements in the promoters of CBP60 genes, highlighting their regulatory potential under stress conditions.

CONCLUSION

The upregulation of OsCBP60bcd-1 in response to Xanthomonas oryzae and the presence of stress-responsive elements in its promoter underscore the importance of CBP60 genes in pathogen defense. These findings provide a basis for further investigation into the functional roles of CBP60 genes in crop disease resistance, with implications for enhancing stress resilience in agricultural species.

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.

Chemical complexity challenge: Is multi‐instance machine learning a solution?

Molecules are complex dynamic objects that can exist in different molecular forms (conformations, tautomers, stereoisomers, protonation states, etc.) and often it is not known which molecular form is responsible for observed physicochemical and biological properties of a given molecule. This raises the problem of the selection of the correct molecular form for machine learning modeling of target properties. The same problem is common to biological molecules (RNA, DNA, proteins)—long sequences where only key segments, which often cannot be located precisely, are involved in biological functions. Multi‐instance machine learning (MIL) is an efficient approach for solving problems where objects under study cannot be uniquely represented by a single instance, but rather by a set of multiple alternative instances. Multi‐instance learning was formalized in 1997 and motivated by the problem of conformation selection in drug activity prediction tasks. Since then MIL has found a lot of applications in various domains, such as information retrieval, computer vision, signal processing, bankruptcy prediction, and so on. In the given review we describe the MIL framework and its applications to the tasks associated with ambiguity in the representation of small and biological molecules in chemoinformatics and bioinformatics. We have collected examples that demonstrate the advantages of MIL over the traditional single‐instance learning (SIL) approach. Special attention was paid to the ability of MIL models to identify key instances responsible for a modeling property.

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CBDT-Oglyc: Prediction of O-glycosylation sites using ChiMIC-based balanced decision table and feature selection

O-glycosylation (Oglyc) plays an important role in various biological processes. The key to understanding the mechanisms of Oglyc is identifying the corresponding glycosylation sites. Two critical steps, feature selection and classifier design, greatly affect the accuracy of computational methods for predicting Oglyc sites. Based on an efficient feature selection algorithm and a classifier capable of handling imbalanced datasets, a new computational method, ChiMIC-based balanced decision table O-glycosylation (CBDT-Oglyc), is proposed. ChiMIC-based balanced decision table for O-glycosylation (CBDT-Oglyc), is proposed to predict Oglyc sites in proteins. Sequence characterization is performed by combining amino acid composition (AAC), undirected composition of [Formula: see text]-spaced amino acid pairs (undirected-CKSAAP) and pseudo-position-specific scoring matrix (PsePSSM). Chi-MIC-share algorithm is used for feature selection, which simplifies the model and improves predictive accuracy. For imbalanced classification, a backtracking method based on local chi-square test is designed, and then cost-sensitive learning is incorporated to construct a novel classifier named ChiMIC-based balanced decision table (CBDT). Based on a 1:49 (positives:negatives) training set, the CBDT classifier achieves significantly better prediction performance than traditional classifiers. Moreover, the independent test results on separate human and mouse glycoproteins show that CBDT-Oglyc outperforms previous methods in global accuracy. CBDT-Oglyc shows great promise in predicting Oglyc sites and is expected to facilitate further experimental studies on protein glycosylation.

PreTP-Stack: Prediction of Therapeutic Peptides Based on the Stacked Ensemble Learing

Therapeutic peptide prediction is critical for drug development and therapeutic therapy. Researchers have developed several computational methods to identify different therapeutic peptide types. However, most computational methods focus on identifying the specific type of therapeutic peptides and fail to accurately predict all types of therapeutic peptides. Moreover, it is still challenging to utilize different properties features to predict the therapeutic peptides. In this study, a novel stacking framework PreTP-Stack is proposed for predicting different types of therapeutic peptide. PreTP-Stack is constructed based on ten different features and four predictors (Random Forest, Linear Discriminant Analysis, XGBoost and Support Vector Machine). Then the proposed method constructs an auto-weighted multi-view learning model as a final meta-classifier to enhance the performance of the basic models. Experimental results showed that the proposed method achieved better or highly comparable performance with the state-of-the-art methods for predicting eight types of therapeutic peptides A user-friendly web-server predictor is available at http://bliulab.net/PreTP-Stack.

