Science of TranshumanGene

Probabilistic genomics and AI-generated mutation modeling for biological reasoning and intervention hypotheses.

The program is presented as a scientific notebook: architecture, assumptions, applications, validation logic and visual evidence for researchers, innovators, students, professors, commercial partners and philanthropic organizations.

TranshumanGene

PROBABILISTIC GENOMICS

MASSIVE AI-GENERATED MUTATIONS THROUGH PREDICTIVE MODELS

GENERATION OF VIRTUAL MOLECULES AND MicroRNA FOR FIGHTING VIRUSES, AMR, CANCERS,

ENHANCING HUMAN GENOMES,

DISCOVERING AND VALIDATING DRUGS

M.VIVIANI, M.BISOGNI, N.L.BRAGAZZI

14,

MASSIVE AI GENERATED MUTATIONS THROUGH PREDICTIVE MODELS

Scientific collaboration model

Research programs are organized around computational hypotheses, validation discipline, experimental caution and ethical review.

Mental health

Our key publications

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General and Personalized Drugs

Validation of genetic studies

Longevity enhancing techniques

SPECIALIZED STARTUPS

VIRUS VARIANT: Predictive medicine

Predictive Virus Mutations

Validation of existing Drugs and Vaccines

Gene Therapies

Side effects genomic link

SPAIRT: Genetic evaluation of the athlete (both professional and amatorial). Possible pharmacological correction to lack in genetic mechanisms.

Health Sphere: a device that keeps tracking of your genetic characteristics.

ICUGENE: Genetic evaluation of admitted patients in ERs and ICUs.

This evaluation determines the best set of drugs to apply, given the patient’s genetic profile. Reduces collateral effects of multiple medications.

METABOLAITE: Genetic cures and optimized drugs for various forms of diabetes

GENEPROTECTOR: Providing monitoring and cures to the deterioration of human DNA and tissues caused by radiations in Space and hazardous environments on Earth.

AMR Antimicrobial resistance: Assessing and preventing antibiotic resistance and proposing genetic solutions.

OPENAIMED: Educational Project to support TranshumanGene and the other Startups in their market expansion. The project aims to help disadvantaged students from all over the world learn new genetic techniques.

WHAT WE HAVE DONE SO FAR

*AI4Omics =

Parallel

OS for

accelerating

normalizing

processes

and data

analysis

MetabolAite

= Solution for

diabetes

cures

10,120 h

drugs and your genome: a reliable predictor of side effects

SARS CoV2 - Viruses - Bacteria: we predict them reliably and fight them efficiently (gene therapies) before they occur

Core business

Human

genome

reliable

prediction

drugs

collateral

effects

Elimination of the side effects of drugs for each person analyzed thanks to the computation of our system that allows you to choose the best drug (personalized medicine) exactly

computation

lethal

Virus/

Bacterium

generated

sequence

Computation and reliable prediction of the lethal genomic sequences that are produced during replication of the virus/bacteria due to the mutations that occur and that our system calculates exactly

Creation of kits/swabs (patentable) to test for the presence of such lethal mutations

Creation of the best enzymes / MicroRNAs as vaccines (patentable) for the given sequences to immunize before getting infected and avoid suffering/death

What is a “Virus Variant”?

V.V. is a

predictive tool

applied to the present virus strands and can tell you the most probable evolution of the virus itself. It’s beneficial to produce efficient vaccines and test the existing ones

TranshumanGene is an AI division specialized in pharmaceutical "in silico" and "in vitro“ R&D

Definition

In Silico: produced using computer modeling or computer simulation

Other critical applications are

ICU – Gene

, a predictive tool that shows the effects of a vaccine on a specific subject or a homogeneous population

Vaccines editing

the production in vitro of compounds that can be added to vaccines to make them more effective

VIRUS VARIANT

The predictive tool for better vaccines

Manaus population seemed to have reached herd immunity. However, given the massive transmission of the so-called Brazilian VOC, it was overwhelmed by the 2 and 3 COVID-19 waves.

Studying and modeling the mutational landscape of SARS-CoV-2 is of paramount importance given the urgent need to predict whether anti-COVID-19 vaccines will be effective or not on the so-called variants of concern (VOCs).

Including compounds able to fight future virus variants could end the pandemic.

Drugs under validation

Abstract

We are working on drugs validations; data will be released after journals publications

has developed a predictive AI-based multi-factorial platform (Virus Variant) that computes all the possible mutations of genomes in viruses and other living forms.

Our priority is the generation of virtual molecules and microRNA essential for fighting viruses, validating vaccines, and foresee the effects on patients.

VirusVariant

works?

Covid-19

Virus DNA

Variants

Calculation

Predicted

Variations

Antigens

mRNAs

Actual

mRNA

Vaccine Procedure

Antigen

mRNA Vaccine

Target Virus

With the help of supercomputers,

calculate

most

likely

mutations

of the virus.

this

could

efficient

. For

reason

apply

a reverse engineering

process

Phase

Predicting

Virus

Since

by the

require

extensive

screening

assessing

their

toxicity

, to be

faster

similar

approach

determine

the target

Antigenes

Calculating

Antigene

Virus Antigene

(Reverse engineering)

Mutated

Genetic

Proteomic

database

validation

After the

, the

resulting

will

associated

with the

Viruses

find

optimal

matches

artificial intelligence-based biophysics

(artificial intelligence + computational biology = computational intelligence)

AI drugs from design in silico to in vitro to trials

What we have achieved

WHAT WE HAVE DONE SO FAR: IN SILICO TO IN VITRO

We have already successfully crystallized an insulin sequence, with an excellent resolution, one of the highest obtained results. This enables us to shed more light on the mechanisms of insulin action at a cellular and molecular level, studying its genetic variants and the primary pathogenic mechanisms leading to diabetes.

Systemic lupus erythematosus (SLE) is a complex, multi-factorial and multi-system autoimmune disease, which imposes a dramatically relevant clinical and societal burden. Steroids (including methylprednisolone) represent the gold-standard option in terms of pharmaceutical treatment, even though their administration can result in side-effects, also serious and life-threatening ones (like malignancies, immune dysregulation/impairment and metabolic syndrome). Molecules obtained and extracted from helminths can be as effective as steroids, if not even superior in terms of pharmacological efficacy, sparing steroid doses and curbing the likelihood of developing severe adverse events. Among the different molecules,

tuftsin

phosphorylcholine appears to be particularly intriguing in that it finely tunes and modulates several immunological cascades and pathways. Utilizing a murine model of SLE nephritis (lupus-prone

NZBxW

/F1 mice) we were able to demonstrate the effectiveness of this novel helminth-based compound. Proteinuria grade, levels of anti-dsDNA autoantibodies and splenic cytokines (like pro-inflammatory cytokines interferon IFN-γ, interleukin IL-1β and IL-6) significantly decreased after administration of

phosphorylcholine, whereas the concentration of anti-inflammatory cytokine IL-10 increased. Summarizing,

phosphorylcholine seems to be a promising pharmacological treatment, even though the investigations conducted so far are preliminary and further research, including randomized clinical trials, is warranted

WHAT WE HAVE DONE SO FAR: IN SILICO TO IN VITRO TO TRIALS

AI4OMICS by TRANSHUMANGENE

A platform for geneticists,

researchers, physicists,

etc

Case Study: AFRICA

We are operating with patients of African origin.

