Protein-protein interaction prediction from primary sequences using supervised machine learning algorithm

Khandelwal, Monika; Rout, Ranjeet Kumar; Umer, Saiyed

doi:10.1109/confluence52989.2022.9734190

Cited by 11 publications

(4 citation statements)

References 30 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Based on this annotation, the complete sequence of a protein is encoded as a vector. This method has been successfully utilized in the field of protein-protein interaction prediction 69 . These descriptor values when plotted across the Cas12-Cas9-Non-Cas training dataset, showed an interesting pattern of significantly lower (negative) descriptor values for Cas9 proteins compared to Cas12 and Non-Cas proteins which had positive values for this feature (Figure 8).…”

Section: Resultsmentioning

confidence: 99%

Identification of Family-Specific Features in Cas9 and Cas12 Proteins: A Machine Learning Approach Using Complete Protein Feature Spectrum

Madugula,

Pujar,

Bharani

et al. 2024

Preprint

View full text Add to dashboard Cite

The recent development of CRISPR-Cas technology holds promise to correct gene-level defects for genetic diseases. The key element of the CRISPR-Cas system is the Cas protein, a nuclease that can edit the gene of interest assisted by guide RNA. However, these Cas proteins suffer from inherent limitations like large size, low cleavage efficiency, and off-target effects, hindering their widespread application as a gene editing tool. Therefore, there is a need to identify novel Cas proteins with improved editing properties, for which it is necessary to understand the underlying features governing the Cas families. In the current study, we aim to elucidate the unique protein attributes associated with Cas9 and Cas12 families and identify the features that distinguish each family from the other. Here, we built Random Forest (RF) binary classifiers to distinguish Cas12 and Cas9 proteins from non-Cas proteins, respectively, using the complete protein feature spectrum (13,495 features) encoding various physiochemical, topological, constitutional, and coevolutionary information of Cas proteins. Furthermore, we built multiclass RF classifiers differentiating Cas9, Cas12, and Non-Cas proteins. All the models were evaluated rigorously on the test and independent datasets. The Cas12 and Cas9 binary models achieved a high overall accuracy of 95% and 97% on their respective independent datasets, while the multiclass classifier achieved a high F1 score of 0.97. We observed that Quasi-sequence-order descriptors like Schneider-lag descriptors and Composition descriptors like charge, volume, and polarizability are essential for the Cas12 family. More interestingly, we discovered that Amino Acid Composition descriptors, especially the Tripeptide Composition (TPC) descriptors, are important for the Cas9 family. Four of the identified important descriptors of Cas9 classification are tripeptides PWN, PYY, HHA, and DHI, which are seen to be conserved across all the Cas9 proteins and were located within different catalytically important domains of the Cas9 protein structure. Among these four tripeptides, tripeptides DHI and HHA are well-known to be involved in the DNA cleavage activity of the Cas9 protein. We therefore propose the the other two tripeptides, PWN and PYY, may also be essential for the Cas9 family. Our identified important descriptors enhanced the understanding of the catalytic mechanisms of Cas9 and Cas12 proteins and provide valuable insights into design of novel Cas systems to achieve enhanced gene-editing properties

show abstract

Section: Resultsmentioning

confidence: 99%

Identification of Family-Specific Features in Cas9 and Cas12 Proteins: A Machine Learning Approach Using Complete Protein Feature Spectrum

Madugula,

Pujar,

Bharani

et al. 2024

Preprint

View full text Add to dashboard Cite

show abstract

“…Based on this annotation, the complete sequence of a protein is encoded as a vector. This method has been successfully utilized in the field of protein–protein interaction prediction . When plotted across the Cas12 vs. Cas9 vs. non-Cas training data set, these descriptor values showed an interesting pattern of significantly lower (negative) descriptor values for Cas9 proteins compared to Cas12 and non-Cas proteins, which had positive values for this feature (Figure ).…”

Section: Resultsmentioning

confidence: 99%

Identification of Family-Specific Features in Cas9 and Cas12 Proteins: A Machine Learning Approach Using Complete Protein Feature Spectrum