ctP2ISP: Protein-Protein Interaction Sites Prediction Using Convolution and Transformer With Data Augmentation

Protein-protein interactions are the basis of many cellular biological processes, such as cellular organization, signal transduction, and immune response. Identifying protein-protein interaction sites is essential for understanding the mechanisms of various biological processes, disease development, and drug design. However, it remains a challenging task to make accurate predictions, as the small amount of training data and severe imbalanced classification reduce the performance of computational methods. We design a deep learning method named ctP²ISP to improve the prediction of protein-protein interaction sites. ctP²ISP employs Convolution and Transformer to extract information and enhance information perception so that semantic features can be mined to identify protein-protein interaction sites. A weighting loss function with different sample weights is designed to suppress the preference of the model toward multi-category prediction. To efficiently reuse the information in the training set, a preprocessing of data augmentation with an improved sample-oriented sampling strategy is applied. The trained ctP²ISP was evaluated against current state-of-the-art methods on six public datasets. The results show that ctP²ISP outperforms all other competing methods on the balance metrics: F1, MCC, and AUPRC. In particular, our prediction on open tests related to viruses may also be consistent with biological insights. The source code and data can be obtained from https://github.com/lennylv/ctP2ISP.

Machine Learning for Pharmacogenomics and Personalized Medicine: A Ranking Model for Drug Sensitivity Prediction

It is infeasible to test many different chemotherapy drugs on actual patients in large clinical trials, which motivates computational methods with the ability to learn and exploit associations between drug effectiveness and patient characteristics. This work proposes a machine learning approach to infer robust predictors of drug responses from patient genomic information. Rather than predicting the exact drug response on a given cell line, we introduce an elastic-net regression methodology to compare a drug-cell line pair against an alternative pair. Using predicted pairwise comparisons we rank the effectiveness of different drugs on the same cell line. A total of 173 cell lines and 100 drug responses were used in various settings for training and testing the proposed models. By comparing our approach against twelve baseline methods, we demonstrate that it outperforms the state-of-the-art methods in the literature. In contrast to most other methods, the algorithm is able to maintain its high performance even when we use a large number of drugs and few cell lines.

Calmodulin binds the N-terminus of the functional amyloid Orb2A inhibiting fibril formation

The cytoplasmic polyadenylation element-binding protein Orb2 is a key regulator of long-term memory (LTM) in Drosophila. The N-terminus of the Orb2 isoform A is required for LTM and forms cross-β fibrils on its own. However, this N-terminus is not part of the core found in ex vivo fibrils. We previously showed that besides forming cross-β fibrils, the N-terminus of Orb2A binds anionic lipid membranes as an amphipathic helix. Here, we show that the Orb2A N-terminus can similarly interact with calcium activated calmodulin (CaM) and that this interaction prevents fibril formation. Because CaM is a known regulator of LTM, this interaction could potentially explain the regulatory role of Orb2A in LTM.

Pathogen-driven coevolution across the CBP60 plant immune regulator subfamilies confers resilience on the regulator module

