Biological Language Model. Qiwen Dong

Чтение книги онлайн.

СКАЧАТЬ people to systematically and completely understand the whole process of transferring biological information from DNA to biologically active proteins as well as to clarify the central law more completely.¹⁴ Having a deeper understanding of the various phenomena in the life process ultimately promotes the rapid development of life sciences.¹⁵ As regards application, it is beneficial for people to analyze disease pathogenesis and find treatment methods, and design proteins with novel biological functions, thereby promoting the rapid development of medicine, agriculture and animal husbandry. Thus, developing efficient computer-based algorithms to predict high-resolution 3D protein structures from their sequences becomes increasingly important.^16,17

       1.2.5 Function prediction

      Proteins are one of the most important molecules in biology as they have a role in many life processes, such as transcription, metabolism and regulation. It is thus of great importance to perform function analysis on proteins to help understand the processes of life.¹⁸ Due to the huge amount of proteins present, it is difficult to verify the function of each and every protein. Computational approaches for function prediction are necessary to assist in the functional identification of the proteome.¹⁹ The related research⁹ includes such aspects as interaction prediction and ontology-based function prediction. Since proteins perform their function by binding with other ligands, including proteins, metal ions, DNA, RNA, etc., it is essential to predict the binding sites of proteins to further explore in detail the function of proteins.²⁰

1.3Organization of the Book Content

      The structure of this book is organized as follows. First, it begins by providing an introduction to the proteome, the biological language model and its application. Then, several research topics of the biological language model are proposed, with detailed introductions on the background and a description of the methods, i.e. linguistic feature analysis of protein sequences, amino acid encoding for protein sequences, protein remote homology detection, protein structure prediction and protein function prediction. For the topic of linguistic feature analysis of protein sequences, the n-grams of whole genome protein sequences from 20 organisms were extracted to obtain statistical sequence analysis results for a large number of genomic and proteomic sequences available for different organisms. Their linguistic features were analyzed by two tests — Zipf’s power law and Shannon’s entropy — developed for analysis of natural languages and symbolic sequences. As regards amino acid encoding, a comprehensive review of the available methods for this is proposed, and these methods are grouped into five categories according to their information sources and information extraction methodologies, which are as follows: binary encoding, physicochemical properties encoding, evolution-based encoding, structure-based encoding and machine-learning encoding. For protein remote homology detection, latent semantic analysis is used to extract and represent the contextual-usage meaning of words of protein sequences by statistical computations, and the auto-cross covariance transformation is introduced to transform protein sequences into fixed-length vectors. For the protein structure prediction topic, a novel index at the profile level is presented for protein domain linker prediction, a building-block library-based method has been presented to predict the local structures and the folding fragments of proteins, conformational entropy is used as an indicator of protein flexibility and a class of novel nonlinear knowledge-based mean force potentials is presented. For the protein function prediction topic, profile-level interface propensities are used for binding site prediction, sequence composition information is used for gene ontology-based protein function prediction and the n-gram biological language model from natural language processing has been used to filter the missing proteins. Finally, the conclusion and future perspectives are proposed.

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