Amino Acid Pairs Research Articles

Quantum Logic Gates Based on DNAtronics, RNAtronics, and Proteintronics

Quantum computers (QCs) adopt an n‐state quantum mechanical system to manipulate the superposition state. However, molecular transistors are not used to build up the quantum logic gate. Herein, it is demonstrated that DNA, RNA, and protein are promising media for QCs, and one uses residue pairs, including nucleotide base pairs and amino acid pairs, via proton‐coupled electron transfer to fabricate a quantum logic gate. In the residue pair, the proton transfer between donor and acceptor states fulfills a qubit. Both the DNA‐CG (3‐qubit) nucleobase pair and nucleotide base pair obey the Toffoli gate. AT (2‐qubit) nucleotide base pair behaves as a SWAP gate and a CNOT gate. Furthermore, the AU and RNA‐CG nucleotide base pairs follow the CNOT gate and Toffoli gate, respectively. In addition, a pair of amino acids achieves 1‐qubit and satisfies the Pauli‐X, ‐Y, and ‐Z gates. The generators of universal quantum logic gates are obtained.

Development of a Software Tool for Identifying Protein Sequence Interdependencies from Multiple Sequence Alignments

Introduction Computational methods for evaluating protein structure have led to a deepened understanding of how aligned sequences of amino acid chains may be used to infer structural relationships. We developed a pattern discovery program for predicting and visualizing higher order group relationships and their correspondences from large scale multiple sequence alignments across multiple species that can enhance the understanding of submolecular protein structure, even for proteins that do not have a solved structure. Methods We developed PSICalc (Protein Sequence Interdependency Calculator) software, using a novel algorithm based originally on the k-modes algorithm that clusters substructural components using normalized mutual information as a measure of intra-site interdependency [Durston, K.K., Chiu, D.K.Y., Wong, A.K.C., Li, G.C.L., Statistical discovery of site inter-dependencies in sub-molecular hierarchical protein structuring, EURASIP J Bioinform Syst Biol. (2012) Jul 13;2012(1):8. doi: 10.1186/1687-4153-2012-8]. To aid in visualizing interactions across multiple subdomains, we developed PSICalc Viewer, a complete graphical user interface that allows researchers to provide a multiple sequence alignment and a set of available input parameters to visualize predicted protein structure as a polytree graph. Results The structure of ubiquitin is well-known and served as a base case for validating the amino acid pairs and groupings identified by the algorithm. The algorithm was able to correctly group amino acids into pairs and clusters representing features of the three dimensional structure of ubiquitin. As one example, the algorithm clustered amino acids 27, 41, and 52, which represent adjacent positions in the three dimensional structure of ubiquitin (1UBQ). Additionally, the algorithm identified pairs and clusters of amino acids around the ATP binding pocket of the ATPase domain of topoisomerase II using a dataset of HATPase domains from the Pfam database. Conclusions The software tool employs the PSICalc algorithm, which allows the researcher to predict features of submolecular protein structure. The pairs and clusters of amino acids identified by the algorithm appear to have structural relevance either through close interactions or mutual participation in larger folds or domains. Additional studies are currently being pursued to apply the software tool to other proteins of interest. It is expected that this tool will aid in the examination of proteins of unknown structure through identification of submolecular interdependencies in protein sequences.

Amino acid δ15N differences consistent with killer whale ecotypes in the Arctic and Northwest Atlantic.

Ecotypes are groups within a species with different ecological adaptations than their conspecifics. Eastern North Pacific (ENP) killer whale (Orcinus orca) ecotypes differ in their diet, behavior, and morphology, but the same is not known for this species in the eastern Canadian Arctic (ECA) and Northwest Atlantic (NWA). Using compound-specific stable isotope analysis (CSIA) of amino acids (AAs), we compared δ15N patterns of the primary trophic and source AA pair, glutamic acid/glutamine (Glx) and phenylalanine (Phe), in dentine collagen of (1) sympatric ENP killer whale ecotypes with well-characterized diet differences and (2) ECA/NWA killer whales with unknown diets. δ15NGlx-Phe was significantly higher in the ENP fish-eating (FE) than mammal-eating (ME) ecotype (19.2 ± 0.4‰ vs. 13.5 ± 0.7‰, respectively). Similar bimodal variation in δ15NGlx-Phe indicated analogous dietary divisions among ECA/NWA killer whales, with two killer whales having higher δ15NGlx-Phe (16.5 ± 0.0‰) than the others (13.5 ± 0.6‰). Inferences of dietary divisions between these killer whales were supported by parallel differences in threonine δ15N (–33.5 ± 1.6‰ and –40.4 ± 1.1‰, respectively), given the negative correlation between δ15NThr and TP across a range of marine consumers. CSIA-AA results for ECA/NWA whales, coupled with differences in tooth wear (a correlate for diet), are consistent with ecotype characteristics reported in ENP and other killer whale populations, thus adding to documented ecological divergence in this species worldwide.