Components of the plant immune signaling network need mechanisms that confer resilience against fast-evolving pathogen effectors that target them. Among eight Arabidopsis CaM-Binding Protein (CBP) 60 family members, AtCBP60g and AtSARD1 are partially functionally redundant, major positive immune regulators, and AtCBP60a is a negative immune regulator. We investigated possible resilience-conferring evolutionary mechanisms among the CBP60a, CBP60g and SARD1 immune regulatory subfamilies. Phylogenetic analysis was used to investigate the times of CBP60 subfamily neofunctionalization. Then, using the pairwise distance rank based on the newly developed analytical platform Protein Evolution Analysis in a Euclidean Space (PEAES), hypotheses of specific coevolutionary mechanisms that could confer resilience on the regulator module were tested. The immune regulator subfamilies diversified around the time of angiosperm divergence and have been evolving very quickly. We detected significant coevolutionary interactions across the immune regulator subfamilies in all of 12 diverse core eudicot species lineages tested. The coevolutionary interactions were consistent with the hypothesized coevolution mechanisms. Despite their unusually fast evolution, members across the CBP60 immune regulator subfamilies have influenced the evolution of each other long after their diversification in a way that could confer resilience on the immune regulator module against fast-evolving pathogen effectors.

Genome-Wide Characterization of the MLO Gene Family in Cannabis sativa Reveals Two Genes as Strong Candidates for Powdery Mildew Susceptibility

Cannabis sativa is increasingly being grown around the world for medicinal, industrial, and recreational purposes. As in all cultivated plants, cannabis is exposed to a wide range of pathogens, including powdery mildew (PM). This fungal disease stresses cannabis plants and reduces flower bud quality, resulting in significant economic losses for licensed producers. The Mildew Locus O (MLO) gene family encodes plant-specific proteins distributed among conserved clades, of which clades IV and V are known to be involved in susceptibility to PM in monocots and dicots, respectively. In several studies, the inactivation of those genes resulted in durable resistance to the disease. In this study, we identified and characterized the MLO gene family members in five different cannabis genomes. Fifteen Cannabis sativa MLO (CsMLO) genes were manually curated in cannabis, with numbers varying between 14, 17, 19, 18, and 18 for CBDRx, Jamaican Lion female, Jamaican Lion male, Purple Kush, and Finola, respectively (when considering paralogs and incomplete genes). Further analysis of the CsMLO genes and their deduced protein sequences revealed that many characteristics of the gene family, such as the presence of seven transmembrane domains, the MLO functional domain, and particular amino acid positions, were present and well conserved. Phylogenetic analysis of the MLO protein sequences from all five cannabis genomes and other plant species indicated seven distinct clades (I through VII), as reported in other crops. Expression analysis revealed that the CsMLOs from clade V, CsMLO1 and CsMLO4, were significantly upregulated following Golovinomyces ambrosiae infection, providing preliminary evidence that they could be involved in PM susceptibility. Finally, the examination of variation within CsMLO1 and CsMLO4 in 32 cannabis cultivars revealed several amino acid changes, which could affect their function. Altogether, cannabis MLO genes were identified and characterized, among which candidates potentially involved in PM susceptibility were noted. The results of this study will lay the foundation for further investigations, such as the functional characterization of clade V MLOs as well as the potential impact of the amino acid changes reported. Those will be useful for breeding purposes in order to develop resistant cultivars.

Far-infrared irradiation inhibits breast cancer cell proliferation independently of DNA damage through increased nuclear Ca2+/calmodulin binding modulated-activation of checkpoint kinase 2

