The human cell is a miserable thing to study. Tens of trillions of them exist in the body, forming an enormous and intricate network that governs every disease and metabolic process. Each cell in that circuit is itself the product of an equally dense and complex interplay among genes, proteins, and other bits of profoundly small biological machinery. Our understanding of this world is hazy and constantly in flux. As recently as a few years ago, scientists thought there were only a few hundred distinct cell types, but new technologies have revealed thousands (and that's just the start).

ChatGPT, a recently developed product by openAI, is successfully leaving its mark as a multi-purpose natural language based chatbot. In this paper, we are more interested in analyzing its potential in the field of computational biology. A major share of work done by computational biologists these days involve coding up bioinformatics algorithms, analyzing data, creating pipelining scripts and even machine learning modeling and feature extraction. This paper focuses on the potential influence (both positive and negative) of ChatGPT in the mentioned aspects with illustrative examples from different perspectives. Compared to other fields of computer science, computational biology has - (1) less coding resources, (2) more sensitivity and bias issues (deals with medical data) and (3) more necessity of coding assistance (people from diverse background come to this field). Keeping such issues in mind, we cover use cases such as code writing, reviewing, debugging, converting, refactoring and pipelining using ChatGPT from the perspective of computational biologists in this paper.

For more than a decade, computational scientist Juan R. Perilla of the University of Delaware has been working to digitally reconstruct a very particular structure of the human immunodeficiency virus (HIV). Perilla and his colleagues set out to create an active three-dimensional digital model of the virus shell, or capsid, that researchers could study and probe as if they were working with an actual particle. The processing power required to build the simulation was significant, according to Perilla, because the model needed to track how a change in one area would impact the interactions of all two million atoms in the particle. Perilla and his group succeeded in constructing the model and demonstrating various means of testing the simulation to ensure it behaves as it would in the real world. "You can actually interrogate the simulated particle, pushing and pulling on the capsid as if you were testing the actual physical system," Perilla says.

TileDB, the database for any complex data and compute, announced the launch of TileDB-SOMA, the first collection of software libraries that implement the open-source SOMA API specification. SOMA and TileDB-SOMA are the result of a collaboration between the Chan Zuckerberg Initiative and TileDB to accelerate single-cell research by eliminating data silos and enable large-scale computations that are otherwise too challenging to execute on commodity hardware. "By streamlining access to enormous datasets, powerful new tools like TileDB-SOMA will accelerate the research efforts of single-cell biologists" New technologies and analysis tools have led to the exponential growth of single-cell RNA sequencing (scRNA-seq) data, requiring new solutions that can accommodate datasets at scale. Advancements in genomics technologies have also enabled researchers to combine multiple modalities of data collected from the same cell samples, increasing the complexity and impact of single-cell analysis. "The unsaid assumption in single-cell research is that dataset size is bound by RAM, but instead of asking researchers to change their computational tools, we're rethinking how the data model itself could do more heavy lifting for scientists," said Stavros Papadopoulos, Founder & CEO, TileDB, Inc. "With TileDB-SOMA for R and Python, computational biologists can work across programming languages and combine data that was previously formatted specifically for Seurat, Anndata/Scanpy or Bioconductor. This breaks down data silos, and allows scientists to collaborate without the hassle of converting or duplicating data. Everyone can access the dataset, stored locally or in the cloud, at any scale."

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What if you could join a rapidly growing company and play a critical role in bringing new medicines to patients through looking at and treating disease in a revolutionary way. Are you a highly motivated and organized computational biologist who is enthusiastic about developing, learning, and applying your computational skills to understand complex biological systems and disease? You will get the opportunity to work in an innovative computational team, driven to deliver high-impact results. You will be in an early group of pioneers developing the world's first AI platform that unveils the causes of emergent disease based on cellular understanding. You will begin your career at Cellarity with a slew of world-class computational biologists and machine learning scientists (https://cellarity.com/the-team),

The ESM Metagenomic Atlas database contains structure predictions for 617 million proteins.Credit: ESM Metagenomic Atlas (CC BY 4.0) When London-based Deep Mind unveiled predicted structures for some 220 million proteins this year, it covered nearly every protein from known organisms in DNA databases. Now, another tech giant is filling in the dark matter of our protein universe. Researchers at Meta (formerly Facebook, headquartered in Menlo Park, California) have used artificial intelligence (AI) to predict the structures of some 600 million proteins from bacteria, viruses and other microbes that haven't been characterized. 'It will change everything': DeepMind's AI makes gigantic leap in solving protein structures "These are the structures we know the least about. These are incredibly mysterious proteins. I think they offer the potential for great insight into biology," says Alexander Rives, the research lead for Meta AI's protein team.

But in the human brain, that volume of tissue contains some 50,000 neural'wires' connected by 134 million synapses. Jeff Lichtman wanted to trace them all. To generate the raw data, he used a protocol known as serial thin-section electron microscopy, imaging thousands of slivers of tissue over 11 months. But the data set was enormous, amounting to 1.4 petabytes -- the equivalent of about 2 million CD-ROMs -- far too much for researchers to handle on their own. "It is simply impossible for human beings to manually trace out all the wires," says Lichtman, a molecular and cell biologist at Harvard University in Cambridge, Massachusetts.

Demis Hassabis (left) and John Jumper (right) from DeepMind developed AlphaFold, an AI that can predict the structure of proteins.Credit: Breakthrough Prize The researchers behind the AlphaFold artificial-intelligence (AI) system have won one of this year's US$3-million Breakthrough prizes -- the most lucrative awards in science. Demis Hassabis and John Jumper, both at DeepMind in London, were recognized for creating the tool that has predicted the 3D structures of almost every known protein on the planet. "Few discoveries so dramatically alter a field, so rapidly," says Mohammed AlQuraishi, a computational biologist at Columbia University in New York City. "It's really changed the practice of structural biology, both computational and experimental." The award was one of five Breakthrough prizes -- awarded for achievements in life sciences, physics and mathematics -- announced on 22 September.

Artificial-intelligence tools are helping to scientists to come up with proteins that are shaped unlike anything in nature.Credit: Ian C Haydon/UW Institute for Protein Design In June, South Korean regulators authorized the first-ever medicine, a COVID vaccine, to be made from a novel protein designed by humans. The vaccine is based on a spherical protein'nanoparticle' that was created by researchers nearly a decade ago, through a labour-intensive trial-and error-process1. Now, thanks to gargantuan advances in artificial intelligence (AI), a team led by David Baker, a biochemist at the University of Washington (UW) in Seattle, reports in Science2,3 that it can design such molecules in seconds instead of months. 'The entire protein universe': AI predicts shape of nearly every known protein Such efforts are a part of a scientific sea change, as AI tools such as DeepMind's protein-structure-prediction software AlphaFold are embraced by life scientists. In July, DeepMind revealed that the latest version of AlphaFold had predicted structures for every protein known to science.