Artificial Intelligence (AI) Tools Constructed via the 5-Steps Rule for Predicting Post-Translational Modifications

Abstract

Identification of the sites of post-translational modifications (PTMs) in protein, RNA, and DNA sequences is currently a very hot topic. This is because the information thus obtained is very useful for in-depth understanding the biological processes at the cellular level and for developing effective drugs against major diseases including cancers and Alzheimer's as well. Although this can be realized by means of various experimental techniques, it is both time-consuming and costly to determine the PTM sites purely based on experiments. With the avalanche of biological sequences generated in the post-genomic age, it is highly desired to develop artificial intelligence (AI) tools for rapidly and effectively identifying the PTM sites. In the last few years, many efforts have been made in this regard, and considerable progresses have been achieved. This review is focused on those AI tools that have the following two features. (1) They have been developed by strictly observing the 5-steps rule so that they each have a user-friendly web-server for the majority of experimental scientists to easily get their desired data without the need to go through the detailed mathematics involved. (2) Their cornerstones have been based on PseAAC (Pseudo Amino Acid Composition) or PseKNC (Pseudo K-tuple Nucleotide Composition), and hence the prediction quality is generally remarkably higher than most of the other PTM prediction methods without such base.

Keywords

Artificial intelligence (AI) tools, Five-step rules, Post-translational modifications, Absolute true rate, Web-server

Introduction

Post-translational modification, or PTM, means the covalent and generally enzymatic modification of proteins right after they are biosynthesized. After being synthesized by ribosomes, proteins may undergo PTM to form the mature protein products. PTMs can occur on the amino acid side chains of a protein or at its C- or N- terminus. They can covalently modify the existing functional group of an amino acid and make it have other functional group. Therefore, the chemical repertoire of the 20 standard amino acids can be considerably extended via the process of PTMs.

According to their occurrence in three different types of biological sequences, PTMs can be classified into the following three different categories: (1) PTLM (post-translational modification) in proteins, (2) PTCM (post-transcriptional modification) in RNA, and (3) PTRM (post-replication modification) in DNA. PTMs play a key role in providing bio-macromolecules with structural and functional diversity, as well as in regulating cellular plasticity and dynamics. Meanwhile, PTMs are also closely associated with many major diseases including cancer, Alzheimer's, and Parkinson's. Therefore, identifying the PTM sites in biological sequences is very important for both basic research and drug development.

Historical Reflection

Before going on, it is illuminative to make a historical reflection. For quite a long period of time, the information derived by the computational approaches were not trusted very much by most experimental scientists due to the notorious local minimum problem [1]. Actually, they only trusted the results determined by the experiments, and thought that computational results were not reliable unless they had been confirmed by experiments. This kind of situation has been changed during the last decade or so owing to the rapid development of structural bioinformatics and sequential bioinformatics. For the 3D structures of proteins, what they trusted most were those determined by the X-ray crystallography. Unfortunately, it is time-consuming and expensive, and not all proteins can be successfully crystallized. Membrane proteins are difficult to crystallize and most of them will not dissolve in normal solvents. Accordingly, so far very few membrane protein structures have been determined. NMR is indeed a very powerful tool in determining the 3D structures of membrane proteins (see, e.g., [2-19]), but it is also time-consuming and costly. In order to acquire the structural information in a timely manner, a series of 3D protein structures have been developed by means of structural bioinformatics tools (see, e.g., [20-32]) and they have been found very useful in conducting mutagenesis studies [33] for rational drug design. Meanwhile, facing the explosive growth of biological sequences discovered in the post-genomic age, to timely use them for drug development, a lot of useful information have been revealed or deducted by various AI tools via the PseAAC approach [34-36] and PseKNC approach [37-39]. Actually, this kind of AI technique has played increasingly important roles in driving the medicinal chemistry into an unprecedented revolution [40,41] by significantly speeding up the process of finding novel drugs [42-44].

As it was in the last few years that many AI tools were developed for predicting the PTM sites in biological sequences [40,45-87] in compliance with the Chou's 5-steps rule [88] by going through the following five procedures: (1) How to select or construct a valid benchmark dataset to train and test the predictor; (2) How to represent the samples with an effective formulation that can truly reflect their intrinsic correlation with the target to be predicted; (3) How to introduce or develop a powerful algorithm to conduct the prediction; (4) How to properly perform cross-validation tests to objectively evaluate the anticipated prediction accuracy; (5) How to establish a user-friendly web-server for the predictor that is accessible to the public.

The AI tools constructed thru the 5-steps rule bear the following notable merits: (1) Crystal clear in logic development, (2) Complete transparent in operation, (3) Quite easy to repeat the reported results by others, (4) Holding high potential in stimulating other sequence-analyzing methods, and (5) Very convenient to be used by broad experimental scientists.

Therefore, focused on the current review paper are only those AI tools that were born through the Chou's 5-steps rule [88]. As for the importance of the 5-steps rule and how to use it in developing new predictor for proteome and genome analyses, see an insightful Wikipedia article at https://en.wikipedia.org/wiki/5-step_rules.

Besides, with the avalanche of biological sequences in the post-genomic era, one of the most important but also most difficult problems in developing AI tools for investigation into biology is how to express a biological sequence with a discrete model or a vector, yet still considerably keep its sequence-order information or key pattern characteristic. This is because all the existing machine-learning algorithms (such as "Optimization" algorithm [89], "Covariance Discriminant" or "CD" algorithm [90,91], "Nearest Neighbor" or "NN" algorithm [92], and "Support Vector Machine" or "SVM" algorithm [92,93]) can only handle vectors as elaborated in a comprehensive review [40].

