Author(s): Omer Ozgur Artificial Intelligence From https://www.researchgate.net/figure/Structure-of-Hemoglobin-Protein-Hemoglobingentr-https-hemoglobingentr-Access_fig22_349573893 Deepmind’s academic paper came out in Nature describing all the details of its CASP-winning AlphaFold v2 model for predicting protein structures. They also made all the code available at Github. Google Colab notebooks were quickly created. This development allowed everyone to predict the protein they like. We will be using Alphafoldv2 in this article to predict the future and how the science world is changing. The Biologist’s Dream According to Levinthal’s paradox, to list all possible configurations for a protein and then find the correct 3D structure would take more time than the age the universe. Proteins are the major component of muscle, bone, organs and skin. Proteins are often referred to by others as the building blocks of our bodies. A protein’s function is directly related to how it is folded. The world’s greatest challenges can be solved by predicting the shape of proteins. This includes finding effective treatment for disease and the right enzymes to perform certain functions. Experimental techniques like X-ray and neutron diffraction (NMR), nuclear magnetic resonance (NMR) or electron cryomicroscopy (also known as cryoelectron microscopy or cryoEM) can help to determine many protein structures. These techniques can be expensive, time-consuming, and difficult to use. These techniques may not work for certain proteins. AlphaFold is here to assist. This neural network-based algorithm has performed amazingly on protein folding problems. We can predict the future of protein folding with algorithms such as Alphafold. It is AI-time in biology. Alphafold 2 From https://deepmind.com/blog/article/alphafold-a-solution-to-a-50-year-old-grand-challenge-in-biology In this section, we’ll look at how Alphafold works in general and where it’s evolving. SPADKTNVKAAWGKVGAHAGEYGAEALERMFLSFPTTKTYFEALERMFLSEALERMFLSFPTTKTYF Alphafold takes the amino acid sequence at the simplest level and predicts the positions and bond angles of molecules in 3D space. Alphafold is similar to GPT-3 in that it uses unsupervised learning. DeepMind posted a diagram showing that MSA (Multiple Sequence Alignment), is an important step in the modeling process. GPT-3 GPT-3 uses unlabelled text data to learn the basic features of the language. Alphafold also learns embedding by looking at similar sequences of proteins. Because they share an evolutionary origin, two protein sequences may be identical. A protein’s ability to serve the same purpose in different organisms is dependent on how similar its amino acid sequences are. While proteins can evolve and mutate, their structure tends to stay the same despite these changes. It is decades-old to use correlated mutations in order to extract structural information form an MSA. You can also collect pieces from other proteins to build your target’s model. AlphaFold 1 was based on CNNs while AlphaFold 2 uses Transformers. Once we have mastered the algorithmic structure, it is possible to learn domain knowledge. Hemoglobin Protein Hemoglobin, a protein molecule found in red blood cells, carries oxygen from the lung to tissues. It also returns carbon dioxide back to the lungs. Hemoglobin is also important in the maintenance of red blood cell shape. A disruption in hemoglobin structure could result in red blood cell formation and impair blood vessel flow. Hemoglobin is made up of subunits. One protein molecule is called a protein subunit. It combines with another protein molecule to create a protein complex. The cell produces different components, much like making a car. One subunit of a protein assembly may be the “catalytic” subunit that catalyzes an enzyme reaction. A “regulatory” subunit will either facilitate or hinder activity. The genes HBA1, HBA2, HBB and HBB code for hemoglobin A, the main hemoglobin form in humans. HBA1 (hemoglobin subunit alpha 1) and HBA2 (hemoglobin subunit beta 2) are codes for by the genes HBA1 & HBA2. HBB codes for subunit beta. Predicting the sequence Before we can make predictions, it is necessary to know the amino acid sequence of the protein. The direct amino acid sequence can be used to extract the sequence of amino acids from DNA. We can use the NCBI database to access information about a protein known and then download that data in fasta format. Send hemoglobin subunital alpha [Homo sapiens]. File – Format(Fasta), – Create File. What is NCBI? As part of the National Library of Medicine’s National Center for Biotechnology Information, (NCBI), it develops and maintains bibliographic and molecular databases. The data they generate is not theirs. There are tons of data available here. What is the Fasta Format? The FASTA Format is used in bioinformatics (biochemistry) to represent nucleotide and amino acid sequences. Nucleotides are represented with single-letter codes. Let’s use Alphafold 2 Google Colaboratory. I have shared the most important codes blocks here. You can access all codes from Colab Notebook. The alphafold2 MSA pipeline is not included in this notebook. This notebook can only be used with one sequence (no MSA). Although accuracy is nearly identical to that of the AlphaFold system for many targets, some target types may have an inaccuracy because there are fewer templates and a smaller MSA. We recommend that you use the complete open-source AlphaFold for maximum reliability. We will now read the fasta file that we have downloaded to get sequence information. from Bio import SeqIO fasta_sequences = SeqIO.parse(“sequence.fasta”,’fasta’) for record in fasta_sequences: print(record) protein_seq =str(record.seq) ID: NP_000508.1 Name: NP_000508.1 Description: NP_000508.1 hemoglobin subunit alpha [Homo sapiens] Number of features: 0 Seq(‘MVLSPADKTNVKAAWGKVGAHAGEYGAEALERMFLSFPTTKTYFPHFDLSHGSA…KYR’) Making Prediction %%time feature_dict = pipeline.make_sequence_features(sequence=protein_seq, description=”none”, num_res=len(query_sequence)), pipeline.make_msa_features(msas=[[query_sequence]], deletion_matrices=[[[0]*len(query_sequence)]]), mk_mock_template(query_sequence) plddts = predict_structure(“alphaH”,feature_dict,model_runners) For hemoglobin subunit alpha, it took 46 min to predict. When the prediction is complete, it will be saved in the directory with alphaH_unrelaxed_model_1.pdb file. Visualization. The pdb file format allows for the description and annotations of nucleic acids and protein structures, including secondary structure assignments and atomic coordinates. p = py3Dmol.view(js=’https://3dmol.org/build/3Dmol.js’)p.addModel(open(“alphaH_unrelaxed_model_1.pdb”,’r’).read,’pdb’)p.setStyle(‘cartoon’: ‘color’:’spectrum’)p.zoomTop.show Image By Author (hemoglobin subunit alpha) And we can visualize confidence per position. Confidence doesn’t seem too high. Image by Author. The transition of Alphafold from academia into the open-source community could bring about many breakthroughs. Alphafold will allow scientists to accelerate their research and make important discoveries. Alphafold could be the first AI ever to receive a Nobel Prize. Predicting the 3D Structure of The Hemoglobin protein With Alphafold 2 originally appeared in on Medium. People are responding and highlighting this story. 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Predicting 3D Structure Of The Hemoglobin Protein With Alphafold 2