"Africa needs not only its genetic library but also a calibrated set of genetic tools."

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Optimization Tool for Omics

The Problem

A multitude of instruments is available in omics and genomics laboratories around the world and it's difficult to get uniformity in datasets. This is one of the biggest problems when it comes to applying AI to aggregate data or simply comparing aggregate data.

Data we are dealing with tend to be complex, big, and scattered. Consequently, solution patterns are often lost in multiple big foggy data clusters.

Most research procedures take a long time to complete, and the results are generally poorly organized. Hence, it takes extra time and resources to run formal algorithms.

The Solution - 1

There are three ways to solve this limiting factor

build a tool capable of harmonizing different data sets (third party management)

create of a platform that allows scientists and researchers to enter and access data in a customized database that has already been structured

create an AI-OS that mediates between these two approaches

AI4Omics

BDM (Basic Data Management)

features

The software is essential in genetic laboratories for running harmonized data analysis.

AI4Omics, will provide the best quality in terms of

arranging genomic data,

measuring or comparing DNA sequence format,

gene expression,

functional annotations,

identifying genes (position, role, and expression domain)

def

has_allowed_alignment

read

"""Determines whether a read's CIGAR string has the allowed alignments."""

return

all

([

c.operation

_ALLOWED_CIGAR_OPS

read.alignment.cigar

is_usable_example

reads

ref_bases

"""Determines whether a particular reference region and read can be used."""

# Discard examples with variants or no mapped reads.

False

# Use only examples where all reads have simple alignment and allowed bases.

(read)

any

(base

_ALLOWED_BASES

read.aligned_sequence

# Reference should only contain allowed bases.

True

The Solution - 2

The BDM embedded in AI4Omics is five to one hundred times more effective than typical parallel programs. We can eliminate errors and bad syncs with enhanced computational power for quicker autonomous algorithms.

In addition to this, we can organize and format heterogeneous data from a variety of sources and store them efficiently in several databases.

It represents a new step in parallel programming, deep learning, reinforcement learning, and machine learning providing new opportunities in science

simulations,

DNA sequencing,

big data analysis,

autonomously generated algorithms,

precision and personalized medicine,

disanguibication

of different data,

friendly interaction with different standards,

integration with existing genomic apps to plan and execute complex tests.

Executive Summary

Ai4Omics' team has a long experience in parallel programming, deep learning, and genomics, creating new possibilities for achieving more significant results in science in general: simulations, DNA sequencing, big data

Ai4Omics has also developed a new Operating System for supercomputers and running on high-end laptops

This OS is called BDM (Basic Data Management)

Ai4Omics’ BDM makes optimization of data complexity

class

BaseHparams

object

"""Default hyperparameters."""

init

self

total_epochs

learning_rate

0.004

0.001

batch_size

window_size

ref_path

'hs37d5.fa.gz'

vcf_path

'NA12878_calls.vcf.gz'

bam_path

'NA12878_sliced.bam'

out_dir

'examples'

model_dir

ngs_model

log_dir

'logs'

.total_epochs

.learning_rate

.l2 = l2

.batch_size

.window_size

.ref_path

.vcf_path

.bam_path

.out_dir

.model_dir

.log_dir

Extrapolation Tool for Omics

An essential feature of AI4Omics

The platform can run both on high-standard commercial computers and supercomputers.

he operator inserts data into our cyber-secure environment, and AI4Omics generates machine learning algorithms that classify, organize the data, forming new data aggregates and structures. The second step is the “hunt” in these classes of patterns useful to create new therapies, drugs, and treatments.

Once we have reached a sufficiently big cluster of data, we can abstract a virtually infinite number of artificial genomes, delivering preventive cures to the real world even before the critical point of illness has arisen. (Predictive Medicine)

Our platform would accelerate the research time for vaccines in the order x 12+

generate_tfrecord_datasets

hparams

"""Writes out

TFRecords

files for training, evaluation, and test datasets."""

os.path.exists

os.makedirs

# Fraction of examples in each dataset.

train_eval_test_split

= [

0.7

0.2

0.1

num_train_examples

num_eval_examples

num_test_examples

# Generate training, test, and evaluation examples.

, _EVAL))

eval_out

, _TEST))

test_out

all_examples

make_ngs_examples

example

r =

random.random

()

r <

train_out.write

(proto=example)

elif

] +

eval_out.write

else

test_out.write

'# of training examples: %d'

'# of evaluation examples: %d'

'# of test examples: %d'

"""Generator function that yields training, evaluation and test examples."""

ref_reader

fasta.IndexedFastaReader

input_path

hparams.ref_path

vcf_reader

vcf.VcfReader

hparams.vcf_path

read_requirements

= reads_pb2.ReadRequirements()

sam_reader

sam.SamReader

hparams.bam_path

OMICA, a platform for genomic analysis

Applied to African descendent patients.

To face the21st century challenges

Health management Africa needs not only its

Own genetic library but also a calibrated set of genetic tools.

Lately, there have been lots of efforts in creating

libraries of genetic information about the population of Africa.

Some projects have worked on how genetic mutations among Africans contribute to conditions like sickle-cell disease and hearing impairments utilizing a limited number of genomes.

Case Study – Cont.

African genes hold a wealth of genetic variation, beyond that, observed by

scientists in Europe and elsewhere.

Too little of the knowledge and applications from genomics has benefited the global south because of inequalities in healthcare systems.

Leading causes of such inequality are high costs in accessing to non-African institutions and small local research workforce due to lack of funding.

Only about 2% of the genomes mapped globally are African, and a good proportion of these are African American. This comes from a lack of prioritizing funding, policies, and training infrastructure, but understanding genetic medicine is partial. And this has enormous consequences.

For example, estimates of genetic risk scores for people of African descent that predict, say, the likelihood of cardiomyopathies or schizophrenia can be unreliable or even misleading using tools that work well in the West or Asia.

The number of three million is the minimum to accurately map genetic variations across Africa, considering that Africa has 1.3 billion inhabitants.

This also affects the variety of diagnostic tests. The gaps in the availability of genomic information relevant to local populations also don’t allow fine-tuning of the tests. For example, it may find a genetic mutation in someone and not know if that variation is associated with a disease or other causes.

Because of the limited data sets of the African genome designed for a Caucasian and Asian population that may or may not have much of an effect on the African people.