However, a vector defined in a discrete model may completely lose all the sequence-pattern information. To avoid completely losing the sequence-pattern information for proteins, the pseudo amino acid composition [34] or PseAAC [35] was proposed. Ever since the concept of Chou's PseAAC was proposed, it has been widely used in nearly all the areas of computational proteomics (see, e.g., [45,48,52,60,67,73,77,78,82,83,85-87,94-251] as well as a long list of references cited in [41]).

Because it has been widely and increasingly used, four powerful open access soft-wares, called 'PseAAC' [252], 'PseAAC-Builder' [128], 'propy' [146], and 'PseAAC-General' [166], were established: the former three are for generating various modes of Chou's special PseAAC [253]; while the 4^th one for those of Chou's general PseAAC [88], including not only all the special modes of feature vectors for proteins but also the higher level feature vectors such as "Functional Domain" mode (see Eqs.9-10 of [88]), "Gene Ontology" mode (see Eqs.11-12 of [88]), and "Sequential Evolution" or "PSSM" mode (see Eqs.13-14 of [88]).

Meanwhile, the idea of PseAAC was extended to generate various modes of feature vectors for DNA and RNA sequences [37-39,254-258], and has been proved very useful as well.

Given an AI tool, its name can be defined as

Name of 𝔸𝕀 tool = 𝔸𝕀(𝕏) (1)

where the wildcard 𝕏 denotes the web-server or software based on which the AI tool has been constructed. For instance: when 𝕏 = Isno-PseAAC, the AI tool is for predicting cysteine S-nitrosylation sites in proteins by incorporating position specific amino acid propensity into pseudo amino acid composition; when 𝕏 = Irna-PseU, the AI tool is for predicting RNA pseudouridine sites; when 𝕏 = Idna-Methyl, the AI tool is for predicting DNA methylation sites via pseudo trinucleotide composition; and so forth.

Sixteen AI Tools for Identifying PTM or PTLM Sites in Protein Sequences

The 16 AI tools are: (1) 𝔸𝕀 SNO-PseAAC) [46]; (2) 𝔸𝕀 (iSNO-AAPair) [47]; (3) 𝔸𝕀 (iMethyl-AAC) [49]; (4) 𝕀 (iHyd-seAAC) [50]; (5) 𝔸𝕀 (iNitro-Tyr) [51]; (6) 𝔸𝕀 (iUbiq-Lys) [54]; (7) 𝔸𝕀 (iSuc-PseOpt) [56]; (8) 𝔸𝕀 (pSuc-Lys) [57]; (9) 𝔸𝕀 Car-PseCp) [58]; (10) 𝔸𝕀 (pSumo-CD) [59]; (11) 𝔸𝕀 (iHyd-PseCp) [62]; (12) 𝔸𝕀 (iPTM-mLys) [63]; (13) 𝔸𝕀 (iPhos-PseEn) [64]; (14) 𝔸𝕀 (iPGK-PseAAC) [68]; (15) 𝔸𝕀 (iPhos-PseEvo) [71]; (16) 𝔸𝕀 (iPreny-PseAAC) [72]. Their functions and web-server links are each given in Table 1.

Seven AI Tools for Identifying PTM or PTCM Sites in RNA Sequences

The 7 AI tools are: (1) 𝔸𝕀 (iRNA-PseU) [55]; (2) 𝔸𝕀 (pRNAm-PC) [61]; (3) 𝔸𝕀 (iRNA-PseColl) [66]; (4) 𝔸𝕀 (iRNA-methyl) [69]; (5) 𝔸𝕀 (iRNAm5C-PseDNC) [70]; (6) 𝔸𝕀 (iRNA(m6A)-PseDNC) [75]; (7) 𝔸𝕀 (iRNA-3typeA) [76]. Their functions and web-server links are each given in Table 2.

One AI Tool for Identifying PTM or PTRM Sites in DNA Sequences

𝔸𝕀 (iDNA-Methyl) is the AI tool for identifying the PTM sites in DNA sequences [259]. Its function and web-server link are given in Table 3.

Discussions

For measuring the success rates of the AI tools, a set of four metrics [260] are usually used in literature. They are: (1) Overall accuracy or Acc, (2) Mathew's correlation coefficient or MCC, (3) Sensitivity or Sn, and (4) Specificity or Sp, as given below

$$\left\{\begin{array}{l}{S}_n=\frac{TP}{TP+FN}\\ S_p=\frac{TN}{TN+FP}\\ Acc=\frac{TP+TN}{TP+TN+FP+FN}\\ MCC=\frac{(TP\times TN)-(FP\times FN)}{\sqrt{\begin{array}{cccc}(TP+FP)& (TP+FN)& (TN+FP)& (TN+FN)\end{array}}}\end{array}\right.(2)$$

Although the above four metrics copied from math books were often t used in literature to measure the prediction quality of a prediction method, they are lacking intuitiveness and no easy-to-understand for most biologists. Particularly the MCC (the Matthews correlation coefficient), which is a very important metrics used for reflecting the stability of a prediction method. Fortunately, based on the Chou's symbols introduced for studying protein signal peptides [261,262], a set of four intuitive metrics were derived [47,263,264], as given below

$$\left\{\begin{array}{l}\text{Sn = 1 }-\frac{N_{-}^{+}}{N^{+}}\text{ 0 }\le \text{ Sn }\le \text{ 1 }\\ {\text{Sp}}_{}\text{ = 1 }-\frac{N_{+}^{-}}{N^{-}}\text{ 0 }\le \text{ Sp }\le \text{ 1 }\\ \text{Acc = }\wedge \text{ = 1 - }\frac{N_{-}^{+}+N_{+}^{-}}{N^{+}\text{ + }{N}^{-}}\text{ 0 }\le Acc\le \text{ 1}\\ \text{MCC = }\frac{1-\text{ (}\frac{N_{-}^{+}}{N^{+}}+\frac{N_{+}^{-}}{N^{-}}\text{) }}{\sqrt{\left(1+\frac{N_{+}^{-}-N_{-}^{+}}{N^{+}}\right)\left(1+\frac{N_{-}^{+}-N_{+}^{-}}{N^{-}}\right)}}\text{ -1 }\le \text{ MCC }\le \text{ 1}\end{array}\right.(3)$$