Far-infrared (FIR) irradiation is reported to inhibit cell proliferation in various types of cancer cells; the underlying mechanism, however, remains unclear. We explored the molecular mechanisms using MDA-MB-231 human breast cancer cells. FIR irradiation significantly inhibited cell proliferation and colony formation compared to hyperthermal stimulus, with no alteration in cell viability. No increase in DNA fragmentation or phosphorylation of DNA damage kinases including ataxia-telangiectasia mutated kinase, ataxia telangiectasia and Rad3-related kinase, and DNA-dependent protein kinase indicated no DNA damage. FIR irradiation increased the phosphorylation of checkpoint kinase 2 (Chk2) at Thr68 (p-Chk2-Thr⁶⁸) but not that of checkpoint kinase 1 at Ser345. Increased nuclear p-Chk2-Thr⁶⁸ and Ca²⁺/CaM accumulations were found in FIR-irradiated cells, as observed in confocal microscopic analyses and cell fractionation assays. In silico analysis predicted that Chk2 possesses a Ca²⁺/calmodulin (CaM) binding motif ahead of its kinase domain. Indeed, Chk2 physically interacted with CaM in the presence of Ca²⁺, with their binding markedly pronounced in FIR-irradiated cells. Pre-treatment with a Ca²⁺ chelator significantly reversed FIR irradiation-increased p-Chk2-Thr⁶⁸ expression. In addition, a CaM antagonist or small interfering RNA-mediated knockdown of the CaM gene expression significantly attenuated FIR irradiation-increased p-Chk2-Thr⁶⁸ expression. Finally, pre-treatment with a potent Chk2 inhibitor significantly reversed both FIR irradiation-stimulated p-Chk2-Thr⁶⁸ expression and irradiation-repressed cell proliferation. In conclusion, our results demonstrate that FIR irradiation inhibited breast cancer cell proliferation, independently of DNA damage, by activating the Ca²⁺/CaM/Chk2 signaling pathway in the nucleus. These results demonstrate a novel Chk2 activation mechanism that functions irrespective of DNA damage.

Structural Aspects and Prediction of Calmodulin-Binding Proteins

Calmodulin (CaM) is an important intracellular protein that binds Ca²⁺ and functions as a critical second messenger involved in numerous biological activities through extensive interactions with proteins and peptides. CaM’s ability to adapt to binding targets with different structures is related to the flexible central helix separating the N- and C-terminal lobes, which allows for conformational changes between extended and collapsed forms of the protein. CaM-binding targets are most often identified using prediction algorithms that utilize sequence and structural data to predict regions of peptides and proteins that can interact with CaM. In this review, we provide an overview of different CaM-binding proteins, the motifs through which they interact with CaM, and shared properties that make them good binding partners for CaM. Additionally, we discuss the historical and current methods for predicting CaM binding, and the similarities and differences between these methods and their relative success at prediction. As new CaM-binding proteins are identified and classified, we will gain a broader understanding of the biological processes regulated through changes in Ca²⁺ concentration through interactions with CaM.

βC1, pathogenicity determinant encoded by Cotton leaf curl Multan betasatellite, interacts with calmodulin-like protein 11 (Gh-CML11) in Gossypium hirsutum

Begomoviruses interfere with host plant machinery to evade host defense mechanism by interacting with plant proteins. In the old world, this group of viruses are usually associated with betasatellite that induces severe disease symptoms by encoding a protein, βC1, which is a pathogenicity determinant. Here, we show that βC1 encoded by Cotton leaf curl Multan betasatellite (CLCuMB) requires Gossypium hirsutum calmodulin-like protein 11 (Gh-CML11) to infect cotton. First, we used the in silico approach to predict the interaction of CLCuMB-βC1 with Gh-CML11. A number of sequence- and structure-based in-silico interaction prediction techniques suggested a strong putative binding of CLCuMB-βC1 with Gh-CML11 in a Ca+2-dependent manner. In-silico interaction prediction was then confirmed by three different experimental approaches: The Gh-CML11 interaction was confirmed using CLCuMB-βC1 in a yeast two hybrid system and pull down assay. These results were further validated using bimolecular fluorescence complementation system showing the interaction in cytoplasmic veins of Nicotiana benthamiana. Bioinformatics and molecular studies suggested that CLCuMB-βC1 induces the overexpression of Gh-CML11 protein and ultimately provides calcium as a nutrient source for virus movement and transmission. This is the first comprehensive study on the interaction between CLCuMB-βC1 and Gh-CML11 proteins which provided insights into our understating of the role of βC1 in cotton leaf curl disease.

Systematic identification of recognition motifs for the hub protein LC8

LC8 is a eukaryotic hub protein that interacts with multifarious partners; analysis of more than 100 binding/nonbinding sequences led to an algorithm that predicts LC8 partners with 78% accuracy.