OMICA wants to be a flexible platform that allows to store and analyze African genomes and as well fine-tuning such tools.

OMICA utilizes a mix of Open Sources and Proprietary programs to make the platform easy to access, cheap to run, and highly customizable.

Major Actors in the Vaccine and

Drug Discovery world,

requiring

advanced AI

Very few AI companies/

StartUp

, operating in this field, make large use of Supercomputers

Perspective Clients

Harvard University

Stanford University

Johns Hopkins Uni.

University of Oxford

University of Cambridge

University of Basel & Zurich

Top Universities

Other Institutions

Contract research organizations (CRO)

Laboratories

Hospitals

National Health Organizations

TranshumanGene main field of interaction

fewer target molecules

definition of the optimal ones

in few weeks instead of years

Our Omni-comprehensive approach requires heavy use of AI and Supercomputers

What We Do: Drugs and Vaccines

Production

FlowChart

Sequenced

New Studies/Drugs

Existing Study/Drug

Studies

Super

Computer

Relevant Mutations

Proteins

Enzymes

Other molecules

Lab Confirmation

Molecule

Prototype

Client

BIO-Digital Engineer Syllabus: OPENAIMED

TRANSHUMANGENE ACADEMIC BIO-Digital Engineering: OPENAIMED

FH Carinthia

The university of applied science in Carinthia has a far-reaching fundament in different expertise. The study program is focused in three sections

Engineering & IT, Health, and Management.

Also they have some special programs in Innovation and a center for further education.

https://www.fh-kaernten.at/en/

https://www.lakeside-labs.com/

Medical University Graz

FH-PROF. MAG.A DR.IN

ASTRID PAULITSCH-FUCHS

Biomedical Analyze

DI Dr. Erich Alois Hartlieb

Entrepreneurship Mastermind

Priv.

Doz

. Mag.

Dr.rer.nat

Gernot

Zarfel

Microbiology

Mag.rer.nat

. Dr.

Klemens Kittinger

TRANSHUMANGENE ACADEMIC BIO-Digital Engineering Syllabus: OPENAIMED

TU Wien

At TU Wien, we have been conducting research, teaching and learning under the motto 'Technology for people' for over 200 years. TU Wien has evolved into an open academic institution where discussions can happen, opinions can be voiced and arguments will be heard. Although everyone may have different individual philosophies and approaches to life, the staff, management personnel and students at TU Wien all promote open-mindedness and tolerance. Also

they have some special programs in Innovation and a center for further education. In Vienna has the university different places and a renowned worldwide reputation on the technical research field.

https://www.tuwien.at/en/tu-wien/

https://www.tuwien.at/en/tu-wien/about-tu-wien/facts-and-figures/rankings/

https://www.imw.tuwien.ac.at/cps/team/sebastian_schlund/

FH Technikum Wien

With around 13,000 graduates thus far and 4,400 students, the University of Applied Sciences

Technikum

Wien is Austria’s only purely technical university of applied sciences. The educational offerings consist of 12 bachelor’s and 18 master’s degree programs, which are offered as full-time, part-time and/or distance study programs. Four degree programs are taught in English. The educational offerings are based on a solid scientific foundation and are also practice-oriented. At UAS

Wien, emphasis is not only placed on providing a high-quality technical education, but also on subjects with a focus on business and personal development. Close ties and collaborations with business and industry give students and graduates excellent career opportunities. The combination of theory and practical application is of central importance in both research and

instruction.The

research and development activities at UAS

Wien have grown significantly in recent years and currently concentrate on our research focuses: Embedded Systems & Cyber-Physical Systems,

Renewable Urban Energy Systems,

Secure Services, eHealth & Mobility,

Tissue Engineering & Molecular Life Science Technologies,

Automation & Robotics.

https://www.technikum-wien.at/en/

Univ.-Prof. Dr.-Ing. Dipl.-Ing.

Sebastian Schlund

Cyber

Physical

Systems

York University

York University (French

Université

York) is a public research university in Toronto, Ontario, Canada. It is Canada's third-largest university and it has approximately 55,700 students, 7,000 faculty and staff, and over 325,000 alumni worldwide.

A community of changemakers working to create a better future: York believes that our diverse community, excellent learning and research, and commitment to collaboration allows us to address complex global challenges to create positive change in the local and global communities we serve. Our staff, students and faculty are passionate about building a more innovative, just and sustainable world.

https://www.yorku.ca/

https://liam.lab.yorku.ca/person/dr-nicola-luigi-bragazzi/

Dr. Nicola Luigi

Bragazzi

BSc

, MD, PhD,

MSc

, MPH

articles

4235

citations

, H-index 30 (

Scopus

Reviewer

scholarly

journals

National/International

Prizes

Young Knight

Italian

Republic

2005,

Guidoniani

Prize 2018, USERN Prize 2019, MAI Prize 2020

International Journal

Functional

Nutrition, Editor

Medicina

Section

Board Member and Editorial Board Member

Environmental Research and Public Health,

Board Member

Current

Autoimmunity

, Editorial Board Member

Epidemiologia

Laboratory

Industrial and Applied

Mathematics

(LIAM)

Department

Statistics

Toronto, ON, Canada

WHERE WE COME FROM: SUPERCOMPUTING ai Applied to

genomics

fighting

diseases

senescence

AI4OMICS)

Mutant*: Proteins impact (IN COLORS) on predicted mutations (SPHERE)

On internal layers (three-dimensional) is possible to represent temporal and cyclical variables (timing and number of mutations)

*mutant = ai engine

MUTANT IS A COMPLEMENTARY TOOL FOR

ProjectING

and VALIDATING DRUGS

Mutant: from many years to few months OF DRUGS development

AGGREGATE THE MUTATIONS

CREATE AND LIST MUTATIONS

Transhumangene Output

How MUTANT works

NORMALIZATION

AGGREGATION

DRUG

At present, there is no common standard for representing genomic data. Each sequencer tends to have its own. For this reason, a normalization tool of genetic data is essential.

Our Software detects and simulates Mutations in DNA/RNA data.

The Aggregation of data obtains the process of analysis. Our AI will calculate every possible pattern on which a molecule can optimize the wanted result. Such outcomes are rarely unique.

AI4OMICS NORMALIZATION OF GENOMICS DATA

Time and cost saving

AI ENGINE MUTANT

Genetic correction

protein

Selection of genes

Virtual Protein

composition

Y=gene

response

1) AI drug composition generation: substances are selected, and virtual molecules are created to populate many computed patterns.

How MUTANT

optimizes

& Gene Therapies studies

2) Every virtual molecule is used for classifying the resulting outcome on the selected genes

3) Virtual proteins are generated and matched with the genomes.

4) The molecules that give the desired outcome on the genome are selected.