According to Eq.3 we can easily see the following. When ${\text{N}}_{-}^{+}\text{ = 0}$ meaning none of the positive samples is mispredicted to be negative, we have the sensitivity Sn = 1; while ${\text{N}}_{-}^{+}{\text{ = N}}^{+}$ meaning that all the positive samples are mispredicted to be negative, we have the sensitivity Sn = 0. Likewise, when ${\text{N}}_{-}^{+}\text{ = 0}$ meaning none of the negative samples is incorrectly predicted to be positive, we have the specificity Sp = 1; while ${\text{N}}_{+}^{-}{\text{ = N}}^{-}$ meaning all the negative samples are incorrectly predicted to be positive, we have the specificity Sp = 0. When ${\text{N}}_{-}^{+}{\text{ = N}}_{+}^{-}\text{ = 0}$ meaning that none of the positive samples and none of the negative samples is incorrectly predicted, we have the overall accuracy Acc = 1; while ${\text{N}}_{-}^{+}{\text{ = N}}^{+}$ and ${\text{N}}_{+}^{-}{\text{ = N}}^{-}$ meaning that all the positive samples and all the negative samples are mispredicted, we have the overall accuracy Acc = 0, and ; MCC = 1: when ${\text{N}}_{-}^{+}{\text{ = N}}^{+}/\text{ 2}$ and ${\text{N}}_{+}^{-}{\text{ = N}}^{-}/\text{ 2}$ we have MCC = 0 meaning no better than random prediction; when ${\text{N}}_{-}^{+}{\text{ = N}}^{+}$ and ${\text{N}}_{+}^{-}{\text{ = N}}^{-}$ we have MCC = -1 meaning total disagreement between prediction and observation. As we can see from the above discussion, it is much more intuitive and easier to understand when using Eq.3 instead of Eq.1 to examine a predictor for its four metrics, particularly for its Mathew's correlation coefficient.

It is instructive to point out, however, that some AI tools may have the multi-label feature, such as 𝔸𝕀 (iDNA-Methyl) [63] having the capacity to identify multiple lysine PTM sites and their different types. Actually, in the real world the multi-label systems (where a sample may simultaneously belong to several classes) have become more frequent in both system biology [265-292] and system medicine [293,294].

To examine the performance of multi-label AI tools, one also needs a set of global metrics [295,296], as elaborated below.

$$\left\{\begin{array}{l}\text{Aiming}\uparrow \text{ = }\frac{1}{N^q}{\displaystyle {\sum }_{k=1}^{N^q}\left(\frac{\left|\right|{\text{L}}_k\cap {\text{L}}_k^{*}\left|\right|}{\left|\right|{\text{L}}_k^{*}\left|\right|}\right),\text{ [0,1]}}\\ \text{Coverage}\uparrow \text{ = }\frac{1}{N^q}{\displaystyle {\sum }_{k=1}^{N^q}\begin{array}{l}\left(\frac{\left|\right|{\text{L}}_k\cap {\text{L}}_k^{*}\left|\right|}{\left|\right|{\text{L}}_k^{*}\left|\right|}\right),\text{ [0,1]}\\ \end{array}}\\ \text{Accuracy}\uparrow \text{ = }\frac{1}{N^q}{\displaystyle {\sum }_{k=1}^{N^q}\left(\frac{\left|\right|{\text{L}}_k\cap {\text{L}}_k^{*}\left|\right|}{\left|\right|{\text{L}}_k^{}{\displaystyle \cup {\text{L}}_k^{*}}\left|\right|}\right),\text{ [0,1] }}\\ \text{Absolute true}\uparrow \text{ = }\frac{1}{N^q}{\displaystyle {\sum }_{k=1}^{N^q}\Delta ({\text{L}}_{k,}}{\text{L}}_k^{*}{)}_{,}\text{ [0,1]}\\ \text{Ansolute false}\downarrow \text{ = }\frac{1}{N^q}{\displaystyle {\sum }_{k=1}^{N^q}\left(\frac{\left|\right|{\text{L}}_k\cup {\text{L}}_k^{*}\left|\right|{\text{ - ||L}}_k\cap {\text{L}}_k^{*}\left|\right|}{M}\right)\text{, [1,0] }}\end{array}\right.\text{ (4) }$$

where N^q is the total number of query or tested samples, M is the total number of different labels for the investigated system, ||U| means the operator acting on the set therein to count the number of its elements, U means the symbol for the "union" in the set theory, ∩ denotes the symbol for the "intersection", $L_k^{}$ the subset that contains all the labels observed by experiments for the k-th tested sample, $L_k^{*}$ represents the subset that contains all the labels predicted for the k-th sample, and

$$\Delta ({\text{L}}_k,{\text{L}}_k^{*})\text{ = }\left\{\begin{array}{l}1,{\text{ if all the labels in L}}_k^{*}are{\text{ identical to those in L}}_{\text{k}}\\ 0,\text{ otherwise }\end{array}\right.\text{ (5)}$$

