Protein Structure Prediction: Biological Basis, Processing Methods

and Deep Learning

Jingkai Wen

College of Life Science and Technology, Huazhong University of Science and Technology, Wuhan, China

Keywords: Protein Structure Prediction, Processing Methods, Deep Learning.

Abstract: As the speed of finding new proteins exceeds that of structural analysis, traditional experimental ways are

time-consuming and cannot meet the need to decipher the structure of proteins in a relatively short time, which

leads to the appearance of protein structure prediction. Protein structure prediction uses deposited protein

structures to predict the newfound and has developed fast with the increase of computational resources and

the refinement of algorithms. This review introduces protein structure prediction based on machine learning,

including sequence encoding and feature extraction. After that, we focus on deep learning and interpret several

common methods used in deep learning algorithms, including sequence alignment, residues contact profile.

Finally, we introduce several representative algorithms and their methods.

1 INTRODUCTION

Protein Structure prediction is crucial to understand

the protein function and inter-protein contact.

Traditionally, scientists use experimental methods to

analyse the three-dimensional (3D) structure, like X-

ray Crystallography, Nuclear Magnetic Resonance

(NMR) spectroscopy and Cryogenic Electron

Microscopy (Cryo-EM)

(https://www.genome.jp/dbget/aaindex.html.);

(Anand, 2008); (Anfinsen) But now, the gap between

protein sequences and know structures is getting

bigger (Bateman, 2021). Meanwhile, the speed of

discovering new proteins surpass that of analysing, so

it is needed to predict the structure of new-finding

proteins based on what we have known. Proteins have

infinite patterns of structure, but the basic forming

elements are conservative among all species. Also,

according to self-assembly theory, only the amino

acid residue is adequate to model the final 3D

structure (Bengio, 1994). That's the theoretical base

for computational prediction. Many methods were

created to predict 3D structures based on sequences.

According to Critical Assessment of Protein

Structure Prediction (CASP), protein structure

prediction can be divided into two categories,

template-based modeling (TBM) and free modeling

https://orcid.org/0000-0003-1252-5843

(FM) (Bernardes, 2013). TBM compares target

sequences with those in Protein Database (PDB)

(https://www.rcsb.org) and finds homologous

fragments, then takes known motifs together and

thread several parts to present the whole 3D structure.

FM, also called ab initio prediction, predict the target

sequence based on inter-residues interaction and

evolutional relationship.

The Dictionary of Protein Secondary Structures

defines eight states of a single amino acid residue to

make the sequence easier to be processed by the

procedure, so the algorithm can easily categorize

residues. Different methods are applied to process the

sequence and get information (Bonetta, 2020).

2 PROTEIN STRUCTURES

2.1

Primary Structure

The primary structure of proteins refers to the

sequence of amino acids in the polypeptide chains,

like ACDE, which is determined by the sequence of

DNA. After transcription and translation, the genetic

information is transformed from DNA to mRNA and

finally to protein (Cai, 2000). Each amino acid is

joined by peptide bonds, formed by dehydration

308

Wen, J.

Protein Structure Prediction: Biological Basis, Processing Methods and Deep Learning.

DOI: 10.5220/0011203900003443

In Proceedings of the 4th International Conference on Biomedical Engineering and Bioinformatics (ICBEB 2022), pages 308-315

ISBN: 978-989-758-595-1

between amino groups and carboxyl groups. In

structure prediction, 20 amino acids are encoded with

20 letters, so the input to the algorithm is actually

character strings. The first protein deciphered was

insulin. Frederick Sanger discovered its amino acid

sequence in 1951 and brought up that proteins have

defining amino acid sequences (Chou, 1995).

2.2 Secondary Structure

Secondary structure refers to regular local sub-

structures defined by the patterns of hydrogen bonds.

The one-dimensional sequence can form three

dimensional local segments through the hydrogen

bond between amino and carboxyl oxygen. Because

secondary structures are elements for protein folding,

prediction from sequence to local segments is critical

and therefore challenging. Pauling assigned

secondary structures to eight types based on

hydrogen bonding patterns. For convenient

expression and encoding, The Dictionary of Protein

Secondary Structures (DSSP) is commonly used to

describe secondary structures with corresponding

eight letters (Bonetta, 2020).