MUTANT speeds up the following workflow

correction

validated

Check of

known

genes

expressions

Check of the AI

projected

MicroRNA

enzyme

Genes stimulating enzymes and microRNA are virtually built and locked to Mutant database

and check for

Mutant database mines the resulting genes expressions computing the collateral effects

AI created molecules (drug) are kept into consideration or discharged and another cycle restarts in every case

Enzyme research

MicroRNA Deep Sequencing

TranshumanGene ACCELERATES studies IN changes OF genomic response to alterations (Collateral effects)

CANCER STUDY AND PREDICTED MUTATIONS

Antimicrobial resistance

Attributable deaths and disability-adjusted life-years caused by infections with antibiotic-resistant bacteria in the EU and the European Economic Area

The Value of Vaccines in the Avoidance of Antimicrobial Resistance

FROM AI4OMICS TO mutant

MUTANT generates machine learning algorithms that classify and organize data, forming new data aggregates and structures.

Once we have reached a sufficiently big cluster of data, we can abstract a virtually infinite number of artificial genomes, facilitating personal preventive cures even before the critical point of illness has arisen. (Predictive Medicine)

MUTANT would accelerate the research time for drugs and vaccines of a factor of 10+

This system is handy for running subsequent AI analysis

Best quality in arranging genomic data,

Measurement or comparison of DNA sequence,

Structural variation,

Functional annotation,

Gene’s biological identity (position, role and its expression domain)

Since the Mutant AI engine runs on big data (which needs optimization), a supercomputer is required, but best results can be reached with Quantum Computers, when available.

MUTANT: normalization ENGINE

MUTANT: Optimization Tool for Omics

MUTANT practical APPLICATIONS

MUTANT

enhances computational power for organizing and giving a standard data format from different sources.

It exerts its utility in

Simulations

Genome sequencing

Big data analysis

Autonomously generated algorithms

Precision and personal medicine

Disambiguation of different data

Friendly interaction with different standards

Integration with existing genomic apps

Planning and execution of complex tests

Proofs of concepts and case study COVID-19

TransHumanGene, Drugs and Vaccines, Artificial Intelligence and Supercomputing

”, by

Nicola Luigi Bragazzi, Maurizio Bisogni and Maurizio Viviani

TransHumanGene and SARS-CoV-2: navigating the mutational landscape by means of Artificial Intelligence and Supercomputing

”, by Nicola Luigi Bragazzi

, Maurizio Bisogni and Maurizio Viviani

TransHumanGene, Senescence, Artificial Intelligence and Supercomputing

” by Nicola Luigi Bragazzi

TransHumanGene, Cancer, Artificial Intelligence and Supercomputing

” by

“How Big Data and Artificial Intelligence Can Help Better Manage the COVID-19 Pandemic”

by Nicola Luigi Bragazzi,

Haijiang

Dai, Giovanni Damiani, Masoud

Behzadifar

, Mariano Martini and

Jianhong

Int. J. Environ. Res. Public Health 2020, 17(9), 3176; doi.org/10.3390/ijerph17093176

Medical education refers to education and training delivered to medical students in order to become a practitioner. In recent decades, medicine has been radically transformed by scientific and computational/digital advances-including the introduction of new information and communication technologies, the discovery of DNA, and the birth of genomics and post-genomics super-specialties (transcriptomics, proteomics,

interactomics

, and metabolomics/

metabonomics

, among others)-which contribute to the generation of an unprecedented amount of data, so-called 'big data'. While these are well-studied in fields such as medical research and methodology, translational medicine, and clinical practice, they remain overlooked and understudied in the field of medical education. For this purpose, we carried out an integrative review of the literature. Twenty-nine studies were retrieved and synthesized in the present review. Included studies were published between 2012 and 2021. Eleven studies were performed in North America: specifically, nine were conducted in the USA and two studies in Canada. Six studies were carried out in Europe: two in France, two in Germany, one in Italy, and one in several European countries. One additional study was conducted in China. Eight papers were commentaries/theoretical or perspective articles, while five were designed as a case study. Five investigations exploited large databases and datasets, while five additional studies were surveys. Two papers employed visual data analytical/data mining techniques. Finally, other two papers were technical papers, describing the development of software, computational tools and/or learning environments/platforms, while two additional studies were literature reviews (one of which being systematic and bibliometric).The following nine sub-topics could be identified: (I) knowledge and awareness of big data among medical students; (II) difficulties and challenges in integrating and implementing big data teaching into the medical syllabus; (III) exploiting big data to review, improve and enhance medical school curriculum; (IV) exploiting big data to monitor the effectiveness of web-based learning environments among medical students; (V) exploiting big data to capture the determinants and signatures of successful academic performance and counteract/prevent drop-out; (VI) exploiting big data to promote equity, inclusion, and diversity; (VII) exploiting big data to enhance integrity and ethics, avoiding plagiarism and duplication rate; (VIII) empowering medical students, improving and enhancing medical practice; and, (IX) exploiting big data in continuous medical education and learning. These sub-themes were subsequently grouped in the following four major themes/topics: namely, (I) big data and medical curricula; (II) big data and medical academic performance; (III) big data and societal/bioethical issues in biomedical education; and (IV) big data and medical career. Despite the increasing importance of big data in biomedicine, current medical curricula and syllabuses appear inadequate to prepare future medical professionals and practitioners that can leverage on big data in their daily clinical practice. Challenges in integrating, incorporating, and implementing big data teaching into medical school need to be overcome to facilitate the training of the next generation of medical professionals. Finally, in the present integrative review, state-of-art and future potential uses of big data in the field of biomedical discussion are envisaged, with a focus on the still ongoing "Coronavirus Disease 2019" (COVID-19) pandemic, which has been acting as a catalyst for innovation and digitalization.

Big Data for Biomedical Education with a Focus on the COVID-19 Era: An Integrative Review of the Literature

https://pubmed.ncbi.nlm.nih.gov/34501581/

Rola

Khamisy

Farah

Peter

Gilbey

Leonardo B Furstenau

Michele Kremer

Sott

Raymond Farah

Maurizio Viviani

Maurizio Bisogni

Jude

Dzevela

Kong

Rosagemma

Ciliberti

Nicola Luigi Bragazzi

“Artificial neural networks can be effectively used to model changes of intracranial pressure (ICP) during spinal surgery using different non invasive ICP surrogate estimators” by

Watad

A, Bragazzi NL,

Bacigaluppi

Amital

S, Sharif K,

Bisharat

B, Siri A,

Mahamid

A, Abu Ras H, Nasr A,

Bilotta

Robba

M. J

Neurosurg

Sci. 2018 Feb 23.

10.23736/S0390-5616.18.04299-6

“Artificial neural networks allow response prediction in squamous cell carcinoma of the scalp treated with radiotherapy” by Damiani G,

Grossi

Berti

E, Conic RRZ, Radhakrishna U,

Pacifico

Piccinno

R, Linder D. J Eur

Acad

Dermatol

Venereol

. 2020 Jun;34(6):1369-1373.