In Eq.4, the first four metrics with an upper arrow $\uparrow$ are called positive metrics, meaning that the larger the rate is the better the prediction quality will be; the 5th metrics with a down arrow $\downarrow$ is called negative metrics, implying just the opposite meaning. As we can see from Eq.1: (1) The "Aiming" defined by the 1^st sub-equation is for checking the rate or percentage of the correctly predicted labels over the practically predicted labels; (2) The "Coverage" defined in the 2^nd sub-equation is for checking the rate of the correctly predicted labels over the actual labels in the system concerned; (3) The "Accuracy" in the 3^rd sub-equation is for checking the average ratio of correctly predicted labels over the total labels including correctly and incorrectly predicted labels as well as those real labels but are missed in the prediction; (4) The "Absolute true" in the 4^th sub-equation is for checking the ratio of the perfectly or completely correct prediction events over the total prediction events; (5) The "Absolute false" in the 5^th sub-equation is for checking the ratio of the completely wrong prediction over the total prediction events.

The five metrics in Eq.4 reflect the quality of a multi-label predictor from five different angles at the global level. It is instructive to point out, however, among the five global metrics the most important one and also the most difficult to improve its success rate is the "Absolute true" or "perfectly correct" rate [295]. Why? This is because the score standard for the absolute true rate is very harsh. According to its definition, for a statistical sample that is actually simultaneously with the states ("A", "B", "C"). If the predicted result is not exactly the three states but ("A", "B") or ("A", "B", "C", "D"), no score whatsoever will be given. In other words, when and only when the predicted outcome for the statistical sample is perfectly identical to its actual status, can we add one point for the absolute true rate; otherwise, zero. That is why many investigators even chose not to mention the metrics of absolute true rate; otherwise they would face the embarrassment of reporting a very low success rate for their prediction methods.

The set of metrics in Eq.4 are used to evaluate the prediction quality of a multi-label AI tool for all the samples in the entire system concerned [296], and hence is called the "set of metrics for the global accuracy" or the "set of global metrics".

Concluding Remarks and Perspectives

The AI tools introduced in this review paper for predicting PTM sites have been all established by following the 5-steps rule [88], and hence they each have a user-friendly web server for the majority of experimental scientists to easily get their desired data. Also, their cornerstones are based on PseAAC [34-36,88,253] or PseKNC [37,254,256, 257,264,297], and hence their prediction quality is usually higher than the other PTM prediction methods without using the PseAAC or PseKNC approach.

As we can see from the Sections 3, 4, and 5, the most web-servers available are for the AI tools aimed at identifying the PTM sites in protein sequences, the next are at DNA sequences, and the least at RNA sequences. It is anticipated, however, that with more experimental data available in the future, the benchmark datasets for the PTM sites in RNA and DNA sequences will be enriched as well. The existing AI tools will not only be easily extended to cover more RNA and DNA sequences, but also further improve the prediction quality in all kinds of biological sequences.

It is worthy of noting that recently the 5-ateps rule has also been used in many different areas [84,298-314].

Meanwhile it has not escaped our notice that using graphic approaches to study biological and medical systems can provide an intuitive vision and useful insights for helping analyze complicated relations therein as shown in the systems of enzyme fast reaction [315-317], graphical rules in molecular biology [318-321], and low-frequency internal motion in biomacromolecules (such as protein and DNA) [322]. Particularly, what happened is that this kind of insightful implication has also been demonstrated in [323] and many follow-up publications [324-339].

Acknowledgement

The author wishes to thank Dr. Michelle Claus for the invitation to write this paper.