2.3 Tertiary Structure

Tertiary structure refers to the three-dimensional

structure of a single protein folding with one or

several domains driven by non-specific hydrophobic

interaction. Tertiary structure is basically the spatial

arrangement of multiple secondary structures,

sometimes accompanied by metal ions.

Therefore, models for structure prediction

actually try to extract local features from the amino

acid sequence (input) and transform them into octet-

state secondary structures, and then assemble

elements into three-dimensional protein structures.

Table 1. The Dictionary of Protein Secondary Structures.

Code Secondary Structure

G 3

helix (3-turn helix)

H  helix

I  helix

T hydrogen-bonded turn

E extended strand in parallel and/or anti-

arallel β-sheet

B Isolated β-bridge

S Bend

C Coil

3 SELF-ASSEMBLY THEORY

In 1961, Anfinsen treated bovine pancreatic

ribonuclease with 8 M urea and got a randomly coiled

polypeptide chain with cysteine residues. Then he

found that though the ribonuclease was denatured, it

could regain the activity under optimal conditions of

polypeptide concentration and pH (Bengio, 1994).

Therefore, he proposed that proteins have their

natural structures, determined by one-dimensional

sequence, and peptide chains will automatically fold

into that conformation. That's the self-assembly

theory. It is the basis for protein structure prediction

because the theory assumes that no other variables

are influencing the final structure of proteins except

for the sequence.

4 FEATURE EXTRACTION

After the input of sequence, a specific encoding

scheme is needed to generate a set of features to

represent the properties of each protein and use those

features as input to machine learning (ML)

algorithms (Chou, 2020). In the past 30 years,

scientists have developed a number of different

descriptors of proteins for different aspects of

prediction, including fold classification, subcellular

location prediction and membrane protein type

prediction (Chou, 2001); (Chou, 2019). Descriptors

are designed to show some information of the

proteins, like isoelectric point (pI), amino acid

residues composition. Here we introduce some

strategies to extract protein features.

4.1 Amino Acid Composition

Proteins are composed of amino acid residues whose

arrangement determines how proteins will fold. So

basically, amino acid composition (AAC) help to find

a specific spatial structure. Originally, AAC was

utilized as a feature descriptor. Based on sequence, a

vector with 20 elements is yielded, and each element

represents the frequency of a specific amino acid

residue (Comet, 2002); (Dayhoff, 1983); (Deng,

2018). However, it was found that only the AAC

descriptor cannot simulate the spatial structure, and

the bias is inevitable. Scientists think some important

information may be neglected, so Zhou proposed the

concept of pseudo amino acid composition

(PseAAC) (Ding, 2013); (Dubchak, 1995). It is just

some additional digital information adding to the

Protein Structure Prediction: Biological Basis, Processing Methods and Deep Learning

309

original 20 elements, yielding a 20 + -dimension

vector:

𝑋=[𝑥



,𝑥



,⋯,𝑥



,𝑥



,⋯,𝑥



]

The factors 𝑥



,𝑥



,⋯,𝑥



are frequencies of 20

natural amino acids, and the factors 𝑥



,⋯,𝑥



are the information along the sequence as

complementary input. PseAAC includes

hydrophobicity, hydrophilicity, etc. With the

development of analysis, more PseAACs showed up,

representing more important information (Dubchak,

1999); (Eddy, 2002). Free resources containing 63

different kinds of PseAACs can be accessed through

a web server named "PseAAC", which was

established by Shen and Chou

(https://www.csbio.sjtu.edu.cn/bioinf/PseAAC/)

(Edgar, 2004); (Elmlund, 2015).

4.2 Sequence Order

Lin and Li take two adjacent amino acid residues as

an unit and use it to predict secondary structure

(Gondro, 2007); (Gonzalez-Lopez, F.). For example,

dipeptide composition is utilized, which means it

yields 400 possible theoretical arrangements. The

output is then a vector containing the occurrence

frequency of a combination of specific two residues.

Similarly, Yu and coworkers use dipeptide,

tripeptide, and tetrapeptide elements to promote

modeling accuracy (Hall, 1964); (Hanson). What's

more, they convert polypeptide composition into a

structural class tendency sequence, which was then

used as a new feature descriptor. Tetrapeptide

arrangement predicts regular structures, for i-th

residue usually interact with i+4-th residue (Hanson).

4.3 Physiochemical Properties

Each amino acid has its specific side chain, which

determines its physiochemical properties, like

isoelectric point, polarizability and hydrophobicity

(Dayhoff, 1983); (Henikoff, 1992); (Hornak, 2006).