In silico method for selecting residue pairs for single-molecule microscopy and spectroscopy

Obtaining (dynamic) structure related information on proteins is key for understanding their function. Methods as single-molecule Förster Resonance Energy Transfer (smFRET) and Electron Paramagnetic Resonance (EPR) that measure distances between labeled residues to obtain dynamic information rely on selection of suitable residue pairs for chemical modification. Selection of pairs of amino acids, that show sufficient distance changes upon activity of the protein, can be a tedious process. Here we present an in silico approach that makes use of two or more structures (or structure models) to filter suitable residue pairs for FRET or EPR from all possible pairs within the protein. We apply the method for the study of the conformational dynamics of the substrate-binding domain of the osmoregulatory ATP-Binding Cassette transporter OpuA. This method speeds up the process of designing mutants, and because of its systematic nature, the chances of missing promising candidates are reduced.

Local Interactions That Contribute Minimal Frustration Determine Foldability.

Earlier experiments suggest that the evolutionary information (conservation and coevolution) encoded in protein sequences is necessary and sufficient to specify the fold of a protein family. However, there is no computational work to quantify the effect of such evolutionary information on the folding process. Here we explore the role of early folding steps for sequences designed using coevolution and conservation through a combination of computational and experimental methods. We simulated a repertoire of native and designed WW domain sequences to analyze early local contact formation and found that the N-terminal β-hairpin turn would not form correctly due to strong non-native local contacts in unfoldable sequences. Through a maximum likelihood approach, we identified five local contacts that play a critical role in folding, suggesting that a small subset of amino acid pairs can be used to solve the "needle in the haystack" problem to design foldable sequences. Thus, using the contact probability of those five local contacts that form during the early stage of folding, we built a classification model that predicts the foldability of a WW sequence with 81% accuracy. This classification model was used to redesign WW domain sequences that could not fold due to frustration and make them foldable by introducing a few mutations that led to the stabilization of these critical local contacts. The experimental analysis shows that a redesigned sequence folds and binds to polyproline peptides with a similar affinity as those observed for native WW domains. Overall, our analysis shows that evolutionary-designed sequences should not only satisfy the folding stability but also ensure a minimally frustrated folding landscape.

PredNTS: Improved and Robust Prediction of Nitrotyrosine Sites by Integrating Multiple Sequence Features.

Nitrotyrosine, which is generated by numerous reactive nitrogen species, is a type of protein post-translational modification. Identification of site-specific nitration modification on tyrosine is a prerequisite to understanding the molecular function of nitrated proteins. Thanks to the progress of machine learning, computational prediction can play a vital role before the biological experimentation. Herein, we developed a computational predictor PredNTS by integrating multiple sequence features including K-mer, composition of k-spaced amino acid pairs (CKSAAP), AAindex, and binary encoding schemes. The important features were selected by the recursive feature elimination approach using a random forest classifier. Finally, we linearly combined the successive random forest (RF) probability scores generated by the different, single encoding-employing RF models. The resultant PredNTS predictor achieved an area under a curve (AUC) of 0.910 using five-fold cross validation. It outperformed the existing predictors on a comprehensive and independent dataset. Furthermore, we investigated several machine learning algorithms to demonstrate the superiority of the employed RF algorithm. The PredNTS is a useful computational resource for the prediction of nitrotyrosine sites. The web-application with the curated datasets of the PredNTS is publicly available.

Prediction and Analysis of Protein Ubiquitin Sites in the Model Plant A. thaliana

Ubiquitin is an important type of protein after translational modification. Ubiquitin has the ability to take part in several cellular regulations among several biological processions. At the same time, ubiquitin plays key roles in the enzymatic process. So as to construct the new tool to classify the ubiquitin amino acid residues, we employed the random forest model to classify the ubiquitin sites utilizing the experimentally identified ubiquitinated protein sequences of A. thaliana. More detailed, we utilized the k-spaced amino acid pair (CKSAAP) encoding and binary encoding to deal with the potential protein segments. The proposed tools can obtain 72.83% in Sp, 72.42% in Sn, 72.63% in Acc, and 0.4525 in MCC. With these performances, such tools can obtain the available results in the dataset of Arabidopsis.

