CN106446604A - Protein structure ab into prediction method based on firefly algorithm - Google Patents

Protein structure ab into prediction method based on firefly algorithm Download PDF

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CN106446604A
CN106446604A CN201610908691.4A CN201610908691A CN106446604A CN 106446604 A CN106446604 A CN 106446604A CN 201610908691 A CN201610908691 A CN 201610908691A CN 106446604 A CN106446604 A CN 106446604A
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conformation
colony
individual
protein
individuality
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张贵军
郝小虎
周晓根
王柳静
李章维
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Zhejiang University of Technology ZJUT
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    • G06N3/006—Artificial life, i.e. computing arrangements simulating life based on simulated virtual individual or collective life forms, e.g. social simulations or particle swarm optimisation [PSO]
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Abstract

The invention discloses a protein structure ab into prediction method based on a firefly algorithm. The method includes that under a basic firefly algorithm frame, a coarseness energy model is adopted to effectively lower conformational space dimension, group property of the firefly algorithm is utilized to guarantee diversity of protein conformation, segment assembling technology is adopted to initialize conformational group, a dihedral angle is used to express position of conformation in space according to a coarseness expression model of the protein conformation, energy ranking is adopted to determine a strongest luminous individual, position of the conformation is updated by calculating attraction degree among individuals, and approximately-natural-state conformation with lowest energy is acquired by searching in the conformational space. By applying the method in protein structure prediction, conformation high in predication accuracy and low in complexity can be acquired.