Those properties are accessible online at the amino

acid index database

(https://www.genome.jp/dbget/aaindex.html)

(Hornak, 2006); (Ilonen, 2003). Chou extracted

information from physicochemical properties with a

set of correlation factors, yielding PseAAC

descriptors. Using different functions, sequence

order correlation factors can be calculated.

According to global protein sequence descriptors

(GPSD) theory, amino acids are classified according

to their unique properties (Henikoff, 1992). GPSD

includes three dimensions: composition, transition

and distribution. Same as AAC, the composition

means the occurrence frequency of each amino acid

residue type. The transition means frequencies that a

specific type of amino acid changes to another one.

The distribution is position-specific information,

showing the distribution of each amino acid residue

along the sequence.

4.4 Secondary-structure-based

Features

According to DSSP above, each amino acid has its

tendency to appear in one or more secondary

structures. So the protein sequence can be converted

into secondary structure descriptors by extracting

information with GPSD. Helix (H), strand (E) and

coil (C) are commonly utilized (Jararweh, 2019).

Also, this method can be integrated with several

methods above to improve accuracy. For example,

Secondary structure features help to calculate the

correlation factors or yield PseAAC (Jumper, J.);

(Kabsch, 1983).

5 SEQUENCE ALIGNMENT

One letter represents a specific amino acid during the

prediction, and the input is a character string. The

algorithm cannot get the information about the

character of each amino acid, so the first thing to do

is to find if deposited proteins are containing the same

sequence fragments. To achieve this, the target

sequence must be compared with every sequence in

the database once, which needs the application of

Pairwise Alignment (PA). If there are some

counterparts in the database, the algorithm will just

take the known structure of counterparts to construct

the model, based on the self-assembly rule proposed

by Christian B. Anfinsen. If there is no sequence

counterpart in the database, then we have to apply the

algorithm to make an ab initio prediction, which is

normally Multiple Sequence Alignment (MSA).

Through MSA, we can know the evolutional

relationship between the target sequence and existing

sequences and then reason probable 3D structure.

5.1 Pairwise Sequence Alignment

(PSA)

Pairwise sequence alignment (PSA) is a method

assessing the similarity between two sequences. The

classic method is Dynamic Programming, also named

the needleman-wunsch (NW) algorithm

ICBEB 2022 - The International Conference on Biomedical Engineering and Bioinformatics

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(Kawashima, 2000). By using the trace-back process,

DP can provide optimum alignment and predict the

objective function (Kinch, 2016). When the input

contains two sequences, which is the simplest case,

DP builds an i×j matrix based on two sequences (i, j

is the sequence length). Each position in the matrix

has a score, representing the similarity of the row and

the column. Finding the path with the highest scores

can get an alignment pattern of two sequences

(Kurgan, 2007). However, DP is time-consuming and

requires high computation resources. The complexity

grows exponentially along with the increase in

sequence length, not to mention that two or more

optimal paths are available. Moreover, DP suffers

from high-dimensional problems in multiple

sequence alignment. Even if the computation

resources are adequate, the optimum alignment from

DP is rarely biologically optimum. For high accuracy

of prediction, protein sequence alignment

substitution matrices were established (Landan,

2009). The substitution scoring matrix includes PAM

and BLOSUM, (Landan, 2009); (Lecun, 1998).

Jararweh and co-workers improved the needleman-

wunsch algorithm by applying three sets of parallel

implementations. They utilized three hardware

solutions: POSIC Threads-based, SIMD Extensions-

based and GPU-based implementations (Lewicki,

2003).

5.2 Multiple Sequence Alignment

(MSA)

MSA aligns multiple sequences, which are normally

related, to get more biological information, like the

estimation of evolutionary divergence and ancestral

sequence profiling (Lin, 2013). It is normally

implemented when we want to know the evolutional

trace of target proteins when it comes to protein

prediction. By simulating the process of evolution

with MSA, we hope to reason the possible structure

of the target sequence right now. To achieve it,

substitution matrix and scoring were assessed (Lin,

2007). DP can work on MSA if the sequence number

is small, but it cannot be applied on hundreds of

sequences for the spur of complexity (Liu, 2020).