“How Big Data and Artificial Intelligence Can Help Better Manage the COVID-19 Pandemic” by Bragazzi NL, Dai H, Damiani G,

M, Martini M, Wu J. Int J Environ Res Public Health. 2020 May 2;17(9):3176.

“From Rheumatology 1.0 to Rheumatology 4.0 and beyond: the contributions of Big Data to the field of rheumatology” by Bragazzi NL, Damiani G, Martini M.

Mediterr

Rheumatol

. 2019 Mar;30(1):3-6.

SleepOMICS

How Big Data Can Revolutionize Sleep Science” by Bragazzi NL,

Guglielmi

Garbarino

S. Int J Environ Res Public Health. 2019 Jan 21;16(2):291.

"Systematic review and meta-analysis of

case-control

studies from 7,000 COVID-19 Pneumonia patients suggests a beneficial impact of Tocilizumab with benefit most evident in non-corticosteroid Exposed Subjects" by Abdulla

, Charlie

Bridgewood

, Muhammad Mansour,

Naim

Mahroum

, Matteo

Riccò

, Ahmed Nasr, Amr Hussein, Omer Gendelman, Yehuda

Shoenfeld

Merav

Lidar, Howard

Amita

Wu, Dennis

McGonagle

SSRN Papers abstract number 3642653

"Rationale for Evaluating PDE4 Inhibition for Mitigating against Severe Inflammation in COVID-19 Pneumonia and Beyond" by

C, Damiani G, Sharif K,

Bragazzi NL

Quartuccio

Savic

Med Assoc J. 2020 Jun;22(6):335-339. PMID: 32558435

"Canada needs to rapidly escalate public health interventions for its COVID-19 mitigation strategies" by

Scarabel

Pellis

, Wu J. Infect Dis Model. 2020;5:316-322.

10.1016/j.idm.2020.03.004.

Epub

2020 Mar 31. PMID: 32518882

"Modeling the impact of mass influenza vaccination and public health interventions on COVID-19 epidemics with limited detection capability" by Li Q, Tang B,

, Xiao Y, Wu J. Math

Biosci

. 2020 Jul;325:108378.

10.1016/j.mbs.2020.108378.

2020 May 16. PMID: 32507746

"Quantifying the role of social distancing, personal protection and case detection in mitigating COVID-19 outbreak in Ontario, Canada" by Wu J, Tang B,

, Nah K, McCarthy Z. J Math Ind. 2020;10(1):15.

10.1186/s13362-020-00083-3.

2020 May 26. PMID: 32501416

"Effects of COVID-19 Home Confinement on Eating

Behaviour

and Physical Activity: Results of the ECLB-COVID19 International Online Survey" Ammar A, Brach M,

Trabelsi

Chtourou

Boukhris

Masmoudi

Bouaziz

Bentlage

E, How D, Ahmed M, Müller P, Müller N,

Aloui

Hammouda

Paineiras-Domingos

LL,

Braakman

Jansen A, Wrede C,

Bastoni

S, Pernambuco CS,

Mataruna

L, Taheri M,

Irandoust

Khacharem

Chamari

K, Glenn JM, Bott NT,

Gargouri

Batatia

H, Ali GM, Abdelkarim O,

Jarraya

M, Abed KE,

Souissi

N, Van

Gemert-Pijnen

L, Riemann BL, Riemann L,

Moalla

W, Gómez-Raja J, Epstein M,

Sanderman

R, Schulz SV,

Jerg

A, Al

Horani

R, Mansi T,

Jmail

M, Barbosa F, Ferreira-Santos F,

Šimunič

Pišot

Gaggioli

A, Bailey SJ,

Steinacker

JM,

Driss

Hoekelmann

A. Nutrients. 2020 May 28;12(6):E1583.

10.3390/nu12061583. PMID: 32481594

"Point-of-Care Diagnostic Tests for Detecting SARS-CoV-2 Antibodies: A Systematic Review and Meta-Analysis of Real-World Data" by

M, Ferraro P,

Gualerzi

Ranzieri

S, Henry BM, Said YB,

Pyatigorskaya

NV,

Nevolina

E, Wu J,

, Signorelli C. J Clin Med. 2020 May 18;9(5):1515.

10.3390/jcm9051515. PMID: 32443459

"De-Escalation by Reversing the Escalation with a Stronger Synergistic Package of Contact Tracing, Quarantine, Isolation and Personal Protection: Feasibility of Preventing a COVID-19 Rebound in Ontario, Canada, as a Case Study" by Tang B,

, McCarthy Z, Glazer M, Xiao Y, Heffernan JM,

Asgary

A, Ogden NH, Wu J. Biology (Basel). 2020 May 16;9(5):100.

10.3390/biology9050100. PMID: 32429450

"SARS-CoV-2 infection and air pollutants: Correlation or causation?" by

Balzarini

Corradi

M. Sci Total Environ. 2020 Sep 10;734:139489.

10.1016/j.scitotenv.2020.139489.

2020 May 16. PMID: 32425256

"Stop playing with data: there is no sound evidence that

Bacille

Calmette

Guérin

may avoid SARS-CoV-2 infection (for now)" by

. Acta Biomed. 2020 May 11;91(2):207-213.

10.23750/abm.v91i2.9700. PMID: 32420947

"Point-of-Care diagnostic of SARS-CoV-2: knowledge, attitudes, and perceptions (KAP) of medical workforce in Italy" by

F, Signorelli C. Acta Biomed. 2020 May 11;91(2):57-67.

10.23750/abm.v91i2.9573. PMID: 32420926

"COVID-19 knowledge prevents biologics discontinuation: Data from an Italian multicenter survey during RED-ZONE declaration" by

Malagoli

Kridin

Pigatto

P, Damiani G. Dermatol

Ther

. 2020 May 16:e13508.

10.1111/dth.13508. Online ahead of print. PMID: 32415727

"Continuous hydroxychloroquine or colchicine therapy does not prevent infection with SARS-CoV-2: Insights from a large healthcare database analysis" by Gendelman O,

Chodick

Autoimmun

Rev. 2020 Jul;19(7):102566.

10.1016/j.autrev.2020.102566.

2020 May 5. PMID: 32380315

"Ensuring adequate health financing to prevent and control the COVID-19 in Iran" by

Ghanbari

MK, Bakhtiari A,

. Version 2. Int J Equity Health. 2020 May 6;19(1):61.

10.1186/s12939-020-01181-9. PMID: 32375787

"How Big Data and Artificial Intelligence Can Help Better Manage the COVID-19 Pandemic" by

, Dai H, Damiani G,

10.3390/ijerph17093176. PMID: 32370204

"Biologics increase the risk of SARS-CoV-2 infection and hospitalization, but not ICU admission and death: Real-life data from a large cohort during red-zone declaration" by Damiani G,

P. Dermatol

. 2020 May 1:e13475.