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210. M Rahimi, MR Bakhtiarizadeh, A Mohammadi-Sangcheshmeh (2017) Ogenesis_Pred: A sequence-based method for predicting oogenesis proteins by six different modes of Chou's pseudo amino acid composition. J Theor Biol 414: 128-136.
211. M Tahir, M Hayat, M Kabir (2017) Sequence based predictor for discrimination of enhancer and their types by applying general form of Chou's trinucleotide composition. Comput Methods Programs Biomed 146: 69-75.
212. P Tripathi, PN Pandey (2017) A novel alignment-free method to classify protein folding types by combining spectral graph clustering with Chou's pseudo amino acid composition. J Theor Biol 424: 49-54.
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214. B Yu, S Li, WY Qiu, et al. (2017) Accurate prediction of subcellular location of apoptosis proteins combining Chou's PseAAC and PsePSSM based on wavelet denoising. Oncotarget 8: 107640-107665.
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216. J Ahmad, M Hayat (2018) MFSC: Multi-voting based feature selection for classification of golgi proteins by adopting the general form of chou's pseaac components. J Theor Biol 463: 99-109.
217. MA Al Maruf, S Shatabda (2018) iRSpot-SF: Prediction of recombination hotspots by incorporating sequence based features into Chou's Pseudo components. Genomics.
218. M Arif, M Hayat, Z Jan (2018) iMem-2LSAAC: A two-level model for discrimination of membrane proteins and their types by extending the notion of SAAC into Chou's pseudo amino acid composition. J Theor Biol 442: 11-21.
219. AH Butt, N Rasool, YD Khan (2018) Predicting membrane proteins and their types by extracting various sequence features into Chou's general PseAAC. Mol Biol Rep.
220. E Contreras-Torres (2018) Predicting structural classes of proteins by incorporating their global and local physicochemical and conformational properties into general Chou's PseAAC. J Theor Biol 454: 139-145.
221. X Cui, Z Yu, B Yu (2018) UbiSitePred: A novel method for improving the accuracy of ubiquitination sites prediction by using LASSO to select the optimal Chou's pseudo components. Chemometrics and Intelligent Laboratory Systems (CHEMOLAB).
222. X Fu, W Zhu, B Liso et al. (2018) Improved DNA-binding protein identification by incorporating evolutionary information into the Chou's PseAAC. IEEE Access 6: 66545-66556.
223. F Javed, M Hayat (2018) Predicting subcellular localizations of multi-label proteins by incorporating the sequence features into Chou's PseAAC. Genomics
224. MS Krishnan (2018) Using Chou's general PseAAC to analyze the evolutionary relationship of receptor associated proteins (RAP) with various folding patterns of protein domains. J Theor Biol 445: 62-74.
225. Y Liang, S Zhang (2018) Identify Gram-negative bacterial secreted protein types by incorporating different modes of PSSM into Chou's general PseAAC via Kullback-Leibler divergence. J Theor Biol 454: 22-29.
226. J Mei, Y Fu, J Zhao (2018) Analysis and prediction of ion channel inhibitors by using feature selection and Chou's general pseudo amino acid composition. J Theor Biol 456: 41-48.
227. J Mei, J Zhao (2018) Prediction of HIV-1 and HIV-2 proteins by using Chou's pseudo amino acid compositions and different classifiers. Scientific Reports 8: 2359.
228. J Mei, J Zhao (2018) Analysis and prediction of presynaptic and postsynaptic neurotoxins by Chou's general pseudo amino acid composition and motif features. J Theor Biol 447: 147-153.
229. M Mousavizadegan, H Mohabatkar (2018) Computational prediction of antifungal peptides via Chou's PseAAC and SVM. J Bioinform Comput Biol 16.
230. W Qiu, S Li, X Cui, et al. (2018) Predicting protein submitochondrial locations by incorporating the pseudo-position specific scoring matrix into the general Chou's pseudo-amino acid composition. J Theor Biol 450: 86-103.
231. SM Rahman, S Shatabda, S Saha, et al. (2018) DPP-PseAAC: A dna-binding protein prediction model using chou's general PseAAC. J Theor Biol 452: 22-34.
232. ES Sankari, DD Manimegalai (2018) Predicting membrane protein types by incorporating a novel feature set into Chou's general PseAAC. J Theor Biol 455: 319-328.
233. A Srivastava, R Kumar, M Kumar, et al. (2018) predicting and classifying beta-lactamase using a 3-tier prediction system via Chou's general PseAAC. J Theor Biol 457: 29-36.
234. M Tahir, H Tayara, KT Chong (2019) iRNA-PseKNC(2methyl): Identify RNA 2'-O-methylation sites by convolution neural network and Chou's pseudo components. J Theor Biol 465: 1-6.
235. L Zhang, L Kong (2018) iRSpot-ADPM: Identify recombination spots by incorporating the associated dinucleotide product model into Chou's pseudo components. J Theor Biol 441: 1-8.
236. L Zhang, L Kong (2019) iRSpot-PDI: Identification of recombination spots by incorporating dinucleotide property diversity information into Chou's pseudo components. Genomics 111: 457-464.
237. S Zhang, X Duan (2018) Prediction of protein subcellular localization with oversampling approach and Chou's general PseAAC. J Theor Biol 437: 239-250.
238. S Zhang, Y Liang (2018) Predicting apoptosis protein subcellular localization by integrating auto-cross correlation and PSSM into Chou's PseAAC. J Theor Biol 457: 163-169.
239. S Zhang, K Yang, Y Lei, et al. (2018) iRSpot-DTS: Predict recombination spots by incorporating the dinucleotide-based spare-cross covariance information into Chou's pseudo components. Genomics.
240. W Zhao, L Wang, TX Zhang, et al. (2018) A brief review on software tools in generating Chou's pseudo-factor representations for all types of biological sequences. Protein Pept Lett 25: 822-829.