Based on that situation, scientists create heuristic

algorithms, sacrificing some accuracy for higher

computational efficiency. For example, MUSCLE,

CLUSTAL and T-COFFEE use progressive

alignment (Moult, 1995); (Moult, 2007), and

MUMMALS and PROMALS use hidden Markov

model based alignments (Nanni); (Needleman,

1970). Comet and Henry present a method that

integrates other information to the classical dynamic

programming algorithms, like the pattern of

PROSITE (Notredame, 1998).

6 CRITICAL ASSESSMENT OF

PROTEIN STRUCTURE

PREDICTION

Critical Assessment of Protein Structure Prediction

(CASP) is a biennial event that assesses the model of

protein structure prediction, held firstly in 1994. For

every CASP competition, participants are asked to

construct models within a stipulated time to predict

the structure of the given protein sequences.

Participants do not know the actual structure of the

sequences, but the actual structure was determined by

experiments before (Pei, 2007). After modeling, the

predicted structure will be compared to the

counterpart from the experiments, and the similarity

will be evaluated. There are two categories for

modeling since CASP7, free modeling (FM) and

template based modeling (TBM) (Pei, 2008). TBM

predicts those targets whose homologies, which

shares similar sequences, have been deposited in

PDB. The FM category is a big challenge for low

prediction accuracy, and fragment-based approaches,

like Rosetta, I-TASSER, and QUARK, dominated

CASP for many years until deep learning was

introduced

(https://www.csbio.sjtu.edu.cn/bioinf/PseAAC/).

6.1 Deep Learning

Deep learning, as a sub-field of machine learning, is

based on artificial neural networks. It was introduced

in predicting protein structure in 2016 because

contact prediction, a key intermediate step for

prediction, emerged in CASP12. A deep learning-

based method, Raptor-X, showed up and reached

about 50% precision when evaluating top L/5 long-

range predictions, which was twice as much precision

in CASP11 (Qian, 1988). After CASP12, an

improved version of the Raptor-X and open-source

deep learning method DNCON2 were released. In

CASP13, AlphaFold and other high-performing

methods were upgraded to use 'distogram' rather than

just contacts (Qin, 2015); (Rumelhart, 1986).

Deep learning (DL) simulates biological neural

networks. Deep learning uses multiple connected

layers to transform input into corresponding output.

To some extent, Deep learning is the advancement of

the Feed Forward Neural Network (FFNN) (Sadique,

2020); (Sanger, 1951). FFNN is an artificial neural

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network system that contains no cycles, dividing

nodes into groups (layers) and process the input

through them. Each node has its parameter and

weight and all the nodes in the same layer calculate a

vector. The layer i+1 gets its values exclusively from

layer i, until the output layer is valued. The FFNN

model can be trained with examples by propagation

algorithm and have universal approximation

properties, which has been proven (Shen, 2008);

(Simpkin, A.J). Typically, the "windowed" version is

applied to protein prediction. A fixed number of

amino acids as a segment is regarded as the input, and

the target is the PSA of the segment. Based on FFNN,

the Deep Learning method variates the connectivity

between the layers, allowing the algorithm to be

applied to different data types. Like FFNN, Deep

Learning can be trained with many examples by

backpropagation automatically (Smyth, 2000).

However, containing numbers of internal parameters

leads to data-greedy and large samples required. Two

mainstream DL algorithms are Convolutional Neural

Networks (CNN) and Recurrent Neural Networks

(RNN).

6.2 Convolutional Neural Networks

Convolutional Neural Networks is designed to

process spatial dependent data (like a base pair in the

DNA sequence). Taking this advantage, CNN layers

apply local convolutional filters on positions in the

data. This strategy avoids the overfitting problem and

is translation invariant. A module of CNN contains

multiple consecutive layers to encode more complex

features (Wang, 2017). "Windowed" FFNN is a

specific, shallow version of CNN, so we keep

referring to CNN as FFNN.

6.3 Recurrent Neural Networks

Recurrent Neural Networks aim to learn global

features from sequential data. RNN modules use

parameterized sub-module to process the sequence

into an internal state vector and use the vector to

summarize the original sequence. Previous state

vector and current input elements determine the

current internal state vector (Wang, 2018). However,

RNN easily suffers from the gradient vanishing or

gradient explosion problem because RNN keeps

using the same function repeatedly (Xu, 2019). Then

Long Short Term Memory, Gated Recurrent Unit and

other Gated RNN are created to contain the problems.