10.1111/dth.13475. Online ahead of print. PMID: 32356577

"Novel Coronavirus Infection (COVID-19) in Humans: A Scoping Review and Meta-Analysis" by Borges do Nascimento IJ,

Cacic

Abdulazeem

HM, von Groote TC,

Jayarajah

Weerasekara

Esfahani

MA, Civile VT,

Marusic

Jeroncic

Carvas

Junior N,

Pericic

TP,

Zakarija-Grkovic

Meirelles

Guimarães

SM,

Luigi Bragazzi N

, Bjorklund M, Sofi-Mahmudi A,

Altujjar

M, Tian M,

Arcani

DMC,

O'Mathúna

DP,

Marcolino

MS. J Clin Med. 2020 Mar 30;9(4):941.

10.3390/jcm9040941. PMID: 32235486

"The effectiveness of quarantine and isolation determine the trend of the COVID-19 epidemics in the final phase of the current outbreak in China" by Tang B, Xia F, Tang S,

, Li Q, Sun X, Liang J, Xiao Y, Wu J. Int J Infect Dis. 2020 Jun;95:288-293.

10.1016/j.ijid.2020.03.018.

2020 Apr 17. PMID: 32171948

"An updated estimation of the risk of transmission of the novel coronavirus (2019-nCov)" by Tang B,

, Li Q, Tang S, Xiao Y, Wu J. Infect Dis Model. 2020 Feb 11;5:248-255.

10.1016/j.idm.2020.02.001.

eCollection

2020. PMID: 32099934

"Estimation of the Transmission Risk of the 2019-nCoV and Its Implication for Public Health Interventions" by Tang B, Wang X, Li Q,

, Tang S, Xiao Y, Wu J. J Clin Med. 2020 Feb 7;9(2):462.

10.3390/jcm9020462. PMID: 32046137

Article

Andrew

Senior

1,4

Richard

Evans

John

Jumper

,James

Kirkpatrick

Laurent

Sifre

Tim

Green

Chongli

Qin

Augustin

Žídek

,Alexander

Nelson

Alex

Bridgland

Hugo

Penedones

Stig

Petersen

Karen

Simonyan

Steve

Crossan

Pushmeet

Kohli

David

Jones

2,3

Silver

Koray

Kavukcuoglu

Demis

Hassabis

structureprediction

canbe

used

three-dimensionalshapeof

its

amino

acid

problem

fundamental

importance

the structure

protein largely determines its function

however,

structures

canbe difficultto

determineexperimentally. Considerableprogress

recentlybeenmadeby

leveraging

geneticinformation.

possibleto

infer

which

residues

contact

analysing

covariation

homologous

sequences,

aidsin

the prediction

Hereweshowthatwe

can train

neural network

make

accurate predictions

the distances between

pairs

residues,

whichconveymore

information about

structurethancontact

predictions.

Using

information,

weconstruct

apotential

meanforce

that

accuratelydescribetheshapeof

protein. Wefind that

resultingpotentialcanbe

optimized

simple

gradient descent

algorithm

generate

without

complex

sampling

procedures.

resultingsystem,

named

AlphaFold,

achieves

high

accuracy,

even

sequences

withfewer

sequences.

Inthe

recent

Critical

Assessment

Structure

(CASP13)—ablindassessmentof

state

field—AlphaFold

created

high-accuracy

structures (with template modelling

(TM)

scores

higher)

freemodelling

domains,

whereasthe

next best method, which used sampling

contact information,

achieved

such

mai

Alpha

nside

adva

in protein-structureprediction.

Weexpectthis

increased accuracyto enable

insights

into

function

malfunction

proteins,

especially

cases

proteinshave

beenexperimentallydetermined

core

biological

processes.

intermediate

(FM/TBM)

category.

Figure

shows

AlphaFold

dependent

structure,

understanding

struc

predicts

domains

accuracy

than

other

system,

tures

been

grand

challenge

biology

decades.

Although

particularly

0.6–0.7

score

range.

score—ranging

several

experimental

determination

techniques

have

between

1—measures

degree

match

overall

(back

developed

improved

they

remain

difficult

time

bone)

shape

proposed

native

structure.

assessors

consuming

result,

decades

theoretical

attempted

ranked

participating

groups

summed,

capped

predict

structures,

separated

according

CASP

biennial

blind

summed

52.8

parameterized

pti

miz

ati

ampled

nitializations

build

differentiable

pool

further

compute

coordinates,

initializations

sampled,

added

noise

inter-residue

distances,

=||

||,

(‘noisy

restarts’),

to be

pool.

express

After

few

hundred

cycles,

optimization

converges

accumulates

terms

marginal

distribu

lowest

chosen

candidate

tion

overrepresentation

prior,

best-scoring

subtract

reference

over

restarts

process,

domain.

ing

iterations

converged.

Noisy

|length)

independent

enable

slightly

higher

small

version

continuing

sample

same

input

features.

(average

0.641

versus

0.636

test

set,

separate

output

head

4).

distogram

(measured

)).

von

Mises

distribution,

difference

(lDDT

Meth

term,

Finally,

ods)

correlates

final

realized

prevent

steric

clashes,

score2_smooth

Rosetta

construction

incorporates

Waals

term.

multipli

Removing

entirely

gives

0.266.

cative

weights

three

Reducing

resolution

below

six

bins

combination

noticeably

outperformed

equal

weighting.

averaging

adjacent

causes

degrade.

in the

total

potential, reference

degrades

functions

respect

slightly.

‘relaxation’

(side-chain

packing

variables

descent.

Here

L-BFGS

leaved

descent)

initialized

values

Talaris2014

fit

reference-corrected

illustrates

single

trajectory

adds

side-chain

atom

yields

showing

greedy

leads

average

improvement

score.

increasing

large-scale

conformation

changes.

sec

carefully

designed

system

ondary

partly

initialization

vide

angle

distributions.

(TM

score)

improves

represents

quickly

steps

Furthermore,

converged

optimum

achieve

1,000

1,200

Iteration

(Å)

Sequen

A features

on distribution

l network

covariation features

quen

r.m.s.d.

Nat.

1 10

2|Thefolding

processillustrated

T0986s2.

probabilities

the network

the uncertainty in

target T0986s2,

=155,

6N9V.

Steps of structure prediction.

The torsion

angle predictions

(as

−1

fitted

network predicts the entire

distogram based on

features,

predictions for

).While

step of gradient descent greedily lowers

accumulating

×64-residue

regions.

global

effected,

(1,200

steps)

shown,

root

mean

well-packed

chain.

submission

overlaid

square

deviation

(r.m.s.d.)

plotted

against

step

five

snapshots

(in

grey).

(across

=377)

the structure. The secondary structure (from

SST

is also shown (helix

blue, potential

against the

repeats of gradient

descent per

strand

red)

along

secondary

(Nat.),

secondary target

(log

scale).