241. S Adilina, DM Farid, S Shatabda (2019) Effective DNA binding protein prediction by using key features via Chou's general PseAAC. J Theor Biol 460: 64-78.
242. J Ahmad, M Hayat (2019) MFSC: Multi-voting based feature selection for classification of Golgi proteins by adopting the general form of Chou's PseAAC components. J Theor Biol 463: 99-109.
243. G Chen, M Cao, J Yu, et al. (2019) Prediction and functional analysis of prokaryote lysine acetylation site by incorporating six types of features into Chou's general PseAAC. J Theor Biol 461: 92-101.
244. W Hussain, YD Khan, N Rasool, et al. (2019) SPrenylC-PseAAC: A sequence-based model developed via Chou's 5-steps rule and general PseAAC for identifying S-prenylation sites in proteins. J Theor Biol 468: 1-11.
245. M Kabir, S Ahmad, M Iqbal, et al. (2019) iNR-2L: A two-level sequence-based predictor developed via Chou's 5-steps rule and general PseAAC for identifying nuclear receptors and their families. Genomics.
246. NQK Le, EKY Yapp, QT Ho, et al. (2019) iEnhancer-5Step: Identifying enhancers using hidden information of DNA sequences via Chou's 5-step rule and word embedding. Anal Biochem 571: 53-61.
247. Y Pan, S Wang, Q Zhang, et al. (2019) Analysis and prediction of animal toxins by various Chou's pseudo components and reduced amino acid compositions. J Theor Biol 462: 221-229.
248. Y Shen, J Tang, F Guo (2019) Identification of protein subcellular localization via integrating evolutionary and physicochemical information into Chou's general PseAAC. J Theor Biol 462: 230-239.
249. M Tahir, M Hayat, SA Khan (2019) iNuc-ext-PseTNC: An efficient ensemble model for identification of nucleosome positioning by extending the concept of Chou's PseAAC to pseudo-tri-nucleotide composition. Mol Genet Genomics 294: 199-210.
250. B Tian, X Wu, C Chen, et al. (2019) Predicting protein-protein interactions by fusing various Chou's pseudo components and using wavelet denoising approach. J Theor Biol 462: 329-346.
251. AH Butt, N Rasool, YD Khan (2019) Prediction of antioxidant proteins by incorporating statistical moments based features into Chou's PseAAC. J Theor Biol 473: 1-8.
252. HB Shen, Chou KC (2008) PseAAC: A flexible web-server for generating various kinds of protein pseudo amino acid composition. Anal Biochem 373: 386-388.
253. KC Chou (2009) Pseudo amino acid composition and its applications in bioinformatics, proteomics and system biology. Current Proteomics 6: 262-274.
254. W Chen, H Lin, KC Chou (2015) Pseudo nucleotide composition or PseKNC: An effective formulation for analyzing genomic sequences. Mol Biosyst 11: 2620-2634.
255. B Liu, F Liu, X Wang, et al. (2015) Pse-in-One: A web server for generating various modes of pseudo components of DNA, RNA, and protein sequences. Nucleic Acids Res 43: 65-71.
256. B Liu, F Liu, L Fang, et al. (2015) repDNA: A Python package to generate various modes of feature vectors for DNA sequences by incorporating user-defined physicochemical properties and sequence-order effects. Bioinformatics 31: 1307-1309.
257. B Liu, F Liu, L Fang, et al. (2016) repRNA: A web server for generating various feature vectors of RNA sequences. Mol Genet Genomics 291: 473-481.
258. B Liu, H Wu, Wang X, et al. (2017) Pse-in-One 2.0: An improved package of web servers for generating various modes of pseudo components of DNA, RNA, and protein sequences. Nucleic Acids Res: 67-91.
259. Z Liu, X Xiao, WR Qiu, et al. (2015) iDNA-Methyl: Identifying DNA methylation sites via pseudo trinucleotide composition. Anal Biochem 474: 69-77.
260. J Chen, H Liu, J Yang, et al. (2007) Prediction of linear B-cell epitopes using amino acid pair antigenicity scale. Amino Acids 33: 423-428.
261. KC Chou (2001) Using subsite coupling to predict signal peptides. Protein Eng 14: 75-79.
262. KC Chou (2001) Prediction of signal peptides using scaled window. Peptides 22: 1973-1979.
263. KC Chou (2001) Prediction of protein signal sequences and their cleavage sites. Proteins 42: 136-139.
264. W Chen, PM Feng, H Lin, et al. (2013) iRSpot-PseDNC: Identify recombination spots with pseudo dinucleotide composition. Nucleic Acids Res 41: e68.
265. KC Chou, HB Shen (2006) Hum-PLoc: A novel ensemble classifier for predicting human protein subcellular localization. Biochem Biophys Res Commun 347: 150-157.
266. KC Chou, HB Shen (2006) Addendum to "Hum-PLoc: A novel ensemble classifier for predicting human protein subcellular localization". Biochem Biophys Res Commun 348: 1479.
267. HB Shen, KC Chou (2007) Gpos-PLoc: An ensemble classifier for predicting subcellular localization of Gram-positive bacterial proteins Protein Eng Des Sel 20: 39-46.
268. HB Shen, KC Chou (2007) Virus-PLoc: A fusion classifier for predicting the subcellular localization of viral proteins within host and virus-infected cells. Biopolymers 85: 233-240.
269. HB Shen, KC Chou (2007) Nuc-PLoc: A new web-server for predicting protein subnuclear localization by fusing PseAA composition and PsePSSM. Protein Eng Des Sel 20: 561-567.
270. HB Shen, J Yang, KC Chou (2007) Euk-PLoc: An ensemble classifier for large-scale eukaryotic protein subcellular location prediction. Amino Acids 33: 57-67.
271. KC Chou, HB Shen (2008) Cell-PLoc: A package of Web servers for predicting subcellular localization of proteins in various organisms. Nat Protoc 3: 153-162.
272. KC Chou, HB Shen (2010) Cell-PLoc 2.0: An improved package of web-servers for predicting subcellular localization of proteins in various organisms. Nat Sci 2: 1090-1103.
273. KC Chou, ZC Wu, X Xiao (2011) iLoc-Euk: A Multi-Label Classifier for Predicting the Subcellular Localization of Singleplex and Multiplex Eukaryotic Proteins PLoS One 3: 18258.