6.4 Alphafold

AlphaFold (AlphaFold v2.0) outperform other

methods in the CASP14 TM group. Its central

essence is the CNN method (Qin, 2015). AlphaFold

implements a distogram for protein structure

prediction by constructing very deep residual neural

networks with 220 residual blocks processing a

representation of dimensionality 64 × 64 × 128. By

customizing the length of the given sequence and plot

an L × L map, AlphaFold can predict inter-residue

distances for sub-regions. Then the map is

transformed into 3D models using minimized

distance potential, which implements sampling and

gradient-descent-based methods. Trained by the

proteins in PDB, it reaches high accuracy even for

targets with fewer homologous sequences.

AlphaFold contains two main stages: the trunk of

the network and the structure module. After the input

of amino acid sequence, the trunk of the network

process it through multiple repeated layers of neural

network block (named Evoformer) and produce two

arrays. N

seq

× N

res

array represents a processed

multiple sequence alignment (MSA), achieved by

aligning the sequence with those of other species in

Protein Database (PDB) and outputting a matrix

representing the similarity between the target and

deposited sequence. On the other hand, Evoformer

also produces a N

res

× N

res

array. By assessing the

interaction of every two amino acid residues, the N

res

× N

res

array can show residue pairs that likely attract

or repulse each other, then predict the three-

dimensional position of residues. The innovative

point of Evoformer is a new mechanism for

information exchange within MSA and pair

representation. It helps to reason spatial and

evolutionary relationship.

Besides, AlphaFold utilizes end-to-end structure

prediction with pair representation. By assuming

each residue is a free-floating rigid body, the module

constructs residue gas. Residue gas represents 3D

backbone structure as the form of N

res

independent

rotations and translations, which prioritize the

orientation of the C backbone, so the side chains are

highly constrained. Meanwhile, the C backbone is

completely unconstrained, and the network

frequently violates the chain constraint, hoping to

find the conformation with minimum global energy.

Finally, exact enforcement of peptide geometry is

completed through post-prediction relaxation with

gradient descent in the Amber force field (Ye, 2011);

(Yu, 2007).

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6.5 RaptorX-Contact

RaptorX-Contact was created for contact map

prediction by Xu's group based on Deep Learning. It

uses a model called deep ResNet, containing two

major residual neural network modules, respectively

called 1D deep ResNet and 2D deep dilated ResNet

(Qian, 1988); (Qin, 2015); (Zhang, T.-L., Y.-S. Ding,

and K.-C. Chou). The 1D and 2D ResNets play

different roles, respectively capturing long-range

sequential and pairwise context. 1D ResNet extracts

sequential features and conducts 1D convolutional

transformations from a L × 26 matrix, as the input,

into a L × n matrix (L is sequence length). After

converting the L × n matrix into a L × L × 3n pairwise

feature matrix, 2D feature is obtained, derived from

1D feature. It merges with a L × L ×3 pairwise feature

matrix, froming the output. The output from the 1D

ResNet is then fed into 2D ResNet. 2D ResNet is a

Residual Neural Network module, conducting 2D

convolutions. It transforming L × L × (3 + 3n) matrix

into a L × L predicted distance matrix by softmax.

Eventually, the output from the 2D module is fed into

logistic regression. The 1D and 2D ResNets contains

of ~7 and ~60 convolutional layers and kernel size of

15 and 5 × 5, respectively. The 1D and 2D ResNets

for 1D and 2D feature learning is a calculative way to

save computational resources. That's also why

RaptorX-Contact produced better results in CASP11,

comparing with other existing approaches.

For classification, interatomic distances are

discretized into 25 bins: <4.5, 4.5-5, 5-5.5, …,15-

15.5, 15.5-16, and >16 Å, and treat each bin as a

label. Contact prediction is achieved by summing up

the probability of all the C

-C

distance bins that fall

into interval [0, 8 Å].

In CASP12, average long-range contact

prediction accuracy of RaptorX Postdict in L, L/2,

L/5, L/10 are respectively 40.18%, 50.20%, 58.87%,

63.93%, ranking the top. In CASP13 RaptorX-

Contact also did the best, when top L, L/2, and L/5

long-range predicted contacts are evaluated, on the

FM targets it has precision 44.731%, 57.787%, and

70.054%, respectively, and F1 values 0.411, 0.362,

and 0.233, respectively.