Whereas

rarely

experi

performance

template-modelling

mental

templates

starting

reach

needed

unprecedented

method

hope

oba

ty (

QTKCEKKKCVCENCERSTYL

ror

3|Predicted

distancedistributions

comparedwith

truedistances.

=41,

5W9F.

structure showing

under

highlighting

bin

highlighted

red,

drawn

black.

green,

non-contacts

blue.

=552,

predicted distance

residue pairs

≤22

Å,

excluding distributions

>3.5

=28,678).

±s.d.

calculated

1Å

bins.

error

the distance

distributions,

excluding

>22

=61,872).

0.25

matrix

distogram for

T0990

2b,

x Downsample

e2_

No torsions

e No

2 3

6 12

2 C

0 10 20 30 40 50

|TM

distogram,

dependency

histogram

on different

components

score versus

used when

downsampling

distogram, compared with

lDDT

Pearson’s

correlation

coefficients,

both

CASP13 different components of the potential,

adding Rosetta relaxation.

=500

decoys

T0999)

datasets.

science

Onl

methods,

additional

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Research

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groups’

chains,

files,

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searched

train

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similar protein

sequences in

Uniclust30

HHblits

returned

profile

position-specific substitution probabilities for

features—the

parameters

regularized

pseu

dolikelihood-trained

Potts

CCMpred

uses

Frobenius

norm

parameters,

feed

feature)

(484

features)

predictioncenter.org/casp13/zscores_final.cgi?formula=assessors

1ashows

involved

construction,

extraction,

prediction.

network.

architecture

two-dimensional

dilated

Previously,

preceded

one-dimensional

following

tools

versions

bedding

layers

tem

subsequent

experiments

March

CATH

throughout

blocks

convolutions

v.3.0

beta.3

(three

iterations,

=1×10

−3

block,

1b,

consists

HHpred

server

2017-10

PSI-BLAST

v.2.6.0

interleave

batchnorm

15December

2017)

1×1projection

×3

convolution

layer

(March

2019)

BioPython

v.1.65

v.3.5

PyMol

2.2.0

exponential

linear

unit

(ELU)

nonlinearities.

Successive

cycle

dilations

pixels

allow

propagation

informa

cropped

region.

layer,

position

bias

used,

biases

indexed

offset

(capped

32)

number.

extract non-redundant

dom

ains

utilizing

35%

trained with stochastic gradient descent using

sequence sim

ilarity

cluster representatives. This

31,247

cross- entropy

loss. The target

quantification

split into

and test

(29,427

1,820

the C

residues (or C

glycine).

divide

respectively),

keeping

2–22

superfamily

(H-level

classification)

partition.

array

uperf

concatenation

CASP12

excluded

set.

took—at

random—a

homologous superfamily

Individual training runs

cross-validated with early

stopping

create

subset

presented

here.

selected

cross-validation

domains.

pages

networkhyperparameters

channels,

cycling

synchronized

stochastic

Batch

size

crops

GPU

workers.

loss

0.005

accessible

face

area

0.001.

losses

cut

factor

steps.

rate

decayed

50%

150,000,

200,000,

250,000

days

explicitly

represent

gaps

deletions

SA.

distograms.

constrain

memory

usage

avoid

overfit

better

shallow

MSAs,

ting,

always

tested

regions

take

half

is,

before

computing

MSA-based

consecutive

another

contains

samples

extract

domain,

entire

split

nto

non-overlapping 64

crops.

off-diagonal crops,

trained with the follow

interaction

between residues

apart than 64

(with

indicated

brackets).

modelled.

crop

consisted

alignments

(scalar).

represented

juxtaposition

64-residue

fragments.

Sequence-length

1-hot

type

(21

needs

profiles

features),

(22

context

window.

note that

to the

non-gapped

bias,

(30

diagonal

encode

deletion

fea

governed

ture)

index

(integer

number,

fragments

ranges

except

multi-segment

encoded

least-significant

crop.

Augmenting

inputs

on-diagonal

bits

scalar).

correspond

provides

Sequence-length-squared

features: Potts

odel parameters

of each fragment

them.

Nesterov

entum

0.99,

reweighting)

(for

instance,

confidently

gap

(1feature).

helices

sheets),

then

strongly

ratio

conditional

ntar

uat

Randomizing

Torsions

kelihood

augmentation

dicted

predic

tein

different

examples.

tions,

multimodal,

jointly

enhanced

adding

proportional

ground

optimize

torsions.

unify

mass,

cost

truth

variation

fidelity

multimodal

unimodal

distances.

(MSA

subsampling

coordinate

noise),

dropout

prevents

overfitting

(Supplementary

equation

(3)).

term

introduced

Rosetta’s

(top)

combined.

edge

effects,

tilings

produced

offsets

averaged

together,

heav

weighting

near

centre

improve

further,

ensemble

four

Structure realization

by gradient

realize

models,

hyperparameters,

minimize

together.

examples

ideal

geometry,

giving

coordinates

complete

three-domain

target,

minimized

distance,

As the

a r

ich representation capable

incorporat

Supplementary equation

(4)).

there is no

argue

guarantee

potentials

equivalent

scale,

scaling

param

directly.

eters

pooling

activations

practice,

penultimate

separately

lead

results.

eight-class

labels

DSSP

angles,

initial

sampled

(distinguishing

marginals,

helix/sheet/coil

classes)

84%,

comparable

descent algorithm,

state-of-the-art

The relative

accessible surface

on the

initial conditions,

repeat the

optimi

(ASA)

predicted.

zation

initializations.

pooled

lowest-potential

maintained

once

full,

initialize

Ramachandran

,MSA(

)),

indepen

90%

trajectories

30°

dently

residue,

approxi

(the

remaining

10%

mated

10°

(1,296

bins).

during

distributions).

distograms,

runs

change

ASA.

taken

second

longer

ASA

optimize,

load

balanced

(50

)/2

torsions,

former

thoroughly

validated.

curves

important

accu

time,

comparing

racy

with contact

restarting

systems)

eff

effective

MSA,

discounting

redundancy

62%sequence

identity

level,

compare

indication

amount

measure

metrics

_TS

measures

geometric

Distance potential.

distogram probabilities

estimated for

candidate structure

and the

alterna

therefore,

tive

interpolated

cubic

spline.

Because

percentage

of native

15Å,

mass

beyond

greater

harder

accurately,

tolerance

value,

toler

(determined

cross-validation),

constant

ances

0.5,

(without

stereochemical

checks),

extrapolation

thereafter.

(bottom)

(5)).

varying

histograms

introduce

dis

togram

DDT

(DLDDT),

directly

Sup

dataset.

conditioned

plementary

(6)).

nearby

account

often

short,

easier

binary

αβ

indicate

whether

determining

fold

topology,

=12,

considering

glycine

atom)

separation

≥12.

protein. we

is created

the negative

likelihood

of DLDDT using

DLDDT

(Pearson’s

=0.92

CASP13)

(1)).

state,

becomes

log-likelihood

pot

gro

deliver

Full chains without

segmentation.