274. ZC Wu, X Xiao, KC Chou (2011) iLoc-Plant: A multi-label classifier for predicting the subcellular localization of plant proteins with both single and multiple sites. Mol Biosyst 7: 3287-3297.
275. X Xiao, ZC Wu, KC Chou (2011) iLoc-Virus: A multi-label learning classifier for identifying the subcellular localization of virus proteins with both single and multiple sites. J Theor Biol 284: 42-51.
276. KC Chou, ZC Wu, X Xiao (2012) iLoc-Hum: Using accumulation-label scale to predict subcellular locations of human proteins with both single and multiple sites. Mol Biosyst 8: 629-641.
277. ZC Wu, X Xiao (2012) iLoc-Gpos: A multi-layer classifier for predicting the subcellular localization of singleplex and multiplex gram-positive bacterial proteins. Protein Pept Lett 19: 4-14.
278. WZ Lin, JA Fang, X Xiao (2013) iLoc-Animal: A multi-label learning classifier for predicting subcellular localization of animal proteins. Mol Biosyst 9: 634-644.
279. ZD Su, Y Huang, ZY Zhang, et al. (2018) iLoc-lncRNA: Predict the subcellular location of lncRNAs by incorporating octamer composition into general PseKNC. Bioinformatics 34: 4196-4204.
280. X Cheng, X Xiao, Chou KC (2017) pLoc-mPlant: Predict subcellular localization of multi-location plant proteins via incorporating the optimal GO information into general PseAAC. Mol Biosyst 13: 1722-1727
281. X Cheng, X Xiao, Chou KC (2017) pLoc-mVirus: Predict subcellular localization of multi-location virus proteins via incorporating the optimal GO information into general PseAAC. Gene 628: 315-321.
282. X Cheng, SG Zhao, WZ Lin, et al. (2017) pLoc-mAnimal: Predict subcellular localization of animal proteins with both single and multiple sites. Bioinformatics 33: 3524-3531.
283. X Xiao, X Cheng, S Su, et al. (2017) pLoc-mGpos: Incorporate key gene ontology information into general PseAAC for predicting subcellular localization of Gram-positive bacterial proteins. Natural Science 9: 331-349.
284. X Cheng, X Xiao (2018) pLoc-mEuk: Predict subcellular localization of multi-label eukaryotic proteins by extracting the key GO information into general PseAAC. Genomics 110: 50-58.
285. X Cheng, X Xiao (2018) pLoc-mGneg: Predict subcellular localization of Gram-negative bacterial proteins by deep gene ontology learning via general PseAAC. Genomics 110: 231-239.
286. X Cheng, X Xiao (2018) pLoc-mHum: Predict subcellular localization of multi-location human proteins via general PseAAC to winnow out the crucial GO information. Bioinformatics 34: 1448-1456.
287. X Cheng, X Xiao (2018) pLoc_bal-mGneg: Predict subcellular localization of Gram-negative bacterial proteins by quasi-balancing training dataset and general PseAAC. J Theor Biol 458: 92-102.
288. X Cheng, X Xiao (2018) pLoc_bal-mPlant: Predict subcellular localization of plant proteins by general PseAAC and balancing training dataset. Curr Pharm Des 24: 4013-4022.
289. KC Chou, X Cheng, Xiao (2018) pLoc_bal-mHum: Predict subcellular localization of human proteins by PseAAC and quasi-balancing training dataset. Genomics.
290. X Xiao, X Cheng, G Chen, et al. (2019) pLoc_bal-mGpos: Predict subcellular localization of Gram-positive bacterial proteins by quasi-balancing training dataset and PseAAC. Genomics 111: 886-892.
291. X Cheng, WZ Lin, X Xiao (2019) pLoc_bal-mAnimal: Predict subcellular localization of animal proteins by balancing training dataset and PseAAC. Bioinformatics 35: 398-406.
292. KC Chou (2019) Progresses in predicting post-translational modification. International Journal of Peptide Research and Therapeutics.
293. X Cheng, SG Zhao, X Xiao (2017) iATC-mISF: A multi-label classifier for predicting the classes of anatomical therapeutic chemicals. Bioinformatics 341-346.
294. X Cheng, SG Zhao, X Xiao (2017) iATC-mHyb: A hybrid multi-label classifier for predicting the classification of anatomical therapeutic chemicals. Oncotarget 8: 58494-58503.
295. KC Chou (2013) Some remarks on predicting multi-label attributes in molecular biosystems. Mol Biosyst 9: 1092-1100.
296. KC Chou (2019) Advance in predicting subcellular localization of multi-label proteins and its implication for developing multi-target drugs. Curr Med Chem.
297. H Lin, EZ Deng, H Ding, et al. (2014) iPro54-PseKNC: A sequence-based predictor for identifying sigma-54 promoters in prokaryote with pseudo k-tuple nucleotide composition. Nucleic Acids Res 42: 12961-12972.
298. X Zhai, M Chen, W Lu (2018) Accelerated search for perovskite materials with higher Curie temperature based on the machine learning methods. Computational Materials Science 151: 41-48.
299. J Jia, X Li, W Qiu, et al. (2019) iPPI-PseAAC(CGR): Identify protein-protein interactions by incorporating chaos game representation into PseAAC. J Theor Biol 460: 195-203.
300. YD Khan, M Jamil, W Hussain, et al. (2019) pSSbond-PseAAC: Prediction of disulfide bonding sites by integration of PseAAC and statistical moments. J Theor Biol 463: 47-55.
301. VK Shyamili, A Vellaichamy (2019) Sequence and structure-based characterization of human and yeast ubiquitination sites by using Chou's sample formulation. Proteins: Structure, function and bioinformatics.
302. M Awais, W Hussain, YD Khan, et al. (2019) iPhosH-PseAAC: Identify phosphohistidine sites in proteins by blending statistical moments and position relative features according to the Chou's 5-step rule and general pseudo amino acid composition. IEEE/ACM Trans Comput Biol Bioinform.
303. P Feng, H Yang, H Ding, et al. (2019) iDNA6mA-PseKNC: Identifying DNA N(6)-methyladenosine sites by incorporating nucleotide physicochemical properties into PseKNC. Genomics 111: 96-102.