7 CONCLUSION

With the advancement of computation and

experiments, we will rely more on algorithm

prediction to predict more protein structures. Frankly,

high prediction accuracy of AlphaFold on FM

illustrates the power of deep learning,and contact

profile and “distogram” are also huge successes and

promote the accuracy of modeling.. But in some way,

it also embodies the deficiency we have in the

theoretical field of structural biology.

Proteins with poor alignment are still hard to

determine the 3D structure. For TBM, deficient

sequence alignment means the difficulties to do

homologous modeling, which for FM it means poor

evolutional relationship accessible. That is to say, we

cannot fully get rid of empiricism at present. Thus,

more sufficient methods for crystallographic

structural analysis are needed to abate the gap

between known proteins and those experimentally

determined. Also, some sequences with low

similarities share with a similar structure, so we need

to find out what really matters to determine

secondary structure, or we can define new feature

descriptors. Thus, more factors that influence the

spatial bias are remained to find. On top of that, new

deep learning methods are indeed successful in

prediction, but integrating with other traditional

methods is still a challenge. It is necessary to build a

comprehensive model to maximize the accuracy

rather than approach the real model through different

aspects. We cannot know if the prediction is well

enough unless cross-validate with PDB because some

steps in protein folding are still remained to discover.

That's why FM is still way behind TBM.

In the future, more details of the protein folding

process are needed to reduce the fluctuation of

modeling. For now we are still not clear about how

domains assemble together dynamically. We have to

know more about the intermediate steps within.

Algorithms need more training and refinement,

basically helped by the database. More advanced

neural network is still remained to discover.

REFERENCES

Amino Acid Index Database. Available from:

https://www.genome.jp/dbget/aaindex.html.

Anand, A., G. Pugalenthi, and P.N. Suganthan, Predicting

protein structural class by SVM with class-wise

optimized features and decision probabilities. Journal

of Theoretical Biology, 2008. 253(2):

Anfinsen, C.B., et al., Kinetics of formation of Native

Ribonuclease during oxidation of reduced polypeptide

chain. Proceedings of the National Academy of

Sciences of the United States of America, 1961. 47(9):

p. 1309-1314.

Bateman, A., et al., UniProt: the universal protein

knowledgebase in 2021. Nucleic Acids Research,

2021. 49(D1): p. D480-D489.

Protein Structure Prediction: Biological Basis, Processing Methods and Deep Learning

313

Bengio, Y., P. Simard, and P. Frasconi, Learning long-term

dependencies with gradient descent is difficult. Ieee

Transactions on Neural Networks, 1994. 5(2): p. 157-

166.

Bernardes, J.S. and C.E. Pedreira, A review of protein

function prediction under machine learning

perspective. Recent patents on biotechnology, 2013.

7(2): p. 122-41.

Bonetta, R. and G. Valentino, Machine learning techniques

for protein function prediction. Proteins-Structure

Function and Bioinformatics, 2020. 88(3): p. 397-413.

Cai, L.M., et al. Evolutionary computation techniques for

multiple sequence alignment. in 2000 Congress on

Evolutionary Computation (CEC2000). 2000. La Jolla,

Ca.

Chou, K.C. and C.T. Zhang, Prediction of protein structural

classes. Critical Reviews in Biochemistry and

Molecular Biology, 1995. 30(4): p. 275-349.

Chou, K.-C., An insightful 20-year recollection since the

birth of pseudo amino acid components. Amino Acids,

2020. 52(5): p. 847-847.

Chou, K.C., Prediction of protein cellular attributes using

pseudo-amino acid composition. Proteins-Structure

Function and Genetics, 2001. 43(3): p. 246-255.

Chou, K.-C., RETRACTION: WITHDRAWN: An

insightful recollection for predicting protein

subcellular locations in multi-label systems. Genomics,

2019.

Comet, J.P. and J. Henry, Pairwise sequence alignment

using a PROSITE pattern-derived similarity score.

Computers & Chemistry, 2002. 26(5): p. 421-436.

Dayhoff, M.O., W.C. Barker, and L.T. Hunt, Establishing

homologies in protein sequences. Methods in

Enzymology, 1983. 91: p. 524-545.

Deng, H.Y., Y. Jia, and Y. Zhang, Protein structure

prediction. International Journal of Modern Physics B,

2018. 32(18).

Ding, S.F., et al., Evolutionary artificial neural networks: a

review. Artificial Intelligence Review, 2013. 39(3): p.

251-260.