Parameterizing proteins

most accurate predictions.

Although AlphaFold

able to

dimension

some

outperform

space

grows

thus,

example,

T0981-D5,

72.8

GDT_TS,

T0957s1-D2,

88.0

uch

difficult.

Traditionally

TBM-hard

addressed

splitting

pieces—termed

12GDT_TS

submission),

domains—that

independently.

segmentation

targets

lags

behind

alone

error-prone.

detailed

hard

avoided

folded

chains.

molecular

Typically,

MSAs

replacement, another study

reported

that the

sliding

window

approach,

full-chain

(raw

B-factors)

led

marginally

baseline

full-sequence

distogram.

gain

group,

indicating

subsequences

chain,

trying

windows

64,

128,

assist

phasing

X-ray

crystallog

multiples

64.

gave

rise

individual

raphy.

corresponded

We averaged

all of these

weighted

Interpretation

distogram neural network.

produce

would

understand

arrives

found.

assessment,

relaxed

tance

and—in

particular—to

relax

+0.2

(weighting

deter

affect

mined

cross-validation)

derstanding

mechanism

suggest

improvem

ents

However, deep

neural networks

nonlinear

inputs,

attribution

difficult,

specified

and an

on-going

topi

research.

Even so,

there

systems,

Integrated

Gradients

location

paper

T0975,

network’s

particular

(and

40-bin

distance.

distributions)

used.

T0975

onward,

newly

64-bin

plots

absolute

Gradi

ent,

,(defined

equations

(7)–(9))

runs)

T0986s2

10,

(five

runs).

top-10

highest

eight

top

AlphaFold.

run)

maps

highly

structured,

reflecting

(top-one)

in-contact

(1,

5),

fifth

pair(s)

members

submis

of.

1,the

helix

connections

sions

0999

strands

either

helix,

submission,

strain

helix.

connect

‘back-fill’

strands,

mixture

inter-strand

T0975.

salient.

involve

later

performed

elements

method,

Fig

non-contacting

pair,

compares

geometrically

322.

expert

visual

inspection

choose

themselves

nearly

twice

tasked

spatial

input,

patterns

discover

impor

relevance

wide

tant

channelling

refine

ments,

generally

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binding

Reportingsummary

alone can

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on research design

in the Nature

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Dataavailability

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Data Fig.

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https://github.com/deepmind/deepmind-research/tree

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Audet,

Crystal

misoprostol

bound

labor

inducer

prostaglandin

abas

ann

alignments.

D170–D176

(2017).

Remmert,

Hauser,

lightning-fast

iterative

HMM–HMM

alignment.

173–175

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Extended Data Fig. 1|Schematicsofthefoldingsystemandneuralnetwork.

system.

extraction

(constructing

yellow; the

structure-prediction

network in

realization

block

residual convolutional network. The dilated convolution is

reduced

dimension.

the representation

the previous

layer.

The bypass

connections of the

residual network

enable gradients

pass

back

undiminished,

permitting

very

distance distributions

(AF)

best-ranked contact

(RaptorX-Contact

(TripletRes

2|CASP13contact

precisions.

Precisions

in groups’

targets,

updated

domain definitions for

T0990.

divides

chain

(D3

inserted

D2)

39,

alignments,

respectively

(from

website).

decoys for domains excluding T0999)

coefficients.

normalized

=377).

number of

effective sequences correlates

=0.634).

measures,

=377),

forms

Top,

the potential,

the effect

relax.

‘P’

significance of

3|Analysisof

structureaccuracies.

‘Full’,

two-tailed

paired

test.

‘Accuracy’).

predicts ‘Bins’ shows the number of bins

the spline before extrapolation

(particularly

medium

long-range

distribution.

splines

Bottom,

original 64-bin distogram predictions

repeatedly downsampled

afactor

bins,

case

Å (the

last quarter

The two-level potential

the final

row,

contact predictions, is constructed

the probability

mass below

constant extrapolation beyond

Å. The

this table

Extended Data Fig. 4

per-target computation

computed asan

averageover

thetestset.

Structure realization requires

modest

budget, which

parallelized

mach

s (o

(blue).

measured

product

(CPU-based)

machines

elapsed

largely parallelized.

targets take longer

optimize. Figure

crea

rep

=377.

5|AlphaFold

the five AlphaFold CASP13 submissions

shown. Simulated annealing with

assembly entries

shown in blue. Gradient-descent entries

yellow.

later,

left

black

line

rad

9 (1,589

residues)

manually segmented based on HHpred

matching.

=104

domains),

submitted, the

best-of-fivemodel

(submission

GDT_TS),

of full-chain gradient descent

for T0975

back-fill for earlier targets)

run of fragment

domain segmentation (using

descent submission for T0999).

The formula-standardized

scores of the

GDT

+QCS

=31)

=12)

competitor

(group

322),

coloured

category. AlphaFold

performs

=0.0032,

tailed

statistic

test).

Extended Data Fig. 6

|Correct

fold identification by structural

CATH.

inferred

finding

homologous proteins

of known function. Here

show that the FM predictions of AlphaFold give

accuracy in

structure-based

for homologous domains

database. For

the FM

domains, the top-one

30,744

S40

non

redundant

ground-truth

(score

>0.5),

show the percentage of

results)

>0.5.

next-best

matching

accurately.

Extended Data Fig. 7

|Accuracy

of predictions for interfaces.

protein interaction is

domain for understanding protein

hitherto

largely

moderate

success

predicted structures

6 Å r.m.s.d.

This figure

shows that the predictions

the interface

hetero-dimer

probably better

candidates

docking,

did

isolated

rather

complexes.

all-groups

heterodimer

full-atom

(residues

inter-chain heavy-atom

<10

Å)

for the chain submissions of all groups

(green),

relative

the target complex. Results

Å are

not shown. AlphaFold (blue)

achieves consistently

and,

out of

erf

Extended Data Fig. 8

|Ligand

pocket visualizations for

T1011.

T1011

(PDB

6M9T)

EP3

receptor

misoprostol-FA

ligand

pocket.

(78.0

TS)

made

knowledge

ligand,

(322,

68.7

the helices close

the ligand

pocket and

visualized with the

interior

position.

Extended Data Fig.

9|Attribution

mapof

distogramnetwork.

The contact

T0986s2,

Gradient,

,of

expected

):(1)

contact,

(2)

strand–strand

(3)

medium-range

strand contact,

(4)

non-contact

(5)

long-range strand–strand

dots

diagrams.

Darker

colours

weight.

|Attribution

on predicted structure.

0.8),

input pairs, including

self-pairs,

weight

lines

(or

spheres

self-pairs)

sensitivity,

lighter

sensitive,

blue

line.