With AlphaFold’s performance, “suddenly

Shanghai is a glorious city. It is a place that constantly witnesses and creates miracles. It has a good ecosystem of harmonious coexistence of diverse cultures, such as ancient and modern, Chinese and Western, refined and popular. In order to guide the majority of primary and secondary school students to establish the feelings of "love their hometown, love Shanghai, love their motherland" from an early age, through reading and observing the history and characteristics of Shanghai, we can tell the story of Shanghai from many aspects of Shanghai, including events, characters, architecture, folk customs, intangible cultural heritage, dialect, calligraphy and painting, opera, film, economy, lifestyle, etc., under the guidance of the Shanghai Municipal Education Association, Shanghai Tongzhi Museum The Professional Committee of Primary and Secondary School Libraries of the Shanghai Education Association and the Shanghai Branch of Tongfang Zhiwang (Beijing) Technology Co., Ltd. will jointly host the "Love Shanghai and Tell Shanghai Stories" - Shanghai Primary and Secondary School Students' Reading and Practical Creation Activity in 2022 ". Relevant activities are hereby notified as follows:


1、 Participants

Students in primary and secondary schools (including secondary vocational schools, the same below) in Shanghai.

2、 Activity time

July 1 to September 15, 2022.

3、 Activity content

The theme of this activity is "Love Shanghai and Tell Shanghai Stories". The following three activities have been set up. Students can choose to participate in one or more of them.

1. With the theme of "I love Shanghai and tell the story of Shanghai", students will write their feelings after reading books about Shanghai's history, geography, economy, culture, local knowledge and other aspects, combined with their personal experience of study, life, family and surrounding environment, and express their feelings of love for the sea.

2. With the theme of "I love Shanghai and tell the story of Shanghai", and in combination with the students' personal experience in learning, life, family, and surrounding environment, we will create literary works (including essays, essays, short stories, poems, and plays) to reflect the Shanghai in the minds of students.

3. With the theme of "I love Shanghai and tell the story of Shanghai", and in combination with the students' personal experience in learning, life, family and surrounding environment, we will create works of art (including calligraphy, painting, photography, camera, model and physical production, etc.) to reflect the history, culture, economy, folk customs and other features and features of Shanghai.

During the event, the organizer will organize two "Shanghai Story Online Lectures", where experts and scholars will be invited by the Shanghai Chronicle Museum to tell the story of Shanghai to the students. For details of Shanghai Story Online Lecture, please pay attention to the activity website: http://zhengwen.shhistory.com 。

The organizer will also assist the relevant departments (units) of education in each district to organize and guide the school to carry out relevant activities such as theme film and television observation, reading report, visiting relevant theme museums, patriotism education base, etc. in combination with this activity, so as to enhance students' perceptual awareness, coordinate and deepen the activities, and enrich students' summer life. (Relevant activities will be notified separately according to the requirements of COVID-19 prevention and control)

4、 Work requirements

1. After reading, literary and artistic works must be original by the students themselves, and the specific content and subject matter are not limited.

2. The number of words after reading is generally 400-600 words for primary school students, 600-800 words for junior high school students and 800-1000 words for senior high school students.

3. Literary works are created in the form of prose, essays, short stories, poems, plays, etc., with unlimited number of words.

4. Artistic works (calligraphy, painting, photography, camera, model and material object production, etc.) are taken or uploaded after shooting. Upload format: jpg, mp4 and other mainstream formats.

The above reading comments and works will be posted on the website before September 15( http://zhengwen.shhistory.com )Submit to the sponsor for selection. For specific registration and upload methods, please follow the "Online Operation Guide" on the homepage of the website.

5、 Selection and recognition

1. Selection and recognition of students' post-reading feelings, literary and artistic works

The organizer will organize experts to evaluate the works submitted by the students and issue certificates to the award-winning students. The specific awards are set as follows: 10 first prizes, 50 second prizes and 100 third prizes, respectively, according to three groups and three activities of primary school, junior high school and senior high school.