Predicting the stability of homotrimeric and heterotrimeric collagen helices.

Robust methods for predicting thermal stabilities of collagen triple helices are critical for understanding natural structure and stability in the collagen family of proteins and also for designing synthetic peptides mimicking these essential proteins. In this work, we determine the relative stability imparted on the collagen triple helix by single amino acids and interactions between amino acid pairs. Using this analysis, we create a comprehensive algorithm, SCEPTTr, for predicting melting temperatures of synthetic triple helices. Critically, our algorithm is compatible with every natural amino acid, can evaluate both homotrimers and heterotrimers, and accounts for all possible helix compositions and registers, including non-canonically staggered helices. We test and optimize our algorithm against 431 published collagen triple helices to demonstrate the quality of our predictive system. Finally, we use this algorithm to successfully guide the design of an ABC heterotrimer possessing high assembly specificity.

Mapping Structural Intermediates during Co-Translational Folding of Hsp70 with Crosslinking Mass Spectrometry

Crosslinking mass spectrometry (XL-MS) is an emerging technique in structural biology in which spatially proximal amino acids amino acids are crosslinked to one another, and then these pairs of amino acids are identified with mass spectrometry. Such crosslinks can then serve as distance restraints for structural modeling. Of note, diazirine-containing amino acids (called photo-amino acids) can serve as UV-activatable crosslinkers that can be incorporated into nascent proteins co-translationally and generate short “zero-length” crosslinks upon photolysis. Here, we describe progress toward a novel approach that employs XL-MS and diazirine photo-amino acids to provide structural information on nascent protein biogenesis in vivo. As a proof of concept, we apply this method to map the intermediates of the molecular chaperone, DnaK (Hsp70) as it emerges from the ribosome in E. coli.

Bioluminescent Proteins Prediction with Voting Strategy

Background: Bioluminescence is a unique and significant phenomenon in nature. Bioluminescence is important for the lifecycle of some organisms and is valuable in biomedical research, including for gene expression analysis and bioluminescence imaging technology. In recent years, researchers have identified a number of methods for predicting bioluminescent proteins (BLPs), which have increased in accuracy, but could be further improved. Method: In this study, a new bioluminescent proteins prediction method, based on a voting algorithm, is proposed. Four methods of feature extraction based on the amino acid sequence were used. 314 dimensional features in total were extracted from amino acid composition, physicochemical properties and k-spacer amino acid pair composition. In order to obtain the highest MCC value to establish the optimal prediction model, a voting algorithm was then used to build the model. To create the best performing model, the selection of base classifiers and vote counting rules are discussed. Results: The proposed model achieved 93.4% accuracy, 93.4% sensitivity and 91.7% specificity in the test set, which was better than any other method. A previous prediction of bioluminescent proteins in three lineages was also improved using the model building method, resulting in greatly improved accuracy.

PrPred: A Predictor to Identify Plant Resistance Proteins by Incorporating k-Spaced Amino Acid (Group) Pairs.

To infect plants successfully, pathogens adopt various strategies to overcome their physical and chemical barriers and interfere with the plant immune system. Plants deploy a large number of resistance (R) proteins to detect invading pathogens. The R proteins are encoded by resistance genes that contain cell surface-localized receptors and intracellular receptors. In this study, a new plant R protein predictor called prPred was developed based on a support vector machine (SVM), which can accurately distinguish plant R proteins from other proteins. Experimental results showed that the accuracy, precision, sensitivity, specificity, F1-score, MCC, and AUC of prPred were 0.935, 1.000, 0.806, 1.000, 0.893, 0.857, and 0.948, respectively, on an independent test set. Moreover, the predictor integrated the HMMscan search tool and Phobius to identify protein domain families and transmembrane protein regions to differentiate subclasses of R proteins. prPred is available at https://github.com/Wangys-prog/prPred. The tool requires a valid Python installation and is run from the command line.

ApoPred: Identification of Apolipoproteins and Their Subfamilies With Multifarious Features.