Dubchak, I., et al., Prediction of protein-folding class using

global description of amino-acid sequence.

Proceedings of the National Academy of Sciences of

the United States of America, 1995. 92(1

Dubchak, I., et al., Recognition of a protein fold in the

context of the SCOP classification. Proteins-Structure

Function and Bioinformatics, 1999. 35(4): p. 401-407.

Eddy, S.R., A memory-efficient dynamic programming

algorithm for optimal alignment of a sequence to an

RNA secondary structure. Bmc Bioinformatics, 2002.

Edgar, R.C., MUSCLE: multiple sequence alignment with

high accuracy and high throughput. Nucleic Acids

Research, 2004. 32(5): p. 1792-1797.

Elmlund, D. and H. Elmlund, Cryogenic Electron

Microscopy and Single-Particle Analysis, in Annual

Review of Biochemistry, Vol 84, R.D. Kornberg,

Editor. 2015. p. 499-517.

Gondro, C. and B.P. Kinghorn, A simple genetic algorithm

for multiple sequence alignment. Genetics and

Molecular Research, 2007. 6(4): p. 964-982.

Gonzalez-Lopez, F., et al. End-to-end prediction of protein-

protein interaction based on embedding and recurrent

neural networks. in IEEE International Conference on

Bioinformatics and Biomedicine

Hall, L.D., Nuclear magnetic resonance. Advances in

Carbohydrate Chemistry, 1964. 19: p. 51-93.

Hanson, J., et al., Improving prediction of protein

secondary structure, backbone angles, solvent

accessibility and contact numbers by using predicted

contact maps and an ensemble of recurrent and and

residual convolutional neural networks.

Bioinformatics, 2019. 35(14): p. 2403-2410.

Henikoff, S. and J.G. Henikoff, Amino-acid substitution

matrices from protein blocks. Proceedings of the

National Academy of Sciences of the United States of

America, 1992. 89(22): p. 10915-10919.

Hornak, V., et al., Comparison of multiple amber force

fields and development of improved protein backbone

parameters. Proteins-Structure Function and

Bioinformatics, 2006. 65(3): p. 712-725.

Ilonen, J., J.K. Kamarainen, and J. Lampinen, Differential

evolution training algorithm for feed-forward neural

networks. Neural Processing Letters, 2003. 17(1): p.

93-105.

Jararweh, Y., et al., Improving the performance of the

needleman-wunsch algorithm using parallelization and

vectorization techniques. Multimedia Tools and

Applications, 2019. 78(4): p. 3961-3977.

Jumper, J., et al., Highly accurate protein structure

prediction with AlphaFold. Nature: p. 12.

Kabsch, W. and C. Sander, Dictionary of Protein

Secondary Structure - pattern-recognition of hydrogen-

bonded and geometrical features. Biopolymers, 1983.

22(12): p. 2577-2637.

Kawashima, S. and M. Kanehisa, AAindex: Amino acid

index database. Nucleic Acids Research, 2000. 28(1):

p. 374-374.

Kinch, L.N., et al., Evaluation of free modeling targets in

CASP11 and ROLL. Proteins-Structure Function and

Bioinformatics, 2016. 84: p. 51-66.

Kurgan, L. and K. Chen, Prediction of protein structural

class for the twilight zone sequences. Biochemical and

Biophysical Research Communications, 2007. 357(2):

p. 453-460.

Landan, G. and D. Graur, Characterization of pairwise and

multiple sequence alignment errors. Gene, 2009.

441(1-2): p. 141-147.

Lecun, Y., et al., Gradient-based learning applied to

document recognition. Proceedings of the Ieee, 1998.

86(11): p. 2278-2324.

Lewicki, G. and G. Marino, Approximation by

superpositions of a sigmoidal function. Zeitschrift Fur

Analysis Und Ihre Anwendungen, 2003. 22(2): p. 463-

470.

Lin, C., et al., Hierarchical Classification of Protein Folds

Using a Novel Ensemble Classifier. Plos One, 2013.

8(2).

ICBEB 2022 - The International Conference on Biomedical Engineering and Bioinformatics

314

Lin, H. and Q.-Z. Li, Using pseudo amino acid composition

to predict protein structural class: Approached by

incorporating 400 dipeptide components. Journal of

Computational Chemistry, 2007. 28(9):

Liu, M.-L., et al., Predicting Preference of Transcription

Factors for Methylated DNA Using Sequence

Information. Molecular Therapy-Nucleic Acids, 2020.

