Alt sekans profil haritaları kullanılarak protein katlanması tanıma
Protein fold recognition using subsequence profile maps
Yazar: RUŞEN HALEPMOLLASI
Danışman: YRD. DOÇ. DR. ÖMER SİNAN SARAÇ
İstanbul Teknik Üniversitesi / Fen Bilimleri Enstitüsü / Bilgisayar Mühendisliği Ana Bilim Dalı / Bilgisayar Mühendisliği Bilim Dalı
Derece: Yüksek Lisans, 2016
Konu: Bilgisayar Mühendisliği Bilimleri-Bilgisayar ve Kontrol = Computer Engineering and Computer Science and Control

Özet:
Yaşamın en temel makro molekülleri olan proteinlerin 3b yapısına ait bilgi
bioinformatik çalışmalarında kilit bir rol oynar. Hücrenin karmaşık yapısı
içersinde nanometre mesafede ve mikro mili saniyede katlanan proteinlerin katlanma
örüntülerini tahmin etmek ne yazık ki oldukça zordur. İki protein aynı düzen
ve topoloji ile aynı ikincil yapıya sahipse ortak bir katlanma örüntüsüne sahiptir
denilebilir.
Katlandıktan sonra görevlerini yerine getirmek için hazır hale gelen proteinlerin
üç boyutlu yapısı fonksiyonlarına uygun olmalıdır. Belli bir sekans bilgisine ve
amino asit özelliklerine dayanarak protein katlanmasını tanıma, proteinlerin 3b
yapılarının ve fonksiyonlarının belirlenmesinde önemli bir aşama olarak düşünülebilir.
Proteinler arasında yakın evrimsel ilişki olduğunda benzerliği tespit etmek için
sekans-sekans eşleştirmesi iyi sonuçlar verir. Ancak iki protein yapısal olarak çok
benzer olsa da aralarında sekans benzerliği yoksa bu tür bir eşleştirme etkili değildir.
Böyle durumlarda sekaslardan çıkarılan özniteliklere makine öğrenme yöntemleri
uygulayarak proteinlerin katlanmasını tahmin etmek daha etkili olur. Bunun için
proteinlerin doğada sınırlı sayıda olduğunu varsayıp belli bir sayıda katlanma sınıfı
üzerinde çalışılmalıdır.
Bu çalışmada, sınırlı sayıda katlanma sınıfı içeren ve literatürde sıkça rastlanan
dört adet veri kümesi kullanıldı. Protein katlanmasının tanınmasında amino
asitlerin fizyokimyasal özelliklerinden faydalanıldı. Ayrıca ilk kez alt sekans
profil haritası (SPMap) kullanılarak makine öğrenme yöntemlerinin uygulanabileceği
öznitelikler çıkarıldı. Her katlanma sınıfı için ayrı ayrı elde edilen öznitelikler, iki
katmanlı bir yaklaşım ile makine öğrenmesi yöntemlerinden faydalanılarak protein
katlanmalarının tahmin edilmesinde kullanıldı. Amino asit özniteliklerine ve alt
sekans bilgilerine dayanarak elde edilen öznitelik kümeleri pozitif ve negatif olarak
etiketlendi ve ikili sınıflandırma modelleri eğitildi. Oluşturulan modeller ile test
veri kümesi sınıflandırıldı. Elde edilen sınıflandırma tahminleri öznitelik vektörleri
gibi düşünülerek birleştirildi ve ikinci katmanda kullanılmak üzere yeni bir öznitelik
kümesi oluşturuldu. İkinci katmanda çoklu sınıflandırma modeli eğitildi. Bu
modeller uygulamanın başında ayrılan test kümesi ile test edildi ve geliştirilen modelin
performansı doğruluk oranı, kesinlik, duyarlılık ve F-ölçütü ile değerlendirildi.
Önerilen sistem ile performans ölçümünde kullanılan DD veri kümesi üzerinde
ortalama %71.7, EDD veri kümesi üzerinde ortalama%75.7, F95 veri kümesi üzerinde
ortalama%75.15 doğruluk başarı oranı elde edildi.

Abstract:
Proteins that are very important macromolecules of life are responsible for some of
the most essential functions in an organism such as metabolism, transport, immune
system, etc. The analysis of proteins tertiary structure is a difficult task because of the
complex structure of the cell. Protein fold recognition helps to understand the tertiary
structure of the protein.
Before the folding, there is a single direction of flow from the DNA linear polymer
consists of four different bases (Adenosine, Guanine, Cytosine, Thymine) to a protein
consists of different 20 amino acids. This process comprises three stages that are
DNA replication, transcription and translation. In the DNA replication stage, two
identical replicas are created from original molecule of DNA. In the second stage
called transcription, a particular segment of DNA is copied into single stranded
RNA (mRNA) by the RNA polymerase enzyme. Next, messenger RNA (mRNA) is
translated into a specific amino acid chain in a process called translation.