Madugula,

Pujar,

Nammi

et al. 2024

J. Chem. Inf. Model.

View full text Add to dashboard Cite

The recent development of CRISPR-Cas technology holds promise to correct gene-level defects for genetic diseases.The key element of the CRISPR-Cas system is the Cas protein, a nuclease that can edit the gene of interest assisted by guide RNA. However, these Cas proteins suffer from inherent limitations such as large size, low cleavage efficiency, and off-target effects, hindering their widespread application as a gene editing tool. Therefore, there is a need to identify novel Cas proteins with improved editing properties, for which it is necessary to understand the underlying features governing the Cas families. In this study, we aim to elucidate the unique protein features associated with Cas9 and Cas12 families and identify the features distinguishing each family from non-Cas proteins. Here, we built Random Forest (RF) binary classifiers to distinguish Cas12 and Cas9 proteins from non-Cas proteins, respectively, using the complete protein feature spectrum (13,494 features) encoding various physiochemical, topological, constitutional, and coevolutionary information on Cas proteins. Furthermore, we built multiclass RF classifiers differentiating Cas9, Cas12, and non-Cas proteins. All the models were evaluated rigorously on the test and independent data sets. The Cas12 and Cas9 binary models achieved a high overall accuracy of 92% and 95% on their respective independent data sets, while the multiclass classifier achieved an F1 score of close to 0.98. We observed that Quasi-Sequence-Order (QSO) descriptors like Schneider.lag and Composition descriptors like charge, volume, and polarizability are predominant in the Cas12 family. Conversely Amino Acid Composition descriptors, especially Tripeptide Composition (TPC), predominate the Cas9 family. Four of the top 10 descriptors identified in Cas9 classification are tripeptides PWN, PYY, HHA, and DHI, which are seen to be conserved across all Cas9 proteins and located within different catalytically important domains of the Streptococcus pyogenes Cas9 (SpCas9) structure. Among these, DHI and HHA are well-known to be involved in the DNA cleavage activity of the SpCas9 protein. Mutation studies have highlighted the significance of the PWN tripeptide in PAM recognition and DNA cleavage activity of SpCas9, while Y450 from the PYY tripeptide plays a crucial role in reducing off-target effects and improving the specificity in SpCas9. Leveraging our machine learning (ML) pipeline, we identified numerous Cas9 and Cas12 family-specific features. These features offer valuable insights for future experimental and computational studies aiming at designing Cas systems with enhanced gene-editing properties. These features suggest plausible structural modifications that can effectively guide the development of Cas proteins with improved editing capabilities.

show abstract

“…SE may be used to determine how much uncertainty or information a sequence contains ( Zurek, 1989 ; Khandelwal et al, 2022b ). The uncertainty affects the distribution of each word.…”

Section: Basic Terminologymentioning

confidence: 99%

Identification of discriminant features from stationary pattern of nucleotide bases and their application to essential gene classification

et al. 2023

Self Cite

View full text Add to dashboard Cite

Introduction: Essential genes are essential for the survival of various species. These genes are a family linked to critical cellular activities for species survival. These genes are coded for proteins that regulate central metabolism, gene translation, deoxyribonucleic acid replication, and fundamental cellular structure and facilitate intracellular and extracellular transport. Essential genes preserve crucial genomics information that may hold the key to a detailed knowledge of life and evolution. Essential gene studies have long been regarded as a vital topic in computational biology due to their relevance. An essential gene is composed of adenine, guanine, cytosine, and thymine and its various combinations.Methods: This paper presents a novel method of extracting information on the stationary patterns of nucleotides such as adenine, guanine, cytosine, and thymine in each gene. For this purpose, some co-occurrence matrices are derived that provide the statistical distribution of stationary patterns of nucleotides in the genes, which is helpful in establishing the relationship between the nucleotides. For extracting discriminant features from each co-occurrence matrix, energy, entropy, homogeneity, contrast, and dissimilarity features are computed, which are extracted from all co-occurrence matrices and then concatenated to form a feature vector representing each essential gene. Finally, supervised machine learning algorithms are applied for essential gene classification based on the extracted fixed-dimensional feature vectors.Results: For comparison, some existing state-of-the-art feature representation techniques such as Shannon entropy (SE), Hurst exponent (HE), fractal dimension (FD), and their combinations have been utilized.Discussion: An extensive experiment has been performed for classifying the essential genes of five species that show the robustness and effectiveness of the proposed methodology.

show abstract

Protein-protein interaction prediction from primary sequences using supervised machine learning algorithm

Cited by 11 publications

References 30 publications

Identification of Family-Specific Features in Cas9 and Cas12 Proteins: A Machine Learning Approach Using Complete Protein Feature Spectrum

Identification of Family-Specific Features in Cas9 and Cas12 Proteins: A Machine Learning Approach Using Complete Protein Feature Spectrum

Identification of Family-Specific Features in Cas9 and Cas12 Proteins: A Machine Learning Approach Using Complete Protein Feature Spectrum

Identification of discriminant features from stationary pattern of nucleotide bases and their application to essential gene classification

Contact Info

Product

Resources

About