22: p. 1043-1050.

Moult, J., et al., A LARGE-SCALE EXPERIMENT TO

ASSESS PROTEIN-STRUCTURE PREDICTION

METHODS. Proteins-Structure Function and Genetics,

1995. 23(3): p. R2-R4.

Moult, J., et al., Critical assessment of methods of protein

structure prediction - Round VII. Proteins-Structure

Function and Bioinformatics, 2007. 69: p. 3-9.

Nanni, L., S. Brahnam, and A. Lumini, Prediction of

protein structure classes by incorporating different

protein descriptors into general Chou's pseudo amino

acid composition. Journal of Theoretica

Needleman, S.B. and C.D. Wunsch, A general method

applicable to search for similarities in amino acid

sequence of 2 proteins. Journal of Molecular Biology,

1970. 48(3): p. 443-453.

Notredame, C., L. Holm, and D.G. Higgins, COFFEE: an

objective function for multiple sequence alignments.

Bioinformatics (Oxford, England), 1998. 14(5): p. 407-

22.

Pei, J.M. and N.V. Grishin, PROMALS: towards accurate

multiple sequence alignments of distantly related

proteins. Bioinformatics, 2007. 23(7): p. 802-808.

Pei, J.M., B.H. Kim, and N.V. Grishin, PROMALS3D: a

tool for multiple protein sequence and structure

alignments. Nucleic Acids Research, 2008. 36(7): p.

2295-2300.

PseAAC. Available from: https://www

.csbio.sjtu.edu.cn/bioinf/PseAAC/.

Qian, N. and T.J. Sejnowski, Predicting the secondary

structure of globular-proteins using neural network

models. Journal of Molecular Biology, 1988. 202(4): p.

865-884.

Qin, Y., et al., Prediction of protein structural class based

on Linear Predictive Coding of PSI-BLAST profiles.

Open Life Sciences, 2015. 10(1): p. 529-536.

Rumelhart, D.E., G.E. Hinton, and R.J. Williams, Learning

representations by back-propagating errors. Nature,

1986. 323(6088): p. 533-536.

Sadique, N., et al., Image-based effective feature

generation for protein structural class and ligand

binding prediction. Peerj Computer Science, 2020.

Sanger, F. and H. Tuppy, The amino-acid sequence in the

phenylalanyl chain of insulin.1. The identification of

lower peptides from partial hydrolysates. Biochemical

Journal, 1951. 49(4): p. 463-481.

Shen, H.-B. and K.-C. Chou, PseAAC: A flexible web

server for generating various kinds of protein pseudo

amino acid composition. Analytical Biochemistry,

2008. 373(2): p. 386-388.

Simpkin, A.J., et al., Evaluation of model refinement in

CASP14. Proteins-Structure Function and

Bioinformatics.

Smyth, M.S. and J.H.J. Martin, x Ray crystallography.

Journal of Clinical Pathology-Molecular Pathology,

2000. 53(1): p. 8-14.

Wang, S., et al., Accurate De Novo Prediction of Protein

Contact Map by Ultra-Deep Learning Model. Plos

Computational Biology, 2017. 13(1): p. 34.

Wang, S., S.Q. Sun, and J.B. Xu, Analysis of deep learning

methods for blind protein contact prediction in

CASP12. Proteins-Structure Function and

Bioinformatics, 2018. 86: p. 67-77.

Xu, J.B. and S. Wang, Analysis of distance-based protein

structure prediction by deep learning in CASP13.

Proteins-Structure Function and Bioinformatics, 2019.

87(12): p. 1069-1081.

Ye, X., G. Wang, and S.F. Altschul, An assessment of

substitution scores for protein profile-profile

comparison. Bioinformatics, 2011. 27(24): p. 3356-

3363.

Yu, T., et al., Structural class tendency of polypeptide: A

new conception in predicting protein structural class.

Physica a-Statistical Mechanics and Its Applications,

2007. 386(1): p. 581-589.

Zhang, T.-L., Y.-S. Ding, and K.-C. Chou, Prediction

protein structural classes with pseudo-amino acid

composition: Approximate entropy and hydrophobicity

pattern. Journal of Theoretical Biology, 2008. 250(1):

p. 186-193.

Protein Structure Prediction: Biological Basis, Processing Methods and Deep Learning

315