In general, there are three levels of protein structure but in some cases, it can be
fourth level. The primary structure is the protein sequence consists of the amino
acid chain. The secondary structure is the first stage of protein folding, in which the
chain is regulated in regular structures as called "a-helix" and "b-sheet". The tertiary
structure formed by the further folding composed of complex and fixed geometric
shapes. Three-dimensional proteins folded to form tertiary structure create quaternary
structure by coming together.
Information belonging to 3D structures of the proteins, which are the most fundamental
macromolecules of life, plays a key role in bioinformatics studies. The 3D structure
of the proteins which are ready to fulfill their liabilities after the folding have to fit to
the functions (the miss folds would cause the Alzheimer, some types of the cancer and
Parkinson's). In other words, the information about the structure of the proteins plays
an important role in the determining of the different type diseases and improving the
effectiveness of the new medicines.
Protein fold recognition from amino acid sequences plays a critical role in prediction
of protein structures and functions. Therefore information of protein 3-dimensional
structures is significant for understanding cellular function and the development of
drug design and the biomedicine. Unfortunately, it is very difficult to predict the pattern
of the folds for the complex structure of the cells during micro milliseconds and in the
nanometric distance. In a case, when two proteins with the same order and topology
have the same secondary structure, it can be said that they have the same fold pattern.
Protein fold recognition based on the particular sequence information and amino acid
properties is the significant step for the determining 3D structure and the functions
of the proteins. Applying the sequence-sequence pairing for the determining the
similarity of the proteins when the proteins have the close evolutionary relationship
gives good results. But when the proteins do not have the sequence similarity even
if such type of the pairing will not be effective. In this cases, fold prediction using
the machine learning methods on the extracted features of the sequences will be more
effective. Assuming that the number of the proteins in the nature is limited will let us
to work on the fold classes which have the certain number.
The purpose of this work it to extract the specific features from the subsequences and
psycho-chemical properties of the amino acids of the proteins, and to predict correctly
the fold classes of these proteins using the machine learning methods. Protein fold
recognition is very difficult subject theoretically and practically because of the complex
structure of the proteins. Crystallization of the proteins using the practical methods
and analyzing these fold structure is very hard and expensive process. That is why
there is need for the theoretical study using the computational techniques. Once the
features are extracted from the protein sequences, any machine learning method can
be employed.
The recognition process of a query protein sequence in this study can be divided
two steps. In the first step, features were extracted from the query sequence. It has
benefited from the physicochemical properties of the amino acids for the protein fold
recognition. Besides, the attributes on which machine learning methods can be applied
are extracted by using subsequence profile map (SPMap) in protein fold recognition
for the first time. In the second step, The features exracted from each fold class
were used in a two-layer approach to train classifiers to predict correct protein fold
belonged to the query sequence. In the first layer, the features, exracted from SPMap
and physicochemical properties of amino acids, labeled as positive and negative. Then
the feature sets trained binary classifiers and the test set was classified by using these
models. The binary classification estimates mind as feature vectors and was combined
together. Thus, a new feature set has been created to be used in the second layer and
multiple classification models were trained. The developed model was tested with test
set separated at the beginning of the application.
We used the binary classifier method on R programming in the fist layer, where as used
the multiclass classifier methods on Weka and R programming in the second layer.
Random Forest was used for the binary classification and Support Vetor Maachine,
Random Forest, Multi-class classifiers and ensemble classifier were tried for multiple
classification.
In this work, we have used four datasets with the limited number of the fold classes.
The first dataset, called DD set, has been comprehensively employed in several studies
for protein fold recognition. We benefit from it as a benchmark dataset. The dataset
which has the most popular 27 fold classes in SCOP database is composed of a
training set and testing set. The training set includes 313 protein domain sequences,
whereas The testing set includes 385 protein domain sequences. The other three
datasets created according to the latest version of SCOP, are called EDD, F95 and
F194. EDD - Extended DD set comprises 3397 protein domain sequences with the
same 27 fold classes of DD set. F95 and F194 sets created to cover more folds, have
less than 40% pairwise sequence identity. F95 - Fold 95 set comprises 6364 protein
domain sequences from 95 folds. F194 - Fold 194 set comprises 8026 protein domain
sequences from 194 folds.
In this study, we used three popular metrics, which are Precision, Recall and F-measure
for evaluation of the results.For the evaluate the overall performance of this study, we
used the overall accuracy. The classifier performance was evaluated with datasets using
our proposed system, and 71.7% for DD dataset, 75.7% for EDD dataset and 75.15%
for F95 dataset average accuracy rates were achieved.
In our future work, we will benefit from SCOP database hierarchy for the further
improvement of prediction accuracy. Besides, we will try feature selection methods
after the extract the features.