Apolipoprotein is a group of plasma proteins that are associated with a variety of diseases, such as hyperlipidemia, atherosclerosis, Alzheimer’s disease, and diabetes. In order to investigate the function of apolipoproteins and to develop effective targets for related diseases, it is necessary to accurately identify and classify apolipoproteins. Although it is possible to identify apolipoproteins accurately through biochemical experiments, they are expensive and time-consuming. This work aims to establish a high-efficiency and high-accuracy prediction model for recognition of apolipoproteins and their subfamilies. We firstly constructed a high-quality benchmark dataset including 270 apolipoproteins and 535 non-apolipoproteins. Based on the dataset, pseudo-amino acid composition (PseAAC) and composition of k-spaced amino acid pairs (CKSAAP) were used as input vectors. To improve the prediction accuracy and eliminate redundant information, analysis of variance (ANOVA) was used to rank the features. And the incremental feature selection was utilized to obtain the best feature subset. Support vector machine (SVM) was proposed to construct the classification model, which could produce the accuracy of 97.27%, sensitivity of 96.30%, and specificity of 97.76% for discriminating apolipoprotein from non-apolipoprotein in 10-fold cross-validation. In addition, the same process was repeated to generate a new model for predicting apolipoprotein subfamilies. The new model could achieve an overall accuracy of 95.93% in 10-fold cross-validation. According to our proposed model, a convenient webserver called ApoPred was established, which can be freely accessed at http://tang-biolab.com/server/ApoPred/service.html. We expect that this work will contribute to apolipoprotein function research and drug development in relevant diseases.

SPServer: split-statistical potentials for the analysis of protein structures and protein\u2013protein interactions

BackgroundStatistical potentials, also named knowledge-based potentials, are scoring functions derived from empirical data that can be used to evaluate the quality of protein folds and protein–protein interaction (PPI) structures. In previous works we decomposed the statistical potentials in different terms, named Split-Statistical Potentials, accounting for the type of amino acid pairs, their hydrophobicity, solvent accessibility and type of secondary structure. These potentials have been successfully used to identify near-native structures in protein structure prediction, rank protein docking poses, and predict PPI binding affinities.ResultsHere, we present the SPServer, a web server that applies the Split-Statistical Potentials to analyze protein folds and protein interfaces. SPServer provides global scores as well as residue/residue-pair profiles presented as score plots and maps. This level of detail allows users to: (1) identify potentially problematic regions on protein structures; (2) identify disrupting amino acid pairs in protein interfaces; and (3) compare and analyze the quality of tertiary and quaternary structural models.ConclusionsWhile there are many web servers that provide scoring functions to assess the quality of either protein folds or PPI structures, SPServer integrates both aspects in a unique easy-to-use web server. Moreover, the server permits to locally assess the quality of the structures and interfaces at a residue level and provides tools to compare the local assessment between structures.Server addresshttps://sbi.upf.edu/spserver/.

Computational approach to design of aptamers to the receptor binding domain of SARS-CoV-2

The aim of the research. In this work, in silico selection of DNA-aptamers to the receptor-binding domain (RBD) of the SARS-CoV-2 spike protein was performed using molecular modeling methods. Material and methods. A new computational approach to aptamer in silico selection is based on a cycle of simulations, including the stages of molecular modeling, molecular docking, molecular dynamic simulations, and quantum chemical calculations. To verify the obtained calculated results flow cytometry, fluorescence polarization, and small-angle X-ray scattering methods were applied. Results. An initial library consisted of 256 16-mer oligonucleotides was modeled. Based on molecular docking results, the only one aptamer (Apt16) was selected from the library as a starting aptamer to the RBD protein. For Apt16/RBD complex, molecular dynamic and quantum chemical calculations revealed the pairs of nucleotides and amino acids whose contribution to the binding between aptamer and RBD is the largest. Taking into account these data, Apt16 was subjected to the structure modifi cations in order to increase the binding with the RBD. Th us, a new aptamer Apt25 was designed. Th e procedure of 1) aptamer structure modeling/modifi cation, 2) molecular docking, 3) molecular dynamic simulations, 4) quantum chemical calculations was performed several times. As a result, four aptamers (Apt16, Apt25, Apt27, Apt31) to the RBD were designed in silico without any preliminary experimental data. Binding of the each modeled aptamer to the RBD was studied in terms of interactions between residues in protein and nucleotides in the aptamers. Based on the simulation results, the strongest binding with the RBD was predicted for two Apt27 and Apt31aptamers. The calculated results are in good agreement with experimental data obtained by flow cytometry, fluorescence polarization, and small-angle X-ray scattering methods. Conclusion. Th e proposed computational approach to selection and refi nement of aptamers is universal and can be used for wide range of molecular ligands and targets