Description

A kind of from the beginning method of the protein structure prediction based on glowworm swarm algorithm
Technical field
The present invention relates to bioinformatics, computer application field, more particularly, to a kind of based on glowworm swarm algorithm Protein structure prediction from the beginning method.
Background technology
Bioinformatics is a study hotspot of life sciences and computer science crossing domain.Bioinformatics research Achievement has been widely used in gene discovery and prediction, the storage management of gene data, data retrieval and excavation, gene at present Expression data analysiss, protein structure prediction, gene and protein homology Relationship Prediction, sequence analysis with than equity.Genome Define all protein constituting this organism, gene defines the aminoacid sequence of constitutive protein matter.Although protein by The linear order composition of aminoacid, but, they only fold formed specific space structure just can have accordingly activity and Corresponding biological function.Understand that the space structure of protein not only contributes to recognize the function of protein, be also beneficial to recognize Protein is how perform function.Determine protein structure be very important.At present, protein sequence database The speed of data accumulation is very fast, however, it is known that the protein of structure compare less.Although protein structure determination technology has More significantly it is in progress, but, determine that by experimental technique the process of protein structure is still extremely complex, cost is higher. Therefore, the protein structure of measuring wants much less than known protein sequence.On the other hand, with DNA sequencing technology Development, human genome and more Model organism genome or will be completely sequenced, and DNA sequence quantity will be anxious Increase, and the progress due to DNA sequence analysis technology and gene recognition method, we can derive substantial amounts of protein from DNA Sequence.This means the protein amounts of known array and measure the protein amounts of structure (as Protein structure databases Data in PDB) gap will be increasing.It is desirable to the speed producing protein structure can keep up with generation protein The speed of sequence, or reduce both gaps.
Technical bottleneck main at present is two aspects, and first aspect is the method for sampling, and prior art is empty to conformation Between ability in sampling not strong, further aspect is that conformation update method, prior art is still not enough to the renewal precision of conformation.Cause This, existing conformational space searching method Shortcomings, need to improve.
Content of the invention
In order to overcome existing protein structure prediction conformational space optimization method to exist, sampling efficiency is relatively low, complexity relatively Deficiency high, that precision of prediction is relatively low, the present invention proposes a kind of from the beginning method of the protein structure prediction based on glowworm swarm algorithm.? Under basic glowworm swarm algorithm framework, conformational space dimension is effectively reduced using coarseness energy model, using glowworm swarm algorithm The multiformity to ensure protein conformation for the group property, using fragment package technique, conformational population is initialized, foundation The coarseness expression model of protein conformation, represents conformation position in space with one group of dihedral angle, is come using energy ranking Determine the individuality that lights the most by force, and the Attraction Degree between individual updates the position of conformation by calculating, finally searches in conformational space Rope obtains the nearly native state conformation of least energy.
The technical solution adopted for the present invention to solve the technical problems is：
A kind of from the beginning method of the protein structure prediction based on glowworm swarm algorithm, the method comprising the steps of：
1) give list entries information；
2) parameter initialization：Setting population size popSize, iterationses generation, light intensity attracting factor γ, position Put renewal step factor α；
3) colony's conformation initialization：According to given list entries, random popSize individuality of generation, to every in colony Individuality does length fragment assembling, and calculates its fluorescent brightness Io, and wherein length is sequence length, and Io=-E, E are logical Cross RosettaSscore3 energy function calculated protein conformation energy value；
4) to step 3) in the fluorescent brightness that calculates sort from big to small, make the maximum individuality of fluorescent brightness be pg；
5) start iteration：
5.1) individual to each in colony, calculate pgAttraction Degree β to it；
5.2) according to xi(t+1)=xi(t)+β(xj(t)–xi(t))+α (rand 0.5) updates each individuality in space Position, wherein xi(t+1), xiT () represents individual piPosition after renewal and current position, xjT () represents individual pgWork as Front position, whereinβ0For the maximum Attraction Degree factor, rijRepresent individual piWith pgThe distance between, rand arrives for 0 Random number between 1, each individual position x in colonyiT () is expressed as ψ is the dihedral angle of the amino acid residue of list entries, and L is fragment length；
5.3) each individuality in colony is carried out with L random fragment assembling, completes colony and swing at random；
5.4) recalculate each individual fluorescent brightness, update pg；
6) judge whether to reach maximum iteration time generation；
6.1) if current iteration number of times is less than generation, return to step 5.1)；
6.2) if current iteration number of times is equal to generation, terminate；
The technology design of the present invention is：Under basic glowworm swarm algorithm framework, effectively to be dropped using coarseness energy model Low conformational space dimension, ensures the multiformity of protein conformation using the group property of glowworm swarm algorithm, using fragment assembling Technology initializes to conformational population, according to the coarseness expression model of protein conformation, represents conformation with one group of dihedral angle Position in space, to be determined the individuality that lights the most by force, and to be updated by calculating the Attraction Degree between individuality using energy ranking The position of conformation, finally in conformational space, search obtains the nearly native state conformation of least energy.
Beneficial effects of the present invention are：The present invention applies in protein structure prediction, can obtain precision of prediction higher, The relatively low conformation of complexity.
Brief description
Fig. 1 is the pre- geodesic structure of protein 2L0G and experimental determination structure immediate conformation schematic three dimensional views.
Specific embodiment
The invention will be further described below in conjunction with the accompanying drawings.
With reference to Fig. 1, a kind of from the beginning method of the protein structure prediction based on glowworm swarm algorithm, methods described includes following step Suddenly：
1) give list entries information；
2) parameter initialization：Setting population size popSize, iterationses generation, light intensity attracting factor γ, position Put renewal step factor α；
3) colony's conformation initialization：According to given list entries, random popSize individuality of generation, to every in colony Individuality does length fragment assembling, and calculates its fluorescent brightness Io, and wherein length is sequence length, and Io=-E, E are logical Cross RosettaSscore3 energy function calculated protein conformation energy value；
4) to step 3) in the fluorescent brightness that calculates sort from big to small, make the maximum individuality of fluorescent brightness be pg；
5) start iteration：
5.1) individual to each in colony, calculate pgAttraction Degree β to it；
5.2) according to xi(t+1)=xi(t)+β(xj(t)–xi(t))+α (rand 0.5) updates each individuality in space Position, wherein xi(t+1), xiT () represents individual piPosition after renewal and current position, xjT () represents individual pgWork as Front position, whereinβ0For the maximum Attraction Degree factor, rijRepresent individual piWith pgThe distance between, rand arrives for 0 Random number between 1, each individual position x in colonyiT () is expressed as ψ is the dihedral angle of the amino acid residue of list entries, and L is fragment length；
5.3) each individuality in colony is carried out with L random fragment assembling, completes colony and swing at random；
5.4) recalculate each individual fluorescent brightness, update pg；
6) judge whether to reach maximum iteration time generation；
6.1) if current iteration number of times is less than generation, return to step 5.1)；
6.2) if current iteration number of times is equal to generation, terminate；
The present embodiment with the test proteins of the entitled 2L0G of PDB as embodiment, a kind of protein based on glowworm swarm algorithm Structure prediction from the beginning method, the method comprising the steps of：
1) give list entries information；
2) parameter initialization：Setting population size popSize=50, iterationses generation=10000, light intensity is inhaled Draw factor gamma=0.5, location updating step factor α=0.7；
3) colony's conformation initialization：According to given list entries, random popSize individuality of generation, to every in colony Individuality does length fragment assembling, and calculates its fluorescent brightness Io, and wherein length is sequence length, and Io=-E, E are logical Cross RosettaSscore3 energy function calculated protein conformation energy value；
4) to step 3) in the fluorescent brightness that calculates sort from big to small, make the maximum individuality of fluorescent brightness be pg；
5) start iteration：
5.1) individual to each in colony, calculate pgAttraction Degree β to it；
5.2) according to xi(t+1)=xi(t)+β(xj(t)–xi(t))+α (rand 0.5) updates each individuality in space Position, wherein xi(t+1), xiT () represents individual piPosition after renewal and current position, xjT () represents individual pgWork as Front position, whereinβ0For the maximum Attraction Degree factor, rijRepresent individual piWith pgThe distance between, rand arrives for 0 Random number between 1, each individual position x in colonyiT () is expressed as ψ is the dihedral angle of the amino acid residue of list entries, and L=3 is fragment length；
5.3) each individuality in colony is carried out with L random fragment assembling, completes colony and swing at random；
5.4) recalculate each individual fluorescent brightness, update pg；
6) judge whether to reach maximum iteration time generation；
6.1) if current iteration number of times is less than generation, return to step 5.1)；
6.2) if current iteration number of times is equal to generation, terminate；
Described above is the excellent results that show of the embodiment that the present invention is given it is clear that the present invention not only fits Close above-described embodiment, can on the premise of without departing from essence spirit of the present invention and without departing from content involved by flesh and blood of the present invention Do many variations to it to be carried out.