2. Selection and commendation of district education departments (units), schools and instructors

The organizer will issue certificates to the district education-related departments (units) and schools and instructors who have made remarkable achievements in organizing and carrying out this activity according to the results of the reading and practical creation activities organized by the district education-related departments (units) and schools and instructors, as well as the number and quality of the articles and works that students participate in, The awards of relevant education departments (units) and schools are set as "Excellent Organization Award", and the awards of instructors are set as the honorary title of "Excellent Instructor".

6、 Contact number

Predictions are more accurate for some proteins than for others. Erroneous predictions could leave some scientists thinking they understand how a protein works when really, they don’t. Painstaking experiments remain crucial to understanding how proteins fold, Forman-Kay says. “There’s this sense now that people don’t have to do experimental structure determination, which is not true.”
Plodding progress
Proteins start out as long chains of amino acids and fold into a host of curlicues and other 3-D shapes. Some resemble the tight corkscrew ringlets of a 1980s perm or the pleats of an accordion. Others could be mistaken for a child’s spiraling scribbles.

A protein’s architecture is more than just aesthetics; it can determine how that protein functions. For instance, proteins called enzymes need a pocket where they can capture small molecules and carry out chemical reactions. And proteins that work in a protein complex, two or more proteins interacting like parts of a machine, need the right shapes to snap into formation with their partners.

Knowing the folds, coils and loops of a protein’s shape may help scientists decipher how, for example, a mutation alters that shape to cause disease. That knowledge could also help researchers make better vaccines and drugs.

For years, scientists have bombarded protein crystals with X-rays, flash frozen cells and examined them under high­powered electron microscopes, and used other methods to discover the secrets of protein shapes. Such experimental methods take “a lot of personnel time, a lot of effort and a lot of money. So it’s been slow,” says Tamir Gonen, a membrane biophysicist and Howard Hughes Medical Institute investigator at the David Geffen School of Medicine at UCLA.
Such meticulous and expensive experimental work has uncovered the 3-D structures of more than 194,000 proteins, their data files stored in the Protein Data Bank, supported by a consortium of research organizations. But the accelerating pace at which geneticists are deciphering the DNA instructions for making proteins has far outstripped structural biologists’ ability to keep up, says systems biologist Nazim Bouatta of Harvard Medical School. “The question for structural biologists was, how do we close the gap?” he says.

For many researchers, the dream has been to have computer programs that could examine the DNA of a gene and predict how the protein it encodes would fold into a 3-D shape.

Here comes AlphaFold
Over many decades, scientists made progress toward that AI goal. But “until two years ago, we were really a long way from anything like a good solution,” says John Moult, a computational biologist at the University of Maryland’s Rockville campus.

Moult is one of the organizers of a competition: the Critical Assessment of protein Structure Prediction, or CASP. Organizers give competitors a set of proteins for their algorithms to fold and compare the machines’ predictions against experimentally determined structures. Most AIs failed to get close to the actual shapes of the proteins.
Then in 2020, AlphaFold showed up in a big way, predicting the structures of 90 percent of test proteins with high accuracy, including two-thirds with accuracy rivaling experimental methods.

Deciphering the structure of single proteins had been the core of the CASP competition since its inception in 1994. With AlphaFold’s performance, “suddenly, that was essentially done,” Moult says.

Since AlphaFold’s 2021 release, more than half a million scientists have accessed its database, Hassabis said in the news briefing. Some researchers, for example, have used AlphaFold’s predictions to help them get closer to completing a massive biological puzzle: the nuclear pore complex. Nuclear pores are key portals that allow molecules in and out of cell nuclei. Without the pores, cells wouldn’t work properly. Each pore is huge, relatively speaking, composed of about 1,000 pieces of 30 or so different proteins. Researchers had previously managed to place about 30 percent of the pieces in the puzzle.
That puzzle is now almost 60 percent complete, after combining AlphaFold predictions with experimental techniques to understand how the pieces fit together, researchers reported in the June 10 Science.

Now that AlphaFold has pretty much solved how to fold single proteins, this year CASP organizers are asking teams to work on the next challenges: Predict the structures of RNA molecules and model how proteins interact with each other and with other molecules.

For those sorts of tasks, Moult says, deep-learning AI methods “look promising but have not yet delivered the goods.”