Computational approach to design of aptamers to the receptor binding domain of sars-cov-2

The aim of the research. In this work, in silico selection of DNA-aptamers to the receptor-binding domain (RBD) of the SARS-CoV-2 spike protein was performed using molecular modeling methods. Material and methods. A new computational approach to aptamer in silico selection is based on a cycle of simulations, including the stages of molecular modeling, molecular docking, molecular dynamic simulations, and quantum chemical calculations. To verify the obtained calculated results flow cytometry, fluorescence polarization, and small-angle X-ray scattering methods were applied. Results. An initial library consisted of 256 16-mer oligonucleotides was modeled. Based on molecular docking results, the only one aptamer (Apt16) was selected from the library as a starting aptamer to the RBD protein. For Apt16/RBD complex, molecular dynamic and quantum chemical calculations revealed the pairs of nucleotides and amino acids whose contribution to the binding between aptamer and RBD is the largest. Taking into account these data, Apt16 was subjected to the structure modifications in order to increase the binding with the RBD. Thus, a new aptamer Apt25 was designed. The procedure of 1) aptamer structure modeling/modification, 2) molecular docking, 3) molecular dynamic simulations, 4) quantum chemical calculations was performed sev-eral times. As a result, four aptamers (Apt16, Apt25, Apt27, Apt31) to the RBD were designed in silico without any preliminary experimental data. Binding of the each modeled aptamer to the RBD was studied in terms of interactions between residues in protein and nucleotides in the aptamers. Based on the simulation results, the strongest binding with the RBD was predicted for two Apt27 and Apt31aptamers. The calculated results are in good agreement with experimental data obtained by flow cytometry, fluorescence polarization, and small-angle X-ray scattering methods. Conclusion. The proposed computational approach to selection and refinement of aptamers is universal and can be used for wide range of molecular ligands and targets. Key words. © ARTYUSHENKO P. V., MIRONOV V. A., MOROZOV D. I., SHCHUGOREVA I. A., BORBONE N., TOMILIN F. N., KICHKAILO A. S.

In Silico Disulfide Bond Engineering to Improve Human LEPTIN Stability

Enhancing the stability of biomolecules is one of the hot topics in industry. In this study, we enhanced the stability of an important protein called LEPTIN. LEPTIN is a hormone secreted by fat cells playing an essential role in body weight and composition, and its deficiency can result in several disorders. The treatment of related LEPTIN dysfunctions is often available in the form of injection. To decrease the cost and the frequency of its applications can be achieved by increasing its lifetime through engineering LEPTIN. In this study, to engineer LEPTIN, we have introduced disulfide bonds. Disulfide By Design server was used to predict the suitable nominate pairs, which suggested three pairs of amino acids to be mutated to cysteine for disulfide bond formation. Additionally, to further evaluate the effect of combined mutations, we combined these three nominated pairs to produce three more mutants. In order to assess the effect of introduced mutations, molecular dynamic (MD) simulation was performed. The result suggests that Mutant-1 is more stable in comparison to wild-type and the other mutants. Moreover, docking results showed that the introduced mutation does not affect the receptor binding performance; therefore, it can be considered a suitable choice for future protein engineering.

Leri: A web-server for identifying protein functional networks from evolutionary couplings

Information on the co-evolution of amino acid pairs in a protein can be used for endeavors such as protein engineering, mutation design, and structure prediction. Here we report a method that captures significant determinants of proteins using estimated co-evolution information to identify networks of residues, termed ”residue communities”, relevant to protein function. On the benchmark dataset (67 proteins with both catalytic and allosteric residues), the Pearson’s correlation between the identified residues in the communities at functional sites is 0.53, and it is higher than 0.8 by taking account of conserved residues derived from the method. On the endoplasmic reticulum-mitochondria encounter structure complex, the results indicate three distinguishable residue communities that are relevant to functional roles in the protein family, suggesting that the residue communities could be general evolutionary signatures in proteins. Based on the method, we provide a webserver for the scientific community to explore the signatures in protein families, which establishes a powerful tool to analyze residue-level profiling for the discovery of functional sites and biological pathway identification. This web-server is freely available for non-commercial users at https://kornmann.bioch.ox.ac.uk/leri/services/ecs.html, neither login nor e-mail required.