Claims (1)

1. a kind of protein structure prediction based on glowworm swarm algorithm from the beginning method it is characterised in that：Methods described includes following Step：
1) give list entries information；
2) parameter initialization：Setting population size popSize, iterationses generation, light intensity attracting factor γ, position is more New step factor α；
3) colony's conformation initialization：According to given list entries, random generate popSize individuality, in colony every each and every one Body does length fragment assembling, and calculates its fluorescent brightness Io, and wherein length is sequence length, and Io=-E, E are to pass through RosettaSscore3 energy function calculated protein conformation energy value；
4) to step 3) in the fluorescent brightness that calculates sort from big to small, make the maximum individuality of fluorescent brightness be pg；
5) start iteration：
5.1) individual to each in colony, calculate pgAttraction Degree β to it；
5.2) according to xi(t+1)=xi(t)+β(xj(t)–xi(t))+α (rand 0.5) updates each individuality position in space Put, wherein xi(t+1), xiT () represents individual piPosition after renewal and current position, xjT () represents individual pgPresent bit Put, whereinβ0For the maximum Attraction Degree factor, rijRepresent individual piWith pgThe distance between, rand be 0 to 1 it Between random number, each individual position x in colonyiT () is expressed as For the dihedral angle of the amino acid residue of list entries, L is fragment length；
5.3) each individuality in colony is carried out with L random fragment assembling, completes colony and swing at random；
5.4) recalculate each individual fluorescent brightness, update pg；
6) judge whether to reach maximum iteration time generation；
6.1) if current iteration number of times is less than generation, return to step 5.1)；
6.2) if current iteration number of times is equal to generation, terminate.
CN201610908691.4A 2016-10-19 2016-10-19 Protein structure ab into prediction method based on firefly algorithm Pending CN106446604A (en)

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Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN107085674A (en) * 2017-03-14 2017-08-22 浙江工业大学 A kind of multi-modal protein conformation space optimization method based on improvement glowworm swarm algorithm

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US20040024536A1 (en) * 2000-09-28 2004-02-05 Torbjorn Rognes Determination of optimal local sequence alignment similarity score
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CN105205348A (en) * 2015-09-22 2015-12-30 浙江工业大学 Method for colony conformation space optimization based on distance constraint selection strategy

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US20060141480A1 (en) * 1999-11-10 2006-06-29 Kalyanaraman Ramnarayan Use of computationally derived protein structures of genetic polymorphisms in pharmacogenomics and clinical applications
US20040024536A1 (en) * 2000-09-28 2004-02-05 Torbjorn Rognes Determination of optimal local sequence alignment similarity score
CN105160196A (en) * 2015-09-22 2015-12-16 浙江工业大学 Dynamic mutation policy based group global optimization method
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* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN107085674A (en) * 2017-03-14 2017-08-22 浙江工业大学 A kind of multi-modal protein conformation space optimization method based on improvement glowworm swarm algorithm

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