Hub proteins participate in cellular regulation by dynamic binding of multiple proteins within interaction networks. The hub protein LC8 reversibly interacts with more than 100 partners through a flexible pocket at its dimer interface. To explore the diversity of the LC8 partner pool, we screened for LC8 binding partners using a proteomic phage display library composed of peptides from the human proteome, which had no bias toward a known LC8 motif. Of the identified hits, we validated binding of 29 peptides using isothermal titration calorimetry. Of the 29 peptides, 19 were entirely novel, and all had the canonical TQT motif anchor. A striking observation is that numerous peptides containing the TQT anchor do not bind LC8, indicating that residues outside of the anchor facilitate LC8 interactions. Using both LC8-binding and nonbinding peptides containing the motif anchor, we developed the “LC8Pred” algorithm that identifies critical residues flanking the anchor and parses random sequences to predict LC8-binding motifs with ∼78% accuracy. Our findings significantly expand the scope of the LC8 hub interactome.

Prediction of bacterial E3 ubiquitin ligase effectors using reduced amino acid peptide fingerprinting

Background

Although pathogenic Gram-negative bacteria lack their own ubiquitination machinery, they have evolved or acquired virulence effectors that can manipulate the host ubiquitination process through structural and/or functional mimicry of host machinery. Many such effectors have been identified in a wide variety of bacterial pathogens that share little sequence similarity amongst themselves or with eukaryotic ubiquitin E3 ligases.

Methods

To allow identification of novel bacterial E3 ubiquitin ligase effectors from protein sequences we have developed a machine learning approach, the SVM-based Identification and Evaluation of Virulence Effector Ubiquitin ligases (SIEVE-Ub). We extend the string kernel approach used previously to sequence classification by introducing reduced amino acid (RED) alphabet encoding for protein sequences.

Results

We found that 14mer peptides with amino acids represented as simply either hydrophobic or hydrophilic provided the best models for discrimination of E3 ligases from other effector proteins with a receiver-operator characteristic area under the curve (AUC) of 0.90. When considering a subset of E3 ubiquitin ligase effectors that do not fall into known sequence based families we found that the AUC was 0.82, demonstrating the effectiveness of our method at identifying novel functional family members. Feature selection was used to identify a parsimonious set of 10 RED peptides that provided good discrimination, and these peptides were found to be located in functionally important regions of the proteins involved in E2 and host target protein binding. Our general approach enables construction of models based on other effector functions. We used SIEVE-Ub to predict nine potential novel E3 ligases from a large set of bacterial genomes. SIEVE-Ub is available for download at https://doi.org/10.6084/m9.figshare.7766984.v1 or https://github.com/biodataganache/SIEVE-Ub for the most current version.

Learning protein binding affinity using privileged information

BACKGROUND

Determining protein-protein interactions and their binding affinity are important in understanding cellular biological processes, discovery and design of novel therapeutics, protein engineering, and mutagenesis studies. Due to the time and effort required in wet lab experiments, computational prediction of binding affinity from sequence or structure is an important area of research. Structure-based methods, though more accurate than sequence-based techniques, are limited in their applicability due to limited availability of protein structure data.

RESULTS

In this study, we propose a novel machine learning method for predicting binding affinity that uses protein 3D structure as privileged information at training time while expecting only protein sequence information during testing. Using the method, which is based on the framework of learning using privileged information (LUPI), we have achieved improved performance over corresponding sequence-based binding affinity prediction methods that do not have access to privileged information during training. Our experiments show that with the proposed framework which uses structure only during training, it is possible to achieve classification performance comparable to that which is obtained using structure-based features. Evaluation on an independent test set shows improved performance over the PPA-Pred2 method as well.

CONCLUSIONS

The proposed method outperforms several baseline learners and a state-of-the-art binding affinity predictor not only in cross-validation, but also on an additional validation dataset, demonstrating the utility of the LUPI framework for problems that would benefit from classification using structure-based features. The implementation of LUPI developed for this work is expected to be useful in other areas of bioinformatics as well.