Identification of cyclin protein using gradient boost decision tree algorithm

Cyclin proteins are capable to regulate the cell cycle by forming a complex with cyclin-dependent kinases to activate cell cycle. Correct recognition of cyclin proteins could provide key clues for studying their functions. However, their sequences share low similarity, which results in poor prediction for sequence similarity-based methods. Thus, it is urgent to construct a machine learning model to identify cyclin proteins. This study aimed to develop a computational model to discriminate cyclin proteins from non-cyclin proteins. In our model, protein sequences were encoded by seven kinds of features that are amino acid composition, composition of k-spaced amino acid pairs, tri peptide composition, pseudo amino acid composition, geary correlation, normalized moreau-broto autocorrelation and composition/transition/distribution. Afterward, these features were optimized by using analysis of variance (ANOVA) and minimum redundancy maximum relevance (mRMR) with incremental feature selection (IFS) technique. A gradient boost decision tree (GBDT) classifier was trained on the optimal features. Five-fold cross-validated results showed that our model would identify cyclins with an accuracy of 93.06% and AUC value of 0.971, which are higher than the two recent studies on the same data.

Identification of antioxidant proteins using a discriminative intelligent model of k-space amino acid pairs based descriptors incorporating with ensemble feature selection

Antioxidant proteins have been discovered closely associated with disease control due to its capability to eradicate excess free radicals. The accurate identification of antioxidant proteins is on the upsurge owing to their therapeutic significance. However, observing the rapid increases of this toxic disease in the human body, several machine learning algorithms have been applied and performed inadequately to identify antioxidant proteins. Therefore, measuring the effectiveness of antioxidant proteins on the human body, a reliable intelligent model is indispensable for the researchers. In this study, primary protein sequences are formulated using evolutionary and sequence-based numerical descriptors. Whereas, evolutionary features are collected using a bigram Position-specific scoring matrix, besides, K-space amino acid pair (KSAAP) and dipeptide composition are utilized to extract sequential information. Furthermore, in order to reduce the computational time and to eradicate irreverent and noisy features, the Sequential forward selection and Support vector machine (SFS-SVM) based ensemble approach is applied to select optimal features. At last, several distinct nature classification learning methods are applied to choose a suitable operational engine for our model. After evaluating the empirical results, SVM using optimal features achieved an accuracy of 97.54%, 93.71% using the training and independent dataset, respectively. It was found that our proposed model outperformed and reported the highest performance than the existing computational models. It is expected that the developed model may be played a useful role in research academia as well as proteomics and drug development. The source code and all datasets are publicly available at https://github.com/salman-khan-mrd/Antioxident_proteins.

Prediction of Neddylation Sites Using the Composition of k-spaced Amino Acid Pairs and Fuzzy SVM

Introduction: Neddylation is the process of ubiquitin-like protein NEDD8 attaching substrate lysine via isopeptide bonds. As a highly dynamic and reversible post-translational modification, lysine neddylation has been found to be involved in various biological processes and closely associated with many diseases. Objective: The accurate identification of neddylation sites is necessary to elucidate the underlying molecular mechanisms of neddylation. As traditional experimental methods are often expensive and time-consuming, it is imperative to design computational methods to identify neddylation sites. Methods: In this study, a novel predictor named CKSAAP_NeddSite is developed to detect neddylation sites. An effective feature encoding technology, the composition of k-spaced amino acid pairs, is used to encode neddylation sites. And the F-score feature selection method is adopted to remove the redundant features. Moreover, a fuzzy support vector machine algorithm is employed to overcome the class imbalance and noise problem. Results: As illustrated by 10-fold cross-validation, CKSAAP_NeddSite achieves an AUC of 0.9848. Independent tests also show that CKSAAP_NeddSite significantly outperforms existing neddylation sites predictor. Therefore, CKSAAP_NeddSite can be a useful bioinformatics tool for the prediction of neddylation sites. Feature analysis shows that some residues around neddylation sites may play an important role in the prediction. Conclusion: The results of analysis and prediction could offer useful information for elucidating the molecular mechanisms of neddylation. A user-friendly web-server for CKSAAP_NeddSite is established at 123.206.31.171/CKSAAP_NeddSite.