Where AI falls short
Being able to model protein interactions would be a big advantage because most proteins don’t operate in isolation. They work with other proteins or other molecules in cells. But AlphaFold’s accuracy at predicting how the shapes of two proteins might change when the proteins interact are “nowhere near” that of its spot-on projections for a slew of single proteins, says Forman-Kay, the University of Toronto protein biophysicist. That’s something AlphaFold’s creators acknowledge too.

The AI trained to fold proteins by examining the contours of known structures. And many fewer multiprotein complexes than single proteins have been solved experimentally.
Forman-Kay studies proteins that refuse to be confined to any particular shape. These intrinsically disordered proteins are typically as floppy as wet noodles (SN: 2/9/13, p. 26). Some will fold into defined forms when they interact with other proteins or molecules. And they can fold into new shapes when paired with different proteins or molecules to do various jobs.

AlphaFold’s predicted shapes reach a high confidence level for about 60 percent of wiggly proteins that Forman-Kay and colleagues examined, the team reported in a preliminary study posted in February at bioRxiv.org. Often the program depicts the shapeshifters as long corkscrews called alpha helices.

Forman-Kay’s group compared AlphaFold’s predictions for three disordered proteins with experimental data. The structure that the AI assigned to a protein called alpha-synuclein resembles the shape that the protein takes when it interacts with lipids, the team found. But that’s not the way the protein looks all the time.

For another protein, called eukaryotic translation initiation factor 4E-binding protein 2, AlphaFold predicted a mishmash of the protein’s two shapes when working with two different partners. That Frankenstein structure, which doesn’t exist in actual organisms, could mislead researchers about how the protein works, Forman-Kay and colleagues say.
AlphaFold may also be a little too rigid in its predictions. A static “structure doesn’t tell you everything about how a protein works,” says Jane Dyson, a structural biologist at the Scripps Research Institute in La Jolla, Calif. Even single proteins with generally well-defined structures aren’t frozen in space. Enzymes, for example, undergo small shape changes when shepherding chemical reactions.

If you ask AlphaFold to predict the structure of an enzyme, it will show a fixed image that may closely resemble what scientists have determined by X-ray crystallography, Dyson says. “But [it will] not show you any of the subtleties that are changing as the different partners” interact with the enzyme.

“The dynamics are what Mr. AlphaFold can’t give you,” Dyson says.

A revolution in the making
The computer renderings do give biologists a head start on solving problems such as how a drug might interact with a protein. But scientists should remember one thing: “These are models,” not experimentally deciphered structures, says Gonen, at UCLA.

He uses AlphaFold’s protein predictions to help make sense of experimental data, but he worries that researchers will accept the AI’s predictions as gospel. If that happens, “the risk is that it will become harder and harder and harder to justify why you need to solve an experimental structure.” That could lead to reduced funding, talent and other resources for the types of experiments needed to check the computer’s work and forge new ground, he says.
Harvard Medical School’s Bouatta is more optimistic. He thinks that researchers probably don’t need to invest experimental resources in the types of proteins that AlphaFold does a good job of predicting, which should help structural biologists triage where to put their time and money.

“There are proteins for which AlphaFold is still struggling,” Bouatta agrees. Researchers should spend their capital there, he says. “Maybe if we generate more [experimental] data for those challenging proteins, we could use them for retraining another AI system” that could make even better predictions.

He and colleagues have already reverse engineered AlphaFold to make a version called OpenFold that researchers can train to solve other problems, such as those gnarly but important protein complexes.

Massive amounts of DNA generated by the Human Genome Project have made a wide range of biological discoveries possible and opened up new fields of research (SN: 2/12/22, p. 22). Having structural information on 200 million proteins could be similarly revolutionary, Bouatta says.

In the future, thanks to AlphaFold and its AI kin, he says, “we don’t even know what sorts of questions we might be asking.”

Shanghai Tongzhi Museum

Professional Committee of Primary and Secondary School Libraries of Shanghai Education Association

Tongfang Zhiwang (Beijing) Technology Co., Ltd. Shanghai Branch

June 16, 2022

The original title: "Notice |" Love Shanghai and Tell Shanghai Stories "- The Reading and Practical Creation Activity of Shanghai Primary and Secondary School Students in 2022 begins!"