304. Lu F, Zhu M, Lin Y, et al. (2019) The preliminary efficacy evaluation of the CTLA-4-Ig treatment against Lupus nephritis through in-silico analyses. J Theor Biol 471: 74-81.
305. Yi Lu, Shuo Wang, Jianying Wang, et al. (2019) An ep10. idemic avian influenza prediction model based on google trends. Letters in Organic Chemistry 16: 303.
306. Niu B, Liang C, Lu Y, et al. (2019) Glioma stages prediction based on machine learning algorithm combined with protein-protein interaction networks. Genomics.
307. Chou KC (2019) An insightful 20-year recollection since the birth of pseudo amino acid components. Proyein & Peptide Letters in press.
308. Chou KC (2019) Artificial intelligence (AI) tools constructed via the 5-steps rule for predicting post-translational modifications. Artificial Intelligence in press.
309. Chou KC (2019) Recent progresses in predicting protein subcellular localization with artificial intelligence (AI) tools developed via the 5-steps rule. Artificial Intelligence in press.
310. Chou KC (2019) An insightful 10-year recollection since the emergence of the 5-steps rule. Current Pharmaceutical Design in press.
311. Chou KC (2019) Impacts of pseudo amino acid components and 5-steps rule to proteomics and proteome analysis. Proteomics in press.
312. Chou KC (2019) Distorted key theory and its implication for drug development. Current Proteomics in press.
313. Chou KC (2019) Proposing pseudo amino acid components is an important milestone for proteome and genome analyses. International Journal for Peptide Research and Therapeutics (IJPRT) in press.
314. Chou KC (2019) Two kinds of metrics for computational biology. Genomics in press.
315. Chou KC, Forsen S (1980) Diffusion-controlled effects in reversible enzymatic fast reaction system - critical spherical shell and proximity rate constants. Biophysical Chemistry 12: 255-263.
316. Chou KC, Li TT, Forsen S (1980) The critical spherical shell in enzymatic fast reaction systems. Biophysical Chemistry 12: 265-269.
317. Li TT, Forsen S (1980) The flow of substrate molecules in fast enzyme-catalyzed reaction systems. Chemica Scripta 16: 192-196.
318. Chou KC, Forsen S (1980) Graphical rules for enzyme-catalyzed rate laws. Biochem J 187: 829-835.
319. Chou KC, Forsen S, Zhou GQ (1980) Three schematic rules for deriving apparent rate constants. Chemica Scripta 16: 109-113.
320. Chou KC, Carter RE, Forsen S (1981) A new graphical method for deriving rate equations for complicated mechanisms. Chemica Scripta 18: 82-86.
321. Chou KC, Forsen S (1981) Graphical rules of steady-state reaction systems. Canadian Journal of Chemistry 59: 737-755.
322. Chou KC, Chen NY, Forsen S (1981) The biological functions of low-frequency phonons: 2. Cooperative effects. Chemica Scripta 18: 126-132.
323. Chou KC, Jiang SP, Liu WM, et al. (1979) Graph theory of enzyme kinetics: 1. Steady-state reaction system. Scientia Sinica 22: 341-358.
324. Zhou GP, Deng MH (1984) An extension of Chou's graphic rules for deriving enzyme kinetic equations to systems involving parallel reaction pathways. Biochem J 222: 169-176.
325. Chou KC (1989) Graphic rules in steady and non-steady enzyme kinetics. Journal of Biological Chemistry 264: 12074-12079.
326. Chou KC (1990) Applications of graph theory to enzyme kinetics and protein folding kinetics: Steady and non-steady state systems. Biophysical Chemistry 35: 1-24.
327. Althaus IW, Chou JJ, Gonzales AJ, et al. (1993) Steady-state kinetic studies with the non-nucleoside HIV-1 reverse transcriptase inhibitor U-87201E. J Biol Chem 268: 6119-6124.
328. Althaus IW, Gonzales AJ, Chou JJ, et al. (1993) The quinoline U-78036 is a potent inhibitor of HIV-1 reverse transcriptase. J Biol Chem 268: 14875-14880.
329. Althaus IW, Chou JJ, Gonzales AJ, et al. (1993) Kinetic studies with the nonnucleoside HIV-1 reverse transcriptase inhibitor U-88204E. J Biol Chem 32: 6548-6554.
330. Althaus IW, Chou JJ, Gonzales AJ, et al. (1994) Steady-state kinetic studies with the polysulfonate U-9843, an HIV reverse transcriptase inhibitor. Cellular and Molecular Life Science (Experientia) 50: 23-28.
331. Althaus IW, Chou JJ, Gonzales AJ, et al. (1994) Kinetic studies with the non-nucleoside human immunodeficiency virus type-1 reverse transcriptase inhibitor U-90152E. Biochem Pharmacol 47: 2017-2028.
332. Chou KC, Kezdy FJ, Reusser F (1994) Kinetics of processive nucleic acid polymerases and nucleases. Analytical Biochemistry 221: 217-230.
333. Althaus IW, Franks KM, Diebel MR, et al. (1996) The benzylthio-pyrididine U-31,355, a potent inhibitor of HIV-1 reverse transcriptase. Biochem Pharmacol 51: 743-750.
334. Andraos J (2008) Kinetic plasticity and the determination of product ratios for kinetic schemes leading to multiple products without rate laws: New methods based on directed graphs. Canadian Journal of Chemistry 86: 342-357.
335. Chou KC, Shen HB (2009) FoldRate: A web-server for predicting protein folding rates from primary sequence. The Open Bioinformatics Journal 3: 31-50.
336. Shen HB, Song JN, Chou K (2009) Prediction of protein folding rates from primary sequence by fusing multiple sequential features. Journal of Biomedical Science and Engineering (JBiSE) 2: 136-143.
337. Chou KC (2010) Graphic rule for drug metabolism systems. Curr Drug Metab 11: 369-378.
338. Chou KC, Lin WZ, Xiao X (2011) Wenxiang: A web-server for drawing wenxiang diagrams. Natural Science 3: 862-865.
339. Zhou GP (2011) The disposition of the LZCC protein residues in wenxiang diagram provides new insights into the protein-protein interaction mechanism. J Theor Biol 284: 142-148.